diff --git a/.gitignore b/.gitignore index ea4317a..12504ac 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,3 @@ __pycache__ -*.json -!configs/*.json -*.relay -!tests/models -tests/params/ -.pkl_memoize_py3 \ No newline at end of file +.pkl_memoize_py3 +tests/data \ No newline at end of file diff --git a/README.md b/README.md index 93d0fef..5651c93 100644 --- a/README.md +++ b/README.md @@ -17,4 +17,27 @@ Flexible Matching via Equality Saturation - Takes the **relay source file**, the output model and eclass analysis data json from EqSat - Compiles the rewritten model back to a relay executable model and saves to a file - Optional argument: `--debug`; instead of inserting accelerator calls, if this argument is passed, the equivalent relay function will be generated - - Example: `python3 compile_model.py models/resmlp.relay resmlp-rewritten.relay resmlp-rewritten.json resmlp-data.json linear-rewrites im2col-rewrites --debug` \ No newline at end of file + - Example: `python3 compile_model.py models/resmlp.relay resmlp-rewritten.relay resmlp-rewritten.json resmlp-data.json linear-rewrites im2col-rewrites --debug` + +# End-to-end compilation validation +```bash +python3 validate_compilation.py model ([--configs CONFIGS+] | [--defaults]) [--use-ilp] [--debug] +``` +- `model`: resnet18; efficientnet; max_pool2d; mobilenet; resmlp +- `configs`: hlscnn-conv2d; flexasr-maxpool; im2col; vta-dense; linear-rewrites + - hlscnn-conv2d: `Conv2D` to HLSCNN + - flexasr-maxpool: `Max_pool2D` to FlexASR + - im2col: Convolutions to matmuls + - linear-rewrites: `nn.Linear` to FlexASR +- `--defaults`: if turned oon, use default configs +- `--use-ilp`: Use CPLEX ILP Solver to extract the rewritten model +- `--debug`: Use debug functions to replace accelerator calls for debug purposes + +Example: `python3 validate_compilation.py resnet18 --configs im2col vta-dense --debug` + +# Rewrite Config Structure +- rewrites :: `Dict[String, Array[Integer]]`. rewrite rules to apply +- composites :: `Dict[String (accelerator func names), String]`. Compiler composite region annotations +- compiler :: `Dict[String (accelerator func names), String]`. which compiler to use +- debug_functions :: `Dict[String (accelerator func names), String]`. debug functions for corresponding accelerator calls +- out_dtypes :: `Dict[String (accelerator func names), String (dtype names)]`. Output data type of the accelerator function. \ No newline at end of file diff --git a/configs/flexasr-lstm.json b/configs/flexasr-lstm.json new file mode 100644 index 0000000..db4532a --- /dev/null +++ b/configs/flexasr-lstm.json @@ -0,0 +1,17 @@ +{ + "rewrites": { + "flex-lstm": [] + }, + "composites": { + "flex-lstm": "ilaflex.lstm" + }, + "compilers": { + "flex-lstm": "ilaflex" + }, + "debug_functions": { + "flex-lstm": "lambda *inputs: tvm.parser.fromtext(open('./models/lstm-for-pldi-pattern.relay').read())['main'](*inputs)" + }, + "out_dtypes": { + "flex-lstm": "float32" + } +} \ No newline at end of file diff --git a/configs/flexasr-maxpool.json b/configs/flexasr-maxpool.json index 986b432..1101758 100644 --- a/configs/flexasr-maxpool.json +++ b/configs/flexasr-maxpool.json @@ -11,12 +11,15 @@ "simplify-reduce-max": [] }, "composites": { - "flexasr-maxpool": "ilaflex.max_pool" + "flex-maxpool": "ilaflex.max_pool" }, "compilers": { - "flexasr-maxpool": "ilaflex" + "flex-maxpool": "ilaflex" }, "debug_functions": { - "flexasr-maxpool": "nn.max_pool2d" + "flex-maxpool": "lambda x: relay.expand_dims(relay.max(x, axis=0), axis=0)" + }, + "out_dtypes": { + "flex-maxpool": "float32" } } \ No newline at end of file diff --git a/configs/hlscnn-conv2d.json b/configs/hlscnn-conv2d.json index 42497bf..f4fd113 100644 --- a/configs/hlscnn-conv2d.json +++ b/configs/hlscnn-conv2d.json @@ -9,6 +9,9 @@ "hlscnn-conv2d": "ilacnn" }, "debug_functions": { - "hlscnn-conv2d": "nn.conv2d" + "hlscnn-conv2d": "RelayOperators.RelayConv2D" + }, + "out_dtypes": { + "hlscnn-conv2d": "float32" } } \ No newline at end of file diff --git a/configs/im2col.json b/configs/im2col.json new file mode 100644 index 0000000..ab948ca --- /dev/null +++ b/configs/im2col.json @@ -0,0 +1,12 @@ +{ + "rewrites": { + "flatten-unflatten-all-accesses": [], + "bubble-reshape-through-cartesian-product": [], + "bubble-reshape-through-compute-dot-product": [], + "access-reshape-to-relay": [] + }, + "composites": {}, + "compilers": {}, + "debug_functions": {}, + "out_dtypes": {} +} \ No newline at end of file diff --git a/configs/linear-rewrites.json b/configs/linear-rewrites.json index 532d774..43edb72 100644 --- a/configs/linear-rewrites.json +++ b/configs/linear-rewrites.json @@ -2,7 +2,9 @@ "rewrites": { "linear-rewrites": [], "access-reshape-to-relay": [], - "flex-linear-rewrite": [] + "flex-linear-rewrite": [], + "add_bias_add_to_dense": [], + "glenside_matmul_to_relay_dense": [] }, "composites": { "flex-linear": "ilaflex.linear" @@ -12,5 +14,8 @@ }, "debug_functions": { "flex-linear": "lambda x, w, b: nn.bias_add(nn.dense(x, w), b)" + }, + "out_dtypes": { + "flex-linear": "float32" } } \ No newline at end of file diff --git a/configs/qnn-vta-dense.json b/configs/qnn-vta-dense.json new file mode 100644 index 0000000..0ec18ba --- /dev/null +++ b/configs/qnn-vta-dense.json @@ -0,0 +1,17 @@ +{ + "rewrites": { + "vta-dense-rewrite": [] + }, + "composites": { + "vta-dense": "ilavta.dense" + }, + "compilers": { + "vta-dense": "ilavata" + }, + "debug_functions": { + "vta-dense": "lambda x,w: nn.dense(x, w, out_dtype='int32')" + }, + "out_dtypes": { + "vta-dense": "int32" + } +} \ No newline at end of file diff --git a/configs/im2col-rewrites.json b/configs/vta-dense.json similarity index 54% rename from configs/im2col-rewrites.json rename to configs/vta-dense.json index bd06b1c..ace7055 100644 --- a/configs/im2col-rewrites.json +++ b/configs/vta-dense.json @@ -1,9 +1,5 @@ { "rewrites": { - "flatten-unflatten-all-accesses": [], - "bubble-reshape-through-cartesian-product": [], - "bubble-reshape-through-compute-dot-product": [], - "access-reshape-to-relay": [], "vta-dense-rewrite": [] }, "composites": { @@ -14,5 +10,8 @@ }, "debug_functions": { "vta-dense": "nn.dense" + }, + "out_dtypes": { + "vta-dense": "float32" } } \ No newline at end of file diff --git a/demo/mobilenetv2/final_mobilenet_cifar10_400_epochs.pth b/demo/mobilenetv2/final_mobilenet_cifar10_400_epochs.pth new file mode 100644 index 0000000..42995ab Binary files /dev/null and b/demo/mobilenetv2/final_mobilenet_cifar10_400_epochs.pth differ diff --git a/demo/mobilenetv2/quantized_mobilenetv2.relay b/demo/mobilenetv2/quantized_mobilenetv2.relay new file mode 100644 index 0000000..bb472ae --- /dev/null +++ b/demo/mobilenetv2/quantized_mobilenetv2.relay @@ -0,0 +1,2651 @@ +#[version = "0.0.5"] +def @main(%input0: Tensor[(1, 3, 32, 32), float32], %conv1_weight: Tensor[(32, 3, 3, 3), float32], %bn1_running_var: Tensor[(32), float32], %bn1_weight: Tensor[(32), float32], %bn1_running_mean: Tensor[(32), float32], %bn1_bias: Tensor[(32), float32], %layers_0_conv1_weight: Tensor[(32, 32, 1, 1), float32], %layers_0_bn1_running_var: Tensor[(32), float32], %layers_0_bn1_weight: Tensor[(32), float32], %layers_0_bn1_running_mean: Tensor[(32), float32], %layers_0_bn1_bias: Tensor[(32), float32], %layers_0_conv2_weight: Tensor[(32, 1, 3, 3), float32], %layers_0_bn2_running_var: Tensor[(32), float32], %layers_0_bn2_weight: Tensor[(32), float32], %layers_0_bn2_running_mean: Tensor[(32), float32], %layers_0_bn2_bias: Tensor[(32), float32], %layers_0_conv3_weight: Tensor[(16, 32, 1, 1), float32], %layers_0_bn3_running_var: Tensor[(16), float32], %layers_0_bn3_weight: Tensor[(16), float32], %layers_0_bn3_running_mean: Tensor[(16), float32], %layers_0_bn3_bias: Tensor[(16), float32], %layers_0_shortcut_0_weight: Tensor[(16, 32, 1, 1), float32], %layers_0_shortcut_1_running_var: Tensor[(16), float32], %layers_0_shortcut_1_weight: Tensor[(16), float32], %layers_0_shortcut_1_running_mean: Tensor[(16), float32], %layers_0_shortcut_1_bias: Tensor[(16), float32], %layers_1_conv1_weight: Tensor[(96, 16, 1, 1), float32], %layers_1_bn1_running_var: Tensor[(96), float32], %layers_1_bn1_weight: Tensor[(96), float32], %layers_1_bn1_running_mean: Tensor[(96), float32], %layers_1_bn1_bias: Tensor[(96), float32], %layers_1_conv2_weight: Tensor[(96, 1, 3, 3), float32], %layers_1_bn2_running_var: Tensor[(96), float32], %layers_1_bn2_weight: Tensor[(96), float32], %layers_1_bn2_running_mean: Tensor[(96), float32], %layers_1_bn2_bias: Tensor[(96), float32], %layers_1_conv3_weight: Tensor[(24, 96, 1, 1), float32], %layers_1_bn3_running_var: Tensor[(24), float32], %layers_1_bn3_weight: Tensor[(24), float32], %layers_1_bn3_running_mean: Tensor[(24), float32], %layers_1_bn3_bias: Tensor[(24), float32], %layers_1_shortcut_0_weight: Tensor[(24, 16, 1, 1), float32], %layers_1_shortcut_1_running_var: Tensor[(24), float32], %layers_1_shortcut_1_weight: Tensor[(24), float32], %layers_1_shortcut_1_running_mean: Tensor[(24), float32], %layers_1_shortcut_1_bias: Tensor[(24), float32], %layers_2_conv1_weight: Tensor[(144, 24, 1, 1), float32], %layers_2_bn1_running_var: Tensor[(144), float32], %layers_2_bn1_weight: Tensor[(144), float32], %layers_2_bn1_running_mean: Tensor[(144), float32], %layers_2_bn1_bias: Tensor[(144), float32], %layers_2_conv2_weight: Tensor[(144, 1, 3, 3), float32], %layers_2_bn2_running_var: Tensor[(144), float32], %layers_2_bn2_weight: Tensor[(144), float32], %layers_2_bn2_running_mean: Tensor[(144), float32], %layers_2_bn2_bias: Tensor[(144), float32], %layers_2_conv3_weight: Tensor[(24, 144, 1, 1), float32], %layers_2_bn3_running_var: Tensor[(24), float32], %layers_2_bn3_weight: Tensor[(24), float32], %layers_2_bn3_running_mean: Tensor[(24), float32], %layers_2_bn3_bias: Tensor[(24), float32], %layers_3_conv1_weight: Tensor[(144, 24, 1, 1), float32], %layers_3_bn1_running_var: Tensor[(144), float32], %layers_3_bn1_weight: Tensor[(144), float32], %layers_3_bn1_running_mean: Tensor[(144), float32], %layers_3_bn1_bias: Tensor[(144), float32], %layers_3_conv2_weight: Tensor[(144, 1, 3, 3), float32], %layers_3_bn2_running_var: Tensor[(144), float32], %layers_3_bn2_weight: Tensor[(144), float32], %layers_3_bn2_running_mean: Tensor[(144), float32], %layers_3_bn2_bias: Tensor[(144), float32], %layers_3_conv3_weight: Tensor[(32, 144, 1, 1), float32], %layers_3_bn3_running_var: Tensor[(32), float32], %layers_3_bn3_weight: Tensor[(32), float32], %layers_3_bn3_running_mean: Tensor[(32), float32], %layers_3_bn3_bias: Tensor[(32), float32], %layers_4_conv1_weight: Tensor[(192, 32, 1, 1), float32], %layers_4_bn1_running_var: Tensor[(192), float32], %layers_4_bn1_weight: Tensor[(192), float32], %layers_4_bn1_running_mean: Tensor[(192), float32], %layers_4_bn1_bias: Tensor[(192), float32], %layers_4_conv2_weight: Tensor[(192, 1, 3, 3), float32], %layers_4_bn2_running_var: Tensor[(192), float32], %layers_4_bn2_weight: Tensor[(192), float32], %layers_4_bn2_running_mean: Tensor[(192), float32], %layers_4_bn2_bias: Tensor[(192), float32], %layers_4_conv3_weight: Tensor[(32, 192, 1, 1), float32], %layers_4_bn3_running_var: Tensor[(32), float32], %layers_4_bn3_weight: Tensor[(32), float32], %layers_4_bn3_running_mean: Tensor[(32), float32], %layers_4_bn3_bias: Tensor[(32), float32], %layers_5_conv1_weight: Tensor[(192, 32, 1, 1), float32], %layers_5_bn1_running_var: Tensor[(192), float32], %layers_5_bn1_weight: Tensor[(192), float32], %layers_5_bn1_running_mean: Tensor[(192), float32], %layers_5_bn1_bias: Tensor[(192), float32], %layers_5_conv2_weight: Tensor[(192, 1, 3, 3), float32], %layers_5_bn2_running_var: Tensor[(192), float32], %layers_5_bn2_weight: Tensor[(192), float32], %layers_5_bn2_running_mean: Tensor[(192), float32], %layers_5_bn2_bias: Tensor[(192), float32], %layers_5_conv3_weight: Tensor[(32, 192, 1, 1), float32], %layers_5_bn3_running_var: Tensor[(32), float32], %layers_5_bn3_weight: Tensor[(32), float32], %layers_5_bn3_running_mean: Tensor[(32), float32], %layers_5_bn3_bias: Tensor[(32), float32], %layers_6_conv1_weight: Tensor[(192, 32, 1, 1), float32], %layers_6_bn1_running_var: Tensor[(192), float32], %layers_6_bn1_weight: Tensor[(192), float32], %layers_6_bn1_running_mean: Tensor[(192), float32], %layers_6_bn1_bias: Tensor[(192), float32], %layers_6_conv2_weight: Tensor[(192, 1, 3, 3), float32], %layers_6_bn2_running_var: Tensor[(192), float32], %layers_6_bn2_weight: Tensor[(192), float32], %layers_6_bn2_running_mean: Tensor[(192), float32], %layers_6_bn2_bias: Tensor[(192), float32], %layers_6_conv3_weight: Tensor[(64, 192, 1, 1), float32], %layers_6_bn3_running_var: Tensor[(64), float32], %layers_6_bn3_weight: Tensor[(64), float32], %layers_6_bn3_running_mean: Tensor[(64), float32], %layers_6_bn3_bias: Tensor[(64), float32], %layers_7_conv1_weight: Tensor[(384, 64, 1, 1), float32], %layers_7_bn1_running_var: Tensor[(384), float32], %layers_7_bn1_weight: Tensor[(384), float32], %layers_7_bn1_running_mean: Tensor[(384), float32], %layers_7_bn1_bias: Tensor[(384), float32], %layers_7_conv2_weight: Tensor[(384, 1, 3, 3), float32], %layers_7_bn2_running_var: Tensor[(384), float32], %layers_7_bn2_weight: Tensor[(384), float32], %layers_7_bn2_running_mean: Tensor[(384), float32], %layers_7_bn2_bias: Tensor[(384), float32], %layers_7_conv3_weight: Tensor[(64, 384, 1, 1), float32], %layers_7_bn3_running_var: Tensor[(64), float32], %layers_7_bn3_weight: Tensor[(64), float32], %layers_7_bn3_running_mean: Tensor[(64), float32], %layers_7_bn3_bias: Tensor[(64), float32], %layers_8_conv1_weight: Tensor[(384, 64, 1, 1), float32], %layers_8_bn1_running_var: Tensor[(384), float32], %layers_8_bn1_weight: Tensor[(384), float32], %layers_8_bn1_running_mean: Tensor[(384), float32], %layers_8_bn1_bias: Tensor[(384), float32], %layers_8_conv2_weight: Tensor[(384, 1, 3, 3), float32], %layers_8_bn2_running_var: Tensor[(384), float32], %layers_8_bn2_weight: Tensor[(384), float32], %layers_8_bn2_running_mean: Tensor[(384), float32], %layers_8_bn2_bias: Tensor[(384), float32], %layers_8_conv3_weight: Tensor[(64, 384, 1, 1), float32], %layers_8_bn3_running_var: Tensor[(64), float32], %layers_8_bn3_weight: Tensor[(64), float32], %layers_8_bn3_running_mean: Tensor[(64), float32], %layers_8_bn3_bias: Tensor[(64), float32], %layers_9_conv1_weight: Tensor[(384, 64, 1, 1), float32], %layers_9_bn1_running_var: Tensor[(384), float32], %layers_9_bn1_weight: Tensor[(384), float32], %layers_9_bn1_running_mean: Tensor[(384), float32], %layers_9_bn1_bias: Tensor[(384), float32], %layers_9_conv2_weight: Tensor[(384, 1, 3, 3), float32], %layers_9_bn2_running_var: Tensor[(384), float32], %layers_9_bn2_weight: Tensor[(384), float32], %layers_9_bn2_running_mean: Tensor[(384), float32], %layers_9_bn2_bias: Tensor[(384), float32], %layers_9_conv3_weight: Tensor[(64, 384, 1, 1), float32], %layers_9_bn3_running_var: Tensor[(64), float32], %layers_9_bn3_weight: Tensor[(64), float32], %layers_9_bn3_running_mean: Tensor[(64), float32], %layers_9_bn3_bias: Tensor[(64), float32], %layers_10_conv1_weight: Tensor[(384, 64, 1, 1), float32], %layers_10_bn1_running_var: Tensor[(384), float32], %layers_10_bn1_weight: Tensor[(384), float32], %layers_10_bn1_running_mean: Tensor[(384), float32], %layers_10_bn1_bias: Tensor[(384), float32], %layers_10_conv2_weight: Tensor[(384, 1, 3, 3), float32], %layers_10_bn2_running_var: Tensor[(384), float32], %layers_10_bn2_weight: Tensor[(384), float32], %layers_10_bn2_running_mean: Tensor[(384), float32], %layers_10_bn2_bias: Tensor[(384), float32], %layers_10_conv3_weight: Tensor[(96, 384, 1, 1), float32], %layers_10_bn3_running_var: Tensor[(96), float32], %layers_10_bn3_weight: Tensor[(96), float32], %layers_10_bn3_running_mean: Tensor[(96), float32], %layers_10_bn3_bias: Tensor[(96), float32], %layers_10_shortcut_0_weight: Tensor[(96, 64, 1, 1), float32], %layers_10_shortcut_1_running_var: Tensor[(96), float32], %layers_10_shortcut_1_weight: Tensor[(96), float32], %layers_10_shortcut_1_running_mean: Tensor[(96), float32], %layers_10_shortcut_1_bias: Tensor[(96), float32], %layers_11_conv1_weight: Tensor[(576, 96, 1, 1), float32], %layers_11_bn1_running_var: Tensor[(576), float32], %layers_11_bn1_weight: Tensor[(576), float32], %layers_11_bn1_running_mean: Tensor[(576), float32], %layers_11_bn1_bias: Tensor[(576), float32], %layers_11_conv2_weight: Tensor[(576, 1, 3, 3), float32], %layers_11_bn2_running_var: Tensor[(576), float32], %layers_11_bn2_weight: Tensor[(576), float32], %layers_11_bn2_running_mean: Tensor[(576), float32], %layers_11_bn2_bias: Tensor[(576), float32], %layers_11_conv3_weight: Tensor[(96, 576, 1, 1), float32], %layers_11_bn3_running_var: Tensor[(96), float32], %layers_11_bn3_weight: Tensor[(96), float32], %layers_11_bn3_running_mean: Tensor[(96), float32], %layers_11_bn3_bias: Tensor[(96), float32], %layers_12_conv1_weight: Tensor[(576, 96, 1, 1), float32], %layers_12_bn1_running_var: Tensor[(576), float32], %layers_12_bn1_weight: Tensor[(576), float32], %layers_12_bn1_running_mean: Tensor[(576), float32], %layers_12_bn1_bias: Tensor[(576), float32], %layers_12_conv2_weight: Tensor[(576, 1, 3, 3), float32], %layers_12_bn2_running_var: Tensor[(576), float32], %layers_12_bn2_weight: Tensor[(576), float32], %layers_12_bn2_running_mean: Tensor[(576), float32], %layers_12_bn2_bias: Tensor[(576), float32], %layers_12_conv3_weight: Tensor[(96, 576, 1, 1), float32], %layers_12_bn3_running_var: Tensor[(96), float32], %layers_12_bn3_weight: Tensor[(96), float32], %layers_12_bn3_running_mean: Tensor[(96), float32], %layers_12_bn3_bias: Tensor[(96), float32], %layers_13_conv1_weight: Tensor[(576, 96, 1, 1), float32], %layers_13_bn1_running_var: Tensor[(576), float32], %layers_13_bn1_weight: Tensor[(576), float32], %layers_13_bn1_running_mean: Tensor[(576), float32], %layers_13_bn1_bias: Tensor[(576), float32], %layers_13_conv2_weight: Tensor[(576, 1, 3, 3), float32], %layers_13_bn2_running_var: Tensor[(576), float32], %layers_13_bn2_weight: Tensor[(576), float32], %layers_13_bn2_running_mean: Tensor[(576), float32], %layers_13_bn2_bias: Tensor[(576), float32], %layers_13_conv3_weight: Tensor[(160, 576, 1, 1), float32], %layers_13_bn3_running_var: Tensor[(160), float32], %layers_13_bn3_weight: Tensor[(160), float32], %layers_13_bn3_running_mean: Tensor[(160), float32], %layers_13_bn3_bias: Tensor[(160), float32], %layers_14_conv1_weight: Tensor[(960, 160, 1, 1), float32], %layers_14_bn1_running_var: Tensor[(960), float32], %layers_14_bn1_weight: Tensor[(960), float32], %layers_14_bn1_running_mean: Tensor[(960), float32], %layers_14_bn1_bias: Tensor[(960), float32], %layers_14_conv2_weight: Tensor[(960, 1, 3, 3), float32], %layers_14_bn2_running_var: Tensor[(960), float32], %layers_14_bn2_weight: Tensor[(960), float32], %layers_14_bn2_running_mean: Tensor[(960), float32], %layers_14_bn2_bias: Tensor[(960), float32], %layers_14_conv3_weight: Tensor[(160, 960, 1, 1), float32], %layers_14_bn3_running_var: Tensor[(160), float32], %layers_14_bn3_weight: Tensor[(160), float32], %layers_14_bn3_running_mean: Tensor[(160), float32], %layers_14_bn3_bias: Tensor[(160), float32], %layers_15_conv1_weight: Tensor[(960, 160, 1, 1), float32], %layers_15_bn1_running_var: Tensor[(960), float32], %layers_15_bn1_weight: Tensor[(960), float32], %layers_15_bn1_running_mean: Tensor[(960), float32], %layers_15_bn1_bias: Tensor[(960), float32], %layers_15_conv2_weight: Tensor[(960, 1, 3, 3), float32], %layers_15_bn2_running_var: Tensor[(960), float32], %layers_15_bn2_weight: Tensor[(960), float32], %layers_15_bn2_running_mean: Tensor[(960), float32], %layers_15_bn2_bias: Tensor[(960), float32], %layers_15_conv3_weight: Tensor[(160, 960, 1, 1), float32], %layers_15_bn3_running_var: Tensor[(160), float32], %layers_15_bn3_weight: Tensor[(160), float32], %layers_15_bn3_running_mean: Tensor[(160), float32], %layers_15_bn3_bias: Tensor[(160), float32], %layers_16_conv1_weight: Tensor[(960, 160, 1, 1), float32], %layers_16_bn1_running_var: Tensor[(960), float32], %layers_16_bn1_weight: Tensor[(960), float32], %layers_16_bn1_running_mean: Tensor[(960), float32], %layers_16_bn1_bias: Tensor[(960), float32], %layers_16_conv2_weight: Tensor[(960, 1, 3, 3), float32], %layers_16_bn2_running_var: Tensor[(960), float32], %layers_16_bn2_weight: Tensor[(960), float32], %layers_16_bn2_running_mean: Tensor[(960), float32], %layers_16_bn2_bias: Tensor[(960), float32], %layers_16_conv3_weight: Tensor[(320, 960, 1, 1), float32], %layers_16_bn3_running_var: Tensor[(320), float32], %layers_16_bn3_weight: Tensor[(320), float32], %layers_16_bn3_running_mean: Tensor[(320), float32], %layers_16_bn3_bias: Tensor[(320), float32], %layers_16_shortcut_0_weight: Tensor[(320, 160, 1, 1), float32], %layers_16_shortcut_1_running_var: Tensor[(320), float32], %layers_16_shortcut_1_weight: Tensor[(320), float32], %layers_16_shortcut_1_running_mean: Tensor[(320), float32], %layers_16_shortcut_1_bias: Tensor[(320), float32], %conv2_weight: Tensor[(1280, 320, 1, 1), float32], %bn2_running_var: Tensor[(1280), float32], %bn2_weight: Tensor[(1280), float32], %bn2_running_mean: Tensor[(1280), float32], %bn2_bias: Tensor[(1280), float32], %linear_weight: Tensor[(10, 1280), float32], %linear_bias: Tensor[(10), float32]) -> Tensor[(1, 10), float32] { + %14 = reshape(%conv2_weight, newshape=[1280, 320]) /* from_string */ /* ty=Tensor[(1280, 320), float32] */; + %15 = max(%14) /* ty=float32 */; + %16 = min(%14) /* ty=float32 */; + %17 = divide(%15, 127f /* ty=float32 */) /* ty=float32 */; + %18 = divide(%16, -127f /* ty=float32 */) /* ty=float32 */; + %19 = maximum(%17, %18) /* ty=float32 */; + %20 = divide(%14, %19) /* ty=Tensor[(1280, 320), float32] */; + %21 = round(%20) /* ty=Tensor[(1280, 320), float32] */; + %29 = reshape(%layers_16_conv3_weight, newshape=[320, 960]) /* from_string */ /* ty=Tensor[(320, 960), float32] */; + %30 = max(%29) /* ty=float32 */; + %31 = min(%29) /* ty=float32 */; + %32 = divide(%30, 127f /* ty=float32 */) /* ty=float32 */; + %33 = divide(%31, -127f /* ty=float32 */) /* ty=float32 */; + %34 = maximum(%32, %33) /* ty=float32 */; + %35 = divide(%29, %34) /* ty=Tensor[(320, 960), float32] */; + %36 = round(%35) /* ty=Tensor[(320, 960), float32] */; + %44 = reshape(%layers_16_conv1_weight, newshape=[960, 160]) /* from_string */ /* ty=Tensor[(960, 160), float32] */; + %45 = max(%44) /* ty=float32 */; + %46 = min(%44) /* ty=float32 */; + %47 = divide(%45, 127f /* ty=float32 */) /* ty=float32 */; + %48 = divide(%46, -127f /* ty=float32 */) /* ty=float32 */; + %49 = maximum(%47, %48) /* ty=float32 */; + %50 = divide(%44, %49) /* ty=Tensor[(960, 160), float32] */; + %51 = round(%50) /* ty=Tensor[(960, 160), float32] */; + %59 = reshape(%layers_15_conv3_weight, newshape=[160, 960]) /* from_string */ /* ty=Tensor[(160, 960), float32] */; + %60 = max(%59) /* ty=float32 */; + %61 = min(%59) /* ty=float32 */; + %62 = divide(%60, 127f /* ty=float32 */) /* ty=float32 */; + %63 = divide(%61, -127f /* ty=float32 */) /* ty=float32 */; + %64 = maximum(%62, %63) /* ty=float32 */; + %65 = divide(%59, %64) /* ty=Tensor[(160, 960), float32] */; + %66 = round(%65) /* ty=Tensor[(160, 960), float32] */; + %74 = reshape(%layers_15_conv1_weight, newshape=[960, 160]) /* from_string */ /* ty=Tensor[(960, 160), float32] */; + %75 = max(%74) /* ty=float32 */; + %76 = min(%74) /* ty=float32 */; + %77 = divide(%75, 127f /* ty=float32 */) /* ty=float32 */; + %78 = divide(%76, -127f /* ty=float32 */) /* ty=float32 */; + %79 = maximum(%77, %78) /* ty=float32 */; + %80 = divide(%74, %79) /* ty=Tensor[(960, 160), float32] */; + %81 = round(%80) /* ty=Tensor[(960, 160), float32] */; + %89 = reshape(%layers_14_conv3_weight, newshape=[160, 960]) /* from_string */ /* ty=Tensor[(160, 960), float32] */; + %90 = max(%89) /* ty=float32 */; + %91 = min(%89) /* ty=float32 */; + %92 = divide(%90, 127f /* ty=float32 */) /* ty=float32 */; + %93 = divide(%91, -127f /* ty=float32 */) /* ty=float32 */; + %94 = maximum(%92, %93) /* ty=float32 */; + %95 = divide(%89, %94) /* ty=Tensor[(160, 960), float32] */; + %96 = round(%95) /* ty=Tensor[(160, 960), float32] */; + %104 = reshape(%layers_14_conv1_weight, newshape=[960, 160]) /* from_string */ /* ty=Tensor[(960, 160), float32] */; + %105 = max(%104) /* ty=float32 */; + %106 = min(%104) /* ty=float32 */; + %107 = divide(%105, 127f /* ty=float32 */) /* ty=float32 */; + %108 = divide(%106, -127f /* ty=float32 */) /* ty=float32 */; + %109 = maximum(%107, %108) /* ty=float32 */; + %110 = divide(%104, %109) /* ty=Tensor[(960, 160), float32] */; + %111 = round(%110) /* ty=Tensor[(960, 160), float32] */; + %119 = reshape(%layers_13_conv3_weight, newshape=[160, 576]) /* from_string */ /* ty=Tensor[(160, 576), float32] */; + %120 = max(%119) /* ty=float32 */; + %121 = min(%119) /* ty=float32 */; + %122 = divide(%120, 127f /* ty=float32 */) /* ty=float32 */; + %123 = divide(%121, -127f /* ty=float32 */) /* ty=float32 */; + %124 = maximum(%122, %123) /* ty=float32 */; + %125 = divide(%119, %124) /* ty=Tensor[(160, 576), float32] */; + %126 = round(%125) /* ty=Tensor[(160, 576), float32] */; + %134 = reshape(%layers_13_conv1_weight, newshape=[576, 96]) /* from_string */ /* ty=Tensor[(576, 96), float32] */; + %135 = max(%134) /* ty=float32 */; + %136 = min(%134) /* ty=float32 */; + %137 = divide(%135, 127f /* ty=float32 */) /* ty=float32 */; + %138 = divide(%136, -127f /* ty=float32 */) /* ty=float32 */; + %139 = maximum(%137, %138) /* ty=float32 */; + %140 = divide(%134, %139) /* ty=Tensor[(576, 96), float32] */; + %141 = round(%140) /* ty=Tensor[(576, 96), float32] */; + %149 = reshape(%layers_12_conv3_weight, newshape=[96, 576]) /* from_string */ /* ty=Tensor[(96, 576), float32] */; + %150 = max(%149) /* ty=float32 */; + %151 = min(%149) /* ty=float32 */; + %152 = divide(%150, 127f /* ty=float32 */) /* ty=float32 */; + %153 = divide(%151, -127f /* ty=float32 */) /* ty=float32 */; + %154 = maximum(%152, %153) /* ty=float32 */; + %155 = divide(%149, %154) /* ty=Tensor[(96, 576), float32] */; + %156 = round(%155) /* ty=Tensor[(96, 576), float32] */; + %164 = reshape(%layers_12_conv1_weight, newshape=[576, 96]) /* from_string */ /* ty=Tensor[(576, 96), float32] */; + %165 = max(%164) /* ty=float32 */; + %166 = min(%164) /* ty=float32 */; + %167 = divide(%165, 127f /* ty=float32 */) /* ty=float32 */; + %168 = divide(%166, -127f /* ty=float32 */) /* ty=float32 */; + %169 = maximum(%167, %168) /* ty=float32 */; + %170 = divide(%164, %169) /* ty=Tensor[(576, 96), float32] */; + %171 = round(%170) /* ty=Tensor[(576, 96), float32] */; + %179 = reshape(%layers_11_conv3_weight, newshape=[96, 576]) /* from_string */ /* ty=Tensor[(96, 576), float32] */; + %180 = max(%179) /* ty=float32 */; + %181 = min(%179) /* ty=float32 */; + %182 = divide(%180, 127f /* ty=float32 */) /* ty=float32 */; + %183 = divide(%181, -127f /* ty=float32 */) /* ty=float32 */; + %184 = maximum(%182, %183) /* ty=float32 */; + %185 = divide(%179, %184) /* ty=Tensor[(96, 576), float32] */; + %186 = round(%185) /* ty=Tensor[(96, 576), float32] */; + %194 = reshape(%layers_11_conv1_weight, newshape=[576, 96]) /* from_string */ /* ty=Tensor[(576, 96), float32] */; + %195 = max(%194) /* ty=float32 */; + %196 = min(%194) /* ty=float32 */; + %197 = divide(%195, 127f /* ty=float32 */) /* ty=float32 */; + %198 = divide(%196, -127f /* ty=float32 */) /* ty=float32 */; + %199 = maximum(%197, %198) /* ty=float32 */; + %200 = divide(%194, %199) /* ty=Tensor[(576, 96), float32] */; + %201 = round(%200) /* ty=Tensor[(576, 96), float32] */; + %209 = reshape(%layers_10_conv3_weight, newshape=[96, 384]) /* from_string */ /* ty=Tensor[(96, 384), float32] */; + %210 = max(%209) /* ty=float32 */; + %211 = min(%209) /* ty=float32 */; + %212 = divide(%210, 127f /* ty=float32 */) /* ty=float32 */; + %213 = divide(%211, -127f /* ty=float32 */) /* ty=float32 */; + %214 = maximum(%212, %213) /* ty=float32 */; + %215 = divide(%209, %214) /* ty=Tensor[(96, 384), float32] */; + %216 = round(%215) /* ty=Tensor[(96, 384), float32] */; + %224 = reshape(%layers_10_conv1_weight, newshape=[384, 64]) /* from_string */ /* ty=Tensor[(384, 64), float32] */; + %225 = max(%224) /* ty=float32 */; + %226 = min(%224) /* ty=float32 */; + %227 = divide(%225, 127f /* ty=float32 */) /* ty=float32 */; + %228 = divide(%226, -127f /* ty=float32 */) /* ty=float32 */; + %229 = maximum(%227, %228) /* ty=float32 */; + %230 = divide(%224, %229) /* ty=Tensor[(384, 64), float32] */; + %231 = round(%230) /* ty=Tensor[(384, 64), float32] */; + %239 = reshape(%layers_9_conv3_weight, newshape=[64, 384]) /* from_string */ /* ty=Tensor[(64, 384), float32] */; + %240 = max(%239) /* ty=float32 */; + %241 = min(%239) /* ty=float32 */; + %242 = divide(%240, 127f /* ty=float32 */) /* ty=float32 */; + %243 = divide(%241, -127f /* ty=float32 */) /* ty=float32 */; + %244 = maximum(%242, %243) /* ty=float32 */; + %245 = divide(%239, %244) /* ty=Tensor[(64, 384), float32] */; + %246 = round(%245) /* ty=Tensor[(64, 384), float32] */; + %254 = reshape(%layers_9_conv1_weight, newshape=[384, 64]) /* from_string */ /* ty=Tensor[(384, 64), float32] */; + %255 = max(%254) /* ty=float32 */; + %256 = min(%254) /* ty=float32 */; + %257 = divide(%255, 127f /* ty=float32 */) /* ty=float32 */; + %258 = divide(%256, -127f /* ty=float32 */) /* ty=float32 */; + %259 = maximum(%257, %258) /* ty=float32 */; + %260 = divide(%254, %259) /* ty=Tensor[(384, 64), float32] */; + %261 = round(%260) /* ty=Tensor[(384, 64), float32] */; + %269 = reshape(%layers_8_conv3_weight, newshape=[64, 384]) /* from_string */ /* ty=Tensor[(64, 384), float32] */; + %270 = max(%269) /* ty=float32 */; + %271 = min(%269) /* ty=float32 */; + %272 = divide(%270, 127f /* ty=float32 */) /* ty=float32 */; + %273 = divide(%271, -127f /* ty=float32 */) /* ty=float32 */; + %274 = maximum(%272, %273) /* ty=float32 */; + %275 = divide(%269, %274) /* ty=Tensor[(64, 384), float32] */; + %276 = round(%275) /* ty=Tensor[(64, 384), float32] */; + %284 = reshape(%layers_8_conv1_weight, newshape=[384, 64]) /* from_string */ /* ty=Tensor[(384, 64), float32] */; + %285 = max(%284) /* ty=float32 */; + %286 = min(%284) /* ty=float32 */; + %287 = divide(%285, 127f /* ty=float32 */) /* ty=float32 */; + %288 = divide(%286, -127f /* ty=float32 */) /* ty=float32 */; + %289 = maximum(%287, %288) /* ty=float32 */; + %290 = divide(%284, %289) /* ty=Tensor[(384, 64), float32] */; + %291 = round(%290) /* ty=Tensor[(384, 64), float32] */; + %299 = reshape(%layers_7_conv3_weight, newshape=[64, 384]) /* from_string */ /* ty=Tensor[(64, 384), float32] */; + %300 = max(%299) /* ty=float32 */; + %301 = min(%299) /* ty=float32 */; + %302 = divide(%300, 127f /* ty=float32 */) /* ty=float32 */; + %303 = divide(%301, -127f /* ty=float32 */) /* ty=float32 */; + %304 = maximum(%302, %303) /* ty=float32 */; + %305 = divide(%299, %304) /* ty=Tensor[(64, 384), float32] */; + %306 = round(%305) /* ty=Tensor[(64, 384), float32] */; + %314 = reshape(%layers_7_conv1_weight, newshape=[384, 64]) /* from_string */ /* ty=Tensor[(384, 64), float32] */; + %315 = max(%314) /* ty=float32 */; + %316 = min(%314) /* ty=float32 */; + %317 = divide(%315, 127f /* ty=float32 */) /* ty=float32 */; + %318 = divide(%316, -127f /* ty=float32 */) /* ty=float32 */; + %319 = maximum(%317, %318) /* ty=float32 */; + %320 = divide(%314, %319) /* ty=Tensor[(384, 64), float32] */; + %321 = round(%320) /* ty=Tensor[(384, 64), float32] */; + %329 = reshape(%layers_6_conv3_weight, newshape=[64, 192]) /* from_string */ /* ty=Tensor[(64, 192), float32] */; + %330 = max(%329) /* ty=float32 */; + %331 = min(%329) /* ty=float32 */; + %332 = divide(%330, 127f /* ty=float32 */) /* ty=float32 */; + %333 = divide(%331, -127f /* ty=float32 */) /* ty=float32 */; + %334 = maximum(%332, %333) /* ty=float32 */; + %335 = divide(%329, %334) /* ty=Tensor[(64, 192), float32] */; + %336 = round(%335) /* ty=Tensor[(64, 192), float32] */; + %344 = reshape(%layers_6_conv1_weight, newshape=[192, 32]) /* from_string */ /* ty=Tensor[(192, 32), float32] */; + %345 = max(%344) /* ty=float32 */; + %346 = min(%344) /* ty=float32 */; + %347 = divide(%345, 127f /* ty=float32 */) /* ty=float32 */; + %348 = divide(%346, -127f /* ty=float32 */) /* ty=float32 */; + %349 = maximum(%347, %348) /* ty=float32 */; + %350 = divide(%344, %349) /* ty=Tensor[(192, 32), float32] */; + %351 = round(%350) /* ty=Tensor[(192, 32), float32] */; + %359 = reshape(%layers_5_conv3_weight, newshape=[32, 192]) /* from_string */ /* ty=Tensor[(32, 192), float32] */; + %360 = max(%359) /* ty=float32 */; + %361 = min(%359) /* ty=float32 */; + %362 = divide(%360, 127f /* ty=float32 */) /* ty=float32 */; + %363 = divide(%361, -127f /* ty=float32 */) /* ty=float32 */; + %364 = maximum(%362, %363) /* ty=float32 */; + %365 = divide(%359, %364) /* ty=Tensor[(32, 192), float32] */; + %366 = round(%365) /* ty=Tensor[(32, 192), float32] */; + %374 = reshape(%layers_5_conv1_weight, newshape=[192, 32]) /* from_string */ /* ty=Tensor[(192, 32), float32] */; + %375 = max(%374) /* ty=float32 */; + %376 = min(%374) /* ty=float32 */; + %377 = divide(%375, 127f /* ty=float32 */) /* ty=float32 */; + %378 = divide(%376, -127f /* ty=float32 */) /* ty=float32 */; + %379 = maximum(%377, %378) /* ty=float32 */; + %380 = divide(%374, %379) /* ty=Tensor[(192, 32), float32] */; + %381 = round(%380) /* ty=Tensor[(192, 32), float32] */; + %389 = reshape(%layers_4_conv3_weight, newshape=[32, 192]) /* from_string */ /* ty=Tensor[(32, 192), float32] */; + %390 = max(%389) /* ty=float32 */; + %391 = min(%389) /* ty=float32 */; + %392 = divide(%390, 127f /* ty=float32 */) /* ty=float32 */; + %393 = divide(%391, -127f /* ty=float32 */) /* ty=float32 */; + %394 = maximum(%392, %393) /* ty=float32 */; + %395 = divide(%389, %394) /* ty=Tensor[(32, 192), float32] */; + %396 = round(%395) /* ty=Tensor[(32, 192), float32] */; + %404 = reshape(%layers_4_conv1_weight, newshape=[192, 32]) /* from_string */ /* ty=Tensor[(192, 32), float32] */; + %405 = max(%404) /* ty=float32 */; + %406 = min(%404) /* ty=float32 */; + %407 = divide(%405, 127f /* ty=float32 */) /* ty=float32 */; + %408 = divide(%406, -127f /* ty=float32 */) /* ty=float32 */; + %409 = maximum(%407, %408) /* ty=float32 */; + %410 = divide(%404, %409) /* ty=Tensor[(192, 32), float32] */; + %411 = round(%410) /* ty=Tensor[(192, 32), float32] */; + %419 = reshape(%layers_3_conv3_weight, newshape=[32, 144]) /* from_string */ /* ty=Tensor[(32, 144), float32] */; + %420 = max(%419) /* ty=float32 */; + %421 = min(%419) /* ty=float32 */; + %422 = divide(%420, 127f /* ty=float32 */) /* ty=float32 */; + %423 = divide(%421, -127f /* ty=float32 */) /* ty=float32 */; + %424 = maximum(%422, %423) /* ty=float32 */; + %425 = divide(%419, %424) /* ty=Tensor[(32, 144), float32] */; + %426 = round(%425) /* ty=Tensor[(32, 144), float32] */; + %434 = reshape(%layers_3_conv1_weight, newshape=[144, 24]) /* from_string */ /* ty=Tensor[(144, 24), float32] */; + %435 = max(%434) /* ty=float32 */; + %436 = min(%434) /* ty=float32 */; + %437 = divide(%435, 127f /* ty=float32 */) /* ty=float32 */; + %438 = divide(%436, -127f /* ty=float32 */) /* ty=float32 */; + %439 = maximum(%437, %438) /* ty=float32 */; + %440 = divide(%434, %439) /* ty=Tensor[(144, 24), float32] */; + %441 = round(%440) /* ty=Tensor[(144, 24), float32] */; + %449 = reshape(%layers_2_conv3_weight, newshape=[24, 144]) /* from_string */ /* ty=Tensor[(24, 144), float32] */; + %450 = max(%449) /* ty=float32 */; + %451 = min(%449) /* ty=float32 */; + %452 = divide(%450, 127f /* ty=float32 */) /* ty=float32 */; + %453 = divide(%451, -127f /* ty=float32 */) /* ty=float32 */; + %454 = maximum(%452, %453) /* ty=float32 */; + %455 = divide(%449, %454) /* ty=Tensor[(24, 144), float32] */; + %456 = round(%455) /* ty=Tensor[(24, 144), float32] */; + %464 = reshape(%layers_2_conv1_weight, newshape=[144, 24]) /* from_string */ /* ty=Tensor[(144, 24), float32] */; + %465 = max(%464) /* ty=float32 */; + %466 = min(%464) /* ty=float32 */; + %467 = divide(%465, 127f /* ty=float32 */) /* ty=float32 */; + %468 = divide(%466, -127f /* ty=float32 */) /* ty=float32 */; + %469 = maximum(%467, %468) /* ty=float32 */; + %470 = divide(%464, %469) /* ty=Tensor[(144, 24), float32] */; + %471 = round(%470) /* ty=Tensor[(144, 24), float32] */; + %479 = reshape(%layers_1_conv3_weight, newshape=[24, 96]) /* from_string */ /* ty=Tensor[(24, 96), float32] */; + %480 = max(%479) /* ty=float32 */; + %481 = min(%479) /* ty=float32 */; + %482 = divide(%480, 127f /* ty=float32 */) /* ty=float32 */; + %483 = divide(%481, -127f /* ty=float32 */) /* ty=float32 */; + %484 = maximum(%482, %483) /* ty=float32 */; + %485 = divide(%479, %484) /* ty=Tensor[(24, 96), float32] */; + %486 = round(%485) /* ty=Tensor[(24, 96), float32] */; + %494 = reshape(%layers_1_conv1_weight, newshape=[96, 16]) /* from_string */ /* ty=Tensor[(96, 16), float32] */; + %495 = max(%494) /* ty=float32 */; + %496 = min(%494) /* ty=float32 */; + %497 = divide(%495, 127f /* ty=float32 */) /* ty=float32 */; + %498 = divide(%496, -127f /* ty=float32 */) /* ty=float32 */; + %499 = maximum(%497, %498) /* ty=float32 */; + %500 = divide(%494, %499) /* ty=Tensor[(96, 16), float32] */; + %501 = round(%500) /* ty=Tensor[(96, 16), float32] */; + %509 = reshape(%layers_0_conv3_weight, newshape=[16, 32]) /* from_string */ /* ty=Tensor[(16, 32), float32] */; + %510 = max(%509) /* ty=float32 */; + %511 = min(%509) /* ty=float32 */; + %512 = divide(%510, 127f /* ty=float32 */) /* ty=float32 */; + %513 = divide(%511, -127f /* ty=float32 */) /* ty=float32 */; + %514 = maximum(%512, %513) /* ty=float32 */; + %515 = divide(%509, %514) /* ty=Tensor[(16, 32), float32] */; + %516 = round(%515) /* ty=Tensor[(16, 32), float32] */; + %524 = reshape(%layers_0_conv1_weight, newshape=[32, 32]) /* from_string */ /* ty=Tensor[(32, 32), float32] */; + %525 = max(%524) /* ty=float32 */; + %526 = min(%524) /* ty=float32 */; + %527 = divide(%525, 127f /* ty=float32 */) /* ty=float32 */; + %528 = divide(%526, -127f /* ty=float32 */) /* ty=float32 */; + %529 = maximum(%527, %528) /* ty=float32 */; + %530 = divide(%524, %529) /* ty=Tensor[(32, 32), float32] */; + %531 = round(%530) /* ty=Tensor[(32, 32), float32] */; + %539 = reshape(%conv1_weight, newshape=[32, 27]) /* from_string */ /* ty=Tensor[(32, 27), float32] */; + %540 = max(%539) /* ty=float32 */; + %541 = min(%539) /* ty=float32 */; + %542 = divide(%540, 127f /* ty=float32 */) /* ty=float32 */; + %543 = divide(%541, -127f /* ty=float32 */) /* ty=float32 */; + %544 = maximum(%542, %543) /* ty=float32 */; + %545 = divide(%539, %544) /* ty=Tensor[(32, 27), float32] */; + %546 = round(%545) /* ty=Tensor[(32, 27), float32] */; + %547 = nn.pad(%input0, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 3, 34, 32), float32] */; + %548 = nn.pad(%547, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* from_string */ /* ty=Tensor[(1, 3, 34, 34), float32] */; + %549 = windows(%548, axis=1, window_shape=[3, 3, 3], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 32, 32, 3, 3, 3), float32] */; + %550 = squeeze(%549, axis=[1]) /* from_string */ /* ty=Tensor[(1, 32, 32, 3, 3, 3), float32] */; + %551 = reshape(%550, newshape=[1024, 27]) /* from_string */ /* ty=Tensor[(1024, 27), float32] */; + %552 = max(%551) /* ty=float32 */; + %553 = min(%551) /* ty=float32 */; + %554 = divide(%552, 127f /* ty=float32 */) /* ty=float32 */; + %555 = divide(%553, -127f /* ty=float32 */) /* ty=float32 */; + %556 = maximum(%554, %555) /* ty=float32 */; + %557 = divide(%551, %556) /* ty=Tensor[(1024, 27), float32] */; + %558 = round(%557) /* ty=Tensor[(1024, 27), float32] */; + %559 = nn.dense(%539, %551, units=None) /* ty=Tensor[(32, 1024), float32] */; + %560 = max(%559) /* ty=float32 */; + %561 = min(%559) /* ty=float32 */; + %562 = divide(%560, 127f /* ty=float32 */) /* ty=float32 */; + %563 = divide(%561, -127f /* ty=float32 */) /* ty=float32 */; + %564 = cast(%546, dtype="int8") /* ty=Tensor[(32, 27), int8] */; + %565 = cast(%558, dtype="int8") /* ty=Tensor[(1024, 27), int8] */; + %566 = maximum(%562, %563) /* ty=float32 */; + %567 = fn (%outer_arg_036: Tensor[(32, 27), int8], %outer_arg_136: Tensor[(1024, 27), int8], %outer_arg_236: float32, %outer_arg_336: float32, %outer_arg_436: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_0") -> Tensor[(32, 1024), int8] { + %538 = fn (%data36: Tensor[(32, 27), int8], %weights36: Tensor[(1024, 27), int8], %s_data36: float32, %s_w36: float32, %s_act36: float32, Composite="ilavta.dense") -> Tensor[(32, 1024), int8] { + %532 = nn.dense(%data36, %weights36, units=None, out_dtype="int32") /* ty=Tensor[(32, 1024), int32] */; + %533 = multiply(%s_data36, %s_w36) /* ty=float32 */; + %534 = cast(%532, dtype="float32") /* ty=Tensor[(32, 1024), float32] */; + %535 = divide(%533, %s_act36) /* ty=float32 */; + %536 = multiply(%534, %535) /* ty=Tensor[(32, 1024), float32] */; + %537 = clip(%536, a_min=-127f, a_max=127f) /* ty=Tensor[(32, 1024), float32] */; + cast(%537, dtype="int8") /* ty=Tensor[(32, 1024), int8] */ + }; + %538(%outer_arg_036, %outer_arg_136, %outer_arg_236, %outer_arg_336, %outer_arg_436) /* ty=Tensor[(32, 1024), int8] */ + }; + %568 = %567(%564, %565, %544, %556, %566) /* ty=Tensor[(32, 1024), int8] */; + %569 = cast(%568, dtype="float32") /* ty=Tensor[(32, 1024), float32] */; + %570 = multiply(%569, %566) /* ty=Tensor[(32, 1024), float32] */; + %571 = reshape(%570, newshape=[32, 1, 32, 32]) /* from_string */ /* ty=Tensor[(32, 1, 32, 32), float32] */; + %572 = add(%bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(32), float32] */; + %573 = expand_dims(1f /* ty=float32 */, axis=0) /* from_string */ /* ty=Tensor[(1), float32] */; + %574 = sqrt(%572) /* from_string */ /* ty=Tensor[(32), float32] */; + %575 = divide(%573, %574) /* from_string */ /* ty=Tensor[(32), float32] */; + %576 = multiply(%575, %bn1_weight) /* from_string */ /* ty=Tensor[(32), float32] */; + %577 = expand_dims(%576, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %578 = transpose(%571, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %579 = expand_dims(%577, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %580 = negative(%bn1_running_mean) /* from_string */ /* ty=Tensor[(32), float32] */; + %581 = multiply(%580, %576) /* from_string */ /* ty=Tensor[(32), float32] */; + %582 = add(%581, %bn1_bias) /* from_string */ /* ty=Tensor[(32), float32] */; + %583 = expand_dims(%582, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %584 = multiply(%578, %579) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %585 = expand_dims(%583, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %586 = add(%584, %585) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %587 = nn.relu(%586) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %588 = nn.pad(%587, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %589 = nn.pad(%588, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %590 = windows(%589, axis=1, window_shape=[32, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 32, 32, 32, 1, 1), float32] */; + %591 = squeeze(%590, axis=[1]) /* from_string */ /* ty=Tensor[(1, 32, 32, 32, 1, 1), float32] */; + %592 = reshape(%591, newshape=[1024, 32]) /* from_string */ /* ty=Tensor[(1024, 32), float32] */; + %593 = max(%592) /* ty=float32 */; + %594 = min(%592) /* ty=float32 */; + %595 = divide(%593, 127f /* ty=float32 */) /* ty=float32 */; + %596 = divide(%594, -127f /* ty=float32 */) /* ty=float32 */; + %597 = maximum(%595, %596) /* ty=float32 */; + %598 = divide(%592, %597) /* ty=Tensor[(1024, 32), float32] */; + %599 = round(%598) /* ty=Tensor[(1024, 32), float32] */; + %600 = nn.dense(%524, %592, units=None) /* ty=Tensor[(32, 1024), float32] */; + %601 = max(%600) /* ty=float32 */; + %602 = min(%600) /* ty=float32 */; + %603 = divide(%601, 127f /* ty=float32 */) /* ty=float32 */; + %604 = divide(%602, -127f /* ty=float32 */) /* ty=float32 */; + %605 = cast(%531, dtype="int8") /* ty=Tensor[(32, 32), int8] */; + %606 = cast(%599, dtype="int8") /* ty=Tensor[(1024, 32), int8] */; + %607 = maximum(%603, %604) /* ty=float32 */; + %608 = fn (%outer_arg_035: Tensor[(32, 32), int8], %outer_arg_135: Tensor[(1024, 32), int8], %outer_arg_235: float32, %outer_arg_335: float32, %outer_arg_435: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_1") -> Tensor[(32, 1024), int8] { + %523 = fn (%data35: Tensor[(32, 32), int8], %weights35: Tensor[(1024, 32), int8], %s_data35: float32, %s_w35: float32, %s_act35: float32, Composite="ilavta.dense") -> Tensor[(32, 1024), int8] { + %517 = nn.dense(%data35, %weights35, units=None, out_dtype="int32") /* ty=Tensor[(32, 1024), int32] */; + %518 = multiply(%s_data35, %s_w35) /* ty=float32 */; + %519 = cast(%517, dtype="float32") /* ty=Tensor[(32, 1024), float32] */; + %520 = divide(%518, %s_act35) /* ty=float32 */; + %521 = multiply(%519, %520) /* ty=Tensor[(32, 1024), float32] */; + %522 = clip(%521, a_min=-127f, a_max=127f) /* ty=Tensor[(32, 1024), float32] */; + cast(%522, dtype="int8") /* ty=Tensor[(32, 1024), int8] */ + }; + %523(%outer_arg_035, %outer_arg_135, %outer_arg_235, %outer_arg_335, %outer_arg_435) /* ty=Tensor[(32, 1024), int8] */ + }; + %609 = %608(%605, %606, %529, %597, %607) /* ty=Tensor[(32, 1024), int8] */; + %610 = cast(%609, dtype="float32") /* ty=Tensor[(32, 1024), float32] */; + %611 = multiply(%610, %607) /* ty=Tensor[(32, 1024), float32] */; + %612 = reshape(%611, newshape=[32, 1, 32, 32]) /* from_string */ /* ty=Tensor[(32, 1, 32, 32), float32] */; + %613 = add(%layers_0_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(32), float32] */; + %614 = sqrt(%613) /* from_string */ /* ty=Tensor[(32), float32] */; + %615 = divide(%573, %614) /* from_string */ /* ty=Tensor[(32), float32] */; + %616 = multiply(%615, %layers_0_bn1_weight) /* from_string */ /* ty=Tensor[(32), float32] */; + %617 = expand_dims(%616, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %618 = transpose(%612, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %619 = expand_dims(%617, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %620 = negative(%layers_0_bn1_running_mean) /* from_string */ /* ty=Tensor[(32), float32] */; + %621 = multiply(%620, %616) /* from_string */ /* ty=Tensor[(32), float32] */; + %622 = add(%621, %layers_0_bn1_bias) /* from_string */ /* ty=Tensor[(32), float32] */; + %623 = expand_dims(%622, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %624 = multiply(%618, %619) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %625 = expand_dims(%623, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %626 = add(%624, %625) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %627 = nn.relu(%626) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %628 = reshape(%layers_0_conv2_weight, newshape=[32, 1, 3, 3]) /* from_string */ /* ty=Tensor[(32, 1, 3, 3), float32] */; + %629 = add(%layers_0_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(32), float32] */; + %630 = sqrt(%629) /* from_string */ /* ty=Tensor[(32), float32] */; + %631 = divide(%573, %630) /* from_string */ /* ty=Tensor[(32), float32] */; + %632 = multiply(%631, %layers_0_bn2_weight) /* from_string */ /* ty=Tensor[(32), float32] */; + %633 = expand_dims(%632, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %634 = nn.conv2d(%627, %628, padding=[1, 1, 1, 1], groups=32, channels=32, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %635 = expand_dims(%633, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %636 = negative(%layers_0_bn2_running_mean) /* from_string */ /* ty=Tensor[(32), float32] */; + %637 = multiply(%636, %632) /* from_string */ /* ty=Tensor[(32), float32] */; + %638 = add(%637, %layers_0_bn2_bias) /* from_string */ /* ty=Tensor[(32), float32] */; + %639 = expand_dims(%638, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %640 = multiply(%634, %635) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %641 = expand_dims(%639, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %642 = add(%640, %641) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %643 = nn.relu(%642) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %644 = nn.pad(%643, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %645 = nn.pad(%644, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), float32] */; + %646 = windows(%645, axis=1, window_shape=[32, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 32, 32, 32, 1, 1), float32] */; + %647 = squeeze(%646, axis=[1]) /* from_string */ /* ty=Tensor[(1, 32, 32, 32, 1, 1), float32] */; + %648 = reshape(%647, newshape=[1024, 32]) /* from_string */ /* ty=Tensor[(1024, 32), float32] */; + %649 = max(%648) /* ty=float32 */; + %650 = min(%648) /* ty=float32 */; + %651 = divide(%649, 127f /* ty=float32 */) /* ty=float32 */; + %652 = divide(%650, -127f /* ty=float32 */) /* ty=float32 */; + %653 = maximum(%651, %652) /* ty=float32 */; + %654 = divide(%648, %653) /* ty=Tensor[(1024, 32), float32] */; + %655 = round(%654) /* ty=Tensor[(1024, 32), float32] */; + %656 = nn.dense(%509, %648, units=None) /* ty=Tensor[(16, 1024), float32] */; + %657 = max(%656) /* ty=float32 */; + %658 = min(%656) /* ty=float32 */; + %659 = divide(%657, 127f /* ty=float32 */) /* ty=float32 */; + %660 = divide(%658, -127f /* ty=float32 */) /* ty=float32 */; + %661 = cast(%516, dtype="int8") /* ty=Tensor[(16, 32), int8] */; + %662 = cast(%655, dtype="int8") /* ty=Tensor[(1024, 32), int8] */; + %663 = maximum(%659, %660) /* ty=float32 */; + %664 = fn (%outer_arg_034: Tensor[(16, 32), int8], %outer_arg_134: Tensor[(1024, 32), int8], %outer_arg_234: float32, %outer_arg_334: float32, %outer_arg_434: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_2") -> Tensor[(16, 1024), int8] { + %508 = fn (%data34: Tensor[(16, 32), int8], %weights34: Tensor[(1024, 32), int8], %s_data34: float32, %s_w34: float32, %s_act34: float32, Composite="ilavta.dense") -> Tensor[(16, 1024), int8] { + %502 = nn.dense(%data34, %weights34, units=None, out_dtype="int32") /* ty=Tensor[(16, 1024), int32] */; + %503 = multiply(%s_data34, %s_w34) /* ty=float32 */; + %504 = cast(%502, dtype="float32") /* ty=Tensor[(16, 1024), float32] */; + %505 = divide(%503, %s_act34) /* ty=float32 */; + %506 = multiply(%504, %505) /* ty=Tensor[(16, 1024), float32] */; + %507 = clip(%506, a_min=-127f, a_max=127f) /* ty=Tensor[(16, 1024), float32] */; + cast(%507, dtype="int8") /* ty=Tensor[(16, 1024), int8] */ + }; + %508(%outer_arg_034, %outer_arg_134, %outer_arg_234, %outer_arg_334, %outer_arg_434) /* ty=Tensor[(16, 1024), int8] */ + }; + %665 = %664(%661, %662, %514, %653, %663) /* ty=Tensor[(16, 1024), int8] */; + %666 = cast(%665, dtype="float32") /* ty=Tensor[(16, 1024), float32] */; + %667 = multiply(%666, %663) /* ty=Tensor[(16, 1024), float32] */; + %668 = reshape(%667, newshape=[16, 1, 32, 32]) /* from_string */ /* ty=Tensor[(16, 1, 32, 32), float32] */; + %669 = add(%layers_0_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(16), float32] */; + %670 = sqrt(%669) /* from_string */ /* ty=Tensor[(16), float32] */; + %671 = divide(%573, %670) /* from_string */ /* ty=Tensor[(16), float32] */; + %672 = multiply(%671, %layers_0_bn3_weight) /* from_string */ /* ty=Tensor[(16), float32] */; + %673 = expand_dims(%672, axis=1) /* from_string */ /* ty=Tensor[(16, 1), float32] */; + %674 = transpose(%668, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %675 = expand_dims(%673, axis=1) /* from_string */ /* ty=Tensor[(16, 1, 1), float32] */; + %676 = negative(%layers_0_bn3_running_mean) /* from_string */ /* ty=Tensor[(16), float32] */; + %677 = multiply(%676, %672) /* from_string */ /* ty=Tensor[(16), float32] */; + %678 = add(%677, %layers_0_bn3_bias) /* from_string */ /* ty=Tensor[(16), float32] */; + %679 = expand_dims(%678, axis=1) /* from_string */ /* ty=Tensor[(16, 1), float32] */; + %680 = multiply(%674, %675) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %681 = expand_dims(%679, axis=1) /* from_string */ /* ty=Tensor[(16, 1, 1), float32] */; + %689 = reshape(%layers_0_shortcut_0_weight, newshape=[16, 32]) /* from_string */ /* ty=Tensor[(16, 32), float32] */; + %690 = max(%689) /* ty=float32 */; + %691 = min(%689) /* ty=float32 */; + %692 = divide(%690, 127f /* ty=float32 */) /* ty=float32 */; + %693 = divide(%691, -127f /* ty=float32 */) /* ty=float32 */; + %694 = maximum(%692, %693) /* ty=float32 */; + %695 = divide(%689, %694) /* ty=Tensor[(16, 32), float32] */; + %696 = round(%695) /* ty=Tensor[(16, 32), float32] */; + %697 = max(%592) /* ty=float32 */; + %698 = min(%592) /* ty=float32 */; + %699 = divide(%697, 127f /* ty=float32 */) /* ty=float32 */; + %700 = divide(%698, -127f /* ty=float32 */) /* ty=float32 */; + %701 = maximum(%699, %700) /* ty=float32 */; + %702 = divide(%592, %701) /* ty=Tensor[(1024, 32), float32] */; + %703 = round(%702) /* ty=Tensor[(1024, 32), float32] */; + %704 = nn.dense(%689, %592, units=None) /* ty=Tensor[(16, 1024), float32] */; + %705 = max(%704) /* ty=float32 */; + %706 = min(%704) /* ty=float32 */; + %707 = divide(%705, 127f /* ty=float32 */) /* ty=float32 */; + %708 = divide(%706, -127f /* ty=float32 */) /* ty=float32 */; + %709 = cast(%696, dtype="int8") /* ty=Tensor[(16, 32), int8] */; + %710 = cast(%703, dtype="int8") /* ty=Tensor[(1024, 32), int8] */; + %711 = maximum(%707, %708) /* ty=float32 */; + %712 = fn (%outer_arg_037: Tensor[(16, 32), int8], %outer_arg_137: Tensor[(1024, 32), int8], %outer_arg_237: float32, %outer_arg_337: float32, %outer_arg_437: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_3") -> Tensor[(16, 1024), int8] { + %688 = fn (%data37: Tensor[(16, 32), int8], %weights37: Tensor[(1024, 32), int8], %s_data37: float32, %s_w37: float32, %s_act37: float32, Composite="ilavta.dense") -> Tensor[(16, 1024), int8] { + %682 = nn.dense(%data37, %weights37, units=None, out_dtype="int32") /* ty=Tensor[(16, 1024), int32] */; + %683 = multiply(%s_data37, %s_w37) /* ty=float32 */; + %684 = cast(%682, dtype="float32") /* ty=Tensor[(16, 1024), float32] */; + %685 = divide(%683, %s_act37) /* ty=float32 */; + %686 = multiply(%684, %685) /* ty=Tensor[(16, 1024), float32] */; + %687 = clip(%686, a_min=-127f, a_max=127f) /* ty=Tensor[(16, 1024), float32] */; + cast(%687, dtype="int8") /* ty=Tensor[(16, 1024), int8] */ + }; + %688(%outer_arg_037, %outer_arg_137, %outer_arg_237, %outer_arg_337, %outer_arg_437) /* ty=Tensor[(16, 1024), int8] */ + }; + %713 = %712(%709, %710, %694, %701, %711) /* ty=Tensor[(16, 1024), int8] */; + %714 = cast(%713, dtype="float32") /* ty=Tensor[(16, 1024), float32] */; + %715 = multiply(%714, %711) /* ty=Tensor[(16, 1024), float32] */; + %716 = reshape(%715, newshape=[16, 1, 32, 32]) /* from_string */ /* ty=Tensor[(16, 1, 32, 32), float32] */; + %717 = add(%layers_0_shortcut_1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(16), float32] */; + %718 = sqrt(%717) /* from_string */ /* ty=Tensor[(16), float32] */; + %719 = divide(%573, %718) /* from_string */ /* ty=Tensor[(16), float32] */; + %720 = multiply(%719, %layers_0_shortcut_1_weight) /* from_string */ /* ty=Tensor[(16), float32] */; + %721 = expand_dims(%720, axis=1) /* from_string */ /* ty=Tensor[(16, 1), float32] */; + %722 = transpose(%716, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %723 = expand_dims(%721, axis=1) /* from_string */ /* ty=Tensor[(16, 1, 1), float32] */; + %724 = negative(%layers_0_shortcut_1_running_mean) /* from_string */ /* ty=Tensor[(16), float32] */; + %725 = multiply(%724, %720) /* from_string */ /* ty=Tensor[(16), float32] */; + %726 = add(%725, %layers_0_shortcut_1_bias) /* from_string */ /* ty=Tensor[(16), float32] */; + %727 = expand_dims(%726, axis=1) /* from_string */ /* ty=Tensor[(16, 1), float32] */; + %728 = multiply(%722, %723) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %729 = expand_dims(%727, axis=1) /* from_string */ /* ty=Tensor[(16, 1, 1), float32] */; + %730 = add(%680, %681) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %731 = add(%728, %729) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %732 = add(%730, %731) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %733 = nn.pad(%732, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %734 = nn.pad(%733, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %735 = windows(%734, axis=1, window_shape=[16, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 32, 32, 16, 1, 1), float32] */; + %736 = squeeze(%735, axis=[1]) /* from_string */ /* ty=Tensor[(1, 32, 32, 16, 1, 1), float32] */; + %737 = reshape(%736, newshape=[1024, 16]) /* from_string */ /* ty=Tensor[(1024, 16), float32] */; + %738 = max(%737) /* ty=float32 */; + %739 = min(%737) /* ty=float32 */; + %740 = divide(%738, 127f /* ty=float32 */) /* ty=float32 */; + %741 = divide(%739, -127f /* ty=float32 */) /* ty=float32 */; + %742 = maximum(%740, %741) /* ty=float32 */; + %743 = divide(%737, %742) /* ty=Tensor[(1024, 16), float32] */; + %744 = round(%743) /* ty=Tensor[(1024, 16), float32] */; + %745 = nn.dense(%494, %737, units=None) /* ty=Tensor[(96, 1024), float32] */; + %746 = max(%745) /* ty=float32 */; + %747 = min(%745) /* ty=float32 */; + %748 = divide(%746, 127f /* ty=float32 */) /* ty=float32 */; + %749 = divide(%747, -127f /* ty=float32 */) /* ty=float32 */; + %750 = cast(%501, dtype="int8") /* ty=Tensor[(96, 16), int8] */; + %751 = cast(%744, dtype="int8") /* ty=Tensor[(1024, 16), int8] */; + %752 = maximum(%748, %749) /* ty=float32 */; + %753 = fn (%outer_arg_033: Tensor[(96, 16), int8], %outer_arg_133: Tensor[(1024, 16), int8], %outer_arg_233: float32, %outer_arg_333: float32, %outer_arg_433: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_4") -> Tensor[(96, 1024), int8] { + %493 = fn (%data33: Tensor[(96, 16), int8], %weights33: Tensor[(1024, 16), int8], %s_data33: float32, %s_w33: float32, %s_act33: float32, Composite="ilavta.dense") -> Tensor[(96, 1024), int8] { + %487 = nn.dense(%data33, %weights33, units=None, out_dtype="int32") /* ty=Tensor[(96, 1024), int32] */; + %488 = multiply(%s_data33, %s_w33) /* ty=float32 */; + %489 = cast(%487, dtype="float32") /* ty=Tensor[(96, 1024), float32] */; + %490 = divide(%488, %s_act33) /* ty=float32 */; + %491 = multiply(%489, %490) /* ty=Tensor[(96, 1024), float32] */; + %492 = clip(%491, a_min=-127f, a_max=127f) /* ty=Tensor[(96, 1024), float32] */; + cast(%492, dtype="int8") /* ty=Tensor[(96, 1024), int8] */ + }; + %493(%outer_arg_033, %outer_arg_133, %outer_arg_233, %outer_arg_333, %outer_arg_433) /* ty=Tensor[(96, 1024), int8] */ + }; + %754 = %753(%750, %751, %499, %742, %752) /* ty=Tensor[(96, 1024), int8] */; + %755 = cast(%754, dtype="float32") /* ty=Tensor[(96, 1024), float32] */; + %756 = multiply(%755, %752) /* ty=Tensor[(96, 1024), float32] */; + %757 = reshape(%756, newshape=[96, 1, 32, 32]) /* from_string */ /* ty=Tensor[(96, 1, 32, 32), float32] */; + %758 = add(%layers_1_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(96), float32] */; + %759 = sqrt(%758) /* from_string */ /* ty=Tensor[(96), float32] */; + %760 = divide(%573, %759) /* from_string */ /* ty=Tensor[(96), float32] */; + %761 = multiply(%760, %layers_1_bn1_weight) /* from_string */ /* ty=Tensor[(96), float32] */; + %762 = expand_dims(%761, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %763 = transpose(%757, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %764 = expand_dims(%762, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %765 = negative(%layers_1_bn1_running_mean) /* from_string */ /* ty=Tensor[(96), float32] */; + %766 = multiply(%765, %761) /* from_string */ /* ty=Tensor[(96), float32] */; + %767 = add(%766, %layers_1_bn1_bias) /* from_string */ /* ty=Tensor[(96), float32] */; + %768 = expand_dims(%767, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %769 = multiply(%763, %764) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %770 = expand_dims(%768, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %771 = add(%769, %770) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %772 = nn.relu(%771) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %773 = reshape(%layers_1_conv2_weight, newshape=[96, 1, 3, 3]) /* from_string */ /* ty=Tensor[(96, 1, 3, 3), float32] */; + %774 = add(%layers_1_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(96), float32] */; + %775 = sqrt(%774) /* from_string */ /* ty=Tensor[(96), float32] */; + %776 = divide(%573, %775) /* from_string */ /* ty=Tensor[(96), float32] */; + %777 = multiply(%776, %layers_1_bn2_weight) /* from_string */ /* ty=Tensor[(96), float32] */; + %778 = expand_dims(%777, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %779 = nn.conv2d(%772, %773, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %780 = expand_dims(%778, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %781 = negative(%layers_1_bn2_running_mean) /* from_string */ /* ty=Tensor[(96), float32] */; + %782 = multiply(%781, %777) /* from_string */ /* ty=Tensor[(96), float32] */; + %783 = add(%782, %layers_1_bn2_bias) /* from_string */ /* ty=Tensor[(96), float32] */; + %784 = expand_dims(%783, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %785 = multiply(%779, %780) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %786 = expand_dims(%784, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %787 = add(%785, %786) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %788 = nn.relu(%787) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %789 = nn.pad(%788, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %790 = nn.pad(%789, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), float32] */; + %791 = windows(%790, axis=1, window_shape=[96, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 32, 32, 96, 1, 1), float32] */; + %792 = squeeze(%791, axis=[1]) /* from_string */ /* ty=Tensor[(1, 32, 32, 96, 1, 1), float32] */; + %793 = reshape(%792, newshape=[1024, 96]) /* from_string */ /* ty=Tensor[(1024, 96), float32] */; + %794 = max(%793) /* ty=float32 */; + %795 = min(%793) /* ty=float32 */; + %796 = divide(%794, 127f /* ty=float32 */) /* ty=float32 */; + %797 = divide(%795, -127f /* ty=float32 */) /* ty=float32 */; + %798 = maximum(%796, %797) /* ty=float32 */; + %799 = divide(%793, %798) /* ty=Tensor[(1024, 96), float32] */; + %800 = round(%799) /* ty=Tensor[(1024, 96), float32] */; + %801 = nn.dense(%479, %793, units=None) /* ty=Tensor[(24, 1024), float32] */; + %802 = max(%801) /* ty=float32 */; + %803 = min(%801) /* ty=float32 */; + %804 = divide(%802, 127f /* ty=float32 */) /* ty=float32 */; + %805 = divide(%803, -127f /* ty=float32 */) /* ty=float32 */; + %806 = cast(%486, dtype="int8") /* ty=Tensor[(24, 96), int8] */; + %807 = cast(%800, dtype="int8") /* ty=Tensor[(1024, 96), int8] */; + %808 = maximum(%804, %805) /* ty=float32 */; + %809 = fn (%outer_arg_032: Tensor[(24, 96), int8], %outer_arg_132: Tensor[(1024, 96), int8], %outer_arg_232: float32, %outer_arg_332: float32, %outer_arg_432: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_5") -> Tensor[(24, 1024), int8] { + %478 = fn (%data32: Tensor[(24, 96), int8], %weights32: Tensor[(1024, 96), int8], %s_data32: float32, %s_w32: float32, %s_act32: float32, Composite="ilavta.dense") -> Tensor[(24, 1024), int8] { + %472 = nn.dense(%data32, %weights32, units=None, out_dtype="int32") /* ty=Tensor[(24, 1024), int32] */; + %473 = multiply(%s_data32, %s_w32) /* ty=float32 */; + %474 = cast(%472, dtype="float32") /* ty=Tensor[(24, 1024), float32] */; + %475 = divide(%473, %s_act32) /* ty=float32 */; + %476 = multiply(%474, %475) /* ty=Tensor[(24, 1024), float32] */; + %477 = clip(%476, a_min=-127f, a_max=127f) /* ty=Tensor[(24, 1024), float32] */; + cast(%477, dtype="int8") /* ty=Tensor[(24, 1024), int8] */ + }; + %478(%outer_arg_032, %outer_arg_132, %outer_arg_232, %outer_arg_332, %outer_arg_432) /* ty=Tensor[(24, 1024), int8] */ + }; + %810 = %809(%806, %807, %484, %798, %808) /* ty=Tensor[(24, 1024), int8] */; + %811 = cast(%810, dtype="float32") /* ty=Tensor[(24, 1024), float32] */; + %812 = multiply(%811, %808) /* ty=Tensor[(24, 1024), float32] */; + %813 = reshape(%812, newshape=[24, 1, 32, 32]) /* from_string */ /* ty=Tensor[(24, 1, 32, 32), float32] */; + %814 = add(%layers_1_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(24), float32] */; + %815 = sqrt(%814) /* from_string */ /* ty=Tensor[(24), float32] */; + %816 = divide(%573, %815) /* from_string */ /* ty=Tensor[(24), float32] */; + %817 = multiply(%816, %layers_1_bn3_weight) /* from_string */ /* ty=Tensor[(24), float32] */; + %818 = expand_dims(%817, axis=1) /* from_string */ /* ty=Tensor[(24, 1), float32] */; + %819 = transpose(%813, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %820 = expand_dims(%818, axis=1) /* from_string */ /* ty=Tensor[(24, 1, 1), float32] */; + %821 = negative(%layers_1_bn3_running_mean) /* from_string */ /* ty=Tensor[(24), float32] */; + %822 = multiply(%821, %817) /* from_string */ /* ty=Tensor[(24), float32] */; + %823 = add(%822, %layers_1_bn3_bias) /* from_string */ /* ty=Tensor[(24), float32] */; + %824 = expand_dims(%823, axis=1) /* from_string */ /* ty=Tensor[(24, 1), float32] */; + %825 = multiply(%819, %820) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %826 = expand_dims(%824, axis=1) /* from_string */ /* ty=Tensor[(24, 1, 1), float32] */; + %834 = reshape(%layers_1_shortcut_0_weight, newshape=[24, 16]) /* from_string */ /* ty=Tensor[(24, 16), float32] */; + %835 = max(%834) /* ty=float32 */; + %836 = min(%834) /* ty=float32 */; + %837 = divide(%835, 127f /* ty=float32 */) /* ty=float32 */; + %838 = divide(%836, -127f /* ty=float32 */) /* ty=float32 */; + %839 = maximum(%837, %838) /* ty=float32 */; + %840 = divide(%834, %839) /* ty=Tensor[(24, 16), float32] */; + %841 = round(%840) /* ty=Tensor[(24, 16), float32] */; + %842 = max(%737) /* ty=float32 */; + %843 = min(%737) /* ty=float32 */; + %844 = divide(%842, 127f /* ty=float32 */) /* ty=float32 */; + %845 = divide(%843, -127f /* ty=float32 */) /* ty=float32 */; + %846 = maximum(%844, %845) /* ty=float32 */; + %847 = divide(%737, %846) /* ty=Tensor[(1024, 16), float32] */; + %848 = round(%847) /* ty=Tensor[(1024, 16), float32] */; + %849 = nn.dense(%834, %737, units=None) /* ty=Tensor[(24, 1024), float32] */; + %850 = max(%849) /* ty=float32 */; + %851 = min(%849) /* ty=float32 */; + %852 = divide(%850, 127f /* ty=float32 */) /* ty=float32 */; + %853 = divide(%851, -127f /* ty=float32 */) /* ty=float32 */; + %854 = cast(%841, dtype="int8") /* ty=Tensor[(24, 16), int8] */; + %855 = cast(%848, dtype="int8") /* ty=Tensor[(1024, 16), int8] */; + %856 = maximum(%852, %853) /* ty=float32 */; + %857 = fn (%outer_arg_038: Tensor[(24, 16), int8], %outer_arg_138: Tensor[(1024, 16), int8], %outer_arg_238: float32, %outer_arg_338: float32, %outer_arg_438: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_6") -> Tensor[(24, 1024), int8] { + %833 = fn (%data38: Tensor[(24, 16), int8], %weights38: Tensor[(1024, 16), int8], %s_data38: float32, %s_w38: float32, %s_act38: float32, Composite="ilavta.dense") -> Tensor[(24, 1024), int8] { + %827 = nn.dense(%data38, %weights38, units=None, out_dtype="int32") /* ty=Tensor[(24, 1024), int32] */; + %828 = multiply(%s_data38, %s_w38) /* ty=float32 */; + %829 = cast(%827, dtype="float32") /* ty=Tensor[(24, 1024), float32] */; + %830 = divide(%828, %s_act38) /* ty=float32 */; + %831 = multiply(%829, %830) /* ty=Tensor[(24, 1024), float32] */; + %832 = clip(%831, a_min=-127f, a_max=127f) /* ty=Tensor[(24, 1024), float32] */; + cast(%832, dtype="int8") /* ty=Tensor[(24, 1024), int8] */ + }; + %833(%outer_arg_038, %outer_arg_138, %outer_arg_238, %outer_arg_338, %outer_arg_438) /* ty=Tensor[(24, 1024), int8] */ + }; + %858 = %857(%854, %855, %839, %846, %856) /* ty=Tensor[(24, 1024), int8] */; + %859 = cast(%858, dtype="float32") /* ty=Tensor[(24, 1024), float32] */; + %860 = multiply(%859, %856) /* ty=Tensor[(24, 1024), float32] */; + %861 = reshape(%860, newshape=[24, 1, 32, 32]) /* from_string */ /* ty=Tensor[(24, 1, 32, 32), float32] */; + %862 = add(%layers_1_shortcut_1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(24), float32] */; + %863 = sqrt(%862) /* from_string */ /* ty=Tensor[(24), float32] */; + %864 = divide(%573, %863) /* from_string */ /* ty=Tensor[(24), float32] */; + %865 = multiply(%864, %layers_1_shortcut_1_weight) /* from_string */ /* ty=Tensor[(24), float32] */; + %866 = expand_dims(%865, axis=1) /* from_string */ /* ty=Tensor[(24, 1), float32] */; + %867 = transpose(%861, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %868 = expand_dims(%866, axis=1) /* from_string */ /* ty=Tensor[(24, 1, 1), float32] */; + %869 = negative(%layers_1_shortcut_1_running_mean) /* from_string */ /* ty=Tensor[(24), float32] */; + %870 = multiply(%869, %865) /* from_string */ /* ty=Tensor[(24), float32] */; + %871 = add(%870, %layers_1_shortcut_1_bias) /* from_string */ /* ty=Tensor[(24), float32] */; + %872 = expand_dims(%871, axis=1) /* from_string */ /* ty=Tensor[(24, 1), float32] */; + %873 = multiply(%867, %868) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %874 = expand_dims(%872, axis=1) /* from_string */ /* ty=Tensor[(24, 1, 1), float32] */; + %875 = add(%825, %826) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %876 = add(%873, %874) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %877 = add(%875, %876) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %878 = nn.pad(%877, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %879 = nn.pad(%878, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %880 = windows(%879, axis=1, window_shape=[24, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 32, 32, 24, 1, 1), float32] */; + %881 = squeeze(%880, axis=[1]) /* from_string */ /* ty=Tensor[(1, 32, 32, 24, 1, 1), float32] */; + %882 = reshape(%881, newshape=[1024, 24]) /* from_string */ /* ty=Tensor[(1024, 24), float32] */; + %883 = max(%882) /* ty=float32 */; + %884 = min(%882) /* ty=float32 */; + %885 = divide(%883, 127f /* ty=float32 */) /* ty=float32 */; + %886 = divide(%884, -127f /* ty=float32 */) /* ty=float32 */; + %887 = maximum(%885, %886) /* ty=float32 */; + %888 = divide(%882, %887) /* ty=Tensor[(1024, 24), float32] */; + %889 = round(%888) /* ty=Tensor[(1024, 24), float32] */; + %890 = nn.dense(%464, %882, units=None) /* ty=Tensor[(144, 1024), float32] */; + %891 = max(%890) /* ty=float32 */; + %892 = min(%890) /* ty=float32 */; + %893 = divide(%891, 127f /* ty=float32 */) /* ty=float32 */; + %894 = divide(%892, -127f /* ty=float32 */) /* ty=float32 */; + %895 = cast(%471, dtype="int8") /* ty=Tensor[(144, 24), int8] */; + %896 = cast(%889, dtype="int8") /* ty=Tensor[(1024, 24), int8] */; + %897 = maximum(%893, %894) /* ty=float32 */; + %898 = fn (%outer_arg_031: Tensor[(144, 24), int8], %outer_arg_131: Tensor[(1024, 24), int8], %outer_arg_231: float32, %outer_arg_331: float32, %outer_arg_431: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_7") -> Tensor[(144, 1024), int8] { + %463 = fn (%data31: Tensor[(144, 24), int8], %weights31: Tensor[(1024, 24), int8], %s_data31: float32, %s_w31: float32, %s_act31: float32, Composite="ilavta.dense") -> Tensor[(144, 1024), int8] { + %457 = nn.dense(%data31, %weights31, units=None, out_dtype="int32") /* ty=Tensor[(144, 1024), int32] */; + %458 = multiply(%s_data31, %s_w31) /* ty=float32 */; + %459 = cast(%457, dtype="float32") /* ty=Tensor[(144, 1024), float32] */; + %460 = divide(%458, %s_act31) /* ty=float32 */; + %461 = multiply(%459, %460) /* ty=Tensor[(144, 1024), float32] */; + %462 = clip(%461, a_min=-127f, a_max=127f) /* ty=Tensor[(144, 1024), float32] */; + cast(%462, dtype="int8") /* ty=Tensor[(144, 1024), int8] */ + }; + %463(%outer_arg_031, %outer_arg_131, %outer_arg_231, %outer_arg_331, %outer_arg_431) /* ty=Tensor[(144, 1024), int8] */ + }; + %899 = %898(%895, %896, %469, %887, %897) /* ty=Tensor[(144, 1024), int8] */; + %900 = cast(%899, dtype="float32") /* ty=Tensor[(144, 1024), float32] */; + %901 = multiply(%900, %897) /* ty=Tensor[(144, 1024), float32] */; + %902 = reshape(%901, newshape=[144, 1, 32, 32]) /* from_string */ /* ty=Tensor[(144, 1, 32, 32), float32] */; + %903 = add(%layers_2_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(144), float32] */; + %904 = sqrt(%903) /* from_string */ /* ty=Tensor[(144), float32] */; + %905 = divide(%573, %904) /* from_string */ /* ty=Tensor[(144), float32] */; + %906 = multiply(%905, %layers_2_bn1_weight) /* from_string */ /* ty=Tensor[(144), float32] */; + %907 = expand_dims(%906, axis=1) /* from_string */ /* ty=Tensor[(144, 1), float32] */; + %908 = transpose(%902, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %909 = expand_dims(%907, axis=1) /* from_string */ /* ty=Tensor[(144, 1, 1), float32] */; + %910 = negative(%layers_2_bn1_running_mean) /* from_string */ /* ty=Tensor[(144), float32] */; + %911 = multiply(%910, %906) /* from_string */ /* ty=Tensor[(144), float32] */; + %912 = add(%911, %layers_2_bn1_bias) /* from_string */ /* ty=Tensor[(144), float32] */; + %913 = expand_dims(%912, axis=1) /* from_string */ /* ty=Tensor[(144, 1), float32] */; + %914 = multiply(%908, %909) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %915 = expand_dims(%913, axis=1) /* from_string */ /* ty=Tensor[(144, 1, 1), float32] */; + %916 = add(%914, %915) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %917 = nn.relu(%916) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %918 = reshape(%layers_2_conv2_weight, newshape=[144, 1, 3, 3]) /* from_string */ /* ty=Tensor[(144, 1, 3, 3), float32] */; + %919 = add(%layers_2_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(144), float32] */; + %920 = sqrt(%919) /* from_string */ /* ty=Tensor[(144), float32] */; + %921 = divide(%573, %920) /* from_string */ /* ty=Tensor[(144), float32] */; + %922 = multiply(%921, %layers_2_bn2_weight) /* from_string */ /* ty=Tensor[(144), float32] */; + %923 = expand_dims(%922, axis=1) /* from_string */ /* ty=Tensor[(144, 1), float32] */; + %924 = nn.conv2d(%917, %918, padding=[1, 1, 1, 1], groups=144, channels=144, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %925 = expand_dims(%923, axis=1) /* from_string */ /* ty=Tensor[(144, 1, 1), float32] */; + %926 = negative(%layers_2_bn2_running_mean) /* from_string */ /* ty=Tensor[(144), float32] */; + %927 = multiply(%926, %922) /* from_string */ /* ty=Tensor[(144), float32] */; + %928 = add(%927, %layers_2_bn2_bias) /* from_string */ /* ty=Tensor[(144), float32] */; + %929 = expand_dims(%928, axis=1) /* from_string */ /* ty=Tensor[(144, 1), float32] */; + %930 = multiply(%924, %925) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %931 = expand_dims(%929, axis=1) /* from_string */ /* ty=Tensor[(144, 1, 1), float32] */; + %932 = add(%930, %931) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %933 = nn.relu(%932) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %934 = nn.pad(%933, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %935 = nn.pad(%934, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %936 = windows(%935, axis=1, window_shape=[144, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 32, 32, 144, 1, 1), float32] */; + %937 = squeeze(%936, axis=[1]) /* from_string */ /* ty=Tensor[(1, 32, 32, 144, 1, 1), float32] */; + %938 = reshape(%937, newshape=[1024, 144]) /* from_string */ /* ty=Tensor[(1024, 144), float32] */; + %939 = max(%938) /* ty=float32 */; + %940 = min(%938) /* ty=float32 */; + %941 = divide(%939, 127f /* ty=float32 */) /* ty=float32 */; + %942 = divide(%940, -127f /* ty=float32 */) /* ty=float32 */; + %943 = maximum(%941, %942) /* ty=float32 */; + %944 = divide(%938, %943) /* ty=Tensor[(1024, 144), float32] */; + %945 = round(%944) /* ty=Tensor[(1024, 144), float32] */; + %946 = nn.dense(%449, %938, units=None) /* ty=Tensor[(24, 1024), float32] */; + %947 = max(%946) /* ty=float32 */; + %948 = min(%946) /* ty=float32 */; + %949 = divide(%947, 127f /* ty=float32 */) /* ty=float32 */; + %950 = divide(%948, -127f /* ty=float32 */) /* ty=float32 */; + %951 = cast(%456, dtype="int8") /* ty=Tensor[(24, 144), int8] */; + %952 = cast(%945, dtype="int8") /* ty=Tensor[(1024, 144), int8] */; + %953 = maximum(%949, %950) /* ty=float32 */; + %954 = fn (%outer_arg_030: Tensor[(24, 144), int8], %outer_arg_130: Tensor[(1024, 144), int8], %outer_arg_230: float32, %outer_arg_330: float32, %outer_arg_430: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_8") -> Tensor[(24, 1024), int8] { + %448 = fn (%data30: Tensor[(24, 144), int8], %weights30: Tensor[(1024, 144), int8], %s_data30: float32, %s_w30: float32, %s_act30: float32, Composite="ilavta.dense") -> Tensor[(24, 1024), int8] { + %442 = nn.dense(%data30, %weights30, units=None, out_dtype="int32") /* ty=Tensor[(24, 1024), int32] */; + %443 = multiply(%s_data30, %s_w30) /* ty=float32 */; + %444 = cast(%442, dtype="float32") /* ty=Tensor[(24, 1024), float32] */; + %445 = divide(%443, %s_act30) /* ty=float32 */; + %446 = multiply(%444, %445) /* ty=Tensor[(24, 1024), float32] */; + %447 = clip(%446, a_min=-127f, a_max=127f) /* ty=Tensor[(24, 1024), float32] */; + cast(%447, dtype="int8") /* ty=Tensor[(24, 1024), int8] */ + }; + %448(%outer_arg_030, %outer_arg_130, %outer_arg_230, %outer_arg_330, %outer_arg_430) /* ty=Tensor[(24, 1024), int8] */ + }; + %955 = %954(%951, %952, %454, %943, %953) /* ty=Tensor[(24, 1024), int8] */; + %956 = cast(%955, dtype="float32") /* ty=Tensor[(24, 1024), float32] */; + %957 = multiply(%956, %953) /* ty=Tensor[(24, 1024), float32] */; + %958 = reshape(%957, newshape=[24, 1, 32, 32]) /* from_string */ /* ty=Tensor[(24, 1, 32, 32), float32] */; + %959 = add(%layers_2_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(24), float32] */; + %960 = sqrt(%959) /* from_string */ /* ty=Tensor[(24), float32] */; + %961 = divide(%573, %960) /* from_string */ /* ty=Tensor[(24), float32] */; + %962 = multiply(%961, %layers_2_bn3_weight) /* from_string */ /* ty=Tensor[(24), float32] */; + %963 = expand_dims(%962, axis=1) /* from_string */ /* ty=Tensor[(24, 1), float32] */; + %964 = transpose(%958, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %965 = expand_dims(%963, axis=1) /* from_string */ /* ty=Tensor[(24, 1, 1), float32] */; + %966 = negative(%layers_2_bn3_running_mean) /* from_string */ /* ty=Tensor[(24), float32] */; + %967 = multiply(%966, %962) /* from_string */ /* ty=Tensor[(24), float32] */; + %968 = add(%967, %layers_2_bn3_bias) /* from_string */ /* ty=Tensor[(24), float32] */; + %969 = expand_dims(%968, axis=1) /* from_string */ /* ty=Tensor[(24, 1), float32] */; + %970 = multiply(%964, %965) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %971 = expand_dims(%969, axis=1) /* from_string */ /* ty=Tensor[(24, 1, 1), float32] */; + %972 = add(%970, %971) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %973 = add(%972, %877) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %974 = nn.pad(%973, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %975 = nn.pad(%974, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), float32] */; + %976 = windows(%975, axis=1, window_shape=[24, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 32, 32, 24, 1, 1), float32] */; + %977 = squeeze(%976, axis=[1]) /* from_string */ /* ty=Tensor[(1, 32, 32, 24, 1, 1), float32] */; + %978 = reshape(%977, newshape=[1024, 24]) /* from_string */ /* ty=Tensor[(1024, 24), float32] */; + %979 = max(%978) /* ty=float32 */; + %980 = min(%978) /* ty=float32 */; + %981 = divide(%979, 127f /* ty=float32 */) /* ty=float32 */; + %982 = divide(%980, -127f /* ty=float32 */) /* ty=float32 */; + %983 = maximum(%981, %982) /* ty=float32 */; + %984 = divide(%978, %983) /* ty=Tensor[(1024, 24), float32] */; + %985 = round(%984) /* ty=Tensor[(1024, 24), float32] */; + %986 = nn.dense(%434, %978, units=None) /* ty=Tensor[(144, 1024), float32] */; + %987 = max(%986) /* ty=float32 */; + %988 = min(%986) /* ty=float32 */; + %989 = divide(%987, 127f /* ty=float32 */) /* ty=float32 */; + %990 = divide(%988, -127f /* ty=float32 */) /* ty=float32 */; + %991 = cast(%441, dtype="int8") /* ty=Tensor[(144, 24), int8] */; + %992 = cast(%985, dtype="int8") /* ty=Tensor[(1024, 24), int8] */; + %993 = maximum(%989, %990) /* ty=float32 */; + %994 = fn (%outer_arg_029: Tensor[(144, 24), int8], %outer_arg_129: Tensor[(1024, 24), int8], %outer_arg_229: float32, %outer_arg_329: float32, %outer_arg_429: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_9") -> Tensor[(144, 1024), int8] { + %433 = fn (%data29: Tensor[(144, 24), int8], %weights29: Tensor[(1024, 24), int8], %s_data29: float32, %s_w29: float32, %s_act29: float32, Composite="ilavta.dense") -> Tensor[(144, 1024), int8] { + %427 = nn.dense(%data29, %weights29, units=None, out_dtype="int32") /* ty=Tensor[(144, 1024), int32] */; + %428 = multiply(%s_data29, %s_w29) /* ty=float32 */; + %429 = cast(%427, dtype="float32") /* ty=Tensor[(144, 1024), float32] */; + %430 = divide(%428, %s_act29) /* ty=float32 */; + %431 = multiply(%429, %430) /* ty=Tensor[(144, 1024), float32] */; + %432 = clip(%431, a_min=-127f, a_max=127f) /* ty=Tensor[(144, 1024), float32] */; + cast(%432, dtype="int8") /* ty=Tensor[(144, 1024), int8] */ + }; + %433(%outer_arg_029, %outer_arg_129, %outer_arg_229, %outer_arg_329, %outer_arg_429) /* ty=Tensor[(144, 1024), int8] */ + }; + %995 = %994(%991, %992, %439, %983, %993) /* ty=Tensor[(144, 1024), int8] */; + %996 = cast(%995, dtype="float32") /* ty=Tensor[(144, 1024), float32] */; + %997 = multiply(%996, %993) /* ty=Tensor[(144, 1024), float32] */; + %998 = reshape(%997, newshape=[144, 1, 32, 32]) /* from_string */ /* ty=Tensor[(144, 1, 32, 32), float32] */; + %999 = add(%layers_3_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(144), float32] */; + %1000 = sqrt(%999) /* from_string */ /* ty=Tensor[(144), float32] */; + %1001 = divide(%573, %1000) /* from_string */ /* ty=Tensor[(144), float32] */; + %1002 = multiply(%1001, %layers_3_bn1_weight) /* from_string */ /* ty=Tensor[(144), float32] */; + %1003 = expand_dims(%1002, axis=1) /* from_string */ /* ty=Tensor[(144, 1), float32] */; + %1004 = transpose(%998, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %1005 = expand_dims(%1003, axis=1) /* from_string */ /* ty=Tensor[(144, 1, 1), float32] */; + %1006 = negative(%layers_3_bn1_running_mean) /* from_string */ /* ty=Tensor[(144), float32] */; + %1007 = multiply(%1006, %1002) /* from_string */ /* ty=Tensor[(144), float32] */; + %1008 = add(%1007, %layers_3_bn1_bias) /* from_string */ /* ty=Tensor[(144), float32] */; + %1009 = expand_dims(%1008, axis=1) /* from_string */ /* ty=Tensor[(144, 1), float32] */; + %1010 = multiply(%1004, %1005) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %1011 = expand_dims(%1009, axis=1) /* from_string */ /* ty=Tensor[(144, 1, 1), float32] */; + %1012 = add(%1010, %1011) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %1013 = nn.relu(%1012) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), float32] */; + %1014 = reshape(%layers_3_conv2_weight, newshape=[144, 1, 3, 3]) /* from_string */ /* ty=Tensor[(144, 1, 3, 3), float32] */; + %1015 = add(%layers_3_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(144), float32] */; + %1016 = sqrt(%1015) /* from_string */ /* ty=Tensor[(144), float32] */; + %1017 = divide(%573, %1016) /* from_string */ /* ty=Tensor[(144), float32] */; + %1018 = multiply(%1017, %layers_3_bn2_weight) /* from_string */ /* ty=Tensor[(144), float32] */; + %1019 = expand_dims(%1018, axis=1) /* from_string */ /* ty=Tensor[(144, 1), float32] */; + %1020 = nn.conv2d(%1013, %1014, strides=[2, 2], padding=[1, 1, 1, 1], groups=144, channels=144, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), float32] */; + %1021 = expand_dims(%1019, axis=1) /* from_string */ /* ty=Tensor[(144, 1, 1), float32] */; + %1022 = negative(%layers_3_bn2_running_mean) /* from_string */ /* ty=Tensor[(144), float32] */; + %1023 = multiply(%1022, %1018) /* from_string */ /* ty=Tensor[(144), float32] */; + %1024 = add(%1023, %layers_3_bn2_bias) /* from_string */ /* ty=Tensor[(144), float32] */; + %1025 = expand_dims(%1024, axis=1) /* from_string */ /* ty=Tensor[(144, 1), float32] */; + %1026 = multiply(%1020, %1021) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), float32] */; + %1027 = expand_dims(%1025, axis=1) /* from_string */ /* ty=Tensor[(144, 1, 1), float32] */; + %1028 = add(%1026, %1027) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), float32] */; + %1029 = nn.relu(%1028) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), float32] */; + %1030 = nn.pad(%1029, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), float32] */; + %1031 = nn.pad(%1030, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), float32] */; + %1032 = windows(%1031, axis=1, window_shape=[144, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 16, 16, 144, 1, 1), float32] */; + %1033 = squeeze(%1032, axis=[1]) /* from_string */ /* ty=Tensor[(1, 16, 16, 144, 1, 1), float32] */; + %1034 = reshape(%1033, newshape=[256, 144]) /* from_string */ /* ty=Tensor[(256, 144), float32] */; + %1035 = max(%1034) /* ty=float32 */; + %1036 = min(%1034) /* ty=float32 */; + %1037 = divide(%1035, 127f /* ty=float32 */) /* ty=float32 */; + %1038 = divide(%1036, -127f /* ty=float32 */) /* ty=float32 */; + %1039 = maximum(%1037, %1038) /* ty=float32 */; + %1040 = divide(%1034, %1039) /* ty=Tensor[(256, 144), float32] */; + %1041 = round(%1040) /* ty=Tensor[(256, 144), float32] */; + %1042 = nn.dense(%419, %1034, units=None) /* ty=Tensor[(32, 256), float32] */; + %1043 = max(%1042) /* ty=float32 */; + %1044 = min(%1042) /* ty=float32 */; + %1045 = divide(%1043, 127f /* ty=float32 */) /* ty=float32 */; + %1046 = divide(%1044, -127f /* ty=float32 */) /* ty=float32 */; + %1047 = cast(%426, dtype="int8") /* ty=Tensor[(32, 144), int8] */; + %1048 = cast(%1041, dtype="int8") /* ty=Tensor[(256, 144), int8] */; + %1049 = maximum(%1045, %1046) /* ty=float32 */; + %1050 = fn (%outer_arg_028: Tensor[(32, 144), int8], %outer_arg_128: Tensor[(256, 144), int8], %outer_arg_228: float32, %outer_arg_328: float32, %outer_arg_428: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_10") -> Tensor[(32, 256), int8] { + %418 = fn (%data28: Tensor[(32, 144), int8], %weights28: Tensor[(256, 144), int8], %s_data28: float32, %s_w28: float32, %s_act28: float32, Composite="ilavta.dense") -> Tensor[(32, 256), int8] { + %412 = nn.dense(%data28, %weights28, units=None, out_dtype="int32") /* ty=Tensor[(32, 256), int32] */; + %413 = multiply(%s_data28, %s_w28) /* ty=float32 */; + %414 = cast(%412, dtype="float32") /* ty=Tensor[(32, 256), float32] */; + %415 = divide(%413, %s_act28) /* ty=float32 */; + %416 = multiply(%414, %415) /* ty=Tensor[(32, 256), float32] */; + %417 = clip(%416, a_min=-127f, a_max=127f) /* ty=Tensor[(32, 256), float32] */; + cast(%417, dtype="int8") /* ty=Tensor[(32, 256), int8] */ + }; + %418(%outer_arg_028, %outer_arg_128, %outer_arg_228, %outer_arg_328, %outer_arg_428) /* ty=Tensor[(32, 256), int8] */ + }; + %1051 = %1050(%1047, %1048, %424, %1039, %1049) /* ty=Tensor[(32, 256), int8] */; + %1052 = cast(%1051, dtype="float32") /* ty=Tensor[(32, 256), float32] */; + %1053 = multiply(%1052, %1049) /* ty=Tensor[(32, 256), float32] */; + %1054 = reshape(%1053, newshape=[32, 1, 16, 16]) /* from_string */ /* ty=Tensor[(32, 1, 16, 16), float32] */; + %1055 = add(%layers_3_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(32), float32] */; + %1056 = sqrt(%1055) /* from_string */ /* ty=Tensor[(32), float32] */; + %1057 = divide(%573, %1056) /* from_string */ /* ty=Tensor[(32), float32] */; + %1058 = multiply(%1057, %layers_3_bn3_weight) /* from_string */ /* ty=Tensor[(32), float32] */; + %1059 = expand_dims(%1058, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %1060 = transpose(%1054, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1061 = expand_dims(%1059, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %1062 = negative(%layers_3_bn3_running_mean) /* from_string */ /* ty=Tensor[(32), float32] */; + %1063 = multiply(%1062, %1058) /* from_string */ /* ty=Tensor[(32), float32] */; + %1064 = add(%1063, %layers_3_bn3_bias) /* from_string */ /* ty=Tensor[(32), float32] */; + %1065 = expand_dims(%1064, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %1066 = multiply(%1060, %1061) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1067 = expand_dims(%1065, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %1068 = add(%1066, %1067) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1069 = nn.pad(%1068, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1070 = nn.pad(%1069, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1071 = windows(%1070, axis=1, window_shape=[32, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 16, 16, 32, 1, 1), float32] */; + %1072 = squeeze(%1071, axis=[1]) /* from_string */ /* ty=Tensor[(1, 16, 16, 32, 1, 1), float32] */; + %1073 = reshape(%1072, newshape=[256, 32]) /* from_string */ /* ty=Tensor[(256, 32), float32] */; + %1074 = max(%1073) /* ty=float32 */; + %1075 = min(%1073) /* ty=float32 */; + %1076 = divide(%1074, 127f /* ty=float32 */) /* ty=float32 */; + %1077 = divide(%1075, -127f /* ty=float32 */) /* ty=float32 */; + %1078 = maximum(%1076, %1077) /* ty=float32 */; + %1079 = divide(%1073, %1078) /* ty=Tensor[(256, 32), float32] */; + %1080 = round(%1079) /* ty=Tensor[(256, 32), float32] */; + %1081 = nn.dense(%404, %1073, units=None) /* ty=Tensor[(192, 256), float32] */; + %1082 = max(%1081) /* ty=float32 */; + %1083 = min(%1081) /* ty=float32 */; + %1084 = divide(%1082, 127f /* ty=float32 */) /* ty=float32 */; + %1085 = divide(%1083, -127f /* ty=float32 */) /* ty=float32 */; + %1086 = cast(%411, dtype="int8") /* ty=Tensor[(192, 32), int8] */; + %1087 = cast(%1080, dtype="int8") /* ty=Tensor[(256, 32), int8] */; + %1088 = maximum(%1084, %1085) /* ty=float32 */; + %1089 = fn (%outer_arg_027: Tensor[(192, 32), int8], %outer_arg_127: Tensor[(256, 32), int8], %outer_arg_227: float32, %outer_arg_327: float32, %outer_arg_427: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_11") -> Tensor[(192, 256), int8] { + %403 = fn (%data27: Tensor[(192, 32), int8], %weights27: Tensor[(256, 32), int8], %s_data27: float32, %s_w27: float32, %s_act27: float32, Composite="ilavta.dense") -> Tensor[(192, 256), int8] { + %397 = nn.dense(%data27, %weights27, units=None, out_dtype="int32") /* ty=Tensor[(192, 256), int32] */; + %398 = multiply(%s_data27, %s_w27) /* ty=float32 */; + %399 = cast(%397, dtype="float32") /* ty=Tensor[(192, 256), float32] */; + %400 = divide(%398, %s_act27) /* ty=float32 */; + %401 = multiply(%399, %400) /* ty=Tensor[(192, 256), float32] */; + %402 = clip(%401, a_min=-127f, a_max=127f) /* ty=Tensor[(192, 256), float32] */; + cast(%402, dtype="int8") /* ty=Tensor[(192, 256), int8] */ + }; + %403(%outer_arg_027, %outer_arg_127, %outer_arg_227, %outer_arg_327, %outer_arg_427) /* ty=Tensor[(192, 256), int8] */ + }; + %1090 = %1089(%1086, %1087, %409, %1078, %1088) /* ty=Tensor[(192, 256), int8] */; + %1091 = cast(%1090, dtype="float32") /* ty=Tensor[(192, 256), float32] */; + %1092 = multiply(%1091, %1088) /* ty=Tensor[(192, 256), float32] */; + %1093 = reshape(%1092, newshape=[192, 1, 16, 16]) /* from_string */ /* ty=Tensor[(192, 1, 16, 16), float32] */; + %1094 = add(%layers_4_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(192), float32] */; + %1095 = sqrt(%1094) /* from_string */ /* ty=Tensor[(192), float32] */; + %1096 = divide(%573, %1095) /* from_string */ /* ty=Tensor[(192), float32] */; + %1097 = multiply(%1096, %layers_4_bn1_weight) /* from_string */ /* ty=Tensor[(192), float32] */; + %1098 = expand_dims(%1097, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1099 = transpose(%1093, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1100 = expand_dims(%1098, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1101 = negative(%layers_4_bn1_running_mean) /* from_string */ /* ty=Tensor[(192), float32] */; + %1102 = multiply(%1101, %1097) /* from_string */ /* ty=Tensor[(192), float32] */; + %1103 = add(%1102, %layers_4_bn1_bias) /* from_string */ /* ty=Tensor[(192), float32] */; + %1104 = expand_dims(%1103, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1105 = multiply(%1099, %1100) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1106 = expand_dims(%1104, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1107 = add(%1105, %1106) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1108 = nn.relu(%1107) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1109 = reshape(%layers_4_conv2_weight, newshape=[192, 1, 3, 3]) /* from_string */ /* ty=Tensor[(192, 1, 3, 3), float32] */; + %1110 = add(%layers_4_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(192), float32] */; + %1111 = sqrt(%1110) /* from_string */ /* ty=Tensor[(192), float32] */; + %1112 = divide(%573, %1111) /* from_string */ /* ty=Tensor[(192), float32] */; + %1113 = multiply(%1112, %layers_4_bn2_weight) /* from_string */ /* ty=Tensor[(192), float32] */; + %1114 = expand_dims(%1113, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1115 = nn.conv2d(%1108, %1109, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1116 = expand_dims(%1114, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1117 = negative(%layers_4_bn2_running_mean) /* from_string */ /* ty=Tensor[(192), float32] */; + %1118 = multiply(%1117, %1113) /* from_string */ /* ty=Tensor[(192), float32] */; + %1119 = add(%1118, %layers_4_bn2_bias) /* from_string */ /* ty=Tensor[(192), float32] */; + %1120 = expand_dims(%1119, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1121 = multiply(%1115, %1116) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1122 = expand_dims(%1120, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1123 = add(%1121, %1122) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1124 = nn.relu(%1123) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1125 = nn.pad(%1124, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1126 = nn.pad(%1125, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1127 = windows(%1126, axis=1, window_shape=[192, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 16, 16, 192, 1, 1), float32] */; + %1128 = squeeze(%1127, axis=[1]) /* from_string */ /* ty=Tensor[(1, 16, 16, 192, 1, 1), float32] */; + %1129 = reshape(%1128, newshape=[256, 192]) /* from_string */ /* ty=Tensor[(256, 192), float32] */; + %1130 = max(%1129) /* ty=float32 */; + %1131 = min(%1129) /* ty=float32 */; + %1132 = divide(%1130, 127f /* ty=float32 */) /* ty=float32 */; + %1133 = divide(%1131, -127f /* ty=float32 */) /* ty=float32 */; + %1134 = maximum(%1132, %1133) /* ty=float32 */; + %1135 = divide(%1129, %1134) /* ty=Tensor[(256, 192), float32] */; + %1136 = round(%1135) /* ty=Tensor[(256, 192), float32] */; + %1137 = nn.dense(%389, %1129, units=None) /* ty=Tensor[(32, 256), float32] */; + %1138 = max(%1137) /* ty=float32 */; + %1139 = min(%1137) /* ty=float32 */; + %1140 = divide(%1138, 127f /* ty=float32 */) /* ty=float32 */; + %1141 = divide(%1139, -127f /* ty=float32 */) /* ty=float32 */; + %1142 = cast(%396, dtype="int8") /* ty=Tensor[(32, 192), int8] */; + %1143 = cast(%1136, dtype="int8") /* ty=Tensor[(256, 192), int8] */; + %1144 = maximum(%1140, %1141) /* ty=float32 */; + %1145 = fn (%outer_arg_026: Tensor[(32, 192), int8], %outer_arg_126: Tensor[(256, 192), int8], %outer_arg_226: float32, %outer_arg_326: float32, %outer_arg_426: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_12") -> Tensor[(32, 256), int8] { + %388 = fn (%data26: Tensor[(32, 192), int8], %weights26: Tensor[(256, 192), int8], %s_data26: float32, %s_w26: float32, %s_act26: float32, Composite="ilavta.dense") -> Tensor[(32, 256), int8] { + %382 = nn.dense(%data26, %weights26, units=None, out_dtype="int32") /* ty=Tensor[(32, 256), int32] */; + %383 = multiply(%s_data26, %s_w26) /* ty=float32 */; + %384 = cast(%382, dtype="float32") /* ty=Tensor[(32, 256), float32] */; + %385 = divide(%383, %s_act26) /* ty=float32 */; + %386 = multiply(%384, %385) /* ty=Tensor[(32, 256), float32] */; + %387 = clip(%386, a_min=-127f, a_max=127f) /* ty=Tensor[(32, 256), float32] */; + cast(%387, dtype="int8") /* ty=Tensor[(32, 256), int8] */ + }; + %388(%outer_arg_026, %outer_arg_126, %outer_arg_226, %outer_arg_326, %outer_arg_426) /* ty=Tensor[(32, 256), int8] */ + }; + %1146 = %1145(%1142, %1143, %394, %1134, %1144) /* ty=Tensor[(32, 256), int8] */; + %1147 = cast(%1146, dtype="float32") /* ty=Tensor[(32, 256), float32] */; + %1148 = multiply(%1147, %1144) /* ty=Tensor[(32, 256), float32] */; + %1149 = reshape(%1148, newshape=[32, 1, 16, 16]) /* from_string */ /* ty=Tensor[(32, 1, 16, 16), float32] */; + %1150 = add(%layers_4_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(32), float32] */; + %1151 = sqrt(%1150) /* from_string */ /* ty=Tensor[(32), float32] */; + %1152 = divide(%573, %1151) /* from_string */ /* ty=Tensor[(32), float32] */; + %1153 = multiply(%1152, %layers_4_bn3_weight) /* from_string */ /* ty=Tensor[(32), float32] */; + %1154 = expand_dims(%1153, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %1155 = transpose(%1149, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1156 = expand_dims(%1154, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %1157 = negative(%layers_4_bn3_running_mean) /* from_string */ /* ty=Tensor[(32), float32] */; + %1158 = multiply(%1157, %1153) /* from_string */ /* ty=Tensor[(32), float32] */; + %1159 = add(%1158, %layers_4_bn3_bias) /* from_string */ /* ty=Tensor[(32), float32] */; + %1160 = expand_dims(%1159, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %1161 = multiply(%1155, %1156) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1162 = expand_dims(%1160, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %1163 = add(%1161, %1162) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1164 = add(%1163, %1068) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1165 = nn.pad(%1164, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1166 = nn.pad(%1165, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1167 = windows(%1166, axis=1, window_shape=[32, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 16, 16, 32, 1, 1), float32] */; + %1168 = squeeze(%1167, axis=[1]) /* from_string */ /* ty=Tensor[(1, 16, 16, 32, 1, 1), float32] */; + %1169 = reshape(%1168, newshape=[256, 32]) /* from_string */ /* ty=Tensor[(256, 32), float32] */; + %1170 = max(%1169) /* ty=float32 */; + %1171 = min(%1169) /* ty=float32 */; + %1172 = divide(%1170, 127f /* ty=float32 */) /* ty=float32 */; + %1173 = divide(%1171, -127f /* ty=float32 */) /* ty=float32 */; + %1174 = maximum(%1172, %1173) /* ty=float32 */; + %1175 = divide(%1169, %1174) /* ty=Tensor[(256, 32), float32] */; + %1176 = round(%1175) /* ty=Tensor[(256, 32), float32] */; + %1177 = nn.dense(%374, %1169, units=None) /* ty=Tensor[(192, 256), float32] */; + %1178 = max(%1177) /* ty=float32 */; + %1179 = min(%1177) /* ty=float32 */; + %1180 = divide(%1178, 127f /* ty=float32 */) /* ty=float32 */; + %1181 = divide(%1179, -127f /* ty=float32 */) /* ty=float32 */; + %1182 = cast(%381, dtype="int8") /* ty=Tensor[(192, 32), int8] */; + %1183 = cast(%1176, dtype="int8") /* ty=Tensor[(256, 32), int8] */; + %1184 = maximum(%1180, %1181) /* ty=float32 */; + %1185 = fn (%outer_arg_025: Tensor[(192, 32), int8], %outer_arg_125: Tensor[(256, 32), int8], %outer_arg_225: float32, %outer_arg_325: float32, %outer_arg_425: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_13") -> Tensor[(192, 256), int8] { + %373 = fn (%data25: Tensor[(192, 32), int8], %weights25: Tensor[(256, 32), int8], %s_data25: float32, %s_w25: float32, %s_act25: float32, Composite="ilavta.dense") -> Tensor[(192, 256), int8] { + %367 = nn.dense(%data25, %weights25, units=None, out_dtype="int32") /* ty=Tensor[(192, 256), int32] */; + %368 = multiply(%s_data25, %s_w25) /* ty=float32 */; + %369 = cast(%367, dtype="float32") /* ty=Tensor[(192, 256), float32] */; + %370 = divide(%368, %s_act25) /* ty=float32 */; + %371 = multiply(%369, %370) /* ty=Tensor[(192, 256), float32] */; + %372 = clip(%371, a_min=-127f, a_max=127f) /* ty=Tensor[(192, 256), float32] */; + cast(%372, dtype="int8") /* ty=Tensor[(192, 256), int8] */ + }; + %373(%outer_arg_025, %outer_arg_125, %outer_arg_225, %outer_arg_325, %outer_arg_425) /* ty=Tensor[(192, 256), int8] */ + }; + %1186 = %1185(%1182, %1183, %379, %1174, %1184) /* ty=Tensor[(192, 256), int8] */; + %1187 = cast(%1186, dtype="float32") /* ty=Tensor[(192, 256), float32] */; + %1188 = multiply(%1187, %1184) /* ty=Tensor[(192, 256), float32] */; + %1189 = reshape(%1188, newshape=[192, 1, 16, 16]) /* from_string */ /* ty=Tensor[(192, 1, 16, 16), float32] */; + %1190 = add(%layers_5_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(192), float32] */; + %1191 = sqrt(%1190) /* from_string */ /* ty=Tensor[(192), float32] */; + %1192 = divide(%573, %1191) /* from_string */ /* ty=Tensor[(192), float32] */; + %1193 = multiply(%1192, %layers_5_bn1_weight) /* from_string */ /* ty=Tensor[(192), float32] */; + %1194 = expand_dims(%1193, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1195 = transpose(%1189, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1196 = expand_dims(%1194, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1197 = negative(%layers_5_bn1_running_mean) /* from_string */ /* ty=Tensor[(192), float32] */; + %1198 = multiply(%1197, %1193) /* from_string */ /* ty=Tensor[(192), float32] */; + %1199 = add(%1198, %layers_5_bn1_bias) /* from_string */ /* ty=Tensor[(192), float32] */; + %1200 = expand_dims(%1199, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1201 = multiply(%1195, %1196) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1202 = expand_dims(%1200, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1203 = add(%1201, %1202) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1204 = nn.relu(%1203) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1205 = reshape(%layers_5_conv2_weight, newshape=[192, 1, 3, 3]) /* from_string */ /* ty=Tensor[(192, 1, 3, 3), float32] */; + %1206 = add(%layers_5_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(192), float32] */; + %1207 = sqrt(%1206) /* from_string */ /* ty=Tensor[(192), float32] */; + %1208 = divide(%573, %1207) /* from_string */ /* ty=Tensor[(192), float32] */; + %1209 = multiply(%1208, %layers_5_bn2_weight) /* from_string */ /* ty=Tensor[(192), float32] */; + %1210 = expand_dims(%1209, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1211 = nn.conv2d(%1204, %1205, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1212 = expand_dims(%1210, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1213 = negative(%layers_5_bn2_running_mean) /* from_string */ /* ty=Tensor[(192), float32] */; + %1214 = multiply(%1213, %1209) /* from_string */ /* ty=Tensor[(192), float32] */; + %1215 = add(%1214, %layers_5_bn2_bias) /* from_string */ /* ty=Tensor[(192), float32] */; + %1216 = expand_dims(%1215, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1217 = multiply(%1211, %1212) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1218 = expand_dims(%1216, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1219 = add(%1217, %1218) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1220 = nn.relu(%1219) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1221 = nn.pad(%1220, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1222 = nn.pad(%1221, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1223 = windows(%1222, axis=1, window_shape=[192, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 16, 16, 192, 1, 1), float32] */; + %1224 = squeeze(%1223, axis=[1]) /* from_string */ /* ty=Tensor[(1, 16, 16, 192, 1, 1), float32] */; + %1225 = reshape(%1224, newshape=[256, 192]) /* from_string */ /* ty=Tensor[(256, 192), float32] */; + %1226 = max(%1225) /* ty=float32 */; + %1227 = min(%1225) /* ty=float32 */; + %1228 = divide(%1226, 127f /* ty=float32 */) /* ty=float32 */; + %1229 = divide(%1227, -127f /* ty=float32 */) /* ty=float32 */; + %1230 = maximum(%1228, %1229) /* ty=float32 */; + %1231 = divide(%1225, %1230) /* ty=Tensor[(256, 192), float32] */; + %1232 = round(%1231) /* ty=Tensor[(256, 192), float32] */; + %1233 = nn.dense(%359, %1225, units=None) /* ty=Tensor[(32, 256), float32] */; + %1234 = max(%1233) /* ty=float32 */; + %1235 = min(%1233) /* ty=float32 */; + %1236 = divide(%1234, 127f /* ty=float32 */) /* ty=float32 */; + %1237 = divide(%1235, -127f /* ty=float32 */) /* ty=float32 */; + %1238 = cast(%366, dtype="int8") /* ty=Tensor[(32, 192), int8] */; + %1239 = cast(%1232, dtype="int8") /* ty=Tensor[(256, 192), int8] */; + %1240 = maximum(%1236, %1237) /* ty=float32 */; + %1241 = fn (%outer_arg_024: Tensor[(32, 192), int8], %outer_arg_124: Tensor[(256, 192), int8], %outer_arg_224: float32, %outer_arg_324: float32, %outer_arg_424: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_14") -> Tensor[(32, 256), int8] { + %358 = fn (%data24: Tensor[(32, 192), int8], %weights24: Tensor[(256, 192), int8], %s_data24: float32, %s_w24: float32, %s_act24: float32, Composite="ilavta.dense") -> Tensor[(32, 256), int8] { + %352 = nn.dense(%data24, %weights24, units=None, out_dtype="int32") /* ty=Tensor[(32, 256), int32] */; + %353 = multiply(%s_data24, %s_w24) /* ty=float32 */; + %354 = cast(%352, dtype="float32") /* ty=Tensor[(32, 256), float32] */; + %355 = divide(%353, %s_act24) /* ty=float32 */; + %356 = multiply(%354, %355) /* ty=Tensor[(32, 256), float32] */; + %357 = clip(%356, a_min=-127f, a_max=127f) /* ty=Tensor[(32, 256), float32] */; + cast(%357, dtype="int8") /* ty=Tensor[(32, 256), int8] */ + }; + %358(%outer_arg_024, %outer_arg_124, %outer_arg_224, %outer_arg_324, %outer_arg_424) /* ty=Tensor[(32, 256), int8] */ + }; + %1242 = %1241(%1238, %1239, %364, %1230, %1240) /* ty=Tensor[(32, 256), int8] */; + %1243 = cast(%1242, dtype="float32") /* ty=Tensor[(32, 256), float32] */; + %1244 = multiply(%1243, %1240) /* ty=Tensor[(32, 256), float32] */; + %1245 = reshape(%1244, newshape=[32, 1, 16, 16]) /* from_string */ /* ty=Tensor[(32, 1, 16, 16), float32] */; + %1246 = add(%layers_5_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(32), float32] */; + %1247 = sqrt(%1246) /* from_string */ /* ty=Tensor[(32), float32] */; + %1248 = divide(%573, %1247) /* from_string */ /* ty=Tensor[(32), float32] */; + %1249 = multiply(%1248, %layers_5_bn3_weight) /* from_string */ /* ty=Tensor[(32), float32] */; + %1250 = expand_dims(%1249, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %1251 = transpose(%1245, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1252 = expand_dims(%1250, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %1253 = negative(%layers_5_bn3_running_mean) /* from_string */ /* ty=Tensor[(32), float32] */; + %1254 = multiply(%1253, %1249) /* from_string */ /* ty=Tensor[(32), float32] */; + %1255 = add(%1254, %layers_5_bn3_bias) /* from_string */ /* ty=Tensor[(32), float32] */; + %1256 = expand_dims(%1255, axis=1) /* from_string */ /* ty=Tensor[(32, 1), float32] */; + %1257 = multiply(%1251, %1252) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1258 = expand_dims(%1256, axis=1) /* from_string */ /* ty=Tensor[(32, 1, 1), float32] */; + %1259 = add(%1257, %1258) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1260 = add(%1259, %1164) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1261 = nn.pad(%1260, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1262 = nn.pad(%1261, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %1263 = windows(%1262, axis=1, window_shape=[32, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 16, 16, 32, 1, 1), float32] */; + %1264 = squeeze(%1263, axis=[1]) /* from_string */ /* ty=Tensor[(1, 16, 16, 32, 1, 1), float32] */; + %1265 = reshape(%1264, newshape=[256, 32]) /* from_string */ /* ty=Tensor[(256, 32), float32] */; + %1266 = max(%1265) /* ty=float32 */; + %1267 = min(%1265) /* ty=float32 */; + %1268 = divide(%1266, 127f /* ty=float32 */) /* ty=float32 */; + %1269 = divide(%1267, -127f /* ty=float32 */) /* ty=float32 */; + %1270 = maximum(%1268, %1269) /* ty=float32 */; + %1271 = divide(%1265, %1270) /* ty=Tensor[(256, 32), float32] */; + %1272 = round(%1271) /* ty=Tensor[(256, 32), float32] */; + %1273 = nn.dense(%344, %1265, units=None) /* ty=Tensor[(192, 256), float32] */; + %1274 = max(%1273) /* ty=float32 */; + %1275 = min(%1273) /* ty=float32 */; + %1276 = divide(%1274, 127f /* ty=float32 */) /* ty=float32 */; + %1277 = divide(%1275, -127f /* ty=float32 */) /* ty=float32 */; + %1278 = cast(%351, dtype="int8") /* ty=Tensor[(192, 32), int8] */; + %1279 = cast(%1272, dtype="int8") /* ty=Tensor[(256, 32), int8] */; + %1280 = maximum(%1276, %1277) /* ty=float32 */; + %1281 = fn (%outer_arg_023: Tensor[(192, 32), int8], %outer_arg_123: Tensor[(256, 32), int8], %outer_arg_223: float32, %outer_arg_323: float32, %outer_arg_423: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_15") -> Tensor[(192, 256), int8] { + %343 = fn (%data23: Tensor[(192, 32), int8], %weights23: Tensor[(256, 32), int8], %s_data23: float32, %s_w23: float32, %s_act23: float32, Composite="ilavta.dense") -> Tensor[(192, 256), int8] { + %337 = nn.dense(%data23, %weights23, units=None, out_dtype="int32") /* ty=Tensor[(192, 256), int32] */; + %338 = multiply(%s_data23, %s_w23) /* ty=float32 */; + %339 = cast(%337, dtype="float32") /* ty=Tensor[(192, 256), float32] */; + %340 = divide(%338, %s_act23) /* ty=float32 */; + %341 = multiply(%339, %340) /* ty=Tensor[(192, 256), float32] */; + %342 = clip(%341, a_min=-127f, a_max=127f) /* ty=Tensor[(192, 256), float32] */; + cast(%342, dtype="int8") /* ty=Tensor[(192, 256), int8] */ + }; + %343(%outer_arg_023, %outer_arg_123, %outer_arg_223, %outer_arg_323, %outer_arg_423) /* ty=Tensor[(192, 256), int8] */ + }; + %1282 = %1281(%1278, %1279, %349, %1270, %1280) /* ty=Tensor[(192, 256), int8] */; + %1283 = cast(%1282, dtype="float32") /* ty=Tensor[(192, 256), float32] */; + %1284 = multiply(%1283, %1280) /* ty=Tensor[(192, 256), float32] */; + %1285 = reshape(%1284, newshape=[192, 1, 16, 16]) /* from_string */ /* ty=Tensor[(192, 1, 16, 16), float32] */; + %1286 = add(%layers_6_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(192), float32] */; + %1287 = sqrt(%1286) /* from_string */ /* ty=Tensor[(192), float32] */; + %1288 = divide(%573, %1287) /* from_string */ /* ty=Tensor[(192), float32] */; + %1289 = multiply(%1288, %layers_6_bn1_weight) /* from_string */ /* ty=Tensor[(192), float32] */; + %1290 = expand_dims(%1289, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1291 = transpose(%1285, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1292 = expand_dims(%1290, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1293 = negative(%layers_6_bn1_running_mean) /* from_string */ /* ty=Tensor[(192), float32] */; + %1294 = multiply(%1293, %1289) /* from_string */ /* ty=Tensor[(192), float32] */; + %1295 = add(%1294, %layers_6_bn1_bias) /* from_string */ /* ty=Tensor[(192), float32] */; + %1296 = expand_dims(%1295, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1297 = multiply(%1291, %1292) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1298 = expand_dims(%1296, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1299 = add(%1297, %1298) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1300 = nn.relu(%1299) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), float32] */; + %1301 = reshape(%layers_6_conv2_weight, newshape=[192, 1, 3, 3]) /* from_string */ /* ty=Tensor[(192, 1, 3, 3), float32] */; + %1302 = add(%layers_6_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(192), float32] */; + %1303 = sqrt(%1302) /* from_string */ /* ty=Tensor[(192), float32] */; + %1304 = divide(%573, %1303) /* from_string */ /* ty=Tensor[(192), float32] */; + %1305 = multiply(%1304, %layers_6_bn2_weight) /* from_string */ /* ty=Tensor[(192), float32] */; + %1306 = expand_dims(%1305, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1307 = nn.conv2d(%1300, %1301, strides=[2, 2], padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), float32] */; + %1308 = expand_dims(%1306, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1309 = negative(%layers_6_bn2_running_mean) /* from_string */ /* ty=Tensor[(192), float32] */; + %1310 = multiply(%1309, %1305) /* from_string */ /* ty=Tensor[(192), float32] */; + %1311 = add(%1310, %layers_6_bn2_bias) /* from_string */ /* ty=Tensor[(192), float32] */; + %1312 = expand_dims(%1311, axis=1) /* from_string */ /* ty=Tensor[(192, 1), float32] */; + %1313 = multiply(%1307, %1308) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), float32] */; + %1314 = expand_dims(%1312, axis=1) /* from_string */ /* ty=Tensor[(192, 1, 1), float32] */; + %1315 = add(%1313, %1314) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), float32] */; + %1316 = nn.relu(%1315) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), float32] */; + %1317 = nn.pad(%1316, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), float32] */; + %1318 = nn.pad(%1317, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), float32] */; + %1319 = windows(%1318, axis=1, window_shape=[192, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 192, 1, 1), float32] */; + %1320 = squeeze(%1319, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 192, 1, 1), float32] */; + %1321 = reshape(%1320, newshape=[64, 192]) /* from_string */ /* ty=Tensor[(64, 192), float32] */; + %1322 = max(%1321) /* ty=float32 */; + %1323 = min(%1321) /* ty=float32 */; + %1324 = divide(%1322, 127f /* ty=float32 */) /* ty=float32 */; + %1325 = divide(%1323, -127f /* ty=float32 */) /* ty=float32 */; + %1326 = maximum(%1324, %1325) /* ty=float32 */; + %1327 = divide(%1321, %1326) /* ty=Tensor[(64, 192), float32] */; + %1328 = round(%1327) /* ty=Tensor[(64, 192), float32] */; + %1329 = nn.dense(%329, %1321, units=None) /* ty=Tensor[(64, 64), float32] */; + %1330 = max(%1329) /* ty=float32 */; + %1331 = min(%1329) /* ty=float32 */; + %1332 = divide(%1330, 127f /* ty=float32 */) /* ty=float32 */; + %1333 = divide(%1331, -127f /* ty=float32 */) /* ty=float32 */; + %1334 = cast(%336, dtype="int8") /* ty=Tensor[(64, 192), int8] */; + %1335 = cast(%1328, dtype="int8") /* ty=Tensor[(64, 192), int8] */; + %1336 = maximum(%1332, %1333) /* ty=float32 */; + %1337 = fn (%outer_arg_022: Tensor[(64, 192), int8], %outer_arg_122: Tensor[(64, 192), int8], %outer_arg_222: float32, %outer_arg_322: float32, %outer_arg_422: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_16") -> Tensor[(64, 64), int8] { + %328 = fn (%data22: Tensor[(64, 192), int8], %weights22: Tensor[(64, 192), int8], %s_data22: float32, %s_w22: float32, %s_act22: float32, Composite="ilavta.dense") -> Tensor[(64, 64), int8] { + %322 = nn.dense(%data22, %weights22, units=None, out_dtype="int32") /* ty=Tensor[(64, 64), int32] */; + %323 = multiply(%s_data22, %s_w22) /* ty=float32 */; + %324 = cast(%322, dtype="float32") /* ty=Tensor[(64, 64), float32] */; + %325 = divide(%323, %s_act22) /* ty=float32 */; + %326 = multiply(%324, %325) /* ty=Tensor[(64, 64), float32] */; + %327 = clip(%326, a_min=-127f, a_max=127f) /* ty=Tensor[(64, 64), float32] */; + cast(%327, dtype="int8") /* ty=Tensor[(64, 64), int8] */ + }; + %328(%outer_arg_022, %outer_arg_122, %outer_arg_222, %outer_arg_322, %outer_arg_422) /* ty=Tensor[(64, 64), int8] */ + }; + %1338 = %1337(%1334, %1335, %334, %1326, %1336) /* ty=Tensor[(64, 64), int8] */; + %1339 = cast(%1338, dtype="float32") /* ty=Tensor[(64, 64), float32] */; + %1340 = multiply(%1339, %1336) /* ty=Tensor[(64, 64), float32] */; + %1341 = reshape(%1340, newshape=[64, 1, 8, 8]) /* from_string */ /* ty=Tensor[(64, 1, 8, 8), float32] */; + %1342 = add(%layers_6_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(64), float32] */; + %1343 = sqrt(%1342) /* from_string */ /* ty=Tensor[(64), float32] */; + %1344 = divide(%573, %1343) /* from_string */ /* ty=Tensor[(64), float32] */; + %1345 = multiply(%1344, %layers_6_bn3_weight) /* from_string */ /* ty=Tensor[(64), float32] */; + %1346 = expand_dims(%1345, axis=1) /* from_string */ /* ty=Tensor[(64, 1), float32] */; + %1347 = transpose(%1341, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1348 = expand_dims(%1346, axis=1) /* from_string */ /* ty=Tensor[(64, 1, 1), float32] */; + %1349 = negative(%layers_6_bn3_running_mean) /* from_string */ /* ty=Tensor[(64), float32] */; + %1350 = multiply(%1349, %1345) /* from_string */ /* ty=Tensor[(64), float32] */; + %1351 = add(%1350, %layers_6_bn3_bias) /* from_string */ /* ty=Tensor[(64), float32] */; + %1352 = expand_dims(%1351, axis=1) /* from_string */ /* ty=Tensor[(64, 1), float32] */; + %1353 = multiply(%1347, %1348) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1354 = expand_dims(%1352, axis=1) /* from_string */ /* ty=Tensor[(64, 1, 1), float32] */; + %1355 = add(%1353, %1354) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1356 = nn.pad(%1355, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1357 = nn.pad(%1356, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1358 = windows(%1357, axis=1, window_shape=[64, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 64, 1, 1), float32] */; + %1359 = squeeze(%1358, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 64, 1, 1), float32] */; + %1360 = reshape(%1359, newshape=[64, 64]) /* from_string */ /* ty=Tensor[(64, 64), float32] */; + %1361 = max(%1360) /* ty=float32 */; + %1362 = min(%1360) /* ty=float32 */; + %1363 = divide(%1361, 127f /* ty=float32 */) /* ty=float32 */; + %1364 = divide(%1362, -127f /* ty=float32 */) /* ty=float32 */; + %1365 = maximum(%1363, %1364) /* ty=float32 */; + %1366 = divide(%1360, %1365) /* ty=Tensor[(64, 64), float32] */; + %1367 = round(%1366) /* ty=Tensor[(64, 64), float32] */; + %1368 = nn.dense(%314, %1360, units=None) /* ty=Tensor[(384, 64), float32] */; + %1369 = max(%1368) /* ty=float32 */; + %1370 = min(%1368) /* ty=float32 */; + %1371 = divide(%1369, 127f /* ty=float32 */) /* ty=float32 */; + %1372 = divide(%1370, -127f /* ty=float32 */) /* ty=float32 */; + %1373 = cast(%321, dtype="int8") /* ty=Tensor[(384, 64), int8] */; + %1374 = cast(%1367, dtype="int8") /* ty=Tensor[(64, 64), int8] */; + %1375 = maximum(%1371, %1372) /* ty=float32 */; + %1376 = fn (%outer_arg_021: Tensor[(384, 64), int8], %outer_arg_121: Tensor[(64, 64), int8], %outer_arg_221: float32, %outer_arg_321: float32, %outer_arg_421: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_17") -> Tensor[(384, 64), int8] { + %313 = fn (%data21: Tensor[(384, 64), int8], %weights21: Tensor[(64, 64), int8], %s_data21: float32, %s_w21: float32, %s_act21: float32, Composite="ilavta.dense") -> Tensor[(384, 64), int8] { + %307 = nn.dense(%data21, %weights21, units=None, out_dtype="int32") /* ty=Tensor[(384, 64), int32] */; + %308 = multiply(%s_data21, %s_w21) /* ty=float32 */; + %309 = cast(%307, dtype="float32") /* ty=Tensor[(384, 64), float32] */; + %310 = divide(%308, %s_act21) /* ty=float32 */; + %311 = multiply(%309, %310) /* ty=Tensor[(384, 64), float32] */; + %312 = clip(%311, a_min=-127f, a_max=127f) /* ty=Tensor[(384, 64), float32] */; + cast(%312, dtype="int8") /* ty=Tensor[(384, 64), int8] */ + }; + %313(%outer_arg_021, %outer_arg_121, %outer_arg_221, %outer_arg_321, %outer_arg_421) /* ty=Tensor[(384, 64), int8] */ + }; + %1377 = %1376(%1373, %1374, %319, %1365, %1375) /* ty=Tensor[(384, 64), int8] */; + %1378 = cast(%1377, dtype="float32") /* ty=Tensor[(384, 64), float32] */; + %1379 = multiply(%1378, %1375) /* ty=Tensor[(384, 64), float32] */; + %1380 = reshape(%1379, newshape=[384, 1, 8, 8]) /* from_string */ /* ty=Tensor[(384, 1, 8, 8), float32] */; + %1381 = add(%layers_7_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(384), float32] */; + %1382 = sqrt(%1381) /* from_string */ /* ty=Tensor[(384), float32] */; + %1383 = divide(%573, %1382) /* from_string */ /* ty=Tensor[(384), float32] */; + %1384 = multiply(%1383, %layers_7_bn1_weight) /* from_string */ /* ty=Tensor[(384), float32] */; + %1385 = expand_dims(%1384, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1386 = transpose(%1380, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1387 = expand_dims(%1385, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1388 = negative(%layers_7_bn1_running_mean) /* from_string */ /* ty=Tensor[(384), float32] */; + %1389 = multiply(%1388, %1384) /* from_string */ /* ty=Tensor[(384), float32] */; + %1390 = add(%1389, %layers_7_bn1_bias) /* from_string */ /* ty=Tensor[(384), float32] */; + %1391 = expand_dims(%1390, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1392 = multiply(%1386, %1387) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1393 = expand_dims(%1391, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1394 = add(%1392, %1393) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1395 = nn.relu(%1394) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1396 = reshape(%layers_7_conv2_weight, newshape=[384, 1, 3, 3]) /* from_string */ /* ty=Tensor[(384, 1, 3, 3), float32] */; + %1397 = add(%layers_7_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(384), float32] */; + %1398 = sqrt(%1397) /* from_string */ /* ty=Tensor[(384), float32] */; + %1399 = divide(%573, %1398) /* from_string */ /* ty=Tensor[(384), float32] */; + %1400 = multiply(%1399, %layers_7_bn2_weight) /* from_string */ /* ty=Tensor[(384), float32] */; + %1401 = expand_dims(%1400, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1402 = nn.conv2d(%1395, %1396, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1403 = expand_dims(%1401, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1404 = negative(%layers_7_bn2_running_mean) /* from_string */ /* ty=Tensor[(384), float32] */; + %1405 = multiply(%1404, %1400) /* from_string */ /* ty=Tensor[(384), float32] */; + %1406 = add(%1405, %layers_7_bn2_bias) /* from_string */ /* ty=Tensor[(384), float32] */; + %1407 = expand_dims(%1406, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1408 = multiply(%1402, %1403) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1409 = expand_dims(%1407, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1410 = add(%1408, %1409) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1411 = nn.relu(%1410) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1412 = nn.pad(%1411, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1413 = nn.pad(%1412, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1414 = windows(%1413, axis=1, window_shape=[384, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 384, 1, 1), float32] */; + %1415 = squeeze(%1414, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 384, 1, 1), float32] */; + %1416 = reshape(%1415, newshape=[64, 384]) /* from_string */ /* ty=Tensor[(64, 384), float32] */; + %1417 = max(%1416) /* ty=float32 */; + %1418 = min(%1416) /* ty=float32 */; + %1419 = divide(%1417, 127f /* ty=float32 */) /* ty=float32 */; + %1420 = divide(%1418, -127f /* ty=float32 */) /* ty=float32 */; + %1421 = maximum(%1419, %1420) /* ty=float32 */; + %1422 = divide(%1416, %1421) /* ty=Tensor[(64, 384), float32] */; + %1423 = round(%1422) /* ty=Tensor[(64, 384), float32] */; + %1424 = nn.dense(%299, %1416, units=None) /* ty=Tensor[(64, 64), float32] */; + %1425 = max(%1424) /* ty=float32 */; + %1426 = min(%1424) /* ty=float32 */; + %1427 = divide(%1425, 127f /* ty=float32 */) /* ty=float32 */; + %1428 = divide(%1426, -127f /* ty=float32 */) /* ty=float32 */; + %1429 = cast(%306, dtype="int8") /* ty=Tensor[(64, 384), int8] */; + %1430 = cast(%1423, dtype="int8") /* ty=Tensor[(64, 384), int8] */; + %1431 = maximum(%1427, %1428) /* ty=float32 */; + %1432 = fn (%outer_arg_020: Tensor[(64, 384), int8], %outer_arg_120: Tensor[(64, 384), int8], %outer_arg_220: float32, %outer_arg_320: float32, %outer_arg_420: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_18") -> Tensor[(64, 64), int8] { + %298 = fn (%data20: Tensor[(64, 384), int8], %weights20: Tensor[(64, 384), int8], %s_data20: float32, %s_w20: float32, %s_act20: float32, Composite="ilavta.dense") -> Tensor[(64, 64), int8] { + %292 = nn.dense(%data20, %weights20, units=None, out_dtype="int32") /* ty=Tensor[(64, 64), int32] */; + %293 = multiply(%s_data20, %s_w20) /* ty=float32 */; + %294 = cast(%292, dtype="float32") /* ty=Tensor[(64, 64), float32] */; + %295 = divide(%293, %s_act20) /* ty=float32 */; + %296 = multiply(%294, %295) /* ty=Tensor[(64, 64), float32] */; + %297 = clip(%296, a_min=-127f, a_max=127f) /* ty=Tensor[(64, 64), float32] */; + cast(%297, dtype="int8") /* ty=Tensor[(64, 64), int8] */ + }; + %298(%outer_arg_020, %outer_arg_120, %outer_arg_220, %outer_arg_320, %outer_arg_420) /* ty=Tensor[(64, 64), int8] */ + }; + %1433 = %1432(%1429, %1430, %304, %1421, %1431) /* ty=Tensor[(64, 64), int8] */; + %1434 = cast(%1433, dtype="float32") /* ty=Tensor[(64, 64), float32] */; + %1435 = multiply(%1434, %1431) /* ty=Tensor[(64, 64), float32] */; + %1436 = reshape(%1435, newshape=[64, 1, 8, 8]) /* from_string */ /* ty=Tensor[(64, 1, 8, 8), float32] */; + %1437 = add(%layers_7_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(64), float32] */; + %1438 = sqrt(%1437) /* from_string */ /* ty=Tensor[(64), float32] */; + %1439 = divide(%573, %1438) /* from_string */ /* ty=Tensor[(64), float32] */; + %1440 = multiply(%1439, %layers_7_bn3_weight) /* from_string */ /* ty=Tensor[(64), float32] */; + %1441 = expand_dims(%1440, axis=1) /* from_string */ /* ty=Tensor[(64, 1), float32] */; + %1442 = transpose(%1436, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1443 = expand_dims(%1441, axis=1) /* from_string */ /* ty=Tensor[(64, 1, 1), float32] */; + %1444 = negative(%layers_7_bn3_running_mean) /* from_string */ /* ty=Tensor[(64), float32] */; + %1445 = multiply(%1444, %1440) /* from_string */ /* ty=Tensor[(64), float32] */; + %1446 = add(%1445, %layers_7_bn3_bias) /* from_string */ /* ty=Tensor[(64), float32] */; + %1447 = expand_dims(%1446, axis=1) /* from_string */ /* ty=Tensor[(64, 1), float32] */; + %1448 = multiply(%1442, %1443) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1449 = expand_dims(%1447, axis=1) /* from_string */ /* ty=Tensor[(64, 1, 1), float32] */; + %1450 = add(%1448, %1449) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1451 = add(%1450, %1355) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1452 = nn.pad(%1451, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1453 = nn.pad(%1452, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1454 = windows(%1453, axis=1, window_shape=[64, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 64, 1, 1), float32] */; + %1455 = squeeze(%1454, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 64, 1, 1), float32] */; + %1456 = reshape(%1455, newshape=[64, 64]) /* from_string */ /* ty=Tensor[(64, 64), float32] */; + %1457 = max(%1456) /* ty=float32 */; + %1458 = min(%1456) /* ty=float32 */; + %1459 = divide(%1457, 127f /* ty=float32 */) /* ty=float32 */; + %1460 = divide(%1458, -127f /* ty=float32 */) /* ty=float32 */; + %1461 = maximum(%1459, %1460) /* ty=float32 */; + %1462 = divide(%1456, %1461) /* ty=Tensor[(64, 64), float32] */; + %1463 = round(%1462) /* ty=Tensor[(64, 64), float32] */; + %1464 = nn.dense(%284, %1456, units=None) /* ty=Tensor[(384, 64), float32] */; + %1465 = max(%1464) /* ty=float32 */; + %1466 = min(%1464) /* ty=float32 */; + %1467 = divide(%1465, 127f /* ty=float32 */) /* ty=float32 */; + %1468 = divide(%1466, -127f /* ty=float32 */) /* ty=float32 */; + %1469 = cast(%291, dtype="int8") /* ty=Tensor[(384, 64), int8] */; + %1470 = cast(%1463, dtype="int8") /* ty=Tensor[(64, 64), int8] */; + %1471 = maximum(%1467, %1468) /* ty=float32 */; + %1472 = fn (%outer_arg_019: Tensor[(384, 64), int8], %outer_arg_119: Tensor[(64, 64), int8], %outer_arg_219: float32, %outer_arg_319: float32, %outer_arg_419: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_19") -> Tensor[(384, 64), int8] { + %283 = fn (%data19: Tensor[(384, 64), int8], %weights19: Tensor[(64, 64), int8], %s_data19: float32, %s_w19: float32, %s_act19: float32, Composite="ilavta.dense") -> Tensor[(384, 64), int8] { + %277 = nn.dense(%data19, %weights19, units=None, out_dtype="int32") /* ty=Tensor[(384, 64), int32] */; + %278 = multiply(%s_data19, %s_w19) /* ty=float32 */; + %279 = cast(%277, dtype="float32") /* ty=Tensor[(384, 64), float32] */; + %280 = divide(%278, %s_act19) /* ty=float32 */; + %281 = multiply(%279, %280) /* ty=Tensor[(384, 64), float32] */; + %282 = clip(%281, a_min=-127f, a_max=127f) /* ty=Tensor[(384, 64), float32] */; + cast(%282, dtype="int8") /* ty=Tensor[(384, 64), int8] */ + }; + %283(%outer_arg_019, %outer_arg_119, %outer_arg_219, %outer_arg_319, %outer_arg_419) /* ty=Tensor[(384, 64), int8] */ + }; + %1473 = %1472(%1469, %1470, %289, %1461, %1471) /* ty=Tensor[(384, 64), int8] */; + %1474 = cast(%1473, dtype="float32") /* ty=Tensor[(384, 64), float32] */; + %1475 = multiply(%1474, %1471) /* ty=Tensor[(384, 64), float32] */; + %1476 = reshape(%1475, newshape=[384, 1, 8, 8]) /* from_string */ /* ty=Tensor[(384, 1, 8, 8), float32] */; + %1477 = add(%layers_8_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(384), float32] */; + %1478 = sqrt(%1477) /* from_string */ /* ty=Tensor[(384), float32] */; + %1479 = divide(%573, %1478) /* from_string */ /* ty=Tensor[(384), float32] */; + %1480 = multiply(%1479, %layers_8_bn1_weight) /* from_string */ /* ty=Tensor[(384), float32] */; + %1481 = expand_dims(%1480, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1482 = transpose(%1476, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1483 = expand_dims(%1481, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1484 = negative(%layers_8_bn1_running_mean) /* from_string */ /* ty=Tensor[(384), float32] */; + %1485 = multiply(%1484, %1480) /* from_string */ /* ty=Tensor[(384), float32] */; + %1486 = add(%1485, %layers_8_bn1_bias) /* from_string */ /* ty=Tensor[(384), float32] */; + %1487 = expand_dims(%1486, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1488 = multiply(%1482, %1483) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1489 = expand_dims(%1487, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1490 = add(%1488, %1489) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1491 = nn.relu(%1490) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1492 = reshape(%layers_8_conv2_weight, newshape=[384, 1, 3, 3]) /* from_string */ /* ty=Tensor[(384, 1, 3, 3), float32] */; + %1493 = add(%layers_8_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(384), float32] */; + %1494 = sqrt(%1493) /* from_string */ /* ty=Tensor[(384), float32] */; + %1495 = divide(%573, %1494) /* from_string */ /* ty=Tensor[(384), float32] */; + %1496 = multiply(%1495, %layers_8_bn2_weight) /* from_string */ /* ty=Tensor[(384), float32] */; + %1497 = expand_dims(%1496, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1498 = nn.conv2d(%1491, %1492, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1499 = expand_dims(%1497, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1500 = negative(%layers_8_bn2_running_mean) /* from_string */ /* ty=Tensor[(384), float32] */; + %1501 = multiply(%1500, %1496) /* from_string */ /* ty=Tensor[(384), float32] */; + %1502 = add(%1501, %layers_8_bn2_bias) /* from_string */ /* ty=Tensor[(384), float32] */; + %1503 = expand_dims(%1502, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1504 = multiply(%1498, %1499) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1505 = expand_dims(%1503, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1506 = add(%1504, %1505) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1507 = nn.relu(%1506) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1508 = nn.pad(%1507, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1509 = nn.pad(%1508, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1510 = windows(%1509, axis=1, window_shape=[384, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 384, 1, 1), float32] */; + %1511 = squeeze(%1510, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 384, 1, 1), float32] */; + %1512 = reshape(%1511, newshape=[64, 384]) /* from_string */ /* ty=Tensor[(64, 384), float32] */; + %1513 = max(%1512) /* ty=float32 */; + %1514 = min(%1512) /* ty=float32 */; + %1515 = divide(%1513, 127f /* ty=float32 */) /* ty=float32 */; + %1516 = divide(%1514, -127f /* ty=float32 */) /* ty=float32 */; + %1517 = maximum(%1515, %1516) /* ty=float32 */; + %1518 = divide(%1512, %1517) /* ty=Tensor[(64, 384), float32] */; + %1519 = round(%1518) /* ty=Tensor[(64, 384), float32] */; + %1520 = nn.dense(%269, %1512, units=None) /* ty=Tensor[(64, 64), float32] */; + %1521 = max(%1520) /* ty=float32 */; + %1522 = min(%1520) /* ty=float32 */; + %1523 = divide(%1521, 127f /* ty=float32 */) /* ty=float32 */; + %1524 = divide(%1522, -127f /* ty=float32 */) /* ty=float32 */; + %1525 = cast(%276, dtype="int8") /* ty=Tensor[(64, 384), int8] */; + %1526 = cast(%1519, dtype="int8") /* ty=Tensor[(64, 384), int8] */; + %1527 = maximum(%1523, %1524) /* ty=float32 */; + %1528 = fn (%outer_arg_018: Tensor[(64, 384), int8], %outer_arg_118: Tensor[(64, 384), int8], %outer_arg_218: float32, %outer_arg_318: float32, %outer_arg_418: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_20") -> Tensor[(64, 64), int8] { + %268 = fn (%data18: Tensor[(64, 384), int8], %weights18: Tensor[(64, 384), int8], %s_data18: float32, %s_w18: float32, %s_act18: float32, Composite="ilavta.dense") -> Tensor[(64, 64), int8] { + %262 = nn.dense(%data18, %weights18, units=None, out_dtype="int32") /* ty=Tensor[(64, 64), int32] */; + %263 = multiply(%s_data18, %s_w18) /* ty=float32 */; + %264 = cast(%262, dtype="float32") /* ty=Tensor[(64, 64), float32] */; + %265 = divide(%263, %s_act18) /* ty=float32 */; + %266 = multiply(%264, %265) /* ty=Tensor[(64, 64), float32] */; + %267 = clip(%266, a_min=-127f, a_max=127f) /* ty=Tensor[(64, 64), float32] */; + cast(%267, dtype="int8") /* ty=Tensor[(64, 64), int8] */ + }; + %268(%outer_arg_018, %outer_arg_118, %outer_arg_218, %outer_arg_318, %outer_arg_418) /* ty=Tensor[(64, 64), int8] */ + }; + %1529 = %1528(%1525, %1526, %274, %1517, %1527) /* ty=Tensor[(64, 64), int8] */; + %1530 = cast(%1529, dtype="float32") /* ty=Tensor[(64, 64), float32] */; + %1531 = multiply(%1530, %1527) /* ty=Tensor[(64, 64), float32] */; + %1532 = reshape(%1531, newshape=[64, 1, 8, 8]) /* from_string */ /* ty=Tensor[(64, 1, 8, 8), float32] */; + %1533 = add(%layers_8_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(64), float32] */; + %1534 = sqrt(%1533) /* from_string */ /* ty=Tensor[(64), float32] */; + %1535 = divide(%573, %1534) /* from_string */ /* ty=Tensor[(64), float32] */; + %1536 = multiply(%1535, %layers_8_bn3_weight) /* from_string */ /* ty=Tensor[(64), float32] */; + %1537 = expand_dims(%1536, axis=1) /* from_string */ /* ty=Tensor[(64, 1), float32] */; + %1538 = transpose(%1532, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1539 = expand_dims(%1537, axis=1) /* from_string */ /* ty=Tensor[(64, 1, 1), float32] */; + %1540 = negative(%layers_8_bn3_running_mean) /* from_string */ /* ty=Tensor[(64), float32] */; + %1541 = multiply(%1540, %1536) /* from_string */ /* ty=Tensor[(64), float32] */; + %1542 = add(%1541, %layers_8_bn3_bias) /* from_string */ /* ty=Tensor[(64), float32] */; + %1543 = expand_dims(%1542, axis=1) /* from_string */ /* ty=Tensor[(64, 1), float32] */; + %1544 = multiply(%1538, %1539) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1545 = expand_dims(%1543, axis=1) /* from_string */ /* ty=Tensor[(64, 1, 1), float32] */; + %1546 = add(%1544, %1545) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1547 = add(%1546, %1451) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1548 = nn.pad(%1547, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1549 = nn.pad(%1548, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1550 = windows(%1549, axis=1, window_shape=[64, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 64, 1, 1), float32] */; + %1551 = squeeze(%1550, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 64, 1, 1), float32] */; + %1552 = reshape(%1551, newshape=[64, 64]) /* from_string */ /* ty=Tensor[(64, 64), float32] */; + %1553 = max(%1552) /* ty=float32 */; + %1554 = min(%1552) /* ty=float32 */; + %1555 = divide(%1553, 127f /* ty=float32 */) /* ty=float32 */; + %1556 = divide(%1554, -127f /* ty=float32 */) /* ty=float32 */; + %1557 = maximum(%1555, %1556) /* ty=float32 */; + %1558 = divide(%1552, %1557) /* ty=Tensor[(64, 64), float32] */; + %1559 = round(%1558) /* ty=Tensor[(64, 64), float32] */; + %1560 = nn.dense(%254, %1552, units=None) /* ty=Tensor[(384, 64), float32] */; + %1561 = max(%1560) /* ty=float32 */; + %1562 = min(%1560) /* ty=float32 */; + %1563 = divide(%1561, 127f /* ty=float32 */) /* ty=float32 */; + %1564 = divide(%1562, -127f /* ty=float32 */) /* ty=float32 */; + %1565 = cast(%261, dtype="int8") /* ty=Tensor[(384, 64), int8] */; + %1566 = cast(%1559, dtype="int8") /* ty=Tensor[(64, 64), int8] */; + %1567 = maximum(%1563, %1564) /* ty=float32 */; + %1568 = fn (%outer_arg_017: Tensor[(384, 64), int8], %outer_arg_117: Tensor[(64, 64), int8], %outer_arg_217: float32, %outer_arg_317: float32, %outer_arg_417: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_21") -> Tensor[(384, 64), int8] { + %253 = fn (%data17: Tensor[(384, 64), int8], %weights17: Tensor[(64, 64), int8], %s_data17: float32, %s_w17: float32, %s_act17: float32, Composite="ilavta.dense") -> Tensor[(384, 64), int8] { + %247 = nn.dense(%data17, %weights17, units=None, out_dtype="int32") /* ty=Tensor[(384, 64), int32] */; + %248 = multiply(%s_data17, %s_w17) /* ty=float32 */; + %249 = cast(%247, dtype="float32") /* ty=Tensor[(384, 64), float32] */; + %250 = divide(%248, %s_act17) /* ty=float32 */; + %251 = multiply(%249, %250) /* ty=Tensor[(384, 64), float32] */; + %252 = clip(%251, a_min=-127f, a_max=127f) /* ty=Tensor[(384, 64), float32] */; + cast(%252, dtype="int8") /* ty=Tensor[(384, 64), int8] */ + }; + %253(%outer_arg_017, %outer_arg_117, %outer_arg_217, %outer_arg_317, %outer_arg_417) /* ty=Tensor[(384, 64), int8] */ + }; + %1569 = %1568(%1565, %1566, %259, %1557, %1567) /* ty=Tensor[(384, 64), int8] */; + %1570 = cast(%1569, dtype="float32") /* ty=Tensor[(384, 64), float32] */; + %1571 = multiply(%1570, %1567) /* ty=Tensor[(384, 64), float32] */; + %1572 = reshape(%1571, newshape=[384, 1, 8, 8]) /* from_string */ /* ty=Tensor[(384, 1, 8, 8), float32] */; + %1573 = add(%layers_9_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(384), float32] */; + %1574 = sqrt(%1573) /* from_string */ /* ty=Tensor[(384), float32] */; + %1575 = divide(%573, %1574) /* from_string */ /* ty=Tensor[(384), float32] */; + %1576 = multiply(%1575, %layers_9_bn1_weight) /* from_string */ /* ty=Tensor[(384), float32] */; + %1577 = expand_dims(%1576, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1578 = transpose(%1572, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1579 = expand_dims(%1577, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1580 = negative(%layers_9_bn1_running_mean) /* from_string */ /* ty=Tensor[(384), float32] */; + %1581 = multiply(%1580, %1576) /* from_string */ /* ty=Tensor[(384), float32] */; + %1582 = add(%1581, %layers_9_bn1_bias) /* from_string */ /* ty=Tensor[(384), float32] */; + %1583 = expand_dims(%1582, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1584 = multiply(%1578, %1579) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1585 = expand_dims(%1583, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1586 = add(%1584, %1585) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1587 = nn.relu(%1586) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1588 = reshape(%layers_9_conv2_weight, newshape=[384, 1, 3, 3]) /* from_string */ /* ty=Tensor[(384, 1, 3, 3), float32] */; + %1589 = add(%layers_9_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(384), float32] */; + %1590 = sqrt(%1589) /* from_string */ /* ty=Tensor[(384), float32] */; + %1591 = divide(%573, %1590) /* from_string */ /* ty=Tensor[(384), float32] */; + %1592 = multiply(%1591, %layers_9_bn2_weight) /* from_string */ /* ty=Tensor[(384), float32] */; + %1593 = expand_dims(%1592, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1594 = nn.conv2d(%1587, %1588, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1595 = expand_dims(%1593, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1596 = negative(%layers_9_bn2_running_mean) /* from_string */ /* ty=Tensor[(384), float32] */; + %1597 = multiply(%1596, %1592) /* from_string */ /* ty=Tensor[(384), float32] */; + %1598 = add(%1597, %layers_9_bn2_bias) /* from_string */ /* ty=Tensor[(384), float32] */; + %1599 = expand_dims(%1598, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1600 = multiply(%1594, %1595) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1601 = expand_dims(%1599, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1602 = add(%1600, %1601) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1603 = nn.relu(%1602) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1604 = nn.pad(%1603, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1605 = nn.pad(%1604, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1606 = windows(%1605, axis=1, window_shape=[384, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 384, 1, 1), float32] */; + %1607 = squeeze(%1606, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 384, 1, 1), float32] */; + %1608 = reshape(%1607, newshape=[64, 384]) /* from_string */ /* ty=Tensor[(64, 384), float32] */; + %1609 = max(%1608) /* ty=float32 */; + %1610 = min(%1608) /* ty=float32 */; + %1611 = divide(%1609, 127f /* ty=float32 */) /* ty=float32 */; + %1612 = divide(%1610, -127f /* ty=float32 */) /* ty=float32 */; + %1613 = maximum(%1611, %1612) /* ty=float32 */; + %1614 = divide(%1608, %1613) /* ty=Tensor[(64, 384), float32] */; + %1615 = round(%1614) /* ty=Tensor[(64, 384), float32] */; + %1616 = nn.dense(%239, %1608, units=None) /* ty=Tensor[(64, 64), float32] */; + %1617 = max(%1616) /* ty=float32 */; + %1618 = min(%1616) /* ty=float32 */; + %1619 = divide(%1617, 127f /* ty=float32 */) /* ty=float32 */; + %1620 = divide(%1618, -127f /* ty=float32 */) /* ty=float32 */; + %1621 = cast(%246, dtype="int8") /* ty=Tensor[(64, 384), int8] */; + %1622 = cast(%1615, dtype="int8") /* ty=Tensor[(64, 384), int8] */; + %1623 = maximum(%1619, %1620) /* ty=float32 */; + %1624 = fn (%outer_arg_016: Tensor[(64, 384), int8], %outer_arg_116: Tensor[(64, 384), int8], %outer_arg_216: float32, %outer_arg_316: float32, %outer_arg_416: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_22") -> Tensor[(64, 64), int8] { + %238 = fn (%data16: Tensor[(64, 384), int8], %weights16: Tensor[(64, 384), int8], %s_data16: float32, %s_w16: float32, %s_act16: float32, Composite="ilavta.dense") -> Tensor[(64, 64), int8] { + %232 = nn.dense(%data16, %weights16, units=None, out_dtype="int32") /* ty=Tensor[(64, 64), int32] */; + %233 = multiply(%s_data16, %s_w16) /* ty=float32 */; + %234 = cast(%232, dtype="float32") /* ty=Tensor[(64, 64), float32] */; + %235 = divide(%233, %s_act16) /* ty=float32 */; + %236 = multiply(%234, %235) /* ty=Tensor[(64, 64), float32] */; + %237 = clip(%236, a_min=-127f, a_max=127f) /* ty=Tensor[(64, 64), float32] */; + cast(%237, dtype="int8") /* ty=Tensor[(64, 64), int8] */ + }; + %238(%outer_arg_016, %outer_arg_116, %outer_arg_216, %outer_arg_316, %outer_arg_416) /* ty=Tensor[(64, 64), int8] */ + }; + %1625 = %1624(%1621, %1622, %244, %1613, %1623) /* ty=Tensor[(64, 64), int8] */; + %1626 = cast(%1625, dtype="float32") /* ty=Tensor[(64, 64), float32] */; + %1627 = multiply(%1626, %1623) /* ty=Tensor[(64, 64), float32] */; + %1628 = reshape(%1627, newshape=[64, 1, 8, 8]) /* from_string */ /* ty=Tensor[(64, 1, 8, 8), float32] */; + %1629 = add(%layers_9_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(64), float32] */; + %1630 = sqrt(%1629) /* from_string */ /* ty=Tensor[(64), float32] */; + %1631 = divide(%573, %1630) /* from_string */ /* ty=Tensor[(64), float32] */; + %1632 = multiply(%1631, %layers_9_bn3_weight) /* from_string */ /* ty=Tensor[(64), float32] */; + %1633 = expand_dims(%1632, axis=1) /* from_string */ /* ty=Tensor[(64, 1), float32] */; + %1634 = transpose(%1628, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1635 = expand_dims(%1633, axis=1) /* from_string */ /* ty=Tensor[(64, 1, 1), float32] */; + %1636 = negative(%layers_9_bn3_running_mean) /* from_string */ /* ty=Tensor[(64), float32] */; + %1637 = multiply(%1636, %1632) /* from_string */ /* ty=Tensor[(64), float32] */; + %1638 = add(%1637, %layers_9_bn3_bias) /* from_string */ /* ty=Tensor[(64), float32] */; + %1639 = expand_dims(%1638, axis=1) /* from_string */ /* ty=Tensor[(64, 1), float32] */; + %1640 = multiply(%1634, %1635) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1641 = expand_dims(%1639, axis=1) /* from_string */ /* ty=Tensor[(64, 1, 1), float32] */; + %1642 = add(%1640, %1641) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1643 = add(%1642, %1547) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1644 = nn.pad(%1643, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1645 = nn.pad(%1644, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %1646 = windows(%1645, axis=1, window_shape=[64, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 64, 1, 1), float32] */; + %1647 = squeeze(%1646, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 64, 1, 1), float32] */; + %1648 = reshape(%1647, newshape=[64, 64]) /* from_string */ /* ty=Tensor[(64, 64), float32] */; + %1649 = max(%1648) /* ty=float32 */; + %1650 = min(%1648) /* ty=float32 */; + %1651 = divide(%1649, 127f /* ty=float32 */) /* ty=float32 */; + %1652 = divide(%1650, -127f /* ty=float32 */) /* ty=float32 */; + %1653 = maximum(%1651, %1652) /* ty=float32 */; + %1654 = divide(%1648, %1653) /* ty=Tensor[(64, 64), float32] */; + %1655 = round(%1654) /* ty=Tensor[(64, 64), float32] */; + %1656 = nn.dense(%224, %1648, units=None) /* ty=Tensor[(384, 64), float32] */; + %1657 = max(%1656) /* ty=float32 */; + %1658 = min(%1656) /* ty=float32 */; + %1659 = divide(%1657, 127f /* ty=float32 */) /* ty=float32 */; + %1660 = divide(%1658, -127f /* ty=float32 */) /* ty=float32 */; + %1661 = cast(%231, dtype="int8") /* ty=Tensor[(384, 64), int8] */; + %1662 = cast(%1655, dtype="int8") /* ty=Tensor[(64, 64), int8] */; + %1663 = maximum(%1659, %1660) /* ty=float32 */; + %1664 = fn (%outer_arg_015: Tensor[(384, 64), int8], %outer_arg_115: Tensor[(64, 64), int8], %outer_arg_215: float32, %outer_arg_315: float32, %outer_arg_415: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_23") -> Tensor[(384, 64), int8] { + %223 = fn (%data15: Tensor[(384, 64), int8], %weights15: Tensor[(64, 64), int8], %s_data15: float32, %s_w15: float32, %s_act15: float32, Composite="ilavta.dense") -> Tensor[(384, 64), int8] { + %217 = nn.dense(%data15, %weights15, units=None, out_dtype="int32") /* ty=Tensor[(384, 64), int32] */; + %218 = multiply(%s_data15, %s_w15) /* ty=float32 */; + %219 = cast(%217, dtype="float32") /* ty=Tensor[(384, 64), float32] */; + %220 = divide(%218, %s_act15) /* ty=float32 */; + %221 = multiply(%219, %220) /* ty=Tensor[(384, 64), float32] */; + %222 = clip(%221, a_min=-127f, a_max=127f) /* ty=Tensor[(384, 64), float32] */; + cast(%222, dtype="int8") /* ty=Tensor[(384, 64), int8] */ + }; + %223(%outer_arg_015, %outer_arg_115, %outer_arg_215, %outer_arg_315, %outer_arg_415) /* ty=Tensor[(384, 64), int8] */ + }; + %1665 = %1664(%1661, %1662, %229, %1653, %1663) /* ty=Tensor[(384, 64), int8] */; + %1666 = cast(%1665, dtype="float32") /* ty=Tensor[(384, 64), float32] */; + %1667 = multiply(%1666, %1663) /* ty=Tensor[(384, 64), float32] */; + %1668 = reshape(%1667, newshape=[384, 1, 8, 8]) /* from_string */ /* ty=Tensor[(384, 1, 8, 8), float32] */; + %1669 = add(%layers_10_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(384), float32] */; + %1670 = sqrt(%1669) /* from_string */ /* ty=Tensor[(384), float32] */; + %1671 = divide(%573, %1670) /* from_string */ /* ty=Tensor[(384), float32] */; + %1672 = multiply(%1671, %layers_10_bn1_weight) /* from_string */ /* ty=Tensor[(384), float32] */; + %1673 = expand_dims(%1672, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1674 = transpose(%1668, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1675 = expand_dims(%1673, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1676 = negative(%layers_10_bn1_running_mean) /* from_string */ /* ty=Tensor[(384), float32] */; + %1677 = multiply(%1676, %1672) /* from_string */ /* ty=Tensor[(384), float32] */; + %1678 = add(%1677, %layers_10_bn1_bias) /* from_string */ /* ty=Tensor[(384), float32] */; + %1679 = expand_dims(%1678, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1680 = multiply(%1674, %1675) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1681 = expand_dims(%1679, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1682 = add(%1680, %1681) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1683 = nn.relu(%1682) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1684 = reshape(%layers_10_conv2_weight, newshape=[384, 1, 3, 3]) /* from_string */ /* ty=Tensor[(384, 1, 3, 3), float32] */; + %1685 = add(%layers_10_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(384), float32] */; + %1686 = sqrt(%1685) /* from_string */ /* ty=Tensor[(384), float32] */; + %1687 = divide(%573, %1686) /* from_string */ /* ty=Tensor[(384), float32] */; + %1688 = multiply(%1687, %layers_10_bn2_weight) /* from_string */ /* ty=Tensor[(384), float32] */; + %1689 = expand_dims(%1688, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1690 = nn.conv2d(%1683, %1684, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1691 = expand_dims(%1689, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1692 = negative(%layers_10_bn2_running_mean) /* from_string */ /* ty=Tensor[(384), float32] */; + %1693 = multiply(%1692, %1688) /* from_string */ /* ty=Tensor[(384), float32] */; + %1694 = add(%1693, %layers_10_bn2_bias) /* from_string */ /* ty=Tensor[(384), float32] */; + %1695 = expand_dims(%1694, axis=1) /* from_string */ /* ty=Tensor[(384, 1), float32] */; + %1696 = multiply(%1690, %1691) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1697 = expand_dims(%1695, axis=1) /* from_string */ /* ty=Tensor[(384, 1, 1), float32] */; + %1698 = add(%1696, %1697) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1699 = nn.relu(%1698) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1700 = nn.pad(%1699, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1701 = nn.pad(%1700, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), float32] */; + %1702 = windows(%1701, axis=1, window_shape=[384, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 384, 1, 1), float32] */; + %1703 = squeeze(%1702, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 384, 1, 1), float32] */; + %1704 = reshape(%1703, newshape=[64, 384]) /* from_string */ /* ty=Tensor[(64, 384), float32] */; + %1705 = max(%1704) /* ty=float32 */; + %1706 = min(%1704) /* ty=float32 */; + %1707 = divide(%1705, 127f /* ty=float32 */) /* ty=float32 */; + %1708 = divide(%1706, -127f /* ty=float32 */) /* ty=float32 */; + %1709 = maximum(%1707, %1708) /* ty=float32 */; + %1710 = divide(%1704, %1709) /* ty=Tensor[(64, 384), float32] */; + %1711 = round(%1710) /* ty=Tensor[(64, 384), float32] */; + %1712 = nn.dense(%209, %1704, units=None) /* ty=Tensor[(96, 64), float32] */; + %1713 = max(%1712) /* ty=float32 */; + %1714 = min(%1712) /* ty=float32 */; + %1715 = divide(%1713, 127f /* ty=float32 */) /* ty=float32 */; + %1716 = divide(%1714, -127f /* ty=float32 */) /* ty=float32 */; + %1717 = cast(%216, dtype="int8") /* ty=Tensor[(96, 384), int8] */; + %1718 = cast(%1711, dtype="int8") /* ty=Tensor[(64, 384), int8] */; + %1719 = maximum(%1715, %1716) /* ty=float32 */; + %1720 = fn (%outer_arg_014: Tensor[(96, 384), int8], %outer_arg_114: Tensor[(64, 384), int8], %outer_arg_214: float32, %outer_arg_314: float32, %outer_arg_414: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_24") -> Tensor[(96, 64), int8] { + %208 = fn (%data14: Tensor[(96, 384), int8], %weights14: Tensor[(64, 384), int8], %s_data14: float32, %s_w14: float32, %s_act14: float32, Composite="ilavta.dense") -> Tensor[(96, 64), int8] { + %202 = nn.dense(%data14, %weights14, units=None, out_dtype="int32") /* ty=Tensor[(96, 64), int32] */; + %203 = multiply(%s_data14, %s_w14) /* ty=float32 */; + %204 = cast(%202, dtype="float32") /* ty=Tensor[(96, 64), float32] */; + %205 = divide(%203, %s_act14) /* ty=float32 */; + %206 = multiply(%204, %205) /* ty=Tensor[(96, 64), float32] */; + %207 = clip(%206, a_min=-127f, a_max=127f) /* ty=Tensor[(96, 64), float32] */; + cast(%207, dtype="int8") /* ty=Tensor[(96, 64), int8] */ + }; + %208(%outer_arg_014, %outer_arg_114, %outer_arg_214, %outer_arg_314, %outer_arg_414) /* ty=Tensor[(96, 64), int8] */ + }; + %1721 = %1720(%1717, %1718, %214, %1709, %1719) /* ty=Tensor[(96, 64), int8] */; + %1722 = cast(%1721, dtype="float32") /* ty=Tensor[(96, 64), float32] */; + %1723 = multiply(%1722, %1719) /* ty=Tensor[(96, 64), float32] */; + %1724 = reshape(%1723, newshape=[96, 1, 8, 8]) /* from_string */ /* ty=Tensor[(96, 1, 8, 8), float32] */; + %1725 = add(%layers_10_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(96), float32] */; + %1726 = sqrt(%1725) /* from_string */ /* ty=Tensor[(96), float32] */; + %1727 = divide(%573, %1726) /* from_string */ /* ty=Tensor[(96), float32] */; + %1728 = multiply(%1727, %layers_10_bn3_weight) /* from_string */ /* ty=Tensor[(96), float32] */; + %1729 = expand_dims(%1728, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %1730 = transpose(%1724, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1731 = expand_dims(%1729, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %1732 = negative(%layers_10_bn3_running_mean) /* from_string */ /* ty=Tensor[(96), float32] */; + %1733 = multiply(%1732, %1728) /* from_string */ /* ty=Tensor[(96), float32] */; + %1734 = add(%1733, %layers_10_bn3_bias) /* from_string */ /* ty=Tensor[(96), float32] */; + %1735 = expand_dims(%1734, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %1736 = multiply(%1730, %1731) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1737 = expand_dims(%1735, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %1745 = reshape(%layers_10_shortcut_0_weight, newshape=[96, 64]) /* from_string */ /* ty=Tensor[(96, 64), float32] */; + %1746 = max(%1745) /* ty=float32 */; + %1747 = min(%1745) /* ty=float32 */; + %1748 = divide(%1746, 127f /* ty=float32 */) /* ty=float32 */; + %1749 = divide(%1747, -127f /* ty=float32 */) /* ty=float32 */; + %1750 = maximum(%1748, %1749) /* ty=float32 */; + %1751 = divide(%1745, %1750) /* ty=Tensor[(96, 64), float32] */; + %1752 = round(%1751) /* ty=Tensor[(96, 64), float32] */; + %1753 = max(%1648) /* ty=float32 */; + %1754 = min(%1648) /* ty=float32 */; + %1755 = divide(%1753, 127f /* ty=float32 */) /* ty=float32 */; + %1756 = divide(%1754, -127f /* ty=float32 */) /* ty=float32 */; + %1757 = maximum(%1755, %1756) /* ty=float32 */; + %1758 = divide(%1648, %1757) /* ty=Tensor[(64, 64), float32] */; + %1759 = round(%1758) /* ty=Tensor[(64, 64), float32] */; + %1760 = nn.dense(%1745, %1648, units=None) /* ty=Tensor[(96, 64), float32] */; + %1761 = max(%1760) /* ty=float32 */; + %1762 = min(%1760) /* ty=float32 */; + %1763 = divide(%1761, 127f /* ty=float32 */) /* ty=float32 */; + %1764 = divide(%1762, -127f /* ty=float32 */) /* ty=float32 */; + %1765 = cast(%1752, dtype="int8") /* ty=Tensor[(96, 64), int8] */; + %1766 = cast(%1759, dtype="int8") /* ty=Tensor[(64, 64), int8] */; + %1767 = maximum(%1763, %1764) /* ty=float32 */; + %1768 = fn (%outer_arg_039: Tensor[(96, 64), int8], %outer_arg_139: Tensor[(64, 64), int8], %outer_arg_239: float32, %outer_arg_339: float32, %outer_arg_439: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_25") -> Tensor[(96, 64), int8] { + %1744 = fn (%data39: Tensor[(96, 64), int8], %weights39: Tensor[(64, 64), int8], %s_data39: float32, %s_w39: float32, %s_act39: float32, Composite="ilavta.dense") -> Tensor[(96, 64), int8] { + %1738 = nn.dense(%data39, %weights39, units=None, out_dtype="int32") /* ty=Tensor[(96, 64), int32] */; + %1739 = multiply(%s_data39, %s_w39) /* ty=float32 */; + %1740 = cast(%1738, dtype="float32") /* ty=Tensor[(96, 64), float32] */; + %1741 = divide(%1739, %s_act39) /* ty=float32 */; + %1742 = multiply(%1740, %1741) /* ty=Tensor[(96, 64), float32] */; + %1743 = clip(%1742, a_min=-127f, a_max=127f) /* ty=Tensor[(96, 64), float32] */; + cast(%1743, dtype="int8") /* ty=Tensor[(96, 64), int8] */ + }; + %1744(%outer_arg_039, %outer_arg_139, %outer_arg_239, %outer_arg_339, %outer_arg_439) /* ty=Tensor[(96, 64), int8] */ + }; + %1769 = %1768(%1765, %1766, %1750, %1757, %1767) /* ty=Tensor[(96, 64), int8] */; + %1770 = cast(%1769, dtype="float32") /* ty=Tensor[(96, 64), float32] */; + %1771 = multiply(%1770, %1767) /* ty=Tensor[(96, 64), float32] */; + %1772 = reshape(%1771, newshape=[96, 1, 8, 8]) /* from_string */ /* ty=Tensor[(96, 1, 8, 8), float32] */; + %1773 = add(%layers_10_shortcut_1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(96), float32] */; + %1774 = sqrt(%1773) /* from_string */ /* ty=Tensor[(96), float32] */; + %1775 = divide(%573, %1774) /* from_string */ /* ty=Tensor[(96), float32] */; + %1776 = multiply(%1775, %layers_10_shortcut_1_weight) /* from_string */ /* ty=Tensor[(96), float32] */; + %1777 = expand_dims(%1776, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %1778 = transpose(%1772, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1779 = expand_dims(%1777, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %1780 = negative(%layers_10_shortcut_1_running_mean) /* from_string */ /* ty=Tensor[(96), float32] */; + %1781 = multiply(%1780, %1776) /* from_string */ /* ty=Tensor[(96), float32] */; + %1782 = add(%1781, %layers_10_shortcut_1_bias) /* from_string */ /* ty=Tensor[(96), float32] */; + %1783 = expand_dims(%1782, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %1784 = multiply(%1778, %1779) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1785 = expand_dims(%1783, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %1786 = add(%1736, %1737) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1787 = add(%1784, %1785) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1788 = add(%1786, %1787) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1789 = nn.pad(%1788, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1790 = nn.pad(%1789, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1791 = windows(%1790, axis=1, window_shape=[96, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 96, 1, 1), float32] */; + %1792 = squeeze(%1791, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 96, 1, 1), float32] */; + %1793 = reshape(%1792, newshape=[64, 96]) /* from_string */ /* ty=Tensor[(64, 96), float32] */; + %1794 = max(%1793) /* ty=float32 */; + %1795 = min(%1793) /* ty=float32 */; + %1796 = divide(%1794, 127f /* ty=float32 */) /* ty=float32 */; + %1797 = divide(%1795, -127f /* ty=float32 */) /* ty=float32 */; + %1798 = maximum(%1796, %1797) /* ty=float32 */; + %1799 = divide(%1793, %1798) /* ty=Tensor[(64, 96), float32] */; + %1800 = round(%1799) /* ty=Tensor[(64, 96), float32] */; + %1801 = nn.dense(%194, %1793, units=None) /* ty=Tensor[(576, 64), float32] */; + %1802 = max(%1801) /* ty=float32 */; + %1803 = min(%1801) /* ty=float32 */; + %1804 = divide(%1802, 127f /* ty=float32 */) /* ty=float32 */; + %1805 = divide(%1803, -127f /* ty=float32 */) /* ty=float32 */; + %1806 = cast(%201, dtype="int8") /* ty=Tensor[(576, 96), int8] */; + %1807 = cast(%1800, dtype="int8") /* ty=Tensor[(64, 96), int8] */; + %1808 = maximum(%1804, %1805) /* ty=float32 */; + %1809 = fn (%outer_arg_013: Tensor[(576, 96), int8], %outer_arg_113: Tensor[(64, 96), int8], %outer_arg_213: float32, %outer_arg_313: float32, %outer_arg_413: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_26") -> Tensor[(576, 64), int8] { + %193 = fn (%data13: Tensor[(576, 96), int8], %weights13: Tensor[(64, 96), int8], %s_data13: float32, %s_w13: float32, %s_act13: float32, Composite="ilavta.dense") -> Tensor[(576, 64), int8] { + %187 = nn.dense(%data13, %weights13, units=None, out_dtype="int32") /* ty=Tensor[(576, 64), int32] */; + %188 = multiply(%s_data13, %s_w13) /* ty=float32 */; + %189 = cast(%187, dtype="float32") /* ty=Tensor[(576, 64), float32] */; + %190 = divide(%188, %s_act13) /* ty=float32 */; + %191 = multiply(%189, %190) /* ty=Tensor[(576, 64), float32] */; + %192 = clip(%191, a_min=-127f, a_max=127f) /* ty=Tensor[(576, 64), float32] */; + cast(%192, dtype="int8") /* ty=Tensor[(576, 64), int8] */ + }; + %193(%outer_arg_013, %outer_arg_113, %outer_arg_213, %outer_arg_313, %outer_arg_413) /* ty=Tensor[(576, 64), int8] */ + }; + %1810 = %1809(%1806, %1807, %199, %1798, %1808) /* ty=Tensor[(576, 64), int8] */; + %1811 = cast(%1810, dtype="float32") /* ty=Tensor[(576, 64), float32] */; + %1812 = multiply(%1811, %1808) /* ty=Tensor[(576, 64), float32] */; + %1813 = reshape(%1812, newshape=[576, 1, 8, 8]) /* from_string */ /* ty=Tensor[(576, 1, 8, 8), float32] */; + %1814 = add(%layers_11_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(576), float32] */; + %1815 = sqrt(%1814) /* from_string */ /* ty=Tensor[(576), float32] */; + %1816 = divide(%573, %1815) /* from_string */ /* ty=Tensor[(576), float32] */; + %1817 = multiply(%1816, %layers_11_bn1_weight) /* from_string */ /* ty=Tensor[(576), float32] */; + %1818 = expand_dims(%1817, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %1819 = transpose(%1813, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1820 = expand_dims(%1818, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %1821 = negative(%layers_11_bn1_running_mean) /* from_string */ /* ty=Tensor[(576), float32] */; + %1822 = multiply(%1821, %1817) /* from_string */ /* ty=Tensor[(576), float32] */; + %1823 = add(%1822, %layers_11_bn1_bias) /* from_string */ /* ty=Tensor[(576), float32] */; + %1824 = expand_dims(%1823, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %1825 = multiply(%1819, %1820) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1826 = expand_dims(%1824, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %1827 = add(%1825, %1826) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1828 = nn.relu(%1827) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1829 = reshape(%layers_11_conv2_weight, newshape=[576, 1, 3, 3]) /* from_string */ /* ty=Tensor[(576, 1, 3, 3), float32] */; + %1830 = add(%layers_11_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(576), float32] */; + %1831 = sqrt(%1830) /* from_string */ /* ty=Tensor[(576), float32] */; + %1832 = divide(%573, %1831) /* from_string */ /* ty=Tensor[(576), float32] */; + %1833 = multiply(%1832, %layers_11_bn2_weight) /* from_string */ /* ty=Tensor[(576), float32] */; + %1834 = expand_dims(%1833, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %1835 = nn.conv2d(%1828, %1829, padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1836 = expand_dims(%1834, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %1837 = negative(%layers_11_bn2_running_mean) /* from_string */ /* ty=Tensor[(576), float32] */; + %1838 = multiply(%1837, %1833) /* from_string */ /* ty=Tensor[(576), float32] */; + %1839 = add(%1838, %layers_11_bn2_bias) /* from_string */ /* ty=Tensor[(576), float32] */; + %1840 = expand_dims(%1839, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %1841 = multiply(%1835, %1836) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1842 = expand_dims(%1840, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %1843 = add(%1841, %1842) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1844 = nn.relu(%1843) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1845 = nn.pad(%1844, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1846 = nn.pad(%1845, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1847 = windows(%1846, axis=1, window_shape=[576, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 576, 1, 1), float32] */; + %1848 = squeeze(%1847, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 576, 1, 1), float32] */; + %1849 = reshape(%1848, newshape=[64, 576]) /* from_string */ /* ty=Tensor[(64, 576), float32] */; + %1850 = max(%1849) /* ty=float32 */; + %1851 = min(%1849) /* ty=float32 */; + %1852 = divide(%1850, 127f /* ty=float32 */) /* ty=float32 */; + %1853 = divide(%1851, -127f /* ty=float32 */) /* ty=float32 */; + %1854 = maximum(%1852, %1853) /* ty=float32 */; + %1855 = divide(%1849, %1854) /* ty=Tensor[(64, 576), float32] */; + %1856 = round(%1855) /* ty=Tensor[(64, 576), float32] */; + %1857 = nn.dense(%179, %1849, units=None) /* ty=Tensor[(96, 64), float32] */; + %1858 = max(%1857) /* ty=float32 */; + %1859 = min(%1857) /* ty=float32 */; + %1860 = divide(%1858, 127f /* ty=float32 */) /* ty=float32 */; + %1861 = divide(%1859, -127f /* ty=float32 */) /* ty=float32 */; + %1862 = cast(%186, dtype="int8") /* ty=Tensor[(96, 576), int8] */; + %1863 = cast(%1856, dtype="int8") /* ty=Tensor[(64, 576), int8] */; + %1864 = maximum(%1860, %1861) /* ty=float32 */; + %1865 = fn (%outer_arg_012: Tensor[(96, 576), int8], %outer_arg_112: Tensor[(64, 576), int8], %outer_arg_212: float32, %outer_arg_312: float32, %outer_arg_412: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_27") -> Tensor[(96, 64), int8] { + %178 = fn (%data12: Tensor[(96, 576), int8], %weights12: Tensor[(64, 576), int8], %s_data12: float32, %s_w12: float32, %s_act12: float32, Composite="ilavta.dense") -> Tensor[(96, 64), int8] { + %172 = nn.dense(%data12, %weights12, units=None, out_dtype="int32") /* ty=Tensor[(96, 64), int32] */; + %173 = multiply(%s_data12, %s_w12) /* ty=float32 */; + %174 = cast(%172, dtype="float32") /* ty=Tensor[(96, 64), float32] */; + %175 = divide(%173, %s_act12) /* ty=float32 */; + %176 = multiply(%174, %175) /* ty=Tensor[(96, 64), float32] */; + %177 = clip(%176, a_min=-127f, a_max=127f) /* ty=Tensor[(96, 64), float32] */; + cast(%177, dtype="int8") /* ty=Tensor[(96, 64), int8] */ + }; + %178(%outer_arg_012, %outer_arg_112, %outer_arg_212, %outer_arg_312, %outer_arg_412) /* ty=Tensor[(96, 64), int8] */ + }; + %1866 = %1865(%1862, %1863, %184, %1854, %1864) /* ty=Tensor[(96, 64), int8] */; + %1867 = cast(%1866, dtype="float32") /* ty=Tensor[(96, 64), float32] */; + %1868 = multiply(%1867, %1864) /* ty=Tensor[(96, 64), float32] */; + %1869 = reshape(%1868, newshape=[96, 1, 8, 8]) /* from_string */ /* ty=Tensor[(96, 1, 8, 8), float32] */; + %1870 = add(%layers_11_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(96), float32] */; + %1871 = sqrt(%1870) /* from_string */ /* ty=Tensor[(96), float32] */; + %1872 = divide(%573, %1871) /* from_string */ /* ty=Tensor[(96), float32] */; + %1873 = multiply(%1872, %layers_11_bn3_weight) /* from_string */ /* ty=Tensor[(96), float32] */; + %1874 = expand_dims(%1873, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %1875 = transpose(%1869, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1876 = expand_dims(%1874, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %1877 = negative(%layers_11_bn3_running_mean) /* from_string */ /* ty=Tensor[(96), float32] */; + %1878 = multiply(%1877, %1873) /* from_string */ /* ty=Tensor[(96), float32] */; + %1879 = add(%1878, %layers_11_bn3_bias) /* from_string */ /* ty=Tensor[(96), float32] */; + %1880 = expand_dims(%1879, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %1881 = multiply(%1875, %1876) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1882 = expand_dims(%1880, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %1883 = add(%1881, %1882) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1884 = add(%1883, %1788) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1885 = nn.pad(%1884, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1886 = nn.pad(%1885, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1887 = windows(%1886, axis=1, window_shape=[96, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 96, 1, 1), float32] */; + %1888 = squeeze(%1887, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 96, 1, 1), float32] */; + %1889 = reshape(%1888, newshape=[64, 96]) /* from_string */ /* ty=Tensor[(64, 96), float32] */; + %1890 = max(%1889) /* ty=float32 */; + %1891 = min(%1889) /* ty=float32 */; + %1892 = divide(%1890, 127f /* ty=float32 */) /* ty=float32 */; + %1893 = divide(%1891, -127f /* ty=float32 */) /* ty=float32 */; + %1894 = maximum(%1892, %1893) /* ty=float32 */; + %1895 = divide(%1889, %1894) /* ty=Tensor[(64, 96), float32] */; + %1896 = round(%1895) /* ty=Tensor[(64, 96), float32] */; + %1897 = nn.dense(%164, %1889, units=None) /* ty=Tensor[(576, 64), float32] */; + %1898 = max(%1897) /* ty=float32 */; + %1899 = min(%1897) /* ty=float32 */; + %1900 = divide(%1898, 127f /* ty=float32 */) /* ty=float32 */; + %1901 = divide(%1899, -127f /* ty=float32 */) /* ty=float32 */; + %1902 = cast(%171, dtype="int8") /* ty=Tensor[(576, 96), int8] */; + %1903 = cast(%1896, dtype="int8") /* ty=Tensor[(64, 96), int8] */; + %1904 = maximum(%1900, %1901) /* ty=float32 */; + %1905 = fn (%outer_arg_011: Tensor[(576, 96), int8], %outer_arg_111: Tensor[(64, 96), int8], %outer_arg_211: float32, %outer_arg_311: float32, %outer_arg_411: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_28") -> Tensor[(576, 64), int8] { + %163 = fn (%data11: Tensor[(576, 96), int8], %weights11: Tensor[(64, 96), int8], %s_data11: float32, %s_w11: float32, %s_act11: float32, Composite="ilavta.dense") -> Tensor[(576, 64), int8] { + %157 = nn.dense(%data11, %weights11, units=None, out_dtype="int32") /* ty=Tensor[(576, 64), int32] */; + %158 = multiply(%s_data11, %s_w11) /* ty=float32 */; + %159 = cast(%157, dtype="float32") /* ty=Tensor[(576, 64), float32] */; + %160 = divide(%158, %s_act11) /* ty=float32 */; + %161 = multiply(%159, %160) /* ty=Tensor[(576, 64), float32] */; + %162 = clip(%161, a_min=-127f, a_max=127f) /* ty=Tensor[(576, 64), float32] */; + cast(%162, dtype="int8") /* ty=Tensor[(576, 64), int8] */ + }; + %163(%outer_arg_011, %outer_arg_111, %outer_arg_211, %outer_arg_311, %outer_arg_411) /* ty=Tensor[(576, 64), int8] */ + }; + %1906 = %1905(%1902, %1903, %169, %1894, %1904) /* ty=Tensor[(576, 64), int8] */; + %1907 = cast(%1906, dtype="float32") /* ty=Tensor[(576, 64), float32] */; + %1908 = multiply(%1907, %1904) /* ty=Tensor[(576, 64), float32] */; + %1909 = reshape(%1908, newshape=[576, 1, 8, 8]) /* from_string */ /* ty=Tensor[(576, 1, 8, 8), float32] */; + %1910 = add(%layers_12_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(576), float32] */; + %1911 = sqrt(%1910) /* from_string */ /* ty=Tensor[(576), float32] */; + %1912 = divide(%573, %1911) /* from_string */ /* ty=Tensor[(576), float32] */; + %1913 = multiply(%1912, %layers_12_bn1_weight) /* from_string */ /* ty=Tensor[(576), float32] */; + %1914 = expand_dims(%1913, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %1915 = transpose(%1909, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1916 = expand_dims(%1914, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %1917 = negative(%layers_12_bn1_running_mean) /* from_string */ /* ty=Tensor[(576), float32] */; + %1918 = multiply(%1917, %1913) /* from_string */ /* ty=Tensor[(576), float32] */; + %1919 = add(%1918, %layers_12_bn1_bias) /* from_string */ /* ty=Tensor[(576), float32] */; + %1920 = expand_dims(%1919, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %1921 = multiply(%1915, %1916) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1922 = expand_dims(%1920, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %1923 = add(%1921, %1922) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1924 = nn.relu(%1923) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1925 = reshape(%layers_12_conv2_weight, newshape=[576, 1, 3, 3]) /* from_string */ /* ty=Tensor[(576, 1, 3, 3), float32] */; + %1926 = add(%layers_12_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(576), float32] */; + %1927 = sqrt(%1926) /* from_string */ /* ty=Tensor[(576), float32] */; + %1928 = divide(%573, %1927) /* from_string */ /* ty=Tensor[(576), float32] */; + %1929 = multiply(%1928, %layers_12_bn2_weight) /* from_string */ /* ty=Tensor[(576), float32] */; + %1930 = expand_dims(%1929, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %1931 = nn.conv2d(%1924, %1925, padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1932 = expand_dims(%1930, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %1933 = negative(%layers_12_bn2_running_mean) /* from_string */ /* ty=Tensor[(576), float32] */; + %1934 = multiply(%1933, %1929) /* from_string */ /* ty=Tensor[(576), float32] */; + %1935 = add(%1934, %layers_12_bn2_bias) /* from_string */ /* ty=Tensor[(576), float32] */; + %1936 = expand_dims(%1935, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %1937 = multiply(%1931, %1932) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1938 = expand_dims(%1936, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %1939 = add(%1937, %1938) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1940 = nn.relu(%1939) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1941 = nn.pad(%1940, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1942 = nn.pad(%1941, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %1943 = windows(%1942, axis=1, window_shape=[576, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 576, 1, 1), float32] */; + %1944 = squeeze(%1943, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 576, 1, 1), float32] */; + %1945 = reshape(%1944, newshape=[64, 576]) /* from_string */ /* ty=Tensor[(64, 576), float32] */; + %1946 = max(%1945) /* ty=float32 */; + %1947 = min(%1945) /* ty=float32 */; + %1948 = divide(%1946, 127f /* ty=float32 */) /* ty=float32 */; + %1949 = divide(%1947, -127f /* ty=float32 */) /* ty=float32 */; + %1950 = maximum(%1948, %1949) /* ty=float32 */; + %1951 = divide(%1945, %1950) /* ty=Tensor[(64, 576), float32] */; + %1952 = round(%1951) /* ty=Tensor[(64, 576), float32] */; + %1953 = nn.dense(%149, %1945, units=None) /* ty=Tensor[(96, 64), float32] */; + %1954 = max(%1953) /* ty=float32 */; + %1955 = min(%1953) /* ty=float32 */; + %1956 = divide(%1954, 127f /* ty=float32 */) /* ty=float32 */; + %1957 = divide(%1955, -127f /* ty=float32 */) /* ty=float32 */; + %1958 = cast(%156, dtype="int8") /* ty=Tensor[(96, 576), int8] */; + %1959 = cast(%1952, dtype="int8") /* ty=Tensor[(64, 576), int8] */; + %1960 = maximum(%1956, %1957) /* ty=float32 */; + %1961 = fn (%outer_arg_010: Tensor[(96, 576), int8], %outer_arg_110: Tensor[(64, 576), int8], %outer_arg_210: float32, %outer_arg_310: float32, %outer_arg_410: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_29") -> Tensor[(96, 64), int8] { + %148 = fn (%data10: Tensor[(96, 576), int8], %weights10: Tensor[(64, 576), int8], %s_data10: float32, %s_w10: float32, %s_act10: float32, Composite="ilavta.dense") -> Tensor[(96, 64), int8] { + %142 = nn.dense(%data10, %weights10, units=None, out_dtype="int32") /* ty=Tensor[(96, 64), int32] */; + %143 = multiply(%s_data10, %s_w10) /* ty=float32 */; + %144 = cast(%142, dtype="float32") /* ty=Tensor[(96, 64), float32] */; + %145 = divide(%143, %s_act10) /* ty=float32 */; + %146 = multiply(%144, %145) /* ty=Tensor[(96, 64), float32] */; + %147 = clip(%146, a_min=-127f, a_max=127f) /* ty=Tensor[(96, 64), float32] */; + cast(%147, dtype="int8") /* ty=Tensor[(96, 64), int8] */ + }; + %148(%outer_arg_010, %outer_arg_110, %outer_arg_210, %outer_arg_310, %outer_arg_410) /* ty=Tensor[(96, 64), int8] */ + }; + %1962 = %1961(%1958, %1959, %154, %1950, %1960) /* ty=Tensor[(96, 64), int8] */; + %1963 = cast(%1962, dtype="float32") /* ty=Tensor[(96, 64), float32] */; + %1964 = multiply(%1963, %1960) /* ty=Tensor[(96, 64), float32] */; + %1965 = reshape(%1964, newshape=[96, 1, 8, 8]) /* from_string */ /* ty=Tensor[(96, 1, 8, 8), float32] */; + %1966 = add(%layers_12_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(96), float32] */; + %1967 = sqrt(%1966) /* from_string */ /* ty=Tensor[(96), float32] */; + %1968 = divide(%573, %1967) /* from_string */ /* ty=Tensor[(96), float32] */; + %1969 = multiply(%1968, %layers_12_bn3_weight) /* from_string */ /* ty=Tensor[(96), float32] */; + %1970 = expand_dims(%1969, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %1971 = transpose(%1965, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1972 = expand_dims(%1970, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %1973 = negative(%layers_12_bn3_running_mean) /* from_string */ /* ty=Tensor[(96), float32] */; + %1974 = multiply(%1973, %1969) /* from_string */ /* ty=Tensor[(96), float32] */; + %1975 = add(%1974, %layers_12_bn3_bias) /* from_string */ /* ty=Tensor[(96), float32] */; + %1976 = expand_dims(%1975, axis=1) /* from_string */ /* ty=Tensor[(96, 1), float32] */; + %1977 = multiply(%1971, %1972) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1978 = expand_dims(%1976, axis=1) /* from_string */ /* ty=Tensor[(96, 1, 1), float32] */; + %1979 = add(%1977, %1978) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1980 = add(%1979, %1884) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1981 = nn.pad(%1980, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1982 = nn.pad(%1981, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), float32] */; + %1983 = windows(%1982, axis=1, window_shape=[96, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 8, 8, 96, 1, 1), float32] */; + %1984 = squeeze(%1983, axis=[1]) /* from_string */ /* ty=Tensor[(1, 8, 8, 96, 1, 1), float32] */; + %1985 = reshape(%1984, newshape=[64, 96]) /* from_string */ /* ty=Tensor[(64, 96), float32] */; + %1986 = max(%1985) /* ty=float32 */; + %1987 = min(%1985) /* ty=float32 */; + %1988 = divide(%1986, 127f /* ty=float32 */) /* ty=float32 */; + %1989 = divide(%1987, -127f /* ty=float32 */) /* ty=float32 */; + %1990 = maximum(%1988, %1989) /* ty=float32 */; + %1991 = divide(%1985, %1990) /* ty=Tensor[(64, 96), float32] */; + %1992 = round(%1991) /* ty=Tensor[(64, 96), float32] */; + %1993 = nn.dense(%134, %1985, units=None) /* ty=Tensor[(576, 64), float32] */; + %1994 = max(%1993) /* ty=float32 */; + %1995 = min(%1993) /* ty=float32 */; + %1996 = divide(%1994, 127f /* ty=float32 */) /* ty=float32 */; + %1997 = divide(%1995, -127f /* ty=float32 */) /* ty=float32 */; + %1998 = cast(%141, dtype="int8") /* ty=Tensor[(576, 96), int8] */; + %1999 = cast(%1992, dtype="int8") /* ty=Tensor[(64, 96), int8] */; + %2000 = maximum(%1996, %1997) /* ty=float32 */; + %2001 = fn (%outer_arg_09: Tensor[(576, 96), int8], %outer_arg_19: Tensor[(64, 96), int8], %outer_arg_29: float32, %outer_arg_39: float32, %outer_arg_49: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_30") -> Tensor[(576, 64), int8] { + %133 = fn (%data9: Tensor[(576, 96), int8], %weights9: Tensor[(64, 96), int8], %s_data9: float32, %s_w9: float32, %s_act9: float32, Composite="ilavta.dense") -> Tensor[(576, 64), int8] { + %127 = nn.dense(%data9, %weights9, units=None, out_dtype="int32") /* ty=Tensor[(576, 64), int32] */; + %128 = multiply(%s_data9, %s_w9) /* ty=float32 */; + %129 = cast(%127, dtype="float32") /* ty=Tensor[(576, 64), float32] */; + %130 = divide(%128, %s_act9) /* ty=float32 */; + %131 = multiply(%129, %130) /* ty=Tensor[(576, 64), float32] */; + %132 = clip(%131, a_min=-127f, a_max=127f) /* ty=Tensor[(576, 64), float32] */; + cast(%132, dtype="int8") /* ty=Tensor[(576, 64), int8] */ + }; + %133(%outer_arg_09, %outer_arg_19, %outer_arg_29, %outer_arg_39, %outer_arg_49) /* ty=Tensor[(576, 64), int8] */ + }; + %2002 = %2001(%1998, %1999, %139, %1990, %2000) /* ty=Tensor[(576, 64), int8] */; + %2003 = cast(%2002, dtype="float32") /* ty=Tensor[(576, 64), float32] */; + %2004 = multiply(%2003, %2000) /* ty=Tensor[(576, 64), float32] */; + %2005 = reshape(%2004, newshape=[576, 1, 8, 8]) /* from_string */ /* ty=Tensor[(576, 1, 8, 8), float32] */; + %2006 = add(%layers_13_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(576), float32] */; + %2007 = sqrt(%2006) /* from_string */ /* ty=Tensor[(576), float32] */; + %2008 = divide(%573, %2007) /* from_string */ /* ty=Tensor[(576), float32] */; + %2009 = multiply(%2008, %layers_13_bn1_weight) /* from_string */ /* ty=Tensor[(576), float32] */; + %2010 = expand_dims(%2009, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %2011 = transpose(%2005, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %2012 = expand_dims(%2010, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %2013 = negative(%layers_13_bn1_running_mean) /* from_string */ /* ty=Tensor[(576), float32] */; + %2014 = multiply(%2013, %2009) /* from_string */ /* ty=Tensor[(576), float32] */; + %2015 = add(%2014, %layers_13_bn1_bias) /* from_string */ /* ty=Tensor[(576), float32] */; + %2016 = expand_dims(%2015, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %2017 = multiply(%2011, %2012) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %2018 = expand_dims(%2016, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %2019 = add(%2017, %2018) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %2020 = nn.relu(%2019) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), float32] */; + %2021 = reshape(%layers_13_conv2_weight, newshape=[576, 1, 3, 3]) /* from_string */ /* ty=Tensor[(576, 1, 3, 3), float32] */; + %2022 = add(%layers_13_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(576), float32] */; + %2023 = sqrt(%2022) /* from_string */ /* ty=Tensor[(576), float32] */; + %2024 = divide(%573, %2023) /* from_string */ /* ty=Tensor[(576), float32] */; + %2025 = multiply(%2024, %layers_13_bn2_weight) /* from_string */ /* ty=Tensor[(576), float32] */; + %2026 = expand_dims(%2025, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %2027 = nn.conv2d(%2020, %2021, strides=[2, 2], padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), float32] */; + %2028 = expand_dims(%2026, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %2029 = negative(%layers_13_bn2_running_mean) /* from_string */ /* ty=Tensor[(576), float32] */; + %2030 = multiply(%2029, %2025) /* from_string */ /* ty=Tensor[(576), float32] */; + %2031 = add(%2030, %layers_13_bn2_bias) /* from_string */ /* ty=Tensor[(576), float32] */; + %2032 = expand_dims(%2031, axis=1) /* from_string */ /* ty=Tensor[(576, 1), float32] */; + %2033 = multiply(%2027, %2028) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), float32] */; + %2034 = expand_dims(%2032, axis=1) /* from_string */ /* ty=Tensor[(576, 1, 1), float32] */; + %2035 = add(%2033, %2034) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), float32] */; + %2036 = nn.relu(%2035) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), float32] */; + %2037 = nn.pad(%2036, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), float32] */; + %2038 = nn.pad(%2037, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), float32] */; + %2039 = windows(%2038, axis=1, window_shape=[576, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 4, 4, 576, 1, 1), float32] */; + %2040 = squeeze(%2039, axis=[1]) /* from_string */ /* ty=Tensor[(1, 4, 4, 576, 1, 1), float32] */; + %2041 = reshape(%2040, newshape=[16, 576]) /* from_string */ /* ty=Tensor[(16, 576), float32] */; + %2042 = max(%2041) /* ty=float32 */; + %2043 = min(%2041) /* ty=float32 */; + %2044 = divide(%2042, 127f /* ty=float32 */) /* ty=float32 */; + %2045 = divide(%2043, -127f /* ty=float32 */) /* ty=float32 */; + %2046 = maximum(%2044, %2045) /* ty=float32 */; + %2047 = divide(%2041, %2046) /* ty=Tensor[(16, 576), float32] */; + %2048 = round(%2047) /* ty=Tensor[(16, 576), float32] */; + %2049 = nn.dense(%119, %2041, units=None) /* ty=Tensor[(160, 16), float32] */; + %2050 = max(%2049) /* ty=float32 */; + %2051 = min(%2049) /* ty=float32 */; + %2052 = divide(%2050, 127f /* ty=float32 */) /* ty=float32 */; + %2053 = divide(%2051, -127f /* ty=float32 */) /* ty=float32 */; + %2054 = cast(%126, dtype="int8") /* ty=Tensor[(160, 576), int8] */; + %2055 = cast(%2048, dtype="int8") /* ty=Tensor[(16, 576), int8] */; + %2056 = maximum(%2052, %2053) /* ty=float32 */; + %2057 = fn (%outer_arg_08: Tensor[(160, 576), int8], %outer_arg_18: Tensor[(16, 576), int8], %outer_arg_28: float32, %outer_arg_38: float32, %outer_arg_48: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_31") -> Tensor[(160, 16), int8] { + %118 = fn (%data8: Tensor[(160, 576), int8], %weights8: Tensor[(16, 576), int8], %s_data8: float32, %s_w8: float32, %s_act8: float32, Composite="ilavta.dense") -> Tensor[(160, 16), int8] { + %112 = nn.dense(%data8, %weights8, units=None, out_dtype="int32") /* ty=Tensor[(160, 16), int32] */; + %113 = multiply(%s_data8, %s_w8) /* ty=float32 */; + %114 = cast(%112, dtype="float32") /* ty=Tensor[(160, 16), float32] */; + %115 = divide(%113, %s_act8) /* ty=float32 */; + %116 = multiply(%114, %115) /* ty=Tensor[(160, 16), float32] */; + %117 = clip(%116, a_min=-127f, a_max=127f) /* ty=Tensor[(160, 16), float32] */; + cast(%117, dtype="int8") /* ty=Tensor[(160, 16), int8] */ + }; + %118(%outer_arg_08, %outer_arg_18, %outer_arg_28, %outer_arg_38, %outer_arg_48) /* ty=Tensor[(160, 16), int8] */ + }; + %2058 = %2057(%2054, %2055, %124, %2046, %2056) /* ty=Tensor[(160, 16), int8] */; + %2059 = cast(%2058, dtype="float32") /* ty=Tensor[(160, 16), float32] */; + %2060 = multiply(%2059, %2056) /* ty=Tensor[(160, 16), float32] */; + %2061 = reshape(%2060, newshape=[160, 1, 4, 4]) /* from_string */ /* ty=Tensor[(160, 1, 4, 4), float32] */; + %2062 = add(%layers_13_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(160), float32] */; + %2063 = sqrt(%2062) /* from_string */ /* ty=Tensor[(160), float32] */; + %2064 = divide(%573, %2063) /* from_string */ /* ty=Tensor[(160), float32] */; + %2065 = multiply(%2064, %layers_13_bn3_weight) /* from_string */ /* ty=Tensor[(160), float32] */; + %2066 = expand_dims(%2065, axis=1) /* from_string */ /* ty=Tensor[(160, 1), float32] */; + %2067 = transpose(%2061, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2068 = expand_dims(%2066, axis=1) /* from_string */ /* ty=Tensor[(160, 1, 1), float32] */; + %2069 = negative(%layers_13_bn3_running_mean) /* from_string */ /* ty=Tensor[(160), float32] */; + %2070 = multiply(%2069, %2065) /* from_string */ /* ty=Tensor[(160), float32] */; + %2071 = add(%2070, %layers_13_bn3_bias) /* from_string */ /* ty=Tensor[(160), float32] */; + %2072 = expand_dims(%2071, axis=1) /* from_string */ /* ty=Tensor[(160, 1), float32] */; + %2073 = multiply(%2067, %2068) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2074 = expand_dims(%2072, axis=1) /* from_string */ /* ty=Tensor[(160, 1, 1), float32] */; + %2075 = add(%2073, %2074) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2076 = nn.pad(%2075, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2077 = nn.pad(%2076, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2078 = windows(%2077, axis=1, window_shape=[160, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 4, 4, 160, 1, 1), float32] */; + %2079 = squeeze(%2078, axis=[1]) /* from_string */ /* ty=Tensor[(1, 4, 4, 160, 1, 1), float32] */; + %2080 = reshape(%2079, newshape=[16, 160]) /* from_string */ /* ty=Tensor[(16, 160), float32] */; + %2081 = max(%2080) /* ty=float32 */; + %2082 = min(%2080) /* ty=float32 */; + %2083 = divide(%2081, 127f /* ty=float32 */) /* ty=float32 */; + %2084 = divide(%2082, -127f /* ty=float32 */) /* ty=float32 */; + %2085 = maximum(%2083, %2084) /* ty=float32 */; + %2086 = divide(%2080, %2085) /* ty=Tensor[(16, 160), float32] */; + %2087 = round(%2086) /* ty=Tensor[(16, 160), float32] */; + %2088 = nn.dense(%104, %2080, units=None) /* ty=Tensor[(960, 16), float32] */; + %2089 = max(%2088) /* ty=float32 */; + %2090 = min(%2088) /* ty=float32 */; + %2091 = divide(%2089, 127f /* ty=float32 */) /* ty=float32 */; + %2092 = divide(%2090, -127f /* ty=float32 */) /* ty=float32 */; + %2093 = cast(%111, dtype="int8") /* ty=Tensor[(960, 160), int8] */; + %2094 = cast(%2087, dtype="int8") /* ty=Tensor[(16, 160), int8] */; + %2095 = maximum(%2091, %2092) /* ty=float32 */; + %2096 = fn (%outer_arg_07: Tensor[(960, 160), int8], %outer_arg_17: Tensor[(16, 160), int8], %outer_arg_27: float32, %outer_arg_37: float32, %outer_arg_47: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_32") -> Tensor[(960, 16), int8] { + %103 = fn (%data7: Tensor[(960, 160), int8], %weights7: Tensor[(16, 160), int8], %s_data7: float32, %s_w7: float32, %s_act7: float32, Composite="ilavta.dense") -> Tensor[(960, 16), int8] { + %97 = nn.dense(%data7, %weights7, units=None, out_dtype="int32") /* ty=Tensor[(960, 16), int32] */; + %98 = multiply(%s_data7, %s_w7) /* ty=float32 */; + %99 = cast(%97, dtype="float32") /* ty=Tensor[(960, 16), float32] */; + %100 = divide(%98, %s_act7) /* ty=float32 */; + %101 = multiply(%99, %100) /* ty=Tensor[(960, 16), float32] */; + %102 = clip(%101, a_min=-127f, a_max=127f) /* ty=Tensor[(960, 16), float32] */; + cast(%102, dtype="int8") /* ty=Tensor[(960, 16), int8] */ + }; + %103(%outer_arg_07, %outer_arg_17, %outer_arg_27, %outer_arg_37, %outer_arg_47) /* ty=Tensor[(960, 16), int8] */ + }; + %2097 = %2096(%2093, %2094, %109, %2085, %2095) /* ty=Tensor[(960, 16), int8] */; + %2098 = cast(%2097, dtype="float32") /* ty=Tensor[(960, 16), float32] */; + %2099 = multiply(%2098, %2095) /* ty=Tensor[(960, 16), float32] */; + %2100 = reshape(%2099, newshape=[960, 1, 4, 4]) /* from_string */ /* ty=Tensor[(960, 1, 4, 4), float32] */; + %2101 = add(%layers_14_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(960), float32] */; + %2102 = sqrt(%2101) /* from_string */ /* ty=Tensor[(960), float32] */; + %2103 = divide(%573, %2102) /* from_string */ /* ty=Tensor[(960), float32] */; + %2104 = multiply(%2103, %layers_14_bn1_weight) /* from_string */ /* ty=Tensor[(960), float32] */; + %2105 = expand_dims(%2104, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2106 = transpose(%2100, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2107 = expand_dims(%2105, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2108 = negative(%layers_14_bn1_running_mean) /* from_string */ /* ty=Tensor[(960), float32] */; + %2109 = multiply(%2108, %2104) /* from_string */ /* ty=Tensor[(960), float32] */; + %2110 = add(%2109, %layers_14_bn1_bias) /* from_string */ /* ty=Tensor[(960), float32] */; + %2111 = expand_dims(%2110, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2112 = multiply(%2106, %2107) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2113 = expand_dims(%2111, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2114 = add(%2112, %2113) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2115 = nn.relu(%2114) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2116 = reshape(%layers_14_conv2_weight, newshape=[960, 1, 3, 3]) /* from_string */ /* ty=Tensor[(960, 1, 3, 3), float32] */; + %2117 = add(%layers_14_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(960), float32] */; + %2118 = sqrt(%2117) /* from_string */ /* ty=Tensor[(960), float32] */; + %2119 = divide(%573, %2118) /* from_string */ /* ty=Tensor[(960), float32] */; + %2120 = multiply(%2119, %layers_14_bn2_weight) /* from_string */ /* ty=Tensor[(960), float32] */; + %2121 = expand_dims(%2120, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2122 = nn.conv2d(%2115, %2116, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2123 = expand_dims(%2121, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2124 = negative(%layers_14_bn2_running_mean) /* from_string */ /* ty=Tensor[(960), float32] */; + %2125 = multiply(%2124, %2120) /* from_string */ /* ty=Tensor[(960), float32] */; + %2126 = add(%2125, %layers_14_bn2_bias) /* from_string */ /* ty=Tensor[(960), float32] */; + %2127 = expand_dims(%2126, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2128 = multiply(%2122, %2123) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2129 = expand_dims(%2127, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2130 = add(%2128, %2129) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2131 = nn.relu(%2130) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2132 = nn.pad(%2131, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2133 = nn.pad(%2132, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2134 = windows(%2133, axis=1, window_shape=[960, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 4, 4, 960, 1, 1), float32] */; + %2135 = squeeze(%2134, axis=[1]) /* from_string */ /* ty=Tensor[(1, 4, 4, 960, 1, 1), float32] */; + %2136 = reshape(%2135, newshape=[16, 960]) /* from_string */ /* ty=Tensor[(16, 960), float32] */; + %2137 = max(%2136) /* ty=float32 */; + %2138 = min(%2136) /* ty=float32 */; + %2139 = divide(%2137, 127f /* ty=float32 */) /* ty=float32 */; + %2140 = divide(%2138, -127f /* ty=float32 */) /* ty=float32 */; + %2141 = maximum(%2139, %2140) /* ty=float32 */; + %2142 = divide(%2136, %2141) /* ty=Tensor[(16, 960), float32] */; + %2143 = round(%2142) /* ty=Tensor[(16, 960), float32] */; + %2144 = nn.dense(%89, %2136, units=None) /* ty=Tensor[(160, 16), float32] */; + %2145 = max(%2144) /* ty=float32 */; + %2146 = min(%2144) /* ty=float32 */; + %2147 = divide(%2145, 127f /* ty=float32 */) /* ty=float32 */; + %2148 = divide(%2146, -127f /* ty=float32 */) /* ty=float32 */; + %2149 = cast(%96, dtype="int8") /* ty=Tensor[(160, 960), int8] */; + %2150 = cast(%2143, dtype="int8") /* ty=Tensor[(16, 960), int8] */; + %2151 = maximum(%2147, %2148) /* ty=float32 */; + %2152 = fn (%outer_arg_06: Tensor[(160, 960), int8], %outer_arg_16: Tensor[(16, 960), int8], %outer_arg_26: float32, %outer_arg_36: float32, %outer_arg_46: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_33") -> Tensor[(160, 16), int8] { + %88 = fn (%data6: Tensor[(160, 960), int8], %weights6: Tensor[(16, 960), int8], %s_data6: float32, %s_w6: float32, %s_act6: float32, Composite="ilavta.dense") -> Tensor[(160, 16), int8] { + %82 = nn.dense(%data6, %weights6, units=None, out_dtype="int32") /* ty=Tensor[(160, 16), int32] */; + %83 = multiply(%s_data6, %s_w6) /* ty=float32 */; + %84 = cast(%82, dtype="float32") /* ty=Tensor[(160, 16), float32] */; + %85 = divide(%83, %s_act6) /* ty=float32 */; + %86 = multiply(%84, %85) /* ty=Tensor[(160, 16), float32] */; + %87 = clip(%86, a_min=-127f, a_max=127f) /* ty=Tensor[(160, 16), float32] */; + cast(%87, dtype="int8") /* ty=Tensor[(160, 16), int8] */ + }; + %88(%outer_arg_06, %outer_arg_16, %outer_arg_26, %outer_arg_36, %outer_arg_46) /* ty=Tensor[(160, 16), int8] */ + }; + %2153 = %2152(%2149, %2150, %94, %2141, %2151) /* ty=Tensor[(160, 16), int8] */; + %2154 = cast(%2153, dtype="float32") /* ty=Tensor[(160, 16), float32] */; + %2155 = multiply(%2154, %2151) /* ty=Tensor[(160, 16), float32] */; + %2156 = reshape(%2155, newshape=[160, 1, 4, 4]) /* from_string */ /* ty=Tensor[(160, 1, 4, 4), float32] */; + %2157 = add(%layers_14_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(160), float32] */; + %2158 = sqrt(%2157) /* from_string */ /* ty=Tensor[(160), float32] */; + %2159 = divide(%573, %2158) /* from_string */ /* ty=Tensor[(160), float32] */; + %2160 = multiply(%2159, %layers_14_bn3_weight) /* from_string */ /* ty=Tensor[(160), float32] */; + %2161 = expand_dims(%2160, axis=1) /* from_string */ /* ty=Tensor[(160, 1), float32] */; + %2162 = transpose(%2156, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2163 = expand_dims(%2161, axis=1) /* from_string */ /* ty=Tensor[(160, 1, 1), float32] */; + %2164 = negative(%layers_14_bn3_running_mean) /* from_string */ /* ty=Tensor[(160), float32] */; + %2165 = multiply(%2164, %2160) /* from_string */ /* ty=Tensor[(160), float32] */; + %2166 = add(%2165, %layers_14_bn3_bias) /* from_string */ /* ty=Tensor[(160), float32] */; + %2167 = expand_dims(%2166, axis=1) /* from_string */ /* ty=Tensor[(160, 1), float32] */; + %2168 = multiply(%2162, %2163) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2169 = expand_dims(%2167, axis=1) /* from_string */ /* ty=Tensor[(160, 1, 1), float32] */; + %2170 = add(%2168, %2169) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2171 = add(%2170, %2075) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2172 = nn.pad(%2171, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2173 = nn.pad(%2172, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2174 = windows(%2173, axis=1, window_shape=[160, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 4, 4, 160, 1, 1), float32] */; + %2175 = squeeze(%2174, axis=[1]) /* from_string */ /* ty=Tensor[(1, 4, 4, 160, 1, 1), float32] */; + %2176 = reshape(%2175, newshape=[16, 160]) /* from_string */ /* ty=Tensor[(16, 160), float32] */; + %2177 = max(%2176) /* ty=float32 */; + %2178 = min(%2176) /* ty=float32 */; + %2179 = divide(%2177, 127f /* ty=float32 */) /* ty=float32 */; + %2180 = divide(%2178, -127f /* ty=float32 */) /* ty=float32 */; + %2181 = maximum(%2179, %2180) /* ty=float32 */; + %2182 = divide(%2176, %2181) /* ty=Tensor[(16, 160), float32] */; + %2183 = round(%2182) /* ty=Tensor[(16, 160), float32] */; + %2184 = nn.dense(%74, %2176, units=None) /* ty=Tensor[(960, 16), float32] */; + %2185 = max(%2184) /* ty=float32 */; + %2186 = min(%2184) /* ty=float32 */; + %2187 = divide(%2185, 127f /* ty=float32 */) /* ty=float32 */; + %2188 = divide(%2186, -127f /* ty=float32 */) /* ty=float32 */; + %2189 = cast(%81, dtype="int8") /* ty=Tensor[(960, 160), int8] */; + %2190 = cast(%2183, dtype="int8") /* ty=Tensor[(16, 160), int8] */; + %2191 = maximum(%2187, %2188) /* ty=float32 */; + %2192 = fn (%outer_arg_05: Tensor[(960, 160), int8], %outer_arg_15: Tensor[(16, 160), int8], %outer_arg_25: float32, %outer_arg_35: float32, %outer_arg_45: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_34") -> Tensor[(960, 16), int8] { + %73 = fn (%data5: Tensor[(960, 160), int8], %weights5: Tensor[(16, 160), int8], %s_data5: float32, %s_w5: float32, %s_act5: float32, Composite="ilavta.dense") -> Tensor[(960, 16), int8] { + %67 = nn.dense(%data5, %weights5, units=None, out_dtype="int32") /* ty=Tensor[(960, 16), int32] */; + %68 = multiply(%s_data5, %s_w5) /* ty=float32 */; + %69 = cast(%67, dtype="float32") /* ty=Tensor[(960, 16), float32] */; + %70 = divide(%68, %s_act5) /* ty=float32 */; + %71 = multiply(%69, %70) /* ty=Tensor[(960, 16), float32] */; + %72 = clip(%71, a_min=-127f, a_max=127f) /* ty=Tensor[(960, 16), float32] */; + cast(%72, dtype="int8") /* ty=Tensor[(960, 16), int8] */ + }; + %73(%outer_arg_05, %outer_arg_15, %outer_arg_25, %outer_arg_35, %outer_arg_45) /* ty=Tensor[(960, 16), int8] */ + }; + %2193 = %2192(%2189, %2190, %79, %2181, %2191) /* ty=Tensor[(960, 16), int8] */; + %2194 = cast(%2193, dtype="float32") /* ty=Tensor[(960, 16), float32] */; + %2195 = multiply(%2194, %2191) /* ty=Tensor[(960, 16), float32] */; + %2196 = reshape(%2195, newshape=[960, 1, 4, 4]) /* from_string */ /* ty=Tensor[(960, 1, 4, 4), float32] */; + %2197 = add(%layers_15_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(960), float32] */; + %2198 = sqrt(%2197) /* from_string */ /* ty=Tensor[(960), float32] */; + %2199 = divide(%573, %2198) /* from_string */ /* ty=Tensor[(960), float32] */; + %2200 = multiply(%2199, %layers_15_bn1_weight) /* from_string */ /* ty=Tensor[(960), float32] */; + %2201 = expand_dims(%2200, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2202 = transpose(%2196, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2203 = expand_dims(%2201, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2204 = negative(%layers_15_bn1_running_mean) /* from_string */ /* ty=Tensor[(960), float32] */; + %2205 = multiply(%2204, %2200) /* from_string */ /* ty=Tensor[(960), float32] */; + %2206 = add(%2205, %layers_15_bn1_bias) /* from_string */ /* ty=Tensor[(960), float32] */; + %2207 = expand_dims(%2206, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2208 = multiply(%2202, %2203) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2209 = expand_dims(%2207, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2210 = add(%2208, %2209) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2211 = nn.relu(%2210) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2212 = reshape(%layers_15_conv2_weight, newshape=[960, 1, 3, 3]) /* from_string */ /* ty=Tensor[(960, 1, 3, 3), float32] */; + %2213 = add(%layers_15_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(960), float32] */; + %2214 = sqrt(%2213) /* from_string */ /* ty=Tensor[(960), float32] */; + %2215 = divide(%573, %2214) /* from_string */ /* ty=Tensor[(960), float32] */; + %2216 = multiply(%2215, %layers_15_bn2_weight) /* from_string */ /* ty=Tensor[(960), float32] */; + %2217 = expand_dims(%2216, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2218 = nn.conv2d(%2211, %2212, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2219 = expand_dims(%2217, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2220 = negative(%layers_15_bn2_running_mean) /* from_string */ /* ty=Tensor[(960), float32] */; + %2221 = multiply(%2220, %2216) /* from_string */ /* ty=Tensor[(960), float32] */; + %2222 = add(%2221, %layers_15_bn2_bias) /* from_string */ /* ty=Tensor[(960), float32] */; + %2223 = expand_dims(%2222, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2224 = multiply(%2218, %2219) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2225 = expand_dims(%2223, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2226 = add(%2224, %2225) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2227 = nn.relu(%2226) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2228 = nn.pad(%2227, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2229 = nn.pad(%2228, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2230 = windows(%2229, axis=1, window_shape=[960, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 4, 4, 960, 1, 1), float32] */; + %2231 = squeeze(%2230, axis=[1]) /* from_string */ /* ty=Tensor[(1, 4, 4, 960, 1, 1), float32] */; + %2232 = reshape(%2231, newshape=[16, 960]) /* from_string */ /* ty=Tensor[(16, 960), float32] */; + %2233 = max(%2232) /* ty=float32 */; + %2234 = min(%2232) /* ty=float32 */; + %2235 = divide(%2233, 127f /* ty=float32 */) /* ty=float32 */; + %2236 = divide(%2234, -127f /* ty=float32 */) /* ty=float32 */; + %2237 = maximum(%2235, %2236) /* ty=float32 */; + %2238 = divide(%2232, %2237) /* ty=Tensor[(16, 960), float32] */; + %2239 = round(%2238) /* ty=Tensor[(16, 960), float32] */; + %2240 = nn.dense(%59, %2232, units=None) /* ty=Tensor[(160, 16), float32] */; + %2241 = max(%2240) /* ty=float32 */; + %2242 = min(%2240) /* ty=float32 */; + %2243 = divide(%2241, 127f /* ty=float32 */) /* ty=float32 */; + %2244 = divide(%2242, -127f /* ty=float32 */) /* ty=float32 */; + %2245 = cast(%66, dtype="int8") /* ty=Tensor[(160, 960), int8] */; + %2246 = cast(%2239, dtype="int8") /* ty=Tensor[(16, 960), int8] */; + %2247 = maximum(%2243, %2244) /* ty=float32 */; + %2248 = fn (%outer_arg_04: Tensor[(160, 960), int8], %outer_arg_14: Tensor[(16, 960), int8], %outer_arg_24: float32, %outer_arg_34: float32, %outer_arg_44: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_35") -> Tensor[(160, 16), int8] { + %58 = fn (%data4: Tensor[(160, 960), int8], %weights4: Tensor[(16, 960), int8], %s_data4: float32, %s_w4: float32, %s_act4: float32, Composite="ilavta.dense") -> Tensor[(160, 16), int8] { + %52 = nn.dense(%data4, %weights4, units=None, out_dtype="int32") /* ty=Tensor[(160, 16), int32] */; + %53 = multiply(%s_data4, %s_w4) /* ty=float32 */; + %54 = cast(%52, dtype="float32") /* ty=Tensor[(160, 16), float32] */; + %55 = divide(%53, %s_act4) /* ty=float32 */; + %56 = multiply(%54, %55) /* ty=Tensor[(160, 16), float32] */; + %57 = clip(%56, a_min=-127f, a_max=127f) /* ty=Tensor[(160, 16), float32] */; + cast(%57, dtype="int8") /* ty=Tensor[(160, 16), int8] */ + }; + %58(%outer_arg_04, %outer_arg_14, %outer_arg_24, %outer_arg_34, %outer_arg_44) /* ty=Tensor[(160, 16), int8] */ + }; + %2249 = %2248(%2245, %2246, %64, %2237, %2247) /* ty=Tensor[(160, 16), int8] */; + %2250 = cast(%2249, dtype="float32") /* ty=Tensor[(160, 16), float32] */; + %2251 = multiply(%2250, %2247) /* ty=Tensor[(160, 16), float32] */; + %2252 = reshape(%2251, newshape=[160, 1, 4, 4]) /* from_string */ /* ty=Tensor[(160, 1, 4, 4), float32] */; + %2253 = add(%layers_15_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(160), float32] */; + %2254 = sqrt(%2253) /* from_string */ /* ty=Tensor[(160), float32] */; + %2255 = divide(%573, %2254) /* from_string */ /* ty=Tensor[(160), float32] */; + %2256 = multiply(%2255, %layers_15_bn3_weight) /* from_string */ /* ty=Tensor[(160), float32] */; + %2257 = expand_dims(%2256, axis=1) /* from_string */ /* ty=Tensor[(160, 1), float32] */; + %2258 = transpose(%2252, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2259 = expand_dims(%2257, axis=1) /* from_string */ /* ty=Tensor[(160, 1, 1), float32] */; + %2260 = negative(%layers_15_bn3_running_mean) /* from_string */ /* ty=Tensor[(160), float32] */; + %2261 = multiply(%2260, %2256) /* from_string */ /* ty=Tensor[(160), float32] */; + %2262 = add(%2261, %layers_15_bn3_bias) /* from_string */ /* ty=Tensor[(160), float32] */; + %2263 = expand_dims(%2262, axis=1) /* from_string */ /* ty=Tensor[(160, 1), float32] */; + %2264 = multiply(%2258, %2259) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2265 = expand_dims(%2263, axis=1) /* from_string */ /* ty=Tensor[(160, 1, 1), float32] */; + %2266 = add(%2264, %2265) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2267 = add(%2266, %2171) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2268 = nn.pad(%2267, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2269 = nn.pad(%2268, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), float32] */; + %2270 = windows(%2269, axis=1, window_shape=[160, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 4, 4, 160, 1, 1), float32] */; + %2271 = squeeze(%2270, axis=[1]) /* from_string */ /* ty=Tensor[(1, 4, 4, 160, 1, 1), float32] */; + %2272 = reshape(%2271, newshape=[16, 160]) /* from_string */ /* ty=Tensor[(16, 160), float32] */; + %2273 = max(%2272) /* ty=float32 */; + %2274 = min(%2272) /* ty=float32 */; + %2275 = divide(%2273, 127f /* ty=float32 */) /* ty=float32 */; + %2276 = divide(%2274, -127f /* ty=float32 */) /* ty=float32 */; + %2277 = maximum(%2275, %2276) /* ty=float32 */; + %2278 = divide(%2272, %2277) /* ty=Tensor[(16, 160), float32] */; + %2279 = round(%2278) /* ty=Tensor[(16, 160), float32] */; + %2280 = nn.dense(%44, %2272, units=None) /* ty=Tensor[(960, 16), float32] */; + %2281 = max(%2280) /* ty=float32 */; + %2282 = min(%2280) /* ty=float32 */; + %2283 = divide(%2281, 127f /* ty=float32 */) /* ty=float32 */; + %2284 = divide(%2282, -127f /* ty=float32 */) /* ty=float32 */; + %2285 = cast(%51, dtype="int8") /* ty=Tensor[(960, 160), int8] */; + %2286 = cast(%2279, dtype="int8") /* ty=Tensor[(16, 160), int8] */; + %2287 = maximum(%2283, %2284) /* ty=float32 */; + %2288 = fn (%outer_arg_03: Tensor[(960, 160), int8], %outer_arg_13: Tensor[(16, 160), int8], %outer_arg_23: float32, %outer_arg_33: float32, %outer_arg_43: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_36") -> Tensor[(960, 16), int8] { + %43 = fn (%data3: Tensor[(960, 160), int8], %weights3: Tensor[(16, 160), int8], %s_data3: float32, %s_w3: float32, %s_act3: float32, Composite="ilavta.dense") -> Tensor[(960, 16), int8] { + %37 = nn.dense(%data3, %weights3, units=None, out_dtype="int32") /* ty=Tensor[(960, 16), int32] */; + %38 = multiply(%s_data3, %s_w3) /* ty=float32 */; + %39 = cast(%37, dtype="float32") /* ty=Tensor[(960, 16), float32] */; + %40 = divide(%38, %s_act3) /* ty=float32 */; + %41 = multiply(%39, %40) /* ty=Tensor[(960, 16), float32] */; + %42 = clip(%41, a_min=-127f, a_max=127f) /* ty=Tensor[(960, 16), float32] */; + cast(%42, dtype="int8") /* ty=Tensor[(960, 16), int8] */ + }; + %43(%outer_arg_03, %outer_arg_13, %outer_arg_23, %outer_arg_33, %outer_arg_43) /* ty=Tensor[(960, 16), int8] */ + }; + %2289 = %2288(%2285, %2286, %49, %2277, %2287) /* ty=Tensor[(960, 16), int8] */; + %2290 = cast(%2289, dtype="float32") /* ty=Tensor[(960, 16), float32] */; + %2291 = multiply(%2290, %2287) /* ty=Tensor[(960, 16), float32] */; + %2292 = reshape(%2291, newshape=[960, 1, 4, 4]) /* from_string */ /* ty=Tensor[(960, 1, 4, 4), float32] */; + %2293 = add(%layers_16_bn1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(960), float32] */; + %2294 = sqrt(%2293) /* from_string */ /* ty=Tensor[(960), float32] */; + %2295 = divide(%573, %2294) /* from_string */ /* ty=Tensor[(960), float32] */; + %2296 = multiply(%2295, %layers_16_bn1_weight) /* from_string */ /* ty=Tensor[(960), float32] */; + %2297 = expand_dims(%2296, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2298 = transpose(%2292, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2299 = expand_dims(%2297, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2300 = negative(%layers_16_bn1_running_mean) /* from_string */ /* ty=Tensor[(960), float32] */; + %2301 = multiply(%2300, %2296) /* from_string */ /* ty=Tensor[(960), float32] */; + %2302 = add(%2301, %layers_16_bn1_bias) /* from_string */ /* ty=Tensor[(960), float32] */; + %2303 = expand_dims(%2302, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2304 = multiply(%2298, %2299) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2305 = expand_dims(%2303, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2306 = add(%2304, %2305) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2307 = nn.relu(%2306) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2308 = reshape(%layers_16_conv2_weight, newshape=[960, 1, 3, 3]) /* from_string */ /* ty=Tensor[(960, 1, 3, 3), float32] */; + %2309 = add(%layers_16_bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(960), float32] */; + %2310 = sqrt(%2309) /* from_string */ /* ty=Tensor[(960), float32] */; + %2311 = divide(%573, %2310) /* from_string */ /* ty=Tensor[(960), float32] */; + %2312 = multiply(%2311, %layers_16_bn2_weight) /* from_string */ /* ty=Tensor[(960), float32] */; + %2313 = expand_dims(%2312, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2314 = nn.conv2d(%2307, %2308, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2315 = expand_dims(%2313, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2316 = negative(%layers_16_bn2_running_mean) /* from_string */ /* ty=Tensor[(960), float32] */; + %2317 = multiply(%2316, %2312) /* from_string */ /* ty=Tensor[(960), float32] */; + %2318 = add(%2317, %layers_16_bn2_bias) /* from_string */ /* ty=Tensor[(960), float32] */; + %2319 = expand_dims(%2318, axis=1) /* from_string */ /* ty=Tensor[(960, 1), float32] */; + %2320 = multiply(%2314, %2315) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2321 = expand_dims(%2319, axis=1) /* from_string */ /* ty=Tensor[(960, 1, 1), float32] */; + %2322 = add(%2320, %2321) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2323 = nn.relu(%2322) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2324 = nn.pad(%2323, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2325 = nn.pad(%2324, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), float32] */; + %2326 = windows(%2325, axis=1, window_shape=[960, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 4, 4, 960, 1, 1), float32] */; + %2327 = squeeze(%2326, axis=[1]) /* from_string */ /* ty=Tensor[(1, 4, 4, 960, 1, 1), float32] */; + %2328 = reshape(%2327, newshape=[16, 960]) /* from_string */ /* ty=Tensor[(16, 960), float32] */; + %2329 = max(%2328) /* ty=float32 */; + %2330 = min(%2328) /* ty=float32 */; + %2331 = divide(%2329, 127f /* ty=float32 */) /* ty=float32 */; + %2332 = divide(%2330, -127f /* ty=float32 */) /* ty=float32 */; + %2333 = maximum(%2331, %2332) /* ty=float32 */; + %2334 = divide(%2328, %2333) /* ty=Tensor[(16, 960), float32] */; + %2335 = round(%2334) /* ty=Tensor[(16, 960), float32] */; + %2336 = nn.dense(%29, %2328, units=None) /* ty=Tensor[(320, 16), float32] */; + %2337 = max(%2336) /* ty=float32 */; + %2338 = min(%2336) /* ty=float32 */; + %2339 = divide(%2337, 127f /* ty=float32 */) /* ty=float32 */; + %2340 = divide(%2338, -127f /* ty=float32 */) /* ty=float32 */; + %2341 = cast(%36, dtype="int8") /* ty=Tensor[(320, 960), int8] */; + %2342 = cast(%2335, dtype="int8") /* ty=Tensor[(16, 960), int8] */; + %2343 = maximum(%2339, %2340) /* ty=float32 */; + %2344 = fn (%outer_arg_02: Tensor[(320, 960), int8], %outer_arg_12: Tensor[(16, 960), int8], %outer_arg_22: float32, %outer_arg_32: float32, %outer_arg_42: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_37") -> Tensor[(320, 16), int8] { + %28 = fn (%data2: Tensor[(320, 960), int8], %weights2: Tensor[(16, 960), int8], %s_data2: float32, %s_w2: float32, %s_act2: float32, Composite="ilavta.dense") -> Tensor[(320, 16), int8] { + %22 = nn.dense(%data2, %weights2, units=None, out_dtype="int32") /* ty=Tensor[(320, 16), int32] */; + %23 = multiply(%s_data2, %s_w2) /* ty=float32 */; + %24 = cast(%22, dtype="float32") /* ty=Tensor[(320, 16), float32] */; + %25 = divide(%23, %s_act2) /* ty=float32 */; + %26 = multiply(%24, %25) /* ty=Tensor[(320, 16), float32] */; + %27 = clip(%26, a_min=-127f, a_max=127f) /* ty=Tensor[(320, 16), float32] */; + cast(%27, dtype="int8") /* ty=Tensor[(320, 16), int8] */ + }; + %28(%outer_arg_02, %outer_arg_12, %outer_arg_22, %outer_arg_32, %outer_arg_42) /* ty=Tensor[(320, 16), int8] */ + }; + %2345 = %2344(%2341, %2342, %34, %2333, %2343) /* ty=Tensor[(320, 16), int8] */; + %2346 = cast(%2345, dtype="float32") /* ty=Tensor[(320, 16), float32] */; + %2347 = multiply(%2346, %2343) /* ty=Tensor[(320, 16), float32] */; + %2348 = reshape(%2347, newshape=[320, 1, 4, 4]) /* from_string */ /* ty=Tensor[(320, 1, 4, 4), float32] */; + %2349 = add(%layers_16_bn3_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(320), float32] */; + %2350 = sqrt(%2349) /* from_string */ /* ty=Tensor[(320), float32] */; + %2351 = divide(%573, %2350) /* from_string */ /* ty=Tensor[(320), float32] */; + %2352 = multiply(%2351, %layers_16_bn3_weight) /* from_string */ /* ty=Tensor[(320), float32] */; + %2353 = expand_dims(%2352, axis=1) /* from_string */ /* ty=Tensor[(320, 1), float32] */; + %2354 = transpose(%2348, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), float32] */; + %2355 = expand_dims(%2353, axis=1) /* from_string */ /* ty=Tensor[(320, 1, 1), float32] */; + %2356 = negative(%layers_16_bn3_running_mean) /* from_string */ /* ty=Tensor[(320), float32] */; + %2357 = multiply(%2356, %2352) /* from_string */ /* ty=Tensor[(320), float32] */; + %2358 = add(%2357, %layers_16_bn3_bias) /* from_string */ /* ty=Tensor[(320), float32] */; + %2359 = expand_dims(%2358, axis=1) /* from_string */ /* ty=Tensor[(320, 1), float32] */; + %2360 = multiply(%2354, %2355) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), float32] */; + %2361 = expand_dims(%2359, axis=1) /* from_string */ /* ty=Tensor[(320, 1, 1), float32] */; + %2369 = reshape(%layers_16_shortcut_0_weight, newshape=[320, 160]) /* from_string */ /* ty=Tensor[(320, 160), float32] */; + %2370 = max(%2369) /* ty=float32 */; + %2371 = min(%2369) /* ty=float32 */; + %2372 = divide(%2370, 127f /* ty=float32 */) /* ty=float32 */; + %2373 = divide(%2371, -127f /* ty=float32 */) /* ty=float32 */; + %2374 = maximum(%2372, %2373) /* ty=float32 */; + %2375 = divide(%2369, %2374) /* ty=Tensor[(320, 160), float32] */; + %2376 = round(%2375) /* ty=Tensor[(320, 160), float32] */; + %2377 = max(%2272) /* ty=float32 */; + %2378 = min(%2272) /* ty=float32 */; + %2379 = divide(%2377, 127f /* ty=float32 */) /* ty=float32 */; + %2380 = divide(%2378, -127f /* ty=float32 */) /* ty=float32 */; + %2381 = maximum(%2379, %2380) /* ty=float32 */; + %2382 = divide(%2272, %2381) /* ty=Tensor[(16, 160), float32] */; + %2383 = round(%2382) /* ty=Tensor[(16, 160), float32] */; + %2384 = nn.dense(%2369, %2272, units=None) /* ty=Tensor[(320, 16), float32] */; + %2385 = max(%2384) /* ty=float32 */; + %2386 = min(%2384) /* ty=float32 */; + %2387 = divide(%2385, 127f /* ty=float32 */) /* ty=float32 */; + %2388 = divide(%2386, -127f /* ty=float32 */) /* ty=float32 */; + %2389 = cast(%2376, dtype="int8") /* ty=Tensor[(320, 160), int8] */; + %2390 = cast(%2383, dtype="int8") /* ty=Tensor[(16, 160), int8] */; + %2391 = maximum(%2387, %2388) /* ty=float32 */; + %2392 = fn (%outer_arg_040: Tensor[(320, 160), int8], %outer_arg_140: Tensor[(16, 160), int8], %outer_arg_240: float32, %outer_arg_340: float32, %outer_arg_440: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_38") -> Tensor[(320, 16), int8] { + %2368 = fn (%data40: Tensor[(320, 160), int8], %weights40: Tensor[(16, 160), int8], %s_data40: float32, %s_w40: float32, %s_act40: float32, Composite="ilavta.dense") -> Tensor[(320, 16), int8] { + %2362 = nn.dense(%data40, %weights40, units=None, out_dtype="int32") /* ty=Tensor[(320, 16), int32] */; + %2363 = multiply(%s_data40, %s_w40) /* ty=float32 */; + %2364 = cast(%2362, dtype="float32") /* ty=Tensor[(320, 16), float32] */; + %2365 = divide(%2363, %s_act40) /* ty=float32 */; + %2366 = multiply(%2364, %2365) /* ty=Tensor[(320, 16), float32] */; + %2367 = clip(%2366, a_min=-127f, a_max=127f) /* ty=Tensor[(320, 16), float32] */; + cast(%2367, dtype="int8") /* ty=Tensor[(320, 16), int8] */ + }; + %2368(%outer_arg_040, %outer_arg_140, %outer_arg_240, %outer_arg_340, %outer_arg_440) /* ty=Tensor[(320, 16), int8] */ + }; + %2393 = %2392(%2389, %2390, %2374, %2381, %2391) /* ty=Tensor[(320, 16), int8] */; + %2394 = cast(%2393, dtype="float32") /* ty=Tensor[(320, 16), float32] */; + %2395 = multiply(%2394, %2391) /* ty=Tensor[(320, 16), float32] */; + %2396 = reshape(%2395, newshape=[320, 1, 4, 4]) /* from_string */ /* ty=Tensor[(320, 1, 4, 4), float32] */; + %2397 = add(%layers_16_shortcut_1_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(320), float32] */; + %2398 = sqrt(%2397) /* from_string */ /* ty=Tensor[(320), float32] */; + %2399 = divide(%573, %2398) /* from_string */ /* ty=Tensor[(320), float32] */; + %2400 = multiply(%2399, %layers_16_shortcut_1_weight) /* from_string */ /* ty=Tensor[(320), float32] */; + %2401 = expand_dims(%2400, axis=1) /* from_string */ /* ty=Tensor[(320, 1), float32] */; + %2402 = transpose(%2396, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), float32] */; + %2403 = expand_dims(%2401, axis=1) /* from_string */ /* ty=Tensor[(320, 1, 1), float32] */; + %2404 = negative(%layers_16_shortcut_1_running_mean) /* from_string */ /* ty=Tensor[(320), float32] */; + %2405 = multiply(%2404, %2400) /* from_string */ /* ty=Tensor[(320), float32] */; + %2406 = add(%2405, %layers_16_shortcut_1_bias) /* from_string */ /* ty=Tensor[(320), float32] */; + %2407 = expand_dims(%2406, axis=1) /* from_string */ /* ty=Tensor[(320, 1), float32] */; + %2408 = multiply(%2402, %2403) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), float32] */; + %2409 = expand_dims(%2407, axis=1) /* from_string */ /* ty=Tensor[(320, 1, 1), float32] */; + %2410 = add(%2360, %2361) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), float32] */; + %2411 = add(%2408, %2409) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), float32] */; + %2412 = add(%2410, %2411) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), float32] */; + %2413 = nn.pad(%2412, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), float32] */; + %2414 = nn.pad(%2413, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), float32] */; + %2415 = windows(%2414, axis=1, window_shape=[320, 1, 1], strides=[1, 1, 1]) /* from_string */ /* ty=Tensor[(1, 1, 4, 4, 320, 1, 1), float32] */; + %2416 = squeeze(%2415, axis=[1]) /* from_string */ /* ty=Tensor[(1, 4, 4, 320, 1, 1), float32] */; + %2417 = reshape(%2416, newshape=[16, 320]) /* from_string */ /* ty=Tensor[(16, 320), float32] */; + %2418 = max(%2417) /* ty=float32 */; + %2419 = min(%2417) /* ty=float32 */; + %2420 = divide(%2418, 127f /* ty=float32 */) /* ty=float32 */; + %2421 = divide(%2419, -127f /* ty=float32 */) /* ty=float32 */; + %2422 = maximum(%2420, %2421) /* ty=float32 */; + %2423 = divide(%2417, %2422) /* ty=Tensor[(16, 320), float32] */; + %2424 = round(%2423) /* ty=Tensor[(16, 320), float32] */; + %2425 = nn.dense(%14, %2417, units=None) /* ty=Tensor[(1280, 16), float32] */; + %2426 = max(%2425) /* ty=float32 */; + %2427 = min(%2425) /* ty=float32 */; + %2428 = divide(%2426, 127f /* ty=float32 */) /* ty=float32 */; + %2429 = divide(%2427, -127f /* ty=float32 */) /* ty=float32 */; + %2430 = cast(%21, dtype="int8") /* ty=Tensor[(1280, 320), int8] */; + %2431 = cast(%2424, dtype="int8") /* ty=Tensor[(16, 320), int8] */; + %2432 = maximum(%2428, %2429) /* ty=float32 */; + %2433 = fn (%outer_arg_01: Tensor[(1280, 320), int8], %outer_arg_11: Tensor[(16, 320), int8], %outer_arg_21: float32, %outer_arg_31: float32, %outer_arg_41: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_39") -> Tensor[(1280, 16), int8] { + %13 = fn (%data1: Tensor[(1280, 320), int8], %weights1: Tensor[(16, 320), int8], %s_data1: float32, %s_w1: float32, %s_act1: float32, Composite="ilavta.dense") -> Tensor[(1280, 16), int8] { + %7 = nn.dense(%data1, %weights1, units=None, out_dtype="int32") /* ty=Tensor[(1280, 16), int32] */; + %8 = multiply(%s_data1, %s_w1) /* ty=float32 */; + %9 = cast(%7, dtype="float32") /* ty=Tensor[(1280, 16), float32] */; + %10 = divide(%8, %s_act1) /* ty=float32 */; + %11 = multiply(%9, %10) /* ty=Tensor[(1280, 16), float32] */; + %12 = clip(%11, a_min=-127f, a_max=127f) /* ty=Tensor[(1280, 16), float32] */; + cast(%12, dtype="int8") /* ty=Tensor[(1280, 16), int8] */ + }; + %13(%outer_arg_01, %outer_arg_11, %outer_arg_21, %outer_arg_31, %outer_arg_41) /* ty=Tensor[(1280, 16), int8] */ + }; + %2434 = %2433(%2430, %2431, %19, %2422, %2432) /* ty=Tensor[(1280, 16), int8] */; + %2435 = cast(%2434, dtype="float32") /* ty=Tensor[(1280, 16), float32] */; + %2436 = multiply(%2435, %2432) /* ty=Tensor[(1280, 16), float32] */; + %2437 = reshape(%2436, newshape=[1280, 1, 4, 4]) /* from_string */ /* ty=Tensor[(1280, 1, 4, 4), float32] */; + %2438 = add(%bn2_running_var, 1e-05f /* ty=float32 */) /* from_string */ /* ty=Tensor[(1280), float32] */; + %2439 = sqrt(%2438) /* from_string */ /* ty=Tensor[(1280), float32] */; + %2440 = divide(%573, %2439) /* from_string */ /* ty=Tensor[(1280), float32] */; + %2441 = multiply(%2440, %bn2_weight) /* from_string */ /* ty=Tensor[(1280), float32] */; + %2442 = expand_dims(%2441, axis=1) /* from_string */ /* ty=Tensor[(1280, 1), float32] */; + %2443 = transpose(%2437, axes=[1, 0, 2, 3]) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), float32] */; + %2444 = expand_dims(%2442, axis=1) /* from_string */ /* ty=Tensor[(1280, 1, 1), float32] */; + %2445 = negative(%bn2_running_mean) /* from_string */ /* ty=Tensor[(1280), float32] */; + %2446 = multiply(%2445, %2441) /* from_string */ /* ty=Tensor[(1280), float32] */; + %2447 = add(%2446, %bn2_bias) /* from_string */ /* ty=Tensor[(1280), float32] */; + %2448 = expand_dims(%2447, axis=1) /* from_string */ /* ty=Tensor[(1280, 1), float32] */; + %2449 = multiply(%2443, %2444) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), float32] */; + %2450 = expand_dims(%2448, axis=1) /* from_string */ /* ty=Tensor[(1280, 1, 1), float32] */; + %2451 = add(%2449, %2450) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), float32] */; + %2452 = nn.relu(%2451) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), float32] */; + %2453 = nn.avg_pool2d(%2452, pool_size=[4, 4], strides=[4, 4], padding=[0, 0, 0, 0]) /* from_string */ /* ty=Tensor[(1, 1280, 1, 1), float32] */; + %2454 = reshape(%2453, newshape=[1, 1280]) /* from_string */ /* ty=Tensor[(1, 1280), float32] */; + %2455 = max(%2454) /* ty=float32 */; + %2456 = min(%2454) /* ty=float32 */; + %2457 = divide(%2455, 127f /* ty=float32 */) /* ty=float32 */; + %2458 = divide(%2456, -127f /* ty=float32 */) /* ty=float32 */; + %2459 = maximum(%2457, %2458) /* ty=float32 */; + %2460 = divide(%2454, %2459) /* ty=Tensor[(1, 1280), float32] */; + %2461 = round(%2460) /* ty=Tensor[(1, 1280), float32] */; + %2462 = transpose(%linear_weight, axes=[1, 0]) /* from_string */ /* ty=Tensor[(1280, 10), float32] */; + %2463 = transpose(%2462, axes=[1, 0]) /* from_string */ /* ty=Tensor[(10, 1280), float32] */; + %2464 = max(%2463) /* ty=float32 */; + %2465 = min(%2463) /* ty=float32 */; + %2466 = divide(%2464, 127f /* ty=float32 */) /* ty=float32 */; + %2467 = divide(%2465, -127f /* ty=float32 */) /* ty=float32 */; + %2468 = maximum(%2466, %2467) /* ty=float32 */; + %2469 = divide(%2463, %2468) /* ty=Tensor[(10, 1280), float32] */; + %2470 = round(%2469) /* ty=Tensor[(10, 1280), float32] */; + %2471 = nn.dense(%2454, %2463, units=None) /* ty=Tensor[(1, 10), float32] */; + %2472 = max(%2471) /* ty=float32 */; + %2473 = min(%2471) /* ty=float32 */; + %2474 = divide(%2472, 127f /* ty=float32 */) /* ty=float32 */; + %2475 = divide(%2473, -127f /* ty=float32 */) /* ty=float32 */; + %2476 = cast(%2461, dtype="int8") /* ty=Tensor[(1, 1280), int8] */; + %2477 = cast(%2470, dtype="int8") /* ty=Tensor[(10, 1280), int8] */; + %2478 = maximum(%2474, %2475) /* ty=float32 */; + %2479 = fn (%outer_arg_0: Tensor[(1, 1280), int8], %outer_arg_1: Tensor[(10, 1280), int8], %outer_arg_2: float32, %outer_arg_3: float32, %outer_arg_4: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_40") -> Tensor[(1, 10), int8] { + %6 = fn (%data: Tensor[(1, 1280), int8], %weights: Tensor[(10, 1280), int8], %s_data: float32, %s_w: float32, %s_act: float32, Composite="ilavta.dense") -> Tensor[(1, 10), int8] { + %0 = nn.dense(%data, %weights, units=None, out_dtype="int32") /* ty=Tensor[(1, 10), int32] */; + %1 = multiply(%s_data, %s_w) /* ty=float32 */; + %2 = cast(%0, dtype="float32") /* ty=Tensor[(1, 10), float32] */; + %3 = divide(%1, %s_act) /* ty=float32 */; + %4 = multiply(%2, %3) /* ty=Tensor[(1, 10), float32] */; + %5 = clip(%4, a_min=-127f, a_max=127f) /* ty=Tensor[(1, 10), float32] */; + cast(%5, dtype="int8") /* ty=Tensor[(1, 10), int8] */ + }; + %6(%outer_arg_0, %outer_arg_1, %outer_arg_2, %outer_arg_3, %outer_arg_4) /* ty=Tensor[(1, 10), int8] */ + }; + %2480 = %2479(%2476, %2477, %2459, %2468, %2478) /* ty=Tensor[(1, 10), int8] */; + %2481 = cast(%2480, dtype="float32") /* ty=Tensor[(1, 10), float32] */; + %2482 = multiply(%2481, %2478) /* ty=Tensor[(1, 10), float32] */; + add(%2482, %linear_bias) /* from_string */ /* ty=Tensor[(1, 10), float32] */ +} diff --git a/demo/mobilenetv2/test_model_on_vta.py b/demo/mobilenetv2/test_model_on_vta.py new file mode 100644 index 0000000..3d7b6b8 --- /dev/null +++ b/demo/mobilenetv2/test_model_on_vta.py @@ -0,0 +1,153 @@ +import logging +import os +import tqdm +import tvm +import numpy as np +import torch +from tvm import relay +from tvm.contrib import graph_executor +from tvm.runtime.ndarray import cpu +from models.mobilenetv2 import MobileNetV2 +import torchvision +import torchvision.transforms as transforms +import models +import test_vta_quantization as quant_utils + +# Data prep code taken from https://github.com/kuangliu/pytorch-cifar/blob/master/main.py +transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), +]) + +transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), +]) +# trainset = torchvision.datasets.CIFAR10('./data', download=True, transform=transform_train) +# trainloader = DataLoader(trainset, batch_size=4, shuffle=True) +testset = torchvision.datasets.CIFAR10( + root='./data', train=False, download=True, transform=transform_test) +testloader = torch.utils.data.DataLoader( + testset, batch_size=1, shuffle=False, num_workers=2) + +classes = ('plane', 'car', 'bird', 'cat', 'deer', + 'dog', 'frog', 'horse', 'ship', 'truck') + +def get_relay_model(param_file, input_shape=(1, 3, 32, 32)): + params = torch.load(param_file) + prefix = "module." + params = {(k[len(prefix):] if k.startswith(prefix) else k): v for k, v in params.items()} + model = MobileNetV2() + model.load_state_dict(params) + trace = torch.jit.trace(model, torch.randn(*input_shape)) + inputs = [('input0', input_shape)] + return relay.frontend.from_pytorch(trace, inputs) + +def test_relay_model(mod, params): + print(mod) + with tvm.transform.PassContext(opt_level=3): + relay_graph, lib, params = relay.build(mod, params=params, target='llvm') + graph_rt = graph_executor.create(relay_graph, lib, device=cpu(0)) + graph_rt.set_input(**params) + total = 0 + correct = 0 + for idx, (inp, targets) in enumerate(testloader): + graph_rt.set_input('input0', inp.numpy().astype('float32')) + graph_rt.run() + output = graph_rt.get_output(0).asnumpy() + prediected = np.argmax(output, axis=1) + total += targets.size(0) + correct += np.sum(np.equal(prediected, targets.numpy())) + if idx % 100 == 0: + print(f'Batch #{idx}, Accuracy: {correct / total}') + if idx > 2000: + break + print(f'Final accuracy: {correct / total}') + +def get_cali_data(): + logging.info('Calibration:') + total = len(testloader) + for (inp, _) in tqdm.tqdm(testloader, total=total): + yield {'input0': inp.cpu().numpy()} + +def bind_params(func, params): + """Bind the params to the expression.""" + name_dict = {} + for arg in func.params: + name = arg.name_hint + if name in name_dict: + name_dict[name] = None + else: + name_dict[name] = arg + bind_dict = {} + for k, v in params.items(): + if k not in name_dict: + continue + arg = name_dict[k] + if arg is None: + raise ValueError("Multiple args in the function have name %s" % k) + bind_dict[arg] = relay.expr.const(v) + return relay.expr.bind(func, bind_dict) + +def run_with_relay_quantization(mod, params, run=True): + BASE_CFG = { + "skip_conv_layers": [], + "skip_dense_layers": False, + "dtype_input": "int8", + "dtype_weight": "int8", + "dtype_activation": "int32", + } + mod['main'] = models.utils.LetInliner().visit(mod['main']) + mod['main'] = bind_params(mod['main'], params) + mod = relay.transform.InferType()(mod) + mod = relay.transform.FoldConstant()(mod) + mod['main'] = models.utils.AlterDense().visit(mod['main']) + with relay.quantize.qconfig(**BASE_CFG, weight_scale='power2', calibration_mode='kl_divergence', skip_dense_layer=False): + qmod = relay.quantize.quantize(mod, params=params, dataset=list(get_cali_data())) + if run: + test_relay_model(qmod, None) + return qmod + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--save-model', required=False, dest='save_model', action='store_true') + parser.add_argument('--relay-model', required=False, dest='relay_model') + parser.add_argument('--quantize', required=False, dest='quantize', action='store_true') + parser.add_argument('--layerwise', required=False, dest='layerwise_debug', action='store_true') + parser.add_argument('--params', required=True, dest='params') + args = parser.parse_args() + # param_file = 'params/final_mobilenet_cifar10_400_epochs.pth' + param_file = args.params + if args.save_model: + mod, params = get_relay_model(param_file) + mod = relay.transform.InferType()(mod) + mod = relay.transform.SimplifyInference()(mod) + mod['main'] = models.RenameMutator({'.': '_'}).visit(mod['main']) + mod = relay.transform.InferType()(mod) + with open(os.path.join('./models/mobilenetv2.relay'), 'w') as fp: + fp.write(mod.astext()) + exit(0) + else: + params = params = torch.load(param_file, map_location=torch.device('cpu')) + prefix = "module." + params = {(k[len(prefix):].replace('.', '_') if k.startswith(prefix) else k): v.cpu().numpy() for k, v in params.items()} + if not args.relay_model: + raise Exception('relay model not set') + with open(args.relay_model, 'r') as fp: + relay_src = fp.read() + mod = tvm.parser.fromtext(relay_src) + if args.layerwise_debug: + inp = [{'input0': next(enumerate(testloader))[1][0], **params}] + quant_utils.lockstep_layerwise(mod, args.relay_model, inp) + elif args.quantize: + # run_with_relay_quantization(mod, params) + # cali_dataset = get_cali_data() + # calibrations = quant_utils.calibrate(mod, params, cali_dataset, ['nn.dense']) + # mod = tvm.parser.fromtext(relay_src) + expr = quant_utils.VTAQuantize([], ['nn.dense']).visit(mod['main'].body) + mod = tvm.ir.IRModule.from_expr(expr) + mod = relay.transform.InferType()(mod) + test_relay_model(mod, params) diff --git a/flexmatch/Cargo.toml b/flexmatch/Cargo.toml index e7f27f7..8ddd91b 100644 --- a/flexmatch/Cargo.toml +++ b/flexmatch/Cargo.toml @@ -5,19 +5,30 @@ edition = "2021" # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html +[features] +default = [] +cplex = ["rplex", "glenside/cplex"] + [dependencies] serde_json = "1.0" serde = "1.0.130" +log = "0.4.14" +env_logger = "0.9.0" [dependencies.glenside] git = "https://github.com/gussmith23/glenside" -branch = "3la-pldi-push-main" +rev = "611a6bc4b9990849f26daaa4d808a8497124fedc" +default-features = false features = ["tvm"] [dependencies.egg] git = "https://github.com/AD1024/egg" -branch = "3la-mod" +rev = "b8f902f554cc476c397e4fd9c7bea229f946d2a9" [dependencies.tvm] git = "ssh://git@github.com/uwsampl/3la-tvm.git" -rev = "bb6c378e5440899083af253c3466db087157acb6" +rev = "1ae704133bc52b36fb95aac7ddc043d25dca0c12" + +[dependencies.rplex] +optional = true +git = "https://github.com/gussmith23/rplex" diff --git a/flexmatch/src/main.rs b/flexmatch/src/main.rs index a8c8a82..9e0cb3b 100644 --- a/flexmatch/src/main.rs +++ b/flexmatch/src/main.rs @@ -1,22 +1,33 @@ mod rewrites; -use egg::{EGraph, Extractor, Runner}; +use egg::{EGraph, Extractor, Language, Runner}; use glenside::{ extraction::AcceleratorCostFunction, - language::{serialize_analysis_data, MyAnalysis}, + language::{serialize_analysis_data, MyAnalysis, MyAnalysisData, RelayOperator}, }; use rewrites::{get_rewrite_from_string, im2col_rewrites, linear_rewrites}; use serde::Deserialize; use serde_json; -use std::{collections::{HashMap, HashSet}, env, fs, path::{Path, PathBuf}, process::exit}; +use std::{ + collections::{BTreeMap, HashMap, HashSet}, + env, fs, + path::{Path, PathBuf}, + process::exit, +}; use tvm; +extern crate env_logger; +extern crate log; + +use log::{debug, info}; + #[derive(Deserialize, Clone, Debug)] struct RewriteConfig { rewrites: HashMap>, composites: HashMap, compilers: HashMap, debug_functions: HashMap, + out_dtypes: HashMap, } fn read_configs(flexmatch_home: &PathBuf, config_files: &[String]) -> Vec { @@ -25,7 +36,7 @@ fn read_configs(flexmatch_home: &PathBuf, config_files: &[String]) -> Vec Vec, + rec_expr: &mut egg::RecExpr, +) { + let mut expr_map: BTreeMap = BTreeMap::new(); + for eclass in egraph.classes() { + assert_eq!(eclass.nodes.len(), 1); + expr_map.insert(eclass.id, eclass.nodes[0].clone()); + } + for (_id, expr) in expr_map.into_iter() { + rec_expr.add(expr); + } +} + +fn save_expr_and_analysis( + rec_expr_file: PathBuf, + analysis_data_file: PathBuf, + input_shapes: &HashMap>, + input_dtypes: &HashMap, + best: &egg::RecExpr, +) { + info!("Saving RecExpr to {:?}", rec_expr_file); + let json_dump = best.serialize(); + let output_file = PathBuf::from(env::current_dir().unwrap()).join(rec_expr_file); + let _ = std::fs::write(output_file, json_dump.to_string()).unwrap(); + let mut egraph = EGraph::new(MyAnalysis { + name_to_shape: input_shapes.clone(), + name_to_dtype: input_dtypes.clone(), + }); + let (_, id_map) = egraph.add_expr_with_record(&best); + let mut native_map = HashMap::new(); + for (k, v) in id_map.into_iter() { + native_map.insert(k, v); + } + let data_json_dump = serialize_analysis_data(&egraph, &native_map); + let data_output = PathBuf::from(env::current_dir().unwrap()).join(analysis_data_file); + let _ = std::fs::write(data_output, data_json_dump.to_string()).unwrap(); +} + fn main() { + env_logger::init(); let args = env::args().collect::>(); let flexmatch_home = PathBuf::from(env::var("FLEXMATCH_HOME").unwrap()); if args.len() < 3 { - println!("flexmatch src_file recexpr_json data_json [config.json]+"); + println!("flexmatch src_file recexpr_json data_json [config.json]+ Optional[--ilp]"); exit(0); } else { let source_file = &args[1]; - let output_file = &args[2]; - let analysis_data_file = &args[3]; - let config_files = &args[4..]; + let output_file = PathBuf::from(&args[2]); + let analysis_data_file = PathBuf::from(&args[3]); + let use_ilp = &args[args.len() - 1] == "--ilp"; + let config_files = if use_ilp { + &args[4..args.len() - 1] + } else { + &args[4..] + }; let aggregated_configs = read_configs(&flexmatch_home, config_files); let mut rewrites = vec![]; let mut rewrite_set = HashSet::new(); + debug!("{:?}", aggregated_configs); for config in aggregated_configs.iter() { for (rws, rw_args) in config.rewrites.iter() { if rewrite_set.contains(rws) { continue; } + info!("Adding rewrite: {:?} with args {:?}", rws, rw_args); rewrite_set.insert(rws.clone()); match rws.as_str() { "im2col" => rewrites.extend(im2col_rewrites()), @@ -64,45 +122,261 @@ fn main() { let relay_src = fs::read_to_string(PathBuf::from(source_file)).unwrap(); let module: tvm::ir::module::IRModule = tvm::ir::module::IRModule::parse("", relay_src).unwrap(); - let (expr, shape_info, equiv_worklist) = - glenside::language::from_relay::from_relay(&module, false, &vec![]); + info!("Compiling to Glenside"); + let (expr, shape_info, dtype_info, equiv_worklist) = + glenside::language::from_relay::from_relay( + &module, + false, + &vec![ + RelayOperator::RelaySigmoid, + RelayOperator::RelayAvgPool2D, + RelayOperator::RelayMaxPool2D, + RelayOperator::RelayTanh, + RelayOperator::RelayLogSoftmax, + RelayOperator::RelayAdd, + RelayOperator::RelayStridedSlice, + ], + ); let mut env = HashMap::default(); for (name, shape) in &shape_info { env.insert(name.clone(), shape.clone()); } let mut egraph = EGraph::new(MyAnalysis { name_to_shape: env.clone(), + name_to_dtype: dtype_info.iter().cloned().collect(), }); - let (id, id_map) = egraph.add_expr_with_record(&expr); + info!("Merging equivalent expressions"); + let (root_expr, id_map) = egraph.add_expr_with_record(&expr); for (left, right) in equiv_worklist { if let (Some(&new_left), Some(&new_right)) = (id_map.get(&left), id_map.get(&right)) { egraph.union(new_left, new_right); } } egraph.rebuild(); - let runner = Runner::<_, _, ()>::new(MyAnalysis::default()) + info!("Running Equality Saturation"); + let mut runner = Runner::<_, _, ()>::new(MyAnalysis::default()) .with_egraph(egraph) .with_time_limit(std::time::Duration::from_secs(5)) - .with_node_limit(500000) - .with_iter_limit(40) + .with_node_limit(100000) + .with_iter_limit(45) .run(&rewrites); - let extractor = Extractor::new(&runner.egraph, AcceleratorCostFunction {}); - let (_cost, best) = extractor.find_best(id); - let json_dump = best.serialize(); - let output_file = - PathBuf::from(env::current_dir().unwrap()).join(PathBuf::from(output_file)); - let _ = std::fs::write(output_file, json_dump.to_string()).unwrap(); - egraph = EGraph::new(MyAnalysis { - name_to_shape: env.clone(), + info!("EqSat Complete"); + let root_expr = runner.egraph.find(root_expr); + // propagate accelerator calls + let mut analysis_worklist = runner.egraph.analysis_update_worklist(|a, _p| match a { + glenside::language::MyAnalysisData::AccessPattern(access) => { + access.contains_accelerator_calls + } + _ => false, }); - let (_, id_map) = egraph.add_expr_with_record(&best); - egraph.rebuild(); - let mut native_map = HashMap::new(); - for (k, v) in id_map.into_iter() { - native_map.insert(k, v); + while analysis_worklist.len() > 0 { + for (ids, _ref_id) in analysis_worklist.iter() { + for id in ids.iter().cloned() { + let analysis_data = &mut runner.egraph[id].data; + match analysis_data { + glenside::language::MyAnalysisData::AccessPattern(access) => { + access.contains_accelerator_calls = true; + } + _ => (), + } + } + } + analysis_worklist.clear(); + analysis_worklist.extend(runner.egraph.analysis_update_worklist( + |a, parents| match a { + glenside::language::MyAnalysisData::AccessPattern(access) => { + if access.contains_accelerator_calls { + parents + .iter() + .map(|x| x.1) + .any(|pid| match &runner.egraph[pid].data { + glenside::language::MyAnalysisData::AccessPattern(access) => { + !access.contains_accelerator_calls + } + _ => false, + }) + } else { + false + } + } + _ => false, + }, + )); + } + info!("Root eclass analysis: {:?}", runner.egraph[root_expr].data); + info!("Root eclass nodes: {:?}", runner.egraph[root_expr].nodes); + if !use_ilp { + info!("Extraction without ILP"); + let extractor = Extractor::new( + &runner.egraph, + AcceleratorCostFunction(runner.egraph.total_size() as f64), + ); + let (_cost, best) = extractor.find_best(root_expr); + save_expr_and_analysis( + output_file, + analysis_data_file, + &env, + &dtype_info.into_iter().collect(), + &best, + ); + } else { + // The following extraction strategy is borrowed from Glenside ISCA demo + /* + #[cfg(feature = "cplex")] + { + use rplex::*; + info!("Extraction with ILP solver"); + let mut cplex_env = Env::new().unwrap(); + cplex_env.set_param(EnvParam::ScreenOutput(true)).unwrap(); + cplex_env + .set_param(EnvParam::MIPLimitsSolutions(1)) + .unwrap(); + // Deterministic time limit in "ticks" + cplex_env + .set_param(EnvParam::DetTimeLimit(6000000.0)) + .unwrap(); + let mut model = glenside::extraction::ilp::create_generic_egraph_lp_model( + &cplex_env, + &runner.egraph, + |node, id, egraph| true && filter_nodes(node, id, egraph), + &[root_expr], + "ilp-extraction", + ); + let mut costs = Constraint::new( + ConstraintType::Eq, /*ignored*/ + 0.0, /*ignored*/ + "costs", + ); + for (_, var) in model.bq_vars.iter() { + costs.add_wvar(WeightedVariable::new_idx(*var, 1.0)); + } + for (_, var) in model.topo_sort_vars.iter() { + costs.add_wvar(WeightedVariable::new_idx(*var, 0.0)); + } + for (&node, var) in model.bn_vars.iter() { + let weight = get_node_weights(node, runner.egraph.total_size() as f64); + costs.add_wvar(WeightedVariable::new_idx(*var, weight)); + } + model + .problem + .set_objective(ObjectiveType::Minimize, costs) + .unwrap(); + info!("objective set"); + + info!("ilp problem created, beginning solving..."); + let result = model.problem.solve().unwrap(); + info!("ilp problem solved"); + + let (expr, _old_id_to_new_id_map) = + glenside::extraction::ilp::extract_single_expression( + &model, + &result.variables, + EGraph::new(MyAnalysis { + name_to_shape: env.clone(), + name_to_dtype: dtype_info.iter().cloned().collect(), + }), + ); + let mut rec_expr = egg::RecExpr::default(); + save_egraph_as_recexpr(&expr, &mut rec_expr); + save_expr_and_analysis( + output_file, + analysis_data_file, + &env, + &dtype_info.iter().cloned().collect(), + &rec_expr, + ); + } */ + } + } +} + +fn check_accelerator_call_by_eid( + ch_id: &egg::Id, + egraph: &EGraph, +) -> bool { + match &egraph[*ch_id].data { + MyAnalysisData::AccessPattern(access) => access.contains_accelerator_calls, + _ => false, + } +} + +fn get_node_weights(node: &glenside::language::Language, total_size: f64) -> f64 { + if let glenside::language::Language::AcceleratorCall(_) = node { + debug!("Accelerator-call encountered"); + } + match node { + glenside::language::Language::AcceleratorCall(_) => -total_size * 10.0, + glenside::language::Language::List(_) + | glenside::language::Language::Shape(_) + | glenside::language::Language::RelayKernelLayout(_) + | glenside::language::Language::RelayActivationLayout(_) + | glenside::language::Language::Usize(_) + | glenside::language::Language::NotNanFloat64(_) + | glenside::language::Language::AccessShape(_) + | glenside::language::Language::AcceleratorFunc(_) + | glenside::language::Language::Symbol(_) + | glenside::language::Language::PadType(_) + | glenside::language::Language::Int32(_) + | glenside::language::Language::Uint8(_) + | glenside::language::Language::ConstantTensor(_) + | glenside::language::Language::Literal(_) + | glenside::language::Language::AccessLiteral(_) + | glenside::language::Language::AccessTensor(_) => 1.0, + glenside::language::Language::RelayOperatorCall(_) => total_size / 100.0, + glenside::language::Language::RelayOperator(_) => 1.0, + glenside::language::Language::AccessTranspose(_) + | glenside::language::Language::AccessPad(_) + | glenside::language::Language::Access(_) + | glenside::language::Language::AccessFlatten(_) + | glenside::language::Language::AccessWindows(_) + | glenside::language::Language::AccessBroadcast(_) + | glenside::language::Language::AccessInsertAxis(_) + | glenside::language::Language::AccessSlice(_) + | glenside::language::Language::AccessSqueeze(_) => total_size / 10.0, + + glenside::language::Language::Compute(_) + | glenside::language::Language::AccessReshape(_) + | glenside::language::Language::ComputeType(_) + | glenside::language::Language::AccessPair(_) => total_size, + _ => total_size / 20.0, + } +} + +fn filter_nodes( + node: &glenside::language::Language, + id: egg::Id, + egraph: &EGraph, +) -> bool { + let id = egraph.find(id); + if let glenside::language::Language::AcceleratorCall(_) = &node { + return true; + } + if egraph[id].nodes.iter().any(|node| match node { + glenside::language::Language::AcceleratorCall(_) => true, + _ => false, + }) { + return false; + } + let contains_accel_call = + if let glenside::language::MyAnalysisData::AccessPattern(access) = &egraph[id].data { + access.contains_accelerator_calls + } else { + false + }; + if contains_accel_call { + let result = node + .children() + .iter() + .any(|cid| check_accelerator_call_by_eid(&egraph.find(*cid), egraph)); + if !result { + debug!( + "Say no to {:?} because it's not an accelerator call from {:?}", + node, &egraph[id] + ); + return false; + } else { + return true; } - let data_json_dump = serialize_analysis_data(&egraph, &native_map); - let data_output = PathBuf::from(env::current_dir().unwrap()).join(analysis_data_file); - let _ = std::fs::write(data_output, data_json_dump.to_string()).unwrap(); } + return true; } diff --git a/flexmatch/src/rewrites.rs b/flexmatch/src/rewrites.rs index c13e331..24df658 100644 --- a/flexmatch/src/rewrites.rs +++ b/flexmatch/src/rewrites.rs @@ -2,14 +2,10 @@ use egg::Rewrite; use glenside::language::rewrites::*; use glenside::language::{Language, MyAnalysis}; -pub fn get_rewrite_from_string( - name: &String, - args: &Box<[i32]>, -) -> Rewrite { +pub fn get_rewrite_from_string(name: &String, args: &Box<[i32]>) -> Rewrite { match name.as_str() { "bubble-reshape-through-cartesian-product" => bubble_reshape_through_cartesian_product(), "flatten-unflatten-all-accesses" => flatten_unflatten_any_access(), - "bubble-reshape-through-linear" => bubble_reshape_through_linear_generalized(), "access-reshape-to-relay" => access_reshape_to_relay(), "bubble-reshape-through-compute-dot-product" => { bubble_reshape_through_compute_dot_product() @@ -28,8 +24,13 @@ pub fn get_rewrite_from_string( "simplify-reduce-max" => simplify_reduce_max(), "flex-linear-rewrite" => linear_layer_accelerator_rewrites(), + "flex-linear-dense" => dot_product_to_linear(), + "flex-lstm" => lstm_to_flexasr(), + "hlscnn-conv2d" => conv2d_on_hlscnn(), "vta-dense-rewrite" => dot_product_with_vta(), "flexasr-maxpool" => flexasr_maxpool(), + "glenside_matmul_to_relay_dense" => glenside_matmul_to_relay_dense(), + "add_bias_add_to_dense" => add_bias_add_to_dense(), _ => { eprintln!("{} not implemented", name); todo!() @@ -46,5 +47,5 @@ pub fn im2col_rewrites() -> Vec> { } pub fn linear_rewrites() -> Vec> { - vec![bubble_reshape_through_linear_generalized()] + bubble_reshape_through_linear_generalized() } diff --git a/megraph/language.py b/megraph/language.py index 63e5c6d..d14497e 100644 --- a/megraph/language.py +++ b/megraph/language.py @@ -1,6 +1,8 @@ from functools import reduce +from numpy import random import tvm import enum +import resource, sys from megraph.eclass import ENode from tvm import relay from tvm.relay import TypeKind, nn @@ -10,6 +12,9 @@ from tvm.relay import ScopeBuilder from typing import List, Tuple +resource.setrlimit(resource.RLIMIT_STACK, (2**29, -1)) +sys.setrecursionlimit(65536) + class Language: pass class RelayOperators(enum.Enum): @@ -34,29 +39,82 @@ class RelayOperators(enum.Enum): RelayErf = relay.erf, RelayConv1D = relay.nn.conv1d, RelayConv2D = relay.nn.conv2d, + RelayCast = relay.cast, + RelayLeftShift = relay.left_shift, + RelayRightShift = relay.right_shift, + RelayClip = relay.clip, + RelayTanh = relay.tanh, + RelayRound = relay.round, + RelayTake = relay.take, + RelayDropout = relay.nn.dropout, + RelayStack = relay.stack, + RelayLogSoftmax = relay.nn.log_softmax, + RelaySplit = relay.split, + RelayLayerNorm = relay.nn.layer_norm, + RelayBatchMatmul = relay.nn.batch_matmul, + RelayStridedSlice = relay.strided_slice, + RelayZeros = relay.zeros, + RelayAdaptiveAvgPool2D = relay.nn.adaptive_avg_pool2d, + RelayCopy = relay.copy, + RelayArgMax = relay.argmax, def __call__(self, *x): - # print(self.value, x) + # Handle special case of relay operator calls + # could be mitigated by spliting parameters from attributes in glenside + if self.value[0] == relay.zeros: + return relay.zeros(shape=x[0], dtype="float32") + if self.value[0] == relay.nn.layer_norm: + return relay.nn.layer_norm(x[0], gamma=x[1], beta=x[2]) + if self.value[0] == relay.split: + return relay.split(x[0], indices_or_sections=int(x[1]), axis=int(x[2])).tuple_value + if self.value[0] == relay.stack: + return relay.stack(x[:-1], axis=int(x[-1])) + if self.value[0] == relay.nn.log_softmax: + return relay.nn.log_softmax(x[0], axis=int(x[1])) + if self.value[0] == relay.nn.dropout: + return relay.nn.dropout_raw(x[0], rate=float(x[1])) + if self.value[0] == relay.take: + return relay.take(x[0], x[1], axis=int(x[2])) if self.value[0] == relay.mean: - return self.value[0](x[0], axis=int(x[1])) + return self.value[0](x[0], axis=x[1], keepdims=int(x[2]) == 1) if self.value[0] == relay.nn.bias_add: return self.value[0](x[0], x[1], axis=int(x[2])) if self.value[0] == relay.nn.batch_norm: return self.value[0](x[0], x[1], x[2], x[3], x[4], axis=int(x[5]), epsilon=float(x[6])) if self.value[0] == relay.nn.max_pool2d or self.value[0] == relay.nn.global_avg_pool2d: - layout = x[-1]; + layout = x[-1] if layout == RelayActivationLayout.NCHW: - return self.value[0](*x[:-1], layout='NCHW') + if len(x) > 2: + return self.value[0](x[0], pool_size=x[1], strides=x[2], padding=x[3], layout='NCHW') + else: + return self.value[0](x[0], layout='NCHW') elif layout == RelayActivationLayout.NHWC: return self.value[0](*x[:-1], layout='NHWC') + if self.value[0] == relay.nn.adaptive_avg_pool2d: + layout = x[-1] + assert layout == RelayActivationLayout.NCHW + return self.value[0](x[0], output_size=x[1]) + if self.value[0] == relay.argmax: + return self.value[0](x[0], axis=x[1], keepdims=int(x[2])) + if self.value[0] == relay.cast: + return self.value[0](x[0], x[1].value) if self.value[0] == relay.nn.softmax: return self.value[0](x[0], axis=int(x[1])) if self.value[0] == relay.nn.conv2d: data_layout = x[-2].value kernel_layout = x[-1].value - return self.value[0](x[0], x[1], strides=tuple(x[2]), padding=tuple(x[3]), + # Strides in Glenside includes the batch dimension, which is + # not the case in relay + # TODO: Assuming data is NCHW and kernel is OHWI + # which means changing to another layout can break the + # compliation (b/c of the `kernel_size` argument) + return self.value[0](x[0], x[1], strides=tuple(x[2][1:]), padding=tuple(x[3]), groups=int(x[4]), channels=int(x[5]), kernel_size=(int(x[6][1]), int(x[6][2])), - data_layout=data_layout, kernel_layout=kernel_layout) + data_layout="NCHW", kernel_layout="OIHW") + if self.value[0] == relay.nn.avg_pool2d: + assert(len(x) == 5) + data_layout = x[-1].value + return nn.avg_pool2d(x[0], pool_size=x[1], strides=x[2], padding=x[3], layout=data_layout) x = list(map(lambda x: relay.const(x) if isinstance(x, float) else x, x)) try: return self.value[0](*x) @@ -69,12 +127,22 @@ class AcceleratorFunc(enum.Enum): FlexLSTM = 'flex-lstm' VTADense = 'vta-dense' VTAConv1D = 'vta-conv1d' + HLSCNNConv2D = 'hlscnn-conv2d' + FlexMaxPool = 'flex-maxpool' def __str__(self): return self.value class PadType(enum.Enum): ZeroPadding = 'zero-padding' + MinPadding = 'min-padding' + +class DType(enum.Enum): + i32 = 'int32' + i64 = 'int64' + f32 = 'float32' + f64 = 'float64' + u8 = 'uint8' class ComputeType(enum.Enum): Relu = 'relu' @@ -82,6 +150,8 @@ class ComputeType(enum.Enum): Sqrt = 'sqrt' ElementwiseDiv = 'elementwise-div' ElementwiseMul = 'elementwise-mul' + ElementwiseAdd = 'elementwise-add' + ReduceMax = 'reduce-max' class RelayActivationLayout(enum.Enum): NCHW = 'NCHW' @@ -90,6 +160,7 @@ class RelayActivationLayout(enum.Enum): class RelayKernelLayout(enum.Enum): OIHW = 'OIHW' OHWI = 'OHWI' + HWIO = 'HWIO' class Compute(ENode): pass @@ -125,6 +196,9 @@ class AccessTensor(ENode): class AccessTranspose(ENode): pass +class AccessConcat(ENode): + pass + class ListNode(ENode): pass @@ -167,6 +241,9 @@ class AccessPair(ENode): class AccessInsertAxis(ENode): pass +class AccessSlice(ENode): + pass + class RecExprCompiler: def __init__(self, composite_lib, compiler_lib, accelerator_func_lib=dict()): self.nodes : List[ENode] = [] @@ -206,7 +283,9 @@ def _to_relay_helper(self, index): if index in self._id_map: return self._id_map[index] enode = self.nodes[index] - if isinstance(enode, RelayActivationLayout) or isinstance(enode, RelayKernelLayout): + if isinstance(enode, RelayActivationLayout) \ + or isinstance(enode, RelayKernelLayout) \ + or isinstance(enode, DType): return enode children_exprs = list(map(self._to_relay_helper, enode.children)) ch_vars = [] @@ -237,13 +316,18 @@ def _to_relay_helper(self, index): elif isinstance(enode, Literal): return float(enode.symbol) elif isinstance(enode, Symbol): - return self.next_var(index, use_symbol=enode.symbol, shape=relay.TensorType(self.input_shapes[enode.symbol])) + if not (enode.symbol in self.input_shapes and enode.symbol in self.input_dtypes): + raise Exception(f'{enode.symbol} is not a proper symbol') + return self.next_var(index, use_symbol=enode.symbol, + shape=relay.TensorType(self.input_shapes[enode.symbol], dtype=self.input_dtypes[enode.symbol])) elif isinstance(enode, Shape): return tuple(map(int, children_exprs)) elif isinstance(enode, TupleGetItem): - return relay.TupleGetItem(ch_vars[0], *ch_vars[1:]) + return relay.TupleGetItem(ch_vars[0], int(children_exprs[1])) elif isinstance(enode, ConstructTuple): return relay.Tuple(ch_vars) + elif isinstance(enode, AccessConcat): + return relay.concatenate([ch_vars[0], ch_vars[1]], axis=int(children_exprs[2])) elif isinstance(enode, AccessInsertAxis): if not isinstance(ch_vars[0], relay.Expr): ch_vars[0] = relay.const(ch_vars[0]) @@ -260,8 +344,12 @@ def _to_relay_helper(self, index): return children_exprs[0] elif isinstance(enode, AccessFlatten): access_pattern = self.eclass_analysis[enode.children[0]] - newshape=[reduce(lambda x, y: x * y, access_pattern['shape']), - reduce(lambda x, y: x * y, access_pattern['item_shape'])] + newshape = [] + if access_pattern['shape'] != []: + newshape += [reduce(lambda x, y: x * y, access_pattern['shape'])] + if access_pattern['item_shape'] != []: + newshape += [reduce(lambda x, y: x * y, access_pattern['item_shape'])] + # print(newshape, self.eclass_analysis[index]['relay_shape']) return relay.reshape(ch_vars[0], newshape) elif isinstance(enode, AccessLiteral) or isinstance(enode, LiteralNode): return children_exprs[0] @@ -274,7 +362,7 @@ def _to_relay_helper(self, index): elif isinstance(enode, AccessPad): assert isinstance(children_exprs[0], relay.Var) or self.eclass_analysis is not None if self.eclass_analysis: - ndim = len(self.eclass_analysis[enode.children[0]]['shape']) + ndim = len(self.eclass_analysis[enode.children[0]]['shape']) + len(self.eclass_analysis[enode.children[0]]['item_shape']) else: ndim = len(children_exprs[0].type_annotation.shape) assert ndim > 0 @@ -295,6 +383,8 @@ def _to_relay_helper(self, index): if isinstance(pad_type, PadType): if pad_type == PadType.ZeroPadding: return relay.nn.pad(ch_vars[0], pad_width, pad_value=0) + elif pad_type == PadType.MinPadding: + return relay.nn.pad(ch_vars[0], pad_width, pad_value=relay.min(ch_vars[0])) else: raise Exception(f'Unkonw PadType: {str(pad_type)}') else: @@ -302,12 +392,20 @@ def _to_relay_helper(self, index): elif isinstance(enode, AccessSqueeze): return relay.squeeze(ch_vars[0], axis=[int(children_exprs[1])]) elif isinstance(enode, AccessWindows): - key = (tuple(self.eclass_analysis[enode.children[0]]['relay_shape']), tuple(children_exprs[1]), tuple(children_exprs[2])) - if key not in self.access_window_memo: - self.access_window_memo[key] = access_window(self.eclass_analysis[enode.children[0]]['relay_shape'], children_exprs[1], children_exprs[2]) - return self.access_window_memo[key](ch_vars[0]) + data = ch_vars[0] + kernel_shape = children_exprs[1] + strides = children_exprs[2] + data_shape = self.eclass_analysis[enode.children[0]]['relay_shape'] + axis = len(data_shape) - len(kernel_shape) + return relay.sliding_window(data, axis, kernel_shape, strides) elif isinstance(enode, AccessPair): return (ch_vars[0], ch_vars[1]) + elif isinstance(enode, AccessSlice): + data = ch_vars[0] + axis = int(ch_vars[1]) + start = int(ch_vars[2]) + end = int(ch_vars[3]) + return access_slice(data, self.eclass_analysis[enode.children[0]]['relay_shape'], axis, start, end) elif isinstance(enode, Compute): compute_type = enode.symbol for i in range(len(ch_vars)): @@ -323,7 +421,10 @@ def _to_relay_helper(self, index): ComputeType.Negative: lambda: relay.negative(ch_vars[0]), ComputeType.Sqrt: lambda: relay.sqrt(ch_vars[0]), ComputeType.ElementwiseMul: lambda: relay.multiply(ch_vars[0][0], ch_vars[0][1]), - ComputeType.ElementwiseDiv: lambda: relay.divide(ch_vars[0][0], ch_vars[0][1]) + ComputeType.ElementwiseDiv: lambda: relay.divide(ch_vars[0][0], ch_vars[0][1]), + ComputeType.ElementwiseAdd: lambda: relay.nn.bias_add(ch_vars[0][0], ch_vars[0][1]), + ComputeType.ReduceMax: lambda: relay.max(ch_vars[0], [i for i in range(len(self.eclass_analysis[enode.children[0]]['shape']), + len(self.eclass_analysis[enode.children[0]]['relay_shape']))]), }.get(compute_type, None) if func: return func() @@ -331,29 +432,34 @@ def _to_relay_helper(self, index): raise Exception(f'Unrecognized compute type: {compute_type}') elif isinstance(enode, AcceleratorCall): func = str(enode.symbol) + # the last argument is the output shape by convention + assert(isinstance(children_exprs[-1], tuple)) if self.use_debug_func: return self.accelerator_func_lib[func](*ch_vars[:-1]) else: # In Glenside, the last parameter to accelerator-call is the inferred type - accelerator_call = relay.accelerator_call(func, children_exprs[-1]) + inferred_type = self.eclass_analysis[index]['relay_shape'] + accelerator_call = relay.accelerator_call(func, inferred_type, out_dtype=self.out_dtypes[func]) composite_name = self.composite_lib[func] compiler_name = self.compiler_lib[func] - inner_args = [relay.Var(f'inner_arg_{i}') for i in range(len(ch_vars) - 1)] - inner_func = relay.Function(inner_args, accelerator_call, ret_type=relay.TensorType(children_exprs[-1])) + ch_vars = list(map(lambda x: relay.const(x) if isinstance(x, float) or isinstance(x, int) else x, ch_vars)) + ch_vars = list(filter(lambda x: isinstance(x, relay.Expr), ch_vars)) + inner_args = [relay.Var(f'inner_arg_{i}') for i in range(len(ch_vars))] + inner_func = relay.Function(inner_args, accelerator_call, ret_type=relay.TensorType(inferred_type)) inner_func = inner_func.with_attr("Composite", composite_name) - outer_args = [relay.var(f'outer_arg_{i}') for i in range(len(ch_vars) - 1)] - outer_func = relay.Function(outer_args, inner_func(*outer_args), ret_type=relay.TensorType(children_exprs[-1])) + outer_args = [relay.var(f'outer_arg_{i}') for i in range(len(ch_vars))] + outer_func = relay.Function(outer_args, inner_func(*outer_args), ret_type=relay.TensorType(inferred_type)) outer_func = outer_func.with_attr("Compiler", compiler_name) outer_func = outer_func.with_attr("Primitive", tvm.tir.IntImm("int32", 1)) outer_func = outer_func.with_attr( "global_symbol", f"{composite_name}_{self.region_counter}") self.region_counter += 1 - return outer_func(*ch_vars[:-1]) + return outer_func(*ch_vars) else: raise Exception(f'{type(enode)} not implemented') - def to_relay_expr(self, expr_data, input_shapes, analysis_data=dict(), use_debug_func=False): + def to_relay_expr(self, expr_data, input_shapes, input_dtypes, analysis_data=dict(), out_dtypes=dict(), use_debug_func=False): self.region_counter = 0 self.var_count = 0 self._id_map.clear() @@ -362,8 +468,10 @@ def to_relay_expr(self, expr_data, input_shapes, analysis_data=dict(), use_debug self.nodes.clear() self._load_json(expr_data) self.input_shapes = input_shapes + self.input_dtypes = input_dtypes self.eclass_analysis = analysis_data self.use_debug_func = use_debug_func + self.out_dtypes = out_dtypes if len(self.nodes) == 0: return None else: @@ -436,9 +544,17 @@ def access_window(data_shape: List[int], kernel_shape: List[int], strides: List[ assert access_axis >= 0 # begin with 0 each time (probably not, so that we could get rid of paddings?) starts = [0 for _ in range(len(data_shape))] - meta_var = relay.var('data', type_annotation=relay.TensorType(data_shape)) + meta_var = relay.var(f'access_window_var_{random.randint(0, 2**31)}', type_annotation=relay.TensorType(data_shape)) return relay.Function([meta_var], _access_window(meta_var, access_axis, 0, data_shape, kernel_shape, starts, strides)) +def access_slice(data: relay.Expr, data_shape: List[int], axis: int, begin: int, end: int): + assert axis < len(data_shape) + starts = [0] * len(data_shape) + ends = data_shape.copy() + starts[axis] = begin + ends[axis] = end + return relay.strided_slice(data, starts, ends) + def downcast(enode: ENode): symbol = enode.symbol lang = { @@ -446,7 +562,7 @@ def downcast(enode: ENode): 'relay-batch-norm-inference': RelayOperators.RelayBatchNormInference, 'relay-softmax': RelayOperators.RelaySoftMax, 'relay-relu': RelayOperators.RelayReLU, - 'relay-leaky_relu': RelayOperators.RelayLeakyReLU, + 'relay-leaky-relu': RelayOperators.RelayLeakyReLU, 'relay-max-pool2d': RelayOperators.RelayMaxPool2D, 'relay-global-avg-pool2d': RelayOperators.RelayGlobalAvgPool2D, 'relay-avg-pool2d': RelayOperators.RelayAvgPool2D, @@ -463,6 +579,24 @@ def downcast(enode: ENode): 'relay-erf': RelayOperators.RelayErf, 'relay-conv1d': RelayOperators.RelayConv1D, 'relay-conv2d': RelayOperators.RelayConv2D, + 'relay-cast': RelayOperators.RelayCast, + 'relay-clip': RelayOperators.RelayClip, + 'relay-left-shift': RelayOperators.RelayLeftShift, + 'relay-right-shift': RelayOperators.RelayRightShift, + 'relay-take': RelayOperators.RelayTake, + 'relay-stack': RelayOperators.RelayStack, + 'relay-dropout': RelayOperators.RelayDropout, + 'relay-tanh': RelayOperators.RelayTanh, + 'relay-log-softmax': RelayOperators.RelayLogSoftmax, + 'relay-round': RelayOperators.RelayRound, + 'relay-split': RelayOperators.RelaySplit, + 'relay-layer-norm': RelayOperators.RelayLayerNorm, + 'relay-batch-matmul': RelayOperators.RelayBatchMatmul, + 'relay-strided-slice': RelayOperators.RelayStridedSlice, + 'relay-zeros': RelayOperators.RelayZeros, + 'relay-adaptive-avg-pool2d': RelayOperators.RelayAdaptiveAvgPool2D, + 'relay-copy': RelayOperators.RelayCopy, + 'relay-argmax': RelayOperators.RelayArgMax, }.get(symbol, None) if lang is not None: return RelayOperatorCall(lang, enode.children) @@ -472,7 +606,9 @@ def downcast(enode: ENode): 'negative': ComputeType.Negative, 'sqrt': ComputeType.Sqrt, 'elementwise-div': ComputeType.ElementwiseDiv, + 'reduce-max': ComputeType.ReduceMax, 'elementwise-mul': ComputeType.ElementwiseMul, + 'elementwise-add': ComputeType.ElementwiseAdd, }.get(symbol, None) if lang: return Compute(lang, enode.children) @@ -482,6 +618,7 @@ def downcast(enode: ENode): 'relay-activation-layout-nhwc': RelayActivationLayout.NHWC, 'relay-kernel-layout-oihw': RelayKernelLayout.OIHW, 'relay-kernel-layout-ohwi': RelayKernelLayout.OHWI, + 'relay-kernel-layout-hwio': RelayKernelLayout.HWIO, }.get(symbol, None) if lang: return lang @@ -491,9 +628,22 @@ def downcast(enode: ENode): 'flex-lstm': AcceleratorFunc.FlexLSTM, 'vta-dense': AcceleratorFunc.VTADense, 'vta-conv1d': AcceleratorFunc.VTAConv1D, + 'hlscnn-conv2d': AcceleratorFunc.HLSCNNConv2D, + 'flex-maxpool': AcceleratorFunc.FlexMaxPool, }.get(symbol) if lang is not None: return AcceleratorCall(lang, enode.children) + + lang = { + 'int64': DType.i64, + 'int32': DType.i32, + 'uint8': DType.u8, + 'float32': DType.f32, + 'float64': DType.f64 + }.get(symbol) + + if lang is not None: + return lang if symbol.isdigit() or is_num(symbol): return Literal(symbol, children=enode.children) @@ -514,8 +664,11 @@ def downcast(enode: ENode): 'access': lambda: Access, 'access-flatten': lambda: AccessFlatten, 'zero-padding': lambda: lambda *_: PadTypeNode(PadType.ZeroPadding), + 'min-padding': lambda: lambda *_: PadTypeNode(PadType.MinPadding), 'access-windows': lambda: AccessWindows, + 'access-slice': lambda: AccessSlice, 'access-pair': lambda: AccessPair, + 'access-concatenate': lambda: AccessConcat, 'literal': lambda: LiteralNode, 'list': lambda: ListNode }.get(symbol, lambda: Symbol)()(enode.symbol, enode.children) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/compile_model.py b/tests/compile_model.py index 8f9684b..e4e3ef7 100644 --- a/tests/compile_model.py +++ b/tests/compile_model.py @@ -5,6 +5,7 @@ import tvm from tvm import relay from tvm.relay import nn +from megraph.language import RelayOperators def main(relay_file, output_filename, model_json, data_json, *configs, debug=False): home_dir = os.environ.get('FLEXMATCH_HOME') @@ -12,12 +13,14 @@ def main(relay_file, output_filename, model_json, data_json, *configs, debug=Fal compilers = dict() composites = dict() debug_funcs = dict() + out_dtypes = dict() for config in configs: try: with open(os.path.join(home_dir, 'configs', f'{config}.json'), 'r') as fp: cfg = json.load(fp) compilers.update(cfg.get('compilers', {})) composites.update(cfg.get('composites', {})) + out_dtypes.update(cfg.get('out_dtypes', {})) debug_funcs.update( dict(map(lambda pi: (pi[0], eval(pi[1])), cfg.get('debug_functions', {}).items())) @@ -34,17 +37,19 @@ def main(relay_file, output_filename, model_json, data_json, *configs, debug=Fal analysis_data = json.load(fp) analysis_data = dict(map(lambda pi: (int(pi[0]), pi[1]), analysis_data.items())) shape_dict = dict() + dtype_dict = dict() for args in source_model['main'].params: shape_dict[args.name_hint] = tuple(args.type_annotation.shape) - + dtype_dict[args.name_hint] = args.type_annotation.dtype recexpr_compiler = megraph.RecExprCompiler(composites, compilers, debug_funcs) - compiled_expr = recexpr_compiler.to_relay_expr(expr_data, shape_dict, analysis_data, use_debug_func=debug) + compiled_expr = recexpr_compiler.to_relay_expr(expr_data, shape_dict, dtype_dict, analysis_data, out_dtypes, use_debug_func=debug) mod = tvm.ir.IRModule.from_expr(compiled_expr) - # print(mod) + with open(output_filename, 'w') as f: + f.write(mod.astext()) mod = relay.transform.InferType()(mod) - mod = relay.transform.LambdaLift()(mod) + # mod = relay.transform.LambdaLift()(mod) mod = relay.transform.FoldConstant()(mod) - # mod = relay.transform.EliminateCommonSubexpr()(mod) + mod = relay.transform.EliminateCommonSubexpr()(mod) # mod = relay.transform.FuseOps()(mod) with open(output_filename, 'w') as fp: fp.write(mod.astext()) diff --git a/tests/flexmatch_eval.txt b/tests/flexmatch_eval.txt new file mode 100644 index 0000000..015207e --- /dev/null +++ b/tests/flexmatch_eval.txt @@ -0,0 +1,351 @@ +Output file written to: /root/flexmatch/tests/efficientnet-rewritten.json /root/flexmatch/tests/efficientnet-data.json +Compiled model saved to efficientnet-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col linear-rewrites +Step 2: Compiling back to Relay with efficientnet-rewritten.json and efficientnet-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 1160 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 35 +ALL Ops: 1134 +ALL Ops in overloads = 35 * #ops per pattern +Output file written to: /root/flexmatch/tests/efficientnet-rewritten.json /root/flexmatch/tests/efficientnet-data.json +Compiled model saved to efficientnet-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with hlscnn-conv2d +Step 2: Compiling back to Relay with efficientnet-rewritten.json and efficientnet-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 1160 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 35 +ALL Ops: 848 +ALL Ops in overloads = 35 * #ops per pattern +Output file written to: /root/flexmatch/tests/efficientnet-rewritten.json /root/flexmatch/tests/efficientnet-data.json +Compiled model saved to efficientnet-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col vta-dense +Step 2: Compiling back to Relay with efficientnet-rewritten.json and efficientnet-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 1160 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 9 +ALL Ops: 920 +ALL Ops in overloads = 9 * #ops per pattern +Output file written to: /root/flexmatch/tests/mobilenetv2-rewritten.json /root/flexmatch/tests/mobilenetv2-data.json +Compiled model saved to mobilenetv2-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col linear-rewrites +Step 2: Compiling back to Relay with mobilenetv2-rewritten.json and mobilenetv2-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 757 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 41 +ALL Ops: 1174 +ALL Ops in overloads = 41 * #ops per pattern +Output file written to: /root/flexmatch/tests/mobilenetv2-rewritten.json /root/flexmatch/tests/mobilenetv2-data.json +Compiled model saved to mobilenetv2-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with hlscnn-conv2d +Step 2: Compiling back to Relay with mobilenetv2-rewritten.json and mobilenetv2-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 757 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 40 +ALL Ops: 871 +ALL Ops in overloads = 40 * #ops per pattern +Output file written to: /root/flexmatch/tests/mobilenetv2-rewritten.json /root/flexmatch/tests/mobilenetv2-data.json +Compiled model saved to mobilenetv2-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col vta-dense +Step 2: Compiling back to Relay with mobilenetv2-rewritten.json and mobilenetv2-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 757 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 34 +ALL Ops: 1128 +ALL Ops in overloads = 34 * #ops per pattern +Output file written to: /root/flexmatch/tests/resmlp-rewritten.json /root/flexmatch/tests/resmlp-data.json +Compiled model saved to resmlp-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col linear-rewrites +Step 2: Compiling back to Relay with resmlp-rewritten.json and resmlp-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 343 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 38 +ALL Ops: 402 +ALL Ops in overloads = 38 * #ops per pattern +Output file written to: /root/flexmatch/tests/resmlp-rewritten.json /root/flexmatch/tests/resmlp-data.json +Compiled model saved to resmlp-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col vta-dense +Step 2: Compiling back to Relay with resmlp-rewritten.json and resmlp-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 343 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 0 +ALL Ops: 343 +ALL Ops in overloads = 0 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet20-rewritten.json /root/flexmatch/tests/resnet20-data.json +Compiled model saved to resnet20-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col linear-rewrites +Step 2: Compiling back to Relay with resnet20-rewritten.json and resnet20-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 294 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 22 +ALL Ops: 495 +ALL Ops in overloads = 22 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet20-rewritten.json /root/flexmatch/tests/resnet20-data.json +Compiled model saved to resnet20-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with hlscnn-conv2d +Step 2: Compiling back to Relay with resnet20-rewritten.json and resnet20-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 294 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 21 +ALL Ops: 325 +ALL Ops in overloads = 21 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet20-rewritten.json /root/flexmatch/tests/resnet20-data.json +Compiled model saved to resnet20-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col vta-dense +Step 2: Compiling back to Relay with resnet20-rewritten.json and resnet20-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 294 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 20 +ALL Ops: 485 +ALL Ops in overloads = 20 * #ops per pattern +Output file written to: /root/flexmatch/tests/mobilenetv2-rewritten.json /root/flexmatch/tests/mobilenetv2-data.json +Compiled model saved to mobilenetv2-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col vta-dense +Step 2: Compiling back to Relay with mobilenetv2-rewritten.json and mobilenetv2-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 757 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 34 +ALL Ops: 1128 +ALL Ops in overloads = 34 * #ops per pattern +Output file written to: /root/flexmatch/tests/lstm-for-pldi-rewritten.json /root/flexmatch/tests/lstm-for-pldi-data.json +Compiled model saved to lstm-for-pldi-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with flexasr-lstm +Step 2: Compiling back to Relay with lstm-for-pldi-rewritten.json and lstm-for-pldi-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 577 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 1 +ALL Ops: 12 +ALL Ops in overloads = 1 * #ops per pattern +Output file written to: /root/flexmatch/tests/lstm-for-pldi-rewritten.json /root/flexmatch/tests/lstm-for-pldi-data.json +Compiled model saved to lstm-for-pldi-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with hlscnn-conv2d +Step 2: Compiling back to Relay with lstm-for-pldi-rewritten.json and lstm-for-pldi-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 577 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 0 +ALL Ops: 543 +ALL Ops in overloads = 0 * #ops per pattern +Output file written to: /root/flexmatch/tests/lstm-for-pldi-rewritten.json /root/flexmatch/tests/lstm-for-pldi-data.json +Compiled model saved to lstm-for-pldi-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with vta-dense +Step 2: Compiling back to Relay with lstm-for-pldi-rewritten.json and lstm-for-pldi-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 577 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 0 +ALL Ops: 543 +ALL Ops in overloads = 0 * #ops per pattern +Output file written to: /root/flexmatch/tests/transformer-rewritten.json /root/flexmatch/tests/transformer-data.json +Compiled model saved to transformer-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with flexasr-lstm linear-rewrites +Step 2: Compiling back to Relay with transformer-rewritten.json and transformer-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 872 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 66 +ALL Ops: 801 +ALL Ops in overloads = 66 * #ops per pattern +Output file written to: /root/flexmatch/tests/transformer-rewritten.json /root/flexmatch/tests/transformer-data.json +Compiled model saved to transformer-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with hlscnn-conv2d +Step 2: Compiling back to Relay with transformer-rewritten.json and transformer-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 872 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 0 +ALL Ops: 867 +ALL Ops in overloads = 0 * #ops per pattern +Output file written to: /root/flexmatch/tests/transformer-rewritten.json /root/flexmatch/tests/transformer-data.json +Compiled model saved to transformer-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with vta-dense +Step 2: Compiling back to Relay with transformer-rewritten.json and transformer-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 872 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 66 +ALL Ops: 867 +ALL Ops in overloads = 66 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet50_simplifyinference_from_tf-rewritten.json /root/flexmatch/tests/resnet50_simplifyinference_from_tf-data.json +Compiled model saved to resnet50_simplifyinference_from_tf-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with flexasr-lstm im2col linear-rewrites +Step 2: Compiling back to Relay with resnet50_simplifyinference_from_tf-rewritten.json and resnet50_simplifyinference_from_tf-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 609 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 54 +ALL Ops: 1183 +ALL Ops in overloads = 54 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet50_simplifyinference_from_tf-rewritten.json /root/flexmatch/tests/resnet50_simplifyinference_from_tf-data.json +Compiled model saved to resnet50_simplifyinference_from_tf-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with hlscnn-conv2d +Step 2: Compiling back to Relay with resnet50_simplifyinference_from_tf-rewritten.json and resnet50_simplifyinference_from_tf-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 609 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 53 +ALL Ops: 766 +ALL Ops in overloads = 53 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet50_simplifyinference_from_tf-rewritten.json /root/flexmatch/tests/resnet50_simplifyinference_from_tf-data.json +Compiled model saved to resnet50_simplifyinference_from_tf-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col vta-dense +Step 2: Compiling back to Relay with resnet50_simplifyinference_from_tf-rewritten.json and resnet50_simplifyinference_from_tf-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 609 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 24 +ALL Ops: 951 +ALL Ops in overloads = 24 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet50_simplifyinference_from_pytorch-rewritten.json /root/flexmatch/tests/resnet50_simplifyinference_from_pytorch-data.json +Compiled model saved to resnet50_simplifyinference_from_pytorch-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with flexasr-lstm im2col linear-rewrites +Step 2: Compiling back to Relay with resnet50_simplifyinference_from_pytorch-rewritten.json and resnet50_simplifyinference_from_pytorch-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 709 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 54 +ALL Ops: 1233 +ALL Ops in overloads = 54 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet50_simplifyinference_from_pytorch-rewritten.json /root/flexmatch/tests/resnet50_simplifyinference_from_pytorch-data.json +Compiled model saved to resnet50_simplifyinference_from_pytorch-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with hlscnn-conv2d +Step 2: Compiling back to Relay with resnet50_simplifyinference_from_pytorch-rewritten.json and resnet50_simplifyinference_from_pytorch-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 709 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 53 +ALL Ops: 816 +ALL Ops in overloads = 53 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet50_simplifyinference_from_pytorch-rewritten.json /root/flexmatch/tests/resnet50_simplifyinference_from_pytorch-data.json +Compiled model saved to resnet50_simplifyinference_from_pytorch-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col vta-dense +Step 2: Compiling back to Relay with resnet50_simplifyinference_from_pytorch-rewritten.json and resnet50_simplifyinference_from_pytorch-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 709 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 24 +ALL Ops: 1001 +ALL Ops in overloads = 24 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet50_simplifyinference_from_onnx-rewritten.json /root/flexmatch/tests/resnet50_simplifyinference_from_onnx-data.json +Compiled model saved to resnet50_simplifyinference_from_onnx-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with flexasr-lstm im2col linear-rewrites +Step 2: Compiling back to Relay with resnet50_simplifyinference_from_onnx-rewritten.json and resnet50_simplifyinference_from_onnx-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 194 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 54 +ALL Ops: 614 +ALL Ops in overloads = 54 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet50_simplifyinference_from_onnx-rewritten.json /root/flexmatch/tests/resnet50_simplifyinference_from_onnx-data.json +Compiled model saved to resnet50_simplifyinference_from_onnx-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with hlscnn-conv2d +Step 2: Compiling back to Relay with resnet50_simplifyinference_from_onnx-rewritten.json and resnet50_simplifyinference_from_onnx-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 194 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 53 +ALL Ops: 197 +ALL Ops in overloads = 53 * #ops per pattern +Output file written to: /root/flexmatch/tests/resnet50_simplifyinference_from_onnx-rewritten.json /root/flexmatch/tests/resnet50_simplifyinference_from_onnx-data.json +Compiled model saved to resnet50_simplifyinference_from_onnx-rewritten.relay +Files already downloaded and verified +Step 1: Run EqSat with im2col vta-dense +Step 2: Compiling back to Relay with resnet50_simplifyinference_from_onnx-rewritten.json and resnet50_simplifyinference_from_onnx-data.json +Vanilla relay: +ALL overloads: 0 +ALL Ops: 194 +ALL Ops in overloads = 0 * #ops per pattern +EqSat model: +ALL overloads: 24 +ALL Ops: 382 +ALL Ops in overloads = 24 * #ops per pattern diff --git a/tests/get_table_stats.sh b/tests/get_table_stats.sh new file mode 100755 index 0000000..19e061e --- /dev/null +++ b/tests/get_table_stats.sh @@ -0,0 +1,58 @@ +#!/bin/bash +# FlexASR Linear with EfficientNet +python3 validate_compilation.py efficientnet --configs im2col linear-rewrites --get-stats +# HLSCNN on EfficientNet +python3 validate_compilation.py efficientnet --configs hlscnn-conv2d --get-stats +# VTA on EfficientNet +python3 validate_compilation.py efficientnet --configs im2col vta-dense --get-stats + +# MobileNet on FlexASR +python3 validate_compilation.py mobilenetv2 --configs im2col linear-rewrites --get-stats +# MobileNet on HLSCNN +python3 validate_compilation.py mobilenetv2 --configs hlscnn-conv2d --get-stats +# MobileNet on VTA +python3 validate_compilation.py mobilenetv2 --configs im2col vta-dense --get-stats + +# ResMLP on FlexASR +python3 validate_compilation.py resmlp --configs im2col linear-rewrites --get-stats +# ResMLP on VTA +python3 validate_compilation.py resmlp --configs im2col vta-dense --get-stats + +# ResNet20 on FlexASR +python3 validate_compilation.py resnet20 --configs im2col linear-rewrites --get-stats +# ResNet20 on HLSCNN +python3 validate_compilation.py resnet20 --configs hlscnn-conv2d --get-stats +# ResNet20 on VTA +python3 validate_compilation.py resnet20 --configs im2col vta-dense --get-stats + +# Q-MobileNet on VTA (quantize after matching) +python3 validate_compilation.py mobilenetv2 --configs im2col vta-dense --get-stats + +# LSTM on FlexASR +python3 validate_compilation.py lstm-for-pldi --configs flexasr-lstm --get-stats +# LSTM on HLSCNN +python3 validate_compilation.py lstm-for-pldi --configs hlscnn-conv2d --get-stats +# LSTM on VTA +python3 validate_compilation.py lstm-for-pldi --configs vta-dense --get-stats + +# Transformer on FlexASR +python3 validate_compilation.py transformer --configs flexasr-lstm linear-rewrites --get-stats +# Transformer on HLSCNN +python3 validate_compilation.py transformer --configs hlscnn-conv2d --get-stats +# Transformer on VTA +python3 validate_compilation.py transformer --configs vta-dense --get-stats + +# Resnet50 from tensorflow +python3 validate_compilation.py resnet50_simplifyinference_from_tf --configs flexasr-lstm im2col linear-rewrites --get-stats +python3 validate_compilation.py resnet50_simplifyinference_from_tf --configs hlscnn-conv2d --get-stats +python3 validate_compilation.py resnet50_simplifyinference_from_tf --configs im2col vta-dense --get-stats + +# Resnet50 from pytorch +python3 validate_compilation.py resnet50_simplifyinference_from_pytorch --configs flexasr-lstm im2col linear-rewrites --get-stats +python3 validate_compilation.py resnet50_simplifyinference_from_pytorch --configs hlscnn-conv2d --get-stats +python3 validate_compilation.py resnet50_simplifyinference_from_pytorch --configs im2col vta-dense --get-stats + +# Resnet50 from onnx +python3 validate_compilation.py resnet50_simplifyinference_from_onnx --configs flexasr-lstm im2col linear-rewrites --get-stats +python3 validate_compilation.py resnet50_simplifyinference_from_onnx --configs hlscnn-conv2d --get-stats +python3 validate_compilation.py resnet50_simplifyinference_from_onnx --configs im2col vta-dense --get-stats \ No newline at end of file diff --git a/tests/measure_vta_perf.py b/tests/measure_vta_perf.py new file mode 100644 index 0000000..bb3de8b --- /dev/null +++ b/tests/measure_vta_perf.py @@ -0,0 +1,153 @@ +import tvm +from tvm import relay + +import re + +from gluoncv.model_zoo import get_model +from tvm import autotvm +import numpy as np +import vta +from vta.testing import simulator +from vta.top import graph_pack +import torchvision +import torch + +from tvm import rpc +from tvm.contrib import utils, graph_executor +import subprocess + +env = vta.get_env() +# print(env.TARGET) +target = tvm.target.vta(model='tsim') + +remote = rpc.LocalSession() +ctx = remote.ext_dev(0) + +def load_model(filename: str): + """Load a model from a file.""" + with open(filename, "rb") as f: + return tvm.parser.fromtext(f.read()) + + +class Extractor(relay.ExprVisitor): + """Extract the VTA instructions from a Relay program.""" + def __init__(self, op_name): + super().__init__() + self.sizes = [] + self.weights = [] + self.paddings = [] + self.op_name = op_name + + def visit_call(self, call): + super().visit_call(call) + if call.op.name == "nn.conv2d": + print(call.args[0].checked_type.shape, call.args[1].checked_type.shape) + self.sizes.append((call.args[0].checked_type.shape, call.args[1].checked_type.shape)) + self.paddings.append(call.attrs['padding']) + + + +class DenseExtractor(relay.ExprVisitor): + def __init__(self): + super().__init__() + self.sizes = [] + + def visit_call(self, call): + if call.op.name == "nn.dense": + self.sizes.append((call.args[0].checked_type.shape, call.args[1].checked_type.shape)) + super().visit_call(call) + + +def profile_dense(model, filename): + # assume a relay model here + model = relay.transform.InferType()(model) + size_extractor = DenseExtractor() + size_extractor.visit(model['main']) + sizes = size_extractor.sizes + run_sizes = [] + results = [] + memo = {} + for data_size, weight_size in sizes: + if data_size[1] % 16 == 0 and weight_size[0] % 16 == 0: + print('Running on', data_size, weight_size) + k = f'{data_size} * {weight_size}' + if k in memo: + print('cached {}'.format(memo[k])) + run_sizes.append((data_size, weight_size)) + results.append(memo[k]) + continue + call = subprocess.run(["python3", "vta_tsim_dense.py", + str(data_size[0]), # batch + str(data_size[1]), # in_feat + str(weight_size[0])], # out_feat + stdout=subprocess.PIPE) + output = call.stdout.decode('utf-8') + print(output) + matched = re.match(r'(\d+)', output) + if matched: + print(f"Running {data_size} * {weight_size} took {int(output)}") + run_sizes.append((data_size, weight_size)) + results.append(int(matched.group(1))) + memo[k] = int(matched.group(1)) + else: + print(f"failed to exec: {data_size} * {weight_size}") + with open(filename, 'w') as f: + for (data_size, weight_size) in run_sizes: + f.write(f"{data_size}x{weight_size}\n") + for result in results: + f.write(f"{result}\n") + + +def profile_perf(model, backend='mxnet', filename='conv2d_perf.txt'): + if backend == 'mxnet': + mod, params = relay.frontend.from_mxnet(model, {"data": (1, 3, 32, 32)}) + elif backend == 'pytorch': + torch_trace = torch.jit.trace(model, torch.rand(1, 3, 32, 32)) + mod, params = relay.frontend.from_pytorch(torch_trace, [("data", (1, 3, 32, 32))]) + model = relay.transform.InferType()(mod) + model = relay.transform.SimplifyInference()(model) + # print(model) + size_extractor = Extractor("nn.conv2d") + size_extractor.visit(model['main']) + sizes = size_extractor.sizes + + for (data_size, wgt_size), padding in zip(sizes, size_extractor.paddings): + call = subprocess.run(["python3", "vta_tsim_conv2d.py", + str(data_size[0]), # N + str(data_size[1]), # C + str(data_size[2]), # H + str(data_size[3]), # W + str(wgt_size[0]), # O + str(wgt_size[2]), # H + str(wgt_size[3]), # W + str(padding[0]), str(padding[1]), str(padding[2]), str(padding[3])], + stdout=subprocess.PIPE) + output = call.stdout.decode('utf-8') + matched = re.match(r'(\d+)', output) + print(output) + with open(filename, "a") as f: + if matched: + f.write(f"{data_size}x{wgt_size} {output}") + else: + print(f"failed to exec: {data_size} * {wgt_size}") + + +def main(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("model", type=str, help="The model to profile.") + args = parser.parse_args() + if args.model == 'resnet': + # model = get_model('cifar_resnet20_v2', pretrained=True, classes=10) + # backend = 'mxnet' + filename = 'resnet_dense_perf.txt' + model = load_model('mxnet-resnet.relay') + elif args.model == 'mobilenet': + model = load_model('mobilenetv2-rewritten.relay') + # model.eval() + # backend = 'pytorch' + filename = 'mobilenet_dense_perf.txt' + profile_dense(model, filename) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tests/models/__init__.py b/tests/models/__init__.py new file mode 100644 index 0000000..90f60fd --- /dev/null +++ b/tests/models/__init__.py @@ -0,0 +1 @@ +from .utils import * \ No newline at end of file diff --git a/tests/models/bert.relay b/tests/models/bert.relay new file mode 100644 index 0000000..f385678 --- /dev/null +++ b/tests/models/bert.relay @@ -0,0 +1,11884 @@ +#[version = "0.0.5"] +def @main(%input_ids: Tensor[(?, 384), int64], %input_mask: Tensor[(?, 384), int64], %segment_ids: Tensor[(?, 384), int64], %bert_embeddings_LayerNorm_bias: Tensor[(1024), float32], %bert_embeddings_LayerNorm_weight: Tensor[(1024), float32], %bert_embeddings_position_embeddings_weight: Tensor[(512, 1024), float32], %bert_embeddings_token_type_embeddings_weight: Tensor[(2, 1024), float32], %bert_embeddings_word_embeddings_weight: Tensor[(30522, 1024), float32], %bert_encoder_layer_0_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_0_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_0_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_0_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_0_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_0_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_0_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_0_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_0_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_0_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_0_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_0_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_0_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_0_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_0_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_0_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_1_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_1_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_1_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_1_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_1_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_1_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_1_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_1_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_1_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_1_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_1_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_1_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_1_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_1_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_1_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_1_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_10_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_10_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_10_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_10_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_10_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_10_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_10_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_10_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_10_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_10_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_10_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_10_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_10_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_10_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_10_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_10_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_11_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_11_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_11_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_11_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_11_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_11_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_11_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_11_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_11_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_11_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_11_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_11_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_11_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_11_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_11_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_11_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_12_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_12_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_12_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_12_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_12_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_12_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_12_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_12_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_12_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_12_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_12_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_12_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_12_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_12_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_12_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_12_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_13_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_13_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_13_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_13_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_13_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_13_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_13_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_13_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_13_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_13_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_13_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_13_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_13_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_13_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_13_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_13_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_14_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_14_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_14_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_14_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_14_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_14_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_14_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_14_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_14_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_14_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_14_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_14_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_14_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_14_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_14_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_14_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_15_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_15_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_15_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_15_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_15_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_15_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_15_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_15_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_15_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_15_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_15_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_15_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_15_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_15_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_15_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_15_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_16_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_16_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_16_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_16_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_16_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_16_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_16_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_16_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_16_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_16_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_16_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_16_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_16_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_16_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_16_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_16_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_17_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_17_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_17_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_17_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_17_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_17_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_17_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_17_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_17_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_17_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_17_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_17_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_17_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_17_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_17_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_17_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_18_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_18_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_18_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_18_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_18_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_18_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_18_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_18_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_18_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_18_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_18_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_18_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_18_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_18_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_18_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_18_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_19_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_19_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_19_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_19_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_19_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_19_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_19_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_19_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_19_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_19_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_19_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_19_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_19_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_19_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_19_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_19_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_2_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_2_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_2_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_2_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_2_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_2_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_2_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_2_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_2_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_2_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_2_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_2_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_2_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_2_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_2_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_2_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_20_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_20_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_20_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_20_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_20_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_20_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_20_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_20_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_20_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_20_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_20_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_20_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_20_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_20_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_20_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_20_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_21_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_21_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_21_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_21_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_21_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_21_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_21_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_21_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_21_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_21_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_21_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_21_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_21_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_21_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_21_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_21_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_22_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_22_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_22_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_22_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_22_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_22_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_22_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_22_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_22_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_22_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_22_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_22_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_22_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_22_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_22_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_22_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_23_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_23_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_23_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_23_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_23_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_23_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_23_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_23_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_23_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_23_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_23_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_23_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_23_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_23_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_23_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_23_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_3_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_3_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_3_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_3_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_3_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_3_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_3_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_3_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_3_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_3_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_3_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_3_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_3_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_3_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_3_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_3_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_4_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_4_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_4_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_4_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_4_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_4_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_4_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_4_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_4_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_4_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_4_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_4_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_4_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_4_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_4_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_4_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_5_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_5_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_5_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_5_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_5_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_5_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_5_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_5_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_5_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_5_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_5_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_5_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_5_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_5_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_5_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_5_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_6_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_6_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_6_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_6_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_6_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_6_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_6_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_6_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_6_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_6_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_6_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_6_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_6_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_6_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_6_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_6_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_7_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_7_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_7_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_7_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_7_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_7_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_7_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_7_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_7_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_7_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_7_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_7_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_7_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_7_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_7_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_7_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_8_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_8_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_8_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_8_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_8_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_8_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_8_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_8_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_8_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_8_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_8_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_8_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_8_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_8_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_8_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_8_output_dense_weight: Tensor[(1024, 4096), float32], %bert_encoder_layer_9_attention_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_9_attention_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_9_attention_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_9_attention_output_dense_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_9_attention_self_key_bias: Tensor[(1024), float32], %bert_encoder_layer_9_attention_self_key_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_9_attention_self_query_bias: Tensor[(1024), float32], %bert_encoder_layer_9_attention_self_query_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_9_attention_self_value_bias: Tensor[(1024), float32], %bert_encoder_layer_9_attention_self_value_weight: Tensor[(1024, 1024), float32], %bert_encoder_layer_9_intermediate_dense_bias: Tensor[(4096), float32], %bert_encoder_layer_9_intermediate_dense_weight: Tensor[(4096, 1024), float32], %bert_encoder_layer_9_output_LayerNorm_bias: Tensor[(1024), float32], %bert_encoder_layer_9_output_LayerNorm_weight: Tensor[(1024), float32], %bert_encoder_layer_9_output_dense_bias: Tensor[(1024), float32], %bert_encoder_layer_9_output_dense_weight: Tensor[(1024, 4096), float32], %qa_outputs_bias: Tensor[(2), float32], %qa_outputs_weight: Tensor[(2, 1024), float32]) { + %0 = shape_of(%bert_embeddings_word_embeddings_weight, dtype="int64"); + %1 = take(%0, 0); + %2 = less(%input_ids, 0); + %3 = add(%input_ids, %1); + %4 = where(%2, %3, %input_ids); + %5 = shape_of(%input_ids, dtype="int64"); + %6 = take(%5, 1, axis=0); + %7 = cast(%6, dtype="int64"); + %8 = arange(0, %7, 1, start=meta[relay.Constant][0], stop=meta[relay.Call][0], step=meta[relay.Constant][1], dtype="int64"); + %9 = expand_dims(%8, axis=0); + %10 = shape_of(%input_ids, dtype="int64"); + %11 = take(%10, 0, axis=0); + %12 = expand_dims(%11, axis=0); + %13 = expand_dims(%6, axis=0); + %14 = (%12, %13); + %15 = shape_of(%9, dtype="int64"); + %16 = concatenate(%14); + %17 = maximum(%15, %16); + %18 = dyn.broadcast_to(%9, %17, meta[relay.attrs.InitOpAttrs][0]); + %19 = shape_of(%bert_embeddings_position_embeddings_weight, dtype="int64"); + %20 = take(%19, 0); + %21 = less(%18, 0); + %22 = add(%18, %20); + %23 = where(%21, %22, %18); + %24 = take(%bert_embeddings_word_embeddings_weight, %4, axis=0); + %25 = take(%bert_embeddings_position_embeddings_weight, %23, axis=0); + %26 = shape_of(%bert_embeddings_token_type_embeddings_weight, dtype="int64"); + %27 = take(%26, 0); + %28 = less(%segment_ids, 0); + %29 = add(%segment_ids, %27); + %30 = where(%28, %29, %segment_ids); + %31 = add(%24, %25); + %32 = take(%bert_embeddings_token_type_embeddings_weight, %30, axis=0); + %33 = add(%31, %32); + %34 = mean(%33, axis=[-1], keepdims=True); + %35 = subtract(%33, %34); + %36 = power(%35, 2f); + %37 = mean(%36, axis=[-1], keepdims=True); + %38 = add(%37, 1e-12f); + %39 = sqrt(%38); + %40 = divide(%35, %39); + %41 = multiply(%40, %bert_embeddings_LayerNorm_weight); + %42 = add(%41, %bert_embeddings_LayerNorm_bias); + %43 = shape_of(%42, dtype="int64"); + %44 = strided_slice(%43, begin=[1], end=[3], strides=[1]); + %45 = (meta[relay.Constant][2], %44); + %46 = concatenate(%45); + %47 = transpose(%bert_encoder_layer_0_attention_self_query_weight, axes=[1, 0]); + %48 = reshape(%47, newshape=[-1, 1024, 1024]); + %49 = dyn.reshape(%42, %46, newshape=[]); + %50 = transpose(%48, axes=[0, 2, 1]); + %51 = strided_slice(%43, begin=[0], end=[1], strides=[1]); + %52 = strided_slice(%43, begin=[1], end=[2], strides=[1]); + %53 = (%51, %52, meta[relay.Constant][3]); + %54 = nn.batch_matmul(%49, %50, meta[relay.attrs.BatchMatmulAttrs][0]); + %55 = concatenate(%53); + %56 = dyn.reshape(%54, %55, newshape=[]); + %57 = add(%56, %bert_encoder_layer_0_attention_self_query_bias); + %58 = shape_of(%57, dtype="int64"); + %59 = take(%58, 0, axis=0); + %60 = shape_of(%57, dtype="int64"); + %61 = take(%60, 1, axis=0); + %62 = expand_dims(%59, axis=0); + %63 = expand_dims(%61, axis=0); + %64 = (%62, %63, meta[relay.Constant][4], meta[relay.Constant][5]); + %65 = concatenate(%64); + %66 = dyn.reshape(%57, %65, newshape=[]); + %67 = transpose(%66, axes=[0, 2, 1, 3]); + %68 = shape_of(%67, dtype="int64"); + %69 = strided_slice(%68, begin=[2], end=[4], strides=[1]); + %70 = (meta[relay.Constant][6], %69); + %71 = concatenate(%70); + %72 = shape_of(%42, dtype="int64"); + %73 = strided_slice(%72, begin=[1], end=[3], strides=[1]); + %74 = (meta[relay.Constant][7], %73); + %75 = concatenate(%74); + %76 = transpose(%bert_encoder_layer_0_attention_self_key_weight, axes=[1, 0]); + %77 = reshape(%76, newshape=[-1, 1024, 1024]); + %78 = dyn.reshape(%42, %75, newshape=[]); + %79 = transpose(%77, axes=[0, 2, 1]); + %80 = strided_slice(%72, begin=[0], end=[1], strides=[1]); + %81 = strided_slice(%72, begin=[1], end=[2], strides=[1]); + %82 = (%80, %81, meta[relay.Constant][8]); + %83 = nn.batch_matmul(%78, %79, meta[relay.attrs.BatchMatmulAttrs][1]); + %84 = concatenate(%82); + %85 = dyn.reshape(%83, %84, newshape=[]); + %86 = add(%85, %bert_encoder_layer_0_attention_self_key_bias); + %87 = shape_of(%86, dtype="int64"); + %88 = take(%87, 0, axis=0); + %89 = shape_of(%86, dtype="int64"); + %90 = take(%89, 1, axis=0); + %91 = expand_dims(%88, axis=0); + %92 = expand_dims(%90, axis=0); + %93 = (%91, %92, meta[relay.Constant][9], meta[relay.Constant][10]); + %94 = concatenate(%93); + %95 = dyn.reshape(%86, %94, newshape=[]); + %96 = transpose(%95, axes=[0, 2, 3, 1]); + %97 = shape_of(%96, dtype="int64"); + %98 = strided_slice(%97, begin=[2], end=[4], strides=[1]); + %99 = (meta[relay.Constant][11], %98); + %100 = concatenate(%99); + %101 = dyn.reshape(%96, %100, newshape=[]); + %102 = dyn.reshape(%67, %71, newshape=[]); + %103 = transpose(%101, axes=[0, 2, 1]); + %104 = strided_slice(%68, begin=[0], end=[1], strides=[1]); + %105 = strided_slice(%97, begin=[0], end=[1], strides=[1]); + %106 = strided_slice(%68, begin=[1], end=[2], strides=[1]); + %107 = strided_slice(%97, begin=[1], end=[2], strides=[1]); + %108 = maximum(%104, %105); + %109 = maximum(%106, %107); + %110 = (%108, %109); + %111 = concatenate(%110); + %112 = strided_slice(%68, begin=[2], end=[3], strides=[1]); + %113 = strided_slice(%97, begin=[3], end=[4], strides=[1]); + %114 = (%111, %112, %113); + %115 = nn.batch_matmul(%102, %103, meta[relay.attrs.BatchMatmulAttrs][2]); + %116 = concatenate(%114); + %117 = dyn.reshape(%115, %116, newshape=[]); + %118 = expand_dims(%input_mask, axis=1); + %119 = expand_dims(%118, axis=2); + %120 = cast(%119, dtype="float32"); + %121 = subtract(1f, %120); + %122 = divide(%117, 8f); + %123 = multiply(%121, -10000f); + %124 = add(%122, %123); + %125 = max(%124, axis=[3], keepdims=True); + %126 = subtract(%124, %125); + %127 = exp(%126); + %128 = sum(%127, axis=[3], keepdims=True); + %129 = divide(%127, %128); + %130 = shape_of(%129, dtype="int64"); + %131 = strided_slice(%130, begin=[2], end=[4], strides=[1]); + %132 = (meta[relay.Constant][12], %131); + %133 = concatenate(%132); + %134 = shape_of(%42, dtype="int64"); + %135 = strided_slice(%134, begin=[1], end=[3], strides=[1]); + %136 = (meta[relay.Constant][13], %135); + %137 = concatenate(%136); + %138 = transpose(%bert_encoder_layer_0_attention_self_value_weight, axes=[1, 0]); + %139 = reshape(%138, newshape=[-1, 1024, 1024]); + %140 = dyn.reshape(%42, %137, newshape=[]); + %141 = transpose(%139, axes=[0, 2, 1]); + %142 = strided_slice(%134, begin=[0], end=[1], strides=[1]); + %143 = strided_slice(%134, begin=[1], end=[2], strides=[1]); + %144 = (%142, %143, meta[relay.Constant][14]); + %145 = nn.batch_matmul(%140, %141, meta[relay.attrs.BatchMatmulAttrs][3]); + %146 = concatenate(%144); + %147 = dyn.reshape(%145, %146, newshape=[]); + %148 = add(%147, %bert_encoder_layer_0_attention_self_value_bias); + %149 = shape_of(%148, dtype="int64"); + %150 = take(%149, 0, axis=0); + %151 = shape_of(%148, dtype="int64"); + %152 = take(%151, 1, axis=0); + %153 = expand_dims(%150, axis=0); + %154 = expand_dims(%152, axis=0); + %155 = (%153, %154, meta[relay.Constant][15], meta[relay.Constant][16]); + %156 = concatenate(%155); + %157 = dyn.reshape(%148, %156, newshape=[]); + %158 = transpose(%157, axes=[0, 2, 1, 3]); + %159 = shape_of(%158, dtype="int64"); + %160 = strided_slice(%159, begin=[2], end=[4], strides=[1]); + %161 = (meta[relay.Constant][17], %160); + %162 = concatenate(%161); + %163 = dyn.reshape(%158, %162, newshape=[]); + %164 = dyn.reshape(%129, %133, newshape=[]); + %165 = transpose(%163, axes=[0, 2, 1]); + %166 = strided_slice(%130, begin=[0], end=[1], strides=[1]); + %167 = strided_slice(%159, begin=[0], end=[1], strides=[1]); + %168 = strided_slice(%130, begin=[1], end=[2], strides=[1]); + %169 = strided_slice(%159, begin=[1], end=[2], strides=[1]); + %170 = maximum(%166, %167); + %171 = maximum(%168, %169); + %172 = (%170, %171); + %173 = concatenate(%172); + %174 = strided_slice(%130, begin=[2], end=[3], strides=[1]); + %175 = strided_slice(%159, begin=[3], end=[4], strides=[1]); + %176 = (%173, %174, %175); + %177 = nn.batch_matmul(%164, %165, meta[relay.attrs.BatchMatmulAttrs][4]); + %178 = concatenate(%176); + %179 = dyn.reshape(%177, %178, newshape=[]); + %180 = transpose(%179, axes=[0, 2, 1, 3]); + %181 = shape_of(%180, dtype="int64"); + %182 = take(%181, 0, axis=0); + %183 = shape_of(%180, dtype="int64"); + %184 = take(%183, 1, axis=0); + %185 = expand_dims(%182, axis=0); + %186 = expand_dims(%184, axis=0); + %187 = (%185, %186, meta[relay.Constant][18]); + %188 = concatenate(%187); + %189 = dyn.reshape(%180, %188, newshape=[]); + %190 = shape_of(%189, dtype="int64"); + %191 = strided_slice(%190, begin=[1], end=[3], strides=[1]); + %192 = (meta[relay.Constant][19], %191); + %193 = concatenate(%192); + %194 = transpose(%bert_encoder_layer_0_attention_output_dense_weight, axes=[1, 0]); + %195 = reshape(%194, newshape=[-1, 1024, 1024]); + %196 = dyn.reshape(%189, %193, newshape=[]); + %197 = transpose(%195, axes=[0, 2, 1]); + %198 = strided_slice(%190, begin=[0], end=[1], strides=[1]); + %199 = strided_slice(%190, begin=[1], end=[2], strides=[1]); + %200 = (%198, %199, meta[relay.Constant][20]); + %201 = nn.batch_matmul(%196, %197, meta[relay.attrs.BatchMatmulAttrs][5]); + %202 = concatenate(%200); + %203 = dyn.reshape(%201, %202, newshape=[]); + %204 = add(%203, %bert_encoder_layer_0_attention_output_dense_bias); + %205 = add(%204, %42); + %206 = mean(%205, axis=[-1], keepdims=True); + %207 = subtract(%205, %206); + %208 = power(%207, 2f); + %209 = mean(%208, axis=[-1], keepdims=True); + %210 = add(%209, 1e-12f); + %211 = sqrt(%210); + %212 = divide(%207, %211); + %213 = multiply(%212, %bert_encoder_layer_0_attention_output_LayerNorm_weight); + %214 = add(%213, %bert_encoder_layer_0_attention_output_LayerNorm_bias); + %215 = shape_of(%214, dtype="int64"); + %216 = strided_slice(%215, begin=[1], end=[3], strides=[1]); + %217 = (meta[relay.Constant][21], %216); + %218 = concatenate(%217); + %219 = transpose(%bert_encoder_layer_0_intermediate_dense_weight, axes=[1, 0]); + %220 = reshape(%219, newshape=[-1, 1024, 4096]); + %221 = dyn.reshape(%214, %218, newshape=[]); + %222 = transpose(%220, axes=[0, 2, 1]); + %223 = strided_slice(%215, begin=[0], end=[1], strides=[1]); + %224 = strided_slice(%215, begin=[1], end=[2], strides=[1]); + %225 = (%223, %224, meta[relay.Constant][22]); + %226 = nn.batch_matmul(%221, %222, meta[relay.attrs.BatchMatmulAttrs][6]); + %227 = concatenate(%225); + %228 = dyn.reshape(%226, %227, newshape=[]); + %229 = add(%228, %bert_encoder_layer_0_intermediate_dense_bias); + %230 = divide(%229, 1.41421f); + %231 = erf(%230); + %232 = multiply(%229, 0.5f); + %233 = add(%231, 1f); + %234 = multiply(%232, %233); + %235 = shape_of(%234, dtype="int64"); + %236 = strided_slice(%235, begin=[1], end=[3], strides=[1]); + %237 = (meta[relay.Constant][23], %236); + %238 = concatenate(%237); + %239 = transpose(%bert_encoder_layer_0_output_dense_weight, axes=[1, 0]); + %240 = reshape(%239, newshape=[-1, 4096, 1024]); + %241 = dyn.reshape(%234, %238, newshape=[]); + %242 = transpose(%240, axes=[0, 2, 1]); + %243 = strided_slice(%235, begin=[0], end=[1], strides=[1]); + %244 = strided_slice(%235, begin=[1], end=[2], strides=[1]); + %245 = (%243, %244, meta[relay.Constant][24]); + %246 = nn.batch_matmul(%241, %242, meta[relay.attrs.BatchMatmulAttrs][7]); + %247 = concatenate(%245); + %248 = dyn.reshape(%246, %247, newshape=[]); + %249 = add(%248, %bert_encoder_layer_0_output_dense_bias); + %250 = add(%249, %214); + %251 = mean(%250, axis=[-1], keepdims=True); + %252 = subtract(%250, %251); + %253 = power(%252, 2f); + %254 = mean(%253, axis=[-1], keepdims=True); + %255 = add(%254, 1e-12f); + %256 = sqrt(%255); + %257 = divide(%252, %256); + %258 = multiply(%257, %bert_encoder_layer_0_output_LayerNorm_weight); + %259 = add(%258, %bert_encoder_layer_0_output_LayerNorm_bias); + %260 = shape_of(%259, dtype="int64"); + %261 = strided_slice(%260, begin=[1], end=[3], strides=[1]); + %262 = (meta[relay.Constant][25], %261); + %263 = concatenate(%262); + %264 = transpose(%bert_encoder_layer_1_attention_self_query_weight, axes=[1, 0]); + %265 = reshape(%264, newshape=[-1, 1024, 1024]); + %266 = dyn.reshape(%259, %263, newshape=[]); + %267 = transpose(%265, axes=[0, 2, 1]); + %268 = strided_slice(%260, begin=[0], end=[1], strides=[1]); + %269 = strided_slice(%260, begin=[1], end=[2], strides=[1]); + %270 = (%268, %269, meta[relay.Constant][26]); + %271 = nn.batch_matmul(%266, %267, meta[relay.attrs.BatchMatmulAttrs][8]); + %272 = concatenate(%270); + %273 = dyn.reshape(%271, %272, newshape=[]); + %274 = add(%273, %bert_encoder_layer_1_attention_self_query_bias); + %275 = shape_of(%274, dtype="int64"); + %276 = take(%275, 0, axis=0); + %277 = shape_of(%274, dtype="int64"); + %278 = take(%277, 1, axis=0); + %279 = expand_dims(%276, axis=0); + %280 = expand_dims(%278, axis=0); + %281 = (%279, %280, meta[relay.Constant][27], meta[relay.Constant][28]); + %282 = concatenate(%281); + %283 = dyn.reshape(%274, %282, newshape=[]); + %284 = transpose(%283, axes=[0, 2, 1, 3]); + %285 = shape_of(%284, dtype="int64"); + %286 = strided_slice(%285, begin=[2], end=[4], strides=[1]); + %287 = (meta[relay.Constant][29], %286); + %288 = concatenate(%287); + %289 = shape_of(%259, dtype="int64"); + %290 = strided_slice(%289, begin=[1], end=[3], strides=[1]); + %291 = (meta[relay.Constant][30], %290); + %292 = concatenate(%291); + %293 = transpose(%bert_encoder_layer_1_attention_self_key_weight, axes=[1, 0]); + %294 = reshape(%293, newshape=[-1, 1024, 1024]); + %295 = dyn.reshape(%259, %292, newshape=[]); + %296 = transpose(%294, axes=[0, 2, 1]); + %297 = strided_slice(%289, begin=[0], end=[1], strides=[1]); + %298 = strided_slice(%289, begin=[1], end=[2], strides=[1]); + %299 = (%297, %298, meta[relay.Constant][31]); + %300 = nn.batch_matmul(%295, %296, meta[relay.attrs.BatchMatmulAttrs][9]); + %301 = concatenate(%299); + %302 = dyn.reshape(%300, %301, newshape=[]); + %303 = add(%302, %bert_encoder_layer_1_attention_self_key_bias); + %304 = shape_of(%303, dtype="int64"); + %305 = take(%304, 0, axis=0); + %306 = shape_of(%303, dtype="int64"); + %307 = take(%306, 1, axis=0); + %308 = expand_dims(%305, axis=0); + %309 = expand_dims(%307, axis=0); + %310 = (%308, %309, meta[relay.Constant][32], meta[relay.Constant][33]); + %311 = concatenate(%310); + %312 = dyn.reshape(%303, %311, newshape=[]); + %313 = transpose(%312, axes=[0, 2, 3, 1]); + %314 = shape_of(%313, dtype="int64"); + %315 = strided_slice(%314, begin=[2], end=[4], strides=[1]); + %316 = (meta[relay.Constant][34], %315); + %317 = concatenate(%316); + %318 = dyn.reshape(%313, %317, newshape=[]); + %319 = dyn.reshape(%284, %288, newshape=[]); + %320 = transpose(%318, axes=[0, 2, 1]); + %321 = strided_slice(%285, begin=[0], end=[1], strides=[1]); + %322 = strided_slice(%314, begin=[0], end=[1], strides=[1]); + %323 = strided_slice(%285, begin=[1], end=[2], strides=[1]); + %324 = strided_slice(%314, begin=[1], end=[2], strides=[1]); + %325 = maximum(%321, %322); + %326 = maximum(%323, %324); + %327 = (%325, %326); + %328 = concatenate(%327); + %329 = strided_slice(%285, begin=[2], end=[3], strides=[1]); + %330 = strided_slice(%314, begin=[3], end=[4], strides=[1]); + %331 = (%328, %329, %330); + %332 = nn.batch_matmul(%319, %320, meta[relay.attrs.BatchMatmulAttrs][10]); + %333 = concatenate(%331); + %334 = dyn.reshape(%332, %333, newshape=[]); + %335 = divide(%334, 8f); + %336 = add(%335, %123); + %337 = max(%336, axis=[3], keepdims=True); + %338 = subtract(%336, %337); + %339 = exp(%338); + %340 = sum(%339, axis=[3], keepdims=True); + %341 = divide(%339, %340); + %342 = shape_of(%341, dtype="int64"); + %343 = strided_slice(%342, begin=[2], end=[4], strides=[1]); + %344 = (meta[relay.Constant][35], %343); + %345 = concatenate(%344); + %346 = shape_of(%259, dtype="int64"); + %347 = strided_slice(%346, begin=[1], end=[3], strides=[1]); + %348 = (meta[relay.Constant][36], %347); + %349 = concatenate(%348); + %350 = transpose(%bert_encoder_layer_1_attention_self_value_weight, axes=[1, 0]); + %351 = reshape(%350, newshape=[-1, 1024, 1024]); + %352 = dyn.reshape(%259, %349, newshape=[]); + %353 = transpose(%351, axes=[0, 2, 1]); + %354 = strided_slice(%346, begin=[0], end=[1], strides=[1]); + %355 = strided_slice(%346, begin=[1], end=[2], strides=[1]); + %356 = (%354, %355, meta[relay.Constant][37]); + %357 = nn.batch_matmul(%352, %353, meta[relay.attrs.BatchMatmulAttrs][11]); + %358 = concatenate(%356); + %359 = dyn.reshape(%357, %358, newshape=[]); + %360 = add(%359, %bert_encoder_layer_1_attention_self_value_bias); + %361 = shape_of(%360, dtype="int64"); + %362 = take(%361, 0, axis=0); + %363 = shape_of(%360, dtype="int64"); + %364 = take(%363, 1, axis=0); + %365 = expand_dims(%362, axis=0); + %366 = expand_dims(%364, axis=0); + %367 = (%365, %366, meta[relay.Constant][38], meta[relay.Constant][39]); + %368 = concatenate(%367); + %369 = dyn.reshape(%360, %368, newshape=[]); + %370 = transpose(%369, axes=[0, 2, 1, 3]); + %371 = shape_of(%370, dtype="int64"); + %372 = strided_slice(%371, begin=[2], end=[4], strides=[1]); + %373 = (meta[relay.Constant][40], %372); + %374 = concatenate(%373); + %375 = dyn.reshape(%370, %374, newshape=[]); + %376 = dyn.reshape(%341, %345, newshape=[]); + %377 = transpose(%375, axes=[0, 2, 1]); + %378 = strided_slice(%342, begin=[0], end=[1], strides=[1]); + %379 = strided_slice(%371, begin=[0], end=[1], strides=[1]); + %380 = strided_slice(%342, begin=[1], end=[2], strides=[1]); + %381 = strided_slice(%371, begin=[1], end=[2], strides=[1]); + %382 = maximum(%378, %379); + %383 = maximum(%380, %381); + %384 = (%382, %383); + %385 = concatenate(%384); + %386 = strided_slice(%342, begin=[2], end=[3], strides=[1]); + %387 = strided_slice(%371, begin=[3], end=[4], strides=[1]); + %388 = (%385, %386, %387); + %389 = nn.batch_matmul(%376, %377, meta[relay.attrs.BatchMatmulAttrs][12]); + %390 = concatenate(%388); + %391 = dyn.reshape(%389, %390, newshape=[]); + %392 = transpose(%391, axes=[0, 2, 1, 3]); + %393 = shape_of(%392, dtype="int64"); + %394 = take(%393, 0, axis=0); + %395 = shape_of(%392, dtype="int64"); + %396 = take(%395, 1, axis=0); + %397 = expand_dims(%394, axis=0); + %398 = expand_dims(%396, axis=0); + %399 = (%397, %398, meta[relay.Constant][41]); + %400 = concatenate(%399); + %401 = dyn.reshape(%392, %400, newshape=[]); + %402 = shape_of(%401, dtype="int64"); + %403 = strided_slice(%402, begin=[1], end=[3], strides=[1]); + %404 = (meta[relay.Constant][42], %403); + %405 = concatenate(%404); + %406 = transpose(%bert_encoder_layer_1_attention_output_dense_weight, axes=[1, 0]); + %407 = reshape(%406, newshape=[-1, 1024, 1024]); + %408 = dyn.reshape(%401, %405, newshape=[]); + %409 = transpose(%407, axes=[0, 2, 1]); + %410 = strided_slice(%402, begin=[0], end=[1], strides=[1]); + %411 = strided_slice(%402, begin=[1], end=[2], strides=[1]); + %412 = (%410, %411, meta[relay.Constant][43]); + %413 = nn.batch_matmul(%408, %409, meta[relay.attrs.BatchMatmulAttrs][13]); + %414 = concatenate(%412); + %415 = dyn.reshape(%413, %414, newshape=[]); + %416 = add(%415, %bert_encoder_layer_1_attention_output_dense_bias); + %417 = add(%416, %259); + %418 = mean(%417, axis=[-1], keepdims=True); + %419 = subtract(%417, %418); + %420 = power(%419, 2f); + %421 = mean(%420, axis=[-1], keepdims=True); + %422 = add(%421, 1e-12f); + %423 = sqrt(%422); + %424 = divide(%419, %423); + %425 = multiply(%424, %bert_encoder_layer_1_attention_output_LayerNorm_weight); + %426 = add(%425, %bert_encoder_layer_1_attention_output_LayerNorm_bias); + %427 = shape_of(%426, dtype="int64"); + %428 = strided_slice(%427, begin=[1], end=[3], strides=[1]); + %429 = (meta[relay.Constant][44], %428); + %430 = concatenate(%429); + %431 = transpose(%bert_encoder_layer_1_intermediate_dense_weight, axes=[1, 0]); + %432 = reshape(%431, newshape=[-1, 1024, 4096]); + %433 = dyn.reshape(%426, %430, newshape=[]); + %434 = transpose(%432, axes=[0, 2, 1]); + %435 = strided_slice(%427, begin=[0], end=[1], strides=[1]); + %436 = strided_slice(%427, begin=[1], end=[2], strides=[1]); + %437 = (%435, %436, meta[relay.Constant][45]); + %438 = nn.batch_matmul(%433, %434, meta[relay.attrs.BatchMatmulAttrs][14]); + %439 = concatenate(%437); + %440 = dyn.reshape(%438, %439, newshape=[]); + %441 = add(%440, %bert_encoder_layer_1_intermediate_dense_bias); + %442 = divide(%441, 1.41421f); + %443 = erf(%442); + %444 = multiply(%441, 0.5f); + %445 = add(%443, 1f); + %446 = multiply(%444, %445); + %447 = shape_of(%446, dtype="int64"); + %448 = strided_slice(%447, begin=[1], end=[3], strides=[1]); + %449 = (meta[relay.Constant][46], %448); + %450 = concatenate(%449); + %451 = transpose(%bert_encoder_layer_1_output_dense_weight, axes=[1, 0]); + %452 = reshape(%451, newshape=[-1, 4096, 1024]); + %453 = dyn.reshape(%446, %450, newshape=[]); + %454 = transpose(%452, axes=[0, 2, 1]); + %455 = strided_slice(%447, begin=[0], end=[1], strides=[1]); + %456 = strided_slice(%447, begin=[1], end=[2], strides=[1]); + %457 = (%455, %456, meta[relay.Constant][47]); + %458 = nn.batch_matmul(%453, %454, meta[relay.attrs.BatchMatmulAttrs][15]); + %459 = concatenate(%457); + %460 = dyn.reshape(%458, %459, newshape=[]); + %461 = add(%460, %bert_encoder_layer_1_output_dense_bias); + %462 = add(%461, %426); + %463 = mean(%462, axis=[-1], keepdims=True); + %464 = subtract(%462, %463); + %465 = power(%464, 2f); + %466 = mean(%465, axis=[-1], keepdims=True); + %467 = add(%466, 1e-12f); + %468 = sqrt(%467); + %469 = divide(%464, %468); + %470 = multiply(%469, %bert_encoder_layer_1_output_LayerNorm_weight); + %471 = add(%470, %bert_encoder_layer_1_output_LayerNorm_bias); + %472 = shape_of(%471, dtype="int64"); + %473 = strided_slice(%472, begin=[1], end=[3], strides=[1]); + %474 = (meta[relay.Constant][48], %473); + %475 = concatenate(%474); + %476 = transpose(%bert_encoder_layer_2_attention_self_query_weight, axes=[1, 0]); + %477 = reshape(%476, newshape=[-1, 1024, 1024]); + %478 = dyn.reshape(%471, %475, newshape=[]); + %479 = transpose(%477, axes=[0, 2, 1]); + %480 = strided_slice(%472, begin=[0], end=[1], strides=[1]); + %481 = strided_slice(%472, begin=[1], end=[2], strides=[1]); + %482 = (%480, %481, meta[relay.Constant][49]); + %483 = nn.batch_matmul(%478, %479, meta[relay.attrs.BatchMatmulAttrs][16]); + %484 = concatenate(%482); + %485 = dyn.reshape(%483, %484, newshape=[]); + %486 = add(%485, %bert_encoder_layer_2_attention_self_query_bias); + %487 = shape_of(%486, dtype="int64"); + %488 = take(%487, 0, axis=0); + %489 = shape_of(%486, dtype="int64"); + %490 = take(%489, 1, axis=0); + %491 = expand_dims(%488, axis=0); + %492 = expand_dims(%490, axis=0); + %493 = (%491, %492, meta[relay.Constant][50], meta[relay.Constant][51]); + %494 = concatenate(%493); + %495 = dyn.reshape(%486, %494, newshape=[]); + %496 = transpose(%495, axes=[0, 2, 1, 3]); + %497 = shape_of(%496, dtype="int64"); + %498 = strided_slice(%497, begin=[2], end=[4], strides=[1]); + %499 = (meta[relay.Constant][52], %498); + %500 = concatenate(%499); + %501 = shape_of(%471, dtype="int64"); + %502 = strided_slice(%501, begin=[1], end=[3], strides=[1]); + %503 = (meta[relay.Constant][53], %502); + %504 = concatenate(%503); + %505 = transpose(%bert_encoder_layer_2_attention_self_key_weight, axes=[1, 0]); + %506 = reshape(%505, newshape=[-1, 1024, 1024]); + %507 = dyn.reshape(%471, %504, newshape=[]); + %508 = transpose(%506, axes=[0, 2, 1]); + %509 = strided_slice(%501, begin=[0], end=[1], strides=[1]); + %510 = strided_slice(%501, begin=[1], end=[2], strides=[1]); + %511 = (%509, %510, meta[relay.Constant][54]); + %512 = nn.batch_matmul(%507, %508, meta[relay.attrs.BatchMatmulAttrs][17]); + %513 = concatenate(%511); + %514 = dyn.reshape(%512, %513, newshape=[]); + %515 = add(%514, %bert_encoder_layer_2_attention_self_key_bias); + %516 = shape_of(%515, dtype="int64"); + %517 = take(%516, 0, axis=0); + %518 = shape_of(%515, dtype="int64"); + %519 = take(%518, 1, axis=0); + %520 = expand_dims(%517, axis=0); + %521 = expand_dims(%519, axis=0); + %522 = (%520, %521, meta[relay.Constant][55], meta[relay.Constant][56]); + %523 = concatenate(%522); + %524 = dyn.reshape(%515, %523, newshape=[]); + %525 = transpose(%524, axes=[0, 2, 3, 1]); + %526 = shape_of(%525, dtype="int64"); + %527 = strided_slice(%526, begin=[2], end=[4], strides=[1]); + %528 = (meta[relay.Constant][57], %527); + %529 = concatenate(%528); + %530 = dyn.reshape(%525, %529, newshape=[]); + %531 = dyn.reshape(%496, %500, newshape=[]); + %532 = transpose(%530, axes=[0, 2, 1]); + %533 = strided_slice(%497, begin=[0], end=[1], strides=[1]); + %534 = strided_slice(%526, begin=[0], end=[1], strides=[1]); + %535 = strided_slice(%497, begin=[1], end=[2], strides=[1]); + %536 = strided_slice(%526, begin=[1], end=[2], strides=[1]); + %537 = maximum(%533, %534); + %538 = maximum(%535, %536); + %539 = (%537, %538); + %540 = concatenate(%539); + %541 = strided_slice(%497, begin=[2], end=[3], strides=[1]); + %542 = strided_slice(%526, begin=[3], end=[4], strides=[1]); + %543 = (%540, %541, %542); + %544 = nn.batch_matmul(%531, %532, meta[relay.attrs.BatchMatmulAttrs][18]); + %545 = concatenate(%543); + %546 = dyn.reshape(%544, %545, newshape=[]); + %547 = divide(%546, 8f); + %548 = add(%547, %123); + %549 = max(%548, axis=[3], keepdims=True); + %550 = subtract(%548, %549); + %551 = exp(%550); + %552 = sum(%551, axis=[3], keepdims=True); + %553 = divide(%551, %552); + %554 = shape_of(%553, dtype="int64"); + %555 = strided_slice(%554, begin=[2], end=[4], strides=[1]); + %556 = (meta[relay.Constant][58], %555); + %557 = concatenate(%556); + %558 = shape_of(%471, dtype="int64"); + %559 = strided_slice(%558, begin=[1], end=[3], strides=[1]); + %560 = (meta[relay.Constant][59], %559); + %561 = concatenate(%560); + %562 = transpose(%bert_encoder_layer_2_attention_self_value_weight, axes=[1, 0]); + %563 = reshape(%562, newshape=[-1, 1024, 1024]); + %564 = dyn.reshape(%471, %561, newshape=[]); + %565 = transpose(%563, axes=[0, 2, 1]); + %566 = strided_slice(%558, begin=[0], end=[1], strides=[1]); + %567 = strided_slice(%558, begin=[1], end=[2], strides=[1]); + %568 = (%566, %567, meta[relay.Constant][60]); + %569 = nn.batch_matmul(%564, %565, meta[relay.attrs.BatchMatmulAttrs][19]); + %570 = concatenate(%568); + %571 = dyn.reshape(%569, %570, newshape=[]); + %572 = add(%571, %bert_encoder_layer_2_attention_self_value_bias); + %573 = shape_of(%572, dtype="int64"); + %574 = take(%573, 0, axis=0); + %575 = shape_of(%572, dtype="int64"); + %576 = take(%575, 1, axis=0); + %577 = expand_dims(%574, axis=0); + %578 = expand_dims(%576, axis=0); + %579 = (%577, %578, meta[relay.Constant][61], meta[relay.Constant][62]); + %580 = concatenate(%579); + %581 = dyn.reshape(%572, %580, newshape=[]); + %582 = transpose(%581, axes=[0, 2, 1, 3]); + %583 = shape_of(%582, dtype="int64"); + %584 = strided_slice(%583, begin=[2], end=[4], strides=[1]); + %585 = (meta[relay.Constant][63], %584); + %586 = concatenate(%585); + %587 = dyn.reshape(%582, %586, newshape=[]); + %588 = dyn.reshape(%553, %557, newshape=[]); + %589 = transpose(%587, axes=[0, 2, 1]); + %590 = strided_slice(%554, begin=[0], end=[1], strides=[1]); + %591 = strided_slice(%583, begin=[0], end=[1], strides=[1]); + %592 = strided_slice(%554, begin=[1], end=[2], strides=[1]); + %593 = strided_slice(%583, begin=[1], end=[2], strides=[1]); + %594 = maximum(%590, %591); + %595 = maximum(%592, %593); + %596 = (%594, %595); + %597 = concatenate(%596); + %598 = strided_slice(%554, begin=[2], end=[3], strides=[1]); + %599 = strided_slice(%583, begin=[3], end=[4], strides=[1]); + %600 = (%597, %598, %599); + %601 = nn.batch_matmul(%588, %589, meta[relay.attrs.BatchMatmulAttrs][20]); + %602 = concatenate(%600); + %603 = dyn.reshape(%601, %602, newshape=[]); + %604 = transpose(%603, axes=[0, 2, 1, 3]); + %605 = shape_of(%604, dtype="int64"); + %606 = take(%605, 0, axis=0); + %607 = shape_of(%604, dtype="int64"); + %608 = take(%607, 1, axis=0); + %609 = expand_dims(%606, axis=0); + %610 = expand_dims(%608, axis=0); + %611 = (%609, %610, meta[relay.Constant][64]); + %612 = concatenate(%611); + %613 = dyn.reshape(%604, %612, newshape=[]); + %614 = shape_of(%613, dtype="int64"); + %615 = strided_slice(%614, begin=[1], end=[3], strides=[1]); + %616 = (meta[relay.Constant][65], %615); + %617 = concatenate(%616); + %618 = transpose(%bert_encoder_layer_2_attention_output_dense_weight, axes=[1, 0]); + %619 = reshape(%618, newshape=[-1, 1024, 1024]); + %620 = dyn.reshape(%613, %617, newshape=[]); + %621 = transpose(%619, axes=[0, 2, 1]); + %622 = strided_slice(%614, begin=[0], end=[1], strides=[1]); + %623 = strided_slice(%614, begin=[1], end=[2], strides=[1]); + %624 = (%622, %623, meta[relay.Constant][66]); + %625 = nn.batch_matmul(%620, %621, meta[relay.attrs.BatchMatmulAttrs][21]); + %626 = concatenate(%624); + %627 = dyn.reshape(%625, %626, newshape=[]); + %628 = add(%627, %bert_encoder_layer_2_attention_output_dense_bias); + %629 = add(%628, %471); + %630 = mean(%629, axis=[-1], keepdims=True); + %631 = subtract(%629, %630); + %632 = power(%631, 2f); + %633 = mean(%632, axis=[-1], keepdims=True); + %634 = add(%633, 1e-12f); + %635 = sqrt(%634); + %636 = divide(%631, %635); + %637 = multiply(%636, %bert_encoder_layer_2_attention_output_LayerNorm_weight); + %638 = add(%637, %bert_encoder_layer_2_attention_output_LayerNorm_bias); + %639 = shape_of(%638, dtype="int64"); + %640 = strided_slice(%639, begin=[1], end=[3], strides=[1]); + %641 = (meta[relay.Constant][67], %640); + %642 = concatenate(%641); + %643 = transpose(%bert_encoder_layer_2_intermediate_dense_weight, axes=[1, 0]); + %644 = reshape(%643, newshape=[-1, 1024, 4096]); + %645 = dyn.reshape(%638, %642, newshape=[]); + %646 = transpose(%644, axes=[0, 2, 1]); + %647 = strided_slice(%639, begin=[0], end=[1], strides=[1]); + %648 = strided_slice(%639, begin=[1], end=[2], strides=[1]); + %649 = (%647, %648, meta[relay.Constant][68]); + %650 = nn.batch_matmul(%645, %646, meta[relay.attrs.BatchMatmulAttrs][22]); + %651 = concatenate(%649); + %652 = dyn.reshape(%650, %651, newshape=[]); + %653 = add(%652, %bert_encoder_layer_2_intermediate_dense_bias); + %654 = divide(%653, 1.41421f); + %655 = erf(%654); + %656 = multiply(%653, 0.5f); + %657 = add(%655, 1f); + %658 = multiply(%656, %657); + %659 = shape_of(%658, dtype="int64"); + %660 = strided_slice(%659, begin=[1], end=[3], strides=[1]); + %661 = (meta[relay.Constant][69], %660); + %662 = concatenate(%661); + %663 = transpose(%bert_encoder_layer_2_output_dense_weight, axes=[1, 0]); + %664 = reshape(%663, newshape=[-1, 4096, 1024]); + %665 = dyn.reshape(%658, %662, newshape=[]); + %666 = transpose(%664, axes=[0, 2, 1]); + %667 = strided_slice(%659, begin=[0], end=[1], strides=[1]); + %668 = strided_slice(%659, begin=[1], end=[2], strides=[1]); + %669 = (%667, %668, meta[relay.Constant][70]); + %670 = nn.batch_matmul(%665, %666, meta[relay.attrs.BatchMatmulAttrs][23]); + %671 = concatenate(%669); + %672 = dyn.reshape(%670, %671, newshape=[]); + %673 = add(%672, %bert_encoder_layer_2_output_dense_bias); + %674 = add(%673, %638); + %675 = mean(%674, axis=[-1], keepdims=True); + %676 = subtract(%674, %675); + %677 = power(%676, 2f); + %678 = mean(%677, axis=[-1], keepdims=True); + %679 = add(%678, 1e-12f); + %680 = sqrt(%679); + %681 = divide(%676, %680); + %682 = multiply(%681, %bert_encoder_layer_2_output_LayerNorm_weight); + %683 = add(%682, %bert_encoder_layer_2_output_LayerNorm_bias); + %684 = shape_of(%683, dtype="int64"); + %685 = strided_slice(%684, begin=[1], end=[3], strides=[1]); + %686 = (meta[relay.Constant][71], %685); + %687 = concatenate(%686); + %688 = transpose(%bert_encoder_layer_3_attention_self_query_weight, axes=[1, 0]); + %689 = reshape(%688, newshape=[-1, 1024, 1024]); + %690 = dyn.reshape(%683, %687, newshape=[]); + %691 = transpose(%689, axes=[0, 2, 1]); + %692 = strided_slice(%684, begin=[0], end=[1], strides=[1]); + %693 = strided_slice(%684, begin=[1], end=[2], strides=[1]); + %694 = (%692, %693, meta[relay.Constant][72]); + %695 = nn.batch_matmul(%690, %691, meta[relay.attrs.BatchMatmulAttrs][24]); + %696 = concatenate(%694); + %697 = dyn.reshape(%695, %696, newshape=[]); + %698 = add(%697, %bert_encoder_layer_3_attention_self_query_bias); + %699 = shape_of(%698, dtype="int64"); + %700 = take(%699, 0, axis=0); + %701 = shape_of(%698, dtype="int64"); + %702 = take(%701, 1, axis=0); + %703 = expand_dims(%700, axis=0); + %704 = expand_dims(%702, axis=0); + %705 = (%703, %704, meta[relay.Constant][73], meta[relay.Constant][74]); + %706 = concatenate(%705); + %707 = dyn.reshape(%698, %706, newshape=[]); + %708 = transpose(%707, axes=[0, 2, 1, 3]); + %709 = shape_of(%708, dtype="int64"); + %710 = strided_slice(%709, begin=[2], end=[4], strides=[1]); + %711 = (meta[relay.Constant][75], %710); + %712 = concatenate(%711); + %713 = shape_of(%683, dtype="int64"); + %714 = strided_slice(%713, begin=[1], end=[3], strides=[1]); + %715 = (meta[relay.Constant][76], %714); + %716 = concatenate(%715); + %717 = transpose(%bert_encoder_layer_3_attention_self_key_weight, axes=[1, 0]); + %718 = reshape(%717, newshape=[-1, 1024, 1024]); + %719 = dyn.reshape(%683, %716, newshape=[]); + %720 = transpose(%718, axes=[0, 2, 1]); + %721 = strided_slice(%713, begin=[0], end=[1], strides=[1]); + %722 = strided_slice(%713, begin=[1], end=[2], strides=[1]); + %723 = (%721, %722, meta[relay.Constant][77]); + %724 = nn.batch_matmul(%719, %720, meta[relay.attrs.BatchMatmulAttrs][25]); + %725 = concatenate(%723); + %726 = dyn.reshape(%724, %725, newshape=[]); + %727 = add(%726, %bert_encoder_layer_3_attention_self_key_bias); + %728 = shape_of(%727, dtype="int64"); + %729 = take(%728, 0, axis=0); + %730 = shape_of(%727, dtype="int64"); + %731 = take(%730, 1, axis=0); + %732 = expand_dims(%729, axis=0); + %733 = expand_dims(%731, axis=0); + %734 = (%732, %733, meta[relay.Constant][78], meta[relay.Constant][79]); + %735 = concatenate(%734); + %736 = dyn.reshape(%727, %735, newshape=[]); + %737 = transpose(%736, axes=[0, 2, 3, 1]); + %738 = shape_of(%737, dtype="int64"); + %739 = strided_slice(%738, begin=[2], end=[4], strides=[1]); + %740 = (meta[relay.Constant][80], %739); + %741 = concatenate(%740); + %742 = dyn.reshape(%737, %741, newshape=[]); + %743 = dyn.reshape(%708, %712, newshape=[]); + %744 = transpose(%742, axes=[0, 2, 1]); + %745 = strided_slice(%709, begin=[0], end=[1], strides=[1]); + %746 = strided_slice(%738, begin=[0], end=[1], strides=[1]); + %747 = strided_slice(%709, begin=[1], end=[2], strides=[1]); + %748 = strided_slice(%738, begin=[1], end=[2], strides=[1]); + %749 = maximum(%745, %746); + %750 = maximum(%747, %748); + %751 = (%749, %750); + %752 = concatenate(%751); + %753 = strided_slice(%709, begin=[2], end=[3], strides=[1]); + %754 = strided_slice(%738, begin=[3], end=[4], strides=[1]); + %755 = (%752, %753, %754); + %756 = nn.batch_matmul(%743, %744, meta[relay.attrs.BatchMatmulAttrs][26]); + %757 = concatenate(%755); + %758 = dyn.reshape(%756, %757, newshape=[]); + %759 = divide(%758, 8f); + %760 = add(%759, %123); + %761 = max(%760, axis=[3], keepdims=True); + %762 = subtract(%760, %761); + %763 = exp(%762); + %764 = sum(%763, axis=[3], keepdims=True); + %765 = divide(%763, %764); + %766 = shape_of(%765, dtype="int64"); + %767 = strided_slice(%766, begin=[2], end=[4], strides=[1]); + %768 = (meta[relay.Constant][81], %767); + %769 = concatenate(%768); + %770 = shape_of(%683, dtype="int64"); + %771 = strided_slice(%770, begin=[1], end=[3], strides=[1]); + %772 = (meta[relay.Constant][82], %771); + %773 = concatenate(%772); + %774 = transpose(%bert_encoder_layer_3_attention_self_value_weight, axes=[1, 0]); + %775 = reshape(%774, newshape=[-1, 1024, 1024]); + %776 = dyn.reshape(%683, %773, newshape=[]); + %777 = transpose(%775, axes=[0, 2, 1]); + %778 = strided_slice(%770, begin=[0], end=[1], strides=[1]); + %779 = strided_slice(%770, begin=[1], end=[2], strides=[1]); + %780 = (%778, %779, meta[relay.Constant][83]); + %781 = nn.batch_matmul(%776, %777, meta[relay.attrs.BatchMatmulAttrs][27]); + %782 = concatenate(%780); + %783 = dyn.reshape(%781, %782, newshape=[]); + %784 = add(%783, %bert_encoder_layer_3_attention_self_value_bias); + %785 = shape_of(%784, dtype="int64"); + %786 = take(%785, 0, axis=0); + %787 = shape_of(%784, dtype="int64"); + %788 = take(%787, 1, axis=0); + %789 = expand_dims(%786, axis=0); + %790 = expand_dims(%788, axis=0); + %791 = (%789, %790, meta[relay.Constant][84], meta[relay.Constant][85]); + %792 = concatenate(%791); + %793 = dyn.reshape(%784, %792, newshape=[]); + %794 = transpose(%793, axes=[0, 2, 1, 3]); + %795 = shape_of(%794, dtype="int64"); + %796 = strided_slice(%795, begin=[2], end=[4], strides=[1]); + %797 = (meta[relay.Constant][86], %796); + %798 = concatenate(%797); + %799 = dyn.reshape(%794, %798, newshape=[]); + %800 = dyn.reshape(%765, %769, newshape=[]); + %801 = transpose(%799, axes=[0, 2, 1]); + %802 = strided_slice(%766, begin=[0], end=[1], strides=[1]); + %803 = strided_slice(%795, begin=[0], end=[1], strides=[1]); + %804 = strided_slice(%766, begin=[1], end=[2], strides=[1]); + %805 = strided_slice(%795, begin=[1], end=[2], strides=[1]); + %806 = maximum(%802, %803); + %807 = maximum(%804, %805); + %808 = (%806, %807); + %809 = concatenate(%808); + %810 = strided_slice(%766, begin=[2], end=[3], strides=[1]); + %811 = strided_slice(%795, begin=[3], end=[4], strides=[1]); + %812 = (%809, %810, %811); + %813 = nn.batch_matmul(%800, %801, meta[relay.attrs.BatchMatmulAttrs][28]); + %814 = concatenate(%812); + %815 = dyn.reshape(%813, %814, newshape=[]); + %816 = transpose(%815, axes=[0, 2, 1, 3]); + %817 = shape_of(%816, dtype="int64"); + %818 = take(%817, 0, axis=0); + %819 = shape_of(%816, dtype="int64"); + %820 = take(%819, 1, axis=0); + %821 = expand_dims(%818, axis=0); + %822 = expand_dims(%820, axis=0); + %823 = (%821, %822, meta[relay.Constant][87]); + %824 = concatenate(%823); + %825 = dyn.reshape(%816, %824, newshape=[]); + %826 = shape_of(%825, dtype="int64"); + %827 = strided_slice(%826, begin=[1], end=[3], strides=[1]); + %828 = (meta[relay.Constant][88], %827); + %829 = concatenate(%828); + %830 = transpose(%bert_encoder_layer_3_attention_output_dense_weight, axes=[1, 0]); + %831 = reshape(%830, newshape=[-1, 1024, 1024]); + %832 = dyn.reshape(%825, %829, newshape=[]); + %833 = transpose(%831, axes=[0, 2, 1]); + %834 = strided_slice(%826, begin=[0], end=[1], strides=[1]); + %835 = strided_slice(%826, begin=[1], end=[2], strides=[1]); + %836 = (%834, %835, meta[relay.Constant][89]); + %837 = nn.batch_matmul(%832, %833, meta[relay.attrs.BatchMatmulAttrs][29]); + %838 = concatenate(%836); + %839 = dyn.reshape(%837, %838, newshape=[]); + %840 = add(%839, %bert_encoder_layer_3_attention_output_dense_bias); + %841 = add(%840, %683); + %842 = mean(%841, axis=[-1], keepdims=True); + %843 = subtract(%841, %842); + %844 = power(%843, 2f); + %845 = mean(%844, axis=[-1], keepdims=True); + %846 = add(%845, 1e-12f); + %847 = sqrt(%846); + %848 = divide(%843, %847); + %849 = multiply(%848, %bert_encoder_layer_3_attention_output_LayerNorm_weight); + %850 = add(%849, %bert_encoder_layer_3_attention_output_LayerNorm_bias); + %851 = shape_of(%850, dtype="int64"); + %852 = strided_slice(%851, begin=[1], end=[3], strides=[1]); + %853 = (meta[relay.Constant][90], %852); + %854 = concatenate(%853); + %855 = transpose(%bert_encoder_layer_3_intermediate_dense_weight, axes=[1, 0]); + %856 = reshape(%855, newshape=[-1, 1024, 4096]); + %857 = dyn.reshape(%850, %854, newshape=[]); + %858 = transpose(%856, axes=[0, 2, 1]); + %859 = strided_slice(%851, begin=[0], end=[1], strides=[1]); + %860 = strided_slice(%851, begin=[1], end=[2], strides=[1]); + %861 = (%859, %860, meta[relay.Constant][91]); + %862 = nn.batch_matmul(%857, %858, meta[relay.attrs.BatchMatmulAttrs][30]); + %863 = concatenate(%861); + %864 = dyn.reshape(%862, %863, newshape=[]); + %865 = add(%864, %bert_encoder_layer_3_intermediate_dense_bias); + %866 = divide(%865, 1.41421f); + %867 = erf(%866); + %868 = multiply(%865, 0.5f); + %869 = add(%867, 1f); + %870 = multiply(%868, %869); + %871 = shape_of(%870, dtype="int64"); + %872 = strided_slice(%871, begin=[1], end=[3], strides=[1]); + %873 = (meta[relay.Constant][92], %872); + %874 = concatenate(%873); + %875 = transpose(%bert_encoder_layer_3_output_dense_weight, axes=[1, 0]); + %876 = reshape(%875, newshape=[-1, 4096, 1024]); + %877 = dyn.reshape(%870, %874, newshape=[]); + %878 = transpose(%876, axes=[0, 2, 1]); + %879 = strided_slice(%871, begin=[0], end=[1], strides=[1]); + %880 = strided_slice(%871, begin=[1], end=[2], strides=[1]); + %881 = (%879, %880, meta[relay.Constant][93]); + %882 = nn.batch_matmul(%877, %878, meta[relay.attrs.BatchMatmulAttrs][31]); + %883 = concatenate(%881); + %884 = dyn.reshape(%882, %883, newshape=[]); + %885 = add(%884, %bert_encoder_layer_3_output_dense_bias); + %886 = add(%885, %850); + %887 = mean(%886, axis=[-1], keepdims=True); + %888 = subtract(%886, %887); + %889 = power(%888, 2f); + %890 = mean(%889, axis=[-1], keepdims=True); + %891 = add(%890, 1e-12f); + %892 = sqrt(%891); + %893 = divide(%888, %892); + %894 = multiply(%893, %bert_encoder_layer_3_output_LayerNorm_weight); + %895 = add(%894, %bert_encoder_layer_3_output_LayerNorm_bias); + %896 = shape_of(%895, dtype="int64"); + %897 = strided_slice(%896, begin=[1], end=[3], strides=[1]); + %898 = (meta[relay.Constant][94], %897); + %899 = concatenate(%898); + %900 = transpose(%bert_encoder_layer_4_attention_self_query_weight, axes=[1, 0]); + %901 = reshape(%900, newshape=[-1, 1024, 1024]); + %902 = dyn.reshape(%895, %899, newshape=[]); + %903 = transpose(%901, axes=[0, 2, 1]); + %904 = strided_slice(%896, begin=[0], end=[1], strides=[1]); + %905 = strided_slice(%896, begin=[1], end=[2], strides=[1]); + %906 = (%904, %905, meta[relay.Constant][95]); + %907 = nn.batch_matmul(%902, %903, meta[relay.attrs.BatchMatmulAttrs][32]); + %908 = concatenate(%906); + %909 = dyn.reshape(%907, %908, newshape=[]); + %910 = add(%909, %bert_encoder_layer_4_attention_self_query_bias); + %911 = shape_of(%910, dtype="int64"); + %912 = take(%911, 0, axis=0); + %913 = shape_of(%910, dtype="int64"); + %914 = take(%913, 1, axis=0); + %915 = expand_dims(%912, axis=0); + %916 = expand_dims(%914, axis=0); + %917 = (%915, %916, meta[relay.Constant][96], meta[relay.Constant][97]); + %918 = concatenate(%917); + %919 = dyn.reshape(%910, %918, newshape=[]); + %920 = transpose(%919, axes=[0, 2, 1, 3]); + %921 = shape_of(%920, dtype="int64"); + %922 = strided_slice(%921, begin=[2], end=[4], strides=[1]); + %923 = (meta[relay.Constant][98], %922); + %924 = concatenate(%923); + %925 = shape_of(%895, dtype="int64"); + %926 = strided_slice(%925, begin=[1], end=[3], strides=[1]); + %927 = (meta[relay.Constant][99], %926); + %928 = concatenate(%927); + %929 = transpose(%bert_encoder_layer_4_attention_self_key_weight, axes=[1, 0]); + %930 = reshape(%929, newshape=[-1, 1024, 1024]); + %931 = dyn.reshape(%895, %928, newshape=[]); + %932 = transpose(%930, axes=[0, 2, 1]); + %933 = strided_slice(%925, begin=[0], end=[1], strides=[1]); + %934 = strided_slice(%925, begin=[1], end=[2], strides=[1]); + %935 = (%933, %934, meta[relay.Constant][100]); + %936 = nn.batch_matmul(%931, %932, meta[relay.attrs.BatchMatmulAttrs][33]); + %937 = concatenate(%935); + %938 = dyn.reshape(%936, %937, newshape=[]); + %939 = add(%938, %bert_encoder_layer_4_attention_self_key_bias); + %940 = shape_of(%939, dtype="int64"); + %941 = take(%940, 0, axis=0); + %942 = shape_of(%939, dtype="int64"); + %943 = take(%942, 1, axis=0); + %944 = expand_dims(%941, axis=0); + %945 = expand_dims(%943, axis=0); + %946 = (%944, %945, meta[relay.Constant][101], meta[relay.Constant][102]); + %947 = concatenate(%946); + %948 = dyn.reshape(%939, %947, newshape=[]); + %949 = transpose(%948, axes=[0, 2, 3, 1]); + %950 = shape_of(%949, dtype="int64"); + %951 = strided_slice(%950, begin=[2], end=[4], strides=[1]); + %952 = (meta[relay.Constant][103], %951); + %953 = concatenate(%952); + %954 = dyn.reshape(%949, %953, newshape=[]); + %955 = dyn.reshape(%920, %924, newshape=[]); + %956 = transpose(%954, axes=[0, 2, 1]); + %957 = strided_slice(%921, begin=[0], end=[1], strides=[1]); + %958 = strided_slice(%950, begin=[0], end=[1], strides=[1]); + %959 = strided_slice(%921, begin=[1], end=[2], strides=[1]); + %960 = strided_slice(%950, begin=[1], end=[2], strides=[1]); + %961 = maximum(%957, %958); + %962 = maximum(%959, %960); + %963 = (%961, %962); + %964 = concatenate(%963); + %965 = strided_slice(%921, begin=[2], end=[3], strides=[1]); + %966 = strided_slice(%950, begin=[3], end=[4], strides=[1]); + %967 = (%964, %965, %966); + %968 = nn.batch_matmul(%955, %956, meta[relay.attrs.BatchMatmulAttrs][34]); + %969 = concatenate(%967); + %970 = dyn.reshape(%968, %969, newshape=[]); + %971 = divide(%970, 8f); + %972 = add(%971, %123); + %973 = max(%972, axis=[3], keepdims=True); + %974 = subtract(%972, %973); + %975 = exp(%974); + %976 = sum(%975, axis=[3], keepdims=True); + %977 = divide(%975, %976); + %978 = shape_of(%977, dtype="int64"); + %979 = strided_slice(%978, begin=[2], end=[4], strides=[1]); + %980 = (meta[relay.Constant][104], %979); + %981 = concatenate(%980); + %982 = shape_of(%895, dtype="int64"); + %983 = strided_slice(%982, begin=[1], end=[3], strides=[1]); + %984 = (meta[relay.Constant][105], %983); + %985 = concatenate(%984); + %986 = transpose(%bert_encoder_layer_4_attention_self_value_weight, axes=[1, 0]); + %987 = reshape(%986, newshape=[-1, 1024, 1024]); + %988 = dyn.reshape(%895, %985, newshape=[]); + %989 = transpose(%987, axes=[0, 2, 1]); + %990 = strided_slice(%982, begin=[0], end=[1], strides=[1]); + %991 = strided_slice(%982, begin=[1], end=[2], strides=[1]); + %992 = (%990, %991, meta[relay.Constant][106]); + %993 = nn.batch_matmul(%988, %989, meta[relay.attrs.BatchMatmulAttrs][35]); + %994 = concatenate(%992); + %995 = dyn.reshape(%993, %994, newshape=[]); + %996 = add(%995, %bert_encoder_layer_4_attention_self_value_bias); + %997 = shape_of(%996, dtype="int64"); + %998 = take(%997, 0, axis=0); + %999 = shape_of(%996, dtype="int64"); + %1000 = take(%999, 1, axis=0); + %1001 = expand_dims(%998, axis=0); + %1002 = expand_dims(%1000, axis=0); + %1003 = (%1001, %1002, meta[relay.Constant][107], meta[relay.Constant][108]); + %1004 = concatenate(%1003); + %1005 = dyn.reshape(%996, %1004, newshape=[]); + %1006 = transpose(%1005, axes=[0, 2, 1, 3]); + %1007 = shape_of(%1006, dtype="int64"); + %1008 = strided_slice(%1007, begin=[2], end=[4], strides=[1]); + %1009 = (meta[relay.Constant][109], %1008); + %1010 = concatenate(%1009); + %1011 = dyn.reshape(%1006, %1010, newshape=[]); + %1012 = dyn.reshape(%977, %981, newshape=[]); + %1013 = transpose(%1011, axes=[0, 2, 1]); + %1014 = strided_slice(%978, begin=[0], end=[1], strides=[1]); + %1015 = strided_slice(%1007, begin=[0], end=[1], strides=[1]); + %1016 = strided_slice(%978, begin=[1], end=[2], strides=[1]); + %1017 = strided_slice(%1007, begin=[1], end=[2], strides=[1]); + %1018 = maximum(%1014, %1015); + %1019 = maximum(%1016, %1017); + %1020 = (%1018, %1019); + %1021 = concatenate(%1020); + %1022 = strided_slice(%978, begin=[2], end=[3], strides=[1]); + %1023 = strided_slice(%1007, begin=[3], end=[4], strides=[1]); + %1024 = (%1021, %1022, %1023); + %1025 = nn.batch_matmul(%1012, %1013, meta[relay.attrs.BatchMatmulAttrs][36]); + %1026 = concatenate(%1024); + %1027 = dyn.reshape(%1025, %1026, newshape=[]); + %1028 = transpose(%1027, axes=[0, 2, 1, 3]); + %1029 = shape_of(%1028, dtype="int64"); + %1030 = take(%1029, 0, axis=0); + %1031 = shape_of(%1028, dtype="int64"); + %1032 = take(%1031, 1, axis=0); + %1033 = expand_dims(%1030, axis=0); + %1034 = expand_dims(%1032, axis=0); + %1035 = (%1033, %1034, meta[relay.Constant][110]); + %1036 = concatenate(%1035); + %1037 = dyn.reshape(%1028, %1036, newshape=[]); + %1038 = shape_of(%1037, dtype="int64"); + %1039 = strided_slice(%1038, begin=[1], end=[3], strides=[1]); + %1040 = (meta[relay.Constant][111], %1039); + %1041 = concatenate(%1040); + %1042 = transpose(%bert_encoder_layer_4_attention_output_dense_weight, axes=[1, 0]); + %1043 = reshape(%1042, newshape=[-1, 1024, 1024]); + %1044 = dyn.reshape(%1037, %1041, newshape=[]); + %1045 = transpose(%1043, axes=[0, 2, 1]); + %1046 = strided_slice(%1038, begin=[0], end=[1], strides=[1]); + %1047 = strided_slice(%1038, begin=[1], end=[2], strides=[1]); + %1048 = (%1046, %1047, meta[relay.Constant][112]); + %1049 = nn.batch_matmul(%1044, %1045, meta[relay.attrs.BatchMatmulAttrs][37]); + %1050 = concatenate(%1048); + %1051 = dyn.reshape(%1049, %1050, newshape=[]); + %1052 = add(%1051, %bert_encoder_layer_4_attention_output_dense_bias); + %1053 = add(%1052, %895); + %1054 = mean(%1053, axis=[-1], keepdims=True); + %1055 = subtract(%1053, %1054); + %1056 = power(%1055, 2f); + %1057 = mean(%1056, axis=[-1], keepdims=True); + %1058 = add(%1057, 1e-12f); + %1059 = sqrt(%1058); + %1060 = divide(%1055, %1059); + %1061 = multiply(%1060, %bert_encoder_layer_4_attention_output_LayerNorm_weight); + %1062 = add(%1061, %bert_encoder_layer_4_attention_output_LayerNorm_bias); + %1063 = shape_of(%1062, dtype="int64"); + %1064 = strided_slice(%1063, begin=[1], end=[3], strides=[1]); + %1065 = (meta[relay.Constant][113], %1064); + %1066 = concatenate(%1065); + %1067 = transpose(%bert_encoder_layer_4_intermediate_dense_weight, axes=[1, 0]); + %1068 = reshape(%1067, newshape=[-1, 1024, 4096]); + %1069 = dyn.reshape(%1062, %1066, newshape=[]); + %1070 = transpose(%1068, axes=[0, 2, 1]); + %1071 = strided_slice(%1063, begin=[0], end=[1], strides=[1]); + %1072 = strided_slice(%1063, begin=[1], end=[2], strides=[1]); + %1073 = (%1071, %1072, meta[relay.Constant][114]); + %1074 = nn.batch_matmul(%1069, %1070, meta[relay.attrs.BatchMatmulAttrs][38]); + %1075 = concatenate(%1073); + %1076 = dyn.reshape(%1074, %1075, newshape=[]); + %1077 = add(%1076, %bert_encoder_layer_4_intermediate_dense_bias); + %1078 = divide(%1077, 1.41421f); + %1079 = erf(%1078); + %1080 = multiply(%1077, 0.5f); + %1081 = add(%1079, 1f); + %1082 = multiply(%1080, %1081); + %1083 = shape_of(%1082, dtype="int64"); + %1084 = strided_slice(%1083, begin=[1], end=[3], strides=[1]); + %1085 = (meta[relay.Constant][115], %1084); + %1086 = concatenate(%1085); + %1087 = transpose(%bert_encoder_layer_4_output_dense_weight, axes=[1, 0]); + %1088 = reshape(%1087, newshape=[-1, 4096, 1024]); + %1089 = dyn.reshape(%1082, %1086, newshape=[]); + %1090 = transpose(%1088, axes=[0, 2, 1]); + %1091 = strided_slice(%1083, begin=[0], end=[1], strides=[1]); + %1092 = strided_slice(%1083, begin=[1], end=[2], strides=[1]); + %1093 = (%1091, %1092, meta[relay.Constant][116]); + %1094 = nn.batch_matmul(%1089, %1090, meta[relay.attrs.BatchMatmulAttrs][39]); + %1095 = concatenate(%1093); + %1096 = dyn.reshape(%1094, %1095, newshape=[]); + %1097 = add(%1096, %bert_encoder_layer_4_output_dense_bias); + %1098 = add(%1097, %1062); + %1099 = mean(%1098, axis=[-1], keepdims=True); + %1100 = subtract(%1098, %1099); + %1101 = power(%1100, 2f); + %1102 = mean(%1101, axis=[-1], keepdims=True); + %1103 = add(%1102, 1e-12f); + %1104 = sqrt(%1103); + %1105 = divide(%1100, %1104); + %1106 = multiply(%1105, %bert_encoder_layer_4_output_LayerNorm_weight); + %1107 = add(%1106, %bert_encoder_layer_4_output_LayerNorm_bias); + %1108 = shape_of(%1107, dtype="int64"); + %1109 = strided_slice(%1108, begin=[1], end=[3], strides=[1]); + %1110 = (meta[relay.Constant][117], %1109); + %1111 = concatenate(%1110); + %1112 = transpose(%bert_encoder_layer_5_attention_self_query_weight, axes=[1, 0]); + %1113 = reshape(%1112, newshape=[-1, 1024, 1024]); + %1114 = dyn.reshape(%1107, %1111, newshape=[]); + %1115 = transpose(%1113, axes=[0, 2, 1]); + %1116 = strided_slice(%1108, begin=[0], end=[1], strides=[1]); + %1117 = strided_slice(%1108, begin=[1], end=[2], strides=[1]); + %1118 = (%1116, %1117, meta[relay.Constant][118]); + %1119 = nn.batch_matmul(%1114, %1115, meta[relay.attrs.BatchMatmulAttrs][40]); + %1120 = concatenate(%1118); + %1121 = dyn.reshape(%1119, %1120, newshape=[]); + %1122 = add(%1121, %bert_encoder_layer_5_attention_self_query_bias); + %1123 = shape_of(%1122, dtype="int64"); + %1124 = take(%1123, 0, axis=0); + %1125 = shape_of(%1122, dtype="int64"); + %1126 = take(%1125, 1, axis=0); + %1127 = expand_dims(%1124, axis=0); + %1128 = expand_dims(%1126, axis=0); + %1129 = (%1127, %1128, meta[relay.Constant][119], meta[relay.Constant][120]); + %1130 = concatenate(%1129); + %1131 = dyn.reshape(%1122, %1130, newshape=[]); + %1132 = transpose(%1131, axes=[0, 2, 1, 3]); + %1133 = shape_of(%1132, dtype="int64"); + %1134 = strided_slice(%1133, begin=[2], end=[4], strides=[1]); + %1135 = (meta[relay.Constant][121], %1134); + %1136 = concatenate(%1135); + %1137 = shape_of(%1107, dtype="int64"); + %1138 = strided_slice(%1137, begin=[1], end=[3], strides=[1]); + %1139 = (meta[relay.Constant][122], %1138); + %1140 = concatenate(%1139); + %1141 = transpose(%bert_encoder_layer_5_attention_self_key_weight, axes=[1, 0]); + %1142 = reshape(%1141, newshape=[-1, 1024, 1024]); + %1143 = dyn.reshape(%1107, %1140, newshape=[]); + %1144 = transpose(%1142, axes=[0, 2, 1]); + %1145 = strided_slice(%1137, begin=[0], end=[1], strides=[1]); + %1146 = strided_slice(%1137, begin=[1], end=[2], strides=[1]); + %1147 = (%1145, %1146, meta[relay.Constant][123]); + %1148 = nn.batch_matmul(%1143, %1144, meta[relay.attrs.BatchMatmulAttrs][41]); + %1149 = concatenate(%1147); + %1150 = dyn.reshape(%1148, %1149, newshape=[]); + %1151 = add(%1150, %bert_encoder_layer_5_attention_self_key_bias); + %1152 = shape_of(%1151, dtype="int64"); + %1153 = take(%1152, 0, axis=0); + %1154 = shape_of(%1151, dtype="int64"); + %1155 = take(%1154, 1, axis=0); + %1156 = expand_dims(%1153, axis=0); + %1157 = expand_dims(%1155, axis=0); + %1158 = (%1156, %1157, meta[relay.Constant][124], meta[relay.Constant][125]); + %1159 = concatenate(%1158); + %1160 = dyn.reshape(%1151, %1159, newshape=[]); + %1161 = transpose(%1160, axes=[0, 2, 3, 1]); + %1162 = shape_of(%1161, dtype="int64"); + %1163 = strided_slice(%1162, begin=[2], end=[4], strides=[1]); + %1164 = (meta[relay.Constant][126], %1163); + %1165 = concatenate(%1164); + %1166 = dyn.reshape(%1161, %1165, newshape=[]); + %1167 = dyn.reshape(%1132, %1136, newshape=[]); + %1168 = transpose(%1166, axes=[0, 2, 1]); + %1169 = strided_slice(%1133, begin=[0], end=[1], strides=[1]); + %1170 = strided_slice(%1162, begin=[0], end=[1], strides=[1]); + %1171 = strided_slice(%1133, begin=[1], end=[2], strides=[1]); + %1172 = strided_slice(%1162, begin=[1], end=[2], strides=[1]); + %1173 = maximum(%1169, %1170); + %1174 = maximum(%1171, %1172); + %1175 = (%1173, %1174); + %1176 = concatenate(%1175); + %1177 = strided_slice(%1133, begin=[2], end=[3], strides=[1]); + %1178 = strided_slice(%1162, begin=[3], end=[4], strides=[1]); + %1179 = (%1176, %1177, %1178); + %1180 = nn.batch_matmul(%1167, %1168, meta[relay.attrs.BatchMatmulAttrs][42]); + %1181 = concatenate(%1179); + %1182 = dyn.reshape(%1180, %1181, newshape=[]); + %1183 = divide(%1182, 8f); + %1184 = add(%1183, %123); + %1185 = max(%1184, axis=[3], keepdims=True); + %1186 = subtract(%1184, %1185); + %1187 = exp(%1186); + %1188 = sum(%1187, axis=[3], keepdims=True); + %1189 = divide(%1187, %1188); + %1190 = shape_of(%1189, dtype="int64"); + %1191 = strided_slice(%1190, begin=[2], end=[4], strides=[1]); + %1192 = (meta[relay.Constant][127], %1191); + %1193 = concatenate(%1192); + %1194 = shape_of(%1107, dtype="int64"); + %1195 = strided_slice(%1194, begin=[1], end=[3], strides=[1]); + %1196 = (meta[relay.Constant][128], %1195); + %1197 = concatenate(%1196); + %1198 = transpose(%bert_encoder_layer_5_attention_self_value_weight, axes=[1, 0]); + %1199 = reshape(%1198, newshape=[-1, 1024, 1024]); + %1200 = dyn.reshape(%1107, %1197, newshape=[]); + %1201 = transpose(%1199, axes=[0, 2, 1]); + %1202 = strided_slice(%1194, begin=[0], end=[1], strides=[1]); + %1203 = strided_slice(%1194, begin=[1], end=[2], strides=[1]); + %1204 = (%1202, %1203, meta[relay.Constant][129]); + %1205 = nn.batch_matmul(%1200, %1201, meta[relay.attrs.BatchMatmulAttrs][43]); + %1206 = concatenate(%1204); + %1207 = dyn.reshape(%1205, %1206, newshape=[]); + %1208 = add(%1207, %bert_encoder_layer_5_attention_self_value_bias); + %1209 = shape_of(%1208, dtype="int64"); + %1210 = take(%1209, 0, axis=0); + %1211 = shape_of(%1208, dtype="int64"); + %1212 = take(%1211, 1, axis=0); + %1213 = expand_dims(%1210, axis=0); + %1214 = expand_dims(%1212, axis=0); + %1215 = (%1213, %1214, meta[relay.Constant][130], meta[relay.Constant][131]); + %1216 = concatenate(%1215); + %1217 = dyn.reshape(%1208, %1216, newshape=[]); + %1218 = transpose(%1217, axes=[0, 2, 1, 3]); + %1219 = shape_of(%1218, dtype="int64"); + %1220 = strided_slice(%1219, begin=[2], end=[4], strides=[1]); + %1221 = (meta[relay.Constant][132], %1220); + %1222 = concatenate(%1221); + %1223 = dyn.reshape(%1218, %1222, newshape=[]); + %1224 = dyn.reshape(%1189, %1193, newshape=[]); + %1225 = transpose(%1223, axes=[0, 2, 1]); + %1226 = strided_slice(%1190, begin=[0], end=[1], strides=[1]); + %1227 = strided_slice(%1219, begin=[0], end=[1], strides=[1]); + %1228 = strided_slice(%1190, begin=[1], end=[2], strides=[1]); + %1229 = strided_slice(%1219, begin=[1], end=[2], strides=[1]); + %1230 = maximum(%1226, %1227); + %1231 = maximum(%1228, %1229); + %1232 = (%1230, %1231); + %1233 = concatenate(%1232); + %1234 = strided_slice(%1190, begin=[2], end=[3], strides=[1]); + %1235 = strided_slice(%1219, begin=[3], end=[4], strides=[1]); + %1236 = (%1233, %1234, %1235); + %1237 = nn.batch_matmul(%1224, %1225, meta[relay.attrs.BatchMatmulAttrs][44]); + %1238 = concatenate(%1236); + %1239 = dyn.reshape(%1237, %1238, newshape=[]); + %1240 = transpose(%1239, axes=[0, 2, 1, 3]); + %1241 = shape_of(%1240, dtype="int64"); + %1242 = take(%1241, 0, axis=0); + %1243 = shape_of(%1240, dtype="int64"); + %1244 = take(%1243, 1, axis=0); + %1245 = expand_dims(%1242, axis=0); + %1246 = expand_dims(%1244, axis=0); + %1247 = (%1245, %1246, meta[relay.Constant][133]); + %1248 = concatenate(%1247); + %1249 = dyn.reshape(%1240, %1248, newshape=[]); + %1250 = shape_of(%1249, dtype="int64"); + %1251 = strided_slice(%1250, begin=[1], end=[3], strides=[1]); + %1252 = (meta[relay.Constant][134], %1251); + %1253 = concatenate(%1252); + %1254 = transpose(%bert_encoder_layer_5_attention_output_dense_weight, axes=[1, 0]); + %1255 = reshape(%1254, newshape=[-1, 1024, 1024]); + %1256 = dyn.reshape(%1249, %1253, newshape=[]); + %1257 = transpose(%1255, axes=[0, 2, 1]); + %1258 = strided_slice(%1250, begin=[0], end=[1], strides=[1]); + %1259 = strided_slice(%1250, begin=[1], end=[2], strides=[1]); + %1260 = (%1258, %1259, meta[relay.Constant][135]); + %1261 = nn.batch_matmul(%1256, %1257, meta[relay.attrs.BatchMatmulAttrs][45]); + %1262 = concatenate(%1260); + %1263 = dyn.reshape(%1261, %1262, newshape=[]); + %1264 = add(%1263, %bert_encoder_layer_5_attention_output_dense_bias); + %1265 = add(%1264, %1107); + %1266 = mean(%1265, axis=[-1], keepdims=True); + %1267 = subtract(%1265, %1266); + %1268 = power(%1267, 2f); + %1269 = mean(%1268, axis=[-1], keepdims=True); + %1270 = add(%1269, 1e-12f); + %1271 = sqrt(%1270); + %1272 = divide(%1267, %1271); + %1273 = multiply(%1272, %bert_encoder_layer_5_attention_output_LayerNorm_weight); + %1274 = add(%1273, %bert_encoder_layer_5_attention_output_LayerNorm_bias); + %1275 = shape_of(%1274, dtype="int64"); + %1276 = strided_slice(%1275, begin=[1], end=[3], strides=[1]); + %1277 = (meta[relay.Constant][136], %1276); + %1278 = concatenate(%1277); + %1279 = transpose(%bert_encoder_layer_5_intermediate_dense_weight, axes=[1, 0]); + %1280 = reshape(%1279, newshape=[-1, 1024, 4096]); + %1281 = dyn.reshape(%1274, %1278, newshape=[]); + %1282 = transpose(%1280, axes=[0, 2, 1]); + %1283 = strided_slice(%1275, begin=[0], end=[1], strides=[1]); + %1284 = strided_slice(%1275, begin=[1], end=[2], strides=[1]); + %1285 = (%1283, %1284, meta[relay.Constant][137]); + %1286 = nn.batch_matmul(%1281, %1282, meta[relay.attrs.BatchMatmulAttrs][46]); + %1287 = concatenate(%1285); + %1288 = dyn.reshape(%1286, %1287, newshape=[]); + %1289 = add(%1288, %bert_encoder_layer_5_intermediate_dense_bias); + %1290 = divide(%1289, 1.41421f); + %1291 = erf(%1290); + %1292 = multiply(%1289, 0.5f); + %1293 = add(%1291, 1f); + %1294 = multiply(%1292, %1293); + %1295 = shape_of(%1294, dtype="int64"); + %1296 = strided_slice(%1295, begin=[1], end=[3], strides=[1]); + %1297 = (meta[relay.Constant][138], %1296); + %1298 = concatenate(%1297); + %1299 = transpose(%bert_encoder_layer_5_output_dense_weight, axes=[1, 0]); + %1300 = reshape(%1299, newshape=[-1, 4096, 1024]); + %1301 = dyn.reshape(%1294, %1298, newshape=[]); + %1302 = transpose(%1300, axes=[0, 2, 1]); + %1303 = strided_slice(%1295, begin=[0], end=[1], strides=[1]); + %1304 = strided_slice(%1295, begin=[1], end=[2], strides=[1]); + %1305 = (%1303, %1304, meta[relay.Constant][139]); + %1306 = nn.batch_matmul(%1301, %1302, meta[relay.attrs.BatchMatmulAttrs][47]); + %1307 = concatenate(%1305); + %1308 = dyn.reshape(%1306, %1307, newshape=[]); + %1309 = add(%1308, %bert_encoder_layer_5_output_dense_bias); + %1310 = add(%1309, %1274); + %1311 = mean(%1310, axis=[-1], keepdims=True); + %1312 = subtract(%1310, %1311); + %1313 = power(%1312, 2f); + %1314 = mean(%1313, axis=[-1], keepdims=True); + %1315 = add(%1314, 1e-12f); + %1316 = sqrt(%1315); + %1317 = divide(%1312, %1316); + %1318 = multiply(%1317, %bert_encoder_layer_5_output_LayerNorm_weight); + %1319 = add(%1318, %bert_encoder_layer_5_output_LayerNorm_bias); + %1320 = shape_of(%1319, dtype="int64"); + %1321 = strided_slice(%1320, begin=[1], end=[3], strides=[1]); + %1322 = (meta[relay.Constant][140], %1321); + %1323 = concatenate(%1322); + %1324 = transpose(%bert_encoder_layer_6_attention_self_query_weight, axes=[1, 0]); + %1325 = reshape(%1324, newshape=[-1, 1024, 1024]); + %1326 = dyn.reshape(%1319, %1323, newshape=[]); + %1327 = transpose(%1325, axes=[0, 2, 1]); + %1328 = strided_slice(%1320, begin=[0], end=[1], strides=[1]); + %1329 = strided_slice(%1320, begin=[1], end=[2], strides=[1]); + %1330 = (%1328, %1329, meta[relay.Constant][141]); + %1331 = nn.batch_matmul(%1326, %1327, meta[relay.attrs.BatchMatmulAttrs][48]); + %1332 = concatenate(%1330); + %1333 = dyn.reshape(%1331, %1332, newshape=[]); + %1334 = add(%1333, %bert_encoder_layer_6_attention_self_query_bias); + %1335 = shape_of(%1334, dtype="int64"); + %1336 = take(%1335, 0, axis=0); + %1337 = shape_of(%1334, dtype="int64"); + %1338 = take(%1337, 1, axis=0); + %1339 = expand_dims(%1336, axis=0); + %1340 = expand_dims(%1338, axis=0); + %1341 = (%1339, %1340, meta[relay.Constant][142], meta[relay.Constant][143]); + %1342 = concatenate(%1341); + %1343 = dyn.reshape(%1334, %1342, newshape=[]); + %1344 = transpose(%1343, axes=[0, 2, 1, 3]); + %1345 = shape_of(%1344, dtype="int64"); + %1346 = strided_slice(%1345, begin=[2], end=[4], strides=[1]); + %1347 = (meta[relay.Constant][144], %1346); + %1348 = concatenate(%1347); + %1349 = shape_of(%1319, dtype="int64"); + %1350 = strided_slice(%1349, begin=[1], end=[3], strides=[1]); + %1351 = (meta[relay.Constant][145], %1350); + %1352 = concatenate(%1351); + %1353 = transpose(%bert_encoder_layer_6_attention_self_key_weight, axes=[1, 0]); + %1354 = reshape(%1353, newshape=[-1, 1024, 1024]); + %1355 = dyn.reshape(%1319, %1352, newshape=[]); + %1356 = transpose(%1354, axes=[0, 2, 1]); + %1357 = strided_slice(%1349, begin=[0], end=[1], strides=[1]); + %1358 = strided_slice(%1349, begin=[1], end=[2], strides=[1]); + %1359 = (%1357, %1358, meta[relay.Constant][146]); + %1360 = nn.batch_matmul(%1355, %1356, meta[relay.attrs.BatchMatmulAttrs][49]); + %1361 = concatenate(%1359); + %1362 = dyn.reshape(%1360, %1361, newshape=[]); + %1363 = add(%1362, %bert_encoder_layer_6_attention_self_key_bias); + %1364 = shape_of(%1363, dtype="int64"); + %1365 = take(%1364, 0, axis=0); + %1366 = shape_of(%1363, dtype="int64"); + %1367 = take(%1366, 1, axis=0); + %1368 = expand_dims(%1365, axis=0); + %1369 = expand_dims(%1367, axis=0); + %1370 = (%1368, %1369, meta[relay.Constant][147], meta[relay.Constant][148]); + %1371 = concatenate(%1370); + %1372 = dyn.reshape(%1363, %1371, newshape=[]); + %1373 = transpose(%1372, axes=[0, 2, 3, 1]); + %1374 = shape_of(%1373, dtype="int64"); + %1375 = strided_slice(%1374, begin=[2], end=[4], strides=[1]); + %1376 = (meta[relay.Constant][149], %1375); + %1377 = concatenate(%1376); + %1378 = dyn.reshape(%1373, %1377, newshape=[]); + %1379 = dyn.reshape(%1344, %1348, newshape=[]); + %1380 = transpose(%1378, axes=[0, 2, 1]); + %1381 = strided_slice(%1345, begin=[0], end=[1], strides=[1]); + %1382 = strided_slice(%1374, begin=[0], end=[1], strides=[1]); + %1383 = strided_slice(%1345, begin=[1], end=[2], strides=[1]); + %1384 = strided_slice(%1374, begin=[1], end=[2], strides=[1]); + %1385 = maximum(%1381, %1382); + %1386 = maximum(%1383, %1384); + %1387 = (%1385, %1386); + %1388 = concatenate(%1387); + %1389 = strided_slice(%1345, begin=[2], end=[3], strides=[1]); + %1390 = strided_slice(%1374, begin=[3], end=[4], strides=[1]); + %1391 = (%1388, %1389, %1390); + %1392 = nn.batch_matmul(%1379, %1380, meta[relay.attrs.BatchMatmulAttrs][50]); + %1393 = concatenate(%1391); + %1394 = dyn.reshape(%1392, %1393, newshape=[]); + %1395 = divide(%1394, 8f); + %1396 = add(%1395, %123); + %1397 = max(%1396, axis=[3], keepdims=True); + %1398 = subtract(%1396, %1397); + %1399 = exp(%1398); + %1400 = sum(%1399, axis=[3], keepdims=True); + %1401 = divide(%1399, %1400); + %1402 = shape_of(%1401, dtype="int64"); + %1403 = strided_slice(%1402, begin=[2], end=[4], strides=[1]); + %1404 = (meta[relay.Constant][150], %1403); + %1405 = concatenate(%1404); + %1406 = shape_of(%1319, dtype="int64"); + %1407 = strided_slice(%1406, begin=[1], end=[3], strides=[1]); + %1408 = (meta[relay.Constant][151], %1407); + %1409 = concatenate(%1408); + %1410 = transpose(%bert_encoder_layer_6_attention_self_value_weight, axes=[1, 0]); + %1411 = reshape(%1410, newshape=[-1, 1024, 1024]); + %1412 = dyn.reshape(%1319, %1409, newshape=[]); + %1413 = transpose(%1411, axes=[0, 2, 1]); + %1414 = strided_slice(%1406, begin=[0], end=[1], strides=[1]); + %1415 = strided_slice(%1406, begin=[1], end=[2], strides=[1]); + %1416 = (%1414, %1415, meta[relay.Constant][152]); + %1417 = nn.batch_matmul(%1412, %1413, meta[relay.attrs.BatchMatmulAttrs][51]); + %1418 = concatenate(%1416); + %1419 = dyn.reshape(%1417, %1418, newshape=[]); + %1420 = add(%1419, %bert_encoder_layer_6_attention_self_value_bias); + %1421 = shape_of(%1420, dtype="int64"); + %1422 = take(%1421, 0, axis=0); + %1423 = shape_of(%1420, dtype="int64"); + %1424 = take(%1423, 1, axis=0); + %1425 = expand_dims(%1422, axis=0); + %1426 = expand_dims(%1424, axis=0); + %1427 = (%1425, %1426, meta[relay.Constant][153], meta[relay.Constant][154]); + %1428 = concatenate(%1427); + %1429 = dyn.reshape(%1420, %1428, newshape=[]); + %1430 = transpose(%1429, axes=[0, 2, 1, 3]); + %1431 = shape_of(%1430, dtype="int64"); + %1432 = strided_slice(%1431, begin=[2], end=[4], strides=[1]); + %1433 = (meta[relay.Constant][155], %1432); + %1434 = concatenate(%1433); + %1435 = dyn.reshape(%1430, %1434, newshape=[]); + %1436 = dyn.reshape(%1401, %1405, newshape=[]); + %1437 = transpose(%1435, axes=[0, 2, 1]); + %1438 = strided_slice(%1402, begin=[0], end=[1], strides=[1]); + %1439 = strided_slice(%1431, begin=[0], end=[1], strides=[1]); + %1440 = strided_slice(%1402, begin=[1], end=[2], strides=[1]); + %1441 = strided_slice(%1431, begin=[1], end=[2], strides=[1]); + %1442 = maximum(%1438, %1439); + %1443 = maximum(%1440, %1441); + %1444 = (%1442, %1443); + %1445 = concatenate(%1444); + %1446 = strided_slice(%1402, begin=[2], end=[3], strides=[1]); + %1447 = strided_slice(%1431, begin=[3], end=[4], strides=[1]); + %1448 = (%1445, %1446, %1447); + %1449 = nn.batch_matmul(%1436, %1437, meta[relay.attrs.BatchMatmulAttrs][52]); + %1450 = concatenate(%1448); + %1451 = dyn.reshape(%1449, %1450, newshape=[]); + %1452 = transpose(%1451, axes=[0, 2, 1, 3]); + %1453 = shape_of(%1452, dtype="int64"); + %1454 = take(%1453, 0, axis=0); + %1455 = shape_of(%1452, dtype="int64"); + %1456 = take(%1455, 1, axis=0); + %1457 = expand_dims(%1454, axis=0); + %1458 = expand_dims(%1456, axis=0); + %1459 = (%1457, %1458, meta[relay.Constant][156]); + %1460 = concatenate(%1459); + %1461 = dyn.reshape(%1452, %1460, newshape=[]); + %1462 = shape_of(%1461, dtype="int64"); + %1463 = strided_slice(%1462, begin=[1], end=[3], strides=[1]); + %1464 = (meta[relay.Constant][157], %1463); + %1465 = concatenate(%1464); + %1466 = transpose(%bert_encoder_layer_6_attention_output_dense_weight, axes=[1, 0]); + %1467 = reshape(%1466, newshape=[-1, 1024, 1024]); + %1468 = dyn.reshape(%1461, %1465, newshape=[]); + %1469 = transpose(%1467, axes=[0, 2, 1]); + %1470 = strided_slice(%1462, begin=[0], end=[1], strides=[1]); + %1471 = strided_slice(%1462, begin=[1], end=[2], strides=[1]); + %1472 = (%1470, %1471, meta[relay.Constant][158]); + %1473 = nn.batch_matmul(%1468, %1469, meta[relay.attrs.BatchMatmulAttrs][53]); + %1474 = concatenate(%1472); + %1475 = dyn.reshape(%1473, %1474, newshape=[]); + %1476 = add(%1475, %bert_encoder_layer_6_attention_output_dense_bias); + %1477 = add(%1476, %1319); + %1478 = mean(%1477, axis=[-1], keepdims=True); + %1479 = subtract(%1477, %1478); + %1480 = power(%1479, 2f); + %1481 = mean(%1480, axis=[-1], keepdims=True); + %1482 = add(%1481, 1e-12f); + %1483 = sqrt(%1482); + %1484 = divide(%1479, %1483); + %1485 = multiply(%1484, %bert_encoder_layer_6_attention_output_LayerNorm_weight); + %1486 = add(%1485, %bert_encoder_layer_6_attention_output_LayerNorm_bias); + %1487 = shape_of(%1486, dtype="int64"); + %1488 = strided_slice(%1487, begin=[1], end=[3], strides=[1]); + %1489 = (meta[relay.Constant][159], %1488); + %1490 = concatenate(%1489); + %1491 = transpose(%bert_encoder_layer_6_intermediate_dense_weight, axes=[1, 0]); + %1492 = reshape(%1491, newshape=[-1, 1024, 4096]); + %1493 = dyn.reshape(%1486, %1490, newshape=[]); + %1494 = transpose(%1492, axes=[0, 2, 1]); + %1495 = strided_slice(%1487, begin=[0], end=[1], strides=[1]); + %1496 = strided_slice(%1487, begin=[1], end=[2], strides=[1]); + %1497 = (%1495, %1496, meta[relay.Constant][160]); + %1498 = nn.batch_matmul(%1493, %1494, meta[relay.attrs.BatchMatmulAttrs][54]); + %1499 = concatenate(%1497); + %1500 = dyn.reshape(%1498, %1499, newshape=[]); + %1501 = add(%1500, %bert_encoder_layer_6_intermediate_dense_bias); + %1502 = divide(%1501, 1.41421f); + %1503 = erf(%1502); + %1504 = multiply(%1501, 0.5f); + %1505 = add(%1503, 1f); + %1506 = multiply(%1504, %1505); + %1507 = shape_of(%1506, dtype="int64"); + %1508 = strided_slice(%1507, begin=[1], end=[3], strides=[1]); + %1509 = (meta[relay.Constant][161], %1508); + %1510 = concatenate(%1509); + %1511 = transpose(%bert_encoder_layer_6_output_dense_weight, axes=[1, 0]); + %1512 = reshape(%1511, newshape=[-1, 4096, 1024]); + %1513 = dyn.reshape(%1506, %1510, newshape=[]); + %1514 = transpose(%1512, axes=[0, 2, 1]); + %1515 = strided_slice(%1507, begin=[0], end=[1], strides=[1]); + %1516 = strided_slice(%1507, begin=[1], end=[2], strides=[1]); + %1517 = (%1515, %1516, meta[relay.Constant][162]); + %1518 = nn.batch_matmul(%1513, %1514, meta[relay.attrs.BatchMatmulAttrs][55]); + %1519 = concatenate(%1517); + %1520 = dyn.reshape(%1518, %1519, newshape=[]); + %1521 = add(%1520, %bert_encoder_layer_6_output_dense_bias); + %1522 = add(%1521, %1486); + %1523 = mean(%1522, axis=[-1], keepdims=True); + %1524 = subtract(%1522, %1523); + %1525 = power(%1524, 2f); + %1526 = mean(%1525, axis=[-1], keepdims=True); + %1527 = add(%1526, 1e-12f); + %1528 = sqrt(%1527); + %1529 = divide(%1524, %1528); + %1530 = multiply(%1529, %bert_encoder_layer_6_output_LayerNorm_weight); + %1531 = add(%1530, %bert_encoder_layer_6_output_LayerNorm_bias); + %1532 = shape_of(%1531, dtype="int64"); + %1533 = strided_slice(%1532, begin=[1], end=[3], strides=[1]); + %1534 = (meta[relay.Constant][163], %1533); + %1535 = concatenate(%1534); + %1536 = transpose(%bert_encoder_layer_7_attention_self_query_weight, axes=[1, 0]); + %1537 = reshape(%1536, newshape=[-1, 1024, 1024]); + %1538 = dyn.reshape(%1531, %1535, newshape=[]); + %1539 = transpose(%1537, axes=[0, 2, 1]); + %1540 = strided_slice(%1532, begin=[0], end=[1], strides=[1]); + %1541 = strided_slice(%1532, begin=[1], end=[2], strides=[1]); + %1542 = (%1540, %1541, meta[relay.Constant][164]); + %1543 = nn.batch_matmul(%1538, %1539, meta[relay.attrs.BatchMatmulAttrs][56]); + %1544 = concatenate(%1542); + %1545 = dyn.reshape(%1543, %1544, newshape=[]); + %1546 = add(%1545, %bert_encoder_layer_7_attention_self_query_bias); + %1547 = shape_of(%1546, dtype="int64"); + %1548 = take(%1547, 0, axis=0); + %1549 = shape_of(%1546, dtype="int64"); + %1550 = take(%1549, 1, axis=0); + %1551 = expand_dims(%1548, axis=0); + %1552 = expand_dims(%1550, axis=0); + %1553 = (%1551, %1552, meta[relay.Constant][165], meta[relay.Constant][166]); + %1554 = concatenate(%1553); + %1555 = dyn.reshape(%1546, %1554, newshape=[]); + %1556 = transpose(%1555, axes=[0, 2, 1, 3]); + %1557 = shape_of(%1556, dtype="int64"); + %1558 = strided_slice(%1557, begin=[2], end=[4], strides=[1]); + %1559 = (meta[relay.Constant][167], %1558); + %1560 = concatenate(%1559); + %1561 = shape_of(%1531, dtype="int64"); + %1562 = strided_slice(%1561, begin=[1], end=[3], strides=[1]); + %1563 = (meta[relay.Constant][168], %1562); + %1564 = concatenate(%1563); + %1565 = transpose(%bert_encoder_layer_7_attention_self_key_weight, axes=[1, 0]); + %1566 = reshape(%1565, newshape=[-1, 1024, 1024]); + %1567 = dyn.reshape(%1531, %1564, newshape=[]); + %1568 = transpose(%1566, axes=[0, 2, 1]); + %1569 = strided_slice(%1561, begin=[0], end=[1], strides=[1]); + %1570 = strided_slice(%1561, begin=[1], end=[2], strides=[1]); + %1571 = (%1569, %1570, meta[relay.Constant][169]); + %1572 = nn.batch_matmul(%1567, %1568, meta[relay.attrs.BatchMatmulAttrs][57]); + %1573 = concatenate(%1571); + %1574 = dyn.reshape(%1572, %1573, newshape=[]); + %1575 = add(%1574, %bert_encoder_layer_7_attention_self_key_bias); + %1576 = shape_of(%1575, dtype="int64"); + %1577 = take(%1576, 0, axis=0); + %1578 = shape_of(%1575, dtype="int64"); + %1579 = take(%1578, 1, axis=0); + %1580 = expand_dims(%1577, axis=0); + %1581 = expand_dims(%1579, axis=0); + %1582 = (%1580, %1581, meta[relay.Constant][170], meta[relay.Constant][171]); + %1583 = concatenate(%1582); + %1584 = dyn.reshape(%1575, %1583, newshape=[]); + %1585 = transpose(%1584, axes=[0, 2, 3, 1]); + %1586 = shape_of(%1585, dtype="int64"); + %1587 = strided_slice(%1586, begin=[2], end=[4], strides=[1]); + %1588 = (meta[relay.Constant][172], %1587); + %1589 = concatenate(%1588); + %1590 = dyn.reshape(%1585, %1589, newshape=[]); + %1591 = dyn.reshape(%1556, %1560, newshape=[]); + %1592 = transpose(%1590, axes=[0, 2, 1]); + %1593 = strided_slice(%1557, begin=[0], end=[1], strides=[1]); + %1594 = strided_slice(%1586, begin=[0], end=[1], strides=[1]); + %1595 = strided_slice(%1557, begin=[1], end=[2], strides=[1]); + %1596 = strided_slice(%1586, begin=[1], end=[2], strides=[1]); + %1597 = maximum(%1593, %1594); + %1598 = maximum(%1595, %1596); + %1599 = (%1597, %1598); + %1600 = concatenate(%1599); + %1601 = strided_slice(%1557, begin=[2], end=[3], strides=[1]); + %1602 = strided_slice(%1586, begin=[3], end=[4], strides=[1]); + %1603 = (%1600, %1601, %1602); + %1604 = nn.batch_matmul(%1591, %1592, meta[relay.attrs.BatchMatmulAttrs][58]); + %1605 = concatenate(%1603); + %1606 = dyn.reshape(%1604, %1605, newshape=[]); + %1607 = divide(%1606, 8f); + %1608 = add(%1607, %123); + %1609 = max(%1608, axis=[3], keepdims=True); + %1610 = subtract(%1608, %1609); + %1611 = exp(%1610); + %1612 = sum(%1611, axis=[3], keepdims=True); + %1613 = divide(%1611, %1612); + %1614 = shape_of(%1613, dtype="int64"); + %1615 = strided_slice(%1614, begin=[2], end=[4], strides=[1]); + %1616 = (meta[relay.Constant][173], %1615); + %1617 = concatenate(%1616); + %1618 = shape_of(%1531, dtype="int64"); + %1619 = strided_slice(%1618, begin=[1], end=[3], strides=[1]); + %1620 = (meta[relay.Constant][174], %1619); + %1621 = concatenate(%1620); + %1622 = transpose(%bert_encoder_layer_7_attention_self_value_weight, axes=[1, 0]); + %1623 = reshape(%1622, newshape=[-1, 1024, 1024]); + %1624 = dyn.reshape(%1531, %1621, newshape=[]); + %1625 = transpose(%1623, axes=[0, 2, 1]); + %1626 = strided_slice(%1618, begin=[0], end=[1], strides=[1]); + %1627 = strided_slice(%1618, begin=[1], end=[2], strides=[1]); + %1628 = (%1626, %1627, meta[relay.Constant][175]); + %1629 = nn.batch_matmul(%1624, %1625, meta[relay.attrs.BatchMatmulAttrs][59]); + %1630 = concatenate(%1628); + %1631 = dyn.reshape(%1629, %1630, newshape=[]); + %1632 = add(%1631, %bert_encoder_layer_7_attention_self_value_bias); + %1633 = shape_of(%1632, dtype="int64"); + %1634 = take(%1633, 0, axis=0); + %1635 = shape_of(%1632, dtype="int64"); + %1636 = take(%1635, 1, axis=0); + %1637 = expand_dims(%1634, axis=0); + %1638 = expand_dims(%1636, axis=0); + %1639 = (%1637, %1638, meta[relay.Constant][176], meta[relay.Constant][177]); + %1640 = concatenate(%1639); + %1641 = dyn.reshape(%1632, %1640, newshape=[]); + %1642 = transpose(%1641, axes=[0, 2, 1, 3]); + %1643 = shape_of(%1642, dtype="int64"); + %1644 = strided_slice(%1643, begin=[2], end=[4], strides=[1]); + %1645 = (meta[relay.Constant][178], %1644); + %1646 = concatenate(%1645); + %1647 = dyn.reshape(%1642, %1646, newshape=[]); + %1648 = dyn.reshape(%1613, %1617, newshape=[]); + %1649 = transpose(%1647, axes=[0, 2, 1]); + %1650 = strided_slice(%1614, begin=[0], end=[1], strides=[1]); + %1651 = strided_slice(%1643, begin=[0], end=[1], strides=[1]); + %1652 = strided_slice(%1614, begin=[1], end=[2], strides=[1]); + %1653 = strided_slice(%1643, begin=[1], end=[2], strides=[1]); + %1654 = maximum(%1650, %1651); + %1655 = maximum(%1652, %1653); + %1656 = (%1654, %1655); + %1657 = concatenate(%1656); + %1658 = strided_slice(%1614, begin=[2], end=[3], strides=[1]); + %1659 = strided_slice(%1643, begin=[3], end=[4], strides=[1]); + %1660 = (%1657, %1658, %1659); + %1661 = nn.batch_matmul(%1648, %1649, meta[relay.attrs.BatchMatmulAttrs][60]); + %1662 = concatenate(%1660); + %1663 = dyn.reshape(%1661, %1662, newshape=[]); + %1664 = transpose(%1663, axes=[0, 2, 1, 3]); + %1665 = shape_of(%1664, dtype="int64"); + %1666 = take(%1665, 0, axis=0); + %1667 = shape_of(%1664, dtype="int64"); + %1668 = take(%1667, 1, axis=0); + %1669 = expand_dims(%1666, axis=0); + %1670 = expand_dims(%1668, axis=0); + %1671 = (%1669, %1670, meta[relay.Constant][179]); + %1672 = concatenate(%1671); + %1673 = dyn.reshape(%1664, %1672, newshape=[]); + %1674 = shape_of(%1673, dtype="int64"); + %1675 = strided_slice(%1674, begin=[1], end=[3], strides=[1]); + %1676 = (meta[relay.Constant][180], %1675); + %1677 = concatenate(%1676); + %1678 = transpose(%bert_encoder_layer_7_attention_output_dense_weight, axes=[1, 0]); + %1679 = reshape(%1678, newshape=[-1, 1024, 1024]); + %1680 = dyn.reshape(%1673, %1677, newshape=[]); + %1681 = transpose(%1679, axes=[0, 2, 1]); + %1682 = strided_slice(%1674, begin=[0], end=[1], strides=[1]); + %1683 = strided_slice(%1674, begin=[1], end=[2], strides=[1]); + %1684 = (%1682, %1683, meta[relay.Constant][181]); + %1685 = nn.batch_matmul(%1680, %1681, meta[relay.attrs.BatchMatmulAttrs][61]); + %1686 = concatenate(%1684); + %1687 = dyn.reshape(%1685, %1686, newshape=[]); + %1688 = add(%1687, %bert_encoder_layer_7_attention_output_dense_bias); + %1689 = add(%1688, %1531); + %1690 = mean(%1689, axis=[-1], keepdims=True); + %1691 = subtract(%1689, %1690); + %1692 = power(%1691, 2f); + %1693 = mean(%1692, axis=[-1], keepdims=True); + %1694 = add(%1693, 1e-12f); + %1695 = sqrt(%1694); + %1696 = divide(%1691, %1695); + %1697 = multiply(%1696, %bert_encoder_layer_7_attention_output_LayerNorm_weight); + %1698 = add(%1697, %bert_encoder_layer_7_attention_output_LayerNorm_bias); + %1699 = shape_of(%1698, dtype="int64"); + %1700 = strided_slice(%1699, begin=[1], end=[3], strides=[1]); + %1701 = (meta[relay.Constant][182], %1700); + %1702 = concatenate(%1701); + %1703 = transpose(%bert_encoder_layer_7_intermediate_dense_weight, axes=[1, 0]); + %1704 = reshape(%1703, newshape=[-1, 1024, 4096]); + %1705 = dyn.reshape(%1698, %1702, newshape=[]); + %1706 = transpose(%1704, axes=[0, 2, 1]); + %1707 = strided_slice(%1699, begin=[0], end=[1], strides=[1]); + %1708 = strided_slice(%1699, begin=[1], end=[2], strides=[1]); + %1709 = (%1707, %1708, meta[relay.Constant][183]); + %1710 = nn.batch_matmul(%1705, %1706, meta[relay.attrs.BatchMatmulAttrs][62]); + %1711 = concatenate(%1709); + %1712 = dyn.reshape(%1710, %1711, newshape=[]); + %1713 = add(%1712, %bert_encoder_layer_7_intermediate_dense_bias); + %1714 = divide(%1713, 1.41421f); + %1715 = erf(%1714); + %1716 = multiply(%1713, 0.5f); + %1717 = add(%1715, 1f); + %1718 = multiply(%1716, %1717); + %1719 = shape_of(%1718, dtype="int64"); + %1720 = strided_slice(%1719, begin=[1], end=[3], strides=[1]); + %1721 = (meta[relay.Constant][184], %1720); + %1722 = concatenate(%1721); + %1723 = transpose(%bert_encoder_layer_7_output_dense_weight, axes=[1, 0]); + %1724 = reshape(%1723, newshape=[-1, 4096, 1024]); + %1725 = dyn.reshape(%1718, %1722, newshape=[]); + %1726 = transpose(%1724, axes=[0, 2, 1]); + %1727 = strided_slice(%1719, begin=[0], end=[1], strides=[1]); + %1728 = strided_slice(%1719, begin=[1], end=[2], strides=[1]); + %1729 = (%1727, %1728, meta[relay.Constant][185]); + %1730 = nn.batch_matmul(%1725, %1726, meta[relay.attrs.BatchMatmulAttrs][63]); + %1731 = concatenate(%1729); + %1732 = dyn.reshape(%1730, %1731, newshape=[]); + %1733 = add(%1732, %bert_encoder_layer_7_output_dense_bias); + %1734 = add(%1733, %1698); + %1735 = mean(%1734, axis=[-1], keepdims=True); + %1736 = subtract(%1734, %1735); + %1737 = power(%1736, 2f); + %1738 = mean(%1737, axis=[-1], keepdims=True); + %1739 = add(%1738, 1e-12f); + %1740 = sqrt(%1739); + %1741 = divide(%1736, %1740); + %1742 = multiply(%1741, %bert_encoder_layer_7_output_LayerNorm_weight); + %1743 = add(%1742, %bert_encoder_layer_7_output_LayerNorm_bias); + %1744 = shape_of(%1743, dtype="int64"); + %1745 = strided_slice(%1744, begin=[1], end=[3], strides=[1]); + %1746 = (meta[relay.Constant][186], %1745); + %1747 = concatenate(%1746); + %1748 = transpose(%bert_encoder_layer_8_attention_self_query_weight, axes=[1, 0]); + %1749 = reshape(%1748, newshape=[-1, 1024, 1024]); + %1750 = dyn.reshape(%1743, %1747, newshape=[]); + %1751 = transpose(%1749, axes=[0, 2, 1]); + %1752 = strided_slice(%1744, begin=[0], end=[1], strides=[1]); + %1753 = strided_slice(%1744, begin=[1], end=[2], strides=[1]); + %1754 = (%1752, %1753, meta[relay.Constant][187]); + %1755 = nn.batch_matmul(%1750, %1751, meta[relay.attrs.BatchMatmulAttrs][64]); + %1756 = concatenate(%1754); + %1757 = dyn.reshape(%1755, %1756, newshape=[]); + %1758 = add(%1757, %bert_encoder_layer_8_attention_self_query_bias); + %1759 = shape_of(%1758, dtype="int64"); + %1760 = take(%1759, 0, axis=0); + %1761 = shape_of(%1758, dtype="int64"); + %1762 = take(%1761, 1, axis=0); + %1763 = expand_dims(%1760, axis=0); + %1764 = expand_dims(%1762, axis=0); + %1765 = (%1763, %1764, meta[relay.Constant][188], meta[relay.Constant][189]); + %1766 = concatenate(%1765); + %1767 = dyn.reshape(%1758, %1766, newshape=[]); + %1768 = transpose(%1767, axes=[0, 2, 1, 3]); + %1769 = shape_of(%1768, dtype="int64"); + %1770 = strided_slice(%1769, begin=[2], end=[4], strides=[1]); + %1771 = (meta[relay.Constant][190], %1770); + %1772 = concatenate(%1771); + %1773 = shape_of(%1743, dtype="int64"); + %1774 = strided_slice(%1773, begin=[1], end=[3], strides=[1]); + %1775 = (meta[relay.Constant][191], %1774); + %1776 = concatenate(%1775); + %1777 = transpose(%bert_encoder_layer_8_attention_self_key_weight, axes=[1, 0]); + %1778 = reshape(%1777, newshape=[-1, 1024, 1024]); + %1779 = dyn.reshape(%1743, %1776, newshape=[]); + %1780 = transpose(%1778, axes=[0, 2, 1]); + %1781 = strided_slice(%1773, begin=[0], end=[1], strides=[1]); + %1782 = strided_slice(%1773, begin=[1], end=[2], strides=[1]); + %1783 = (%1781, %1782, meta[relay.Constant][192]); + %1784 = nn.batch_matmul(%1779, %1780, meta[relay.attrs.BatchMatmulAttrs][65]); + %1785 = concatenate(%1783); + %1786 = dyn.reshape(%1784, %1785, newshape=[]); + %1787 = add(%1786, %bert_encoder_layer_8_attention_self_key_bias); + %1788 = shape_of(%1787, dtype="int64"); + %1789 = take(%1788, 0, axis=0); + %1790 = shape_of(%1787, dtype="int64"); + %1791 = take(%1790, 1, axis=0); + %1792 = expand_dims(%1789, axis=0); + %1793 = expand_dims(%1791, axis=0); + %1794 = (%1792, %1793, meta[relay.Constant][193], meta[relay.Constant][194]); + %1795 = concatenate(%1794); + %1796 = dyn.reshape(%1787, %1795, newshape=[]); + %1797 = transpose(%1796, axes=[0, 2, 3, 1]); + %1798 = shape_of(%1797, dtype="int64"); + %1799 = strided_slice(%1798, begin=[2], end=[4], strides=[1]); + %1800 = (meta[relay.Constant][195], %1799); + %1801 = concatenate(%1800); + %1802 = dyn.reshape(%1797, %1801, newshape=[]); + %1803 = dyn.reshape(%1768, %1772, newshape=[]); + %1804 = transpose(%1802, axes=[0, 2, 1]); + %1805 = strided_slice(%1769, begin=[0], end=[1], strides=[1]); + %1806 = strided_slice(%1798, begin=[0], end=[1], strides=[1]); + %1807 = strided_slice(%1769, begin=[1], end=[2], strides=[1]); + %1808 = strided_slice(%1798, begin=[1], end=[2], strides=[1]); + %1809 = maximum(%1805, %1806); + %1810 = maximum(%1807, %1808); + %1811 = (%1809, %1810); + %1812 = concatenate(%1811); + %1813 = strided_slice(%1769, begin=[2], end=[3], strides=[1]); + %1814 = strided_slice(%1798, begin=[3], end=[4], strides=[1]); + %1815 = (%1812, %1813, %1814); + %1816 = nn.batch_matmul(%1803, %1804, meta[relay.attrs.BatchMatmulAttrs][66]); + %1817 = concatenate(%1815); + %1818 = dyn.reshape(%1816, %1817, newshape=[]); + %1819 = divide(%1818, 8f); + %1820 = add(%1819, %123); + %1821 = max(%1820, axis=[3], keepdims=True); + %1822 = subtract(%1820, %1821); + %1823 = exp(%1822); + %1824 = sum(%1823, axis=[3], keepdims=True); + %1825 = divide(%1823, %1824); + %1826 = shape_of(%1825, dtype="int64"); + %1827 = strided_slice(%1826, begin=[2], end=[4], strides=[1]); + %1828 = (meta[relay.Constant][196], %1827); + %1829 = concatenate(%1828); + %1830 = shape_of(%1743, dtype="int64"); + %1831 = strided_slice(%1830, begin=[1], end=[3], strides=[1]); + %1832 = (meta[relay.Constant][197], %1831); + %1833 = concatenate(%1832); + %1834 = transpose(%bert_encoder_layer_8_attention_self_value_weight, axes=[1, 0]); + %1835 = reshape(%1834, newshape=[-1, 1024, 1024]); + %1836 = dyn.reshape(%1743, %1833, newshape=[]); + %1837 = transpose(%1835, axes=[0, 2, 1]); + %1838 = strided_slice(%1830, begin=[0], end=[1], strides=[1]); + %1839 = strided_slice(%1830, begin=[1], end=[2], strides=[1]); + %1840 = (%1838, %1839, meta[relay.Constant][198]); + %1841 = nn.batch_matmul(%1836, %1837, meta[relay.attrs.BatchMatmulAttrs][67]); + %1842 = concatenate(%1840); + %1843 = dyn.reshape(%1841, %1842, newshape=[]); + %1844 = add(%1843, %bert_encoder_layer_8_attention_self_value_bias); + %1845 = shape_of(%1844, dtype="int64"); + %1846 = take(%1845, 0, axis=0); + %1847 = shape_of(%1844, dtype="int64"); + %1848 = take(%1847, 1, axis=0); + %1849 = expand_dims(%1846, axis=0); + %1850 = expand_dims(%1848, axis=0); + %1851 = (%1849, %1850, meta[relay.Constant][199], meta[relay.Constant][200]); + %1852 = concatenate(%1851); + %1853 = dyn.reshape(%1844, %1852, newshape=[]); + %1854 = transpose(%1853, axes=[0, 2, 1, 3]); + %1855 = shape_of(%1854, dtype="int64"); + %1856 = strided_slice(%1855, begin=[2], end=[4], strides=[1]); + %1857 = (meta[relay.Constant][201], %1856); + %1858 = concatenate(%1857); + %1859 = dyn.reshape(%1854, %1858, newshape=[]); + %1860 = dyn.reshape(%1825, %1829, newshape=[]); + %1861 = transpose(%1859, axes=[0, 2, 1]); + %1862 = strided_slice(%1826, begin=[0], end=[1], strides=[1]); + %1863 = strided_slice(%1855, begin=[0], end=[1], strides=[1]); + %1864 = strided_slice(%1826, begin=[1], end=[2], strides=[1]); + %1865 = strided_slice(%1855, begin=[1], end=[2], strides=[1]); + %1866 = maximum(%1862, %1863); + %1867 = maximum(%1864, %1865); + %1868 = (%1866, %1867); + %1869 = concatenate(%1868); + %1870 = strided_slice(%1826, begin=[2], end=[3], strides=[1]); + %1871 = strided_slice(%1855, begin=[3], end=[4], strides=[1]); + %1872 = (%1869, %1870, %1871); + %1873 = nn.batch_matmul(%1860, %1861, meta[relay.attrs.BatchMatmulAttrs][68]); + %1874 = concatenate(%1872); + %1875 = dyn.reshape(%1873, %1874, newshape=[]); + %1876 = transpose(%1875, axes=[0, 2, 1, 3]); + %1877 = shape_of(%1876, dtype="int64"); + %1878 = take(%1877, 0, axis=0); + %1879 = shape_of(%1876, dtype="int64"); + %1880 = take(%1879, 1, axis=0); + %1881 = expand_dims(%1878, axis=0); + %1882 = expand_dims(%1880, axis=0); + %1883 = (%1881, %1882, meta[relay.Constant][202]); + %1884 = concatenate(%1883); + %1885 = dyn.reshape(%1876, %1884, newshape=[]); + %1886 = shape_of(%1885, dtype="int64"); + %1887 = strided_slice(%1886, begin=[1], end=[3], strides=[1]); + %1888 = (meta[relay.Constant][203], %1887); + %1889 = concatenate(%1888); + %1890 = transpose(%bert_encoder_layer_8_attention_output_dense_weight, axes=[1, 0]); + %1891 = reshape(%1890, newshape=[-1, 1024, 1024]); + %1892 = dyn.reshape(%1885, %1889, newshape=[]); + %1893 = transpose(%1891, axes=[0, 2, 1]); + %1894 = strided_slice(%1886, begin=[0], end=[1], strides=[1]); + %1895 = strided_slice(%1886, begin=[1], end=[2], strides=[1]); + %1896 = (%1894, %1895, meta[relay.Constant][204]); + %1897 = nn.batch_matmul(%1892, %1893, meta[relay.attrs.BatchMatmulAttrs][69]); + %1898 = concatenate(%1896); + %1899 = dyn.reshape(%1897, %1898, newshape=[]); + %1900 = add(%1899, %bert_encoder_layer_8_attention_output_dense_bias); + %1901 = add(%1900, %1743); + %1902 = mean(%1901, axis=[-1], keepdims=True); + %1903 = subtract(%1901, %1902); + %1904 = power(%1903, 2f); + %1905 = mean(%1904, axis=[-1], keepdims=True); + %1906 = add(%1905, 1e-12f); + %1907 = sqrt(%1906); + %1908 = divide(%1903, %1907); + %1909 = multiply(%1908, %bert_encoder_layer_8_attention_output_LayerNorm_weight); + %1910 = add(%1909, %bert_encoder_layer_8_attention_output_LayerNorm_bias); + %1911 = shape_of(%1910, dtype="int64"); + %1912 = strided_slice(%1911, begin=[1], end=[3], strides=[1]); + %1913 = (meta[relay.Constant][205], %1912); + %1914 = concatenate(%1913); + %1915 = transpose(%bert_encoder_layer_8_intermediate_dense_weight, axes=[1, 0]); + %1916 = reshape(%1915, newshape=[-1, 1024, 4096]); + %1917 = dyn.reshape(%1910, %1914, newshape=[]); + %1918 = transpose(%1916, axes=[0, 2, 1]); + %1919 = strided_slice(%1911, begin=[0], end=[1], strides=[1]); + %1920 = strided_slice(%1911, begin=[1], end=[2], strides=[1]); + %1921 = (%1919, %1920, meta[relay.Constant][206]); + %1922 = nn.batch_matmul(%1917, %1918, meta[relay.attrs.BatchMatmulAttrs][70]); + %1923 = concatenate(%1921); + %1924 = dyn.reshape(%1922, %1923, newshape=[]); + %1925 = add(%1924, %bert_encoder_layer_8_intermediate_dense_bias); + %1926 = divide(%1925, 1.41421f); + %1927 = erf(%1926); + %1928 = multiply(%1925, 0.5f); + %1929 = add(%1927, 1f); + %1930 = multiply(%1928, %1929); + %1931 = shape_of(%1930, dtype="int64"); + %1932 = strided_slice(%1931, begin=[1], end=[3], strides=[1]); + %1933 = (meta[relay.Constant][207], %1932); + %1934 = concatenate(%1933); + %1935 = transpose(%bert_encoder_layer_8_output_dense_weight, axes=[1, 0]); + %1936 = reshape(%1935, newshape=[-1, 4096, 1024]); + %1937 = dyn.reshape(%1930, %1934, newshape=[]); + %1938 = transpose(%1936, axes=[0, 2, 1]); + %1939 = strided_slice(%1931, begin=[0], end=[1], strides=[1]); + %1940 = strided_slice(%1931, begin=[1], end=[2], strides=[1]); + %1941 = (%1939, %1940, meta[relay.Constant][208]); + %1942 = nn.batch_matmul(%1937, %1938, meta[relay.attrs.BatchMatmulAttrs][71]); + %1943 = concatenate(%1941); + %1944 = dyn.reshape(%1942, %1943, newshape=[]); + %1945 = add(%1944, %bert_encoder_layer_8_output_dense_bias); + %1946 = add(%1945, %1910); + %1947 = mean(%1946, axis=[-1], keepdims=True); + %1948 = subtract(%1946, %1947); + %1949 = power(%1948, 2f); + %1950 = mean(%1949, axis=[-1], keepdims=True); + %1951 = add(%1950, 1e-12f); + %1952 = sqrt(%1951); + %1953 = divide(%1948, %1952); + %1954 = multiply(%1953, %bert_encoder_layer_8_output_LayerNorm_weight); + %1955 = add(%1954, %bert_encoder_layer_8_output_LayerNorm_bias); + %1956 = shape_of(%1955, dtype="int64"); + %1957 = strided_slice(%1956, begin=[1], end=[3], strides=[1]); + %1958 = (meta[relay.Constant][209], %1957); + %1959 = concatenate(%1958); + %1960 = transpose(%bert_encoder_layer_9_attention_self_query_weight, axes=[1, 0]); + %1961 = reshape(%1960, newshape=[-1, 1024, 1024]); + %1962 = dyn.reshape(%1955, %1959, newshape=[]); + %1963 = transpose(%1961, axes=[0, 2, 1]); + %1964 = strided_slice(%1956, begin=[0], end=[1], strides=[1]); + %1965 = strided_slice(%1956, begin=[1], end=[2], strides=[1]); + %1966 = (%1964, %1965, meta[relay.Constant][210]); + %1967 = nn.batch_matmul(%1962, %1963, meta[relay.attrs.BatchMatmulAttrs][72]); + %1968 = concatenate(%1966); + %1969 = dyn.reshape(%1967, %1968, newshape=[]); + %1970 = add(%1969, %bert_encoder_layer_9_attention_self_query_bias); + %1971 = shape_of(%1970, dtype="int64"); + %1972 = take(%1971, 0, axis=0); + %1973 = shape_of(%1970, dtype="int64"); + %1974 = take(%1973, 1, axis=0); + %1975 = expand_dims(%1972, axis=0); + %1976 = expand_dims(%1974, axis=0); + %1977 = (%1975, %1976, meta[relay.Constant][211], meta[relay.Constant][212]); + %1978 = concatenate(%1977); + %1979 = dyn.reshape(%1970, %1978, newshape=[]); + %1980 = transpose(%1979, axes=[0, 2, 1, 3]); + %1981 = shape_of(%1980, dtype="int64"); + %1982 = strided_slice(%1981, begin=[2], end=[4], strides=[1]); + %1983 = (meta[relay.Constant][213], %1982); + %1984 = concatenate(%1983); + %1985 = shape_of(%1955, dtype="int64"); + %1986 = strided_slice(%1985, begin=[1], end=[3], strides=[1]); + %1987 = (meta[relay.Constant][214], %1986); + %1988 = concatenate(%1987); + %1989 = transpose(%bert_encoder_layer_9_attention_self_key_weight, axes=[1, 0]); + %1990 = reshape(%1989, newshape=[-1, 1024, 1024]); + %1991 = dyn.reshape(%1955, %1988, newshape=[]); + %1992 = transpose(%1990, axes=[0, 2, 1]); + %1993 = strided_slice(%1985, begin=[0], end=[1], strides=[1]); + %1994 = strided_slice(%1985, begin=[1], end=[2], strides=[1]); + %1995 = (%1993, %1994, meta[relay.Constant][215]); + %1996 = nn.batch_matmul(%1991, %1992, meta[relay.attrs.BatchMatmulAttrs][73]); + %1997 = concatenate(%1995); + %1998 = dyn.reshape(%1996, %1997, newshape=[]); + %1999 = add(%1998, %bert_encoder_layer_9_attention_self_key_bias); + %2000 = shape_of(%1999, dtype="int64"); + %2001 = take(%2000, 0, axis=0); + %2002 = shape_of(%1999, dtype="int64"); + %2003 = take(%2002, 1, axis=0); + %2004 = expand_dims(%2001, axis=0); + %2005 = expand_dims(%2003, axis=0); + %2006 = (%2004, %2005, meta[relay.Constant][216], meta[relay.Constant][217]); + %2007 = concatenate(%2006); + %2008 = dyn.reshape(%1999, %2007, newshape=[]); + %2009 = transpose(%2008, axes=[0, 2, 3, 1]); + %2010 = shape_of(%2009, dtype="int64"); + %2011 = strided_slice(%2010, begin=[2], end=[4], strides=[1]); + %2012 = (meta[relay.Constant][218], %2011); + %2013 = concatenate(%2012); + %2014 = dyn.reshape(%2009, %2013, newshape=[]); + %2015 = dyn.reshape(%1980, %1984, newshape=[]); + %2016 = transpose(%2014, axes=[0, 2, 1]); + %2017 = strided_slice(%1981, begin=[0], end=[1], strides=[1]); + %2018 = strided_slice(%2010, begin=[0], end=[1], strides=[1]); + %2019 = strided_slice(%1981, begin=[1], end=[2], strides=[1]); + %2020 = strided_slice(%2010, begin=[1], end=[2], strides=[1]); + %2021 = maximum(%2017, %2018); + %2022 = maximum(%2019, %2020); + %2023 = (%2021, %2022); + %2024 = concatenate(%2023); + %2025 = strided_slice(%1981, begin=[2], end=[3], strides=[1]); + %2026 = strided_slice(%2010, begin=[3], end=[4], strides=[1]); + %2027 = (%2024, %2025, %2026); + %2028 = nn.batch_matmul(%2015, %2016, meta[relay.attrs.BatchMatmulAttrs][74]); + %2029 = concatenate(%2027); + %2030 = dyn.reshape(%2028, %2029, newshape=[]); + %2031 = divide(%2030, 8f); + %2032 = add(%2031, %123); + %2033 = max(%2032, axis=[3], keepdims=True); + %2034 = subtract(%2032, %2033); + %2035 = exp(%2034); + %2036 = sum(%2035, axis=[3], keepdims=True); + %2037 = divide(%2035, %2036); + %2038 = shape_of(%2037, dtype="int64"); + %2039 = strided_slice(%2038, begin=[2], end=[4], strides=[1]); + %2040 = (meta[relay.Constant][219], %2039); + %2041 = concatenate(%2040); + %2042 = shape_of(%1955, dtype="int64"); + %2043 = strided_slice(%2042, begin=[1], end=[3], strides=[1]); + %2044 = (meta[relay.Constant][220], %2043); + %2045 = concatenate(%2044); + %2046 = transpose(%bert_encoder_layer_9_attention_self_value_weight, axes=[1, 0]); + %2047 = reshape(%2046, newshape=[-1, 1024, 1024]); + %2048 = dyn.reshape(%1955, %2045, newshape=[]); + %2049 = transpose(%2047, axes=[0, 2, 1]); + %2050 = strided_slice(%2042, begin=[0], end=[1], strides=[1]); + %2051 = strided_slice(%2042, begin=[1], end=[2], strides=[1]); + %2052 = (%2050, %2051, meta[relay.Constant][221]); + %2053 = nn.batch_matmul(%2048, %2049, meta[relay.attrs.BatchMatmulAttrs][75]); + %2054 = concatenate(%2052); + %2055 = dyn.reshape(%2053, %2054, newshape=[]); + %2056 = add(%2055, %bert_encoder_layer_9_attention_self_value_bias); + %2057 = shape_of(%2056, dtype="int64"); + %2058 = take(%2057, 0, axis=0); + %2059 = shape_of(%2056, dtype="int64"); + %2060 = take(%2059, 1, axis=0); + %2061 = expand_dims(%2058, axis=0); + %2062 = expand_dims(%2060, axis=0); + %2063 = (%2061, %2062, meta[relay.Constant][222], meta[relay.Constant][223]); + %2064 = concatenate(%2063); + %2065 = dyn.reshape(%2056, %2064, newshape=[]); + %2066 = transpose(%2065, axes=[0, 2, 1, 3]); + %2067 = shape_of(%2066, dtype="int64"); + %2068 = strided_slice(%2067, begin=[2], end=[4], strides=[1]); + %2069 = (meta[relay.Constant][224], %2068); + %2070 = concatenate(%2069); + %2071 = dyn.reshape(%2066, %2070, newshape=[]); + %2072 = dyn.reshape(%2037, %2041, newshape=[]); + %2073 = transpose(%2071, axes=[0, 2, 1]); + %2074 = strided_slice(%2038, begin=[0], end=[1], strides=[1]); + %2075 = strided_slice(%2067, begin=[0], end=[1], strides=[1]); + %2076 = strided_slice(%2038, begin=[1], end=[2], strides=[1]); + %2077 = strided_slice(%2067, begin=[1], end=[2], strides=[1]); + %2078 = maximum(%2074, %2075); + %2079 = maximum(%2076, %2077); + %2080 = (%2078, %2079); + %2081 = concatenate(%2080); + %2082 = strided_slice(%2038, begin=[2], end=[3], strides=[1]); + %2083 = strided_slice(%2067, begin=[3], end=[4], strides=[1]); + %2084 = (%2081, %2082, %2083); + %2085 = nn.batch_matmul(%2072, %2073, meta[relay.attrs.BatchMatmulAttrs][76]); + %2086 = concatenate(%2084); + %2087 = dyn.reshape(%2085, %2086, newshape=[]); + %2088 = transpose(%2087, axes=[0, 2, 1, 3]); + %2089 = shape_of(%2088, dtype="int64"); + %2090 = take(%2089, 0, axis=0); + %2091 = shape_of(%2088, dtype="int64"); + %2092 = take(%2091, 1, axis=0); + %2093 = expand_dims(%2090, axis=0); + %2094 = expand_dims(%2092, axis=0); + %2095 = (%2093, %2094, meta[relay.Constant][225]); + %2096 = concatenate(%2095); + %2097 = dyn.reshape(%2088, %2096, newshape=[]); + %2098 = shape_of(%2097, dtype="int64"); + %2099 = strided_slice(%2098, begin=[1], end=[3], strides=[1]); + %2100 = (meta[relay.Constant][226], %2099); + %2101 = concatenate(%2100); + %2102 = transpose(%bert_encoder_layer_9_attention_output_dense_weight, axes=[1, 0]); + %2103 = reshape(%2102, newshape=[-1, 1024, 1024]); + %2104 = dyn.reshape(%2097, %2101, newshape=[]); + %2105 = transpose(%2103, axes=[0, 2, 1]); + %2106 = strided_slice(%2098, begin=[0], end=[1], strides=[1]); + %2107 = strided_slice(%2098, begin=[1], end=[2], strides=[1]); + %2108 = (%2106, %2107, meta[relay.Constant][227]); + %2109 = nn.batch_matmul(%2104, %2105, meta[relay.attrs.BatchMatmulAttrs][77]); + %2110 = concatenate(%2108); + %2111 = dyn.reshape(%2109, %2110, newshape=[]); + %2112 = add(%2111, %bert_encoder_layer_9_attention_output_dense_bias); + %2113 = add(%2112, %1955); + %2114 = mean(%2113, axis=[-1], keepdims=True); + %2115 = subtract(%2113, %2114); + %2116 = power(%2115, 2f); + %2117 = mean(%2116, axis=[-1], keepdims=True); + %2118 = add(%2117, 1e-12f); + %2119 = sqrt(%2118); + %2120 = divide(%2115, %2119); + %2121 = multiply(%2120, %bert_encoder_layer_9_attention_output_LayerNorm_weight); + %2122 = add(%2121, %bert_encoder_layer_9_attention_output_LayerNorm_bias); + %2123 = shape_of(%2122, dtype="int64"); + %2124 = strided_slice(%2123, begin=[1], end=[3], strides=[1]); + %2125 = (meta[relay.Constant][228], %2124); + %2126 = concatenate(%2125); + %2127 = transpose(%bert_encoder_layer_9_intermediate_dense_weight, axes=[1, 0]); + %2128 = reshape(%2127, newshape=[-1, 1024, 4096]); + %2129 = dyn.reshape(%2122, %2126, newshape=[]); + %2130 = transpose(%2128, axes=[0, 2, 1]); + %2131 = strided_slice(%2123, begin=[0], end=[1], strides=[1]); + %2132 = strided_slice(%2123, begin=[1], end=[2], strides=[1]); + %2133 = (%2131, %2132, meta[relay.Constant][229]); + %2134 = nn.batch_matmul(%2129, %2130, meta[relay.attrs.BatchMatmulAttrs][78]); + %2135 = concatenate(%2133); + %2136 = dyn.reshape(%2134, %2135, newshape=[]); + %2137 = add(%2136, %bert_encoder_layer_9_intermediate_dense_bias); + %2138 = divide(%2137, 1.41421f); + %2139 = erf(%2138); + %2140 = multiply(%2137, 0.5f); + %2141 = add(%2139, 1f); + %2142 = multiply(%2140, %2141); + %2143 = shape_of(%2142, dtype="int64"); + %2144 = strided_slice(%2143, begin=[1], end=[3], strides=[1]); + %2145 = (meta[relay.Constant][230], %2144); + %2146 = concatenate(%2145); + %2147 = transpose(%bert_encoder_layer_9_output_dense_weight, axes=[1, 0]); + %2148 = reshape(%2147, newshape=[-1, 4096, 1024]); + %2149 = dyn.reshape(%2142, %2146, newshape=[]); + %2150 = transpose(%2148, axes=[0, 2, 1]); + %2151 = strided_slice(%2143, begin=[0], end=[1], strides=[1]); + %2152 = strided_slice(%2143, begin=[1], end=[2], strides=[1]); + %2153 = (%2151, %2152, meta[relay.Constant][231]); + %2154 = nn.batch_matmul(%2149, %2150, meta[relay.attrs.BatchMatmulAttrs][79]); + %2155 = concatenate(%2153); + %2156 = dyn.reshape(%2154, %2155, newshape=[]); + %2157 = add(%2156, %bert_encoder_layer_9_output_dense_bias); + %2158 = add(%2157, %2122); + %2159 = mean(%2158, axis=[-1], keepdims=True); + %2160 = subtract(%2158, %2159); + %2161 = power(%2160, 2f); + %2162 = mean(%2161, axis=[-1], keepdims=True); + %2163 = add(%2162, 1e-12f); + %2164 = sqrt(%2163); + %2165 = divide(%2160, %2164); + %2166 = multiply(%2165, %bert_encoder_layer_9_output_LayerNorm_weight); + %2167 = add(%2166, %bert_encoder_layer_9_output_LayerNorm_bias); + %2168 = shape_of(%2167, dtype="int64"); + %2169 = strided_slice(%2168, begin=[1], end=[3], strides=[1]); + %2170 = (meta[relay.Constant][232], %2169); + %2171 = concatenate(%2170); + %2172 = transpose(%bert_encoder_layer_10_attention_self_query_weight, axes=[1, 0]); + %2173 = reshape(%2172, newshape=[-1, 1024, 1024]); + %2174 = dyn.reshape(%2167, %2171, newshape=[]); + %2175 = transpose(%2173, axes=[0, 2, 1]); + %2176 = strided_slice(%2168, begin=[0], end=[1], strides=[1]); + %2177 = strided_slice(%2168, begin=[1], end=[2], strides=[1]); + %2178 = (%2176, %2177, meta[relay.Constant][233]); + %2179 = nn.batch_matmul(%2174, %2175, meta[relay.attrs.BatchMatmulAttrs][80]); + %2180 = concatenate(%2178); + %2181 = dyn.reshape(%2179, %2180, newshape=[]); + %2182 = add(%2181, %bert_encoder_layer_10_attention_self_query_bias); + %2183 = shape_of(%2182, dtype="int64"); + %2184 = take(%2183, 0, axis=0); + %2185 = shape_of(%2182, dtype="int64"); + %2186 = take(%2185, 1, axis=0); + %2187 = expand_dims(%2184, axis=0); + %2188 = expand_dims(%2186, axis=0); + %2189 = (%2187, %2188, meta[relay.Constant][234], meta[relay.Constant][235]); + %2190 = concatenate(%2189); + %2191 = dyn.reshape(%2182, %2190, newshape=[]); + %2192 = transpose(%2191, axes=[0, 2, 1, 3]); + %2193 = shape_of(%2192, dtype="int64"); + %2194 = strided_slice(%2193, begin=[2], end=[4], strides=[1]); + %2195 = (meta[relay.Constant][236], %2194); + %2196 = concatenate(%2195); + %2197 = shape_of(%2167, dtype="int64"); + %2198 = strided_slice(%2197, begin=[1], end=[3], strides=[1]); + %2199 = (meta[relay.Constant][237], %2198); + %2200 = concatenate(%2199); + %2201 = transpose(%bert_encoder_layer_10_attention_self_key_weight, axes=[1, 0]); + %2202 = reshape(%2201, newshape=[-1, 1024, 1024]); + %2203 = dyn.reshape(%2167, %2200, newshape=[]); + %2204 = transpose(%2202, axes=[0, 2, 1]); + %2205 = strided_slice(%2197, begin=[0], end=[1], strides=[1]); + %2206 = strided_slice(%2197, begin=[1], end=[2], strides=[1]); + %2207 = (%2205, %2206, meta[relay.Constant][238]); + %2208 = nn.batch_matmul(%2203, %2204, meta[relay.attrs.BatchMatmulAttrs][81]); + %2209 = concatenate(%2207); + %2210 = dyn.reshape(%2208, %2209, newshape=[]); + %2211 = add(%2210, %bert_encoder_layer_10_attention_self_key_bias); + %2212 = shape_of(%2211, dtype="int64"); + %2213 = take(%2212, 0, axis=0); + %2214 = shape_of(%2211, dtype="int64"); + %2215 = take(%2214, 1, axis=0); + %2216 = expand_dims(%2213, axis=0); + %2217 = expand_dims(%2215, axis=0); + %2218 = (%2216, %2217, meta[relay.Constant][239], meta[relay.Constant][240]); + %2219 = concatenate(%2218); + %2220 = dyn.reshape(%2211, %2219, newshape=[]); + %2221 = transpose(%2220, axes=[0, 2, 3, 1]); + %2222 = shape_of(%2221, dtype="int64"); + %2223 = strided_slice(%2222, begin=[2], end=[4], strides=[1]); + %2224 = (meta[relay.Constant][241], %2223); + %2225 = concatenate(%2224); + %2226 = dyn.reshape(%2221, %2225, newshape=[]); + %2227 = dyn.reshape(%2192, %2196, newshape=[]); + %2228 = transpose(%2226, axes=[0, 2, 1]); + %2229 = strided_slice(%2193, begin=[0], end=[1], strides=[1]); + %2230 = strided_slice(%2222, begin=[0], end=[1], strides=[1]); + %2231 = strided_slice(%2193, begin=[1], end=[2], strides=[1]); + %2232 = strided_slice(%2222, begin=[1], end=[2], strides=[1]); + %2233 = maximum(%2229, %2230); + %2234 = maximum(%2231, %2232); + %2235 = (%2233, %2234); + %2236 = concatenate(%2235); + %2237 = strided_slice(%2193, begin=[2], end=[3], strides=[1]); + %2238 = strided_slice(%2222, begin=[3], end=[4], strides=[1]); + %2239 = (%2236, %2237, %2238); + %2240 = nn.batch_matmul(%2227, %2228, meta[relay.attrs.BatchMatmulAttrs][82]); + %2241 = concatenate(%2239); + %2242 = dyn.reshape(%2240, %2241, newshape=[]); + %2243 = divide(%2242, 8f); + %2244 = add(%2243, %123); + %2245 = max(%2244, axis=[3], keepdims=True); + %2246 = subtract(%2244, %2245); + %2247 = exp(%2246); + %2248 = sum(%2247, axis=[3], keepdims=True); + %2249 = divide(%2247, %2248); + %2250 = shape_of(%2249, dtype="int64"); + %2251 = strided_slice(%2250, begin=[2], end=[4], strides=[1]); + %2252 = (meta[relay.Constant][242], %2251); + %2253 = concatenate(%2252); + %2254 = shape_of(%2167, dtype="int64"); + %2255 = strided_slice(%2254, begin=[1], end=[3], strides=[1]); + %2256 = (meta[relay.Constant][243], %2255); + %2257 = concatenate(%2256); + %2258 = transpose(%bert_encoder_layer_10_attention_self_value_weight, axes=[1, 0]); + %2259 = reshape(%2258, newshape=[-1, 1024, 1024]); + %2260 = dyn.reshape(%2167, %2257, newshape=[]); + %2261 = transpose(%2259, axes=[0, 2, 1]); + %2262 = strided_slice(%2254, begin=[0], end=[1], strides=[1]); + %2263 = strided_slice(%2254, begin=[1], end=[2], strides=[1]); + %2264 = (%2262, %2263, meta[relay.Constant][244]); + %2265 = nn.batch_matmul(%2260, %2261, meta[relay.attrs.BatchMatmulAttrs][83]); + %2266 = concatenate(%2264); + %2267 = dyn.reshape(%2265, %2266, newshape=[]); + %2268 = add(%2267, %bert_encoder_layer_10_attention_self_value_bias); + %2269 = shape_of(%2268, dtype="int64"); + %2270 = take(%2269, 0, axis=0); + %2271 = shape_of(%2268, dtype="int64"); + %2272 = take(%2271, 1, axis=0); + %2273 = expand_dims(%2270, axis=0); + %2274 = expand_dims(%2272, axis=0); + %2275 = (%2273, %2274, meta[relay.Constant][245], meta[relay.Constant][246]); + %2276 = concatenate(%2275); + %2277 = dyn.reshape(%2268, %2276, newshape=[]); + %2278 = transpose(%2277, axes=[0, 2, 1, 3]); + %2279 = shape_of(%2278, dtype="int64"); + %2280 = strided_slice(%2279, begin=[2], end=[4], strides=[1]); + %2281 = (meta[relay.Constant][247], %2280); + %2282 = concatenate(%2281); + %2283 = dyn.reshape(%2278, %2282, newshape=[]); + %2284 = dyn.reshape(%2249, %2253, newshape=[]); + %2285 = transpose(%2283, axes=[0, 2, 1]); + %2286 = strided_slice(%2250, begin=[0], end=[1], strides=[1]); + %2287 = strided_slice(%2279, begin=[0], end=[1], strides=[1]); + %2288 = strided_slice(%2250, begin=[1], end=[2], strides=[1]); + %2289 = strided_slice(%2279, begin=[1], end=[2], strides=[1]); + %2290 = maximum(%2286, %2287); + %2291 = maximum(%2288, %2289); + %2292 = (%2290, %2291); + %2293 = concatenate(%2292); + %2294 = strided_slice(%2250, begin=[2], end=[3], strides=[1]); + %2295 = strided_slice(%2279, begin=[3], end=[4], strides=[1]); + %2296 = (%2293, %2294, %2295); + %2297 = nn.batch_matmul(%2284, %2285, meta[relay.attrs.BatchMatmulAttrs][84]); + %2298 = concatenate(%2296); + %2299 = dyn.reshape(%2297, %2298, newshape=[]); + %2300 = transpose(%2299, axes=[0, 2, 1, 3]); + %2301 = shape_of(%2300, dtype="int64"); + %2302 = take(%2301, 0, axis=0); + %2303 = shape_of(%2300, dtype="int64"); + %2304 = take(%2303, 1, axis=0); + %2305 = expand_dims(%2302, axis=0); + %2306 = expand_dims(%2304, axis=0); + %2307 = (%2305, %2306, meta[relay.Constant][248]); + %2308 = concatenate(%2307); + %2309 = dyn.reshape(%2300, %2308, newshape=[]); + %2310 = shape_of(%2309, dtype="int64"); + %2311 = strided_slice(%2310, begin=[1], end=[3], strides=[1]); + %2312 = (meta[relay.Constant][249], %2311); + %2313 = concatenate(%2312); + %2314 = transpose(%bert_encoder_layer_10_attention_output_dense_weight, axes=[1, 0]); + %2315 = reshape(%2314, newshape=[-1, 1024, 1024]); + %2316 = dyn.reshape(%2309, %2313, newshape=[]); + %2317 = transpose(%2315, axes=[0, 2, 1]); + %2318 = strided_slice(%2310, begin=[0], end=[1], strides=[1]); + %2319 = strided_slice(%2310, begin=[1], end=[2], strides=[1]); + %2320 = (%2318, %2319, meta[relay.Constant][250]); + %2321 = nn.batch_matmul(%2316, %2317, meta[relay.attrs.BatchMatmulAttrs][85]); + %2322 = concatenate(%2320); + %2323 = dyn.reshape(%2321, %2322, newshape=[]); + %2324 = add(%2323, %bert_encoder_layer_10_attention_output_dense_bias); + %2325 = add(%2324, %2167); + %2326 = mean(%2325, axis=[-1], keepdims=True); + %2327 = subtract(%2325, %2326); + %2328 = power(%2327, 2f); + %2329 = mean(%2328, axis=[-1], keepdims=True); + %2330 = add(%2329, 1e-12f); + %2331 = sqrt(%2330); + %2332 = divide(%2327, %2331); + %2333 = multiply(%2332, %bert_encoder_layer_10_attention_output_LayerNorm_weight); + %2334 = add(%2333, %bert_encoder_layer_10_attention_output_LayerNorm_bias); + %2335 = shape_of(%2334, dtype="int64"); + %2336 = strided_slice(%2335, begin=[1], end=[3], strides=[1]); + %2337 = (meta[relay.Constant][251], %2336); + %2338 = concatenate(%2337); + %2339 = transpose(%bert_encoder_layer_10_intermediate_dense_weight, axes=[1, 0]); + %2340 = reshape(%2339, newshape=[-1, 1024, 4096]); + %2341 = dyn.reshape(%2334, %2338, newshape=[]); + %2342 = transpose(%2340, axes=[0, 2, 1]); + %2343 = strided_slice(%2335, begin=[0], end=[1], strides=[1]); + %2344 = strided_slice(%2335, begin=[1], end=[2], strides=[1]); + %2345 = (%2343, %2344, meta[relay.Constant][252]); + %2346 = nn.batch_matmul(%2341, %2342, meta[relay.attrs.BatchMatmulAttrs][86]); + %2347 = concatenate(%2345); + %2348 = dyn.reshape(%2346, %2347, newshape=[]); + %2349 = add(%2348, %bert_encoder_layer_10_intermediate_dense_bias); + %2350 = divide(%2349, 1.41421f); + %2351 = erf(%2350); + %2352 = multiply(%2349, 0.5f); + %2353 = add(%2351, 1f); + %2354 = multiply(%2352, %2353); + %2355 = shape_of(%2354, dtype="int64"); + %2356 = strided_slice(%2355, begin=[1], end=[3], strides=[1]); + %2357 = (meta[relay.Constant][253], %2356); + %2358 = concatenate(%2357); + %2359 = transpose(%bert_encoder_layer_10_output_dense_weight, axes=[1, 0]); + %2360 = reshape(%2359, newshape=[-1, 4096, 1024]); + %2361 = dyn.reshape(%2354, %2358, newshape=[]); + %2362 = transpose(%2360, axes=[0, 2, 1]); + %2363 = strided_slice(%2355, begin=[0], end=[1], strides=[1]); + %2364 = strided_slice(%2355, begin=[1], end=[2], strides=[1]); + %2365 = (%2363, %2364, meta[relay.Constant][254]); + %2366 = nn.batch_matmul(%2361, %2362, meta[relay.attrs.BatchMatmulAttrs][87]); + %2367 = concatenate(%2365); + %2368 = dyn.reshape(%2366, %2367, newshape=[]); + %2369 = add(%2368, %bert_encoder_layer_10_output_dense_bias); + %2370 = add(%2369, %2334); + %2371 = mean(%2370, axis=[-1], keepdims=True); + %2372 = subtract(%2370, %2371); + %2373 = power(%2372, 2f); + %2374 = mean(%2373, axis=[-1], keepdims=True); + %2375 = add(%2374, 1e-12f); + %2376 = sqrt(%2375); + %2377 = divide(%2372, %2376); + %2378 = multiply(%2377, %bert_encoder_layer_10_output_LayerNorm_weight); + %2379 = add(%2378, %bert_encoder_layer_10_output_LayerNorm_bias); + %2380 = shape_of(%2379, dtype="int64"); + %2381 = strided_slice(%2380, begin=[1], end=[3], strides=[1]); + %2382 = (meta[relay.Constant][255], %2381); + %2383 = concatenate(%2382); + %2384 = transpose(%bert_encoder_layer_11_attention_self_query_weight, axes=[1, 0]); + %2385 = reshape(%2384, newshape=[-1, 1024, 1024]); + %2386 = dyn.reshape(%2379, %2383, newshape=[]); + %2387 = transpose(%2385, axes=[0, 2, 1]); + %2388 = strided_slice(%2380, begin=[0], end=[1], strides=[1]); + %2389 = strided_slice(%2380, begin=[1], end=[2], strides=[1]); + %2390 = (%2388, %2389, meta[relay.Constant][256]); + %2391 = nn.batch_matmul(%2386, %2387, meta[relay.attrs.BatchMatmulAttrs][88]); + %2392 = concatenate(%2390); + %2393 = dyn.reshape(%2391, %2392, newshape=[]); + %2394 = add(%2393, %bert_encoder_layer_11_attention_self_query_bias); + %2395 = shape_of(%2394, dtype="int64"); + %2396 = take(%2395, 0, axis=0); + %2397 = shape_of(%2394, dtype="int64"); + %2398 = take(%2397, 1, axis=0); + %2399 = expand_dims(%2396, axis=0); + %2400 = expand_dims(%2398, axis=0); + %2401 = (%2399, %2400, meta[relay.Constant][257], meta[relay.Constant][258]); + %2402 = concatenate(%2401); + %2403 = dyn.reshape(%2394, %2402, newshape=[]); + %2404 = transpose(%2403, axes=[0, 2, 1, 3]); + %2405 = shape_of(%2404, dtype="int64"); + %2406 = strided_slice(%2405, begin=[2], end=[4], strides=[1]); + %2407 = (meta[relay.Constant][259], %2406); + %2408 = concatenate(%2407); + %2409 = shape_of(%2379, dtype="int64"); + %2410 = strided_slice(%2409, begin=[1], end=[3], strides=[1]); + %2411 = (meta[relay.Constant][260], %2410); + %2412 = concatenate(%2411); + %2413 = transpose(%bert_encoder_layer_11_attention_self_key_weight, axes=[1, 0]); + %2414 = reshape(%2413, newshape=[-1, 1024, 1024]); + %2415 = dyn.reshape(%2379, %2412, newshape=[]); + %2416 = transpose(%2414, axes=[0, 2, 1]); + %2417 = strided_slice(%2409, begin=[0], end=[1], strides=[1]); + %2418 = strided_slice(%2409, begin=[1], end=[2], strides=[1]); + %2419 = (%2417, %2418, meta[relay.Constant][261]); + %2420 = nn.batch_matmul(%2415, %2416, meta[relay.attrs.BatchMatmulAttrs][89]); + %2421 = concatenate(%2419); + %2422 = dyn.reshape(%2420, %2421, newshape=[]); + %2423 = add(%2422, %bert_encoder_layer_11_attention_self_key_bias); + %2424 = shape_of(%2423, dtype="int64"); + %2425 = take(%2424, 0, axis=0); + %2426 = shape_of(%2423, dtype="int64"); + %2427 = take(%2426, 1, axis=0); + %2428 = expand_dims(%2425, axis=0); + %2429 = expand_dims(%2427, axis=0); + %2430 = (%2428, %2429, meta[relay.Constant][262], meta[relay.Constant][263]); + %2431 = concatenate(%2430); + %2432 = dyn.reshape(%2423, %2431, newshape=[]); + %2433 = transpose(%2432, axes=[0, 2, 3, 1]); + %2434 = shape_of(%2433, dtype="int64"); + %2435 = strided_slice(%2434, begin=[2], end=[4], strides=[1]); + %2436 = (meta[relay.Constant][264], %2435); + %2437 = concatenate(%2436); + %2438 = dyn.reshape(%2433, %2437, newshape=[]); + %2439 = dyn.reshape(%2404, %2408, newshape=[]); + %2440 = transpose(%2438, axes=[0, 2, 1]); + %2441 = strided_slice(%2405, begin=[0], end=[1], strides=[1]); + %2442 = strided_slice(%2434, begin=[0], end=[1], strides=[1]); + %2443 = strided_slice(%2405, begin=[1], end=[2], strides=[1]); + %2444 = strided_slice(%2434, begin=[1], end=[2], strides=[1]); + %2445 = maximum(%2441, %2442); + %2446 = maximum(%2443, %2444); + %2447 = (%2445, %2446); + %2448 = concatenate(%2447); + %2449 = strided_slice(%2405, begin=[2], end=[3], strides=[1]); + %2450 = strided_slice(%2434, begin=[3], end=[4], strides=[1]); + %2451 = (%2448, %2449, %2450); + %2452 = nn.batch_matmul(%2439, %2440, meta[relay.attrs.BatchMatmulAttrs][90]); + %2453 = concatenate(%2451); + %2454 = dyn.reshape(%2452, %2453, newshape=[]); + %2455 = divide(%2454, 8f); + %2456 = add(%2455, %123); + %2457 = max(%2456, axis=[3], keepdims=True); + %2458 = subtract(%2456, %2457); + %2459 = exp(%2458); + %2460 = sum(%2459, axis=[3], keepdims=True); + %2461 = divide(%2459, %2460); + %2462 = shape_of(%2461, dtype="int64"); + %2463 = strided_slice(%2462, begin=[2], end=[4], strides=[1]); + %2464 = (meta[relay.Constant][265], %2463); + %2465 = concatenate(%2464); + %2466 = shape_of(%2379, dtype="int64"); + %2467 = strided_slice(%2466, begin=[1], end=[3], strides=[1]); + %2468 = (meta[relay.Constant][266], %2467); + %2469 = concatenate(%2468); + %2470 = transpose(%bert_encoder_layer_11_attention_self_value_weight, axes=[1, 0]); + %2471 = reshape(%2470, newshape=[-1, 1024, 1024]); + %2472 = dyn.reshape(%2379, %2469, newshape=[]); + %2473 = transpose(%2471, axes=[0, 2, 1]); + %2474 = strided_slice(%2466, begin=[0], end=[1], strides=[1]); + %2475 = strided_slice(%2466, begin=[1], end=[2], strides=[1]); + %2476 = (%2474, %2475, meta[relay.Constant][267]); + %2477 = nn.batch_matmul(%2472, %2473, meta[relay.attrs.BatchMatmulAttrs][91]); + %2478 = concatenate(%2476); + %2479 = dyn.reshape(%2477, %2478, newshape=[]); + %2480 = add(%2479, %bert_encoder_layer_11_attention_self_value_bias); + %2481 = shape_of(%2480, dtype="int64"); + %2482 = take(%2481, 0, axis=0); + %2483 = shape_of(%2480, dtype="int64"); + %2484 = take(%2483, 1, axis=0); + %2485 = expand_dims(%2482, axis=0); + %2486 = expand_dims(%2484, axis=0); + %2487 = (%2485, %2486, meta[relay.Constant][268], meta[relay.Constant][269]); + %2488 = concatenate(%2487); + %2489 = dyn.reshape(%2480, %2488, newshape=[]); + %2490 = transpose(%2489, axes=[0, 2, 1, 3]); + %2491 = shape_of(%2490, dtype="int64"); + %2492 = strided_slice(%2491, begin=[2], end=[4], strides=[1]); + %2493 = (meta[relay.Constant][270], %2492); + %2494 = concatenate(%2493); + %2495 = dyn.reshape(%2490, %2494, newshape=[]); + %2496 = dyn.reshape(%2461, %2465, newshape=[]); + %2497 = transpose(%2495, axes=[0, 2, 1]); + %2498 = strided_slice(%2462, begin=[0], end=[1], strides=[1]); + %2499 = strided_slice(%2491, begin=[0], end=[1], strides=[1]); + %2500 = strided_slice(%2462, begin=[1], end=[2], strides=[1]); + %2501 = strided_slice(%2491, begin=[1], end=[2], strides=[1]); + %2502 = maximum(%2498, %2499); + %2503 = maximum(%2500, %2501); + %2504 = (%2502, %2503); + %2505 = concatenate(%2504); + %2506 = strided_slice(%2462, begin=[2], end=[3], strides=[1]); + %2507 = strided_slice(%2491, begin=[3], end=[4], strides=[1]); + %2508 = (%2505, %2506, %2507); + %2509 = nn.batch_matmul(%2496, %2497, meta[relay.attrs.BatchMatmulAttrs][92]); + %2510 = concatenate(%2508); + %2511 = dyn.reshape(%2509, %2510, newshape=[]); + %2512 = transpose(%2511, axes=[0, 2, 1, 3]); + %2513 = shape_of(%2512, dtype="int64"); + %2514 = take(%2513, 0, axis=0); + %2515 = shape_of(%2512, dtype="int64"); + %2516 = take(%2515, 1, axis=0); + %2517 = expand_dims(%2514, axis=0); + %2518 = expand_dims(%2516, axis=0); + %2519 = (%2517, %2518, meta[relay.Constant][271]); + %2520 = concatenate(%2519); + %2521 = dyn.reshape(%2512, %2520, newshape=[]); + %2522 = shape_of(%2521, dtype="int64"); + %2523 = strided_slice(%2522, begin=[1], end=[3], strides=[1]); + %2524 = (meta[relay.Constant][272], %2523); + %2525 = concatenate(%2524); + %2526 = transpose(%bert_encoder_layer_11_attention_output_dense_weight, axes=[1, 0]); + %2527 = reshape(%2526, newshape=[-1, 1024, 1024]); + %2528 = dyn.reshape(%2521, %2525, newshape=[]); + %2529 = transpose(%2527, axes=[0, 2, 1]); + %2530 = strided_slice(%2522, begin=[0], end=[1], strides=[1]); + %2531 = strided_slice(%2522, begin=[1], end=[2], strides=[1]); + %2532 = (%2530, %2531, meta[relay.Constant][273]); + %2533 = nn.batch_matmul(%2528, %2529, meta[relay.attrs.BatchMatmulAttrs][93]); + %2534 = concatenate(%2532); + %2535 = dyn.reshape(%2533, %2534, newshape=[]); + %2536 = add(%2535, %bert_encoder_layer_11_attention_output_dense_bias); + %2537 = add(%2536, %2379); + %2538 = mean(%2537, axis=[-1], keepdims=True); + %2539 = subtract(%2537, %2538); + %2540 = power(%2539, 2f); + %2541 = mean(%2540, axis=[-1], keepdims=True); + %2542 = add(%2541, 1e-12f); + %2543 = sqrt(%2542); + %2544 = divide(%2539, %2543); + %2545 = multiply(%2544, %bert_encoder_layer_11_attention_output_LayerNorm_weight); + %2546 = add(%2545, %bert_encoder_layer_11_attention_output_LayerNorm_bias); + %2547 = shape_of(%2546, dtype="int64"); + %2548 = strided_slice(%2547, begin=[1], end=[3], strides=[1]); + %2549 = (meta[relay.Constant][274], %2548); + %2550 = concatenate(%2549); + %2551 = transpose(%bert_encoder_layer_11_intermediate_dense_weight, axes=[1, 0]); + %2552 = reshape(%2551, newshape=[-1, 1024, 4096]); + %2553 = dyn.reshape(%2546, %2550, newshape=[]); + %2554 = transpose(%2552, axes=[0, 2, 1]); + %2555 = strided_slice(%2547, begin=[0], end=[1], strides=[1]); + %2556 = strided_slice(%2547, begin=[1], end=[2], strides=[1]); + %2557 = (%2555, %2556, meta[relay.Constant][275]); + %2558 = nn.batch_matmul(%2553, %2554, meta[relay.attrs.BatchMatmulAttrs][94]); + %2559 = concatenate(%2557); + %2560 = dyn.reshape(%2558, %2559, newshape=[]); + %2561 = add(%2560, %bert_encoder_layer_11_intermediate_dense_bias); + %2562 = divide(%2561, 1.41421f); + %2563 = erf(%2562); + %2564 = multiply(%2561, 0.5f); + %2565 = add(%2563, 1f); + %2566 = multiply(%2564, %2565); + %2567 = shape_of(%2566, dtype="int64"); + %2568 = strided_slice(%2567, begin=[1], end=[3], strides=[1]); + %2569 = (meta[relay.Constant][276], %2568); + %2570 = concatenate(%2569); + %2571 = transpose(%bert_encoder_layer_11_output_dense_weight, axes=[1, 0]); + %2572 = reshape(%2571, newshape=[-1, 4096, 1024]); + %2573 = dyn.reshape(%2566, %2570, newshape=[]); + %2574 = transpose(%2572, axes=[0, 2, 1]); + %2575 = strided_slice(%2567, begin=[0], end=[1], strides=[1]); + %2576 = strided_slice(%2567, begin=[1], end=[2], strides=[1]); + %2577 = (%2575, %2576, meta[relay.Constant][277]); + %2578 = nn.batch_matmul(%2573, %2574, meta[relay.attrs.BatchMatmulAttrs][95]); + %2579 = concatenate(%2577); + %2580 = dyn.reshape(%2578, %2579, newshape=[]); + %2581 = add(%2580, %bert_encoder_layer_11_output_dense_bias); + %2582 = add(%2581, %2546); + %2583 = mean(%2582, axis=[-1], keepdims=True); + %2584 = subtract(%2582, %2583); + %2585 = power(%2584, 2f); + %2586 = mean(%2585, axis=[-1], keepdims=True); + %2587 = add(%2586, 1e-12f); + %2588 = sqrt(%2587); + %2589 = divide(%2584, %2588); + %2590 = multiply(%2589, %bert_encoder_layer_11_output_LayerNorm_weight); + %2591 = add(%2590, %bert_encoder_layer_11_output_LayerNorm_bias); + %2592 = shape_of(%2591, dtype="int64"); + %2593 = strided_slice(%2592, begin=[1], end=[3], strides=[1]); + %2594 = (meta[relay.Constant][278], %2593); + %2595 = concatenate(%2594); + %2596 = transpose(%bert_encoder_layer_12_attention_self_query_weight, axes=[1, 0]); + %2597 = reshape(%2596, newshape=[-1, 1024, 1024]); + %2598 = dyn.reshape(%2591, %2595, newshape=[]); + %2599 = transpose(%2597, axes=[0, 2, 1]); + %2600 = strided_slice(%2592, begin=[0], end=[1], strides=[1]); + %2601 = strided_slice(%2592, begin=[1], end=[2], strides=[1]); + %2602 = (%2600, %2601, meta[relay.Constant][279]); + %2603 = nn.batch_matmul(%2598, %2599, meta[relay.attrs.BatchMatmulAttrs][96]); + %2604 = concatenate(%2602); + %2605 = dyn.reshape(%2603, %2604, newshape=[]); + %2606 = add(%2605, %bert_encoder_layer_12_attention_self_query_bias); + %2607 = shape_of(%2606, dtype="int64"); + %2608 = take(%2607, 0, axis=0); + %2609 = shape_of(%2606, dtype="int64"); + %2610 = take(%2609, 1, axis=0); + %2611 = expand_dims(%2608, axis=0); + %2612 = expand_dims(%2610, axis=0); + %2613 = (%2611, %2612, meta[relay.Constant][280], meta[relay.Constant][281]); + %2614 = concatenate(%2613); + %2615 = dyn.reshape(%2606, %2614, newshape=[]); + %2616 = transpose(%2615, axes=[0, 2, 1, 3]); + %2617 = shape_of(%2616, dtype="int64"); + %2618 = strided_slice(%2617, begin=[2], end=[4], strides=[1]); + %2619 = (meta[relay.Constant][282], %2618); + %2620 = concatenate(%2619); + %2621 = shape_of(%2591, dtype="int64"); + %2622 = strided_slice(%2621, begin=[1], end=[3], strides=[1]); + %2623 = (meta[relay.Constant][283], %2622); + %2624 = concatenate(%2623); + %2625 = transpose(%bert_encoder_layer_12_attention_self_key_weight, axes=[1, 0]); + %2626 = reshape(%2625, newshape=[-1, 1024, 1024]); + %2627 = dyn.reshape(%2591, %2624, newshape=[]); + %2628 = transpose(%2626, axes=[0, 2, 1]); + %2629 = strided_slice(%2621, begin=[0], end=[1], strides=[1]); + %2630 = strided_slice(%2621, begin=[1], end=[2], strides=[1]); + %2631 = (%2629, %2630, meta[relay.Constant][284]); + %2632 = nn.batch_matmul(%2627, %2628, meta[relay.attrs.BatchMatmulAttrs][97]); + %2633 = concatenate(%2631); + %2634 = dyn.reshape(%2632, %2633, newshape=[]); + %2635 = add(%2634, %bert_encoder_layer_12_attention_self_key_bias); + %2636 = shape_of(%2635, dtype="int64"); + %2637 = take(%2636, 0, axis=0); + %2638 = shape_of(%2635, dtype="int64"); + %2639 = take(%2638, 1, axis=0); + %2640 = expand_dims(%2637, axis=0); + %2641 = expand_dims(%2639, axis=0); + %2642 = (%2640, %2641, meta[relay.Constant][285], meta[relay.Constant][286]); + %2643 = concatenate(%2642); + %2644 = dyn.reshape(%2635, %2643, newshape=[]); + %2645 = transpose(%2644, axes=[0, 2, 3, 1]); + %2646 = shape_of(%2645, dtype="int64"); + %2647 = strided_slice(%2646, begin=[2], end=[4], strides=[1]); + %2648 = (meta[relay.Constant][287], %2647); + %2649 = concatenate(%2648); + %2650 = dyn.reshape(%2645, %2649, newshape=[]); + %2651 = dyn.reshape(%2616, %2620, newshape=[]); + %2652 = transpose(%2650, axes=[0, 2, 1]); + %2653 = strided_slice(%2617, begin=[0], end=[1], strides=[1]); + %2654 = strided_slice(%2646, begin=[0], end=[1], strides=[1]); + %2655 = strided_slice(%2617, begin=[1], end=[2], strides=[1]); + %2656 = strided_slice(%2646, begin=[1], end=[2], strides=[1]); + %2657 = maximum(%2653, %2654); + %2658 = maximum(%2655, %2656); + %2659 = (%2657, %2658); + %2660 = concatenate(%2659); + %2661 = strided_slice(%2617, begin=[2], end=[3], strides=[1]); + %2662 = strided_slice(%2646, begin=[3], end=[4], strides=[1]); + %2663 = (%2660, %2661, %2662); + %2664 = nn.batch_matmul(%2651, %2652, meta[relay.attrs.BatchMatmulAttrs][98]); + %2665 = concatenate(%2663); + %2666 = dyn.reshape(%2664, %2665, newshape=[]); + %2667 = divide(%2666, 8f); + %2668 = add(%2667, %123); + %2669 = max(%2668, axis=[3], keepdims=True); + %2670 = subtract(%2668, %2669); + %2671 = exp(%2670); + %2672 = sum(%2671, axis=[3], keepdims=True); + %2673 = divide(%2671, %2672); + %2674 = shape_of(%2673, dtype="int64"); + %2675 = strided_slice(%2674, begin=[2], end=[4], strides=[1]); + %2676 = (meta[relay.Constant][288], %2675); + %2677 = concatenate(%2676); + %2678 = shape_of(%2591, dtype="int64"); + %2679 = strided_slice(%2678, begin=[1], end=[3], strides=[1]); + %2680 = (meta[relay.Constant][289], %2679); + %2681 = concatenate(%2680); + %2682 = transpose(%bert_encoder_layer_12_attention_self_value_weight, axes=[1, 0]); + %2683 = reshape(%2682, newshape=[-1, 1024, 1024]); + %2684 = dyn.reshape(%2591, %2681, newshape=[]); + %2685 = transpose(%2683, axes=[0, 2, 1]); + %2686 = strided_slice(%2678, begin=[0], end=[1], strides=[1]); + %2687 = strided_slice(%2678, begin=[1], end=[2], strides=[1]); + %2688 = (%2686, %2687, meta[relay.Constant][290]); + %2689 = nn.batch_matmul(%2684, %2685, meta[relay.attrs.BatchMatmulAttrs][99]); + %2690 = concatenate(%2688); + %2691 = dyn.reshape(%2689, %2690, newshape=[]); + %2692 = add(%2691, %bert_encoder_layer_12_attention_self_value_bias); + %2693 = shape_of(%2692, dtype="int64"); + %2694 = take(%2693, 0, axis=0); + %2695 = shape_of(%2692, dtype="int64"); + %2696 = take(%2695, 1, axis=0); + %2697 = expand_dims(%2694, axis=0); + %2698 = expand_dims(%2696, axis=0); + %2699 = (%2697, %2698, meta[relay.Constant][291], meta[relay.Constant][292]); + %2700 = concatenate(%2699); + %2701 = dyn.reshape(%2692, %2700, newshape=[]); + %2702 = transpose(%2701, axes=[0, 2, 1, 3]); + %2703 = shape_of(%2702, dtype="int64"); + %2704 = strided_slice(%2703, begin=[2], end=[4], strides=[1]); + %2705 = (meta[relay.Constant][293], %2704); + %2706 = concatenate(%2705); + %2707 = dyn.reshape(%2702, %2706, newshape=[]); + %2708 = dyn.reshape(%2673, %2677, newshape=[]); + %2709 = transpose(%2707, axes=[0, 2, 1]); + %2710 = strided_slice(%2674, begin=[0], end=[1], strides=[1]); + %2711 = strided_slice(%2703, begin=[0], end=[1], strides=[1]); + %2712 = strided_slice(%2674, begin=[1], end=[2], strides=[1]); + %2713 = strided_slice(%2703, begin=[1], end=[2], strides=[1]); + %2714 = maximum(%2710, %2711); + %2715 = maximum(%2712, %2713); + %2716 = (%2714, %2715); + %2717 = concatenate(%2716); + %2718 = strided_slice(%2674, begin=[2], end=[3], strides=[1]); + %2719 = strided_slice(%2703, begin=[3], end=[4], strides=[1]); + %2720 = (%2717, %2718, %2719); + %2721 = nn.batch_matmul(%2708, %2709, meta[relay.attrs.BatchMatmulAttrs][100]); + %2722 = concatenate(%2720); + %2723 = dyn.reshape(%2721, %2722, newshape=[]); + %2724 = transpose(%2723, axes=[0, 2, 1, 3]); + %2725 = shape_of(%2724, dtype="int64"); + %2726 = take(%2725, 0, axis=0); + %2727 = shape_of(%2724, dtype="int64"); + %2728 = take(%2727, 1, axis=0); + %2729 = expand_dims(%2726, axis=0); + %2730 = expand_dims(%2728, axis=0); + %2731 = (%2729, %2730, meta[relay.Constant][294]); + %2732 = concatenate(%2731); + %2733 = dyn.reshape(%2724, %2732, newshape=[]); + %2734 = shape_of(%2733, dtype="int64"); + %2735 = strided_slice(%2734, begin=[1], end=[3], strides=[1]); + %2736 = (meta[relay.Constant][295], %2735); + %2737 = concatenate(%2736); + %2738 = transpose(%bert_encoder_layer_12_attention_output_dense_weight, axes=[1, 0]); + %2739 = reshape(%2738, newshape=[-1, 1024, 1024]); + %2740 = dyn.reshape(%2733, %2737, newshape=[]); + %2741 = transpose(%2739, axes=[0, 2, 1]); + %2742 = strided_slice(%2734, begin=[0], end=[1], strides=[1]); + %2743 = strided_slice(%2734, begin=[1], end=[2], strides=[1]); + %2744 = (%2742, %2743, meta[relay.Constant][296]); + %2745 = nn.batch_matmul(%2740, %2741, meta[relay.attrs.BatchMatmulAttrs][101]); + %2746 = concatenate(%2744); + %2747 = dyn.reshape(%2745, %2746, newshape=[]); + %2748 = add(%2747, %bert_encoder_layer_12_attention_output_dense_bias); + %2749 = add(%2748, %2591); + %2750 = mean(%2749, axis=[-1], keepdims=True); + %2751 = subtract(%2749, %2750); + %2752 = power(%2751, 2f); + %2753 = mean(%2752, axis=[-1], keepdims=True); + %2754 = add(%2753, 1e-12f); + %2755 = sqrt(%2754); + %2756 = divide(%2751, %2755); + %2757 = multiply(%2756, %bert_encoder_layer_12_attention_output_LayerNorm_weight); + %2758 = add(%2757, %bert_encoder_layer_12_attention_output_LayerNorm_bias); + %2759 = shape_of(%2758, dtype="int64"); + %2760 = strided_slice(%2759, begin=[1], end=[3], strides=[1]); + %2761 = (meta[relay.Constant][297], %2760); + %2762 = concatenate(%2761); + %2763 = transpose(%bert_encoder_layer_12_intermediate_dense_weight, axes=[1, 0]); + %2764 = reshape(%2763, newshape=[-1, 1024, 4096]); + %2765 = dyn.reshape(%2758, %2762, newshape=[]); + %2766 = transpose(%2764, axes=[0, 2, 1]); + %2767 = strided_slice(%2759, begin=[0], end=[1], strides=[1]); + %2768 = strided_slice(%2759, begin=[1], end=[2], strides=[1]); + %2769 = (%2767, %2768, meta[relay.Constant][298]); + %2770 = nn.batch_matmul(%2765, %2766, meta[relay.attrs.BatchMatmulAttrs][102]); + %2771 = concatenate(%2769); + %2772 = dyn.reshape(%2770, %2771, newshape=[]); + %2773 = add(%2772, %bert_encoder_layer_12_intermediate_dense_bias); + %2774 = divide(%2773, 1.41421f); + %2775 = erf(%2774); + %2776 = multiply(%2773, 0.5f); + %2777 = add(%2775, 1f); + %2778 = multiply(%2776, %2777); + %2779 = shape_of(%2778, dtype="int64"); + %2780 = strided_slice(%2779, begin=[1], end=[3], strides=[1]); + %2781 = (meta[relay.Constant][299], %2780); + %2782 = concatenate(%2781); + %2783 = transpose(%bert_encoder_layer_12_output_dense_weight, axes=[1, 0]); + %2784 = reshape(%2783, newshape=[-1, 4096, 1024]); + %2785 = dyn.reshape(%2778, %2782, newshape=[]); + %2786 = transpose(%2784, axes=[0, 2, 1]); + %2787 = strided_slice(%2779, begin=[0], end=[1], strides=[1]); + %2788 = strided_slice(%2779, begin=[1], end=[2], strides=[1]); + %2789 = (%2787, %2788, meta[relay.Constant][300]); + %2790 = nn.batch_matmul(%2785, %2786, meta[relay.attrs.BatchMatmulAttrs][103]); + %2791 = concatenate(%2789); + %2792 = dyn.reshape(%2790, %2791, newshape=[]); + %2793 = add(%2792, %bert_encoder_layer_12_output_dense_bias); + %2794 = add(%2793, %2758); + %2795 = mean(%2794, axis=[-1], keepdims=True); + %2796 = subtract(%2794, %2795); + %2797 = power(%2796, 2f); + %2798 = mean(%2797, axis=[-1], keepdims=True); + %2799 = add(%2798, 1e-12f); + %2800 = sqrt(%2799); + %2801 = divide(%2796, %2800); + %2802 = multiply(%2801, %bert_encoder_layer_12_output_LayerNorm_weight); + %2803 = add(%2802, %bert_encoder_layer_12_output_LayerNorm_bias); + %2804 = shape_of(%2803, dtype="int64"); + %2805 = strided_slice(%2804, begin=[1], end=[3], strides=[1]); + %2806 = (meta[relay.Constant][301], %2805); + %2807 = concatenate(%2806); + %2808 = transpose(%bert_encoder_layer_13_attention_self_query_weight, axes=[1, 0]); + %2809 = reshape(%2808, newshape=[-1, 1024, 1024]); + %2810 = dyn.reshape(%2803, %2807, newshape=[]); + %2811 = transpose(%2809, axes=[0, 2, 1]); + %2812 = strided_slice(%2804, begin=[0], end=[1], strides=[1]); + %2813 = strided_slice(%2804, begin=[1], end=[2], strides=[1]); + %2814 = (%2812, %2813, meta[relay.Constant][302]); + %2815 = nn.batch_matmul(%2810, %2811, meta[relay.attrs.BatchMatmulAttrs][104]); + %2816 = concatenate(%2814); + %2817 = dyn.reshape(%2815, %2816, newshape=[]); + %2818 = add(%2817, %bert_encoder_layer_13_attention_self_query_bias); + %2819 = shape_of(%2818, dtype="int64"); + %2820 = take(%2819, 0, axis=0); + %2821 = shape_of(%2818, dtype="int64"); + %2822 = take(%2821, 1, axis=0); + %2823 = expand_dims(%2820, axis=0); + %2824 = expand_dims(%2822, axis=0); + %2825 = (%2823, %2824, meta[relay.Constant][303], meta[relay.Constant][304]); + %2826 = concatenate(%2825); + %2827 = dyn.reshape(%2818, %2826, newshape=[]); + %2828 = transpose(%2827, axes=[0, 2, 1, 3]); + %2829 = shape_of(%2828, dtype="int64"); + %2830 = strided_slice(%2829, begin=[2], end=[4], strides=[1]); + %2831 = (meta[relay.Constant][305], %2830); + %2832 = concatenate(%2831); + %2833 = shape_of(%2803, dtype="int64"); + %2834 = strided_slice(%2833, begin=[1], end=[3], strides=[1]); + %2835 = (meta[relay.Constant][306], %2834); + %2836 = concatenate(%2835); + %2837 = transpose(%bert_encoder_layer_13_attention_self_key_weight, axes=[1, 0]); + %2838 = reshape(%2837, newshape=[-1, 1024, 1024]); + %2839 = dyn.reshape(%2803, %2836, newshape=[]); + %2840 = transpose(%2838, axes=[0, 2, 1]); + %2841 = strided_slice(%2833, begin=[0], end=[1], strides=[1]); + %2842 = strided_slice(%2833, begin=[1], end=[2], strides=[1]); + %2843 = (%2841, %2842, meta[relay.Constant][307]); + %2844 = nn.batch_matmul(%2839, %2840, meta[relay.attrs.BatchMatmulAttrs][105]); + %2845 = concatenate(%2843); + %2846 = dyn.reshape(%2844, %2845, newshape=[]); + %2847 = add(%2846, %bert_encoder_layer_13_attention_self_key_bias); + %2848 = shape_of(%2847, dtype="int64"); + %2849 = take(%2848, 0, axis=0); + %2850 = shape_of(%2847, dtype="int64"); + %2851 = take(%2850, 1, axis=0); + %2852 = expand_dims(%2849, axis=0); + %2853 = expand_dims(%2851, axis=0); + %2854 = (%2852, %2853, meta[relay.Constant][308], meta[relay.Constant][309]); + %2855 = concatenate(%2854); + %2856 = dyn.reshape(%2847, %2855, newshape=[]); + %2857 = transpose(%2856, axes=[0, 2, 3, 1]); + %2858 = shape_of(%2857, dtype="int64"); + %2859 = strided_slice(%2858, begin=[2], end=[4], strides=[1]); + %2860 = (meta[relay.Constant][310], %2859); + %2861 = concatenate(%2860); + %2862 = dyn.reshape(%2857, %2861, newshape=[]); + %2863 = dyn.reshape(%2828, %2832, newshape=[]); + %2864 = transpose(%2862, axes=[0, 2, 1]); + %2865 = strided_slice(%2829, begin=[0], end=[1], strides=[1]); + %2866 = strided_slice(%2858, begin=[0], end=[1], strides=[1]); + %2867 = strided_slice(%2829, begin=[1], end=[2], strides=[1]); + %2868 = strided_slice(%2858, begin=[1], end=[2], strides=[1]); + %2869 = maximum(%2865, %2866); + %2870 = maximum(%2867, %2868); + %2871 = (%2869, %2870); + %2872 = concatenate(%2871); + %2873 = strided_slice(%2829, begin=[2], end=[3], strides=[1]); + %2874 = strided_slice(%2858, begin=[3], end=[4], strides=[1]); + %2875 = (%2872, %2873, %2874); + %2876 = nn.batch_matmul(%2863, %2864, meta[relay.attrs.BatchMatmulAttrs][106]); + %2877 = concatenate(%2875); + %2878 = dyn.reshape(%2876, %2877, newshape=[]); + %2879 = divide(%2878, 8f); + %2880 = add(%2879, %123); + %2881 = max(%2880, axis=[3], keepdims=True); + %2882 = subtract(%2880, %2881); + %2883 = exp(%2882); + %2884 = sum(%2883, axis=[3], keepdims=True); + %2885 = divide(%2883, %2884); + %2886 = shape_of(%2885, dtype="int64"); + %2887 = strided_slice(%2886, begin=[2], end=[4], strides=[1]); + %2888 = (meta[relay.Constant][311], %2887); + %2889 = concatenate(%2888); + %2890 = shape_of(%2803, dtype="int64"); + %2891 = strided_slice(%2890, begin=[1], end=[3], strides=[1]); + %2892 = (meta[relay.Constant][312], %2891); + %2893 = concatenate(%2892); + %2894 = transpose(%bert_encoder_layer_13_attention_self_value_weight, axes=[1, 0]); + %2895 = reshape(%2894, newshape=[-1, 1024, 1024]); + %2896 = dyn.reshape(%2803, %2893, newshape=[]); + %2897 = transpose(%2895, axes=[0, 2, 1]); + %2898 = strided_slice(%2890, begin=[0], end=[1], strides=[1]); + %2899 = strided_slice(%2890, begin=[1], end=[2], strides=[1]); + %2900 = (%2898, %2899, meta[relay.Constant][313]); + %2901 = nn.batch_matmul(%2896, %2897, meta[relay.attrs.BatchMatmulAttrs][107]); + %2902 = concatenate(%2900); + %2903 = dyn.reshape(%2901, %2902, newshape=[]); + %2904 = add(%2903, %bert_encoder_layer_13_attention_self_value_bias); + %2905 = shape_of(%2904, dtype="int64"); + %2906 = take(%2905, 0, axis=0); + %2907 = shape_of(%2904, dtype="int64"); + %2908 = take(%2907, 1, axis=0); + %2909 = expand_dims(%2906, axis=0); + %2910 = expand_dims(%2908, axis=0); + %2911 = (%2909, %2910, meta[relay.Constant][314], meta[relay.Constant][315]); + %2912 = concatenate(%2911); + %2913 = dyn.reshape(%2904, %2912, newshape=[]); + %2914 = transpose(%2913, axes=[0, 2, 1, 3]); + %2915 = shape_of(%2914, dtype="int64"); + %2916 = strided_slice(%2915, begin=[2], end=[4], strides=[1]); + %2917 = (meta[relay.Constant][316], %2916); + %2918 = concatenate(%2917); + %2919 = dyn.reshape(%2914, %2918, newshape=[]); + %2920 = dyn.reshape(%2885, %2889, newshape=[]); + %2921 = transpose(%2919, axes=[0, 2, 1]); + %2922 = strided_slice(%2886, begin=[0], end=[1], strides=[1]); + %2923 = strided_slice(%2915, begin=[0], end=[1], strides=[1]); + %2924 = strided_slice(%2886, begin=[1], end=[2], strides=[1]); + %2925 = strided_slice(%2915, begin=[1], end=[2], strides=[1]); + %2926 = maximum(%2922, %2923); + %2927 = maximum(%2924, %2925); + %2928 = (%2926, %2927); + %2929 = concatenate(%2928); + %2930 = strided_slice(%2886, begin=[2], end=[3], strides=[1]); + %2931 = strided_slice(%2915, begin=[3], end=[4], strides=[1]); + %2932 = (%2929, %2930, %2931); + %2933 = nn.batch_matmul(%2920, %2921, meta[relay.attrs.BatchMatmulAttrs][108]); + %2934 = concatenate(%2932); + %2935 = dyn.reshape(%2933, %2934, newshape=[]); + %2936 = transpose(%2935, axes=[0, 2, 1, 3]); + %2937 = shape_of(%2936, dtype="int64"); + %2938 = take(%2937, 0, axis=0); + %2939 = shape_of(%2936, dtype="int64"); + %2940 = take(%2939, 1, axis=0); + %2941 = expand_dims(%2938, axis=0); + %2942 = expand_dims(%2940, axis=0); + %2943 = (%2941, %2942, meta[relay.Constant][317]); + %2944 = concatenate(%2943); + %2945 = dyn.reshape(%2936, %2944, newshape=[]); + %2946 = shape_of(%2945, dtype="int64"); + %2947 = strided_slice(%2946, begin=[1], end=[3], strides=[1]); + %2948 = (meta[relay.Constant][318], %2947); + %2949 = concatenate(%2948); + %2950 = transpose(%bert_encoder_layer_13_attention_output_dense_weight, axes=[1, 0]); + %2951 = reshape(%2950, newshape=[-1, 1024, 1024]); + %2952 = dyn.reshape(%2945, %2949, newshape=[]); + %2953 = transpose(%2951, axes=[0, 2, 1]); + %2954 = strided_slice(%2946, begin=[0], end=[1], strides=[1]); + %2955 = strided_slice(%2946, begin=[1], end=[2], strides=[1]); + %2956 = (%2954, %2955, meta[relay.Constant][319]); + %2957 = nn.batch_matmul(%2952, %2953, meta[relay.attrs.BatchMatmulAttrs][109]); + %2958 = concatenate(%2956); + %2959 = dyn.reshape(%2957, %2958, newshape=[]); + %2960 = add(%2959, %bert_encoder_layer_13_attention_output_dense_bias); + %2961 = add(%2960, %2803); + %2962 = mean(%2961, axis=[-1], keepdims=True); + %2963 = subtract(%2961, %2962); + %2964 = power(%2963, 2f); + %2965 = mean(%2964, axis=[-1], keepdims=True); + %2966 = add(%2965, 1e-12f); + %2967 = sqrt(%2966); + %2968 = divide(%2963, %2967); + %2969 = multiply(%2968, %bert_encoder_layer_13_attention_output_LayerNorm_weight); + %2970 = add(%2969, %bert_encoder_layer_13_attention_output_LayerNorm_bias); + %2971 = shape_of(%2970, dtype="int64"); + %2972 = strided_slice(%2971, begin=[1], end=[3], strides=[1]); + %2973 = (meta[relay.Constant][320], %2972); + %2974 = concatenate(%2973); + %2975 = transpose(%bert_encoder_layer_13_intermediate_dense_weight, axes=[1, 0]); + %2976 = reshape(%2975, newshape=[-1, 1024, 4096]); + %2977 = dyn.reshape(%2970, %2974, newshape=[]); + %2978 = transpose(%2976, axes=[0, 2, 1]); + %2979 = strided_slice(%2971, begin=[0], end=[1], strides=[1]); + %2980 = strided_slice(%2971, begin=[1], end=[2], strides=[1]); + %2981 = (%2979, %2980, meta[relay.Constant][321]); + %2982 = nn.batch_matmul(%2977, %2978, meta[relay.attrs.BatchMatmulAttrs][110]); + %2983 = concatenate(%2981); + %2984 = dyn.reshape(%2982, %2983, newshape=[]); + %2985 = add(%2984, %bert_encoder_layer_13_intermediate_dense_bias); + %2986 = divide(%2985, 1.41421f); + %2987 = erf(%2986); + %2988 = multiply(%2985, 0.5f); + %2989 = add(%2987, 1f); + %2990 = multiply(%2988, %2989); + %2991 = shape_of(%2990, dtype="int64"); + %2992 = strided_slice(%2991, begin=[1], end=[3], strides=[1]); + %2993 = (meta[relay.Constant][322], %2992); + %2994 = concatenate(%2993); + %2995 = transpose(%bert_encoder_layer_13_output_dense_weight, axes=[1, 0]); + %2996 = reshape(%2995, newshape=[-1, 4096, 1024]); + %2997 = dyn.reshape(%2990, %2994, newshape=[]); + %2998 = transpose(%2996, axes=[0, 2, 1]); + %2999 = strided_slice(%2991, begin=[0], end=[1], strides=[1]); + %3000 = strided_slice(%2991, begin=[1], end=[2], strides=[1]); + %3001 = (%2999, %3000, meta[relay.Constant][323]); + %3002 = nn.batch_matmul(%2997, %2998, meta[relay.attrs.BatchMatmulAttrs][111]); + %3003 = concatenate(%3001); + %3004 = dyn.reshape(%3002, %3003, newshape=[]); + %3005 = add(%3004, %bert_encoder_layer_13_output_dense_bias); + %3006 = add(%3005, %2970); + %3007 = mean(%3006, axis=[-1], keepdims=True); + %3008 = subtract(%3006, %3007); + %3009 = power(%3008, 2f); + %3010 = mean(%3009, axis=[-1], keepdims=True); + %3011 = add(%3010, 1e-12f); + %3012 = sqrt(%3011); + %3013 = divide(%3008, %3012); + %3014 = multiply(%3013, %bert_encoder_layer_13_output_LayerNorm_weight); + %3015 = add(%3014, %bert_encoder_layer_13_output_LayerNorm_bias); + %3016 = shape_of(%3015, dtype="int64"); + %3017 = strided_slice(%3016, begin=[1], end=[3], strides=[1]); + %3018 = (meta[relay.Constant][324], %3017); + %3019 = concatenate(%3018); + %3020 = transpose(%bert_encoder_layer_14_attention_self_query_weight, axes=[1, 0]); + %3021 = reshape(%3020, newshape=[-1, 1024, 1024]); + %3022 = dyn.reshape(%3015, %3019, newshape=[]); + %3023 = transpose(%3021, axes=[0, 2, 1]); + %3024 = strided_slice(%3016, begin=[0], end=[1], strides=[1]); + %3025 = strided_slice(%3016, begin=[1], end=[2], strides=[1]); + %3026 = (%3024, %3025, meta[relay.Constant][325]); + %3027 = nn.batch_matmul(%3022, %3023, meta[relay.attrs.BatchMatmulAttrs][112]); + %3028 = concatenate(%3026); + %3029 = dyn.reshape(%3027, %3028, newshape=[]); + %3030 = add(%3029, %bert_encoder_layer_14_attention_self_query_bias); + %3031 = shape_of(%3030, dtype="int64"); + %3032 = take(%3031, 0, axis=0); + %3033 = shape_of(%3030, dtype="int64"); + %3034 = take(%3033, 1, axis=0); + %3035 = expand_dims(%3032, axis=0); + %3036 = expand_dims(%3034, axis=0); + %3037 = (%3035, %3036, meta[relay.Constant][326], meta[relay.Constant][327]); + %3038 = concatenate(%3037); + %3039 = dyn.reshape(%3030, %3038, newshape=[]); + %3040 = transpose(%3039, axes=[0, 2, 1, 3]); + %3041 = shape_of(%3040, dtype="int64"); + %3042 = strided_slice(%3041, begin=[2], end=[4], strides=[1]); + %3043 = (meta[relay.Constant][328], %3042); + %3044 = concatenate(%3043); + %3045 = shape_of(%3015, dtype="int64"); + %3046 = strided_slice(%3045, begin=[1], end=[3], strides=[1]); + %3047 = (meta[relay.Constant][329], %3046); + %3048 = concatenate(%3047); + %3049 = transpose(%bert_encoder_layer_14_attention_self_key_weight, axes=[1, 0]); + %3050 = reshape(%3049, newshape=[-1, 1024, 1024]); + %3051 = dyn.reshape(%3015, %3048, newshape=[]); + %3052 = transpose(%3050, axes=[0, 2, 1]); + %3053 = strided_slice(%3045, begin=[0], end=[1], strides=[1]); + %3054 = strided_slice(%3045, begin=[1], end=[2], strides=[1]); + %3055 = (%3053, %3054, meta[relay.Constant][330]); + %3056 = nn.batch_matmul(%3051, %3052, meta[relay.attrs.BatchMatmulAttrs][113]); + %3057 = concatenate(%3055); + %3058 = dyn.reshape(%3056, %3057, newshape=[]); + %3059 = add(%3058, %bert_encoder_layer_14_attention_self_key_bias); + %3060 = shape_of(%3059, dtype="int64"); + %3061 = take(%3060, 0, axis=0); + %3062 = shape_of(%3059, dtype="int64"); + %3063 = take(%3062, 1, axis=0); + %3064 = expand_dims(%3061, axis=0); + %3065 = expand_dims(%3063, axis=0); + %3066 = (%3064, %3065, meta[relay.Constant][331], meta[relay.Constant][332]); + %3067 = concatenate(%3066); + %3068 = dyn.reshape(%3059, %3067, newshape=[]); + %3069 = transpose(%3068, axes=[0, 2, 3, 1]); + %3070 = shape_of(%3069, dtype="int64"); + %3071 = strided_slice(%3070, begin=[2], end=[4], strides=[1]); + %3072 = (meta[relay.Constant][333], %3071); + %3073 = concatenate(%3072); + %3074 = dyn.reshape(%3069, %3073, newshape=[]); + %3075 = dyn.reshape(%3040, %3044, newshape=[]); + %3076 = transpose(%3074, axes=[0, 2, 1]); + %3077 = strided_slice(%3041, begin=[0], end=[1], strides=[1]); + %3078 = strided_slice(%3070, begin=[0], end=[1], strides=[1]); + %3079 = strided_slice(%3041, begin=[1], end=[2], strides=[1]); + %3080 = strided_slice(%3070, begin=[1], end=[2], strides=[1]); + %3081 = maximum(%3077, %3078); + %3082 = maximum(%3079, %3080); + %3083 = (%3081, %3082); + %3084 = concatenate(%3083); + %3085 = strided_slice(%3041, begin=[2], end=[3], strides=[1]); + %3086 = strided_slice(%3070, begin=[3], end=[4], strides=[1]); + %3087 = (%3084, %3085, %3086); + %3088 = nn.batch_matmul(%3075, %3076, meta[relay.attrs.BatchMatmulAttrs][114]); + %3089 = concatenate(%3087); + %3090 = dyn.reshape(%3088, %3089, newshape=[]); + %3091 = divide(%3090, 8f); + %3092 = add(%3091, %123); + %3093 = max(%3092, axis=[3], keepdims=True); + %3094 = subtract(%3092, %3093); + %3095 = exp(%3094); + %3096 = sum(%3095, axis=[3], keepdims=True); + %3097 = divide(%3095, %3096); + %3098 = shape_of(%3097, dtype="int64"); + %3099 = strided_slice(%3098, begin=[2], end=[4], strides=[1]); + %3100 = (meta[relay.Constant][334], %3099); + %3101 = concatenate(%3100); + %3102 = shape_of(%3015, dtype="int64"); + %3103 = strided_slice(%3102, begin=[1], end=[3], strides=[1]); + %3104 = (meta[relay.Constant][335], %3103); + %3105 = concatenate(%3104); + %3106 = transpose(%bert_encoder_layer_14_attention_self_value_weight, axes=[1, 0]); + %3107 = reshape(%3106, newshape=[-1, 1024, 1024]); + %3108 = dyn.reshape(%3015, %3105, newshape=[]); + %3109 = transpose(%3107, axes=[0, 2, 1]); + %3110 = strided_slice(%3102, begin=[0], end=[1], strides=[1]); + %3111 = strided_slice(%3102, begin=[1], end=[2], strides=[1]); + %3112 = (%3110, %3111, meta[relay.Constant][336]); + %3113 = nn.batch_matmul(%3108, %3109, meta[relay.attrs.BatchMatmulAttrs][115]); + %3114 = concatenate(%3112); + %3115 = dyn.reshape(%3113, %3114, newshape=[]); + %3116 = add(%3115, %bert_encoder_layer_14_attention_self_value_bias); + %3117 = shape_of(%3116, dtype="int64"); + %3118 = take(%3117, 0, axis=0); + %3119 = shape_of(%3116, dtype="int64"); + %3120 = take(%3119, 1, axis=0); + %3121 = expand_dims(%3118, axis=0); + %3122 = expand_dims(%3120, axis=0); + %3123 = (%3121, %3122, meta[relay.Constant][337], meta[relay.Constant][338]); + %3124 = concatenate(%3123); + %3125 = dyn.reshape(%3116, %3124, newshape=[]); + %3126 = transpose(%3125, axes=[0, 2, 1, 3]); + %3127 = shape_of(%3126, dtype="int64"); + %3128 = strided_slice(%3127, begin=[2], end=[4], strides=[1]); + %3129 = (meta[relay.Constant][339], %3128); + %3130 = concatenate(%3129); + %3131 = dyn.reshape(%3126, %3130, newshape=[]); + %3132 = dyn.reshape(%3097, %3101, newshape=[]); + %3133 = transpose(%3131, axes=[0, 2, 1]); + %3134 = strided_slice(%3098, begin=[0], end=[1], strides=[1]); + %3135 = strided_slice(%3127, begin=[0], end=[1], strides=[1]); + %3136 = strided_slice(%3098, begin=[1], end=[2], strides=[1]); + %3137 = strided_slice(%3127, begin=[1], end=[2], strides=[1]); + %3138 = maximum(%3134, %3135); + %3139 = maximum(%3136, %3137); + %3140 = (%3138, %3139); + %3141 = concatenate(%3140); + %3142 = strided_slice(%3098, begin=[2], end=[3], strides=[1]); + %3143 = strided_slice(%3127, begin=[3], end=[4], strides=[1]); + %3144 = (%3141, %3142, %3143); + %3145 = nn.batch_matmul(%3132, %3133, meta[relay.attrs.BatchMatmulAttrs][116]); + %3146 = concatenate(%3144); + %3147 = dyn.reshape(%3145, %3146, newshape=[]); + %3148 = transpose(%3147, axes=[0, 2, 1, 3]); + %3149 = shape_of(%3148, dtype="int64"); + %3150 = take(%3149, 0, axis=0); + %3151 = shape_of(%3148, dtype="int64"); + %3152 = take(%3151, 1, axis=0); + %3153 = expand_dims(%3150, axis=0); + %3154 = expand_dims(%3152, axis=0); + %3155 = (%3153, %3154, meta[relay.Constant][340]); + %3156 = concatenate(%3155); + %3157 = dyn.reshape(%3148, %3156, newshape=[]); + %3158 = shape_of(%3157, dtype="int64"); + %3159 = strided_slice(%3158, begin=[1], end=[3], strides=[1]); + %3160 = (meta[relay.Constant][341], %3159); + %3161 = concatenate(%3160); + %3162 = transpose(%bert_encoder_layer_14_attention_output_dense_weight, axes=[1, 0]); + %3163 = reshape(%3162, newshape=[-1, 1024, 1024]); + %3164 = dyn.reshape(%3157, %3161, newshape=[]); + %3165 = transpose(%3163, axes=[0, 2, 1]); + %3166 = strided_slice(%3158, begin=[0], end=[1], strides=[1]); + %3167 = strided_slice(%3158, begin=[1], end=[2], strides=[1]); + %3168 = (%3166, %3167, meta[relay.Constant][342]); + %3169 = nn.batch_matmul(%3164, %3165, meta[relay.attrs.BatchMatmulAttrs][117]); + %3170 = concatenate(%3168); + %3171 = dyn.reshape(%3169, %3170, newshape=[]); + %3172 = add(%3171, %bert_encoder_layer_14_attention_output_dense_bias); + %3173 = add(%3172, %3015); + %3174 = mean(%3173, axis=[-1], keepdims=True); + %3175 = subtract(%3173, %3174); + %3176 = power(%3175, 2f); + %3177 = mean(%3176, axis=[-1], keepdims=True); + %3178 = add(%3177, 1e-12f); + %3179 = sqrt(%3178); + %3180 = divide(%3175, %3179); + %3181 = multiply(%3180, %bert_encoder_layer_14_attention_output_LayerNorm_weight); + %3182 = add(%3181, %bert_encoder_layer_14_attention_output_LayerNorm_bias); + %3183 = shape_of(%3182, dtype="int64"); + %3184 = strided_slice(%3183, begin=[1], end=[3], strides=[1]); + %3185 = (meta[relay.Constant][343], %3184); + %3186 = concatenate(%3185); + %3187 = transpose(%bert_encoder_layer_14_intermediate_dense_weight, axes=[1, 0]); + %3188 = reshape(%3187, newshape=[-1, 1024, 4096]); + %3189 = dyn.reshape(%3182, %3186, newshape=[]); + %3190 = transpose(%3188, axes=[0, 2, 1]); + %3191 = strided_slice(%3183, begin=[0], end=[1], strides=[1]); + %3192 = strided_slice(%3183, begin=[1], end=[2], strides=[1]); + %3193 = (%3191, %3192, meta[relay.Constant][344]); + %3194 = nn.batch_matmul(%3189, %3190, meta[relay.attrs.BatchMatmulAttrs][118]); + %3195 = concatenate(%3193); + %3196 = dyn.reshape(%3194, %3195, newshape=[]); + %3197 = add(%3196, %bert_encoder_layer_14_intermediate_dense_bias); + %3198 = divide(%3197, 1.41421f); + %3199 = erf(%3198); + %3200 = multiply(%3197, 0.5f); + %3201 = add(%3199, 1f); + %3202 = multiply(%3200, %3201); + %3203 = shape_of(%3202, dtype="int64"); + %3204 = strided_slice(%3203, begin=[1], end=[3], strides=[1]); + %3205 = (meta[relay.Constant][345], %3204); + %3206 = concatenate(%3205); + %3207 = transpose(%bert_encoder_layer_14_output_dense_weight, axes=[1, 0]); + %3208 = reshape(%3207, newshape=[-1, 4096, 1024]); + %3209 = dyn.reshape(%3202, %3206, newshape=[]); + %3210 = transpose(%3208, axes=[0, 2, 1]); + %3211 = strided_slice(%3203, begin=[0], end=[1], strides=[1]); + %3212 = strided_slice(%3203, begin=[1], end=[2], strides=[1]); + %3213 = (%3211, %3212, meta[relay.Constant][346]); + %3214 = nn.batch_matmul(%3209, %3210, meta[relay.attrs.BatchMatmulAttrs][119]); + %3215 = concatenate(%3213); + %3216 = dyn.reshape(%3214, %3215, newshape=[]); + %3217 = add(%3216, %bert_encoder_layer_14_output_dense_bias); + %3218 = add(%3217, %3182); + %3219 = mean(%3218, axis=[-1], keepdims=True); + %3220 = subtract(%3218, %3219); + %3221 = power(%3220, 2f); + %3222 = mean(%3221, axis=[-1], keepdims=True); + %3223 = add(%3222, 1e-12f); + %3224 = sqrt(%3223); + %3225 = divide(%3220, %3224); + %3226 = multiply(%3225, %bert_encoder_layer_14_output_LayerNorm_weight); + %3227 = add(%3226, %bert_encoder_layer_14_output_LayerNorm_bias); + %3228 = shape_of(%3227, dtype="int64"); + %3229 = strided_slice(%3228, begin=[1], end=[3], strides=[1]); + %3230 = (meta[relay.Constant][347], %3229); + %3231 = concatenate(%3230); + %3232 = transpose(%bert_encoder_layer_15_attention_self_query_weight, axes=[1, 0]); + %3233 = reshape(%3232, newshape=[-1, 1024, 1024]); + %3234 = dyn.reshape(%3227, %3231, newshape=[]); + %3235 = transpose(%3233, axes=[0, 2, 1]); + %3236 = strided_slice(%3228, begin=[0], end=[1], strides=[1]); + %3237 = strided_slice(%3228, begin=[1], end=[2], strides=[1]); + %3238 = (%3236, %3237, meta[relay.Constant][348]); + %3239 = nn.batch_matmul(%3234, %3235, meta[relay.attrs.BatchMatmulAttrs][120]); + %3240 = concatenate(%3238); + %3241 = dyn.reshape(%3239, %3240, newshape=[]); + %3242 = add(%3241, %bert_encoder_layer_15_attention_self_query_bias); + %3243 = shape_of(%3242, dtype="int64"); + %3244 = take(%3243, 0, axis=0); + %3245 = shape_of(%3242, dtype="int64"); + %3246 = take(%3245, 1, axis=0); + %3247 = expand_dims(%3244, axis=0); + %3248 = expand_dims(%3246, axis=0); + %3249 = (%3247, %3248, meta[relay.Constant][349], meta[relay.Constant][350]); + %3250 = concatenate(%3249); + %3251 = dyn.reshape(%3242, %3250, newshape=[]); + %3252 = transpose(%3251, axes=[0, 2, 1, 3]); + %3253 = shape_of(%3252, dtype="int64"); + %3254 = strided_slice(%3253, begin=[2], end=[4], strides=[1]); + %3255 = (meta[relay.Constant][351], %3254); + %3256 = concatenate(%3255); + %3257 = shape_of(%3227, dtype="int64"); + %3258 = strided_slice(%3257, begin=[1], end=[3], strides=[1]); + %3259 = (meta[relay.Constant][352], %3258); + %3260 = concatenate(%3259); + %3261 = transpose(%bert_encoder_layer_15_attention_self_key_weight, axes=[1, 0]); + %3262 = reshape(%3261, newshape=[-1, 1024, 1024]); + %3263 = dyn.reshape(%3227, %3260, newshape=[]); + %3264 = transpose(%3262, axes=[0, 2, 1]); + %3265 = strided_slice(%3257, begin=[0], end=[1], strides=[1]); + %3266 = strided_slice(%3257, begin=[1], end=[2], strides=[1]); + %3267 = (%3265, %3266, meta[relay.Constant][353]); + %3268 = nn.batch_matmul(%3263, %3264, meta[relay.attrs.BatchMatmulAttrs][121]); + %3269 = concatenate(%3267); + %3270 = dyn.reshape(%3268, %3269, newshape=[]); + %3271 = add(%3270, %bert_encoder_layer_15_attention_self_key_bias); + %3272 = shape_of(%3271, dtype="int64"); + %3273 = take(%3272, 0, axis=0); + %3274 = shape_of(%3271, dtype="int64"); + %3275 = take(%3274, 1, axis=0); + %3276 = expand_dims(%3273, axis=0); + %3277 = expand_dims(%3275, axis=0); + %3278 = (%3276, %3277, meta[relay.Constant][354], meta[relay.Constant][355]); + %3279 = concatenate(%3278); + %3280 = dyn.reshape(%3271, %3279, newshape=[]); + %3281 = transpose(%3280, axes=[0, 2, 3, 1]); + %3282 = shape_of(%3281, dtype="int64"); + %3283 = strided_slice(%3282, begin=[2], end=[4], strides=[1]); + %3284 = (meta[relay.Constant][356], %3283); + %3285 = concatenate(%3284); + %3286 = dyn.reshape(%3281, %3285, newshape=[]); + %3287 = dyn.reshape(%3252, %3256, newshape=[]); + %3288 = transpose(%3286, axes=[0, 2, 1]); + %3289 = strided_slice(%3253, begin=[0], end=[1], strides=[1]); + %3290 = strided_slice(%3282, begin=[0], end=[1], strides=[1]); + %3291 = strided_slice(%3253, begin=[1], end=[2], strides=[1]); + %3292 = strided_slice(%3282, begin=[1], end=[2], strides=[1]); + %3293 = maximum(%3289, %3290); + %3294 = maximum(%3291, %3292); + %3295 = (%3293, %3294); + %3296 = concatenate(%3295); + %3297 = strided_slice(%3253, begin=[2], end=[3], strides=[1]); + %3298 = strided_slice(%3282, begin=[3], end=[4], strides=[1]); + %3299 = (%3296, %3297, %3298); + %3300 = nn.batch_matmul(%3287, %3288, meta[relay.attrs.BatchMatmulAttrs][122]); + %3301 = concatenate(%3299); + %3302 = dyn.reshape(%3300, %3301, newshape=[]); + %3303 = divide(%3302, 8f); + %3304 = add(%3303, %123); + %3305 = max(%3304, axis=[3], keepdims=True); + %3306 = subtract(%3304, %3305); + %3307 = exp(%3306); + %3308 = sum(%3307, axis=[3], keepdims=True); + %3309 = divide(%3307, %3308); + %3310 = shape_of(%3309, dtype="int64"); + %3311 = strided_slice(%3310, begin=[2], end=[4], strides=[1]); + %3312 = (meta[relay.Constant][357], %3311); + %3313 = concatenate(%3312); + %3314 = shape_of(%3227, dtype="int64"); + %3315 = strided_slice(%3314, begin=[1], end=[3], strides=[1]); + %3316 = (meta[relay.Constant][358], %3315); + %3317 = concatenate(%3316); + %3318 = transpose(%bert_encoder_layer_15_attention_self_value_weight, axes=[1, 0]); + %3319 = reshape(%3318, newshape=[-1, 1024, 1024]); + %3320 = dyn.reshape(%3227, %3317, newshape=[]); + %3321 = transpose(%3319, axes=[0, 2, 1]); + %3322 = strided_slice(%3314, begin=[0], end=[1], strides=[1]); + %3323 = strided_slice(%3314, begin=[1], end=[2], strides=[1]); + %3324 = (%3322, %3323, meta[relay.Constant][359]); + %3325 = nn.batch_matmul(%3320, %3321, meta[relay.attrs.BatchMatmulAttrs][123]); + %3326 = concatenate(%3324); + %3327 = dyn.reshape(%3325, %3326, newshape=[]); + %3328 = add(%3327, %bert_encoder_layer_15_attention_self_value_bias); + %3329 = shape_of(%3328, dtype="int64"); + %3330 = take(%3329, 0, axis=0); + %3331 = shape_of(%3328, dtype="int64"); + %3332 = take(%3331, 1, axis=0); + %3333 = expand_dims(%3330, axis=0); + %3334 = expand_dims(%3332, axis=0); + %3335 = (%3333, %3334, meta[relay.Constant][360], meta[relay.Constant][361]); + %3336 = concatenate(%3335); + %3337 = dyn.reshape(%3328, %3336, newshape=[]); + %3338 = transpose(%3337, axes=[0, 2, 1, 3]); + %3339 = shape_of(%3338, dtype="int64"); + %3340 = strided_slice(%3339, begin=[2], end=[4], strides=[1]); + %3341 = (meta[relay.Constant][362], %3340); + %3342 = concatenate(%3341); + %3343 = dyn.reshape(%3338, %3342, newshape=[]); + %3344 = dyn.reshape(%3309, %3313, newshape=[]); + %3345 = transpose(%3343, axes=[0, 2, 1]); + %3346 = strided_slice(%3310, begin=[0], end=[1], strides=[1]); + %3347 = strided_slice(%3339, begin=[0], end=[1], strides=[1]); + %3348 = strided_slice(%3310, begin=[1], end=[2], strides=[1]); + %3349 = strided_slice(%3339, begin=[1], end=[2], strides=[1]); + %3350 = maximum(%3346, %3347); + %3351 = maximum(%3348, %3349); + %3352 = (%3350, %3351); + %3353 = concatenate(%3352); + %3354 = strided_slice(%3310, begin=[2], end=[3], strides=[1]); + %3355 = strided_slice(%3339, begin=[3], end=[4], strides=[1]); + %3356 = (%3353, %3354, %3355); + %3357 = nn.batch_matmul(%3344, %3345, meta[relay.attrs.BatchMatmulAttrs][124]); + %3358 = concatenate(%3356); + %3359 = dyn.reshape(%3357, %3358, newshape=[]); + %3360 = transpose(%3359, axes=[0, 2, 1, 3]); + %3361 = shape_of(%3360, dtype="int64"); + %3362 = take(%3361, 0, axis=0); + %3363 = shape_of(%3360, dtype="int64"); + %3364 = take(%3363, 1, axis=0); + %3365 = expand_dims(%3362, axis=0); + %3366 = expand_dims(%3364, axis=0); + %3367 = (%3365, %3366, meta[relay.Constant][363]); + %3368 = concatenate(%3367); + %3369 = dyn.reshape(%3360, %3368, newshape=[]); + %3370 = shape_of(%3369, dtype="int64"); + %3371 = strided_slice(%3370, begin=[1], end=[3], strides=[1]); + %3372 = (meta[relay.Constant][364], %3371); + %3373 = concatenate(%3372); + %3374 = transpose(%bert_encoder_layer_15_attention_output_dense_weight, axes=[1, 0]); + %3375 = reshape(%3374, newshape=[-1, 1024, 1024]); + %3376 = dyn.reshape(%3369, %3373, newshape=[]); + %3377 = transpose(%3375, axes=[0, 2, 1]); + %3378 = strided_slice(%3370, begin=[0], end=[1], strides=[1]); + %3379 = strided_slice(%3370, begin=[1], end=[2], strides=[1]); + %3380 = (%3378, %3379, meta[relay.Constant][365]); + %3381 = nn.batch_matmul(%3376, %3377, meta[relay.attrs.BatchMatmulAttrs][125]); + %3382 = concatenate(%3380); + %3383 = dyn.reshape(%3381, %3382, newshape=[]); + %3384 = add(%3383, %bert_encoder_layer_15_attention_output_dense_bias); + %3385 = add(%3384, %3227); + %3386 = mean(%3385, axis=[-1], keepdims=True); + %3387 = subtract(%3385, %3386); + %3388 = power(%3387, 2f); + %3389 = mean(%3388, axis=[-1], keepdims=True); + %3390 = add(%3389, 1e-12f); + %3391 = sqrt(%3390); + %3392 = divide(%3387, %3391); + %3393 = multiply(%3392, %bert_encoder_layer_15_attention_output_LayerNorm_weight); + %3394 = add(%3393, %bert_encoder_layer_15_attention_output_LayerNorm_bias); + %3395 = shape_of(%3394, dtype="int64"); + %3396 = strided_slice(%3395, begin=[1], end=[3], strides=[1]); + %3397 = (meta[relay.Constant][366], %3396); + %3398 = concatenate(%3397); + %3399 = transpose(%bert_encoder_layer_15_intermediate_dense_weight, axes=[1, 0]); + %3400 = reshape(%3399, newshape=[-1, 1024, 4096]); + %3401 = dyn.reshape(%3394, %3398, newshape=[]); + %3402 = transpose(%3400, axes=[0, 2, 1]); + %3403 = strided_slice(%3395, begin=[0], end=[1], strides=[1]); + %3404 = strided_slice(%3395, begin=[1], end=[2], strides=[1]); + %3405 = (%3403, %3404, meta[relay.Constant][367]); + %3406 = nn.batch_matmul(%3401, %3402, meta[relay.attrs.BatchMatmulAttrs][126]); + %3407 = concatenate(%3405); + %3408 = dyn.reshape(%3406, %3407, newshape=[]); + %3409 = add(%3408, %bert_encoder_layer_15_intermediate_dense_bias); + %3410 = divide(%3409, 1.41421f); + %3411 = erf(%3410); + %3412 = multiply(%3409, 0.5f); + %3413 = add(%3411, 1f); + %3414 = multiply(%3412, %3413); + %3415 = shape_of(%3414, dtype="int64"); + %3416 = strided_slice(%3415, begin=[1], end=[3], strides=[1]); + %3417 = (meta[relay.Constant][368], %3416); + %3418 = concatenate(%3417); + %3419 = transpose(%bert_encoder_layer_15_output_dense_weight, axes=[1, 0]); + %3420 = reshape(%3419, newshape=[-1, 4096, 1024]); + %3421 = dyn.reshape(%3414, %3418, newshape=[]); + %3422 = transpose(%3420, axes=[0, 2, 1]); + %3423 = strided_slice(%3415, begin=[0], end=[1], strides=[1]); + %3424 = strided_slice(%3415, begin=[1], end=[2], strides=[1]); + %3425 = (%3423, %3424, meta[relay.Constant][369]); + %3426 = nn.batch_matmul(%3421, %3422, meta[relay.attrs.BatchMatmulAttrs][127]); + %3427 = concatenate(%3425); + %3428 = dyn.reshape(%3426, %3427, newshape=[]); + %3429 = add(%3428, %bert_encoder_layer_15_output_dense_bias); + %3430 = add(%3429, %3394); + %3431 = mean(%3430, axis=[-1], keepdims=True); + %3432 = subtract(%3430, %3431); + %3433 = power(%3432, 2f); + %3434 = mean(%3433, axis=[-1], keepdims=True); + %3435 = add(%3434, 1e-12f); + %3436 = sqrt(%3435); + %3437 = divide(%3432, %3436); + %3438 = multiply(%3437, %bert_encoder_layer_15_output_LayerNorm_weight); + %3439 = add(%3438, %bert_encoder_layer_15_output_LayerNorm_bias); + %3440 = shape_of(%3439, dtype="int64"); + %3441 = strided_slice(%3440, begin=[1], end=[3], strides=[1]); + %3442 = (meta[relay.Constant][370], %3441); + %3443 = concatenate(%3442); + %3444 = transpose(%bert_encoder_layer_16_attention_self_query_weight, axes=[1, 0]); + %3445 = reshape(%3444, newshape=[-1, 1024, 1024]); + %3446 = dyn.reshape(%3439, %3443, newshape=[]); + %3447 = transpose(%3445, axes=[0, 2, 1]); + %3448 = strided_slice(%3440, begin=[0], end=[1], strides=[1]); + %3449 = strided_slice(%3440, begin=[1], end=[2], strides=[1]); + %3450 = (%3448, %3449, meta[relay.Constant][371]); + %3451 = nn.batch_matmul(%3446, %3447, meta[relay.attrs.BatchMatmulAttrs][128]); + %3452 = concatenate(%3450); + %3453 = dyn.reshape(%3451, %3452, newshape=[]); + %3454 = add(%3453, %bert_encoder_layer_16_attention_self_query_bias); + %3455 = shape_of(%3454, dtype="int64"); + %3456 = take(%3455, 0, axis=0); + %3457 = shape_of(%3454, dtype="int64"); + %3458 = take(%3457, 1, axis=0); + %3459 = expand_dims(%3456, axis=0); + %3460 = expand_dims(%3458, axis=0); + %3461 = (%3459, %3460, meta[relay.Constant][372], meta[relay.Constant][373]); + %3462 = concatenate(%3461); + %3463 = dyn.reshape(%3454, %3462, newshape=[]); + %3464 = transpose(%3463, axes=[0, 2, 1, 3]); + %3465 = shape_of(%3464, dtype="int64"); + %3466 = strided_slice(%3465, begin=[2], end=[4], strides=[1]); + %3467 = (meta[relay.Constant][374], %3466); + %3468 = concatenate(%3467); + %3469 = shape_of(%3439, dtype="int64"); + %3470 = strided_slice(%3469, begin=[1], end=[3], strides=[1]); + %3471 = (meta[relay.Constant][375], %3470); + %3472 = concatenate(%3471); + %3473 = transpose(%bert_encoder_layer_16_attention_self_key_weight, axes=[1, 0]); + %3474 = reshape(%3473, newshape=[-1, 1024, 1024]); + %3475 = dyn.reshape(%3439, %3472, newshape=[]); + %3476 = transpose(%3474, axes=[0, 2, 1]); + %3477 = strided_slice(%3469, begin=[0], end=[1], strides=[1]); + %3478 = strided_slice(%3469, begin=[1], end=[2], strides=[1]); + %3479 = (%3477, %3478, meta[relay.Constant][376]); + %3480 = nn.batch_matmul(%3475, %3476, meta[relay.attrs.BatchMatmulAttrs][129]); + %3481 = concatenate(%3479); + %3482 = dyn.reshape(%3480, %3481, newshape=[]); + %3483 = add(%3482, %bert_encoder_layer_16_attention_self_key_bias); + %3484 = shape_of(%3483, dtype="int64"); + %3485 = take(%3484, 0, axis=0); + %3486 = shape_of(%3483, dtype="int64"); + %3487 = take(%3486, 1, axis=0); + %3488 = expand_dims(%3485, axis=0); + %3489 = expand_dims(%3487, axis=0); + %3490 = (%3488, %3489, meta[relay.Constant][377], meta[relay.Constant][378]); + %3491 = concatenate(%3490); + %3492 = dyn.reshape(%3483, %3491, newshape=[]); + %3493 = transpose(%3492, axes=[0, 2, 3, 1]); + %3494 = shape_of(%3493, dtype="int64"); + %3495 = strided_slice(%3494, begin=[2], end=[4], strides=[1]); + %3496 = (meta[relay.Constant][379], %3495); + %3497 = concatenate(%3496); + %3498 = dyn.reshape(%3493, %3497, newshape=[]); + %3499 = dyn.reshape(%3464, %3468, newshape=[]); + %3500 = transpose(%3498, axes=[0, 2, 1]); + %3501 = strided_slice(%3465, begin=[0], end=[1], strides=[1]); + %3502 = strided_slice(%3494, begin=[0], end=[1], strides=[1]); + %3503 = strided_slice(%3465, begin=[1], end=[2], strides=[1]); + %3504 = strided_slice(%3494, begin=[1], end=[2], strides=[1]); + %3505 = maximum(%3501, %3502); + %3506 = maximum(%3503, %3504); + %3507 = (%3505, %3506); + %3508 = concatenate(%3507); + %3509 = strided_slice(%3465, begin=[2], end=[3], strides=[1]); + %3510 = strided_slice(%3494, begin=[3], end=[4], strides=[1]); + %3511 = (%3508, %3509, %3510); + %3512 = nn.batch_matmul(%3499, %3500, meta[relay.attrs.BatchMatmulAttrs][130]); + %3513 = concatenate(%3511); + %3514 = dyn.reshape(%3512, %3513, newshape=[]); + %3515 = divide(%3514, 8f); + %3516 = add(%3515, %123); + %3517 = max(%3516, axis=[3], keepdims=True); + %3518 = subtract(%3516, %3517); + %3519 = exp(%3518); + %3520 = sum(%3519, axis=[3], keepdims=True); + %3521 = divide(%3519, %3520); + %3522 = shape_of(%3521, dtype="int64"); + %3523 = strided_slice(%3522, begin=[2], end=[4], strides=[1]); + %3524 = (meta[relay.Constant][380], %3523); + %3525 = concatenate(%3524); + %3526 = shape_of(%3439, dtype="int64"); + %3527 = strided_slice(%3526, begin=[1], end=[3], strides=[1]); + %3528 = (meta[relay.Constant][381], %3527); + %3529 = concatenate(%3528); + %3530 = transpose(%bert_encoder_layer_16_attention_self_value_weight, axes=[1, 0]); + %3531 = reshape(%3530, newshape=[-1, 1024, 1024]); + %3532 = dyn.reshape(%3439, %3529, newshape=[]); + %3533 = transpose(%3531, axes=[0, 2, 1]); + %3534 = strided_slice(%3526, begin=[0], end=[1], strides=[1]); + %3535 = strided_slice(%3526, begin=[1], end=[2], strides=[1]); + %3536 = (%3534, %3535, meta[relay.Constant][382]); + %3537 = nn.batch_matmul(%3532, %3533, meta[relay.attrs.BatchMatmulAttrs][131]); + %3538 = concatenate(%3536); + %3539 = dyn.reshape(%3537, %3538, newshape=[]); + %3540 = add(%3539, %bert_encoder_layer_16_attention_self_value_bias); + %3541 = shape_of(%3540, dtype="int64"); + %3542 = take(%3541, 0, axis=0); + %3543 = shape_of(%3540, dtype="int64"); + %3544 = take(%3543, 1, axis=0); + %3545 = expand_dims(%3542, axis=0); + %3546 = expand_dims(%3544, axis=0); + %3547 = (%3545, %3546, meta[relay.Constant][383], meta[relay.Constant][384]); + %3548 = concatenate(%3547); + %3549 = dyn.reshape(%3540, %3548, newshape=[]); + %3550 = transpose(%3549, axes=[0, 2, 1, 3]); + %3551 = shape_of(%3550, dtype="int64"); + %3552 = strided_slice(%3551, begin=[2], end=[4], strides=[1]); + %3553 = (meta[relay.Constant][385], %3552); + %3554 = concatenate(%3553); + %3555 = dyn.reshape(%3550, %3554, newshape=[]); + %3556 = dyn.reshape(%3521, %3525, newshape=[]); + %3557 = transpose(%3555, axes=[0, 2, 1]); + %3558 = strided_slice(%3522, begin=[0], end=[1], strides=[1]); + %3559 = strided_slice(%3551, begin=[0], end=[1], strides=[1]); + %3560 = strided_slice(%3522, begin=[1], end=[2], strides=[1]); + %3561 = strided_slice(%3551, begin=[1], end=[2], strides=[1]); + %3562 = maximum(%3558, %3559); + %3563 = maximum(%3560, %3561); + %3564 = (%3562, %3563); + %3565 = concatenate(%3564); + %3566 = strided_slice(%3522, begin=[2], end=[3], strides=[1]); + %3567 = strided_slice(%3551, begin=[3], end=[4], strides=[1]); + %3568 = (%3565, %3566, %3567); + %3569 = nn.batch_matmul(%3556, %3557, meta[relay.attrs.BatchMatmulAttrs][132]); + %3570 = concatenate(%3568); + %3571 = dyn.reshape(%3569, %3570, newshape=[]); + %3572 = transpose(%3571, axes=[0, 2, 1, 3]); + %3573 = shape_of(%3572, dtype="int64"); + %3574 = take(%3573, 0, axis=0); + %3575 = shape_of(%3572, dtype="int64"); + %3576 = take(%3575, 1, axis=0); + %3577 = expand_dims(%3574, axis=0); + %3578 = expand_dims(%3576, axis=0); + %3579 = (%3577, %3578, meta[relay.Constant][386]); + %3580 = concatenate(%3579); + %3581 = dyn.reshape(%3572, %3580, newshape=[]); + %3582 = shape_of(%3581, dtype="int64"); + %3583 = strided_slice(%3582, begin=[1], end=[3], strides=[1]); + %3584 = (meta[relay.Constant][387], %3583); + %3585 = concatenate(%3584); + %3586 = transpose(%bert_encoder_layer_16_attention_output_dense_weight, axes=[1, 0]); + %3587 = reshape(%3586, newshape=[-1, 1024, 1024]); + %3588 = dyn.reshape(%3581, %3585, newshape=[]); + %3589 = transpose(%3587, axes=[0, 2, 1]); + %3590 = strided_slice(%3582, begin=[0], end=[1], strides=[1]); + %3591 = strided_slice(%3582, begin=[1], end=[2], strides=[1]); + %3592 = (%3590, %3591, meta[relay.Constant][388]); + %3593 = nn.batch_matmul(%3588, %3589, meta[relay.attrs.BatchMatmulAttrs][133]); + %3594 = concatenate(%3592); + %3595 = dyn.reshape(%3593, %3594, newshape=[]); + %3596 = add(%3595, %bert_encoder_layer_16_attention_output_dense_bias); + %3597 = add(%3596, %3439); + %3598 = mean(%3597, axis=[-1], keepdims=True); + %3599 = subtract(%3597, %3598); + %3600 = power(%3599, 2f); + %3601 = mean(%3600, axis=[-1], keepdims=True); + %3602 = add(%3601, 1e-12f); + %3603 = sqrt(%3602); + %3604 = divide(%3599, %3603); + %3605 = multiply(%3604, %bert_encoder_layer_16_attention_output_LayerNorm_weight); + %3606 = add(%3605, %bert_encoder_layer_16_attention_output_LayerNorm_bias); + %3607 = shape_of(%3606, dtype="int64"); + %3608 = strided_slice(%3607, begin=[1], end=[3], strides=[1]); + %3609 = (meta[relay.Constant][389], %3608); + %3610 = concatenate(%3609); + %3611 = transpose(%bert_encoder_layer_16_intermediate_dense_weight, axes=[1, 0]); + %3612 = reshape(%3611, newshape=[-1, 1024, 4096]); + %3613 = dyn.reshape(%3606, %3610, newshape=[]); + %3614 = transpose(%3612, axes=[0, 2, 1]); + %3615 = strided_slice(%3607, begin=[0], end=[1], strides=[1]); + %3616 = strided_slice(%3607, begin=[1], end=[2], strides=[1]); + %3617 = (%3615, %3616, meta[relay.Constant][390]); + %3618 = nn.batch_matmul(%3613, %3614, meta[relay.attrs.BatchMatmulAttrs][134]); + %3619 = concatenate(%3617); + %3620 = dyn.reshape(%3618, %3619, newshape=[]); + %3621 = add(%3620, %bert_encoder_layer_16_intermediate_dense_bias); + %3622 = divide(%3621, 1.41421f); + %3623 = erf(%3622); + %3624 = multiply(%3621, 0.5f); + %3625 = add(%3623, 1f); + %3626 = multiply(%3624, %3625); + %3627 = shape_of(%3626, dtype="int64"); + %3628 = strided_slice(%3627, begin=[1], end=[3], strides=[1]); + %3629 = (meta[relay.Constant][391], %3628); + %3630 = concatenate(%3629); + %3631 = transpose(%bert_encoder_layer_16_output_dense_weight, axes=[1, 0]); + %3632 = reshape(%3631, newshape=[-1, 4096, 1024]); + %3633 = dyn.reshape(%3626, %3630, newshape=[]); + %3634 = transpose(%3632, axes=[0, 2, 1]); + %3635 = strided_slice(%3627, begin=[0], end=[1], strides=[1]); + %3636 = strided_slice(%3627, begin=[1], end=[2], strides=[1]); + %3637 = (%3635, %3636, meta[relay.Constant][392]); + %3638 = nn.batch_matmul(%3633, %3634, meta[relay.attrs.BatchMatmulAttrs][135]); + %3639 = concatenate(%3637); + %3640 = dyn.reshape(%3638, %3639, newshape=[]); + %3641 = add(%3640, %bert_encoder_layer_16_output_dense_bias); + %3642 = add(%3641, %3606); + %3643 = mean(%3642, axis=[-1], keepdims=True); + %3644 = subtract(%3642, %3643); + %3645 = power(%3644, 2f); + %3646 = mean(%3645, axis=[-1], keepdims=True); + %3647 = add(%3646, 1e-12f); + %3648 = sqrt(%3647); + %3649 = divide(%3644, %3648); + %3650 = multiply(%3649, %bert_encoder_layer_16_output_LayerNorm_weight); + %3651 = add(%3650, %bert_encoder_layer_16_output_LayerNorm_bias); + %3652 = shape_of(%3651, dtype="int64"); + %3653 = strided_slice(%3652, begin=[1], end=[3], strides=[1]); + %3654 = (meta[relay.Constant][393], %3653); + %3655 = concatenate(%3654); + %3656 = transpose(%bert_encoder_layer_17_attention_self_query_weight, axes=[1, 0]); + %3657 = reshape(%3656, newshape=[-1, 1024, 1024]); + %3658 = dyn.reshape(%3651, %3655, newshape=[]); + %3659 = transpose(%3657, axes=[0, 2, 1]); + %3660 = strided_slice(%3652, begin=[0], end=[1], strides=[1]); + %3661 = strided_slice(%3652, begin=[1], end=[2], strides=[1]); + %3662 = (%3660, %3661, meta[relay.Constant][394]); + %3663 = nn.batch_matmul(%3658, %3659, meta[relay.attrs.BatchMatmulAttrs][136]); + %3664 = concatenate(%3662); + %3665 = dyn.reshape(%3663, %3664, newshape=[]); + %3666 = add(%3665, %bert_encoder_layer_17_attention_self_query_bias); + %3667 = shape_of(%3666, dtype="int64"); + %3668 = take(%3667, 0, axis=0); + %3669 = shape_of(%3666, dtype="int64"); + %3670 = take(%3669, 1, axis=0); + %3671 = expand_dims(%3668, axis=0); + %3672 = expand_dims(%3670, axis=0); + %3673 = (%3671, %3672, meta[relay.Constant][395], meta[relay.Constant][396]); + %3674 = concatenate(%3673); + %3675 = dyn.reshape(%3666, %3674, newshape=[]); + %3676 = transpose(%3675, axes=[0, 2, 1, 3]); + %3677 = shape_of(%3676, dtype="int64"); + %3678 = strided_slice(%3677, begin=[2], end=[4], strides=[1]); + %3679 = (meta[relay.Constant][397], %3678); + %3680 = concatenate(%3679); + %3681 = shape_of(%3651, dtype="int64"); + %3682 = strided_slice(%3681, begin=[1], end=[3], strides=[1]); + %3683 = (meta[relay.Constant][398], %3682); + %3684 = concatenate(%3683); + %3685 = transpose(%bert_encoder_layer_17_attention_self_key_weight, axes=[1, 0]); + %3686 = reshape(%3685, newshape=[-1, 1024, 1024]); + %3687 = dyn.reshape(%3651, %3684, newshape=[]); + %3688 = transpose(%3686, axes=[0, 2, 1]); + %3689 = strided_slice(%3681, begin=[0], end=[1], strides=[1]); + %3690 = strided_slice(%3681, begin=[1], end=[2], strides=[1]); + %3691 = (%3689, %3690, meta[relay.Constant][399]); + %3692 = nn.batch_matmul(%3687, %3688, meta[relay.attrs.BatchMatmulAttrs][137]); + %3693 = concatenate(%3691); + %3694 = dyn.reshape(%3692, %3693, newshape=[]); + %3695 = add(%3694, %bert_encoder_layer_17_attention_self_key_bias); + %3696 = shape_of(%3695, dtype="int64"); + %3697 = take(%3696, 0, axis=0); + %3698 = shape_of(%3695, dtype="int64"); + %3699 = take(%3698, 1, axis=0); + %3700 = expand_dims(%3697, axis=0); + %3701 = expand_dims(%3699, axis=0); + %3702 = (%3700, %3701, meta[relay.Constant][400], meta[relay.Constant][401]); + %3703 = concatenate(%3702); + %3704 = dyn.reshape(%3695, %3703, newshape=[]); + %3705 = transpose(%3704, axes=[0, 2, 3, 1]); + %3706 = shape_of(%3705, dtype="int64"); + %3707 = strided_slice(%3706, begin=[2], end=[4], strides=[1]); + %3708 = (meta[relay.Constant][402], %3707); + %3709 = concatenate(%3708); + %3710 = dyn.reshape(%3705, %3709, newshape=[]); + %3711 = dyn.reshape(%3676, %3680, newshape=[]); + %3712 = transpose(%3710, axes=[0, 2, 1]); + %3713 = strided_slice(%3677, begin=[0], end=[1], strides=[1]); + %3714 = strided_slice(%3706, begin=[0], end=[1], strides=[1]); + %3715 = strided_slice(%3677, begin=[1], end=[2], strides=[1]); + %3716 = strided_slice(%3706, begin=[1], end=[2], strides=[1]); + %3717 = maximum(%3713, %3714); + %3718 = maximum(%3715, %3716); + %3719 = (%3717, %3718); + %3720 = concatenate(%3719); + %3721 = strided_slice(%3677, begin=[2], end=[3], strides=[1]); + %3722 = strided_slice(%3706, begin=[3], end=[4], strides=[1]); + %3723 = (%3720, %3721, %3722); + %3724 = nn.batch_matmul(%3711, %3712, meta[relay.attrs.BatchMatmulAttrs][138]); + %3725 = concatenate(%3723); + %3726 = dyn.reshape(%3724, %3725, newshape=[]); + %3727 = divide(%3726, 8f); + %3728 = add(%3727, %123); + %3729 = max(%3728, axis=[3], keepdims=True); + %3730 = subtract(%3728, %3729); + %3731 = exp(%3730); + %3732 = sum(%3731, axis=[3], keepdims=True); + %3733 = divide(%3731, %3732); + %3734 = shape_of(%3733, dtype="int64"); + %3735 = strided_slice(%3734, begin=[2], end=[4], strides=[1]); + %3736 = (meta[relay.Constant][403], %3735); + %3737 = concatenate(%3736); + %3738 = shape_of(%3651, dtype="int64"); + %3739 = strided_slice(%3738, begin=[1], end=[3], strides=[1]); + %3740 = (meta[relay.Constant][404], %3739); + %3741 = concatenate(%3740); + %3742 = transpose(%bert_encoder_layer_17_attention_self_value_weight, axes=[1, 0]); + %3743 = reshape(%3742, newshape=[-1, 1024, 1024]); + %3744 = dyn.reshape(%3651, %3741, newshape=[]); + %3745 = transpose(%3743, axes=[0, 2, 1]); + %3746 = strided_slice(%3738, begin=[0], end=[1], strides=[1]); + %3747 = strided_slice(%3738, begin=[1], end=[2], strides=[1]); + %3748 = (%3746, %3747, meta[relay.Constant][405]); + %3749 = nn.batch_matmul(%3744, %3745, meta[relay.attrs.BatchMatmulAttrs][139]); + %3750 = concatenate(%3748); + %3751 = dyn.reshape(%3749, %3750, newshape=[]); + %3752 = add(%3751, %bert_encoder_layer_17_attention_self_value_bias); + %3753 = shape_of(%3752, dtype="int64"); + %3754 = take(%3753, 0, axis=0); + %3755 = shape_of(%3752, dtype="int64"); + %3756 = take(%3755, 1, axis=0); + %3757 = expand_dims(%3754, axis=0); + %3758 = expand_dims(%3756, axis=0); + %3759 = (%3757, %3758, meta[relay.Constant][406], meta[relay.Constant][407]); + %3760 = concatenate(%3759); + %3761 = dyn.reshape(%3752, %3760, newshape=[]); + %3762 = transpose(%3761, axes=[0, 2, 1, 3]); + %3763 = shape_of(%3762, dtype="int64"); + %3764 = strided_slice(%3763, begin=[2], end=[4], strides=[1]); + %3765 = (meta[relay.Constant][408], %3764); + %3766 = concatenate(%3765); + %3767 = dyn.reshape(%3762, %3766, newshape=[]); + %3768 = dyn.reshape(%3733, %3737, newshape=[]); + %3769 = transpose(%3767, axes=[0, 2, 1]); + %3770 = strided_slice(%3734, begin=[0], end=[1], strides=[1]); + %3771 = strided_slice(%3763, begin=[0], end=[1], strides=[1]); + %3772 = strided_slice(%3734, begin=[1], end=[2], strides=[1]); + %3773 = strided_slice(%3763, begin=[1], end=[2], strides=[1]); + %3774 = maximum(%3770, %3771); + %3775 = maximum(%3772, %3773); + %3776 = (%3774, %3775); + %3777 = concatenate(%3776); + %3778 = strided_slice(%3734, begin=[2], end=[3], strides=[1]); + %3779 = strided_slice(%3763, begin=[3], end=[4], strides=[1]); + %3780 = (%3777, %3778, %3779); + %3781 = nn.batch_matmul(%3768, %3769, meta[relay.attrs.BatchMatmulAttrs][140]); + %3782 = concatenate(%3780); + %3783 = dyn.reshape(%3781, %3782, newshape=[]); + %3784 = transpose(%3783, axes=[0, 2, 1, 3]); + %3785 = shape_of(%3784, dtype="int64"); + %3786 = take(%3785, 0, axis=0); + %3787 = shape_of(%3784, dtype="int64"); + %3788 = take(%3787, 1, axis=0); + %3789 = expand_dims(%3786, axis=0); + %3790 = expand_dims(%3788, axis=0); + %3791 = (%3789, %3790, meta[relay.Constant][409]); + %3792 = concatenate(%3791); + %3793 = dyn.reshape(%3784, %3792, newshape=[]); + %3794 = shape_of(%3793, dtype="int64"); + %3795 = strided_slice(%3794, begin=[1], end=[3], strides=[1]); + %3796 = (meta[relay.Constant][410], %3795); + %3797 = concatenate(%3796); + %3798 = transpose(%bert_encoder_layer_17_attention_output_dense_weight, axes=[1, 0]); + %3799 = reshape(%3798, newshape=[-1, 1024, 1024]); + %3800 = dyn.reshape(%3793, %3797, newshape=[]); + %3801 = transpose(%3799, axes=[0, 2, 1]); + %3802 = strided_slice(%3794, begin=[0], end=[1], strides=[1]); + %3803 = strided_slice(%3794, begin=[1], end=[2], strides=[1]); + %3804 = (%3802, %3803, meta[relay.Constant][411]); + %3805 = nn.batch_matmul(%3800, %3801, meta[relay.attrs.BatchMatmulAttrs][141]); + %3806 = concatenate(%3804); + %3807 = dyn.reshape(%3805, %3806, newshape=[]); + %3808 = add(%3807, %bert_encoder_layer_17_attention_output_dense_bias); + %3809 = add(%3808, %3651); + %3810 = mean(%3809, axis=[-1], keepdims=True); + %3811 = subtract(%3809, %3810); + %3812 = power(%3811, 2f); + %3813 = mean(%3812, axis=[-1], keepdims=True); + %3814 = add(%3813, 1e-12f); + %3815 = sqrt(%3814); + %3816 = divide(%3811, %3815); + %3817 = multiply(%3816, %bert_encoder_layer_17_attention_output_LayerNorm_weight); + %3818 = add(%3817, %bert_encoder_layer_17_attention_output_LayerNorm_bias); + %3819 = shape_of(%3818, dtype="int64"); + %3820 = strided_slice(%3819, begin=[1], end=[3], strides=[1]); + %3821 = (meta[relay.Constant][412], %3820); + %3822 = concatenate(%3821); + %3823 = transpose(%bert_encoder_layer_17_intermediate_dense_weight, axes=[1, 0]); + %3824 = reshape(%3823, newshape=[-1, 1024, 4096]); + %3825 = dyn.reshape(%3818, %3822, newshape=[]); + %3826 = transpose(%3824, axes=[0, 2, 1]); + %3827 = strided_slice(%3819, begin=[0], end=[1], strides=[1]); + %3828 = strided_slice(%3819, begin=[1], end=[2], strides=[1]); + %3829 = (%3827, %3828, meta[relay.Constant][413]); + %3830 = nn.batch_matmul(%3825, %3826, meta[relay.attrs.BatchMatmulAttrs][142]); + %3831 = concatenate(%3829); + %3832 = dyn.reshape(%3830, %3831, newshape=[]); + %3833 = add(%3832, %bert_encoder_layer_17_intermediate_dense_bias); + %3834 = divide(%3833, 1.41421f); + %3835 = erf(%3834); + %3836 = multiply(%3833, 0.5f); + %3837 = add(%3835, 1f); + %3838 = multiply(%3836, %3837); + %3839 = shape_of(%3838, dtype="int64"); + %3840 = strided_slice(%3839, begin=[1], end=[3], strides=[1]); + %3841 = (meta[relay.Constant][414], %3840); + %3842 = concatenate(%3841); + %3843 = transpose(%bert_encoder_layer_17_output_dense_weight, axes=[1, 0]); + %3844 = reshape(%3843, newshape=[-1, 4096, 1024]); + %3845 = dyn.reshape(%3838, %3842, newshape=[]); + %3846 = transpose(%3844, axes=[0, 2, 1]); + %3847 = strided_slice(%3839, begin=[0], end=[1], strides=[1]); + %3848 = strided_slice(%3839, begin=[1], end=[2], strides=[1]); + %3849 = (%3847, %3848, meta[relay.Constant][415]); + %3850 = nn.batch_matmul(%3845, %3846, meta[relay.attrs.BatchMatmulAttrs][143]); + %3851 = concatenate(%3849); + %3852 = dyn.reshape(%3850, %3851, newshape=[]); + %3853 = add(%3852, %bert_encoder_layer_17_output_dense_bias); + %3854 = add(%3853, %3818); + %3855 = mean(%3854, axis=[-1], keepdims=True); + %3856 = subtract(%3854, %3855); + %3857 = power(%3856, 2f); + %3858 = mean(%3857, axis=[-1], keepdims=True); + %3859 = add(%3858, 1e-12f); + %3860 = sqrt(%3859); + %3861 = divide(%3856, %3860); + %3862 = multiply(%3861, %bert_encoder_layer_17_output_LayerNorm_weight); + %3863 = add(%3862, %bert_encoder_layer_17_output_LayerNorm_bias); + %3864 = shape_of(%3863, dtype="int64"); + %3865 = strided_slice(%3864, begin=[1], end=[3], strides=[1]); + %3866 = (meta[relay.Constant][416], %3865); + %3867 = concatenate(%3866); + %3868 = transpose(%bert_encoder_layer_18_attention_self_query_weight, axes=[1, 0]); + %3869 = reshape(%3868, newshape=[-1, 1024, 1024]); + %3870 = dyn.reshape(%3863, %3867, newshape=[]); + %3871 = transpose(%3869, axes=[0, 2, 1]); + %3872 = strided_slice(%3864, begin=[0], end=[1], strides=[1]); + %3873 = strided_slice(%3864, begin=[1], end=[2], strides=[1]); + %3874 = (%3872, %3873, meta[relay.Constant][417]); + %3875 = nn.batch_matmul(%3870, %3871, meta[relay.attrs.BatchMatmulAttrs][144]); + %3876 = concatenate(%3874); + %3877 = dyn.reshape(%3875, %3876, newshape=[]); + %3878 = add(%3877, %bert_encoder_layer_18_attention_self_query_bias); + %3879 = shape_of(%3878, dtype="int64"); + %3880 = take(%3879, 0, axis=0); + %3881 = shape_of(%3878, dtype="int64"); + %3882 = take(%3881, 1, axis=0); + %3883 = expand_dims(%3880, axis=0); + %3884 = expand_dims(%3882, axis=0); + %3885 = (%3883, %3884, meta[relay.Constant][418], meta[relay.Constant][419]); + %3886 = concatenate(%3885); + %3887 = dyn.reshape(%3878, %3886, newshape=[]); + %3888 = transpose(%3887, axes=[0, 2, 1, 3]); + %3889 = shape_of(%3888, dtype="int64"); + %3890 = strided_slice(%3889, begin=[2], end=[4], strides=[1]); + %3891 = (meta[relay.Constant][420], %3890); + %3892 = concatenate(%3891); + %3893 = shape_of(%3863, dtype="int64"); + %3894 = strided_slice(%3893, begin=[1], end=[3], strides=[1]); + %3895 = (meta[relay.Constant][421], %3894); + %3896 = concatenate(%3895); + %3897 = transpose(%bert_encoder_layer_18_attention_self_key_weight, axes=[1, 0]); + %3898 = reshape(%3897, newshape=[-1, 1024, 1024]); + %3899 = dyn.reshape(%3863, %3896, newshape=[]); + %3900 = transpose(%3898, axes=[0, 2, 1]); + %3901 = strided_slice(%3893, begin=[0], end=[1], strides=[1]); + %3902 = strided_slice(%3893, begin=[1], end=[2], strides=[1]); + %3903 = (%3901, %3902, meta[relay.Constant][422]); + %3904 = nn.batch_matmul(%3899, %3900, meta[relay.attrs.BatchMatmulAttrs][145]); + %3905 = concatenate(%3903); + %3906 = dyn.reshape(%3904, %3905, newshape=[]); + %3907 = add(%3906, %bert_encoder_layer_18_attention_self_key_bias); + %3908 = shape_of(%3907, dtype="int64"); + %3909 = take(%3908, 0, axis=0); + %3910 = shape_of(%3907, dtype="int64"); + %3911 = take(%3910, 1, axis=0); + %3912 = expand_dims(%3909, axis=0); + %3913 = expand_dims(%3911, axis=0); + %3914 = (%3912, %3913, meta[relay.Constant][423], meta[relay.Constant][424]); + %3915 = concatenate(%3914); + %3916 = dyn.reshape(%3907, %3915, newshape=[]); + %3917 = transpose(%3916, axes=[0, 2, 3, 1]); + %3918 = shape_of(%3917, dtype="int64"); + %3919 = strided_slice(%3918, begin=[2], end=[4], strides=[1]); + %3920 = (meta[relay.Constant][425], %3919); + %3921 = concatenate(%3920); + %3922 = dyn.reshape(%3917, %3921, newshape=[]); + %3923 = dyn.reshape(%3888, %3892, newshape=[]); + %3924 = transpose(%3922, axes=[0, 2, 1]); + %3925 = strided_slice(%3889, begin=[0], end=[1], strides=[1]); + %3926 = strided_slice(%3918, begin=[0], end=[1], strides=[1]); + %3927 = strided_slice(%3889, begin=[1], end=[2], strides=[1]); + %3928 = strided_slice(%3918, begin=[1], end=[2], strides=[1]); + %3929 = maximum(%3925, %3926); + %3930 = maximum(%3927, %3928); + %3931 = (%3929, %3930); + %3932 = concatenate(%3931); + %3933 = strided_slice(%3889, begin=[2], end=[3], strides=[1]); + %3934 = strided_slice(%3918, begin=[3], end=[4], strides=[1]); + %3935 = (%3932, %3933, %3934); + %3936 = nn.batch_matmul(%3923, %3924, meta[relay.attrs.BatchMatmulAttrs][146]); + %3937 = concatenate(%3935); + %3938 = dyn.reshape(%3936, %3937, newshape=[]); + %3939 = divide(%3938, 8f); + %3940 = add(%3939, %123); + %3941 = max(%3940, axis=[3], keepdims=True); + %3942 = subtract(%3940, %3941); + %3943 = exp(%3942); + %3944 = sum(%3943, axis=[3], keepdims=True); + %3945 = divide(%3943, %3944); + %3946 = shape_of(%3945, dtype="int64"); + %3947 = strided_slice(%3946, begin=[2], end=[4], strides=[1]); + %3948 = (meta[relay.Constant][426], %3947); + %3949 = concatenate(%3948); + %3950 = shape_of(%3863, dtype="int64"); + %3951 = strided_slice(%3950, begin=[1], end=[3], strides=[1]); + %3952 = (meta[relay.Constant][427], %3951); + %3953 = concatenate(%3952); + %3954 = transpose(%bert_encoder_layer_18_attention_self_value_weight, axes=[1, 0]); + %3955 = reshape(%3954, newshape=[-1, 1024, 1024]); + %3956 = dyn.reshape(%3863, %3953, newshape=[]); + %3957 = transpose(%3955, axes=[0, 2, 1]); + %3958 = strided_slice(%3950, begin=[0], end=[1], strides=[1]); + %3959 = strided_slice(%3950, begin=[1], end=[2], strides=[1]); + %3960 = (%3958, %3959, meta[relay.Constant][428]); + %3961 = nn.batch_matmul(%3956, %3957, meta[relay.attrs.BatchMatmulAttrs][147]); + %3962 = concatenate(%3960); + %3963 = dyn.reshape(%3961, %3962, newshape=[]); + %3964 = add(%3963, %bert_encoder_layer_18_attention_self_value_bias); + %3965 = shape_of(%3964, dtype="int64"); + %3966 = take(%3965, 0, axis=0); + %3967 = shape_of(%3964, dtype="int64"); + %3968 = take(%3967, 1, axis=0); + %3969 = expand_dims(%3966, axis=0); + %3970 = expand_dims(%3968, axis=0); + %3971 = (%3969, %3970, meta[relay.Constant][429], meta[relay.Constant][430]); + %3972 = concatenate(%3971); + %3973 = dyn.reshape(%3964, %3972, newshape=[]); + %3974 = transpose(%3973, axes=[0, 2, 1, 3]); + %3975 = shape_of(%3974, dtype="int64"); + %3976 = strided_slice(%3975, begin=[2], end=[4], strides=[1]); + %3977 = (meta[relay.Constant][431], %3976); + %3978 = concatenate(%3977); + %3979 = dyn.reshape(%3974, %3978, newshape=[]); + %3980 = dyn.reshape(%3945, %3949, newshape=[]); + %3981 = transpose(%3979, axes=[0, 2, 1]); + %3982 = strided_slice(%3946, begin=[0], end=[1], strides=[1]); + %3983 = strided_slice(%3975, begin=[0], end=[1], strides=[1]); + %3984 = strided_slice(%3946, begin=[1], end=[2], strides=[1]); + %3985 = strided_slice(%3975, begin=[1], end=[2], strides=[1]); + %3986 = maximum(%3982, %3983); + %3987 = maximum(%3984, %3985); + %3988 = (%3986, %3987); + %3989 = concatenate(%3988); + %3990 = strided_slice(%3946, begin=[2], end=[3], strides=[1]); + %3991 = strided_slice(%3975, begin=[3], end=[4], strides=[1]); + %3992 = (%3989, %3990, %3991); + %3993 = nn.batch_matmul(%3980, %3981, meta[relay.attrs.BatchMatmulAttrs][148]); + %3994 = concatenate(%3992); + %3995 = dyn.reshape(%3993, %3994, newshape=[]); + %3996 = transpose(%3995, axes=[0, 2, 1, 3]); + %3997 = shape_of(%3996, dtype="int64"); + %3998 = take(%3997, 0, axis=0); + %3999 = shape_of(%3996, dtype="int64"); + %4000 = take(%3999, 1, axis=0); + %4001 = expand_dims(%3998, axis=0); + %4002 = expand_dims(%4000, axis=0); + %4003 = (%4001, %4002, meta[relay.Constant][432]); + %4004 = concatenate(%4003); + %4005 = dyn.reshape(%3996, %4004, newshape=[]); + %4006 = shape_of(%4005, dtype="int64"); + %4007 = strided_slice(%4006, begin=[1], end=[3], strides=[1]); + %4008 = (meta[relay.Constant][433], %4007); + %4009 = concatenate(%4008); + %4010 = transpose(%bert_encoder_layer_18_attention_output_dense_weight, axes=[1, 0]); + %4011 = reshape(%4010, newshape=[-1, 1024, 1024]); + %4012 = dyn.reshape(%4005, %4009, newshape=[]); + %4013 = transpose(%4011, axes=[0, 2, 1]); + %4014 = strided_slice(%4006, begin=[0], end=[1], strides=[1]); + %4015 = strided_slice(%4006, begin=[1], end=[2], strides=[1]); + %4016 = (%4014, %4015, meta[relay.Constant][434]); + %4017 = nn.batch_matmul(%4012, %4013, meta[relay.attrs.BatchMatmulAttrs][149]); + %4018 = concatenate(%4016); + %4019 = dyn.reshape(%4017, %4018, newshape=[]); + %4020 = add(%4019, %bert_encoder_layer_18_attention_output_dense_bias); + %4021 = add(%4020, %3863); + %4022 = mean(%4021, axis=[-1], keepdims=True); + %4023 = subtract(%4021, %4022); + %4024 = power(%4023, 2f); + %4025 = mean(%4024, axis=[-1], keepdims=True); + %4026 = add(%4025, 1e-12f); + %4027 = sqrt(%4026); + %4028 = divide(%4023, %4027); + %4029 = multiply(%4028, %bert_encoder_layer_18_attention_output_LayerNorm_weight); + %4030 = add(%4029, %bert_encoder_layer_18_attention_output_LayerNorm_bias); + %4031 = shape_of(%4030, dtype="int64"); + %4032 = strided_slice(%4031, begin=[1], end=[3], strides=[1]); + %4033 = (meta[relay.Constant][435], %4032); + %4034 = concatenate(%4033); + %4035 = transpose(%bert_encoder_layer_18_intermediate_dense_weight, axes=[1, 0]); + %4036 = reshape(%4035, newshape=[-1, 1024, 4096]); + %4037 = dyn.reshape(%4030, %4034, newshape=[]); + %4038 = transpose(%4036, axes=[0, 2, 1]); + %4039 = strided_slice(%4031, begin=[0], end=[1], strides=[1]); + %4040 = strided_slice(%4031, begin=[1], end=[2], strides=[1]); + %4041 = (%4039, %4040, meta[relay.Constant][436]); + %4042 = nn.batch_matmul(%4037, %4038, meta[relay.attrs.BatchMatmulAttrs][150]); + %4043 = concatenate(%4041); + %4044 = dyn.reshape(%4042, %4043, newshape=[]); + %4045 = add(%4044, %bert_encoder_layer_18_intermediate_dense_bias); + %4046 = divide(%4045, 1.41421f); + %4047 = erf(%4046); + %4048 = multiply(%4045, 0.5f); + %4049 = add(%4047, 1f); + %4050 = multiply(%4048, %4049); + %4051 = shape_of(%4050, dtype="int64"); + %4052 = strided_slice(%4051, begin=[1], end=[3], strides=[1]); + %4053 = (meta[relay.Constant][437], %4052); + %4054 = concatenate(%4053); + %4055 = transpose(%bert_encoder_layer_18_output_dense_weight, axes=[1, 0]); + %4056 = reshape(%4055, newshape=[-1, 4096, 1024]); + %4057 = dyn.reshape(%4050, %4054, newshape=[]); + %4058 = transpose(%4056, axes=[0, 2, 1]); + %4059 = strided_slice(%4051, begin=[0], end=[1], strides=[1]); + %4060 = strided_slice(%4051, begin=[1], end=[2], strides=[1]); + %4061 = (%4059, %4060, meta[relay.Constant][438]); + %4062 = nn.batch_matmul(%4057, %4058, meta[relay.attrs.BatchMatmulAttrs][151]); + %4063 = concatenate(%4061); + %4064 = dyn.reshape(%4062, %4063, newshape=[]); + %4065 = add(%4064, %bert_encoder_layer_18_output_dense_bias); + %4066 = add(%4065, %4030); + %4067 = mean(%4066, axis=[-1], keepdims=True); + %4068 = subtract(%4066, %4067); + %4069 = power(%4068, 2f); + %4070 = mean(%4069, axis=[-1], keepdims=True); + %4071 = add(%4070, 1e-12f); + %4072 = sqrt(%4071); + %4073 = divide(%4068, %4072); + %4074 = multiply(%4073, %bert_encoder_layer_18_output_LayerNorm_weight); + %4075 = add(%4074, %bert_encoder_layer_18_output_LayerNorm_bias); + %4076 = shape_of(%4075, dtype="int64"); + %4077 = strided_slice(%4076, begin=[1], end=[3], strides=[1]); + %4078 = (meta[relay.Constant][439], %4077); + %4079 = concatenate(%4078); + %4080 = transpose(%bert_encoder_layer_19_attention_self_query_weight, axes=[1, 0]); + %4081 = reshape(%4080, newshape=[-1, 1024, 1024]); + %4082 = dyn.reshape(%4075, %4079, newshape=[]); + %4083 = transpose(%4081, axes=[0, 2, 1]); + %4084 = strided_slice(%4076, begin=[0], end=[1], strides=[1]); + %4085 = strided_slice(%4076, begin=[1], end=[2], strides=[1]); + %4086 = (%4084, %4085, meta[relay.Constant][440]); + %4087 = nn.batch_matmul(%4082, %4083, meta[relay.attrs.BatchMatmulAttrs][152]); + %4088 = concatenate(%4086); + %4089 = dyn.reshape(%4087, %4088, newshape=[]); + %4090 = add(%4089, %bert_encoder_layer_19_attention_self_query_bias); + %4091 = shape_of(%4090, dtype="int64"); + %4092 = take(%4091, 0, axis=0); + %4093 = shape_of(%4090, dtype="int64"); + %4094 = take(%4093, 1, axis=0); + %4095 = expand_dims(%4092, axis=0); + %4096 = expand_dims(%4094, axis=0); + %4097 = (%4095, %4096, meta[relay.Constant][441], meta[relay.Constant][442]); + %4098 = concatenate(%4097); + %4099 = dyn.reshape(%4090, %4098, newshape=[]); + %4100 = transpose(%4099, axes=[0, 2, 1, 3]); + %4101 = shape_of(%4100, dtype="int64"); + %4102 = strided_slice(%4101, begin=[2], end=[4], strides=[1]); + %4103 = (meta[relay.Constant][443], %4102); + %4104 = concatenate(%4103); + %4105 = shape_of(%4075, dtype="int64"); + %4106 = strided_slice(%4105, begin=[1], end=[3], strides=[1]); + %4107 = (meta[relay.Constant][444], %4106); + %4108 = concatenate(%4107); + %4109 = transpose(%bert_encoder_layer_19_attention_self_key_weight, axes=[1, 0]); + %4110 = reshape(%4109, newshape=[-1, 1024, 1024]); + %4111 = dyn.reshape(%4075, %4108, newshape=[]); + %4112 = transpose(%4110, axes=[0, 2, 1]); + %4113 = strided_slice(%4105, begin=[0], end=[1], strides=[1]); + %4114 = strided_slice(%4105, begin=[1], end=[2], strides=[1]); + %4115 = (%4113, %4114, meta[relay.Constant][445]); + %4116 = nn.batch_matmul(%4111, %4112, meta[relay.attrs.BatchMatmulAttrs][153]); + %4117 = concatenate(%4115); + %4118 = dyn.reshape(%4116, %4117, newshape=[]); + %4119 = add(%4118, %bert_encoder_layer_19_attention_self_key_bias); + %4120 = shape_of(%4119, dtype="int64"); + %4121 = take(%4120, 0, axis=0); + %4122 = shape_of(%4119, dtype="int64"); + %4123 = take(%4122, 1, axis=0); + %4124 = expand_dims(%4121, axis=0); + %4125 = expand_dims(%4123, axis=0); + %4126 = (%4124, %4125, meta[relay.Constant][446], meta[relay.Constant][447]); + %4127 = concatenate(%4126); + %4128 = dyn.reshape(%4119, %4127, newshape=[]); + %4129 = transpose(%4128, axes=[0, 2, 3, 1]); + %4130 = shape_of(%4129, dtype="int64"); + %4131 = strided_slice(%4130, begin=[2], end=[4], strides=[1]); + %4132 = (meta[relay.Constant][448], %4131); + %4133 = concatenate(%4132); + %4134 = dyn.reshape(%4129, %4133, newshape=[]); + %4135 = dyn.reshape(%4100, %4104, newshape=[]); + %4136 = transpose(%4134, axes=[0, 2, 1]); + %4137 = strided_slice(%4101, begin=[0], end=[1], strides=[1]); + %4138 = strided_slice(%4130, begin=[0], end=[1], strides=[1]); + %4139 = strided_slice(%4101, begin=[1], end=[2], strides=[1]); + %4140 = strided_slice(%4130, begin=[1], end=[2], strides=[1]); + %4141 = maximum(%4137, %4138); + %4142 = maximum(%4139, %4140); + %4143 = (%4141, %4142); + %4144 = concatenate(%4143); + %4145 = strided_slice(%4101, begin=[2], end=[3], strides=[1]); + %4146 = strided_slice(%4130, begin=[3], end=[4], strides=[1]); + %4147 = (%4144, %4145, %4146); + %4148 = nn.batch_matmul(%4135, %4136, meta[relay.attrs.BatchMatmulAttrs][154]); + %4149 = concatenate(%4147); + %4150 = dyn.reshape(%4148, %4149, newshape=[]); + %4151 = divide(%4150, 8f); + %4152 = add(%4151, %123); + %4153 = max(%4152, axis=[3], keepdims=True); + %4154 = subtract(%4152, %4153); + %4155 = exp(%4154); + %4156 = sum(%4155, axis=[3], keepdims=True); + %4157 = divide(%4155, %4156); + %4158 = shape_of(%4157, dtype="int64"); + %4159 = strided_slice(%4158, begin=[2], end=[4], strides=[1]); + %4160 = (meta[relay.Constant][449], %4159); + %4161 = concatenate(%4160); + %4162 = shape_of(%4075, dtype="int64"); + %4163 = strided_slice(%4162, begin=[1], end=[3], strides=[1]); + %4164 = (meta[relay.Constant][450], %4163); + %4165 = concatenate(%4164); + %4166 = transpose(%bert_encoder_layer_19_attention_self_value_weight, axes=[1, 0]); + %4167 = reshape(%4166, newshape=[-1, 1024, 1024]); + %4168 = dyn.reshape(%4075, %4165, newshape=[]); + %4169 = transpose(%4167, axes=[0, 2, 1]); + %4170 = strided_slice(%4162, begin=[0], end=[1], strides=[1]); + %4171 = strided_slice(%4162, begin=[1], end=[2], strides=[1]); + %4172 = (%4170, %4171, meta[relay.Constant][451]); + %4173 = nn.batch_matmul(%4168, %4169, meta[relay.attrs.BatchMatmulAttrs][155]); + %4174 = concatenate(%4172); + %4175 = dyn.reshape(%4173, %4174, newshape=[]); + %4176 = add(%4175, %bert_encoder_layer_19_attention_self_value_bias); + %4177 = shape_of(%4176, dtype="int64"); + %4178 = take(%4177, 0, axis=0); + %4179 = shape_of(%4176, dtype="int64"); + %4180 = take(%4179, 1, axis=0); + %4181 = expand_dims(%4178, axis=0); + %4182 = expand_dims(%4180, axis=0); + %4183 = (%4181, %4182, meta[relay.Constant][452], meta[relay.Constant][453]); + %4184 = concatenate(%4183); + %4185 = dyn.reshape(%4176, %4184, newshape=[]); + %4186 = transpose(%4185, axes=[0, 2, 1, 3]); + %4187 = shape_of(%4186, dtype="int64"); + %4188 = strided_slice(%4187, begin=[2], end=[4], strides=[1]); + %4189 = (meta[relay.Constant][454], %4188); + %4190 = concatenate(%4189); + %4191 = dyn.reshape(%4186, %4190, newshape=[]); + %4192 = dyn.reshape(%4157, %4161, newshape=[]); + %4193 = transpose(%4191, axes=[0, 2, 1]); + %4194 = strided_slice(%4158, begin=[0], end=[1], strides=[1]); + %4195 = strided_slice(%4187, begin=[0], end=[1], strides=[1]); + %4196 = strided_slice(%4158, begin=[1], end=[2], strides=[1]); + %4197 = strided_slice(%4187, begin=[1], end=[2], strides=[1]); + %4198 = maximum(%4194, %4195); + %4199 = maximum(%4196, %4197); + %4200 = (%4198, %4199); + %4201 = concatenate(%4200); + %4202 = strided_slice(%4158, begin=[2], end=[3], strides=[1]); + %4203 = strided_slice(%4187, begin=[3], end=[4], strides=[1]); + %4204 = (%4201, %4202, %4203); + %4205 = nn.batch_matmul(%4192, %4193, meta[relay.attrs.BatchMatmulAttrs][156]); + %4206 = concatenate(%4204); + %4207 = dyn.reshape(%4205, %4206, newshape=[]); + %4208 = transpose(%4207, axes=[0, 2, 1, 3]); + %4209 = shape_of(%4208, dtype="int64"); + %4210 = take(%4209, 0, axis=0); + %4211 = shape_of(%4208, dtype="int64"); + %4212 = take(%4211, 1, axis=0); + %4213 = expand_dims(%4210, axis=0); + %4214 = expand_dims(%4212, axis=0); + %4215 = (%4213, %4214, meta[relay.Constant][455]); + %4216 = concatenate(%4215); + %4217 = dyn.reshape(%4208, %4216, newshape=[]); + %4218 = shape_of(%4217, dtype="int64"); + %4219 = strided_slice(%4218, begin=[1], end=[3], strides=[1]); + %4220 = (meta[relay.Constant][456], %4219); + %4221 = concatenate(%4220); + %4222 = transpose(%bert_encoder_layer_19_attention_output_dense_weight, axes=[1, 0]); + %4223 = reshape(%4222, newshape=[-1, 1024, 1024]); + %4224 = dyn.reshape(%4217, %4221, newshape=[]); + %4225 = transpose(%4223, axes=[0, 2, 1]); + %4226 = strided_slice(%4218, begin=[0], end=[1], strides=[1]); + %4227 = strided_slice(%4218, begin=[1], end=[2], strides=[1]); + %4228 = (%4226, %4227, meta[relay.Constant][457]); + %4229 = nn.batch_matmul(%4224, %4225, meta[relay.attrs.BatchMatmulAttrs][157]); + %4230 = concatenate(%4228); + %4231 = dyn.reshape(%4229, %4230, newshape=[]); + %4232 = add(%4231, %bert_encoder_layer_19_attention_output_dense_bias); + %4233 = add(%4232, %4075); + %4234 = mean(%4233, axis=[-1], keepdims=True); + %4235 = subtract(%4233, %4234); + %4236 = power(%4235, 2f); + %4237 = mean(%4236, axis=[-1], keepdims=True); + %4238 = add(%4237, 1e-12f); + %4239 = sqrt(%4238); + %4240 = divide(%4235, %4239); + %4241 = multiply(%4240, %bert_encoder_layer_19_attention_output_LayerNorm_weight); + %4242 = add(%4241, %bert_encoder_layer_19_attention_output_LayerNorm_bias); + %4243 = shape_of(%4242, dtype="int64"); + %4244 = strided_slice(%4243, begin=[1], end=[3], strides=[1]); + %4245 = (meta[relay.Constant][458], %4244); + %4246 = concatenate(%4245); + %4247 = transpose(%bert_encoder_layer_19_intermediate_dense_weight, axes=[1, 0]); + %4248 = reshape(%4247, newshape=[-1, 1024, 4096]); + %4249 = dyn.reshape(%4242, %4246, newshape=[]); + %4250 = transpose(%4248, axes=[0, 2, 1]); + %4251 = strided_slice(%4243, begin=[0], end=[1], strides=[1]); + %4252 = strided_slice(%4243, begin=[1], end=[2], strides=[1]); + %4253 = (%4251, %4252, meta[relay.Constant][459]); + %4254 = nn.batch_matmul(%4249, %4250, meta[relay.attrs.BatchMatmulAttrs][158]); + %4255 = concatenate(%4253); + %4256 = dyn.reshape(%4254, %4255, newshape=[]); + %4257 = add(%4256, %bert_encoder_layer_19_intermediate_dense_bias); + %4258 = divide(%4257, 1.41421f); + %4259 = erf(%4258); + %4260 = multiply(%4257, 0.5f); + %4261 = add(%4259, 1f); + %4262 = multiply(%4260, %4261); + %4263 = shape_of(%4262, dtype="int64"); + %4264 = strided_slice(%4263, begin=[1], end=[3], strides=[1]); + %4265 = (meta[relay.Constant][460], %4264); + %4266 = concatenate(%4265); + %4267 = transpose(%bert_encoder_layer_19_output_dense_weight, axes=[1, 0]); + %4268 = reshape(%4267, newshape=[-1, 4096, 1024]); + %4269 = dyn.reshape(%4262, %4266, newshape=[]); + %4270 = transpose(%4268, axes=[0, 2, 1]); + %4271 = strided_slice(%4263, begin=[0], end=[1], strides=[1]); + %4272 = strided_slice(%4263, begin=[1], end=[2], strides=[1]); + %4273 = (%4271, %4272, meta[relay.Constant][461]); + %4274 = nn.batch_matmul(%4269, %4270, meta[relay.attrs.BatchMatmulAttrs][159]); + %4275 = concatenate(%4273); + %4276 = dyn.reshape(%4274, %4275, newshape=[]); + %4277 = add(%4276, %bert_encoder_layer_19_output_dense_bias); + %4278 = add(%4277, %4242); + %4279 = mean(%4278, axis=[-1], keepdims=True); + %4280 = subtract(%4278, %4279); + %4281 = power(%4280, 2f); + %4282 = mean(%4281, axis=[-1], keepdims=True); + %4283 = add(%4282, 1e-12f); + %4284 = sqrt(%4283); + %4285 = divide(%4280, %4284); + %4286 = multiply(%4285, %bert_encoder_layer_19_output_LayerNorm_weight); + %4287 = add(%4286, %bert_encoder_layer_19_output_LayerNorm_bias); + %4288 = shape_of(%4287, dtype="int64"); + %4289 = strided_slice(%4288, begin=[1], end=[3], strides=[1]); + %4290 = (meta[relay.Constant][462], %4289); + %4291 = concatenate(%4290); + %4292 = transpose(%bert_encoder_layer_20_attention_self_query_weight, axes=[1, 0]); + %4293 = reshape(%4292, newshape=[-1, 1024, 1024]); + %4294 = dyn.reshape(%4287, %4291, newshape=[]); + %4295 = transpose(%4293, axes=[0, 2, 1]); + %4296 = strided_slice(%4288, begin=[0], end=[1], strides=[1]); + %4297 = strided_slice(%4288, begin=[1], end=[2], strides=[1]); + %4298 = (%4296, %4297, meta[relay.Constant][463]); + %4299 = nn.batch_matmul(%4294, %4295, meta[relay.attrs.BatchMatmulAttrs][160]); + %4300 = concatenate(%4298); + %4301 = dyn.reshape(%4299, %4300, newshape=[]); + %4302 = add(%4301, %bert_encoder_layer_20_attention_self_query_bias); + %4303 = shape_of(%4302, dtype="int64"); + %4304 = take(%4303, 0, axis=0); + %4305 = shape_of(%4302, dtype="int64"); + %4306 = take(%4305, 1, axis=0); + %4307 = expand_dims(%4304, axis=0); + %4308 = expand_dims(%4306, axis=0); + %4309 = (%4307, %4308, meta[relay.Constant][464], meta[relay.Constant][465]); + %4310 = concatenate(%4309); + %4311 = dyn.reshape(%4302, %4310, newshape=[]); + %4312 = transpose(%4311, axes=[0, 2, 1, 3]); + %4313 = shape_of(%4312, dtype="int64"); + %4314 = strided_slice(%4313, begin=[2], end=[4], strides=[1]); + %4315 = (meta[relay.Constant][466], %4314); + %4316 = concatenate(%4315); + %4317 = shape_of(%4287, dtype="int64"); + %4318 = strided_slice(%4317, begin=[1], end=[3], strides=[1]); + %4319 = (meta[relay.Constant][467], %4318); + %4320 = concatenate(%4319); + %4321 = transpose(%bert_encoder_layer_20_attention_self_key_weight, axes=[1, 0]); + %4322 = reshape(%4321, newshape=[-1, 1024, 1024]); + %4323 = dyn.reshape(%4287, %4320, newshape=[]); + %4324 = transpose(%4322, axes=[0, 2, 1]); + %4325 = strided_slice(%4317, begin=[0], end=[1], strides=[1]); + %4326 = strided_slice(%4317, begin=[1], end=[2], strides=[1]); + %4327 = (%4325, %4326, meta[relay.Constant][468]); + %4328 = nn.batch_matmul(%4323, %4324, meta[relay.attrs.BatchMatmulAttrs][161]); + %4329 = concatenate(%4327); + %4330 = dyn.reshape(%4328, %4329, newshape=[]); + %4331 = add(%4330, %bert_encoder_layer_20_attention_self_key_bias); + %4332 = shape_of(%4331, dtype="int64"); + %4333 = take(%4332, 0, axis=0); + %4334 = shape_of(%4331, dtype="int64"); + %4335 = take(%4334, 1, axis=0); + %4336 = expand_dims(%4333, axis=0); + %4337 = expand_dims(%4335, axis=0); + %4338 = (%4336, %4337, meta[relay.Constant][469], meta[relay.Constant][470]); + %4339 = concatenate(%4338); + %4340 = dyn.reshape(%4331, %4339, newshape=[]); + %4341 = transpose(%4340, axes=[0, 2, 3, 1]); + %4342 = shape_of(%4341, dtype="int64"); + %4343 = strided_slice(%4342, begin=[2], end=[4], strides=[1]); + %4344 = (meta[relay.Constant][471], %4343); + %4345 = concatenate(%4344); + %4346 = dyn.reshape(%4341, %4345, newshape=[]); + %4347 = dyn.reshape(%4312, %4316, newshape=[]); + %4348 = transpose(%4346, axes=[0, 2, 1]); + %4349 = strided_slice(%4313, begin=[0], end=[1], strides=[1]); + %4350 = strided_slice(%4342, begin=[0], end=[1], strides=[1]); + %4351 = strided_slice(%4313, begin=[1], end=[2], strides=[1]); + %4352 = strided_slice(%4342, begin=[1], end=[2], strides=[1]); + %4353 = maximum(%4349, %4350); + %4354 = maximum(%4351, %4352); + %4355 = (%4353, %4354); + %4356 = concatenate(%4355); + %4357 = strided_slice(%4313, begin=[2], end=[3], strides=[1]); + %4358 = strided_slice(%4342, begin=[3], end=[4], strides=[1]); + %4359 = (%4356, %4357, %4358); + %4360 = nn.batch_matmul(%4347, %4348, meta[relay.attrs.BatchMatmulAttrs][162]); + %4361 = concatenate(%4359); + %4362 = dyn.reshape(%4360, %4361, newshape=[]); + %4363 = divide(%4362, 8f); + %4364 = add(%4363, %123); + %4365 = max(%4364, axis=[3], keepdims=True); + %4366 = subtract(%4364, %4365); + %4367 = exp(%4366); + %4368 = sum(%4367, axis=[3], keepdims=True); + %4369 = divide(%4367, %4368); + %4370 = shape_of(%4369, dtype="int64"); + %4371 = strided_slice(%4370, begin=[2], end=[4], strides=[1]); + %4372 = (meta[relay.Constant][472], %4371); + %4373 = concatenate(%4372); + %4374 = shape_of(%4287, dtype="int64"); + %4375 = strided_slice(%4374, begin=[1], end=[3], strides=[1]); + %4376 = (meta[relay.Constant][473], %4375); + %4377 = concatenate(%4376); + %4378 = transpose(%bert_encoder_layer_20_attention_self_value_weight, axes=[1, 0]); + %4379 = reshape(%4378, newshape=[-1, 1024, 1024]); + %4380 = dyn.reshape(%4287, %4377, newshape=[]); + %4381 = transpose(%4379, axes=[0, 2, 1]); + %4382 = strided_slice(%4374, begin=[0], end=[1], strides=[1]); + %4383 = strided_slice(%4374, begin=[1], end=[2], strides=[1]); + %4384 = (%4382, %4383, meta[relay.Constant][474]); + %4385 = nn.batch_matmul(%4380, %4381, meta[relay.attrs.BatchMatmulAttrs][163]); + %4386 = concatenate(%4384); + %4387 = dyn.reshape(%4385, %4386, newshape=[]); + %4388 = add(%4387, %bert_encoder_layer_20_attention_self_value_bias); + %4389 = shape_of(%4388, dtype="int64"); + %4390 = take(%4389, 0, axis=0); + %4391 = shape_of(%4388, dtype="int64"); + %4392 = take(%4391, 1, axis=0); + %4393 = expand_dims(%4390, axis=0); + %4394 = expand_dims(%4392, axis=0); + %4395 = (%4393, %4394, meta[relay.Constant][475], meta[relay.Constant][476]); + %4396 = concatenate(%4395); + %4397 = dyn.reshape(%4388, %4396, newshape=[]); + %4398 = transpose(%4397, axes=[0, 2, 1, 3]); + %4399 = shape_of(%4398, dtype="int64"); + %4400 = strided_slice(%4399, begin=[2], end=[4], strides=[1]); + %4401 = (meta[relay.Constant][477], %4400); + %4402 = concatenate(%4401); + %4403 = dyn.reshape(%4398, %4402, newshape=[]); + %4404 = dyn.reshape(%4369, %4373, newshape=[]); + %4405 = transpose(%4403, axes=[0, 2, 1]); + %4406 = strided_slice(%4370, begin=[0], end=[1], strides=[1]); + %4407 = strided_slice(%4399, begin=[0], end=[1], strides=[1]); + %4408 = strided_slice(%4370, begin=[1], end=[2], strides=[1]); + %4409 = strided_slice(%4399, begin=[1], end=[2], strides=[1]); + %4410 = maximum(%4406, %4407); + %4411 = maximum(%4408, %4409); + %4412 = (%4410, %4411); + %4413 = concatenate(%4412); + %4414 = strided_slice(%4370, begin=[2], end=[3], strides=[1]); + %4415 = strided_slice(%4399, begin=[3], end=[4], strides=[1]); + %4416 = (%4413, %4414, %4415); + %4417 = nn.batch_matmul(%4404, %4405, meta[relay.attrs.BatchMatmulAttrs][164]); + %4418 = concatenate(%4416); + %4419 = dyn.reshape(%4417, %4418, newshape=[]); + %4420 = transpose(%4419, axes=[0, 2, 1, 3]); + %4421 = shape_of(%4420, dtype="int64"); + %4422 = take(%4421, 0, axis=0); + %4423 = shape_of(%4420, dtype="int64"); + %4424 = take(%4423, 1, axis=0); + %4425 = expand_dims(%4422, axis=0); + %4426 = expand_dims(%4424, axis=0); + %4427 = (%4425, %4426, meta[relay.Constant][478]); + %4428 = concatenate(%4427); + %4429 = dyn.reshape(%4420, %4428, newshape=[]); + %4430 = shape_of(%4429, dtype="int64"); + %4431 = strided_slice(%4430, begin=[1], end=[3], strides=[1]); + %4432 = (meta[relay.Constant][479], %4431); + %4433 = concatenate(%4432); + %4434 = transpose(%bert_encoder_layer_20_attention_output_dense_weight, axes=[1, 0]); + %4435 = reshape(%4434, newshape=[-1, 1024, 1024]); + %4436 = dyn.reshape(%4429, %4433, newshape=[]); + %4437 = transpose(%4435, axes=[0, 2, 1]); + %4438 = strided_slice(%4430, begin=[0], end=[1], strides=[1]); + %4439 = strided_slice(%4430, begin=[1], end=[2], strides=[1]); + %4440 = (%4438, %4439, meta[relay.Constant][480]); + %4441 = nn.batch_matmul(%4436, %4437, meta[relay.attrs.BatchMatmulAttrs][165]); + %4442 = concatenate(%4440); + %4443 = dyn.reshape(%4441, %4442, newshape=[]); + %4444 = add(%4443, %bert_encoder_layer_20_attention_output_dense_bias); + %4445 = add(%4444, %4287); + %4446 = mean(%4445, axis=[-1], keepdims=True); + %4447 = subtract(%4445, %4446); + %4448 = power(%4447, 2f); + %4449 = mean(%4448, axis=[-1], keepdims=True); + %4450 = add(%4449, 1e-12f); + %4451 = sqrt(%4450); + %4452 = divide(%4447, %4451); + %4453 = multiply(%4452, %bert_encoder_layer_20_attention_output_LayerNorm_weight); + %4454 = add(%4453, %bert_encoder_layer_20_attention_output_LayerNorm_bias); + %4455 = shape_of(%4454, dtype="int64"); + %4456 = strided_slice(%4455, begin=[1], end=[3], strides=[1]); + %4457 = (meta[relay.Constant][481], %4456); + %4458 = concatenate(%4457); + %4459 = transpose(%bert_encoder_layer_20_intermediate_dense_weight, axes=[1, 0]); + %4460 = reshape(%4459, newshape=[-1, 1024, 4096]); + %4461 = dyn.reshape(%4454, %4458, newshape=[]); + %4462 = transpose(%4460, axes=[0, 2, 1]); + %4463 = strided_slice(%4455, begin=[0], end=[1], strides=[1]); + %4464 = strided_slice(%4455, begin=[1], end=[2], strides=[1]); + %4465 = (%4463, %4464, meta[relay.Constant][482]); + %4466 = nn.batch_matmul(%4461, %4462, meta[relay.attrs.BatchMatmulAttrs][166]); + %4467 = concatenate(%4465); + %4468 = dyn.reshape(%4466, %4467, newshape=[]); + %4469 = add(%4468, %bert_encoder_layer_20_intermediate_dense_bias); + %4470 = divide(%4469, 1.41421f); + %4471 = erf(%4470); + %4472 = multiply(%4469, 0.5f); + %4473 = add(%4471, 1f); + %4474 = multiply(%4472, %4473); + %4475 = shape_of(%4474, dtype="int64"); + %4476 = strided_slice(%4475, begin=[1], end=[3], strides=[1]); + %4477 = (meta[relay.Constant][483], %4476); + %4478 = concatenate(%4477); + %4479 = transpose(%bert_encoder_layer_20_output_dense_weight, axes=[1, 0]); + %4480 = reshape(%4479, newshape=[-1, 4096, 1024]); + %4481 = dyn.reshape(%4474, %4478, newshape=[]); + %4482 = transpose(%4480, axes=[0, 2, 1]); + %4483 = strided_slice(%4475, begin=[0], end=[1], strides=[1]); + %4484 = strided_slice(%4475, begin=[1], end=[2], strides=[1]); + %4485 = (%4483, %4484, meta[relay.Constant][484]); + %4486 = nn.batch_matmul(%4481, %4482, meta[relay.attrs.BatchMatmulAttrs][167]); + %4487 = concatenate(%4485); + %4488 = dyn.reshape(%4486, %4487, newshape=[]); + %4489 = add(%4488, %bert_encoder_layer_20_output_dense_bias); + %4490 = add(%4489, %4454); + %4491 = mean(%4490, axis=[-1], keepdims=True); + %4492 = subtract(%4490, %4491); + %4493 = power(%4492, 2f); + %4494 = mean(%4493, axis=[-1], keepdims=True); + %4495 = add(%4494, 1e-12f); + %4496 = sqrt(%4495); + %4497 = divide(%4492, %4496); + %4498 = multiply(%4497, %bert_encoder_layer_20_output_LayerNorm_weight); + %4499 = add(%4498, %bert_encoder_layer_20_output_LayerNorm_bias); + %4500 = shape_of(%4499, dtype="int64"); + %4501 = strided_slice(%4500, begin=[1], end=[3], strides=[1]); + %4502 = (meta[relay.Constant][485], %4501); + %4503 = concatenate(%4502); + %4504 = transpose(%bert_encoder_layer_21_attention_self_query_weight, axes=[1, 0]); + %4505 = reshape(%4504, newshape=[-1, 1024, 1024]); + %4506 = dyn.reshape(%4499, %4503, newshape=[]); + %4507 = transpose(%4505, axes=[0, 2, 1]); + %4508 = strided_slice(%4500, begin=[0], end=[1], strides=[1]); + %4509 = strided_slice(%4500, begin=[1], end=[2], strides=[1]); + %4510 = (%4508, %4509, meta[relay.Constant][486]); + %4511 = nn.batch_matmul(%4506, %4507, meta[relay.attrs.BatchMatmulAttrs][168]); + %4512 = concatenate(%4510); + %4513 = dyn.reshape(%4511, %4512, newshape=[]); + %4514 = add(%4513, %bert_encoder_layer_21_attention_self_query_bias); + %4515 = shape_of(%4514, dtype="int64"); + %4516 = take(%4515, 0, axis=0); + %4517 = shape_of(%4514, dtype="int64"); + %4518 = take(%4517, 1, axis=0); + %4519 = expand_dims(%4516, axis=0); + %4520 = expand_dims(%4518, axis=0); + %4521 = (%4519, %4520, meta[relay.Constant][487], meta[relay.Constant][488]); + %4522 = concatenate(%4521); + %4523 = dyn.reshape(%4514, %4522, newshape=[]); + %4524 = transpose(%4523, axes=[0, 2, 1, 3]); + %4525 = shape_of(%4524, dtype="int64"); + %4526 = strided_slice(%4525, begin=[2], end=[4], strides=[1]); + %4527 = (meta[relay.Constant][489], %4526); + %4528 = concatenate(%4527); + %4529 = shape_of(%4499, dtype="int64"); + %4530 = strided_slice(%4529, begin=[1], end=[3], strides=[1]); + %4531 = (meta[relay.Constant][490], %4530); + %4532 = concatenate(%4531); + %4533 = transpose(%bert_encoder_layer_21_attention_self_key_weight, axes=[1, 0]); + %4534 = reshape(%4533, newshape=[-1, 1024, 1024]); + %4535 = dyn.reshape(%4499, %4532, newshape=[]); + %4536 = transpose(%4534, axes=[0, 2, 1]); + %4537 = strided_slice(%4529, begin=[0], end=[1], strides=[1]); + %4538 = strided_slice(%4529, begin=[1], end=[2], strides=[1]); + %4539 = (%4537, %4538, meta[relay.Constant][491]); + %4540 = nn.batch_matmul(%4535, %4536, meta[relay.attrs.BatchMatmulAttrs][169]); + %4541 = concatenate(%4539); + %4542 = dyn.reshape(%4540, %4541, newshape=[]); + %4543 = add(%4542, %bert_encoder_layer_21_attention_self_key_bias); + %4544 = shape_of(%4543, dtype="int64"); + %4545 = take(%4544, 0, axis=0); + %4546 = shape_of(%4543, dtype="int64"); + %4547 = take(%4546, 1, axis=0); + %4548 = expand_dims(%4545, axis=0); + %4549 = expand_dims(%4547, axis=0); + %4550 = (%4548, %4549, meta[relay.Constant][492], meta[relay.Constant][493]); + %4551 = concatenate(%4550); + %4552 = dyn.reshape(%4543, %4551, newshape=[]); + %4553 = transpose(%4552, axes=[0, 2, 3, 1]); + %4554 = shape_of(%4553, dtype="int64"); + %4555 = strided_slice(%4554, begin=[2], end=[4], strides=[1]); + %4556 = (meta[relay.Constant][494], %4555); + %4557 = concatenate(%4556); + %4558 = dyn.reshape(%4553, %4557, newshape=[]); + %4559 = dyn.reshape(%4524, %4528, newshape=[]); + %4560 = transpose(%4558, axes=[0, 2, 1]); + %4561 = strided_slice(%4525, begin=[0], end=[1], strides=[1]); + %4562 = strided_slice(%4554, begin=[0], end=[1], strides=[1]); + %4563 = strided_slice(%4525, begin=[1], end=[2], strides=[1]); + %4564 = strided_slice(%4554, begin=[1], end=[2], strides=[1]); + %4565 = maximum(%4561, %4562); + %4566 = maximum(%4563, %4564); + %4567 = (%4565, %4566); + %4568 = concatenate(%4567); + %4569 = strided_slice(%4525, begin=[2], end=[3], strides=[1]); + %4570 = strided_slice(%4554, begin=[3], end=[4], strides=[1]); + %4571 = (%4568, %4569, %4570); + %4572 = nn.batch_matmul(%4559, %4560, meta[relay.attrs.BatchMatmulAttrs][170]); + %4573 = concatenate(%4571); + %4574 = dyn.reshape(%4572, %4573, newshape=[]); + %4575 = divide(%4574, 8f); + %4576 = add(%4575, %123); + %4577 = max(%4576, axis=[3], keepdims=True); + %4578 = subtract(%4576, %4577); + %4579 = exp(%4578); + %4580 = sum(%4579, axis=[3], keepdims=True); + %4581 = divide(%4579, %4580); + %4582 = shape_of(%4581, dtype="int64"); + %4583 = strided_slice(%4582, begin=[2], end=[4], strides=[1]); + %4584 = (meta[relay.Constant][495], %4583); + %4585 = concatenate(%4584); + %4586 = shape_of(%4499, dtype="int64"); + %4587 = strided_slice(%4586, begin=[1], end=[3], strides=[1]); + %4588 = (meta[relay.Constant][496], %4587); + %4589 = concatenate(%4588); + %4590 = transpose(%bert_encoder_layer_21_attention_self_value_weight, axes=[1, 0]); + %4591 = reshape(%4590, newshape=[-1, 1024, 1024]); + %4592 = dyn.reshape(%4499, %4589, newshape=[]); + %4593 = transpose(%4591, axes=[0, 2, 1]); + %4594 = strided_slice(%4586, begin=[0], end=[1], strides=[1]); + %4595 = strided_slice(%4586, begin=[1], end=[2], strides=[1]); + %4596 = (%4594, %4595, meta[relay.Constant][497]); + %4597 = nn.batch_matmul(%4592, %4593, meta[relay.attrs.BatchMatmulAttrs][171]); + %4598 = concatenate(%4596); + %4599 = dyn.reshape(%4597, %4598, newshape=[]); + %4600 = add(%4599, %bert_encoder_layer_21_attention_self_value_bias); + %4601 = shape_of(%4600, dtype="int64"); + %4602 = take(%4601, 0, axis=0); + %4603 = shape_of(%4600, dtype="int64"); + %4604 = take(%4603, 1, axis=0); + %4605 = expand_dims(%4602, axis=0); + %4606 = expand_dims(%4604, axis=0); + %4607 = (%4605, %4606, meta[relay.Constant][498], meta[relay.Constant][499]); + %4608 = concatenate(%4607); + %4609 = dyn.reshape(%4600, %4608, newshape=[]); + %4610 = transpose(%4609, axes=[0, 2, 1, 3]); + %4611 = shape_of(%4610, dtype="int64"); + %4612 = strided_slice(%4611, begin=[2], end=[4], strides=[1]); + %4613 = (meta[relay.Constant][500], %4612); + %4614 = concatenate(%4613); + %4615 = dyn.reshape(%4610, %4614, newshape=[]); + %4616 = dyn.reshape(%4581, %4585, newshape=[]); + %4617 = transpose(%4615, axes=[0, 2, 1]); + %4618 = strided_slice(%4582, begin=[0], end=[1], strides=[1]); + %4619 = strided_slice(%4611, begin=[0], end=[1], strides=[1]); + %4620 = strided_slice(%4582, begin=[1], end=[2], strides=[1]); + %4621 = strided_slice(%4611, begin=[1], end=[2], strides=[1]); + %4622 = maximum(%4618, %4619); + %4623 = maximum(%4620, %4621); + %4624 = (%4622, %4623); + %4625 = concatenate(%4624); + %4626 = strided_slice(%4582, begin=[2], end=[3], strides=[1]); + %4627 = strided_slice(%4611, begin=[3], end=[4], strides=[1]); + %4628 = (%4625, %4626, %4627); + %4629 = nn.batch_matmul(%4616, %4617, meta[relay.attrs.BatchMatmulAttrs][172]); + %4630 = concatenate(%4628); + %4631 = dyn.reshape(%4629, %4630, newshape=[]); + %4632 = transpose(%4631, axes=[0, 2, 1, 3]); + %4633 = shape_of(%4632, dtype="int64"); + %4634 = take(%4633, 0, axis=0); + %4635 = shape_of(%4632, dtype="int64"); + %4636 = take(%4635, 1, axis=0); + %4637 = expand_dims(%4634, axis=0); + %4638 = expand_dims(%4636, axis=0); + %4639 = (%4637, %4638, meta[relay.Constant][501]); + %4640 = concatenate(%4639); + %4641 = dyn.reshape(%4632, %4640, newshape=[]); + %4642 = shape_of(%4641, dtype="int64"); + %4643 = strided_slice(%4642, begin=[1], end=[3], strides=[1]); + %4644 = (meta[relay.Constant][502], %4643); + %4645 = concatenate(%4644); + %4646 = transpose(%bert_encoder_layer_21_attention_output_dense_weight, axes=[1, 0]); + %4647 = reshape(%4646, newshape=[-1, 1024, 1024]); + %4648 = dyn.reshape(%4641, %4645, newshape=[]); + %4649 = transpose(%4647, axes=[0, 2, 1]); + %4650 = strided_slice(%4642, begin=[0], end=[1], strides=[1]); + %4651 = strided_slice(%4642, begin=[1], end=[2], strides=[1]); + %4652 = (%4650, %4651, meta[relay.Constant][503]); + %4653 = nn.batch_matmul(%4648, %4649, meta[relay.attrs.BatchMatmulAttrs][173]); + %4654 = concatenate(%4652); + %4655 = dyn.reshape(%4653, %4654, newshape=[]); + %4656 = add(%4655, %bert_encoder_layer_21_attention_output_dense_bias); + %4657 = add(%4656, %4499); + %4658 = mean(%4657, axis=[-1], keepdims=True); + %4659 = subtract(%4657, %4658); + %4660 = power(%4659, 2f); + %4661 = mean(%4660, axis=[-1], keepdims=True); + %4662 = add(%4661, 1e-12f); + %4663 = sqrt(%4662); + %4664 = divide(%4659, %4663); + %4665 = multiply(%4664, %bert_encoder_layer_21_attention_output_LayerNorm_weight); + %4666 = add(%4665, %bert_encoder_layer_21_attention_output_LayerNorm_bias); + %4667 = shape_of(%4666, dtype="int64"); + %4668 = strided_slice(%4667, begin=[1], end=[3], strides=[1]); + %4669 = (meta[relay.Constant][504], %4668); + %4670 = concatenate(%4669); + %4671 = transpose(%bert_encoder_layer_21_intermediate_dense_weight, axes=[1, 0]); + %4672 = reshape(%4671, newshape=[-1, 1024, 4096]); + %4673 = dyn.reshape(%4666, %4670, newshape=[]); + %4674 = transpose(%4672, axes=[0, 2, 1]); + %4675 = strided_slice(%4667, begin=[0], end=[1], strides=[1]); + %4676 = strided_slice(%4667, begin=[1], end=[2], strides=[1]); + %4677 = (%4675, %4676, meta[relay.Constant][505]); + %4678 = nn.batch_matmul(%4673, %4674, meta[relay.attrs.BatchMatmulAttrs][174]); + %4679 = concatenate(%4677); + %4680 = dyn.reshape(%4678, %4679, newshape=[]); + %4681 = add(%4680, %bert_encoder_layer_21_intermediate_dense_bias); + %4682 = divide(%4681, 1.41421f); + %4683 = erf(%4682); + %4684 = multiply(%4681, 0.5f); + %4685 = add(%4683, 1f); + %4686 = multiply(%4684, %4685); + %4687 = shape_of(%4686, dtype="int64"); + %4688 = strided_slice(%4687, begin=[1], end=[3], strides=[1]); + %4689 = (meta[relay.Constant][506], %4688); + %4690 = concatenate(%4689); + %4691 = transpose(%bert_encoder_layer_21_output_dense_weight, axes=[1, 0]); + %4692 = reshape(%4691, newshape=[-1, 4096, 1024]); + %4693 = dyn.reshape(%4686, %4690, newshape=[]); + %4694 = transpose(%4692, axes=[0, 2, 1]); + %4695 = strided_slice(%4687, begin=[0], end=[1], strides=[1]); + %4696 = strided_slice(%4687, begin=[1], end=[2], strides=[1]); + %4697 = (%4695, %4696, meta[relay.Constant][507]); + %4698 = nn.batch_matmul(%4693, %4694, meta[relay.attrs.BatchMatmulAttrs][175]); + %4699 = concatenate(%4697); + %4700 = dyn.reshape(%4698, %4699, newshape=[]); + %4701 = add(%4700, %bert_encoder_layer_21_output_dense_bias); + %4702 = add(%4701, %4666); + %4703 = mean(%4702, axis=[-1], keepdims=True); + %4704 = subtract(%4702, %4703); + %4705 = power(%4704, 2f); + %4706 = mean(%4705, axis=[-1], keepdims=True); + %4707 = add(%4706, 1e-12f); + %4708 = sqrt(%4707); + %4709 = divide(%4704, %4708); + %4710 = multiply(%4709, %bert_encoder_layer_21_output_LayerNorm_weight); + %4711 = add(%4710, %bert_encoder_layer_21_output_LayerNorm_bias); + %4712 = shape_of(%4711, dtype="int64"); + %4713 = strided_slice(%4712, begin=[1], end=[3], strides=[1]); + %4714 = (meta[relay.Constant][508], %4713); + %4715 = concatenate(%4714); + %4716 = transpose(%bert_encoder_layer_22_attention_self_query_weight, axes=[1, 0]); + %4717 = reshape(%4716, newshape=[-1, 1024, 1024]); + %4718 = dyn.reshape(%4711, %4715, newshape=[]); + %4719 = transpose(%4717, axes=[0, 2, 1]); + %4720 = strided_slice(%4712, begin=[0], end=[1], strides=[1]); + %4721 = strided_slice(%4712, begin=[1], end=[2], strides=[1]); + %4722 = (%4720, %4721, meta[relay.Constant][509]); + %4723 = nn.batch_matmul(%4718, %4719, meta[relay.attrs.BatchMatmulAttrs][176]); + %4724 = concatenate(%4722); + %4725 = dyn.reshape(%4723, %4724, newshape=[]); + %4726 = add(%4725, %bert_encoder_layer_22_attention_self_query_bias); + %4727 = shape_of(%4726, dtype="int64"); + %4728 = take(%4727, 0, axis=0); + %4729 = shape_of(%4726, dtype="int64"); + %4730 = take(%4729, 1, axis=0); + %4731 = expand_dims(%4728, axis=0); + %4732 = expand_dims(%4730, axis=0); + %4733 = (%4731, %4732, meta[relay.Constant][510], meta[relay.Constant][511]); + %4734 = concatenate(%4733); + %4735 = dyn.reshape(%4726, %4734, newshape=[]); + %4736 = transpose(%4735, axes=[0, 2, 1, 3]); + %4737 = shape_of(%4736, dtype="int64"); + %4738 = strided_slice(%4737, begin=[2], end=[4], strides=[1]); + %4739 = (meta[relay.Constant][512], %4738); + %4740 = concatenate(%4739); + %4741 = shape_of(%4711, dtype="int64"); + %4742 = strided_slice(%4741, begin=[1], end=[3], strides=[1]); + %4743 = (meta[relay.Constant][513], %4742); + %4744 = concatenate(%4743); + %4745 = transpose(%bert_encoder_layer_22_attention_self_key_weight, axes=[1, 0]); + %4746 = reshape(%4745, newshape=[-1, 1024, 1024]); + %4747 = dyn.reshape(%4711, %4744, newshape=[]); + %4748 = transpose(%4746, axes=[0, 2, 1]); + %4749 = strided_slice(%4741, begin=[0], end=[1], strides=[1]); + %4750 = strided_slice(%4741, begin=[1], end=[2], strides=[1]); + %4751 = (%4749, %4750, meta[relay.Constant][514]); + %4752 = nn.batch_matmul(%4747, %4748, meta[relay.attrs.BatchMatmulAttrs][177]); + %4753 = concatenate(%4751); + %4754 = dyn.reshape(%4752, %4753, newshape=[]); + %4755 = add(%4754, %bert_encoder_layer_22_attention_self_key_bias); + %4756 = shape_of(%4755, dtype="int64"); + %4757 = take(%4756, 0, axis=0); + %4758 = shape_of(%4755, dtype="int64"); + %4759 = take(%4758, 1, axis=0); + %4760 = expand_dims(%4757, axis=0); + %4761 = expand_dims(%4759, axis=0); + %4762 = (%4760, %4761, meta[relay.Constant][515], meta[relay.Constant][516]); + %4763 = concatenate(%4762); + %4764 = dyn.reshape(%4755, %4763, newshape=[]); + %4765 = transpose(%4764, axes=[0, 2, 3, 1]); + %4766 = shape_of(%4765, dtype="int64"); + %4767 = strided_slice(%4766, begin=[2], end=[4], strides=[1]); + %4768 = (meta[relay.Constant][517], %4767); + %4769 = concatenate(%4768); + %4770 = dyn.reshape(%4765, %4769, newshape=[]); + %4771 = dyn.reshape(%4736, %4740, newshape=[]); + %4772 = transpose(%4770, axes=[0, 2, 1]); + %4773 = strided_slice(%4737, begin=[0], end=[1], strides=[1]); + %4774 = strided_slice(%4766, begin=[0], end=[1], strides=[1]); + %4775 = strided_slice(%4737, begin=[1], end=[2], strides=[1]); + %4776 = strided_slice(%4766, begin=[1], end=[2], strides=[1]); + %4777 = maximum(%4773, %4774); + %4778 = maximum(%4775, %4776); + %4779 = (%4777, %4778); + %4780 = concatenate(%4779); + %4781 = strided_slice(%4737, begin=[2], end=[3], strides=[1]); + %4782 = strided_slice(%4766, begin=[3], end=[4], strides=[1]); + %4783 = (%4780, %4781, %4782); + %4784 = nn.batch_matmul(%4771, %4772, meta[relay.attrs.BatchMatmulAttrs][178]); + %4785 = concatenate(%4783); + %4786 = dyn.reshape(%4784, %4785, newshape=[]); + %4787 = divide(%4786, 8f); + %4788 = add(%4787, %123); + %4789 = max(%4788, axis=[3], keepdims=True); + %4790 = subtract(%4788, %4789); + %4791 = exp(%4790); + %4792 = sum(%4791, axis=[3], keepdims=True); + %4793 = divide(%4791, %4792); + %4794 = shape_of(%4793, dtype="int64"); + %4795 = strided_slice(%4794, begin=[2], end=[4], strides=[1]); + %4796 = (meta[relay.Constant][518], %4795); + %4797 = concatenate(%4796); + %4798 = shape_of(%4711, dtype="int64"); + %4799 = strided_slice(%4798, begin=[1], end=[3], strides=[1]); + %4800 = (meta[relay.Constant][519], %4799); + %4801 = concatenate(%4800); + %4802 = transpose(%bert_encoder_layer_22_attention_self_value_weight, axes=[1, 0]); + %4803 = reshape(%4802, newshape=[-1, 1024, 1024]); + %4804 = dyn.reshape(%4711, %4801, newshape=[]); + %4805 = transpose(%4803, axes=[0, 2, 1]); + %4806 = strided_slice(%4798, begin=[0], end=[1], strides=[1]); + %4807 = strided_slice(%4798, begin=[1], end=[2], strides=[1]); + %4808 = (%4806, %4807, meta[relay.Constant][520]); + %4809 = nn.batch_matmul(%4804, %4805, meta[relay.attrs.BatchMatmulAttrs][179]); + %4810 = concatenate(%4808); + %4811 = dyn.reshape(%4809, %4810, newshape=[]); + %4812 = add(%4811, %bert_encoder_layer_22_attention_self_value_bias); + %4813 = shape_of(%4812, dtype="int64"); + %4814 = take(%4813, 0, axis=0); + %4815 = shape_of(%4812, dtype="int64"); + %4816 = take(%4815, 1, axis=0); + %4817 = expand_dims(%4814, axis=0); + %4818 = expand_dims(%4816, axis=0); + %4819 = (%4817, %4818, meta[relay.Constant][521], meta[relay.Constant][522]); + %4820 = concatenate(%4819); + %4821 = dyn.reshape(%4812, %4820, newshape=[]); + %4822 = transpose(%4821, axes=[0, 2, 1, 3]); + %4823 = shape_of(%4822, dtype="int64"); + %4824 = strided_slice(%4823, begin=[2], end=[4], strides=[1]); + %4825 = (meta[relay.Constant][523], %4824); + %4826 = concatenate(%4825); + %4827 = dyn.reshape(%4822, %4826, newshape=[]); + %4828 = dyn.reshape(%4793, %4797, newshape=[]); + %4829 = transpose(%4827, axes=[0, 2, 1]); + %4830 = strided_slice(%4794, begin=[0], end=[1], strides=[1]); + %4831 = strided_slice(%4823, begin=[0], end=[1], strides=[1]); + %4832 = strided_slice(%4794, begin=[1], end=[2], strides=[1]); + %4833 = strided_slice(%4823, begin=[1], end=[2], strides=[1]); + %4834 = maximum(%4830, %4831); + %4835 = maximum(%4832, %4833); + %4836 = (%4834, %4835); + %4837 = concatenate(%4836); + %4838 = strided_slice(%4794, begin=[2], end=[3], strides=[1]); + %4839 = strided_slice(%4823, begin=[3], end=[4], strides=[1]); + %4840 = (%4837, %4838, %4839); + %4841 = nn.batch_matmul(%4828, %4829, meta[relay.attrs.BatchMatmulAttrs][180]); + %4842 = concatenate(%4840); + %4843 = dyn.reshape(%4841, %4842, newshape=[]); + %4844 = transpose(%4843, axes=[0, 2, 1, 3]); + %4845 = shape_of(%4844, dtype="int64"); + %4846 = take(%4845, 0, axis=0); + %4847 = shape_of(%4844, dtype="int64"); + %4848 = take(%4847, 1, axis=0); + %4849 = expand_dims(%4846, axis=0); + %4850 = expand_dims(%4848, axis=0); + %4851 = (%4849, %4850, meta[relay.Constant][524]); + %4852 = concatenate(%4851); + %4853 = dyn.reshape(%4844, %4852, newshape=[]); + %4854 = shape_of(%4853, dtype="int64"); + %4855 = strided_slice(%4854, begin=[1], end=[3], strides=[1]); + %4856 = (meta[relay.Constant][525], %4855); + %4857 = concatenate(%4856); + %4858 = transpose(%bert_encoder_layer_22_attention_output_dense_weight, axes=[1, 0]); + %4859 = reshape(%4858, newshape=[-1, 1024, 1024]); + %4860 = dyn.reshape(%4853, %4857, newshape=[]); + %4861 = transpose(%4859, axes=[0, 2, 1]); + %4862 = strided_slice(%4854, begin=[0], end=[1], strides=[1]); + %4863 = strided_slice(%4854, begin=[1], end=[2], strides=[1]); + %4864 = (%4862, %4863, meta[relay.Constant][526]); + %4865 = nn.batch_matmul(%4860, %4861, meta[relay.attrs.BatchMatmulAttrs][181]); + %4866 = concatenate(%4864); + %4867 = dyn.reshape(%4865, %4866, newshape=[]); + %4868 = add(%4867, %bert_encoder_layer_22_attention_output_dense_bias); + %4869 = add(%4868, %4711); + %4870 = mean(%4869, axis=[-1], keepdims=True); + %4871 = subtract(%4869, %4870); + %4872 = power(%4871, 2f); + %4873 = mean(%4872, axis=[-1], keepdims=True); + %4874 = add(%4873, 1e-12f); + %4875 = sqrt(%4874); + %4876 = divide(%4871, %4875); + %4877 = multiply(%4876, %bert_encoder_layer_22_attention_output_LayerNorm_weight); + %4878 = add(%4877, %bert_encoder_layer_22_attention_output_LayerNorm_bias); + %4879 = shape_of(%4878, dtype="int64"); + %4880 = strided_slice(%4879, begin=[1], end=[3], strides=[1]); + %4881 = (meta[relay.Constant][527], %4880); + %4882 = concatenate(%4881); + %4883 = transpose(%bert_encoder_layer_22_intermediate_dense_weight, axes=[1, 0]); + %4884 = reshape(%4883, newshape=[-1, 1024, 4096]); + %4885 = dyn.reshape(%4878, %4882, newshape=[]); + %4886 = transpose(%4884, axes=[0, 2, 1]); + %4887 = strided_slice(%4879, begin=[0], end=[1], strides=[1]); + %4888 = strided_slice(%4879, begin=[1], end=[2], strides=[1]); + %4889 = (%4887, %4888, meta[relay.Constant][528]); + %4890 = nn.batch_matmul(%4885, %4886, meta[relay.attrs.BatchMatmulAttrs][182]); + %4891 = concatenate(%4889); + %4892 = dyn.reshape(%4890, %4891, newshape=[]); + %4893 = add(%4892, %bert_encoder_layer_22_intermediate_dense_bias); + %4894 = divide(%4893, 1.41421f); + %4895 = erf(%4894); + %4896 = multiply(%4893, 0.5f); + %4897 = add(%4895, 1f); + %4898 = multiply(%4896, %4897); + %4899 = shape_of(%4898, dtype="int64"); + %4900 = strided_slice(%4899, begin=[1], end=[3], strides=[1]); + %4901 = (meta[relay.Constant][529], %4900); + %4902 = concatenate(%4901); + %4903 = transpose(%bert_encoder_layer_22_output_dense_weight, axes=[1, 0]); + %4904 = reshape(%4903, newshape=[-1, 4096, 1024]); + %4905 = dyn.reshape(%4898, %4902, newshape=[]); + %4906 = transpose(%4904, axes=[0, 2, 1]); + %4907 = strided_slice(%4899, begin=[0], end=[1], strides=[1]); + %4908 = strided_slice(%4899, begin=[1], end=[2], strides=[1]); + %4909 = (%4907, %4908, meta[relay.Constant][530]); + %4910 = nn.batch_matmul(%4905, %4906, meta[relay.attrs.BatchMatmulAttrs][183]); + %4911 = concatenate(%4909); + %4912 = dyn.reshape(%4910, %4911, newshape=[]); + %4913 = add(%4912, %bert_encoder_layer_22_output_dense_bias); + %4914 = add(%4913, %4878); + %4915 = mean(%4914, axis=[-1], keepdims=True); + %4916 = subtract(%4914, %4915); + %4917 = power(%4916, 2f); + %4918 = mean(%4917, axis=[-1], keepdims=True); + %4919 = add(%4918, 1e-12f); + %4920 = sqrt(%4919); + %4921 = divide(%4916, %4920); + %4922 = multiply(%4921, %bert_encoder_layer_22_output_LayerNorm_weight); + %4923 = add(%4922, %bert_encoder_layer_22_output_LayerNorm_bias); + %4924 = shape_of(%4923, dtype="int64"); + %4925 = strided_slice(%4924, begin=[1], end=[3], strides=[1]); + %4926 = (meta[relay.Constant][531], %4925); + %4927 = concatenate(%4926); + %4928 = transpose(%bert_encoder_layer_23_attention_self_query_weight, axes=[1, 0]); + %4929 = reshape(%4928, newshape=[-1, 1024, 1024]); + %4930 = dyn.reshape(%4923, %4927, newshape=[]); + %4931 = transpose(%4929, axes=[0, 2, 1]); + %4932 = strided_slice(%4924, begin=[0], end=[1], strides=[1]); + %4933 = strided_slice(%4924, begin=[1], end=[2], strides=[1]); + %4934 = (%4932, %4933, meta[relay.Constant][532]); + %4935 = nn.batch_matmul(%4930, %4931, meta[relay.attrs.BatchMatmulAttrs][184]); + %4936 = concatenate(%4934); + %4937 = dyn.reshape(%4935, %4936, newshape=[]); + %4938 = add(%4937, %bert_encoder_layer_23_attention_self_query_bias); + %4939 = shape_of(%4938, dtype="int64"); + %4940 = take(%4939, 0, axis=0); + %4941 = shape_of(%4938, dtype="int64"); + %4942 = take(%4941, 1, axis=0); + %4943 = expand_dims(%4940, axis=0); + %4944 = expand_dims(%4942, axis=0); + %4945 = (%4943, %4944, meta[relay.Constant][533], meta[relay.Constant][534]); + %4946 = concatenate(%4945); + %4947 = dyn.reshape(%4938, %4946, newshape=[]); + %4948 = transpose(%4947, axes=[0, 2, 1, 3]); + %4949 = shape_of(%4948, dtype="int64"); + %4950 = strided_slice(%4949, begin=[2], end=[4], strides=[1]); + %4951 = (meta[relay.Constant][535], %4950); + %4952 = concatenate(%4951); + %4953 = shape_of(%4923, dtype="int64"); + %4954 = strided_slice(%4953, begin=[1], end=[3], strides=[1]); + %4955 = (meta[relay.Constant][536], %4954); + %4956 = concatenate(%4955); + %4957 = transpose(%bert_encoder_layer_23_attention_self_key_weight, axes=[1, 0]); + %4958 = reshape(%4957, newshape=[-1, 1024, 1024]); + %4959 = dyn.reshape(%4923, %4956, newshape=[]); + %4960 = transpose(%4958, axes=[0, 2, 1]); + %4961 = strided_slice(%4953, begin=[0], end=[1], strides=[1]); + %4962 = strided_slice(%4953, begin=[1], end=[2], strides=[1]); + %4963 = (%4961, %4962, meta[relay.Constant][537]); + %4964 = nn.batch_matmul(%4959, %4960, meta[relay.attrs.BatchMatmulAttrs][185]); + %4965 = concatenate(%4963); + %4966 = dyn.reshape(%4964, %4965, newshape=[]); + %4967 = add(%4966, %bert_encoder_layer_23_attention_self_key_bias); + %4968 = shape_of(%4967, dtype="int64"); + %4969 = take(%4968, 0, axis=0); + %4970 = shape_of(%4967, dtype="int64"); + %4971 = take(%4970, 1, axis=0); + %4972 = expand_dims(%4969, axis=0); + %4973 = expand_dims(%4971, axis=0); + %4974 = (%4972, %4973, meta[relay.Constant][538], meta[relay.Constant][539]); + %4975 = concatenate(%4974); + %4976 = dyn.reshape(%4967, %4975, newshape=[]); + %4977 = transpose(%4976, axes=[0, 2, 3, 1]); + %4978 = shape_of(%4977, dtype="int64"); + %4979 = strided_slice(%4978, begin=[2], end=[4], strides=[1]); + %4980 = (meta[relay.Constant][540], %4979); + %4981 = concatenate(%4980); + %4982 = dyn.reshape(%4977, %4981, newshape=[]); + %4983 = dyn.reshape(%4948, %4952, newshape=[]); + %4984 = transpose(%4982, axes=[0, 2, 1]); + %4985 = strided_slice(%4949, begin=[0], end=[1], strides=[1]); + %4986 = strided_slice(%4978, begin=[0], end=[1], strides=[1]); + %4987 = strided_slice(%4949, begin=[1], end=[2], strides=[1]); + %4988 = strided_slice(%4978, begin=[1], end=[2], strides=[1]); + %4989 = maximum(%4985, %4986); + %4990 = maximum(%4987, %4988); + %4991 = (%4989, %4990); + %4992 = concatenate(%4991); + %4993 = strided_slice(%4949, begin=[2], end=[3], strides=[1]); + %4994 = strided_slice(%4978, begin=[3], end=[4], strides=[1]); + %4995 = (%4992, %4993, %4994); + %4996 = nn.batch_matmul(%4983, %4984, meta[relay.attrs.BatchMatmulAttrs][186]); + %4997 = concatenate(%4995); + %4998 = dyn.reshape(%4996, %4997, newshape=[]); + %4999 = divide(%4998, 8f); + %5000 = add(%4999, %123); + %5001 = max(%5000, axis=[3], keepdims=True); + %5002 = subtract(%5000, %5001); + %5003 = exp(%5002); + %5004 = sum(%5003, axis=[3], keepdims=True); + %5005 = divide(%5003, %5004); + %5006 = shape_of(%5005, dtype="int64"); + %5007 = strided_slice(%5006, begin=[2], end=[4], strides=[1]); + %5008 = (meta[relay.Constant][541], %5007); + %5009 = concatenate(%5008); + %5010 = shape_of(%4923, dtype="int64"); + %5011 = strided_slice(%5010, begin=[1], end=[3], strides=[1]); + %5012 = (meta[relay.Constant][542], %5011); + %5013 = concatenate(%5012); + %5014 = transpose(%bert_encoder_layer_23_attention_self_value_weight, axes=[1, 0]); + %5015 = reshape(%5014, newshape=[-1, 1024, 1024]); + %5016 = dyn.reshape(%4923, %5013, newshape=[]); + %5017 = transpose(%5015, axes=[0, 2, 1]); + %5018 = strided_slice(%5010, begin=[0], end=[1], strides=[1]); + %5019 = strided_slice(%5010, begin=[1], end=[2], strides=[1]); + %5020 = (%5018, %5019, meta[relay.Constant][543]); + %5021 = nn.batch_matmul(%5016, %5017, meta[relay.attrs.BatchMatmulAttrs][187]); + %5022 = concatenate(%5020); + %5023 = dyn.reshape(%5021, %5022, newshape=[]); + %5024 = add(%5023, %bert_encoder_layer_23_attention_self_value_bias); + %5025 = shape_of(%5024, dtype="int64"); + %5026 = take(%5025, 0, axis=0); + %5027 = shape_of(%5024, dtype="int64"); + %5028 = take(%5027, 1, axis=0); + %5029 = expand_dims(%5026, axis=0); + %5030 = expand_dims(%5028, axis=0); + %5031 = (%5029, %5030, meta[relay.Constant][544], meta[relay.Constant][545]); + %5032 = concatenate(%5031); + %5033 = dyn.reshape(%5024, %5032, newshape=[]); + %5034 = transpose(%5033, axes=[0, 2, 1, 3]); + %5035 = shape_of(%5034, dtype="int64"); + %5036 = strided_slice(%5035, begin=[2], end=[4], strides=[1]); + %5037 = (meta[relay.Constant][546], %5036); + %5038 = concatenate(%5037); + %5039 = dyn.reshape(%5034, %5038, newshape=[]); + %5040 = dyn.reshape(%5005, %5009, newshape=[]); + %5041 = transpose(%5039, axes=[0, 2, 1]); + %5042 = strided_slice(%5006, begin=[0], end=[1], strides=[1]); + %5043 = strided_slice(%5035, begin=[0], end=[1], strides=[1]); + %5044 = strided_slice(%5006, begin=[1], end=[2], strides=[1]); + %5045 = strided_slice(%5035, begin=[1], end=[2], strides=[1]); + %5046 = maximum(%5042, %5043); + %5047 = maximum(%5044, %5045); + %5048 = (%5046, %5047); + %5049 = concatenate(%5048); + %5050 = strided_slice(%5006, begin=[2], end=[3], strides=[1]); + %5051 = strided_slice(%5035, begin=[3], end=[4], strides=[1]); + %5052 = (%5049, %5050, %5051); + %5053 = nn.batch_matmul(%5040, %5041, meta[relay.attrs.BatchMatmulAttrs][188]); + %5054 = concatenate(%5052); + %5055 = dyn.reshape(%5053, %5054, newshape=[]); + %5056 = transpose(%5055, axes=[0, 2, 1, 3]); + %5057 = shape_of(%5056, dtype="int64"); + %5058 = take(%5057, 0, axis=0); + %5059 = shape_of(%5056, dtype="int64"); + %5060 = take(%5059, 1, axis=0); + %5061 = expand_dims(%5058, axis=0); + %5062 = expand_dims(%5060, axis=0); + %5063 = (%5061, %5062, meta[relay.Constant][547]); + %5064 = concatenate(%5063); + %5065 = dyn.reshape(%5056, %5064, newshape=[]); + %5066 = shape_of(%5065, dtype="int64"); + %5067 = strided_slice(%5066, begin=[1], end=[3], strides=[1]); + %5068 = (meta[relay.Constant][548], %5067); + %5069 = concatenate(%5068); + %5070 = transpose(%bert_encoder_layer_23_attention_output_dense_weight, axes=[1, 0]); + %5071 = reshape(%5070, newshape=[-1, 1024, 1024]); + %5072 = dyn.reshape(%5065, %5069, newshape=[]); + %5073 = transpose(%5071, axes=[0, 2, 1]); + %5074 = strided_slice(%5066, begin=[0], end=[1], strides=[1]); + %5075 = strided_slice(%5066, begin=[1], end=[2], strides=[1]); + %5076 = (%5074, %5075, meta[relay.Constant][549]); + %5077 = nn.batch_matmul(%5072, %5073, meta[relay.attrs.BatchMatmulAttrs][189]); + %5078 = concatenate(%5076); + %5079 = dyn.reshape(%5077, %5078, newshape=[]); + %5080 = add(%5079, %bert_encoder_layer_23_attention_output_dense_bias); + %5081 = add(%5080, %4923); + %5082 = mean(%5081, axis=[-1], keepdims=True); + %5083 = subtract(%5081, %5082); + %5084 = power(%5083, 2f); + %5085 = mean(%5084, axis=[-1], keepdims=True); + %5086 = add(%5085, 1e-12f); + %5087 = sqrt(%5086); + %5088 = divide(%5083, %5087); + %5089 = multiply(%5088, %bert_encoder_layer_23_attention_output_LayerNorm_weight); + %5090 = add(%5089, %bert_encoder_layer_23_attention_output_LayerNorm_bias); + %5091 = shape_of(%5090, dtype="int64"); + %5092 = strided_slice(%5091, begin=[1], end=[3], strides=[1]); + %5093 = (meta[relay.Constant][550], %5092); + %5094 = concatenate(%5093); + %5095 = transpose(%bert_encoder_layer_23_intermediate_dense_weight, axes=[1, 0]); + %5096 = reshape(%5095, newshape=[-1, 1024, 4096]); + %5097 = dyn.reshape(%5090, %5094, newshape=[]); + %5098 = transpose(%5096, axes=[0, 2, 1]); + %5099 = strided_slice(%5091, begin=[0], end=[1], strides=[1]); + %5100 = strided_slice(%5091, begin=[1], end=[2], strides=[1]); + %5101 = (%5099, %5100, meta[relay.Constant][551]); + %5102 = nn.batch_matmul(%5097, %5098, meta[relay.attrs.BatchMatmulAttrs][190]); + %5103 = concatenate(%5101); + %5104 = dyn.reshape(%5102, %5103, newshape=[]); + %5105 = add(%5104, %bert_encoder_layer_23_intermediate_dense_bias); + %5106 = divide(%5105, 1.41421f); + %5107 = erf(%5106); + %5108 = multiply(%5105, 0.5f); + %5109 = add(%5107, 1f); + %5110 = multiply(%5108, %5109); + %5111 = shape_of(%5110, dtype="int64"); + %5112 = strided_slice(%5111, begin=[1], end=[3], strides=[1]); + %5113 = (meta[relay.Constant][552], %5112); + %5114 = concatenate(%5113); + %5115 = transpose(%bert_encoder_layer_23_output_dense_weight, axes=[1, 0]); + %5116 = reshape(%5115, newshape=[-1, 4096, 1024]); + %5117 = dyn.reshape(%5110, %5114, newshape=[]); + %5118 = transpose(%5116, axes=[0, 2, 1]); + %5119 = strided_slice(%5111, begin=[0], end=[1], strides=[1]); + %5120 = strided_slice(%5111, begin=[1], end=[2], strides=[1]); + %5121 = (%5119, %5120, meta[relay.Constant][553]); + %5122 = nn.batch_matmul(%5117, %5118, meta[relay.attrs.BatchMatmulAttrs][191]); + %5123 = concatenate(%5121); + %5124 = dyn.reshape(%5122, %5123, newshape=[]); + %5125 = add(%5124, %bert_encoder_layer_23_output_dense_bias); + %5126 = add(%5125, %5090); + %5127 = mean(%5126, axis=[-1], keepdims=True); + %5128 = subtract(%5126, %5127); + %5129 = power(%5128, 2f); + %5130 = mean(%5129, axis=[-1], keepdims=True); + %5131 = add(%5130, 1e-12f); + %5132 = sqrt(%5131); + %5133 = divide(%5128, %5132); + %5134 = multiply(%5133, %bert_encoder_layer_23_output_LayerNorm_weight); + %5135 = add(%5134, %bert_encoder_layer_23_output_LayerNorm_bias); + %5136 = shape_of(%5135, dtype="int64"); + %5137 = strided_slice(%5136, begin=[1], end=[3], strides=[1]); + %5138 = (meta[relay.Constant][554], %5137); + %5139 = concatenate(%5138); + %5140 = transpose(%qa_outputs_weight, axes=[1, 0]); + %5141 = reshape(%5140, newshape=[-1, 1024, 2]); + %5142 = dyn.reshape(%5135, %5139, newshape=[]); + %5143 = transpose(%5141, axes=[0, 2, 1]); + %5144 = strided_slice(%5136, begin=[0], end=[1], strides=[1]); + %5145 = strided_slice(%5136, begin=[1], end=[2], strides=[1]); + %5146 = (%5144, %5145, meta[relay.Constant][555]); + %5147 = nn.batch_matmul(%5142, %5143, meta[relay.attrs.BatchMatmulAttrs][192]); + %5148 = concatenate(%5146); + %5149 = dyn.reshape(%5147, %5148, newshape=[]); + %5150 = add(%5149, %qa_outputs_bias); + %5151 = split(%5150, indices_or_sections=[1], axis=-1); + %5152 = %5151.0; + %5153 = %5151.1; + %5154 = squeeze(%5152, axis=[-1]); + %5155 = squeeze(%5153, axis=[-1]); + (%5154, %5155) +} + +#[metadata] +{ + "root": 1, + "nodes": [ + { + "type_key": "" + }, + { + "type_key": "Map", + "keys": [ + "relay.attrs.InitOpAttrs", + "relay.Constant", + "relay.Call", + "relay.attrs.BatchMatmulAttrs" + ], + "data": [2, 4, 561, 587] + }, + { + "type_key": "Array", + "data": [3] + }, + { + "type_key": "relay.attrs.InitOpAttrs", 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--- /dev/null +++ b/tests/models/efficientnet.relay @@ -0,0 +1,1163 @@ +#[version = "0.0.5"] +def @main(%data: Tensor[(1, 3, 224, 224), float32], %efficientnet0_features_conv0_weight: Tensor[(32, 3, 3, 3), float32], %efficientnet0_features_batchnorm0_gamma: Tensor[(32), float32], %efficientnet0_features_batchnorm0_beta: Tensor[(32), float32], %efficientnet0_features_batchnorm0_running_mean: Tensor[(32), float32], %efficientnet0_features_batchnorm0_running_var: Tensor[(32), float32], %efficientnet0_features_mbconv0_conv0_weight: Tensor[(32, 32, 1, 1), float32], %efficientnet0_features_mbconv0_batchnorm0_gamma: Tensor[(32), float32], %efficientnet0_features_mbconv0_batchnorm0_beta: Tensor[(32), float32], %efficientnet0_features_mbconv0_batchnorm0_running_mean: Tensor[(32), float32], %efficientnet0_features_mbconv0_batchnorm0_running_var: Tensor[(32), float32], %efficientnet0_features_mbconv0_conv1_weight: Tensor[(32, 1, 3, 3), float32], %efficientnet0_features_mbconv0_batchnorm1_gamma: Tensor[(32), float32], %efficientnet0_features_mbconv0_batchnorm1_beta: Tensor[(32), float32], %efficientnet0_features_mbconv0_batchnorm1_running_mean: Tensor[(32), float32], %efficientnet0_features_mbconv0_batchnorm1_running_var: Tensor[(32), float32], %efficientnet0_features_mbconv0_conv2_weight: Tensor[(16, 32, 1, 1), float32], %efficientnet0_features_mbconv0_batchnorm2_gamma: Tensor[(16), float32], %efficientnet0_features_mbconv0_batchnorm2_beta: Tensor[(16), float32], %efficientnet0_features_mbconv0_batchnorm2_running_mean: Tensor[(16), float32], %efficientnet0_features_mbconv0_batchnorm2_running_var: Tensor[(16), float32], %efficientnet0_features_mbconv1_conv0_weight: Tensor[(96, 16, 1, 1), float32], %efficientnet0_features_mbconv1_batchnorm0_gamma: Tensor[(96), float32], %efficientnet0_features_mbconv1_batchnorm0_beta: Tensor[(96), float32], %efficientnet0_features_mbconv1_batchnorm0_running_mean: Tensor[(96), float32], %efficientnet0_features_mbconv1_batchnorm0_running_var: Tensor[(96), float32], %efficientnet0_features_mbconv1_conv1_weight: Tensor[(96, 1, 3, 3), float32], %efficientnet0_features_mbconv1_batchnorm1_gamma: Tensor[(96), float32], %efficientnet0_features_mbconv1_batchnorm1_beta: Tensor[(96), float32], %efficientnet0_features_mbconv1_batchnorm1_running_mean: Tensor[(96), float32], %efficientnet0_features_mbconv1_batchnorm1_running_var: Tensor[(96), float32], %efficientnet0_features_mbconv1_conv2_weight: Tensor[(24, 96, 1, 1), float32], %efficientnet0_features_mbconv1_batchnorm2_gamma: Tensor[(24), float32], %efficientnet0_features_mbconv1_batchnorm2_beta: Tensor[(24), float32], %efficientnet0_features_mbconv1_batchnorm2_running_mean: Tensor[(24), float32], %efficientnet0_features_mbconv1_batchnorm2_running_var: Tensor[(24), float32], %efficientnet0_features_mbconv2_conv0_weight: Tensor[(144, 24, 1, 1), float32], %efficientnet0_features_mbconv2_batchnorm0_gamma: Tensor[(144), float32], %efficientnet0_features_mbconv2_batchnorm0_beta: Tensor[(144), float32], %efficientnet0_features_mbconv2_batchnorm0_running_mean: Tensor[(144), float32], %efficientnet0_features_mbconv2_batchnorm0_running_var: Tensor[(144), float32], %efficientnet0_features_mbconv2_conv1_weight: Tensor[(144, 1, 3, 3), float32], %efficientnet0_features_mbconv2_batchnorm1_gamma: Tensor[(144), float32], %efficientnet0_features_mbconv2_batchnorm1_beta: Tensor[(144), float32], %efficientnet0_features_mbconv2_batchnorm1_running_mean: Tensor[(144), float32], %efficientnet0_features_mbconv2_batchnorm1_running_var: Tensor[(144), float32], %efficientnet0_features_mbconv2_conv2_weight: Tensor[(24, 144, 1, 1), float32], %efficientnet0_features_mbconv2_batchnorm2_gamma: Tensor[(24), float32], %efficientnet0_features_mbconv2_batchnorm2_beta: Tensor[(24), float32], %efficientnet0_features_mbconv2_batchnorm2_running_mean: Tensor[(24), float32], %efficientnet0_features_mbconv2_batchnorm2_running_var: Tensor[(24), float32], %efficientnet0_features_mbconv3_conv0_weight: Tensor[(144, 24, 1, 1), float32], %efficientnet0_features_mbconv3_batchnorm0_gamma: Tensor[(144), float32], %efficientnet0_features_mbconv3_batchnorm0_beta: Tensor[(144), float32], %efficientnet0_features_mbconv3_batchnorm0_running_mean: Tensor[(144), float32], %efficientnet0_features_mbconv3_batchnorm0_running_var: Tensor[(144), float32], %efficientnet0_features_mbconv3_conv1_weight: Tensor[(144, 1, 5, 5), float32], %efficientnet0_features_mbconv3_batchnorm1_gamma: Tensor[(144), float32], %efficientnet0_features_mbconv3_batchnorm1_beta: Tensor[(144), float32], %efficientnet0_features_mbconv3_batchnorm1_running_mean: Tensor[(144), float32], %efficientnet0_features_mbconv3_batchnorm1_running_var: Tensor[(144), float32], %efficientnet0_features_mbconv3_conv2_weight: Tensor[(40, 144, 1, 1), float32], %efficientnet0_features_mbconv3_batchnorm2_gamma: Tensor[(40), float32], %efficientnet0_features_mbconv3_batchnorm2_beta: Tensor[(40), float32], %efficientnet0_features_mbconv3_batchnorm2_running_mean: Tensor[(40), float32], %efficientnet0_features_mbconv3_batchnorm2_running_var: Tensor[(40), float32], %efficientnet0_features_mbconv4_conv0_weight: Tensor[(240, 40, 1, 1), float32], %efficientnet0_features_mbconv4_batchnorm0_gamma: Tensor[(240), float32], %efficientnet0_features_mbconv4_batchnorm0_beta: Tensor[(240), float32], %efficientnet0_features_mbconv4_batchnorm0_running_mean: Tensor[(240), float32], %efficientnet0_features_mbconv4_batchnorm0_running_var: Tensor[(240), float32], %efficientnet0_features_mbconv4_conv1_weight: Tensor[(240, 1, 5, 5), float32], %efficientnet0_features_mbconv4_batchnorm1_gamma: Tensor[(240), float32], %efficientnet0_features_mbconv4_batchnorm1_beta: Tensor[(240), float32], %efficientnet0_features_mbconv4_batchnorm1_running_mean: Tensor[(240), float32], %efficientnet0_features_mbconv4_batchnorm1_running_var: Tensor[(240), float32], %efficientnet0_features_mbconv4_conv2_weight: Tensor[(40, 240, 1, 1), float32], %efficientnet0_features_mbconv4_batchnorm2_gamma: Tensor[(40), float32], %efficientnet0_features_mbconv4_batchnorm2_beta: Tensor[(40), float32], %efficientnet0_features_mbconv4_batchnorm2_running_mean: Tensor[(40), float32], %efficientnet0_features_mbconv4_batchnorm2_running_var: Tensor[(40), float32], %efficientnet0_features_mbconv5_conv0_weight: Tensor[(240, 40, 1, 1), float32], %efficientnet0_features_mbconv5_batchnorm0_gamma: Tensor[(240), float32], %efficientnet0_features_mbconv5_batchnorm0_beta: Tensor[(240), float32], %efficientnet0_features_mbconv5_batchnorm0_running_mean: Tensor[(240), float32], %efficientnet0_features_mbconv5_batchnorm0_running_var: Tensor[(240), float32], %efficientnet0_features_mbconv5_conv1_weight: Tensor[(240, 1, 3, 3), float32], %efficientnet0_features_mbconv5_batchnorm1_gamma: Tensor[(240), float32], %efficientnet0_features_mbconv5_batchnorm1_beta: Tensor[(240), float32], %efficientnet0_features_mbconv5_batchnorm1_running_mean: Tensor[(240), float32], %efficientnet0_features_mbconv5_batchnorm1_running_var: Tensor[(240), float32], %efficientnet0_features_mbconv5_conv2_weight: Tensor[(80, 240, 1, 1), float32], %efficientnet0_features_mbconv5_batchnorm2_gamma: Tensor[(80), float32], %efficientnet0_features_mbconv5_batchnorm2_beta: Tensor[(80), float32], %efficientnet0_features_mbconv5_batchnorm2_running_mean: Tensor[(80), float32], %efficientnet0_features_mbconv5_batchnorm2_running_var: Tensor[(80), float32], %efficientnet0_features_mbconv6_conv0_weight: Tensor[(480, 80, 1, 1), float32], %efficientnet0_features_mbconv6_batchnorm0_gamma: Tensor[(480), float32], %efficientnet0_features_mbconv6_batchnorm0_beta: Tensor[(480), float32], %efficientnet0_features_mbconv6_batchnorm0_running_mean: Tensor[(480), float32], %efficientnet0_features_mbconv6_batchnorm0_running_var: Tensor[(480), float32], %efficientnet0_features_mbconv6_conv1_weight: Tensor[(480, 1, 3, 3), float32], %efficientnet0_features_mbconv6_batchnorm1_gamma: Tensor[(480), float32], %efficientnet0_features_mbconv6_batchnorm1_beta: Tensor[(480), float32], %efficientnet0_features_mbconv6_batchnorm1_running_mean: Tensor[(480), float32], %efficientnet0_features_mbconv6_batchnorm1_running_var: Tensor[(480), float32], %efficientnet0_features_mbconv6_conv2_weight: Tensor[(80, 480, 1, 1), float32], %efficientnet0_features_mbconv6_batchnorm2_gamma: Tensor[(80), float32], %efficientnet0_features_mbconv6_batchnorm2_beta: Tensor[(80), float32], %efficientnet0_features_mbconv6_batchnorm2_running_mean: Tensor[(80), float32], %efficientnet0_features_mbconv6_batchnorm2_running_var: Tensor[(80), float32], %efficientnet0_features_mbconv7_conv0_weight: Tensor[(480, 80, 1, 1), float32], %efficientnet0_features_mbconv7_batchnorm0_gamma: Tensor[(480), float32], %efficientnet0_features_mbconv7_batchnorm0_beta: Tensor[(480), float32], %efficientnet0_features_mbconv7_batchnorm0_running_mean: Tensor[(480), float32], %efficientnet0_features_mbconv7_batchnorm0_running_var: Tensor[(480), float32], %efficientnet0_features_mbconv7_conv1_weight: Tensor[(480, 1, 3, 3), float32], %efficientnet0_features_mbconv7_batchnorm1_gamma: Tensor[(480), float32], %efficientnet0_features_mbconv7_batchnorm1_beta: Tensor[(480), float32], %efficientnet0_features_mbconv7_batchnorm1_running_mean: Tensor[(480), float32], %efficientnet0_features_mbconv7_batchnorm1_running_var: Tensor[(480), float32], %efficientnet0_features_mbconv7_conv2_weight: Tensor[(80, 480, 1, 1), float32], %efficientnet0_features_mbconv7_batchnorm2_gamma: Tensor[(80), float32], %efficientnet0_features_mbconv7_batchnorm2_beta: Tensor[(80), float32], %efficientnet0_features_mbconv7_batchnorm2_running_mean: Tensor[(80), float32], %efficientnet0_features_mbconv7_batchnorm2_running_var: Tensor[(80), float32], %efficientnet0_features_mbconv8_conv0_weight: Tensor[(480, 80, 1, 1), float32], %efficientnet0_features_mbconv8_batchnorm0_gamma: Tensor[(480), float32], %efficientnet0_features_mbconv8_batchnorm0_beta: Tensor[(480), float32], %efficientnet0_features_mbconv8_batchnorm0_running_mean: Tensor[(480), float32], %efficientnet0_features_mbconv8_batchnorm0_running_var: Tensor[(480), float32], %efficientnet0_features_mbconv8_conv1_weight: Tensor[(480, 1, 5, 5), float32], %efficientnet0_features_mbconv8_batchnorm1_gamma: Tensor[(480), float32], %efficientnet0_features_mbconv8_batchnorm1_beta: Tensor[(480), float32], %efficientnet0_features_mbconv8_batchnorm1_running_mean: Tensor[(480), float32], %efficientnet0_features_mbconv8_batchnorm1_running_var: Tensor[(480), float32], %efficientnet0_features_mbconv8_conv2_weight: Tensor[(112, 480, 1, 1), float32], %efficientnet0_features_mbconv8_batchnorm2_gamma: Tensor[(112), float32], %efficientnet0_features_mbconv8_batchnorm2_beta: Tensor[(112), float32], %efficientnet0_features_mbconv8_batchnorm2_running_mean: Tensor[(112), float32], %efficientnet0_features_mbconv8_batchnorm2_running_var: Tensor[(112), float32], %efficientnet0_features_mbconv9_conv0_weight: Tensor[(672, 112, 1, 1), float32], %efficientnet0_features_mbconv9_batchnorm0_gamma: Tensor[(672), float32], %efficientnet0_features_mbconv9_batchnorm0_beta: Tensor[(672), float32], %efficientnet0_features_mbconv9_batchnorm0_running_mean: Tensor[(672), float32], %efficientnet0_features_mbconv9_batchnorm0_running_var: Tensor[(672), float32], %efficientnet0_features_mbconv9_conv1_weight: Tensor[(672, 1, 5, 5), float32], %efficientnet0_features_mbconv9_batchnorm1_gamma: Tensor[(672), float32], %efficientnet0_features_mbconv9_batchnorm1_beta: Tensor[(672), float32], %efficientnet0_features_mbconv9_batchnorm1_running_mean: Tensor[(672), float32], %efficientnet0_features_mbconv9_batchnorm1_running_var: Tensor[(672), float32], %efficientnet0_features_mbconv9_conv2_weight: Tensor[(112, 672, 1, 1), float32], %efficientnet0_features_mbconv9_batchnorm2_gamma: Tensor[(112), float32], %efficientnet0_features_mbconv9_batchnorm2_beta: Tensor[(112), float32], %efficientnet0_features_mbconv9_batchnorm2_running_mean: Tensor[(112), float32], %efficientnet0_features_mbconv9_batchnorm2_running_var: Tensor[(112), float32], %efficientnet0_features_mbconv10_conv0_weight: Tensor[(672, 112, 1, 1), float32], %efficientnet0_features_mbconv10_batchnorm0_gamma: Tensor[(672), float32], %efficientnet0_features_mbconv10_batchnorm0_beta: Tensor[(672), float32], %efficientnet0_features_mbconv10_batchnorm0_running_mean: Tensor[(672), float32], %efficientnet0_features_mbconv10_batchnorm0_running_var: Tensor[(672), float32], %efficientnet0_features_mbconv10_conv1_weight: Tensor[(672, 1, 5, 5), float32], %efficientnet0_features_mbconv10_batchnorm1_gamma: Tensor[(672), float32], %efficientnet0_features_mbconv10_batchnorm1_beta: Tensor[(672), float32], %efficientnet0_features_mbconv10_batchnorm1_running_mean: Tensor[(672), float32], %efficientnet0_features_mbconv10_batchnorm1_running_var: Tensor[(672), float32], %efficientnet0_features_mbconv10_conv2_weight: Tensor[(112, 672, 1, 1), float32], %efficientnet0_features_mbconv10_batchnorm2_gamma: Tensor[(112), float32], %efficientnet0_features_mbconv10_batchnorm2_beta: Tensor[(112), float32], %efficientnet0_features_mbconv10_batchnorm2_running_mean: Tensor[(112), float32], %efficientnet0_features_mbconv10_batchnorm2_running_var: Tensor[(112), float32], %efficientnet0_features_mbconv11_conv0_weight: Tensor[(672, 112, 1, 1), float32], %efficientnet0_features_mbconv11_batchnorm0_gamma: Tensor[(672), float32], %efficientnet0_features_mbconv11_batchnorm0_beta: Tensor[(672), float32], %efficientnet0_features_mbconv11_batchnorm0_running_mean: Tensor[(672), float32], %efficientnet0_features_mbconv11_batchnorm0_running_var: Tensor[(672), float32], %efficientnet0_features_mbconv11_conv1_weight: Tensor[(672, 1, 5, 5), float32], %efficientnet0_features_mbconv11_batchnorm1_gamma: Tensor[(672), float32], %efficientnet0_features_mbconv11_batchnorm1_beta: Tensor[(672), float32], %efficientnet0_features_mbconv11_batchnorm1_running_mean: Tensor[(672), float32], %efficientnet0_features_mbconv11_batchnorm1_running_var: Tensor[(672), float32], %efficientnet0_features_mbconv11_conv2_weight: Tensor[(192, 672, 1, 1), float32], %efficientnet0_features_mbconv11_batchnorm2_gamma: Tensor[(192), float32], %efficientnet0_features_mbconv11_batchnorm2_beta: Tensor[(192), float32], %efficientnet0_features_mbconv11_batchnorm2_running_mean: Tensor[(192), float32], %efficientnet0_features_mbconv11_batchnorm2_running_var: Tensor[(192), float32], %efficientnet0_features_mbconv12_conv0_weight: Tensor[(1152, 192, 1, 1), float32], %efficientnet0_features_mbconv12_batchnorm0_gamma: Tensor[(1152), float32], %efficientnet0_features_mbconv12_batchnorm0_beta: Tensor[(1152), float32], %efficientnet0_features_mbconv12_batchnorm0_running_mean: Tensor[(1152), float32], %efficientnet0_features_mbconv12_batchnorm0_running_var: Tensor[(1152), float32], %efficientnet0_features_mbconv12_conv1_weight: Tensor[(1152, 1, 5, 5), float32], %efficientnet0_features_mbconv12_batchnorm1_gamma: Tensor[(1152), float32], %efficientnet0_features_mbconv12_batchnorm1_beta: Tensor[(1152), float32], %efficientnet0_features_mbconv12_batchnorm1_running_mean: Tensor[(1152), float32], %efficientnet0_features_mbconv12_batchnorm1_running_var: Tensor[(1152), float32], %efficientnet0_features_mbconv12_conv2_weight: Tensor[(192, 1152, 1, 1), float32], %efficientnet0_features_mbconv12_batchnorm2_gamma: Tensor[(192), float32], %efficientnet0_features_mbconv12_batchnorm2_beta: Tensor[(192), float32], %efficientnet0_features_mbconv12_batchnorm2_running_mean: Tensor[(192), float32], %efficientnet0_features_mbconv12_batchnorm2_running_var: Tensor[(192), float32], %efficientnet0_features_mbconv13_conv0_weight: Tensor[(1152, 192, 1, 1), float32], %efficientnet0_features_mbconv13_batchnorm0_gamma: Tensor[(1152), float32], %efficientnet0_features_mbconv13_batchnorm0_beta: Tensor[(1152), float32], %efficientnet0_features_mbconv13_batchnorm0_running_mean: Tensor[(1152), float32], %efficientnet0_features_mbconv13_batchnorm0_running_var: Tensor[(1152), float32], %efficientnet0_features_mbconv13_conv1_weight: Tensor[(1152, 1, 5, 5), float32], %efficientnet0_features_mbconv13_batchnorm1_gamma: Tensor[(1152), float32], %efficientnet0_features_mbconv13_batchnorm1_beta: Tensor[(1152), float32], %efficientnet0_features_mbconv13_batchnorm1_running_mean: Tensor[(1152), float32], %efficientnet0_features_mbconv13_batchnorm1_running_var: Tensor[(1152), float32], %efficientnet0_features_mbconv13_conv2_weight: Tensor[(192, 1152, 1, 1), float32], %efficientnet0_features_mbconv13_batchnorm2_gamma: Tensor[(192), float32], %efficientnet0_features_mbconv13_batchnorm2_beta: Tensor[(192), float32], %efficientnet0_features_mbconv13_batchnorm2_running_mean: Tensor[(192), float32], %efficientnet0_features_mbconv13_batchnorm2_running_var: Tensor[(192), float32], %efficientnet0_features_mbconv14_conv0_weight: Tensor[(1152, 192, 1, 1), float32], %efficientnet0_features_mbconv14_batchnorm0_gamma: Tensor[(1152), float32], %efficientnet0_features_mbconv14_batchnorm0_beta: Tensor[(1152), float32], %efficientnet0_features_mbconv14_batchnorm0_running_mean: Tensor[(1152), float32], %efficientnet0_features_mbconv14_batchnorm0_running_var: Tensor[(1152), float32], %efficientnet0_features_mbconv14_conv1_weight: Tensor[(1152, 1, 5, 5), float32], %efficientnet0_features_mbconv14_batchnorm1_gamma: Tensor[(1152), float32], %efficientnet0_features_mbconv14_batchnorm1_beta: Tensor[(1152), float32], %efficientnet0_features_mbconv14_batchnorm1_running_mean: Tensor[(1152), float32], %efficientnet0_features_mbconv14_batchnorm1_running_var: Tensor[(1152), float32], %efficientnet0_features_mbconv14_conv2_weight: Tensor[(192, 1152, 1, 1), float32], %efficientnet0_features_mbconv14_batchnorm2_gamma: Tensor[(192), float32], %efficientnet0_features_mbconv14_batchnorm2_beta: Tensor[(192), float32], %efficientnet0_features_mbconv14_batchnorm2_running_mean: Tensor[(192), float32], %efficientnet0_features_mbconv14_batchnorm2_running_var: Tensor[(192), float32], %efficientnet0_features_mbconv15_conv0_weight: Tensor[(1152, 192, 1, 1), float32], %efficientnet0_features_mbconv15_batchnorm0_gamma: Tensor[(1152), float32], %efficientnet0_features_mbconv15_batchnorm0_beta: Tensor[(1152), float32], %efficientnet0_features_mbconv15_batchnorm0_running_mean: Tensor[(1152), float32], %efficientnet0_features_mbconv15_batchnorm0_running_var: Tensor[(1152), float32], %efficientnet0_features_mbconv15_conv1_weight: Tensor[(1152, 1, 3, 3), float32], %efficientnet0_features_mbconv15_batchnorm1_gamma: Tensor[(1152), float32], %efficientnet0_features_mbconv15_batchnorm1_beta: Tensor[(1152), float32], %efficientnet0_features_mbconv15_batchnorm1_running_mean: Tensor[(1152), float32], %efficientnet0_features_mbconv15_batchnorm1_running_var: Tensor[(1152), float32], %efficientnet0_features_mbconv15_conv2_weight: Tensor[(320, 1152, 1, 1), float32], %efficientnet0_features_mbconv15_batchnorm2_gamma: Tensor[(320), float32], %efficientnet0_features_mbconv15_batchnorm2_beta: Tensor[(320), float32], %efficientnet0_features_mbconv15_batchnorm2_running_mean: Tensor[(320), float32], %efficientnet0_features_mbconv15_batchnorm2_running_var: Tensor[(320), float32], %efficientnet0_features_conv1_weight: Tensor[(1280, 320, 1, 1), float32], %efficientnet0_features_batchnorm1_gamma: Tensor[(1280), float32], %efficientnet0_features_batchnorm1_beta: Tensor[(1280), float32], %efficientnet0_features_batchnorm1_running_mean: Tensor[(1280), float32], %efficientnet0_features_batchnorm1_running_var: Tensor[(1280), float32], %efficientnet0_output_pred_weight: Tensor[(1000, 1280, 1, 1), float32]) -> Tensor[(1, 1000), float32] { + %0 = nn.pad(%data, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [1, 1]]) /* ty=Tensor[(1, 3, 226, 226), float32] */; + %1 = add(%efficientnet0_features_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %2 = sqrt(%1) /* ty=Tensor[(32), float32] */; + %3 = divide(1f /* ty=float32 */, %2) /* ty=Tensor[(32), float32] */; + %4 = multiply(%3, %efficientnet0_features_batchnorm0_gamma) /* ty=Tensor[(32), float32] */; + %5 = nn.conv2d(%0, %efficientnet0_features_conv0_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=32, kernel_size=[3, 3]) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %6 = expand_dims(%4, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %7 = negative(%efficientnet0_features_batchnorm0_running_mean) /* ty=Tensor[(32), float32] */; + %8 = multiply(%7, %4) /* ty=Tensor[(32), float32] */; + %9 = add(%8, %efficientnet0_features_batchnorm0_beta) /* ty=Tensor[(32), float32] */; + %10 = multiply(%5, %6) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %11 = expand_dims(%9, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %12 = add(%efficientnet0_features_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %13 = sqrt(%12) /* ty=Tensor[(32), float32] */; + %14 = divide(1f /* ty=float32 */, %13) /* ty=Tensor[(32), float32] */; + %15 = multiply(%14, %efficientnet0_features_batchnorm0_gamma) /* ty=Tensor[(32), float32] */; + %16 = expand_dims(%15, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %17 = negative(%efficientnet0_features_batchnorm0_running_mean) /* ty=Tensor[(32), float32] */; + %18 = multiply(%17, %15) /* ty=Tensor[(32), float32] */; + %19 = add(%18, %efficientnet0_features_batchnorm0_beta) /* ty=Tensor[(32), float32] */; + %20 = multiply(%5, %16) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %21 = expand_dims(%19, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %22 = add(%20, %21) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %23 = multiply(%22, 1f /* ty=float32 */) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %24 = add(%10, %11) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %25 = sigmoid(%23) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %26 = multiply(%24, %25) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %27 = add(%efficientnet0_features_mbconv0_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %28 = sqrt(%27) /* ty=Tensor[(32), float32] */; + %29 = divide(1f /* ty=float32 */, %28) /* ty=Tensor[(32), float32] */; + %30 = multiply(%29, %efficientnet0_features_mbconv0_batchnorm0_gamma) /* ty=Tensor[(32), float32] */; + %31 = nn.conv2d(%26, %efficientnet0_features_mbconv0_conv0_weight, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1]) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %32 = expand_dims(%30, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %33 = negative(%efficientnet0_features_mbconv0_batchnorm0_running_mean) /* ty=Tensor[(32), float32] */; + %34 = multiply(%33, %30) /* ty=Tensor[(32), float32] */; + %35 = add(%34, %efficientnet0_features_mbconv0_batchnorm0_beta) /* ty=Tensor[(32), float32] */; + %36 = multiply(%31, %32) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %37 = expand_dims(%35, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %38 = add(%efficientnet0_features_mbconv0_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %39 = sqrt(%38) /* ty=Tensor[(32), float32] */; + %40 = divide(1f /* ty=float32 */, %39) /* ty=Tensor[(32), float32] */; + %41 = multiply(%40, %efficientnet0_features_mbconv0_batchnorm0_gamma) /* ty=Tensor[(32), float32] */; + %42 = expand_dims(%41, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %43 = negative(%efficientnet0_features_mbconv0_batchnorm0_running_mean) /* ty=Tensor[(32), float32] */; + %44 = multiply(%43, %41) /* ty=Tensor[(32), float32] */; + %45 = add(%44, %efficientnet0_features_mbconv0_batchnorm0_beta) /* ty=Tensor[(32), float32] */; + %46 = multiply(%31, %42) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %47 = expand_dims(%45, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %48 = add(%46, %47) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %49 = multiply(%48, 1f /* ty=float32 */) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %50 = add(%36, %37) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %51 = sigmoid(%49) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %52 = multiply(%50, %51) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %53 = nn.pad(%52, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [1, 1]]) /* ty=Tensor[(1, 32, 114, 114), float32] */; + %54 = add(%efficientnet0_features_mbconv0_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %55 = sqrt(%54) /* ty=Tensor[(32), float32] */; + %56 = divide(1f /* ty=float32 */, %55) /* ty=Tensor[(32), float32] */; + %57 = multiply(%56, %efficientnet0_features_mbconv0_batchnorm1_gamma) /* ty=Tensor[(32), float32] */; + %58 = nn.conv2d(%53, %efficientnet0_features_mbconv0_conv1_weight, padding=[0, 0, 0, 0], groups=32, channels=32, kernel_size=[3, 3]) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %59 = expand_dims(%57, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %60 = negative(%efficientnet0_features_mbconv0_batchnorm1_running_mean) /* ty=Tensor[(32), float32] */; + %61 = multiply(%60, %57) /* ty=Tensor[(32), float32] */; + %62 = add(%61, %efficientnet0_features_mbconv0_batchnorm1_beta) /* ty=Tensor[(32), float32] */; + %63 = multiply(%58, %59) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %64 = expand_dims(%62, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %65 = add(%efficientnet0_features_mbconv0_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %66 = sqrt(%65) /* ty=Tensor[(32), float32] */; + %67 = divide(1f /* ty=float32 */, %66) /* ty=Tensor[(32), float32] */; + %68 = multiply(%67, %efficientnet0_features_mbconv0_batchnorm1_gamma) /* ty=Tensor[(32), float32] */; + %69 = expand_dims(%68, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %70 = negative(%efficientnet0_features_mbconv0_batchnorm1_running_mean) /* ty=Tensor[(32), float32] */; + %71 = multiply(%70, %68) /* ty=Tensor[(32), float32] */; + %72 = add(%71, %efficientnet0_features_mbconv0_batchnorm1_beta) /* ty=Tensor[(32), float32] */; + %73 = multiply(%58, %69) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %74 = expand_dims(%72, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %75 = add(%73, %74) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %76 = multiply(%75, 1f /* ty=float32 */) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %77 = add(%63, %64) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %78 = sigmoid(%76) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %79 = multiply(%77, %78) /* ty=Tensor[(1, 32, 112, 112), float32] */; + %80 = add(%efficientnet0_features_mbconv0_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %81 = sqrt(%80) /* ty=Tensor[(16), float32] */; + %82 = divide(1f /* ty=float32 */, %81) /* ty=Tensor[(16), float32] */; + %83 = multiply(%82, %efficientnet0_features_mbconv0_batchnorm2_gamma) /* ty=Tensor[(16), float32] */; + %84 = nn.conv2d(%79, %efficientnet0_features_mbconv0_conv2_weight, padding=[0, 0, 0, 0], channels=16, kernel_size=[1, 1]) /* ty=Tensor[(1, 16, 112, 112), float32] */; + %85 = expand_dims(%83, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %86 = negative(%efficientnet0_features_mbconv0_batchnorm2_running_mean) /* ty=Tensor[(16), float32] */; + %87 = multiply(%86, %83) /* ty=Tensor[(16), float32] */; + %88 = add(%87, %efficientnet0_features_mbconv0_batchnorm2_beta) /* ty=Tensor[(16), float32] */; + %89 = multiply(%84, %85) /* ty=Tensor[(1, 16, 112, 112), float32] */; + %90 = expand_dims(%88, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %91 = add(%89, %90) /* ty=Tensor[(1, 16, 112, 112), float32] */; + %92 = add(%efficientnet0_features_mbconv1_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %93 = sqrt(%92) /* ty=Tensor[(96), float32] */; + %94 = divide(1f /* ty=float32 */, %93) /* ty=Tensor[(96), float32] */; + %95 = multiply(%94, %efficientnet0_features_mbconv1_batchnorm0_gamma) /* ty=Tensor[(96), float32] */; + %96 = nn.conv2d(%91, %efficientnet0_features_mbconv1_conv0_weight, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 112, 112), float32] */; + %97 = expand_dims(%95, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %98 = negative(%efficientnet0_features_mbconv1_batchnorm0_running_mean) /* ty=Tensor[(96), float32] */; + %99 = multiply(%98, %95) /* ty=Tensor[(96), float32] */; + %100 = add(%99, %efficientnet0_features_mbconv1_batchnorm0_beta) /* ty=Tensor[(96), float32] */; + %101 = multiply(%96, %97) /* ty=Tensor[(1, 96, 112, 112), float32] */; + %102 = expand_dims(%100, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %103 = add(%efficientnet0_features_mbconv1_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %104 = sqrt(%103) /* ty=Tensor[(96), float32] */; + %105 = divide(1f /* ty=float32 */, %104) /* ty=Tensor[(96), float32] */; + %106 = multiply(%105, %efficientnet0_features_mbconv1_batchnorm0_gamma) /* ty=Tensor[(96), float32] */; + %107 = expand_dims(%106, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %108 = negative(%efficientnet0_features_mbconv1_batchnorm0_running_mean) /* ty=Tensor[(96), float32] */; + %109 = multiply(%108, %106) /* ty=Tensor[(96), float32] */; + %110 = add(%109, %efficientnet0_features_mbconv1_batchnorm0_beta) /* ty=Tensor[(96), float32] */; + %111 = multiply(%96, %107) /* ty=Tensor[(1, 96, 112, 112), float32] */; + %112 = expand_dims(%110, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %113 = add(%111, %112) /* ty=Tensor[(1, 96, 112, 112), float32] */; + %114 = multiply(%113, 1f /* ty=float32 */) /* ty=Tensor[(1, 96, 112, 112), float32] */; + %115 = add(%101, %102) /* ty=Tensor[(1, 96, 112, 112), float32] */; + %116 = sigmoid(%114) /* ty=Tensor[(1, 96, 112, 112), float32] */; + %117 = multiply(%115, %116) /* ty=Tensor[(1, 96, 112, 112), float32] */; + %118 = nn.pad(%117, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [1, 1]]) /* ty=Tensor[(1, 96, 114, 114), float32] */; + %119 = add(%efficientnet0_features_mbconv1_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %120 = sqrt(%119) /* ty=Tensor[(96), float32] */; + %121 = divide(1f /* ty=float32 */, %120) /* ty=Tensor[(96), float32] */; + %122 = multiply(%121, %efficientnet0_features_mbconv1_batchnorm1_gamma) /* ty=Tensor[(96), float32] */; + %123 = nn.conv2d(%118, %efficientnet0_features_mbconv1_conv1_weight, strides=[2, 2], padding=[0, 0, 0, 0], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 56, 56), float32] */; + %124 = expand_dims(%122, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %125 = negative(%efficientnet0_features_mbconv1_batchnorm1_running_mean) /* ty=Tensor[(96), float32] */; + %126 = multiply(%125, %122) /* ty=Tensor[(96), float32] */; + %127 = add(%126, %efficientnet0_features_mbconv1_batchnorm1_beta) /* ty=Tensor[(96), float32] */; + %128 = multiply(%123, %124) /* ty=Tensor[(1, 96, 56, 56), float32] */; + %129 = expand_dims(%127, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %130 = add(%efficientnet0_features_mbconv1_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %131 = sqrt(%130) /* ty=Tensor[(96), float32] */; + %132 = divide(1f /* ty=float32 */, %131) /* ty=Tensor[(96), float32] */; + %133 = multiply(%132, %efficientnet0_features_mbconv1_batchnorm1_gamma) /* ty=Tensor[(96), float32] */; + %134 = expand_dims(%133, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %135 = negative(%efficientnet0_features_mbconv1_batchnorm1_running_mean) /* ty=Tensor[(96), float32] */; + %136 = multiply(%135, %133) /* ty=Tensor[(96), float32] */; + %137 = add(%136, %efficientnet0_features_mbconv1_batchnorm1_beta) /* ty=Tensor[(96), float32] */; + %138 = multiply(%123, %134) /* ty=Tensor[(1, 96, 56, 56), float32] */; + %139 = expand_dims(%137, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %140 = add(%138, %139) /* ty=Tensor[(1, 96, 56, 56), float32] */; + %141 = multiply(%140, 1f /* ty=float32 */) /* ty=Tensor[(1, 96, 56, 56), float32] */; + %142 = add(%128, %129) /* ty=Tensor[(1, 96, 56, 56), float32] */; + %143 = sigmoid(%141) /* ty=Tensor[(1, 96, 56, 56), float32] */; + %144 = multiply(%142, %143) /* ty=Tensor[(1, 96, 56, 56), float32] */; + %145 = add(%efficientnet0_features_mbconv1_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(24), float32] */; + %146 = sqrt(%145) /* ty=Tensor[(24), float32] */; + %147 = divide(1f /* ty=float32 */, %146) /* ty=Tensor[(24), float32] */; + %148 = multiply(%147, %efficientnet0_features_mbconv1_batchnorm2_gamma) /* ty=Tensor[(24), float32] */; + %149 = nn.conv2d(%144, %efficientnet0_features_mbconv1_conv2_weight, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1]) /* ty=Tensor[(1, 24, 56, 56), float32] */; + %150 = expand_dims(%148, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %151 = negative(%efficientnet0_features_mbconv1_batchnorm2_running_mean) /* ty=Tensor[(24), float32] */; + %152 = multiply(%151, %148) /* ty=Tensor[(24), float32] */; + %153 = add(%152, %efficientnet0_features_mbconv1_batchnorm2_beta) /* ty=Tensor[(24), float32] */; + %154 = multiply(%149, %150) /* ty=Tensor[(1, 24, 56, 56), float32] */; + %155 = expand_dims(%153, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %156 = add(%154, %155) /* ty=Tensor[(1, 24, 56, 56), float32] */; + %157 = add(%efficientnet0_features_mbconv2_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %158 = sqrt(%157) /* ty=Tensor[(144), float32] */; + %159 = divide(1f /* ty=float32 */, %158) /* ty=Tensor[(144), float32] */; + %160 = multiply(%159, %efficientnet0_features_mbconv2_batchnorm0_gamma) /* ty=Tensor[(144), float32] */; + %161 = nn.conv2d(%156, %efficientnet0_features_mbconv2_conv0_weight, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1]) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %162 = expand_dims(%160, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %163 = negative(%efficientnet0_features_mbconv2_batchnorm0_running_mean) /* ty=Tensor[(144), float32] */; + %164 = multiply(%163, %160) /* ty=Tensor[(144), float32] */; + %165 = add(%164, %efficientnet0_features_mbconv2_batchnorm0_beta) /* ty=Tensor[(144), float32] */; + %166 = multiply(%161, %162) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %167 = expand_dims(%165, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %168 = add(%efficientnet0_features_mbconv2_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %169 = sqrt(%168) /* ty=Tensor[(144), float32] */; + %170 = divide(1f /* ty=float32 */, %169) /* ty=Tensor[(144), float32] */; + %171 = multiply(%170, %efficientnet0_features_mbconv2_batchnorm0_gamma) /* ty=Tensor[(144), float32] */; + %172 = expand_dims(%171, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %173 = negative(%efficientnet0_features_mbconv2_batchnorm0_running_mean) /* ty=Tensor[(144), float32] */; + %174 = multiply(%173, %171) /* ty=Tensor[(144), float32] */; + %175 = add(%174, %efficientnet0_features_mbconv2_batchnorm0_beta) /* ty=Tensor[(144), float32] */; + %176 = multiply(%161, %172) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %177 = expand_dims(%175, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %178 = add(%176, %177) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %179 = multiply(%178, 1f /* ty=float32 */) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %180 = add(%166, %167) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %181 = sigmoid(%179) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %182 = multiply(%180, %181) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %183 = nn.pad(%182, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [1, 1]]) /* ty=Tensor[(1, 144, 58, 58), float32] */; + %184 = add(%efficientnet0_features_mbconv2_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %185 = sqrt(%184) /* ty=Tensor[(144), float32] */; + %186 = divide(1f /* ty=float32 */, %185) /* ty=Tensor[(144), float32] */; + %187 = multiply(%186, %efficientnet0_features_mbconv2_batchnorm1_gamma) /* ty=Tensor[(144), float32] */; + %188 = nn.conv2d(%183, %efficientnet0_features_mbconv2_conv1_weight, padding=[0, 0, 0, 0], groups=144, channels=144, kernel_size=[3, 3]) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %189 = expand_dims(%187, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %190 = negative(%efficientnet0_features_mbconv2_batchnorm1_running_mean) /* ty=Tensor[(144), float32] */; + %191 = multiply(%190, %187) /* ty=Tensor[(144), float32] */; + %192 = add(%191, %efficientnet0_features_mbconv2_batchnorm1_beta) /* ty=Tensor[(144), float32] */; + %193 = multiply(%188, %189) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %194 = expand_dims(%192, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %195 = add(%efficientnet0_features_mbconv2_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %196 = sqrt(%195) /* ty=Tensor[(144), float32] */; + %197 = divide(1f /* ty=float32 */, %196) /* ty=Tensor[(144), float32] */; + %198 = multiply(%197, %efficientnet0_features_mbconv2_batchnorm1_gamma) /* ty=Tensor[(144), float32] */; + %199 = expand_dims(%198, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %200 = negative(%efficientnet0_features_mbconv2_batchnorm1_running_mean) /* ty=Tensor[(144), float32] */; + %201 = multiply(%200, %198) /* ty=Tensor[(144), float32] */; + %202 = add(%201, %efficientnet0_features_mbconv2_batchnorm1_beta) /* ty=Tensor[(144), float32] */; + %203 = multiply(%188, %199) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %204 = expand_dims(%202, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %205 = add(%203, %204) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %206 = multiply(%205, 1f /* ty=float32 */) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %207 = add(%193, %194) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %208 = sigmoid(%206) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %209 = multiply(%207, %208) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %210 = add(%efficientnet0_features_mbconv2_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(24), float32] */; + %211 = sqrt(%210) /* ty=Tensor[(24), float32] */; + %212 = divide(1f /* ty=float32 */, %211) /* ty=Tensor[(24), float32] */; + %213 = multiply(%212, %efficientnet0_features_mbconv2_batchnorm2_gamma) /* ty=Tensor[(24), float32] */; + %214 = nn.conv2d(%209, %efficientnet0_features_mbconv2_conv2_weight, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1]) /* ty=Tensor[(1, 24, 56, 56), float32] */; + %215 = expand_dims(%213, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %216 = negative(%efficientnet0_features_mbconv2_batchnorm2_running_mean) /* ty=Tensor[(24), float32] */; + %217 = multiply(%216, %213) /* ty=Tensor[(24), float32] */; + %218 = add(%217, %efficientnet0_features_mbconv2_batchnorm2_beta) /* ty=Tensor[(24), float32] */; + %219 = multiply(%214, %215) /* ty=Tensor[(1, 24, 56, 56), float32] */; + %220 = expand_dims(%218, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %221 = add(%efficientnet0_features_mbconv1_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(24), float32] */; + %222 = sqrt(%221) /* ty=Tensor[(24), float32] */; + %223 = divide(1f /* ty=float32 */, %222) /* ty=Tensor[(24), float32] */; + %224 = multiply(%223, %efficientnet0_features_mbconv1_batchnorm2_gamma) /* ty=Tensor[(24), float32] */; + %225 = expand_dims(%224, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %226 = negative(%efficientnet0_features_mbconv1_batchnorm2_running_mean) /* ty=Tensor[(24), float32] */; + %227 = multiply(%226, %224) /* ty=Tensor[(24), float32] */; + %228 = add(%227, %efficientnet0_features_mbconv1_batchnorm2_beta) /* ty=Tensor[(24), float32] */; + %229 = multiply(%149, %225) /* ty=Tensor[(1, 24, 56, 56), float32] */; + %230 = expand_dims(%228, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %231 = add(%219, %220) /* ty=Tensor[(1, 24, 56, 56), float32] */; + %232 = add(%229, %230) /* ty=Tensor[(1, 24, 56, 56), float32] */; + %233 = add(%231, %232) /* ty=Tensor[(1, 24, 56, 56), float32] */; + %234 = add(%efficientnet0_features_mbconv3_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %235 = sqrt(%234) /* ty=Tensor[(144), float32] */; + %236 = divide(1f /* ty=float32 */, %235) /* ty=Tensor[(144), float32] */; + %237 = multiply(%236, %efficientnet0_features_mbconv3_batchnorm0_gamma) /* ty=Tensor[(144), float32] */; + %238 = nn.conv2d(%233, %efficientnet0_features_mbconv3_conv0_weight, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1]) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %239 = expand_dims(%237, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %240 = negative(%efficientnet0_features_mbconv3_batchnorm0_running_mean) /* ty=Tensor[(144), float32] */; + %241 = multiply(%240, %237) /* ty=Tensor[(144), float32] */; + %242 = add(%241, %efficientnet0_features_mbconv3_batchnorm0_beta) /* ty=Tensor[(144), float32] */; + %243 = multiply(%238, %239) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %244 = expand_dims(%242, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %245 = add(%efficientnet0_features_mbconv3_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %246 = sqrt(%245) /* ty=Tensor[(144), float32] */; + %247 = divide(1f /* ty=float32 */, %246) /* ty=Tensor[(144), float32] */; + %248 = multiply(%247, %efficientnet0_features_mbconv3_batchnorm0_gamma) /* ty=Tensor[(144), float32] */; + %249 = expand_dims(%248, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %250 = negative(%efficientnet0_features_mbconv3_batchnorm0_running_mean) /* ty=Tensor[(144), float32] */; + %251 = multiply(%250, %248) /* ty=Tensor[(144), float32] */; + %252 = add(%251, %efficientnet0_features_mbconv3_batchnorm0_beta) /* ty=Tensor[(144), float32] */; + %253 = multiply(%238, %249) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %254 = expand_dims(%252, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %255 = add(%253, %254) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %256 = multiply(%255, 1f /* ty=float32 */) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %257 = add(%243, %244) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %258 = sigmoid(%256) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %259 = multiply(%257, %258) /* ty=Tensor[(1, 144, 56, 56), float32] */; + %260 = nn.pad(%259, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [2, 2], [2, 2]]) /* ty=Tensor[(1, 144, 60, 60), float32] */; + %261 = add(%efficientnet0_features_mbconv3_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %262 = sqrt(%261) /* ty=Tensor[(144), float32] */; + %263 = divide(1f /* ty=float32 */, %262) /* ty=Tensor[(144), float32] */; + %264 = multiply(%263, %efficientnet0_features_mbconv3_batchnorm1_gamma) /* ty=Tensor[(144), float32] */; + %265 = nn.conv2d(%260, %efficientnet0_features_mbconv3_conv1_weight, strides=[2, 2], padding=[0, 0, 0, 0], groups=144, channels=144, kernel_size=[5, 5]) /* ty=Tensor[(1, 144, 28, 28), float32] */; + %266 = expand_dims(%264, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %267 = negative(%efficientnet0_features_mbconv3_batchnorm1_running_mean) /* ty=Tensor[(144), float32] */; + %268 = multiply(%267, %264) /* ty=Tensor[(144), float32] */; + %269 = add(%268, %efficientnet0_features_mbconv3_batchnorm1_beta) /* ty=Tensor[(144), float32] */; + %270 = multiply(%265, %266) /* ty=Tensor[(1, 144, 28, 28), float32] */; + %271 = expand_dims(%269, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %272 = add(%efficientnet0_features_mbconv3_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %273 = sqrt(%272) /* ty=Tensor[(144), float32] */; + %274 = divide(1f /* ty=float32 */, %273) /* ty=Tensor[(144), float32] */; + %275 = multiply(%274, %efficientnet0_features_mbconv3_batchnorm1_gamma) /* ty=Tensor[(144), float32] */; + %276 = expand_dims(%275, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %277 = negative(%efficientnet0_features_mbconv3_batchnorm1_running_mean) /* ty=Tensor[(144), float32] */; + %278 = multiply(%277, %275) /* ty=Tensor[(144), float32] */; + %279 = add(%278, %efficientnet0_features_mbconv3_batchnorm1_beta) /* ty=Tensor[(144), float32] */; + %280 = multiply(%265, %276) /* ty=Tensor[(1, 144, 28, 28), float32] */; + %281 = expand_dims(%279, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %282 = add(%280, %281) /* ty=Tensor[(1, 144, 28, 28), float32] */; + %283 = multiply(%282, 1f /* ty=float32 */) /* ty=Tensor[(1, 144, 28, 28), float32] */; + %284 = add(%270, %271) /* ty=Tensor[(1, 144, 28, 28), float32] */; + %285 = sigmoid(%283) /* ty=Tensor[(1, 144, 28, 28), float32] */; + %286 = multiply(%284, %285) /* ty=Tensor[(1, 144, 28, 28), float32] */; + %287 = add(%efficientnet0_features_mbconv3_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(40), float32] */; + %288 = sqrt(%287) /* ty=Tensor[(40), float32] */; + %289 = divide(1f /* ty=float32 */, %288) /* ty=Tensor[(40), float32] */; + %290 = multiply(%289, %efficientnet0_features_mbconv3_batchnorm2_gamma) /* ty=Tensor[(40), float32] */; + %291 = nn.conv2d(%286, %efficientnet0_features_mbconv3_conv2_weight, padding=[0, 0, 0, 0], channels=40, kernel_size=[1, 1]) /* ty=Tensor[(1, 40, 28, 28), float32] */; + %292 = expand_dims(%290, axis=1, num_newaxis=2) /* ty=Tensor[(40, 1, 1), float32] */; + %293 = negative(%efficientnet0_features_mbconv3_batchnorm2_running_mean) /* ty=Tensor[(40), float32] */; + %294 = multiply(%293, %290) /* ty=Tensor[(40), float32] */; + %295 = add(%294, %efficientnet0_features_mbconv3_batchnorm2_beta) /* ty=Tensor[(40), float32] */; + %296 = multiply(%291, %292) /* ty=Tensor[(1, 40, 28, 28), float32] */; + %297 = expand_dims(%295, axis=1, num_newaxis=2) /* ty=Tensor[(40, 1, 1), float32] */; + %298 = add(%296, %297) /* ty=Tensor[(1, 40, 28, 28), float32] */; + %299 = add(%efficientnet0_features_mbconv4_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(240), float32] */; + %300 = sqrt(%299) /* ty=Tensor[(240), float32] */; + %301 = divide(1f /* ty=float32 */, %300) /* ty=Tensor[(240), float32] */; + %302 = multiply(%301, %efficientnet0_features_mbconv4_batchnorm0_gamma) /* ty=Tensor[(240), float32] */; + %303 = nn.conv2d(%298, %efficientnet0_features_mbconv4_conv0_weight, padding=[0, 0, 0, 0], channels=240, kernel_size=[1, 1]) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %304 = expand_dims(%302, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %305 = negative(%efficientnet0_features_mbconv4_batchnorm0_running_mean) /* ty=Tensor[(240), float32] */; + %306 = multiply(%305, %302) /* ty=Tensor[(240), float32] */; + %307 = add(%306, %efficientnet0_features_mbconv4_batchnorm0_beta) /* ty=Tensor[(240), float32] */; + %308 = multiply(%303, %304) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %309 = expand_dims(%307, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %310 = add(%efficientnet0_features_mbconv4_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(240), float32] */; + %311 = sqrt(%310) /* ty=Tensor[(240), float32] */; + %312 = divide(1f /* ty=float32 */, %311) /* ty=Tensor[(240), float32] */; + %313 = multiply(%312, %efficientnet0_features_mbconv4_batchnorm0_gamma) /* ty=Tensor[(240), float32] */; + %314 = expand_dims(%313, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %315 = negative(%efficientnet0_features_mbconv4_batchnorm0_running_mean) /* ty=Tensor[(240), float32] */; + %316 = multiply(%315, %313) /* ty=Tensor[(240), float32] */; + %317 = add(%316, %efficientnet0_features_mbconv4_batchnorm0_beta) /* ty=Tensor[(240), float32] */; + %318 = multiply(%303, %314) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %319 = expand_dims(%317, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %320 = add(%318, %319) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %321 = multiply(%320, 1f /* ty=float32 */) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %322 = add(%308, %309) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %323 = sigmoid(%321) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %324 = multiply(%322, %323) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %325 = nn.pad(%324, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [2, 2], [2, 2]]) /* ty=Tensor[(1, 240, 32, 32), float32] */; + %326 = add(%efficientnet0_features_mbconv4_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(240), float32] */; + %327 = sqrt(%326) /* ty=Tensor[(240), float32] */; + %328 = divide(1f /* ty=float32 */, %327) /* ty=Tensor[(240), float32] */; + %329 = multiply(%328, %efficientnet0_features_mbconv4_batchnorm1_gamma) /* ty=Tensor[(240), float32] */; + %330 = nn.conv2d(%325, %efficientnet0_features_mbconv4_conv1_weight, padding=[0, 0, 0, 0], groups=240, channels=240, kernel_size=[5, 5]) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %331 = expand_dims(%329, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %332 = negative(%efficientnet0_features_mbconv4_batchnorm1_running_mean) /* ty=Tensor[(240), float32] */; + %333 = multiply(%332, %329) /* ty=Tensor[(240), float32] */; + %334 = add(%333, %efficientnet0_features_mbconv4_batchnorm1_beta) /* ty=Tensor[(240), float32] */; + %335 = multiply(%330, %331) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %336 = expand_dims(%334, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %337 = add(%efficientnet0_features_mbconv4_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(240), float32] */; + %338 = sqrt(%337) /* ty=Tensor[(240), float32] */; + %339 = divide(1f /* ty=float32 */, %338) /* ty=Tensor[(240), float32] */; + %340 = multiply(%339, %efficientnet0_features_mbconv4_batchnorm1_gamma) /* ty=Tensor[(240), float32] */; + %341 = expand_dims(%340, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %342 = negative(%efficientnet0_features_mbconv4_batchnorm1_running_mean) /* ty=Tensor[(240), float32] */; + %343 = multiply(%342, %340) /* ty=Tensor[(240), float32] */; + %344 = add(%343, %efficientnet0_features_mbconv4_batchnorm1_beta) /* ty=Tensor[(240), float32] */; + %345 = multiply(%330, %341) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %346 = expand_dims(%344, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %347 = add(%345, %346) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %348 = multiply(%347, 1f /* ty=float32 */) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %349 = add(%335, %336) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %350 = sigmoid(%348) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %351 = multiply(%349, %350) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %352 = add(%efficientnet0_features_mbconv4_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(40), float32] */; + %353 = sqrt(%352) /* ty=Tensor[(40), float32] */; + %354 = divide(1f /* ty=float32 */, %353) /* ty=Tensor[(40), float32] */; + %355 = multiply(%354, %efficientnet0_features_mbconv4_batchnorm2_gamma) /* ty=Tensor[(40), float32] */; + %356 = nn.conv2d(%351, %efficientnet0_features_mbconv4_conv2_weight, padding=[0, 0, 0, 0], channels=40, kernel_size=[1, 1]) /* ty=Tensor[(1, 40, 28, 28), float32] */; + %357 = expand_dims(%355, axis=1, num_newaxis=2) /* ty=Tensor[(40, 1, 1), float32] */; + %358 = negative(%efficientnet0_features_mbconv4_batchnorm2_running_mean) /* ty=Tensor[(40), float32] */; + %359 = multiply(%358, %355) /* ty=Tensor[(40), float32] */; + %360 = add(%359, %efficientnet0_features_mbconv4_batchnorm2_beta) /* ty=Tensor[(40), float32] */; + %361 = multiply(%356, %357) /* ty=Tensor[(1, 40, 28, 28), float32] */; + %362 = expand_dims(%360, axis=1, num_newaxis=2) /* ty=Tensor[(40, 1, 1), float32] */; + %363 = add(%efficientnet0_features_mbconv3_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(40), float32] */; + %364 = sqrt(%363) /* ty=Tensor[(40), float32] */; + %365 = divide(1f /* ty=float32 */, %364) /* ty=Tensor[(40), float32] */; + %366 = multiply(%365, %efficientnet0_features_mbconv3_batchnorm2_gamma) /* ty=Tensor[(40), float32] */; + %367 = expand_dims(%366, axis=1, num_newaxis=2) /* ty=Tensor[(40, 1, 1), float32] */; + %368 = negative(%efficientnet0_features_mbconv3_batchnorm2_running_mean) /* ty=Tensor[(40), float32] */; + %369 = multiply(%368, %366) /* ty=Tensor[(40), float32] */; + %370 = add(%369, %efficientnet0_features_mbconv3_batchnorm2_beta) /* ty=Tensor[(40), float32] */; + %371 = multiply(%291, %367) /* ty=Tensor[(1, 40, 28, 28), float32] */; + %372 = expand_dims(%370, axis=1, num_newaxis=2) /* ty=Tensor[(40, 1, 1), float32] */; + %373 = add(%361, %362) /* ty=Tensor[(1, 40, 28, 28), float32] */; + %374 = add(%371, %372) /* ty=Tensor[(1, 40, 28, 28), float32] */; + %375 = add(%373, %374) /* ty=Tensor[(1, 40, 28, 28), float32] */; + %376 = add(%efficientnet0_features_mbconv5_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(240), float32] */; + %377 = sqrt(%376) /* ty=Tensor[(240), float32] */; + %378 = divide(1f /* ty=float32 */, %377) /* ty=Tensor[(240), float32] */; + %379 = multiply(%378, %efficientnet0_features_mbconv5_batchnorm0_gamma) /* ty=Tensor[(240), float32] */; + %380 = nn.conv2d(%375, %efficientnet0_features_mbconv5_conv0_weight, padding=[0, 0, 0, 0], channels=240, kernel_size=[1, 1]) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %381 = expand_dims(%379, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %382 = negative(%efficientnet0_features_mbconv5_batchnorm0_running_mean) /* ty=Tensor[(240), float32] */; + %383 = multiply(%382, %379) /* ty=Tensor[(240), float32] */; + %384 = add(%383, %efficientnet0_features_mbconv5_batchnorm0_beta) /* ty=Tensor[(240), float32] */; + %385 = multiply(%380, %381) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %386 = expand_dims(%384, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %387 = add(%efficientnet0_features_mbconv5_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(240), float32] */; + %388 = sqrt(%387) /* ty=Tensor[(240), float32] */; + %389 = divide(1f /* ty=float32 */, %388) /* ty=Tensor[(240), float32] */; + %390 = multiply(%389, %efficientnet0_features_mbconv5_batchnorm0_gamma) /* ty=Tensor[(240), float32] */; + %391 = expand_dims(%390, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %392 = negative(%efficientnet0_features_mbconv5_batchnorm0_running_mean) /* ty=Tensor[(240), float32] */; + %393 = multiply(%392, %390) /* ty=Tensor[(240), float32] */; + %394 = add(%393, %efficientnet0_features_mbconv5_batchnorm0_beta) /* ty=Tensor[(240), float32] */; + %395 = multiply(%380, %391) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %396 = expand_dims(%394, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %397 = add(%395, %396) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %398 = multiply(%397, 1f /* ty=float32 */) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %399 = add(%385, %386) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %400 = sigmoid(%398) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %401 = multiply(%399, %400) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %402 = nn.pad(%401, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [1, 1]]) /* ty=Tensor[(1, 240, 30, 30), float32] */; + %403 = add(%efficientnet0_features_mbconv5_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(240), float32] */; + %404 = sqrt(%403) /* ty=Tensor[(240), float32] */; + %405 = divide(1f /* ty=float32 */, %404) /* ty=Tensor[(240), float32] */; + %406 = multiply(%405, %efficientnet0_features_mbconv5_batchnorm1_gamma) /* ty=Tensor[(240), float32] */; + %407 = nn.conv2d(%402, %efficientnet0_features_mbconv5_conv1_weight, padding=[0, 0, 0, 0], groups=240, channels=240, kernel_size=[3, 3]) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %408 = expand_dims(%406, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %409 = negative(%efficientnet0_features_mbconv5_batchnorm1_running_mean) /* ty=Tensor[(240), float32] */; + %410 = multiply(%409, %406) /* ty=Tensor[(240), float32] */; + %411 = add(%410, %efficientnet0_features_mbconv5_batchnorm1_beta) /* ty=Tensor[(240), float32] */; + %412 = multiply(%407, %408) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %413 = expand_dims(%411, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %414 = add(%efficientnet0_features_mbconv5_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(240), float32] */; + %415 = sqrt(%414) /* ty=Tensor[(240), float32] */; + %416 = divide(1f /* ty=float32 */, %415) /* ty=Tensor[(240), float32] */; + %417 = multiply(%416, %efficientnet0_features_mbconv5_batchnorm1_gamma) /* ty=Tensor[(240), float32] */; + %418 = expand_dims(%417, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %419 = negative(%efficientnet0_features_mbconv5_batchnorm1_running_mean) /* ty=Tensor[(240), float32] */; + %420 = multiply(%419, %417) /* ty=Tensor[(240), float32] */; + %421 = add(%420, %efficientnet0_features_mbconv5_batchnorm1_beta) /* ty=Tensor[(240), float32] */; + %422 = multiply(%407, %418) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %423 = expand_dims(%421, axis=1, num_newaxis=2) /* ty=Tensor[(240, 1, 1), float32] */; + %424 = add(%422, %423) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %425 = multiply(%424, 1f /* ty=float32 */) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %426 = add(%412, %413) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %427 = sigmoid(%425) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %428 = multiply(%426, %427) /* ty=Tensor[(1, 240, 28, 28), float32] */; + %429 = add(%efficientnet0_features_mbconv5_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(80), float32] */; + %430 = sqrt(%429) /* ty=Tensor[(80), float32] */; + %431 = divide(1f /* ty=float32 */, %430) /* ty=Tensor[(80), float32] */; + %432 = multiply(%431, %efficientnet0_features_mbconv5_batchnorm2_gamma) /* ty=Tensor[(80), float32] */; + %433 = nn.conv2d(%428, %efficientnet0_features_mbconv5_conv2_weight, padding=[0, 0, 0, 0], channels=80, kernel_size=[1, 1]) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %434 = expand_dims(%432, axis=1, num_newaxis=2) /* ty=Tensor[(80, 1, 1), float32] */; + %435 = negative(%efficientnet0_features_mbconv5_batchnorm2_running_mean) /* ty=Tensor[(80), float32] */; + %436 = multiply(%435, %432) /* ty=Tensor[(80), float32] */; + %437 = add(%436, %efficientnet0_features_mbconv5_batchnorm2_beta) /* ty=Tensor[(80), float32] */; + %438 = multiply(%433, %434) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %439 = expand_dims(%437, axis=1, num_newaxis=2) /* ty=Tensor[(80, 1, 1), float32] */; + %440 = add(%438, %439) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %441 = add(%efficientnet0_features_mbconv6_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %442 = sqrt(%441) /* ty=Tensor[(480), float32] */; + %443 = divide(1f /* ty=float32 */, %442) /* ty=Tensor[(480), float32] */; + %444 = multiply(%443, %efficientnet0_features_mbconv6_batchnorm0_gamma) /* ty=Tensor[(480), float32] */; + %445 = nn.conv2d(%440, %efficientnet0_features_mbconv6_conv0_weight, padding=[0, 0, 0, 0], channels=480, kernel_size=[1, 1]) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %446 = expand_dims(%444, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %447 = negative(%efficientnet0_features_mbconv6_batchnorm0_running_mean) /* ty=Tensor[(480), float32] */; + %448 = multiply(%447, %444) /* ty=Tensor[(480), float32] */; + %449 = add(%448, %efficientnet0_features_mbconv6_batchnorm0_beta) /* ty=Tensor[(480), float32] */; + %450 = multiply(%445, %446) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %451 = expand_dims(%449, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %452 = add(%efficientnet0_features_mbconv6_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %453 = sqrt(%452) /* ty=Tensor[(480), float32] */; + %454 = divide(1f /* ty=float32 */, %453) /* ty=Tensor[(480), float32] */; + %455 = multiply(%454, %efficientnet0_features_mbconv6_batchnorm0_gamma) /* ty=Tensor[(480), float32] */; + %456 = expand_dims(%455, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %457 = negative(%efficientnet0_features_mbconv6_batchnorm0_running_mean) /* ty=Tensor[(480), float32] */; + %458 = multiply(%457, %455) /* ty=Tensor[(480), float32] */; + %459 = add(%458, %efficientnet0_features_mbconv6_batchnorm0_beta) /* ty=Tensor[(480), float32] */; + %460 = multiply(%445, %456) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %461 = expand_dims(%459, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %462 = add(%460, %461) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %463 = multiply(%462, 1f /* ty=float32 */) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %464 = add(%450, %451) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %465 = sigmoid(%463) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %466 = multiply(%464, %465) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %467 = nn.pad(%466, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [1, 1]]) /* ty=Tensor[(1, 480, 30, 30), float32] */; + %468 = add(%efficientnet0_features_mbconv6_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %469 = sqrt(%468) /* ty=Tensor[(480), float32] */; + %470 = divide(1f /* ty=float32 */, %469) /* ty=Tensor[(480), float32] */; + %471 = multiply(%470, %efficientnet0_features_mbconv6_batchnorm1_gamma) /* ty=Tensor[(480), float32] */; + %472 = nn.conv2d(%467, %efficientnet0_features_mbconv6_conv1_weight, padding=[0, 0, 0, 0], groups=480, channels=480, kernel_size=[3, 3]) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %473 = expand_dims(%471, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %474 = negative(%efficientnet0_features_mbconv6_batchnorm1_running_mean) /* ty=Tensor[(480), float32] */; + %475 = multiply(%474, %471) /* ty=Tensor[(480), float32] */; + %476 = add(%475, %efficientnet0_features_mbconv6_batchnorm1_beta) /* ty=Tensor[(480), float32] */; + %477 = multiply(%472, %473) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %478 = expand_dims(%476, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %479 = add(%efficientnet0_features_mbconv6_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %480 = sqrt(%479) /* ty=Tensor[(480), float32] */; + %481 = divide(1f /* ty=float32 */, %480) /* ty=Tensor[(480), float32] */; + %482 = multiply(%481, %efficientnet0_features_mbconv6_batchnorm1_gamma) /* ty=Tensor[(480), float32] */; + %483 = expand_dims(%482, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %484 = negative(%efficientnet0_features_mbconv6_batchnorm1_running_mean) /* ty=Tensor[(480), float32] */; + %485 = multiply(%484, %482) /* ty=Tensor[(480), float32] */; + %486 = add(%485, %efficientnet0_features_mbconv6_batchnorm1_beta) /* ty=Tensor[(480), float32] */; + %487 = multiply(%472, %483) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %488 = expand_dims(%486, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %489 = add(%487, %488) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %490 = multiply(%489, 1f /* ty=float32 */) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %491 = add(%477, %478) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %492 = sigmoid(%490) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %493 = multiply(%491, %492) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %494 = add(%efficientnet0_features_mbconv6_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(80), float32] */; + %495 = sqrt(%494) /* ty=Tensor[(80), float32] */; + %496 = divide(1f /* ty=float32 */, %495) /* ty=Tensor[(80), float32] */; + %497 = multiply(%496, %efficientnet0_features_mbconv6_batchnorm2_gamma) /* ty=Tensor[(80), float32] */; + %498 = nn.conv2d(%493, %efficientnet0_features_mbconv6_conv2_weight, padding=[0, 0, 0, 0], channels=80, kernel_size=[1, 1]) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %499 = expand_dims(%497, axis=1, num_newaxis=2) /* ty=Tensor[(80, 1, 1), float32] */; + %500 = negative(%efficientnet0_features_mbconv6_batchnorm2_running_mean) /* ty=Tensor[(80), float32] */; + %501 = multiply(%500, %497) /* ty=Tensor[(80), float32] */; + %502 = add(%501, %efficientnet0_features_mbconv6_batchnorm2_beta) /* ty=Tensor[(80), float32] */; + %503 = multiply(%498, %499) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %504 = expand_dims(%502, axis=1, num_newaxis=2) /* ty=Tensor[(80, 1, 1), float32] */; + %505 = add(%efficientnet0_features_mbconv5_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(80), float32] */; + %506 = sqrt(%505) /* ty=Tensor[(80), float32] */; + %507 = divide(1f /* ty=float32 */, %506) /* ty=Tensor[(80), float32] */; + %508 = multiply(%507, %efficientnet0_features_mbconv5_batchnorm2_gamma) /* ty=Tensor[(80), float32] */; + %509 = expand_dims(%508, axis=1, num_newaxis=2) /* ty=Tensor[(80, 1, 1), float32] */; + %510 = negative(%efficientnet0_features_mbconv5_batchnorm2_running_mean) /* ty=Tensor[(80), float32] */; + %511 = multiply(%510, %508) /* ty=Tensor[(80), float32] */; + %512 = add(%511, %efficientnet0_features_mbconv5_batchnorm2_beta) /* ty=Tensor[(80), float32] */; + %513 = multiply(%433, %509) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %514 = expand_dims(%512, axis=1, num_newaxis=2) /* ty=Tensor[(80, 1, 1), float32] */; + %515 = add(%503, %504) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %516 = add(%513, %514) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %517 = add(%515, %516) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %518 = add(%efficientnet0_features_mbconv7_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %519 = sqrt(%518) /* ty=Tensor[(480), float32] */; + %520 = divide(1f /* ty=float32 */, %519) /* ty=Tensor[(480), float32] */; + %521 = multiply(%520, %efficientnet0_features_mbconv7_batchnorm0_gamma) /* ty=Tensor[(480), float32] */; + %522 = nn.conv2d(%517, %efficientnet0_features_mbconv7_conv0_weight, padding=[0, 0, 0, 0], channels=480, kernel_size=[1, 1]) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %523 = expand_dims(%521, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %524 = negative(%efficientnet0_features_mbconv7_batchnorm0_running_mean) /* ty=Tensor[(480), float32] */; + %525 = multiply(%524, %521) /* ty=Tensor[(480), float32] */; + %526 = add(%525, %efficientnet0_features_mbconv7_batchnorm0_beta) /* ty=Tensor[(480), float32] */; + %527 = multiply(%522, %523) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %528 = expand_dims(%526, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %529 = add(%efficientnet0_features_mbconv7_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %530 = sqrt(%529) /* ty=Tensor[(480), float32] */; + %531 = divide(1f /* ty=float32 */, %530) /* ty=Tensor[(480), float32] */; + %532 = multiply(%531, %efficientnet0_features_mbconv7_batchnorm0_gamma) /* ty=Tensor[(480), float32] */; + %533 = expand_dims(%532, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %534 = negative(%efficientnet0_features_mbconv7_batchnorm0_running_mean) /* ty=Tensor[(480), float32] */; + %535 = multiply(%534, %532) /* ty=Tensor[(480), float32] */; + %536 = add(%535, %efficientnet0_features_mbconv7_batchnorm0_beta) /* ty=Tensor[(480), float32] */; + %537 = multiply(%522, %533) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %538 = expand_dims(%536, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %539 = add(%537, %538) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %540 = multiply(%539, 1f /* ty=float32 */) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %541 = add(%527, %528) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %542 = sigmoid(%540) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %543 = multiply(%541, %542) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %544 = nn.pad(%543, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [1, 1]]) /* ty=Tensor[(1, 480, 30, 30), float32] */; + %545 = add(%efficientnet0_features_mbconv7_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %546 = sqrt(%545) /* ty=Tensor[(480), float32] */; + %547 = divide(1f /* ty=float32 */, %546) /* ty=Tensor[(480), float32] */; + %548 = multiply(%547, %efficientnet0_features_mbconv7_batchnorm1_gamma) /* ty=Tensor[(480), float32] */; + %549 = nn.conv2d(%544, %efficientnet0_features_mbconv7_conv1_weight, padding=[0, 0, 0, 0], groups=480, channels=480, kernel_size=[3, 3]) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %550 = expand_dims(%548, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %551 = negative(%efficientnet0_features_mbconv7_batchnorm1_running_mean) /* ty=Tensor[(480), float32] */; + %552 = multiply(%551, %548) /* ty=Tensor[(480), float32] */; + %553 = add(%552, %efficientnet0_features_mbconv7_batchnorm1_beta) /* ty=Tensor[(480), float32] */; + %554 = multiply(%549, %550) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %555 = expand_dims(%553, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %556 = add(%efficientnet0_features_mbconv7_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %557 = sqrt(%556) /* ty=Tensor[(480), float32] */; + %558 = divide(1f /* ty=float32 */, %557) /* ty=Tensor[(480), float32] */; + %559 = multiply(%558, %efficientnet0_features_mbconv7_batchnorm1_gamma) /* ty=Tensor[(480), float32] */; + %560 = expand_dims(%559, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %561 = negative(%efficientnet0_features_mbconv7_batchnorm1_running_mean) /* ty=Tensor[(480), float32] */; + %562 = multiply(%561, %559) /* ty=Tensor[(480), float32] */; + %563 = add(%562, %efficientnet0_features_mbconv7_batchnorm1_beta) /* ty=Tensor[(480), float32] */; + %564 = multiply(%549, %560) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %565 = expand_dims(%563, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %566 = add(%564, %565) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %567 = multiply(%566, 1f /* ty=float32 */) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %568 = add(%554, %555) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %569 = sigmoid(%567) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %570 = multiply(%568, %569) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %571 = add(%efficientnet0_features_mbconv7_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(80), float32] */; + %572 = sqrt(%571) /* ty=Tensor[(80), float32] */; + %573 = divide(1f /* ty=float32 */, %572) /* ty=Tensor[(80), float32] */; + %574 = multiply(%573, %efficientnet0_features_mbconv7_batchnorm2_gamma) /* ty=Tensor[(80), float32] */; + %575 = nn.conv2d(%570, %efficientnet0_features_mbconv7_conv2_weight, padding=[0, 0, 0, 0], channels=80, kernel_size=[1, 1]) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %576 = expand_dims(%574, axis=1, num_newaxis=2) /* ty=Tensor[(80, 1, 1), float32] */; + %577 = negative(%efficientnet0_features_mbconv7_batchnorm2_running_mean) /* ty=Tensor[(80), float32] */; + %578 = multiply(%577, %574) /* ty=Tensor[(80), float32] */; + %579 = add(%578, %efficientnet0_features_mbconv7_batchnorm2_beta) /* ty=Tensor[(80), float32] */; + %580 = multiply(%575, %576) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %581 = expand_dims(%579, axis=1, num_newaxis=2) /* ty=Tensor[(80, 1, 1), float32] */; + %582 = add(%580, %581) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %583 = add(%582, %517) /* ty=Tensor[(1, 80, 28, 28), float32] */; + %584 = add(%efficientnet0_features_mbconv8_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %585 = sqrt(%584) /* ty=Tensor[(480), float32] */; + %586 = divide(1f /* ty=float32 */, %585) /* ty=Tensor[(480), float32] */; + %587 = multiply(%586, %efficientnet0_features_mbconv8_batchnorm0_gamma) /* ty=Tensor[(480), float32] */; + %588 = nn.conv2d(%583, %efficientnet0_features_mbconv8_conv0_weight, padding=[0, 0, 0, 0], channels=480, kernel_size=[1, 1]) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %589 = expand_dims(%587, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %590 = negative(%efficientnet0_features_mbconv8_batchnorm0_running_mean) /* ty=Tensor[(480), float32] */; + %591 = multiply(%590, %587) /* ty=Tensor[(480), float32] */; + %592 = add(%591, %efficientnet0_features_mbconv8_batchnorm0_beta) /* ty=Tensor[(480), float32] */; + %593 = multiply(%588, %589) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %594 = expand_dims(%592, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %595 = add(%efficientnet0_features_mbconv8_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %596 = sqrt(%595) /* ty=Tensor[(480), float32] */; + %597 = divide(1f /* ty=float32 */, %596) /* ty=Tensor[(480), float32] */; + %598 = multiply(%597, %efficientnet0_features_mbconv8_batchnorm0_gamma) /* ty=Tensor[(480), float32] */; + %599 = expand_dims(%598, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %600 = negative(%efficientnet0_features_mbconv8_batchnorm0_running_mean) /* ty=Tensor[(480), float32] */; + %601 = multiply(%600, %598) /* ty=Tensor[(480), float32] */; + %602 = add(%601, %efficientnet0_features_mbconv8_batchnorm0_beta) /* ty=Tensor[(480), float32] */; + %603 = multiply(%588, %599) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %604 = expand_dims(%602, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %605 = add(%603, %604) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %606 = multiply(%605, 1f /* ty=float32 */) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %607 = add(%593, %594) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %608 = sigmoid(%606) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %609 = multiply(%607, %608) /* ty=Tensor[(1, 480, 28, 28), float32] */; + %610 = nn.pad(%609, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [2, 2], [2, 2]]) /* ty=Tensor[(1, 480, 32, 32), float32] */; + %611 = add(%efficientnet0_features_mbconv8_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %612 = sqrt(%611) /* ty=Tensor[(480), float32] */; + %613 = divide(1f /* ty=float32 */, %612) /* ty=Tensor[(480), float32] */; + %614 = multiply(%613, %efficientnet0_features_mbconv8_batchnorm1_gamma) /* ty=Tensor[(480), float32] */; + %615 = nn.conv2d(%610, %efficientnet0_features_mbconv8_conv1_weight, strides=[2, 2], padding=[0, 0, 0, 0], groups=480, channels=480, kernel_size=[5, 5]) /* ty=Tensor[(1, 480, 14, 14), float32] */; + %616 = expand_dims(%614, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %617 = negative(%efficientnet0_features_mbconv8_batchnorm1_running_mean) /* ty=Tensor[(480), float32] */; + %618 = multiply(%617, %614) /* ty=Tensor[(480), float32] */; + %619 = add(%618, %efficientnet0_features_mbconv8_batchnorm1_beta) /* ty=Tensor[(480), float32] */; + %620 = multiply(%615, %616) /* ty=Tensor[(1, 480, 14, 14), float32] */; + %621 = expand_dims(%619, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %622 = add(%efficientnet0_features_mbconv8_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(480), float32] */; + %623 = sqrt(%622) /* ty=Tensor[(480), float32] */; + %624 = divide(1f /* ty=float32 */, %623) /* ty=Tensor[(480), float32] */; + %625 = multiply(%624, %efficientnet0_features_mbconv8_batchnorm1_gamma) /* ty=Tensor[(480), float32] */; + %626 = expand_dims(%625, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %627 = negative(%efficientnet0_features_mbconv8_batchnorm1_running_mean) /* ty=Tensor[(480), float32] */; + %628 = multiply(%627, %625) /* ty=Tensor[(480), float32] */; + %629 = add(%628, %efficientnet0_features_mbconv8_batchnorm1_beta) /* ty=Tensor[(480), float32] */; + %630 = multiply(%615, %626) /* ty=Tensor[(1, 480, 14, 14), float32] */; + %631 = expand_dims(%629, axis=1, num_newaxis=2) /* ty=Tensor[(480, 1, 1), float32] */; + %632 = add(%630, %631) /* ty=Tensor[(1, 480, 14, 14), float32] */; + %633 = multiply(%632, 1f /* ty=float32 */) /* ty=Tensor[(1, 480, 14, 14), float32] */; + %634 = add(%620, %621) /* ty=Tensor[(1, 480, 14, 14), float32] */; + %635 = sigmoid(%633) /* ty=Tensor[(1, 480, 14, 14), float32] */; + %636 = multiply(%634, %635) /* ty=Tensor[(1, 480, 14, 14), float32] */; + %637 = add(%efficientnet0_features_mbconv8_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(112), float32] */; + %638 = sqrt(%637) /* ty=Tensor[(112), float32] */; + %639 = divide(1f /* ty=float32 */, %638) /* ty=Tensor[(112), float32] */; + %640 = multiply(%639, %efficientnet0_features_mbconv8_batchnorm2_gamma) /* ty=Tensor[(112), float32] */; + %641 = nn.conv2d(%636, %efficientnet0_features_mbconv8_conv2_weight, padding=[0, 0, 0, 0], channels=112, kernel_size=[1, 1]) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %642 = expand_dims(%640, axis=1, num_newaxis=2) /* ty=Tensor[(112, 1, 1), float32] */; + %643 = negative(%efficientnet0_features_mbconv8_batchnorm2_running_mean) /* ty=Tensor[(112), float32] */; + %644 = multiply(%643, %640) /* ty=Tensor[(112), float32] */; + %645 = add(%644, %efficientnet0_features_mbconv8_batchnorm2_beta) /* ty=Tensor[(112), float32] */; + %646 = multiply(%641, %642) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %647 = expand_dims(%645, axis=1, num_newaxis=2) /* ty=Tensor[(112, 1, 1), float32] */; + %648 = add(%646, %647) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %649 = add(%efficientnet0_features_mbconv9_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(672), float32] */; + %650 = 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/* ty=Tensor[(112), float32] */; + %716 = multiply(%715, %efficientnet0_features_mbconv8_batchnorm2_gamma) /* ty=Tensor[(112), float32] */; + %717 = expand_dims(%716, axis=1, num_newaxis=2) /* ty=Tensor[(112, 1, 1), float32] */; + %718 = negative(%efficientnet0_features_mbconv8_batchnorm2_running_mean) /* ty=Tensor[(112), float32] */; + %719 = multiply(%718, %716) /* ty=Tensor[(112), float32] */; + %720 = add(%719, %efficientnet0_features_mbconv8_batchnorm2_beta) /* ty=Tensor[(112), float32] */; + %721 = multiply(%641, %717) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %722 = expand_dims(%720, axis=1, num_newaxis=2) /* ty=Tensor[(112, 1, 1), float32] */; + %723 = add(%711, %712) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %724 = add(%721, %722) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %725 = add(%723, %724) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %726 = add(%efficientnet0_features_mbconv10_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(672), float32] */; + 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1), float32] */; + %759 = negative(%efficientnet0_features_mbconv10_batchnorm1_running_mean) /* ty=Tensor[(672), float32] */; + %760 = multiply(%759, %756) /* ty=Tensor[(672), float32] */; + %761 = add(%760, %efficientnet0_features_mbconv10_batchnorm1_beta) /* ty=Tensor[(672), float32] */; + %762 = multiply(%757, %758) /* ty=Tensor[(1, 672, 14, 14), float32] */; + %763 = expand_dims(%761, axis=1, num_newaxis=2) /* ty=Tensor[(672, 1, 1), float32] */; + %764 = add(%efficientnet0_features_mbconv10_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(672), float32] */; + %765 = sqrt(%764) /* ty=Tensor[(672), float32] */; + %766 = divide(1f /* ty=float32 */, %765) /* ty=Tensor[(672), float32] */; + %767 = multiply(%766, %efficientnet0_features_mbconv10_batchnorm1_gamma) /* ty=Tensor[(672), float32] */; + %768 = expand_dims(%767, axis=1, num_newaxis=2) /* ty=Tensor[(672, 1, 1), float32] */; + %769 = negative(%efficientnet0_features_mbconv10_batchnorm1_running_mean) /* ty=Tensor[(672), float32] */; + %770 = multiply(%769, %767) /* ty=Tensor[(672), float32] */; + %771 = add(%770, %efficientnet0_features_mbconv10_batchnorm1_beta) /* ty=Tensor[(672), float32] */; + %772 = multiply(%757, %768) /* ty=Tensor[(1, 672, 14, 14), float32] */; + %773 = expand_dims(%771, axis=1, num_newaxis=2) /* ty=Tensor[(672, 1, 1), float32] */; + %774 = add(%772, %773) /* ty=Tensor[(1, 672, 14, 14), float32] */; + %775 = multiply(%774, 1f /* ty=float32 */) /* ty=Tensor[(1, 672, 14, 14), float32] */; + %776 = add(%762, %763) /* ty=Tensor[(1, 672, 14, 14), float32] */; + %777 = sigmoid(%775) /* ty=Tensor[(1, 672, 14, 14), float32] */; + %778 = multiply(%776, %777) /* ty=Tensor[(1, 672, 14, 14), float32] */; + %779 = add(%efficientnet0_features_mbconv10_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(112), float32] */; + %780 = sqrt(%779) /* ty=Tensor[(112), float32] */; + %781 = divide(1f /* ty=float32 */, %780) /* ty=Tensor[(112), float32] */; + %782 = multiply(%781, %efficientnet0_features_mbconv10_batchnorm2_gamma) /* ty=Tensor[(112), float32] */; + %783 = nn.conv2d(%778, %efficientnet0_features_mbconv10_conv2_weight, padding=[0, 0, 0, 0], channels=112, kernel_size=[1, 1]) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %784 = expand_dims(%782, axis=1, num_newaxis=2) /* ty=Tensor[(112, 1, 1), float32] */; + %785 = negative(%efficientnet0_features_mbconv10_batchnorm2_running_mean) /* ty=Tensor[(112), float32] */; + %786 = multiply(%785, %782) /* ty=Tensor[(112), float32] */; + %787 = add(%786, %efficientnet0_features_mbconv10_batchnorm2_beta) /* ty=Tensor[(112), float32] */; + %788 = multiply(%783, %784) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %789 = expand_dims(%787, axis=1, num_newaxis=2) /* ty=Tensor[(112, 1, 1), float32] */; + %790 = add(%788, %789) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %791 = add(%790, %725) /* ty=Tensor[(1, 112, 14, 14), float32] */; + %792 = 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num_newaxis=2) /* ty=Tensor[(672, 1, 1), float32] */; + %803 = add(%efficientnet0_features_mbconv11_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(672), float32] */; + %804 = sqrt(%803) /* ty=Tensor[(672), float32] */; + %805 = divide(1f /* ty=float32 */, %804) /* ty=Tensor[(672), float32] */; + %806 = multiply(%805, %efficientnet0_features_mbconv11_batchnorm0_gamma) /* ty=Tensor[(672), float32] */; + %807 = expand_dims(%806, axis=1, num_newaxis=2) /* ty=Tensor[(672, 1, 1), float32] */; + %808 = negative(%efficientnet0_features_mbconv11_batchnorm0_running_mean) /* ty=Tensor[(672), float32] */; + %809 = multiply(%808, %806) /* ty=Tensor[(672), float32] */; + %810 = add(%809, %efficientnet0_features_mbconv11_batchnorm0_beta) /* ty=Tensor[(672), float32] */; + %811 = multiply(%796, %807) /* ty=Tensor[(1, 672, 14, 14), float32] */; + %812 = expand_dims(%810, axis=1, num_newaxis=2) /* ty=Tensor[(672, 1, 1), float32] */; + %813 = add(%811, %812) /* ty=Tensor[(1, 672, 14, 14), 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*/; + %824 = expand_dims(%822, axis=1, num_newaxis=2) /* ty=Tensor[(672, 1, 1), float32] */; + %825 = negative(%efficientnet0_features_mbconv11_batchnorm1_running_mean) /* ty=Tensor[(672), float32] */; + %826 = multiply(%825, %822) /* ty=Tensor[(672), float32] */; + %827 = add(%826, %efficientnet0_features_mbconv11_batchnorm1_beta) /* ty=Tensor[(672), float32] */; + %828 = multiply(%823, %824) /* ty=Tensor[(1, 672, 7, 7), float32] */; + %829 = expand_dims(%827, axis=1, num_newaxis=2) /* ty=Tensor[(672, 1, 1), float32] */; + %830 = add(%efficientnet0_features_mbconv11_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(672), float32] */; + %831 = sqrt(%830) /* ty=Tensor[(672), float32] */; + %832 = divide(1f /* ty=float32 */, %831) /* ty=Tensor[(672), float32] */; + %833 = multiply(%832, %efficientnet0_features_mbconv11_batchnorm1_gamma) /* ty=Tensor[(672), float32] */; + %834 = expand_dims(%833, axis=1, num_newaxis=2) /* ty=Tensor[(672, 1, 1), float32] */; + %835 = 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float32] */; + %1010 = expand_dims(%1008, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1011 = add(%efficientnet0_features_mbconv14_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(1152), float32] */; + %1012 = sqrt(%1011) /* ty=Tensor[(1152), float32] */; + %1013 = divide(1f /* ty=float32 */, %1012) /* ty=Tensor[(1152), float32] */; + %1014 = multiply(%1013, %efficientnet0_features_mbconv14_batchnorm0_gamma) /* ty=Tensor[(1152), float32] */; + %1015 = expand_dims(%1014, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1016 = negative(%efficientnet0_features_mbconv14_batchnorm0_running_mean) /* ty=Tensor[(1152), float32] */; + %1017 = multiply(%1016, %1014) /* ty=Tensor[(1152), float32] */; + %1018 = add(%1017, %efficientnet0_features_mbconv14_batchnorm0_beta) /* ty=Tensor[(1152), float32] */; + %1019 = multiply(%1004, %1015) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1020 = expand_dims(%1018, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1021 = add(%1019, %1020) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1022 = multiply(%1021, 1f /* ty=float32 */) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1023 = add(%1009, %1010) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1024 = sigmoid(%1022) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1025 = multiply(%1023, %1024) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1026 = nn.pad(%1025, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [2, 2], [2, 2]]) /* ty=Tensor[(1, 1152, 11, 11), float32] */; + %1027 = add(%efficientnet0_features_mbconv14_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(1152), float32] */; + %1028 = sqrt(%1027) /* ty=Tensor[(1152), float32] */; + %1029 = divide(1f /* ty=float32 */, %1028) /* ty=Tensor[(1152), float32] */; + %1030 = multiply(%1029, %efficientnet0_features_mbconv14_batchnorm1_gamma) /* ty=Tensor[(1152), float32] */; + %1031 = nn.conv2d(%1026, %efficientnet0_features_mbconv14_conv1_weight, padding=[0, 0, 0, 0], groups=1152, channels=1152, kernel_size=[5, 5]) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1032 = expand_dims(%1030, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1033 = negative(%efficientnet0_features_mbconv14_batchnorm1_running_mean) /* ty=Tensor[(1152), float32] */; + %1034 = multiply(%1033, %1030) /* ty=Tensor[(1152), float32] */; + %1035 = add(%1034, %efficientnet0_features_mbconv14_batchnorm1_beta) /* ty=Tensor[(1152), float32] */; + %1036 = multiply(%1031, %1032) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1037 = expand_dims(%1035, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1038 = add(%efficientnet0_features_mbconv14_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(1152), float32] */; + %1039 = sqrt(%1038) /* ty=Tensor[(1152), float32] */; + %1040 = divide(1f /* ty=float32 */, %1039) /* ty=Tensor[(1152), float32] */; + %1041 = multiply(%1040, %efficientnet0_features_mbconv14_batchnorm1_gamma) /* ty=Tensor[(1152), float32] */; + %1042 = expand_dims(%1041, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1043 = negative(%efficientnet0_features_mbconv14_batchnorm1_running_mean) /* ty=Tensor[(1152), float32] */; + %1044 = multiply(%1043, %1041) /* ty=Tensor[(1152), float32] */; + %1045 = add(%1044, %efficientnet0_features_mbconv14_batchnorm1_beta) /* ty=Tensor[(1152), float32] */; + %1046 = multiply(%1031, %1042) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1047 = expand_dims(%1045, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1048 = add(%1046, %1047) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1049 = multiply(%1048, 1f /* ty=float32 */) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1050 = add(%1036, %1037) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1051 = sigmoid(%1049) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1052 = multiply(%1050, %1051) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1053 = add(%efficientnet0_features_mbconv14_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(192), float32] */; + %1054 = sqrt(%1053) /* ty=Tensor[(192), float32] */; + %1055 = divide(1f /* ty=float32 */, %1054) /* ty=Tensor[(192), float32] */; + %1056 = multiply(%1055, %efficientnet0_features_mbconv14_batchnorm2_gamma) /* ty=Tensor[(192), float32] */; + %1057 = nn.conv2d(%1052, %efficientnet0_features_mbconv14_conv2_weight, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1]) /* ty=Tensor[(1, 192, 7, 7), float32] */; + %1058 = expand_dims(%1056, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %1059 = negative(%efficientnet0_features_mbconv14_batchnorm2_running_mean) /* ty=Tensor[(192), float32] */; + %1060 = multiply(%1059, %1056) /* ty=Tensor[(192), float32] */; + %1061 = add(%1060, %efficientnet0_features_mbconv14_batchnorm2_beta) /* ty=Tensor[(192), float32] */; + %1062 = multiply(%1057, %1058) /* ty=Tensor[(1, 192, 7, 7), float32] */; + %1063 = expand_dims(%1061, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %1064 = add(%1062, %1063) /* ty=Tensor[(1, 192, 7, 7), float32] */; + %1065 = add(%1064, %999) /* ty=Tensor[(1, 192, 7, 7), float32] */; + %1066 = add(%efficientnet0_features_mbconv15_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(1152), float32] */; + %1067 = sqrt(%1066) /* ty=Tensor[(1152), float32] */; + %1068 = divide(1f /* ty=float32 */, %1067) /* ty=Tensor[(1152), float32] */; + %1069 = multiply(%1068, %efficientnet0_features_mbconv15_batchnorm0_gamma) /* ty=Tensor[(1152), float32] */; + %1070 = nn.conv2d(%1065, %efficientnet0_features_mbconv15_conv0_weight, padding=[0, 0, 0, 0], channels=1152, kernel_size=[1, 1]) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1071 = expand_dims(%1069, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1072 = negative(%efficientnet0_features_mbconv15_batchnorm0_running_mean) /* ty=Tensor[(1152), float32] */; + %1073 = multiply(%1072, %1069) /* ty=Tensor[(1152), float32] */; + %1074 = add(%1073, %efficientnet0_features_mbconv15_batchnorm0_beta) /* ty=Tensor[(1152), float32] */; + %1075 = multiply(%1070, %1071) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1076 = expand_dims(%1074, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1077 = add(%efficientnet0_features_mbconv15_batchnorm0_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(1152), float32] */; + %1078 = sqrt(%1077) /* ty=Tensor[(1152), float32] */; + %1079 = divide(1f /* ty=float32 */, %1078) /* ty=Tensor[(1152), float32] */; + %1080 = multiply(%1079, %efficientnet0_features_mbconv15_batchnorm0_gamma) /* ty=Tensor[(1152), float32] */; + %1081 = expand_dims(%1080, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1082 = negative(%efficientnet0_features_mbconv15_batchnorm0_running_mean) /* ty=Tensor[(1152), float32] */; + %1083 = multiply(%1082, %1080) /* ty=Tensor[(1152), float32] */; + %1084 = add(%1083, %efficientnet0_features_mbconv15_batchnorm0_beta) /* ty=Tensor[(1152), float32] */; + %1085 = multiply(%1070, %1081) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1086 = expand_dims(%1084, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1087 = add(%1085, %1086) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1088 = multiply(%1087, 1f /* ty=float32 */) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1089 = add(%1075, %1076) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1090 = sigmoid(%1088) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1091 = multiply(%1089, %1090) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1092 = nn.pad(%1091, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [1, 1]]) /* ty=Tensor[(1, 1152, 9, 9), float32] */; + %1093 = add(%efficientnet0_features_mbconv15_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(1152), float32] */; + %1094 = sqrt(%1093) /* ty=Tensor[(1152), float32] */; + %1095 = divide(1f /* ty=float32 */, %1094) /* ty=Tensor[(1152), float32] */; + %1096 = multiply(%1095, %efficientnet0_features_mbconv15_batchnorm1_gamma) /* ty=Tensor[(1152), float32] */; + %1097 = nn.conv2d(%1092, %efficientnet0_features_mbconv15_conv1_weight, padding=[0, 0, 0, 0], groups=1152, channels=1152, kernel_size=[3, 3]) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1098 = expand_dims(%1096, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1099 = negative(%efficientnet0_features_mbconv15_batchnorm1_running_mean) /* ty=Tensor[(1152), float32] */; + %1100 = multiply(%1099, %1096) /* ty=Tensor[(1152), float32] */; + %1101 = add(%1100, %efficientnet0_features_mbconv15_batchnorm1_beta) /* ty=Tensor[(1152), float32] */; + %1102 = multiply(%1097, %1098) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1103 = expand_dims(%1101, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1104 = add(%efficientnet0_features_mbconv15_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(1152), float32] */; + %1105 = sqrt(%1104) /* ty=Tensor[(1152), float32] */; + %1106 = divide(1f /* ty=float32 */, %1105) /* ty=Tensor[(1152), float32] */; + %1107 = multiply(%1106, %efficientnet0_features_mbconv15_batchnorm1_gamma) /* ty=Tensor[(1152), float32] */; + %1108 = expand_dims(%1107, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1109 = negative(%efficientnet0_features_mbconv15_batchnorm1_running_mean) /* ty=Tensor[(1152), float32] */; + %1110 = multiply(%1109, %1107) /* ty=Tensor[(1152), float32] */; + %1111 = add(%1110, %efficientnet0_features_mbconv15_batchnorm1_beta) /* ty=Tensor[(1152), float32] */; + %1112 = multiply(%1097, %1108) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1113 = expand_dims(%1111, axis=1, num_newaxis=2) /* ty=Tensor[(1152, 1, 1), float32] */; + %1114 = add(%1112, %1113) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1115 = multiply(%1114, 1f /* ty=float32 */) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1116 = add(%1102, %1103) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1117 = sigmoid(%1115) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1118 = multiply(%1116, %1117) /* ty=Tensor[(1, 1152, 7, 7), float32] */; + %1119 = add(%efficientnet0_features_mbconv15_batchnorm2_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(320), float32] */; + %1120 = sqrt(%1119) /* ty=Tensor[(320), float32] */; + %1121 = divide(1f /* ty=float32 */, %1120) /* ty=Tensor[(320), float32] */; + %1122 = multiply(%1121, %efficientnet0_features_mbconv15_batchnorm2_gamma) /* ty=Tensor[(320), float32] */; + %1123 = nn.conv2d(%1118, %efficientnet0_features_mbconv15_conv2_weight, padding=[0, 0, 0, 0], channels=320, kernel_size=[1, 1]) /* ty=Tensor[(1, 320, 7, 7), float32] */; + %1124 = expand_dims(%1122, axis=1, num_newaxis=2) /* ty=Tensor[(320, 1, 1), float32] */; + %1125 = negative(%efficientnet0_features_mbconv15_batchnorm2_running_mean) /* ty=Tensor[(320), float32] */; + %1126 = multiply(%1125, %1122) /* ty=Tensor[(320), float32] */; + %1127 = add(%1126, %efficientnet0_features_mbconv15_batchnorm2_beta) /* ty=Tensor[(320), float32] */; + %1128 = multiply(%1123, %1124) /* ty=Tensor[(1, 320, 7, 7), float32] */; + %1129 = expand_dims(%1127, axis=1, num_newaxis=2) /* ty=Tensor[(320, 1, 1), float32] */; + %1130 = add(%1128, %1129) /* ty=Tensor[(1, 320, 7, 7), float32] */; + %1131 = add(%efficientnet0_features_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(1280), float32] */; + %1132 = sqrt(%1131) /* ty=Tensor[(1280), float32] */; + %1133 = divide(1f /* ty=float32 */, %1132) /* ty=Tensor[(1280), float32] */; + %1134 = multiply(%1133, %efficientnet0_features_batchnorm1_gamma) /* ty=Tensor[(1280), float32] */; + %1135 = nn.conv2d(%1130, %efficientnet0_features_conv1_weight, padding=[0, 0, 0, 0], channels=1280, kernel_size=[1, 1]) /* ty=Tensor[(1, 1280, 7, 7), float32] */; + %1136 = expand_dims(%1134, axis=1, num_newaxis=2) /* ty=Tensor[(1280, 1, 1), float32] */; + %1137 = negative(%efficientnet0_features_batchnorm1_running_mean) /* ty=Tensor[(1280), float32] */; + %1138 = multiply(%1137, %1134) /* ty=Tensor[(1280), float32] */; + %1139 = add(%1138, %efficientnet0_features_batchnorm1_beta) /* ty=Tensor[(1280), float32] */; + %1140 = multiply(%1135, %1136) /* ty=Tensor[(1, 1280, 7, 7), float32] */; + %1141 = expand_dims(%1139, axis=1, num_newaxis=2) /* ty=Tensor[(1280, 1, 1), float32] */; + %1142 = add(%efficientnet0_features_batchnorm1_running_var, 0.001f /* ty=float32 */) /* ty=Tensor[(1280), float32] */; + %1143 = sqrt(%1142) /* ty=Tensor[(1280), float32] */; + %1144 = divide(1f /* ty=float32 */, %1143) /* ty=Tensor[(1280), float32] */; + %1145 = multiply(%1144, %efficientnet0_features_batchnorm1_gamma) /* ty=Tensor[(1280), float32] */; + %1146 = expand_dims(%1145, axis=1, num_newaxis=2) /* ty=Tensor[(1280, 1, 1), float32] */; + %1147 = negative(%efficientnet0_features_batchnorm1_running_mean) /* ty=Tensor[(1280), float32] */; + %1148 = multiply(%1147, %1145) /* ty=Tensor[(1280), float32] */; + %1149 = add(%1148, %efficientnet0_features_batchnorm1_beta) /* ty=Tensor[(1280), float32] */; + %1150 = multiply(%1135, %1146) /* ty=Tensor[(1, 1280, 7, 7), float32] */; + %1151 = expand_dims(%1149, axis=1, num_newaxis=2) /* ty=Tensor[(1280, 1, 1), float32] */; + %1152 = add(%1150, %1151) /* ty=Tensor[(1, 1280, 7, 7), float32] */; + %1153 = multiply(%1152, 1f /* ty=float32 */) /* ty=Tensor[(1, 1280, 7, 7), float32] */; + %1154 = add(%1140, %1141) /* ty=Tensor[(1, 1280, 7, 7), float32] */; + %1155 = sigmoid(%1153) /* ty=Tensor[(1, 1280, 7, 7), float32] */; + %1156 = multiply(%1154, %1155) /* ty=Tensor[(1, 1280, 7, 7), float32] */; + %1157 = nn.global_avg_pool2d(%1156) /* ty=Tensor[(1, 1280, 1, 1), float32] */; + %1158 = nn.conv2d(%1157, %efficientnet0_output_pred_weight, padding=[0, 0, 0, 0], channels=1000, kernel_size=[1, 1]) /* ty=Tensor[(1, 1000, 1, 1), float32] */; + nn.batch_flatten(%1158) /* ty=Tensor[(1, 1000), float32] */ +} diff --git a/tests/models/lstm-for-pldi-pattern.relay b/tests/models/lstm-for-pldi-pattern.relay new file mode 100644 index 0000000..be66924 --- /dev/null +++ b/tests/models/lstm-for-pldi-pattern.relay @@ -0,0 +1,859 @@ +#[version = "0.0.5"] +def @main(%x: Tensor[(35, 1, 128), float32], %hidden0: Tensor[(1, 1, 128), float32], %hidden1: Tensor[(1, 1, 128), float32], %rnn_weight_ih_l0: Tensor[(512, 128), float32], %rnn_weight_hh_l0: Tensor[(512, 128), float32], %rnn_bias_ih_l0: Tensor[(512), float32], %rnn_bias_hh_l0: Tensor[(512), float32]) { + %0 = split(%x, indices_or_sections=35); + %1 = %0.0; + %2 = %0.1; + %3 = %0.2; + %4 = %0.3; + %5 = %0.4; + %6 = %0.5; + %7 = %0.6; + %8 = %0.7; + %9 = %0.8; + %10 = %0.9; + %11 = %0.10; + %12 = %0.11; + %13 = %0.12; + %14 = %0.13; + %15 = %0.14; + %16 = %0.15; + %17 = %0.16; + %18 = %0.17; + %19 = %0.18; + %20 = %0.19; + %21 = %0.20; + %22 = %0.21; + %23 = %0.22; + %24 = %0.23; + %25 = %0.24; + %26 = %0.25; + %27 = %0.26; + %28 = %0.27; + %29 = %0.28; + %30 = %0.29; + %31 = %0.30; + %32 = %0.31; + %33 = %0.32; + %34 = %0.33; + %35 = %0.34; + %36 = squeeze(%1, axis=[0]); + %37 = squeeze(%2, axis=[0]); + %38 = squeeze(%3, axis=[0]); + %39 = squeeze(%4, axis=[0]); + %40 = squeeze(%5, axis=[0]); + %41 = squeeze(%6, axis=[0]); + %42 = squeeze(%7, axis=[0]); + %43 = squeeze(%8, axis=[0]); + %44 = squeeze(%9, axis=[0]); + %45 = squeeze(%10, axis=[0]); + %46 = squeeze(%11, axis=[0]); + %47 = squeeze(%12, axis=[0]); + %48 = squeeze(%13, axis=[0]); + %49 = squeeze(%14, axis=[0]); + %50 = squeeze(%15, axis=[0]); + %51 = squeeze(%16, axis=[0]); + %52 = squeeze(%17, axis=[0]); + %53 = squeeze(%18, axis=[0]); + %54 = squeeze(%19, axis=[0]); + %55 = squeeze(%20, axis=[0]); + %56 = squeeze(%21, axis=[0]); + %57 = squeeze(%22, axis=[0]); + %58 = squeeze(%23, axis=[0]); + %59 = squeeze(%24, axis=[0]); + %60 = squeeze(%25, axis=[0]); + %61 = squeeze(%26, axis=[0]); + %62 = squeeze(%27, axis=[0]); + %63 = squeeze(%28, axis=[0]); + %64 = squeeze(%29, axis=[0]); + %65 = squeeze(%30, axis=[0]); + %66 = squeeze(%31, axis=[0]); + %67 = squeeze(%32, axis=[0]); + %68 = squeeze(%33, axis=[0]); + %69 = squeeze(%34, axis=[0]); + %70 = squeeze(%35, axis=[0]); + %71 = (%36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63, %64, %65, %66, %67, %68, %69, %70); + %72 = %hidden0; + %73 = split(%72, indices_or_sections=1); + %74 = %73.0; + %75 = squeeze(%74, axis=[0]); + %76 = (%75,); + %77 = %71.0; + %78 = %76.0; + %79 = (%77, %78); + %80 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %81 = concatenate(%79, axis=1); + %82 = concatenate(%80, axis=1); + %83 = nn.dense(%81, %82, units=None); + %84 = add(%83, %rnn_bias_ih_l0); + %85 = add(%84, %rnn_bias_hh_l0); + %86 = split(%85, indices_or_sections=4, axis=-1); + %87 = %86.3; + %88 = %86.1; + %89 = %hidden1; + %90 = split(%89, indices_or_sections=1); + %91 = %90.0; + %92 = squeeze(%91, axis=[0]); + %93 = (%92,); + %94 = sigmoid(%88); + %95 = %93.0; + %96 = %86.0; + %97 = %86.2; + %98 = sigmoid(%96); + %99 = tanh(%97); + %100 = multiply(%94, %95); + %101 = multiply(%98, %99); + %102 = add(%100, %101); + %103 = sigmoid(%87); + %104 = tanh(%102); + %105 = %71.1; + %106 = multiply(%103, %104); + %107 = (%105, %106); + %108 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %109 = concatenate(%107, axis=1); + %110 = concatenate(%108, axis=1); + %111 = nn.dense(%109, %110, units=None); + %112 = add(%111, %rnn_bias_ih_l0); + %113 = add(%112, %rnn_bias_hh_l0); + %114 = split(%113, indices_or_sections=4, axis=-1); + %115 = %114.3; + %116 = %114.1; + %117 = sigmoid(%116); + %118 = %114.0; + %119 = %114.2; + %120 = sigmoid(%118); + %121 = tanh(%119); + %122 = multiply(%117, %102); + %123 = multiply(%120, %121); + %124 = add(%122, %123); + %125 = sigmoid(%115); + %126 = tanh(%124); + %127 = %71.2; + %128 = multiply(%125, %126); + %129 = (%127, %128); + %130 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %131 = concatenate(%129, axis=1); + %132 = concatenate(%130, axis=1); + %133 = nn.dense(%131, %132, units=None); + %134 = add(%133, %rnn_bias_ih_l0); + %135 = add(%134, %rnn_bias_hh_l0); + %136 = split(%135, indices_or_sections=4, axis=-1); + %137 = %136.3; + %138 = %136.1; + %139 = sigmoid(%138); + %140 = %136.0; + %141 = %136.2; + %142 = sigmoid(%140); + %143 = tanh(%141); + %144 = multiply(%139, %124); + %145 = multiply(%142, %143); + %146 = add(%144, %145); + %147 = sigmoid(%137); + %148 = tanh(%146); + %149 = %71.3; + %150 = multiply(%147, %148); + %151 = (%149, %150); + %152 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %153 = concatenate(%151, axis=1); + %154 = concatenate(%152, axis=1); + %155 = nn.dense(%153, %154, units=None); + %156 = add(%155, %rnn_bias_ih_l0); + %157 = add(%156, %rnn_bias_hh_l0); + %158 = split(%157, indices_or_sections=4, axis=-1); + %159 = %158.3; + %160 = %158.1; + %161 = sigmoid(%160); + %162 = %158.0; + %163 = %158.2; + %164 = sigmoid(%162); + %165 = tanh(%163); + %166 = multiply(%161, %146); + %167 = multiply(%164, %165); + %168 = add(%166, %167); + %169 = sigmoid(%159); + %170 = tanh(%168); + %171 = %71.4; + %172 = multiply(%169, %170); + %173 = (%171, %172); + %174 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %175 = concatenate(%173, axis=1); + %176 = concatenate(%174, axis=1); + %177 = nn.dense(%175, %176, units=None); + %178 = add(%177, %rnn_bias_ih_l0); + %179 = add(%178, %rnn_bias_hh_l0); + %180 = split(%179, indices_or_sections=4, axis=-1); + %181 = %180.3; + %182 = %180.1; + %183 = sigmoid(%182); + %184 = %180.0; + %185 = %180.2; + %186 = sigmoid(%184); + %187 = tanh(%185); + %188 = multiply(%183, %168); + %189 = multiply(%186, %187); + %190 = add(%188, %189); + %191 = sigmoid(%181); + %192 = tanh(%190); + %193 = %71.5; + %194 = multiply(%191, %192); + %195 = (%193, %194); + %196 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %197 = concatenate(%195, axis=1); + %198 = concatenate(%196, axis=1); + %199 = nn.dense(%197, %198, units=None); + %200 = add(%199, %rnn_bias_ih_l0); + %201 = add(%200, %rnn_bias_hh_l0); + %202 = split(%201, indices_or_sections=4, axis=-1); + %203 = %202.3; + %204 = %202.1; + %205 = sigmoid(%204); + %206 = %202.0; + %207 = %202.2; + %208 = sigmoid(%206); + %209 = tanh(%207); + %210 = multiply(%205, %190); + %211 = multiply(%208, %209); + %212 = add(%210, %211); + %213 = sigmoid(%203); + %214 = tanh(%212); + %215 = %71.6; + %216 = multiply(%213, %214); + %217 = (%215, %216); + %218 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %219 = concatenate(%217, axis=1); + %220 = concatenate(%218, axis=1); + %221 = nn.dense(%219, %220, units=None); + %222 = add(%221, %rnn_bias_ih_l0); + %223 = add(%222, %rnn_bias_hh_l0); + %224 = split(%223, indices_or_sections=4, axis=-1); + %225 = %224.3; + %226 = %224.1; + %227 = sigmoid(%226); + %228 = %224.0; + %229 = %224.2; + %230 = sigmoid(%228); + %231 = tanh(%229); + %232 = multiply(%227, %212); + %233 = multiply(%230, %231); + %234 = add(%232, %233); + %235 = sigmoid(%225); + %236 = tanh(%234); + %237 = %71.7; + %238 = multiply(%235, %236); + %239 = (%237, %238); + %240 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %241 = concatenate(%239, axis=1); + %242 = concatenate(%240, axis=1); + %243 = nn.dense(%241, %242, units=None); + %244 = add(%243, %rnn_bias_ih_l0); + %245 = add(%244, %rnn_bias_hh_l0); + %246 = split(%245, indices_or_sections=4, axis=-1); + %247 = %246.3; + %248 = %246.1; + %249 = sigmoid(%248); + %250 = %246.0; + %251 = %246.2; + %252 = sigmoid(%250); + %253 = tanh(%251); + %254 = multiply(%249, %234); + %255 = multiply(%252, %253); + %256 = add(%254, %255); + %257 = sigmoid(%247); + %258 = tanh(%256); + %259 = %71.8; + %260 = multiply(%257, %258); + %261 = (%259, %260); + %262 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %263 = concatenate(%261, axis=1); + %264 = concatenate(%262, axis=1); + %265 = nn.dense(%263, %264, units=None); + %266 = add(%265, %rnn_bias_ih_l0); + %267 = add(%266, %rnn_bias_hh_l0); + %268 = split(%267, indices_or_sections=4, axis=-1); + %269 = %268.3; + %270 = %268.1; + %271 = sigmoid(%270); + %272 = %268.0; + %273 = %268.2; + %274 = sigmoid(%272); + %275 = tanh(%273); + %276 = multiply(%271, %256); + %277 = multiply(%274, %275); + %278 = add(%276, %277); + %279 = sigmoid(%269); + %280 = tanh(%278); + %281 = %71.9; + %282 = multiply(%279, %280); + %283 = (%281, %282); + %284 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %285 = concatenate(%283, axis=1); + %286 = concatenate(%284, axis=1); + %287 = nn.dense(%285, %286, units=None); + %288 = add(%287, %rnn_bias_ih_l0); + %289 = add(%288, %rnn_bias_hh_l0); + %290 = split(%289, indices_or_sections=4, axis=-1); + %291 = %290.3; + %292 = %290.1; + %293 = sigmoid(%292); + %294 = %290.0; + %295 = %290.2; + %296 = sigmoid(%294); + %297 = tanh(%295); + %298 = multiply(%293, %278); + %299 = multiply(%296, %297); + %300 = add(%298, %299); + %301 = sigmoid(%291); + %302 = tanh(%300); + %303 = %71.10; + %304 = multiply(%301, %302); + %305 = (%303, %304); + %306 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %307 = concatenate(%305, axis=1); + %308 = concatenate(%306, axis=1); + %309 = nn.dense(%307, %308, units=None); + %310 = add(%309, %rnn_bias_ih_l0); + %311 = add(%310, %rnn_bias_hh_l0); + %312 = split(%311, indices_or_sections=4, axis=-1); + %313 = %312.3; + %314 = %312.1; + %315 = sigmoid(%314); + %316 = %312.0; + %317 = %312.2; + %318 = sigmoid(%316); + %319 = tanh(%317); + %320 = multiply(%315, %300); + %321 = multiply(%318, %319); + %322 = add(%320, %321); + %323 = sigmoid(%313); + %324 = tanh(%322); + %325 = %71.11; + %326 = multiply(%323, %324); + %327 = (%325, %326); + %328 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %329 = concatenate(%327, axis=1); + %330 = concatenate(%328, axis=1); + %331 = nn.dense(%329, %330, units=None); + %332 = add(%331, %rnn_bias_ih_l0); + %333 = add(%332, %rnn_bias_hh_l0); + %334 = split(%333, indices_or_sections=4, axis=-1); + %335 = %334.3; + %336 = %334.1; + %337 = sigmoid(%336); + %338 = %334.0; + %339 = %334.2; + %340 = sigmoid(%338); + %341 = tanh(%339); + %342 = multiply(%337, %322); + %343 = multiply(%340, %341); + %344 = add(%342, %343); + %345 = sigmoid(%335); + %346 = tanh(%344); + %347 = %71.12; + %348 = multiply(%345, %346); + %349 = (%347, %348); + %350 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %351 = concatenate(%349, axis=1); + %352 = concatenate(%350, axis=1); + %353 = nn.dense(%351, %352, units=None); + %354 = add(%353, %rnn_bias_ih_l0); + %355 = add(%354, %rnn_bias_hh_l0); + %356 = split(%355, indices_or_sections=4, axis=-1); + %357 = %356.3; + %358 = %356.1; + %359 = sigmoid(%358); + %360 = %356.0; + %361 = %356.2; + %362 = sigmoid(%360); + %363 = tanh(%361); + %364 = multiply(%359, %344); + %365 = multiply(%362, %363); + %366 = add(%364, %365); + %367 = sigmoid(%357); + %368 = tanh(%366); + %369 = %71.13; + %370 = multiply(%367, %368); + %371 = (%369, %370); + %372 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %373 = concatenate(%371, axis=1); + %374 = concatenate(%372, axis=1); + %375 = nn.dense(%373, %374, units=None); + %376 = add(%375, %rnn_bias_ih_l0); + %377 = add(%376, %rnn_bias_hh_l0); + %378 = split(%377, indices_or_sections=4, axis=-1); + %379 = %378.3; + %380 = %378.1; + %381 = sigmoid(%380); + %382 = %378.0; + %383 = %378.2; + %384 = sigmoid(%382); + %385 = tanh(%383); + %386 = multiply(%381, %366); + %387 = multiply(%384, %385); + %388 = add(%386, %387); + %389 = sigmoid(%379); + %390 = tanh(%388); + %391 = %71.14; + %392 = multiply(%389, %390); + %393 = (%391, %392); + %394 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %395 = concatenate(%393, axis=1); + %396 = concatenate(%394, axis=1); + %397 = nn.dense(%395, %396, units=None); + %398 = add(%397, %rnn_bias_ih_l0); + %399 = add(%398, %rnn_bias_hh_l0); + %400 = split(%399, indices_or_sections=4, axis=-1); + %401 = %400.3; + %402 = %400.1; + %403 = sigmoid(%402); + %404 = %400.0; + %405 = %400.2; + %406 = sigmoid(%404); + %407 = tanh(%405); + %408 = multiply(%403, %388); + %409 = multiply(%406, %407); + %410 = add(%408, %409); + %411 = sigmoid(%401); + %412 = tanh(%410); + %413 = %71.15; + %414 = multiply(%411, %412); + %415 = (%413, %414); + %416 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %417 = concatenate(%415, axis=1); + %418 = concatenate(%416, axis=1); + %419 = nn.dense(%417, %418, units=None); + %420 = add(%419, %rnn_bias_ih_l0); + %421 = add(%420, %rnn_bias_hh_l0); + %422 = split(%421, indices_or_sections=4, axis=-1); + %423 = %422.3; + %424 = %422.1; + %425 = sigmoid(%424); + %426 = %422.0; + %427 = %422.2; + %428 = sigmoid(%426); + %429 = tanh(%427); + %430 = multiply(%425, %410); + %431 = multiply(%428, %429); + %432 = add(%430, %431); + %433 = sigmoid(%423); + %434 = tanh(%432); + %435 = %71.16; + %436 = multiply(%433, %434); + %437 = (%435, %436); + %438 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %439 = concatenate(%437, axis=1); + %440 = concatenate(%438, axis=1); + %441 = nn.dense(%439, %440, units=None); + %442 = add(%441, %rnn_bias_ih_l0); + %443 = add(%442, %rnn_bias_hh_l0); + %444 = split(%443, indices_or_sections=4, axis=-1); + %445 = %444.3; + %446 = %444.1; + %447 = sigmoid(%446); + %448 = %444.0; + %449 = %444.2; + %450 = sigmoid(%448); + %451 = tanh(%449); + %452 = multiply(%447, %432); + %453 = multiply(%450, %451); + %454 = add(%452, %453); + %455 = sigmoid(%445); + %456 = tanh(%454); + %457 = %71.17; + %458 = multiply(%455, %456); + %459 = (%457, %458); + %460 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %461 = concatenate(%459, axis=1); + %462 = concatenate(%460, axis=1); + %463 = nn.dense(%461, %462, units=None); + %464 = add(%463, %rnn_bias_ih_l0); + %465 = add(%464, %rnn_bias_hh_l0); + %466 = split(%465, indices_or_sections=4, axis=-1); + %467 = %466.3; + %468 = %466.1; + %469 = sigmoid(%468); + %470 = %466.0; + %471 = %466.2; + %472 = sigmoid(%470); + %473 = tanh(%471); + %474 = multiply(%469, %454); + %475 = multiply(%472, %473); + %476 = add(%474, %475); + %477 = sigmoid(%467); + %478 = tanh(%476); + %479 = %71.18; + %480 = multiply(%477, %478); + %481 = (%479, %480); + %482 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %483 = concatenate(%481, axis=1); + %484 = concatenate(%482, axis=1); + %485 = nn.dense(%483, %484, units=None); + %486 = add(%485, %rnn_bias_ih_l0); + %487 = add(%486, %rnn_bias_hh_l0); + %488 = split(%487, indices_or_sections=4, axis=-1); + %489 = %488.3; + %490 = %488.1; + %491 = sigmoid(%490); + %492 = %488.0; + %493 = %488.2; + %494 = sigmoid(%492); + %495 = tanh(%493); + %496 = multiply(%491, %476); + %497 = multiply(%494, %495); + %498 = add(%496, %497); + %499 = sigmoid(%489); + %500 = tanh(%498); + %501 = %71.19; + %502 = multiply(%499, %500); + %503 = (%501, %502); + %504 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %505 = concatenate(%503, axis=1); + %506 = concatenate(%504, axis=1); + %507 = nn.dense(%505, %506, units=None); + %508 = add(%507, %rnn_bias_ih_l0); + %509 = add(%508, %rnn_bias_hh_l0); + %510 = split(%509, indices_or_sections=4, axis=-1); + %511 = %510.3; + %512 = %510.1; + %513 = sigmoid(%512); + %514 = %510.0; + %515 = %510.2; + %516 = sigmoid(%514); + %517 = tanh(%515); + %518 = multiply(%513, %498); + %519 = multiply(%516, %517); + %520 = add(%518, %519); + %521 = sigmoid(%511); + %522 = tanh(%520); + %523 = %71.20; + %524 = multiply(%521, %522); + %525 = (%523, %524); + %526 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %527 = concatenate(%525, axis=1); + %528 = concatenate(%526, axis=1); + %529 = nn.dense(%527, %528, units=None); + %530 = add(%529, %rnn_bias_ih_l0); + %531 = add(%530, %rnn_bias_hh_l0); + %532 = split(%531, indices_or_sections=4, axis=-1); + %533 = %532.3; + %534 = %532.1; + %535 = sigmoid(%534); + %536 = %532.0; + %537 = %532.2; + %538 = sigmoid(%536); + %539 = tanh(%537); + %540 = multiply(%535, %520); + %541 = multiply(%538, %539); + %542 = add(%540, %541); + %543 = sigmoid(%533); + %544 = tanh(%542); + %545 = %71.21; + %546 = multiply(%543, %544); + %547 = (%545, %546); + %548 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %549 = concatenate(%547, axis=1); + %550 = concatenate(%548, axis=1); + %551 = nn.dense(%549, %550, units=None); + %552 = add(%551, %rnn_bias_ih_l0); + %553 = add(%552, %rnn_bias_hh_l0); + %554 = split(%553, indices_or_sections=4, axis=-1); + %555 = %554.3; + %556 = %554.1; + %557 = sigmoid(%556); + %558 = %554.0; + %559 = %554.2; + %560 = sigmoid(%558); + %561 = tanh(%559); + %562 = multiply(%557, %542); + %563 = multiply(%560, %561); + %564 = add(%562, %563); + %565 = sigmoid(%555); + %566 = tanh(%564); + %567 = %71.22; + %568 = multiply(%565, %566); + %569 = (%567, %568); + %570 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %571 = concatenate(%569, axis=1); + %572 = concatenate(%570, axis=1); + %573 = nn.dense(%571, %572, units=None); + %574 = add(%573, %rnn_bias_ih_l0); + %575 = add(%574, %rnn_bias_hh_l0); + %576 = split(%575, indices_or_sections=4, axis=-1); + %577 = %576.3; + %578 = %576.1; + %579 = sigmoid(%578); + %580 = %576.0; + %581 = %576.2; + %582 = sigmoid(%580); + %583 = tanh(%581); + %584 = multiply(%579, %564); + %585 = multiply(%582, %583); + %586 = add(%584, %585); + %587 = sigmoid(%577); + %588 = tanh(%586); + %589 = %71.23; + %590 = multiply(%587, %588); + %591 = (%589, %590); + %592 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %593 = concatenate(%591, axis=1); + %594 = concatenate(%592, axis=1); + %595 = nn.dense(%593, %594, units=None); + %596 = add(%595, %rnn_bias_ih_l0); + %597 = add(%596, %rnn_bias_hh_l0); + %598 = split(%597, indices_or_sections=4, axis=-1); + %599 = %598.3; + %600 = %598.1; + %601 = sigmoid(%600); + %602 = %598.0; + %603 = %598.2; + %604 = sigmoid(%602); + %605 = tanh(%603); + %606 = multiply(%601, %586); + %607 = multiply(%604, %605); + %608 = add(%606, %607); + %609 = sigmoid(%599); + %610 = tanh(%608); + %611 = %71.24; + %612 = multiply(%609, %610); + %613 = (%611, %612); + %614 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %615 = concatenate(%613, axis=1); + %616 = concatenate(%614, axis=1); + %617 = nn.dense(%615, %616, units=None); + %618 = add(%617, %rnn_bias_ih_l0); + %619 = add(%618, %rnn_bias_hh_l0); + %620 = split(%619, indices_or_sections=4, axis=-1); + %621 = %620.3; + %622 = %620.1; + %623 = sigmoid(%622); + %624 = %620.0; + %625 = %620.2; + %626 = sigmoid(%624); + %627 = tanh(%625); + %628 = multiply(%623, %608); + %629 = multiply(%626, %627); + %630 = add(%628, %629); + %631 = sigmoid(%621); + %632 = tanh(%630); + %633 = %71.25; + %634 = multiply(%631, %632); + %635 = (%633, %634); + %636 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %637 = concatenate(%635, axis=1); + %638 = concatenate(%636, axis=1); + %639 = nn.dense(%637, %638, units=None); + %640 = add(%639, %rnn_bias_ih_l0); + %641 = add(%640, %rnn_bias_hh_l0); + %642 = split(%641, indices_or_sections=4, axis=-1); + %643 = %642.3; + %644 = %642.1; + %645 = sigmoid(%644); + %646 = %642.0; + %647 = %642.2; + %648 = sigmoid(%646); + %649 = tanh(%647); + %650 = multiply(%645, %630); + %651 = multiply(%648, %649); + %652 = add(%650, %651); + %653 = sigmoid(%643); + %654 = tanh(%652); + %655 = %71.26; + %656 = multiply(%653, %654); + %657 = (%655, %656); + %658 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %659 = concatenate(%657, axis=1); + %660 = concatenate(%658, axis=1); + %661 = nn.dense(%659, %660, units=None); + %662 = add(%661, %rnn_bias_ih_l0); + %663 = add(%662, %rnn_bias_hh_l0); + %664 = split(%663, indices_or_sections=4, axis=-1); + %665 = %664.3; + %666 = %664.1; + %667 = sigmoid(%666); + %668 = %664.0; + %669 = %664.2; + %670 = sigmoid(%668); + %671 = tanh(%669); + %672 = multiply(%667, %652); + %673 = multiply(%670, %671); + %674 = add(%672, %673); + %675 = sigmoid(%665); + %676 = tanh(%674); + %677 = %71.27; + %678 = multiply(%675, %676); + %679 = (%677, %678); + %680 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %681 = concatenate(%679, axis=1); + %682 = concatenate(%680, axis=1); + %683 = nn.dense(%681, %682, units=None); + %684 = add(%683, %rnn_bias_ih_l0); + %685 = add(%684, %rnn_bias_hh_l0); + %686 = split(%685, indices_or_sections=4, axis=-1); + %687 = %686.3; + %688 = %686.1; + %689 = sigmoid(%688); + %690 = %686.0; + %691 = %686.2; + %692 = sigmoid(%690); + %693 = tanh(%691); + %694 = multiply(%689, %674); + %695 = multiply(%692, %693); + %696 = add(%694, %695); + %697 = sigmoid(%687); + %698 = tanh(%696); + %699 = %71.28; + %700 = multiply(%697, %698); + %701 = (%699, %700); + %702 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %703 = concatenate(%701, axis=1); + %704 = concatenate(%702, axis=1); + %705 = nn.dense(%703, %704, units=None); + %706 = add(%705, %rnn_bias_ih_l0); + %707 = add(%706, %rnn_bias_hh_l0); + %708 = split(%707, indices_or_sections=4, axis=-1); + %709 = %708.3; + %710 = %708.1; + %711 = sigmoid(%710); + %712 = %708.0; + %713 = %708.2; + %714 = sigmoid(%712); + %715 = tanh(%713); + %716 = multiply(%711, %696); + %717 = multiply(%714, %715); + %718 = add(%716, %717); + %719 = sigmoid(%709); + %720 = tanh(%718); + %721 = %71.29; + %722 = multiply(%719, %720); + %723 = (%721, %722); + %724 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %725 = concatenate(%723, axis=1); + %726 = concatenate(%724, axis=1); + %727 = nn.dense(%725, %726, units=None); + %728 = add(%727, %rnn_bias_ih_l0); + %729 = add(%728, %rnn_bias_hh_l0); + %730 = split(%729, indices_or_sections=4, axis=-1); + %731 = %730.3; + %732 = %730.1; + %733 = sigmoid(%732); + %734 = %730.0; + %735 = %730.2; + %736 = sigmoid(%734); + %737 = tanh(%735); + %738 = multiply(%733, %718); + %739 = multiply(%736, %737); + %740 = add(%738, %739); + %741 = sigmoid(%731); + %742 = tanh(%740); + %743 = %71.30; + %744 = multiply(%741, %742); + %745 = (%743, %744); + %746 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %747 = concatenate(%745, axis=1); + %748 = concatenate(%746, axis=1); + %749 = nn.dense(%747, %748, units=None); + %750 = add(%749, %rnn_bias_ih_l0); + %751 = add(%750, %rnn_bias_hh_l0); + %752 = split(%751, indices_or_sections=4, axis=-1); + %753 = %752.3; + %754 = %752.1; + %755 = sigmoid(%754); + %756 = %752.0; + %757 = %752.2; + %758 = sigmoid(%756); + %759 = tanh(%757); + %760 = multiply(%755, %740); + %761 = multiply(%758, %759); + %762 = add(%760, %761); + %763 = sigmoid(%753); + %764 = tanh(%762); + %765 = %71.31; + %766 = multiply(%763, %764); + %767 = (%765, %766); + %768 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %769 = concatenate(%767, axis=1); + %770 = concatenate(%768, axis=1); + %771 = nn.dense(%769, %770, units=None); + %772 = add(%771, %rnn_bias_ih_l0); + %773 = add(%772, %rnn_bias_hh_l0); + %774 = split(%773, indices_or_sections=4, axis=-1); + %775 = %774.3; + %776 = %774.1; + %777 = sigmoid(%776); + %778 = %774.0; + %779 = %774.2; + %780 = sigmoid(%778); + %781 = tanh(%779); + %782 = multiply(%777, %762); + %783 = multiply(%780, %781); + %784 = add(%782, %783); + %785 = sigmoid(%775); + %786 = tanh(%784); + %787 = %71.32; + %788 = multiply(%785, %786); + %789 = (%787, %788); + %790 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %791 = concatenate(%789, axis=1); + %792 = concatenate(%790, axis=1); + %793 = nn.dense(%791, %792, units=None); + %794 = add(%793, %rnn_bias_ih_l0); + %795 = add(%794, %rnn_bias_hh_l0); + %796 = split(%795, indices_or_sections=4, axis=-1); + %797 = %796.3; + %798 = %796.1; + %799 = sigmoid(%798); + %800 = %796.0; + %801 = %796.2; + %802 = sigmoid(%800); + %803 = tanh(%801); + %804 = multiply(%799, %784); + %805 = multiply(%802, %803); + %806 = add(%804, %805); + %807 = sigmoid(%797); + %808 = tanh(%806); + %809 = %71.33; + %810 = multiply(%807, %808); + %811 = (%809, %810); + %812 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %813 = concatenate(%811, axis=1); + %814 = concatenate(%812, axis=1); + %815 = nn.dense(%813, %814, units=None); + %816 = add(%815, %rnn_bias_ih_l0); + %817 = add(%816, %rnn_bias_hh_l0); + %818 = split(%817, indices_or_sections=4, axis=-1); + %819 = %818.3; + %820 = %818.1; + %821 = sigmoid(%820); + %822 = %818.0; + %823 = %818.2; + %824 = sigmoid(%822); + %825 = tanh(%823); + %826 = multiply(%821, %806); + %827 = multiply(%824, %825); + %828 = add(%826, %827); + %829 = sigmoid(%819); + %830 = tanh(%828); + %831 = %71.34; + %832 = multiply(%829, %830); + %833 = (%831, %832); + %834 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %835 = concatenate(%833, axis=1); + %836 = concatenate(%834, axis=1); + %837 = nn.dense(%835, %836, units=None); + %838 = add(%837, %rnn_bias_ih_l0); + %839 = add(%838, %rnn_bias_hh_l0); + %840 = split(%839, indices_or_sections=4, axis=-1); + %841 = %840.3; + %842 = %840.1; + %843 = sigmoid(%842); + %844 = %840.0; + %845 = %840.2; + %846 = sigmoid(%844); + %847 = tanh(%845); + %848 = multiply(%843, %828); + %849 = multiply(%846, %847); + %850 = add(%848, %849); + %851 = sigmoid(%841); + %852 = tanh(%850); + %853 = multiply(%851, %852); + %854 = (%106, %128, %150, %172, %194, %216, %238, %260, %282, %304, %326, %348, %370, %392, %414, %436, %458, %480, %502, %524, %546, %568, %590, %612, %634, %656, %678, %700, %722, %744, %766, %788, %810, %832, %853); + stack(%854) +} diff --git a/tests/models/lstm-for-pldi.relay b/tests/models/lstm-for-pldi.relay new file mode 100644 index 0000000..260cd35 --- /dev/null +++ b/tests/models/lstm-for-pldi.relay @@ -0,0 +1,883 @@ +#[version = "0.0.5"] +// LSTM with the following modifications: +// We unwrap the "%hidden" argument from a tuple into two separate tensors. +// We remove the first line that casts data to int32 from int64. we just assume data is int32. +def @main(%data: Tensor[(35, 10), int32], %hidden0: Tensor[(1, 10, 128), float32], %hidden1: Tensor[(1, 10, 128), float32], %encoder_weight: Tensor[(33278, 128), float32], %rnn_weight_ih_l0: Tensor[(512, 128), float32], %rnn_weight_hh_l0: Tensor[(512, 128), float32], %rnn_bias_ih_l0: Tensor[(512), float32], %rnn_bias_hh_l0: Tensor[(512), float32], %decoder_weight: Tensor[(33278, 128), float32], %decoder_bias: Tensor[(33278), float32]) { + %1 = take(%encoder_weight, %data, axis=0); + %2 = nn.dropout(%1, rate=0.2f); + %3 = %2.0; + %4 = split(%3, indices_or_sections=35); + %5 = %4.0; + %6 = %4.1; + %7 = %4.2; + %8 = %4.3; + %9 = %4.4; + %10 = %4.5; + %11 = %4.6; + %12 = %4.7; + %13 = %4.8; + %14 = %4.9; + %15 = %4.10; + %16 = %4.11; + %17 = %4.12; + %18 = %4.13; + %19 = %4.14; + %20 = %4.15; + %21 = %4.16; + %22 = %4.17; + %23 = %4.18; + %24 = %4.19; + %25 = %4.20; + %26 = %4.21; + %27 = %4.22; + %28 = %4.23; + %29 = %4.24; + %30 = %4.25; + %31 = %4.26; + %32 = %4.27; + %33 = %4.28; + %34 = %4.29; + %35 = %4.30; + %36 = %4.31; + %37 = %4.32; + %38 = %4.33; + %39 = %4.34; + %40 = squeeze(%5, axis=[0]); + %41 = squeeze(%6, axis=[0]); + %42 = squeeze(%7, axis=[0]); + %43 = squeeze(%8, axis=[0]); + %44 = squeeze(%9, axis=[0]); + %45 = squeeze(%10, axis=[0]); + %46 = squeeze(%11, axis=[0]); + %47 = squeeze(%12, axis=[0]); + %48 = squeeze(%13, axis=[0]); + %49 = squeeze(%14, axis=[0]); + %50 = squeeze(%15, axis=[0]); + %51 = squeeze(%16, axis=[0]); + %52 = squeeze(%17, axis=[0]); + %53 = squeeze(%18, axis=[0]); + %54 = squeeze(%19, axis=[0]); + %55 = squeeze(%20, axis=[0]); + %56 = squeeze(%21, axis=[0]); + %57 = squeeze(%22, axis=[0]); + %58 = squeeze(%23, axis=[0]); + %59 = squeeze(%24, axis=[0]); + %60 = squeeze(%25, axis=[0]); + %61 = squeeze(%26, axis=[0]); + %62 = squeeze(%27, axis=[0]); + %63 = squeeze(%28, axis=[0]); + %64 = squeeze(%29, axis=[0]); + %65 = squeeze(%30, axis=[0]); + %66 = squeeze(%31, axis=[0]); + %67 = squeeze(%32, axis=[0]); + %68 = squeeze(%33, axis=[0]); + %69 = squeeze(%34, axis=[0]); + %70 = squeeze(%35, axis=[0]); + %71 = squeeze(%36, axis=[0]); + %72 = squeeze(%37, axis=[0]); + %73 = squeeze(%38, axis=[0]); + %74 = squeeze(%39, axis=[0]); + %75 = (%40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63, %64, %65, %66, %67, %68, %69, %70, %71, %72, %73, %74); + %76 = %hidden0; + %77 = split(%76, indices_or_sections=1); + %78 = %77.0; + %79 = squeeze(%78, axis=[0]); + %80 = (%79,); + %81 = %75.0; + %82 = %80.0; + %83 = (%81, %82); + %84 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %85 = concatenate(%83, axis=1); + %86 = concatenate(%84, axis=1); + %87 = nn.dense(%85, %86, units=None); + %88 = add(%87, %rnn_bias_ih_l0); + %89 = add(%88, %rnn_bias_hh_l0); + %90 = split(%89, indices_or_sections=4, axis=-1); + %91 = %90.3; + %92 = %90.1; + %93 = %hidden1; + %94 = split(%93, indices_or_sections=1); + %95 = %94.0; + %96 = squeeze(%95, axis=[0]); + %97 = (%96,); + %98 = sigmoid(%92); + %99 = %97.0; + %100 = %90.0; + %101 = %90.2; + %102 = sigmoid(%100); + %103 = tanh(%101); + %104 = multiply(%98, %99); + %105 = multiply(%102, %103); + %106 = add(%104, %105); + %107 = sigmoid(%91); + %108 = tanh(%106); + %109 = %75.1; + %110 = multiply(%107, %108); + %111 = (%109, %110); + %112 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %113 = concatenate(%111, axis=1); + %114 = concatenate(%112, axis=1); + %115 = nn.dense(%113, %114, units=None); + %116 = add(%115, %rnn_bias_ih_l0); + %117 = add(%116, %rnn_bias_hh_l0); + %118 = split(%117, indices_or_sections=4, axis=-1); + %119 = %118.3; + %120 = %118.1; + %121 = sigmoid(%120); + %122 = %118.0; + %123 = %118.2; + %124 = sigmoid(%122); + %125 = tanh(%123); + %126 = multiply(%121, %106); + %127 = multiply(%124, %125); + %128 = add(%126, %127); + %129 = sigmoid(%119); + %130 = tanh(%128); + %131 = %75.2; + %132 = multiply(%129, %130); + %133 = (%131, %132); + %134 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %135 = concatenate(%133, axis=1); + %136 = concatenate(%134, axis=1); + %137 = nn.dense(%135, %136, units=None); + %138 = add(%137, %rnn_bias_ih_l0); + %139 = add(%138, %rnn_bias_hh_l0); + %140 = split(%139, indices_or_sections=4, axis=-1); + %141 = %140.3; + %142 = %140.1; + %143 = sigmoid(%142); + %144 = %140.0; + %145 = %140.2; + %146 = sigmoid(%144); + %147 = tanh(%145); + %148 = multiply(%143, %128); + %149 = multiply(%146, %147); + %150 = add(%148, %149); + %151 = sigmoid(%141); + %152 = tanh(%150); + %153 = %75.3; + %154 = multiply(%151, %152); + %155 = (%153, %154); + %156 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %157 = concatenate(%155, axis=1); + %158 = concatenate(%156, axis=1); + %159 = nn.dense(%157, %158, units=None); + %160 = add(%159, %rnn_bias_ih_l0); + %161 = add(%160, %rnn_bias_hh_l0); + %162 = split(%161, indices_or_sections=4, axis=-1); + %163 = %162.3; + %164 = %162.1; + %165 = sigmoid(%164); + %166 = %162.0; + %167 = %162.2; + %168 = sigmoid(%166); + %169 = tanh(%167); + %170 = multiply(%165, %150); + %171 = multiply(%168, %169); + %172 = add(%170, %171); + %173 = sigmoid(%163); + %174 = tanh(%172); + %175 = %75.4; + %176 = multiply(%173, %174); + %177 = (%175, %176); + %178 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %179 = concatenate(%177, axis=1); + %180 = concatenate(%178, axis=1); + %181 = nn.dense(%179, %180, units=None); + %182 = add(%181, %rnn_bias_ih_l0); + %183 = add(%182, %rnn_bias_hh_l0); + %184 = split(%183, indices_or_sections=4, axis=-1); + %185 = %184.3; + %186 = %184.1; + %187 = sigmoid(%186); + %188 = %184.0; + %189 = %184.2; + %190 = sigmoid(%188); + %191 = tanh(%189); + %192 = multiply(%187, %172); + %193 = multiply(%190, %191); + %194 = add(%192, %193); + %195 = sigmoid(%185); + %196 = tanh(%194); + %197 = %75.5; + %198 = multiply(%195, %196); + %199 = (%197, %198); + %200 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %201 = concatenate(%199, axis=1); + %202 = concatenate(%200, axis=1); + %203 = nn.dense(%201, %202, units=None); + %204 = add(%203, %rnn_bias_ih_l0); + %205 = add(%204, %rnn_bias_hh_l0); + %206 = split(%205, indices_or_sections=4, axis=-1); + %207 = %206.3; + %208 = %206.1; + %209 = sigmoid(%208); + %210 = %206.0; + %211 = %206.2; + %212 = sigmoid(%210); + %213 = tanh(%211); + %214 = multiply(%209, %194); + %215 = multiply(%212, %213); + %216 = add(%214, %215); + %217 = sigmoid(%207); + %218 = tanh(%216); + %219 = %75.6; + %220 = multiply(%217, %218); + %221 = (%219, %220); + %222 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %223 = concatenate(%221, axis=1); + %224 = concatenate(%222, axis=1); + %225 = nn.dense(%223, %224, units=None); + %226 = add(%225, %rnn_bias_ih_l0); + %227 = add(%226, %rnn_bias_hh_l0); + %228 = split(%227, indices_or_sections=4, axis=-1); + %229 = %228.3; + %230 = %228.1; + %231 = sigmoid(%230); + %232 = %228.0; + %233 = %228.2; + %234 = sigmoid(%232); + %235 = tanh(%233); + %236 = multiply(%231, %216); + %237 = multiply(%234, %235); + %238 = add(%236, %237); + %239 = sigmoid(%229); + %240 = tanh(%238); + %241 = %75.7; + %242 = multiply(%239, %240); + %243 = (%241, %242); + %244 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %245 = concatenate(%243, axis=1); + %246 = concatenate(%244, axis=1); + %247 = nn.dense(%245, %246, units=None); + %248 = add(%247, %rnn_bias_ih_l0); + %249 = add(%248, %rnn_bias_hh_l0); + %250 = split(%249, indices_or_sections=4, axis=-1); + %251 = %250.3; + %252 = %250.1; + %253 = sigmoid(%252); + %254 = %250.0; + %255 = %250.2; + %256 = sigmoid(%254); + %257 = tanh(%255); + %258 = multiply(%253, %238); + %259 = multiply(%256, %257); + %260 = add(%258, %259); + %261 = sigmoid(%251); + %262 = tanh(%260); + %263 = %75.8; + %264 = multiply(%261, %262); + %265 = (%263, %264); + %266 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %267 = concatenate(%265, axis=1); + %268 = concatenate(%266, axis=1); + %269 = nn.dense(%267, %268, units=None); + %270 = add(%269, %rnn_bias_ih_l0); + %271 = add(%270, %rnn_bias_hh_l0); + %272 = split(%271, indices_or_sections=4, axis=-1); + %273 = %272.3; + %274 = %272.1; + %275 = sigmoid(%274); + %276 = %272.0; + %277 = %272.2; + %278 = sigmoid(%276); + %279 = tanh(%277); + %280 = multiply(%275, %260); + %281 = multiply(%278, %279); + %282 = add(%280, %281); + %283 = sigmoid(%273); + %284 = tanh(%282); + %285 = %75.9; + %286 = multiply(%283, %284); + %287 = (%285, %286); + %288 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %289 = concatenate(%287, axis=1); + %290 = concatenate(%288, axis=1); + %291 = nn.dense(%289, %290, units=None); + %292 = add(%291, %rnn_bias_ih_l0); + %293 = add(%292, %rnn_bias_hh_l0); + %294 = split(%293, indices_or_sections=4, axis=-1); + %295 = %294.3; + %296 = %294.1; + %297 = sigmoid(%296); + %298 = %294.0; + %299 = %294.2; + %300 = sigmoid(%298); + %301 = tanh(%299); + %302 = multiply(%297, %282); + %303 = multiply(%300, %301); + %304 = add(%302, %303); + %305 = sigmoid(%295); + %306 = tanh(%304); + %307 = %75.10; + %308 = multiply(%305, %306); + %309 = (%307, %308); + %310 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %311 = concatenate(%309, axis=1); + %312 = concatenate(%310, axis=1); + %313 = nn.dense(%311, %312, units=None); + %314 = add(%313, %rnn_bias_ih_l0); + %315 = add(%314, %rnn_bias_hh_l0); + %316 = split(%315, indices_or_sections=4, axis=-1); + %317 = %316.3; + %318 = %316.1; + %319 = sigmoid(%318); + %320 = %316.0; + %321 = %316.2; + %322 = sigmoid(%320); + %323 = tanh(%321); + %324 = multiply(%319, %304); + %325 = multiply(%322, %323); + %326 = add(%324, %325); + %327 = sigmoid(%317); + %328 = tanh(%326); + %329 = %75.11; + %330 = multiply(%327, %328); + %331 = (%329, %330); + %332 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %333 = concatenate(%331, axis=1); + %334 = concatenate(%332, axis=1); + %335 = nn.dense(%333, %334, units=None); + %336 = add(%335, %rnn_bias_ih_l0); + %337 = add(%336, %rnn_bias_hh_l0); + %338 = split(%337, indices_or_sections=4, axis=-1); + %339 = %338.3; + %340 = %338.1; + %341 = sigmoid(%340); + %342 = %338.0; + %343 = %338.2; + %344 = sigmoid(%342); + %345 = tanh(%343); + %346 = multiply(%341, %326); + %347 = multiply(%344, %345); + %348 = add(%346, %347); + %349 = sigmoid(%339); + %350 = tanh(%348); + %351 = %75.12; + %352 = multiply(%349, %350); + %353 = (%351, %352); + %354 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %355 = concatenate(%353, axis=1); + %356 = concatenate(%354, axis=1); + %357 = nn.dense(%355, %356, units=None); + %358 = add(%357, %rnn_bias_ih_l0); + %359 = add(%358, %rnn_bias_hh_l0); + %360 = split(%359, indices_or_sections=4, axis=-1); + %361 = %360.3; + %362 = %360.1; + %363 = sigmoid(%362); + %364 = %360.0; + %365 = %360.2; + %366 = sigmoid(%364); + %367 = tanh(%365); + %368 = multiply(%363, %348); + %369 = multiply(%366, %367); + %370 = add(%368, %369); + %371 = sigmoid(%361); + %372 = tanh(%370); + %373 = %75.13; + %374 = multiply(%371, %372); + %375 = (%373, %374); + %376 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %377 = concatenate(%375, axis=1); + %378 = concatenate(%376, axis=1); + %379 = nn.dense(%377, %378, units=None); + %380 = add(%379, %rnn_bias_ih_l0); + %381 = add(%380, %rnn_bias_hh_l0); + %382 = split(%381, indices_or_sections=4, axis=-1); + %383 = %382.3; + %384 = %382.1; + %385 = sigmoid(%384); + %386 = %382.0; + %387 = %382.2; + %388 = sigmoid(%386); + %389 = tanh(%387); + %390 = multiply(%385, %370); + %391 = multiply(%388, %389); + %392 = add(%390, %391); + %393 = sigmoid(%383); + %394 = tanh(%392); + %395 = %75.14; + %396 = multiply(%393, %394); + %397 = (%395, %396); + %398 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %399 = concatenate(%397, axis=1); + %400 = concatenate(%398, axis=1); + %401 = nn.dense(%399, %400, units=None); + %402 = add(%401, %rnn_bias_ih_l0); + %403 = add(%402, %rnn_bias_hh_l0); + %404 = split(%403, indices_or_sections=4, axis=-1); + %405 = %404.3; + %406 = %404.1; + %407 = sigmoid(%406); + %408 = %404.0; + %409 = %404.2; + %410 = sigmoid(%408); + %411 = tanh(%409); + %412 = multiply(%407, %392); + %413 = multiply(%410, %411); + %414 = add(%412, %413); + %415 = sigmoid(%405); + %416 = tanh(%414); + %417 = %75.15; + %418 = multiply(%415, %416); + %419 = (%417, %418); + %420 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %421 = concatenate(%419, axis=1); + %422 = concatenate(%420, axis=1); + %423 = nn.dense(%421, %422, units=None); + %424 = add(%423, %rnn_bias_ih_l0); + %425 = add(%424, %rnn_bias_hh_l0); + %426 = split(%425, indices_or_sections=4, axis=-1); + %427 = %426.3; + %428 = %426.1; + %429 = sigmoid(%428); + %430 = %426.0; + %431 = %426.2; + %432 = sigmoid(%430); + %433 = tanh(%431); + %434 = multiply(%429, %414); + %435 = multiply(%432, %433); + %436 = add(%434, %435); + %437 = sigmoid(%427); + %438 = tanh(%436); + %439 = %75.16; + %440 = multiply(%437, %438); + %441 = (%439, %440); + %442 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %443 = concatenate(%441, axis=1); + %444 = concatenate(%442, axis=1); + %445 = nn.dense(%443, %444, units=None); + %446 = add(%445, %rnn_bias_ih_l0); + %447 = add(%446, %rnn_bias_hh_l0); + %448 = split(%447, indices_or_sections=4, axis=-1); + %449 = %448.3; + %450 = %448.1; + %451 = sigmoid(%450); + %452 = %448.0; + %453 = %448.2; + %454 = sigmoid(%452); + %455 = tanh(%453); + %456 = multiply(%451, %436); + %457 = multiply(%454, %455); + %458 = add(%456, %457); + %459 = sigmoid(%449); + %460 = tanh(%458); + %461 = %75.17; + %462 = multiply(%459, %460); + %463 = (%461, %462); + %464 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %465 = concatenate(%463, axis=1); + %466 = concatenate(%464, axis=1); + %467 = nn.dense(%465, %466, units=None); + %468 = add(%467, %rnn_bias_ih_l0); + %469 = add(%468, %rnn_bias_hh_l0); + %470 = split(%469, indices_or_sections=4, axis=-1); + %471 = %470.3; + %472 = %470.1; + %473 = sigmoid(%472); + %474 = %470.0; + %475 = %470.2; + %476 = sigmoid(%474); + %477 = tanh(%475); + %478 = multiply(%473, %458); + %479 = multiply(%476, %477); + %480 = add(%478, %479); + %481 = sigmoid(%471); + %482 = tanh(%480); + %483 = %75.18; + %484 = multiply(%481, %482); + %485 = (%483, %484); + %486 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %487 = concatenate(%485, axis=1); + %488 = concatenate(%486, axis=1); + %489 = nn.dense(%487, %488, units=None); + %490 = add(%489, %rnn_bias_ih_l0); + %491 = add(%490, %rnn_bias_hh_l0); + %492 = split(%491, indices_or_sections=4, axis=-1); + %493 = %492.3; + %494 = %492.1; + %495 = sigmoid(%494); + %496 = %492.0; + %497 = %492.2; + %498 = sigmoid(%496); + %499 = tanh(%497); + %500 = multiply(%495, %480); + %501 = multiply(%498, %499); + %502 = add(%500, %501); + %503 = sigmoid(%493); + %504 = tanh(%502); + %505 = %75.19; + %506 = multiply(%503, %504); + %507 = (%505, %506); + %508 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %509 = concatenate(%507, axis=1); + %510 = concatenate(%508, axis=1); + %511 = nn.dense(%509, %510, units=None); + %512 = add(%511, %rnn_bias_ih_l0); + %513 = add(%512, %rnn_bias_hh_l0); + %514 = split(%513, indices_or_sections=4, axis=-1); + %515 = %514.3; + %516 = %514.1; + %517 = sigmoid(%516); + %518 = %514.0; + %519 = %514.2; + %520 = sigmoid(%518); + %521 = tanh(%519); + %522 = multiply(%517, %502); + %523 = multiply(%520, %521); + %524 = add(%522, %523); + %525 = sigmoid(%515); + %526 = tanh(%524); + %527 = %75.20; + %528 = multiply(%525, %526); + %529 = (%527, %528); + %530 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %531 = concatenate(%529, axis=1); + %532 = concatenate(%530, axis=1); + %533 = nn.dense(%531, %532, units=None); + %534 = add(%533, %rnn_bias_ih_l0); + %535 = add(%534, %rnn_bias_hh_l0); + %536 = split(%535, indices_or_sections=4, axis=-1); + %537 = %536.3; + %538 = %536.1; + %539 = sigmoid(%538); + %540 = %536.0; + %541 = %536.2; + %542 = sigmoid(%540); + %543 = tanh(%541); + %544 = multiply(%539, %524); + %545 = multiply(%542, %543); + %546 = add(%544, %545); + %547 = sigmoid(%537); + %548 = tanh(%546); + %549 = %75.21; + %550 = multiply(%547, %548); + %551 = (%549, %550); + %552 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %553 = concatenate(%551, axis=1); + %554 = concatenate(%552, axis=1); + %555 = nn.dense(%553, %554, units=None); + %556 = add(%555, %rnn_bias_ih_l0); + %557 = add(%556, %rnn_bias_hh_l0); + %558 = split(%557, indices_or_sections=4, axis=-1); + %559 = %558.3; + %560 = %558.1; + %561 = sigmoid(%560); + %562 = %558.0; + %563 = %558.2; + %564 = sigmoid(%562); + %565 = tanh(%563); + %566 = multiply(%561, %546); + %567 = multiply(%564, %565); + %568 = add(%566, %567); + %569 = sigmoid(%559); + %570 = tanh(%568); + %571 = %75.22; + %572 = multiply(%569, %570); + %573 = (%571, %572); + %574 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %575 = concatenate(%573, axis=1); + %576 = concatenate(%574, axis=1); + %577 = nn.dense(%575, %576, units=None); + %578 = add(%577, %rnn_bias_ih_l0); + %579 = add(%578, %rnn_bias_hh_l0); + %580 = split(%579, indices_or_sections=4, axis=-1); + %581 = %580.3; + %582 = %580.1; + %583 = sigmoid(%582); + %584 = %580.0; + %585 = %580.2; + %586 = sigmoid(%584); + %587 = tanh(%585); + %588 = multiply(%583, %568); + %589 = multiply(%586, %587); + %590 = add(%588, %589); + %591 = sigmoid(%581); + %592 = tanh(%590); + %593 = %75.23; + %594 = multiply(%591, %592); + %595 = (%593, %594); + %596 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %597 = concatenate(%595, axis=1); + %598 = concatenate(%596, axis=1); + %599 = nn.dense(%597, %598, units=None); + %600 = add(%599, %rnn_bias_ih_l0); + %601 = add(%600, %rnn_bias_hh_l0); + %602 = split(%601, indices_or_sections=4, axis=-1); + %603 = %602.3; + %604 = %602.1; + %605 = sigmoid(%604); + %606 = %602.0; + %607 = %602.2; + %608 = sigmoid(%606); + %609 = tanh(%607); + %610 = multiply(%605, %590); + %611 = multiply(%608, %609); + %612 = add(%610, %611); + %613 = sigmoid(%603); + %614 = tanh(%612); + %615 = %75.24; + %616 = multiply(%613, %614); + %617 = (%615, %616); + %618 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %619 = concatenate(%617, axis=1); + %620 = concatenate(%618, axis=1); + %621 = nn.dense(%619, %620, units=None); + %622 = add(%621, %rnn_bias_ih_l0); + %623 = add(%622, %rnn_bias_hh_l0); + %624 = split(%623, indices_or_sections=4, axis=-1); + %625 = %624.3; + %626 = %624.1; + %627 = sigmoid(%626); + %628 = %624.0; + %629 = %624.2; + %630 = sigmoid(%628); + %631 = tanh(%629); + %632 = multiply(%627, %612); + %633 = multiply(%630, %631); + %634 = add(%632, %633); + %635 = sigmoid(%625); + %636 = tanh(%634); + %637 = %75.25; + %638 = multiply(%635, %636); + %639 = (%637, %638); + %640 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %641 = concatenate(%639, axis=1); + %642 = concatenate(%640, axis=1); + %643 = nn.dense(%641, %642, units=None); + %644 = add(%643, %rnn_bias_ih_l0); + %645 = add(%644, %rnn_bias_hh_l0); + %646 = split(%645, indices_or_sections=4, axis=-1); + %647 = %646.3; + %648 = %646.1; + %649 = sigmoid(%648); + %650 = %646.0; + %651 = %646.2; + %652 = sigmoid(%650); + %653 = tanh(%651); + %654 = multiply(%649, %634); + %655 = multiply(%652, %653); + %656 = add(%654, %655); + %657 = sigmoid(%647); + %658 = tanh(%656); + %659 = %75.26; + %660 = multiply(%657, %658); + %661 = (%659, %660); + %662 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %663 = concatenate(%661, axis=1); + %664 = concatenate(%662, axis=1); + %665 = nn.dense(%663, %664, units=None); + %666 = add(%665, %rnn_bias_ih_l0); + %667 = add(%666, %rnn_bias_hh_l0); + %668 = split(%667, indices_or_sections=4, axis=-1); + %669 = %668.3; + %670 = %668.1; + %671 = sigmoid(%670); + %672 = %668.0; + %673 = %668.2; + %674 = sigmoid(%672); + %675 = tanh(%673); + %676 = multiply(%671, %656); + %677 = multiply(%674, %675); + %678 = add(%676, %677); + %679 = sigmoid(%669); + %680 = tanh(%678); + %681 = %75.27; + %682 = multiply(%679, %680); + %683 = (%681, %682); + %684 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %685 = concatenate(%683, axis=1); + %686 = concatenate(%684, axis=1); + %687 = nn.dense(%685, %686, units=None); + %688 = add(%687, %rnn_bias_ih_l0); + %689 = add(%688, %rnn_bias_hh_l0); + %690 = split(%689, indices_or_sections=4, axis=-1); + %691 = %690.3; + %692 = %690.1; + %693 = sigmoid(%692); + %694 = %690.0; + %695 = %690.2; + %696 = sigmoid(%694); + %697 = tanh(%695); + %698 = multiply(%693, %678); + %699 = multiply(%696, %697); + %700 = add(%698, %699); + %701 = sigmoid(%691); + %702 = tanh(%700); + %703 = %75.28; + %704 = multiply(%701, %702); + %705 = (%703, %704); + %706 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %707 = concatenate(%705, axis=1); + %708 = concatenate(%706, axis=1); + %709 = nn.dense(%707, %708, units=None); + %710 = add(%709, %rnn_bias_ih_l0); + %711 = add(%710, %rnn_bias_hh_l0); + %712 = split(%711, indices_or_sections=4, axis=-1); + %713 = %712.3; + %714 = %712.1; + %715 = sigmoid(%714); + %716 = %712.0; + %717 = %712.2; + %718 = sigmoid(%716); + %719 = tanh(%717); + %720 = multiply(%715, %700); + %721 = multiply(%718, %719); + %722 = add(%720, %721); + %723 = sigmoid(%713); + %724 = tanh(%722); + %725 = %75.29; + %726 = multiply(%723, %724); + %727 = (%725, %726); + %728 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %729 = concatenate(%727, axis=1); + %730 = concatenate(%728, axis=1); + %731 = nn.dense(%729, %730, units=None); + %732 = add(%731, %rnn_bias_ih_l0); + %733 = add(%732, %rnn_bias_hh_l0); + %734 = split(%733, indices_or_sections=4, axis=-1); + %735 = %734.3; + %736 = %734.1; + %737 = sigmoid(%736); + %738 = %734.0; + %739 = %734.2; + %740 = sigmoid(%738); + %741 = tanh(%739); + %742 = multiply(%737, %722); + %743 = multiply(%740, %741); + %744 = add(%742, %743); + %745 = sigmoid(%735); + %746 = tanh(%744); + %747 = %75.30; + %748 = multiply(%745, %746); + %749 = (%747, %748); + %750 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %751 = concatenate(%749, axis=1); + %752 = concatenate(%750, axis=1); + %753 = nn.dense(%751, %752, units=None); + %754 = add(%753, %rnn_bias_ih_l0); + %755 = add(%754, %rnn_bias_hh_l0); + %756 = split(%755, indices_or_sections=4, axis=-1); + %757 = %756.3; + %758 = %756.1; + %759 = sigmoid(%758); + %760 = %756.0; + %761 = %756.2; + %762 = sigmoid(%760); + %763 = tanh(%761); + %764 = multiply(%759, %744); + %765 = multiply(%762, %763); + %766 = add(%764, %765); + %767 = sigmoid(%757); + %768 = tanh(%766); + %769 = %75.31; + %770 = multiply(%767, %768); + %771 = (%769, %770); + %772 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %773 = concatenate(%771, axis=1); + %774 = concatenate(%772, axis=1); + %775 = nn.dense(%773, %774, units=None); + %776 = add(%775, %rnn_bias_ih_l0); + %777 = add(%776, %rnn_bias_hh_l0); + %778 = split(%777, indices_or_sections=4, axis=-1); + %779 = %778.3; + %780 = %778.1; + %781 = sigmoid(%780); + %782 = %778.0; + %783 = %778.2; + %784 = sigmoid(%782); + %785 = tanh(%783); + %786 = multiply(%781, %766); + %787 = multiply(%784, %785); + %788 = add(%786, %787); + %789 = sigmoid(%779); + %790 = tanh(%788); + %791 = %75.32; + %792 = multiply(%789, %790); + %793 = (%791, %792); + %794 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %795 = concatenate(%793, axis=1); + %796 = concatenate(%794, axis=1); + %797 = nn.dense(%795, %796, units=None); + %798 = add(%797, %rnn_bias_ih_l0); + %799 = add(%798, %rnn_bias_hh_l0); + %800 = split(%799, indices_or_sections=4, axis=-1); + %801 = %800.3; + %802 = %800.1; + %803 = sigmoid(%802); + %804 = %800.0; + %805 = %800.2; + %806 = sigmoid(%804); + %807 = tanh(%805); + %808 = multiply(%803, %788); + %809 = multiply(%806, %807); + %810 = add(%808, %809); + %811 = sigmoid(%801); + %812 = tanh(%810); + %813 = %75.33; + %814 = multiply(%811, %812); + %815 = (%813, %814); + %816 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %817 = concatenate(%815, axis=1); + %818 = concatenate(%816, axis=1); + %819 = nn.dense(%817, %818, units=None); + %820 = add(%819, %rnn_bias_ih_l0); + %821 = add(%820, %rnn_bias_hh_l0); + %822 = split(%821, indices_or_sections=4, axis=-1); + %823 = %822.3; + %824 = %822.1; + %825 = sigmoid(%824); + %826 = %822.0; + %827 = %822.2; + %828 = sigmoid(%826); + %829 = tanh(%827); + %830 = multiply(%825, %810); + %831 = multiply(%828, %829); + %832 = add(%830, %831); + %833 = sigmoid(%823); + %834 = tanh(%832); + %835 = %75.34; + %836 = multiply(%833, %834); + %837 = (%835, %836); + %838 = (%rnn_weight_ih_l0, %rnn_weight_hh_l0); + %839 = concatenate(%837, axis=1); + %840 = concatenate(%838, axis=1); + %841 = nn.dense(%839, %840, units=None); + %842 = add(%841, %rnn_bias_ih_l0); + %843 = add(%842, %rnn_bias_hh_l0); + %844 = split(%843, indices_or_sections=4, axis=-1); + %845 = %844.3; + %846 = %844.1; + %847 = sigmoid(%846); + %848 = %844.0; + %849 = %844.2; + %850 = sigmoid(%848); + %851 = tanh(%849); + %852 = multiply(%847, %832); + %853 = multiply(%850, %851); + %854 = add(%852, %853); + %855 = sigmoid(%845); + %856 = tanh(%854); + %857 = multiply(%855, %856); + %858 = (%110, %132, %154, %176, %198, %220, %242, %264, %286, %308, %330, %352, %374, %396, %418, %440, %462, %484, %506, %528, %550, %572, %594, %616, %638, %660, %682, %704, %726, %748, %770, %792, %814, %836, %857); + %859 = stack(%858); + %860 = (); + %861 = (); + %862 = (%859, %860, %861); + %863 = %862.0; + %864 = nn.dropout(%863, rate=0.2f); + %865 = %864.0; + %866 = transpose(%decoder_weight, axes=[1, 0]); + %867 = reshape(%865, newshape=[-1, 128]); + %868 = transpose(%866, axes=[1, 0]); + %869 = nn.dense(%867, %868, units=None); + %870 = reshape(%869, newshape=[35, 10, 33278]); + %871 = add(%870, %decoder_bias); + %872 = reshape(%871, newshape=[-1, 33278]); + %873 = %862.1; + %874 = %862.2; + %875 = nn.log_softmax(%872, axis=1); + %876 = (%873, %874); + (%875, %876) +} \ No newline at end of file diff --git a/tests/models/lstm_s2t.py b/tests/models/lstm_s2t.py new file mode 100644 index 0000000..53de1dd --- /dev/null +++ b/tests/models/lstm_s2t.py @@ -0,0 +1,127 @@ +""" +LSTM definition taken from +https://github.com/uwsampl/3la-tvm/blob/3la-pldi-push-main/tests/python/byo3la/end_to_end_speech_to_text.py +""" +import tvm +from tvm import relay +import numpy as np + +def relay_lstm_cell(batch_size, input_size, hidden_size): + # based on https://pytorch.org/docs/stable/generated/torch.nn.GRU.html#torch.nn.GRU + state_tensor_type = relay.TensorType((batch_size, hidden_size)) + state_tuple_type = relay.TupleType([state_tensor_type, state_tensor_type]) + + inp = relay.var("input", shape=(batch_size, input_size)) + state = relay.Var("state", type_annotation=state_tuple_type) + + w_ih = relay.var("w_ih", shape=(4*hidden_size, input_size)) + w_hh = relay.var("w_hh", shape=(4*hidden_size, hidden_size)) + b_ih = relay.var("b_ih", shape=(4*hidden_size,)) + b_hh = relay.var("b_hh", shape=(4*hidden_size,)) + + hidden = relay.TupleGetItem(state, 0) + cell_state = relay.TupleGetItem(state, 1) + + # PyTorch packs the i2h and h2h weights and biases together so we will match that here + w_i_splits = relay.split(w_ih, 4, 0) + w_h_splits = relay.split(w_hh, 4, 0) + b_i_splits = relay.split(b_ih, 4, 0) + b_h_splits = relay.split(b_hh, 4, 0) + w_ii, w_if, w_ig, w_io = w_i_splits[0], w_i_splits[1], w_i_splits[2], w_i_splits[3] + w_hi, w_hf, w_hg, w_ho = w_h_splits[0], w_h_splits[1], w_h_splits[2], w_h_splits[3] + b_ii, b_if, b_ig, b_io = b_i_splits[0], b_i_splits[1], b_i_splits[2], b_i_splits[3] + b_hi, b_hf, b_hg, b_ho = b_h_splits[0], b_h_splits[1], b_h_splits[2], b_h_splits[3] + + def weighted_value(weight, value, bias): + return relay.transpose(relay.nn.dense(weight, value) + relay.reshape(bias, (hidden_size, 1))) + + i_t = relay.sigmoid(weighted_value(w_ii, inp, b_ii) + weighted_value(w_hi, hidden, b_hi)) + f_t = relay.sigmoid(weighted_value(w_if, inp, b_if) + weighted_value(w_hf, hidden, b_hf)) + g_t = relay.tanh(weighted_value(w_ig, inp, b_ig) + weighted_value(w_hg, hidden, b_hg)) + o_t = relay.sigmoid(weighted_value(w_io, inp, b_io) + weighted_value(w_ho, hidden, b_ho)) + c_t = f_t*cell_state + i_t*g_t + h_t = o_t*relay.tanh(c_t) + + h_var = relay.Var("h") + c_var = relay.Var("c") + return relay.Function([inp, state, w_ih, w_hh, b_ih, b_hh], + relay.Let(h_var, h_t, + relay.Let(c_var, c_t, + relay.Tuple([h_var, relay.Tuple([h_var, c_var])]))), + ret_type=relay.TupleType([state_tensor_type, state_tuple_type])) + + +def lstm_body(data, state, i2h_weight, h2h_weight, i2h_bias, h2h_bias, + batch_size, input_size, hidden_size, time_steps, time_axis=1): + builder = relay.ScopeBuilder() + cell = builder.let("lstm_cell", relay_lstm_cell(batch_size, input_size, hidden_size)) + splits = builder.let("splits", relay.split(data, time_steps, time_axis).astuple()) + last_state = state + seq_outs = [] + for i in range(time_steps): + squeezed = builder.let(f"squeezed_{i}", relay.squeeze(relay.TupleGetItem(splits, i), axis=[time_axis])) + cell_out = builder.let(f"cell_out_{i}", + cell(squeezed, last_state, + i2h_weight, h2h_weight, + i2h_bias, i2h_bias)) + new_seq_out = builder.let(f"seq_out_{i}", relay.TupleGetItem(cell_out, 0)) + seq_outs.append(new_seq_out) + new_hidden = builder.let(f"state_update_{i}", relay.TupleGetItem(cell_out, 1)) + last_state = new_hidden + + stacked = builder.let("stacked", relay.stack(seq_outs, axis=time_axis)) + # builder.ret(relay.Tuple([stacked, reshape_hidden, reshape_cell])) + builder.ret(relay.Tuple([stacked])) + return builder.get() + + +# Warning! This is an unrolled RNN! If you want a truly dynamic RNN, +# you should define it using a list ADT and apply the LSTM cell recursively. +# We can easily do that, though note that interacting +# with the ADT objects in the BYOC codegen would be tricky +def lstm_definition(batch_size, input_size, hidden_size, time_steps, + time_axis=1): + """ + Wrap the LSTM body in a function + """ + state_tensor_type = relay.TensorType((batch_size, hidden_size)) + state_tuple_type = relay.TupleType([state_tensor_type, state_tensor_type]) + + input_var = relay.var("input", shape=(batch_size, time_steps, input_size)) + state_var = relay.var("state", type_annotation=state_tuple_type) + i2h_weight_var = relay.var("i2h_weight", shape=(4*hidden_size, input_size)) + h2h_weight_var = relay.var("h2h_weight", shape=(4*hidden_size, hidden_size)) + i2h_bias_var = relay.var("i2h_bias", shape=(4*hidden_size,)) + h2h_bias_var = relay.var("h2h_bias", shape=(4*hidden_size,)) + + ret_type = relay.TupleType([ + relay.TensorType((batch_size, time_steps, hidden_size)), + relay.TensorType((1, batch_size, hidden_size)), + relay.TensorType((1, batch_size, hidden_size)) + ]) + + return relay.Function( + [input_var, state_var, i2h_weight_var, h2h_weight_var, + i2h_bias_var, h2h_bias_var], + lstm_body(input_var, state_var, + i2h_weight_var, h2h_weight_var, i2h_bias_var, h2h_bias_var, + batch_size, input_size, hidden_size, time_steps, time_axis=time_axis), + ret_type=ret_type) + +def get_lstm_pattern(batch_size, input_size, hidden_size, time_steps): + lstm_pattern = lstm_definition(batch_size, input_size, hidden_size, time_steps).body + mod = tvm.ir.IRModule.from_expr(lstm_pattern) + mod = relay.transform.SimplifyInference()(mod) + with open('lstm_pattern.relay', 'w') as fp: + fp.write(mod.astext()) + print('Pattern written to lstm_pattern.relay') + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--batch', type=int, required=True) + parser.add_argument('--input_size', type=int, required=True) + parser.add_argument('--hidden_size', type=int, required=True) + parser.add_argument('--time_steps', type=int, required=True) + args = parser.parse_args() + get_lstm_pattern(args.batch, args.input_size, args.hidden_size, args.time_steps) diff --git a/tests/max_pool2d.py b/tests/models/max_pool2d.py similarity index 87% rename from tests/max_pool2d.py rename to tests/models/max_pool2d.py index f472b88..380076d 100644 --- a/tests/max_pool2d.py +++ b/tests/models/max_pool2d.py @@ -7,7 +7,7 @@ def main(b, c, h, w, kh, kw): result = relay.nn.max_pool2d(data, [kh, kw]) mod = tvm.ir.IRModule.from_expr(result) mod = relay.transform.InferType()(mod) - with open('./models/max_pool2d.relay', 'w') as fp: + with open('max_pool2d.relay', 'w') as fp: fp.write(mod.astext()) if __name__ == '__main__': @@ -17,7 +17,7 @@ def main(b, c, h, w, kh, kw): parser.add_argument('h', type=int, help='height') parser.add_argument('w', type=int, help='width') parser.add_argument('kh', type=int, help='window height') - parser.add_argument('kh', type=int, help='window width') + parser.add_argument('kw', type=int, help='window width') args = parser.parse_args() main(args.b, args.c, args.h, args.w, args.kh, args.kw) diff --git a/tests/models/max_pool2d.relay b/tests/models/max_pool2d.relay new file mode 100644 index 0000000..b20ff82 --- /dev/null +++ b/tests/models/max_pool2d.relay @@ -0,0 +1,4 @@ +#[version = "0.0.5"] +def @main(%data: Tensor[(5, 7, 256, 256), float32]) -> Tensor[(5, 7, 241, 241), float32] { + nn.max_pool2d(%data, pool_size=[16, 16], padding=[0, 0, 0, 0]) /* ty=Tensor[(5, 7, 241, 241), float32] */ +} diff --git a/tests/models/mobilenet.py b/tests/models/mobilenet.py new file mode 100644 index 0000000..171d918 --- /dev/null +++ b/tests/models/mobilenet.py @@ -0,0 +1,14 @@ +import tvm +import sys +import tvm.relay +import tvm.relay.testing + +def main(batch, num_classes, image_shape=(3, 32, 32)): + mod, _ = tvm.relay.testing.mobilenet.get_workload(batch, num_classes, image_shape=image_shape) + mod = tvm.relay.transform.InferType()(mod) + mod = tvm.relay.transform.SimplifyInference()(mod) + with open('mobilenet.relay', 'w') as fp: + fp.write(mod.astext()) + +if __name__ == '__main__': + main(int(sys.argv[1]), int(sys.argv[2])) \ No newline at end of file diff --git a/tests/models/mobilenet.relay b/tests/models/mobilenet.relay new file mode 100644 index 0000000..2e49453 --- /dev/null +++ b/tests/models/mobilenet.relay @@ -0,0 +1,359 @@ +#[version = "0.0.5"] +def @main(%data: Tensor[(1, 3, 32, 32), float32], %conv_block_1_conv_weight: Tensor[(32, 3, 3, 3), float32], %conv_block_1_bn_gamma: Tensor[(32), float32], %conv_block_1_bn_beta: Tensor[(32), float32], %conv_block_1_bn_moving_mean: Tensor[(32), float32], %conv_block_1_bn_moving_var: Tensor[(32), float32], %separable_conv_block_1_weight: Tensor[(32, 1, 3, 3), float32], %separable_conv_block_1_bn1_gamma: Tensor[(32), float32], %separable_conv_block_1_bn1_beta: Tensor[(32), float32], %separable_conv_block_1_bn1_moving_mean: Tensor[(32), float32], %separable_conv_block_1_bn1_moving_var: Tensor[(32), float32], %separable_conv_block_1_conv2_weight: Tensor[(64, 32, 1, 1), float32], %separable_conv_block_1_bn2_gamma: Tensor[(64), float32], %separable_conv_block_1_bn2_beta: Tensor[(64), float32], %separable_conv_block_1_bn2_moving_mean: Tensor[(64), float32], %separable_conv_block_1_bn2_moving_var: Tensor[(64), float32], %separable_conv_block_2_weight: Tensor[(64, 1, 3, 3), float32], %separable_conv_block_2_bn1_gamma: Tensor[(64), float32], %separable_conv_block_2_bn1_beta: Tensor[(64), float32], %separable_conv_block_2_bn1_moving_mean: Tensor[(64), float32], %separable_conv_block_2_bn1_moving_var: Tensor[(64), float32], %separable_conv_block_2_conv2_weight: Tensor[(128, 64, 1, 1), float32], %separable_conv_block_2_bn2_gamma: Tensor[(128), float32], %separable_conv_block_2_bn2_beta: Tensor[(128), float32], %separable_conv_block_2_bn2_moving_mean: Tensor[(128), float32], %separable_conv_block_2_bn2_moving_var: Tensor[(128), float32], %separable_conv_block_3_weight: Tensor[(128, 1, 3, 3), float32], %separable_conv_block_3_bn1_gamma: Tensor[(128), float32], %separable_conv_block_3_bn1_beta: Tensor[(128), float32], %separable_conv_block_3_bn1_moving_mean: Tensor[(128), float32], %separable_conv_block_3_bn1_moving_var: Tensor[(128), float32], %separable_conv_block_3_conv2_weight: Tensor[(128, 128, 1, 1), float32], %separable_conv_block_3_bn2_gamma: Tensor[(128), float32], %separable_conv_block_3_bn2_beta: Tensor[(128), float32], %separable_conv_block_3_bn2_moving_mean: Tensor[(128), float32], %separable_conv_block_3_bn2_moving_var: Tensor[(128), float32], %separable_conv_block_4_weight: Tensor[(128, 1, 3, 3), float32], %separable_conv_block_4_bn1_gamma: Tensor[(128), float32], %separable_conv_block_4_bn1_beta: Tensor[(128), float32], %separable_conv_block_4_bn1_moving_mean: Tensor[(128), float32], %separable_conv_block_4_bn1_moving_var: Tensor[(128), float32], %separable_conv_block_4_conv2_weight: Tensor[(256, 128, 1, 1), float32], %separable_conv_block_4_bn2_gamma: Tensor[(256), float32], %separable_conv_block_4_bn2_beta: Tensor[(256), float32], %separable_conv_block_4_bn2_moving_mean: Tensor[(256), float32], %separable_conv_block_4_bn2_moving_var: Tensor[(256), float32], %separable_conv_block_5_weight: Tensor[(256, 1, 3, 3), float32], %separable_conv_block_5_bn1_gamma: Tensor[(256), float32], %separable_conv_block_5_bn1_beta: Tensor[(256), float32], %separable_conv_block_5_bn1_moving_mean: Tensor[(256), float32], %separable_conv_block_5_bn1_moving_var: Tensor[(256), float32], %separable_conv_block_5_conv2_weight: Tensor[(256, 256, 1, 1), float32], %separable_conv_block_5_bn2_gamma: Tensor[(256), float32], %separable_conv_block_5_bn2_beta: Tensor[(256), float32], %separable_conv_block_5_bn2_moving_mean: Tensor[(256), float32], %separable_conv_block_5_bn2_moving_var: Tensor[(256), float32], %separable_conv_block_6_weight: Tensor[(256, 1, 3, 3), float32], %separable_conv_block_6_bn1_gamma: Tensor[(256), float32], %separable_conv_block_6_bn1_beta: Tensor[(256), float32], %separable_conv_block_6_bn1_moving_mean: Tensor[(256), float32], %separable_conv_block_6_bn1_moving_var: Tensor[(256), float32], %separable_conv_block_6_conv2_weight: Tensor[(512, 256, 1, 1), float32], %separable_conv_block_6_bn2_gamma: Tensor[(512), float32], %separable_conv_block_6_bn2_beta: Tensor[(512), float32], %separable_conv_block_6_bn2_moving_mean: Tensor[(512), float32], %separable_conv_block_6_bn2_moving_var: Tensor[(512), float32], %separable_conv_block_7_weight: Tensor[(512, 1, 3, 3), float32], %separable_conv_block_7_bn1_gamma: Tensor[(512), float32], %separable_conv_block_7_bn1_beta: Tensor[(512), float32], %separable_conv_block_7_bn1_moving_mean: Tensor[(512), float32], %separable_conv_block_7_bn1_moving_var: Tensor[(512), float32], %separable_conv_block_7_conv2_weight: Tensor[(512, 512, 1, 1), float32], %separable_conv_block_7_bn2_gamma: Tensor[(512), float32], %separable_conv_block_7_bn2_beta: Tensor[(512), float32], %separable_conv_block_7_bn2_moving_mean: Tensor[(512), float32], %separable_conv_block_7_bn2_moving_var: Tensor[(512), float32], %separable_conv_block_8_weight: Tensor[(512, 1, 3, 3), float32], %separable_conv_block_8_bn1_gamma: Tensor[(512), float32], %separable_conv_block_8_bn1_beta: Tensor[(512), float32], %separable_conv_block_8_bn1_moving_mean: Tensor[(512), float32], %separable_conv_block_8_bn1_moving_var: Tensor[(512), float32], %separable_conv_block_8_conv2_weight: Tensor[(512, 512, 1, 1), float32], %separable_conv_block_8_bn2_gamma: Tensor[(512), float32], %separable_conv_block_8_bn2_beta: Tensor[(512), float32], %separable_conv_block_8_bn2_moving_mean: Tensor[(512), float32], %separable_conv_block_8_bn2_moving_var: Tensor[(512), float32], %separable_conv_block_9_weight: Tensor[(512, 1, 3, 3), float32], %separable_conv_block_9_bn1_gamma: Tensor[(512), float32], %separable_conv_block_9_bn1_beta: Tensor[(512), float32], %separable_conv_block_9_bn1_moving_mean: Tensor[(512), float32], %separable_conv_block_9_bn1_moving_var: Tensor[(512), float32], %separable_conv_block_9_conv2_weight: Tensor[(512, 512, 1, 1), float32], %separable_conv_block_9_bn2_gamma: Tensor[(512), float32], %separable_conv_block_9_bn2_beta: Tensor[(512), float32], %separable_conv_block_9_bn2_moving_mean: Tensor[(512), float32], %separable_conv_block_9_bn2_moving_var: Tensor[(512), float32], %separable_conv_block_10_weight: Tensor[(512, 1, 3, 3), float32], %separable_conv_block_10_bn1_gamma: Tensor[(512), float32], %separable_conv_block_10_bn1_beta: Tensor[(512), float32], %separable_conv_block_10_bn1_moving_mean: Tensor[(512), float32], %separable_conv_block_10_bn1_moving_var: Tensor[(512), float32], %separable_conv_block_10_conv2_weight: Tensor[(512, 512, 1, 1), float32], %separable_conv_block_10_bn2_gamma: Tensor[(512), float32], %separable_conv_block_10_bn2_beta: Tensor[(512), float32], %separable_conv_block_10_bn2_moving_mean: Tensor[(512), float32], %separable_conv_block_10_bn2_moving_var: Tensor[(512), float32], %separable_conv_block_11_weight: Tensor[(512, 1, 3, 3), float32], %separable_conv_block_11_bn1_gamma: Tensor[(512), float32], %separable_conv_block_11_bn1_beta: Tensor[(512), float32], %separable_conv_block_11_bn1_moving_mean: Tensor[(512), float32], %separable_conv_block_11_bn1_moving_var: Tensor[(512), float32], %separable_conv_block_11_conv2_weight: Tensor[(512, 512, 1, 1), float32], %separable_conv_block_11_bn2_gamma: Tensor[(512), float32], %separable_conv_block_11_bn2_beta: Tensor[(512), float32], %separable_conv_block_11_bn2_moving_mean: Tensor[(512), float32], %separable_conv_block_11_bn2_moving_var: Tensor[(512), float32], %separable_conv_block_12_weight: Tensor[(512, 1, 3, 3), float32], %separable_conv_block_12_bn1_gamma: Tensor[(512), float32], %separable_conv_block_12_bn1_beta: Tensor[(512), float32], %separable_conv_block_12_bn1_moving_mean: Tensor[(512), float32], %separable_conv_block_12_bn1_moving_var: Tensor[(512), float32], %separable_conv_block_12_conv2_weight: Tensor[(1024, 512, 1, 1), float32], %separable_conv_block_12_bn2_gamma: Tensor[(1024), float32], %separable_conv_block_12_bn2_beta: Tensor[(1024), float32], %separable_conv_block_12_bn2_moving_mean: Tensor[(1024), float32], %separable_conv_block_12_bn2_moving_var: Tensor[(1024), float32], %separable_conv_block_13_weight: Tensor[(1024, 1, 3, 3), float32], %separable_conv_block_13_bn1_gamma: Tensor[(1024), float32], %separable_conv_block_13_bn1_beta: Tensor[(1024), float32], %separable_conv_block_13_bn1_moving_mean: Tensor[(1024), float32], %separable_conv_block_13_bn1_moving_var: Tensor[(1024), float32], %separable_conv_block_13_conv2_weight: Tensor[(1024, 1024, 1, 1), float32], %separable_conv_block_13_bn2_gamma: Tensor[(1024), float32], %separable_conv_block_13_bn2_beta: Tensor[(1024), float32], %separable_conv_block_13_bn2_moving_mean: Tensor[(1024), float32], %separable_conv_block_13_bn2_moving_var: Tensor[(1024), float32], %fc_weight: Tensor[(1000, 1024), float32], %fc_bias: Tensor[(1000), float32]) -> Tensor[(1, 1000), float32] { + %0 = add(%conv_block_1_bn_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %1 = sqrt(%0) /* ty=Tensor[(32), float32] */; + %2 = divide(1f /* ty=float32 */, %1) /* ty=Tensor[(32), float32] */; + %3 = multiply(%2, %conv_block_1_bn_gamma) /* ty=Tensor[(32), float32] */; + %4 = nn.conv2d(%data, %conv_block_1_conv_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %5 = expand_dims(%3, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %6 = negative(%conv_block_1_bn_moving_mean) /* ty=Tensor[(32), float32] */; + %7 = multiply(%6, %3) /* ty=Tensor[(32), float32] */; + %8 = add(%7, %conv_block_1_bn_beta) /* ty=Tensor[(32), float32] */; + %9 = multiply(%4, %5) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %10 = expand_dims(%8, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %11 = add(%9, %10) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %12 = nn.relu(%11) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %13 = add(%separable_conv_block_1_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %14 = sqrt(%13) /* ty=Tensor[(32), float32] */; + %15 = divide(1f /* ty=float32 */, %14) /* ty=Tensor[(32), float32] */; + %16 = multiply(%15, %separable_conv_block_1_bn1_gamma) /* ty=Tensor[(32), float32] */; + %17 = nn.conv2d(%12, %separable_conv_block_1_weight, padding=[1, 1, 1, 1], groups=32, channels=32, kernel_size=[3, 3]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %18 = expand_dims(%16, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %19 = negative(%separable_conv_block_1_bn1_moving_mean) /* ty=Tensor[(32), float32] */; + %20 = multiply(%19, %16) /* ty=Tensor[(32), float32] */; + %21 = add(%20, %separable_conv_block_1_bn1_beta) /* ty=Tensor[(32), float32] */; + %22 = multiply(%17, %18) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %23 = expand_dims(%21, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %24 = add(%22, %23) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %25 = nn.relu(%24) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %26 = add(%separable_conv_block_1_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %27 = sqrt(%26) /* ty=Tensor[(64), float32] */; + %28 = divide(1f /* ty=float32 */, %27) /* ty=Tensor[(64), float32] */; + %29 = multiply(%28, %separable_conv_block_1_bn2_gamma) /* ty=Tensor[(64), float32] */; + %30 = nn.conv2d(%25, %separable_conv_block_1_conv2_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 16, 16), float32] */; + %31 = expand_dims(%29, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %32 = negative(%separable_conv_block_1_bn2_moving_mean) /* ty=Tensor[(64), float32] */; + %33 = multiply(%32, %29) /* ty=Tensor[(64), float32] */; + %34 = add(%33, %separable_conv_block_1_bn2_beta) /* ty=Tensor[(64), float32] */; + %35 = multiply(%30, %31) /* ty=Tensor[(1, 64, 16, 16), float32] */; + %36 = expand_dims(%34, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %37 = add(%35, %36) /* ty=Tensor[(1, 64, 16, 16), float32] */; + %38 = nn.relu(%37) /* ty=Tensor[(1, 64, 16, 16), float32] */; + %39 = add(%separable_conv_block_2_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %40 = sqrt(%39) /* ty=Tensor[(64), float32] */; + %41 = divide(1f /* ty=float32 */, %40) /* ty=Tensor[(64), float32] */; + %42 = multiply(%41, %separable_conv_block_2_bn1_gamma) /* ty=Tensor[(64), float32] */; + %43 = nn.conv2d(%38, %separable_conv_block_2_weight, strides=[2, 2], padding=[1, 1, 1, 1], groups=64, channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %44 = expand_dims(%42, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %45 = negative(%separable_conv_block_2_bn1_moving_mean) /* ty=Tensor[(64), float32] */; + %46 = multiply(%45, %42) /* ty=Tensor[(64), float32] */; + %47 = add(%46, %separable_conv_block_2_bn1_beta) /* ty=Tensor[(64), float32] */; + %48 = multiply(%43, %44) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %49 = expand_dims(%47, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %50 = add(%48, %49) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %51 = nn.relu(%50) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %52 = add(%separable_conv_block_2_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %53 = sqrt(%52) /* ty=Tensor[(128), float32] */; + %54 = divide(1f /* ty=float32 */, %53) /* ty=Tensor[(128), float32] */; + %55 = multiply(%54, %separable_conv_block_2_bn2_gamma) /* ty=Tensor[(128), float32] */; + %56 = nn.conv2d(%51, %separable_conv_block_2_conv2_weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %57 = expand_dims(%55, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %58 = negative(%separable_conv_block_2_bn2_moving_mean) /* ty=Tensor[(128), float32] */; + %59 = multiply(%58, %55) /* ty=Tensor[(128), float32] */; + %60 = add(%59, %separable_conv_block_2_bn2_beta) /* ty=Tensor[(128), float32] */; + %61 = multiply(%56, %57) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %62 = expand_dims(%60, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %63 = add(%61, %62) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %64 = nn.relu(%63) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %65 = add(%separable_conv_block_3_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %66 = sqrt(%65) /* ty=Tensor[(128), float32] */; + %67 = divide(1f /* ty=float32 */, %66) /* ty=Tensor[(128), float32] */; + %68 = multiply(%67, %separable_conv_block_3_bn1_gamma) /* ty=Tensor[(128), float32] */; + %69 = nn.conv2d(%64, %separable_conv_block_3_weight, padding=[1, 1, 1, 1], groups=128, channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %70 = expand_dims(%68, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %71 = negative(%separable_conv_block_3_bn1_moving_mean) /* ty=Tensor[(128), float32] */; + %72 = multiply(%71, %68) /* ty=Tensor[(128), float32] */; + %73 = add(%72, %separable_conv_block_3_bn1_beta) /* ty=Tensor[(128), float32] */; + %74 = multiply(%69, %70) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %75 = expand_dims(%73, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %76 = add(%74, %75) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %77 = nn.relu(%76) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %78 = add(%separable_conv_block_3_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %79 = sqrt(%78) /* ty=Tensor[(128), float32] */; + %80 = divide(1f /* ty=float32 */, %79) /* ty=Tensor[(128), float32] */; + %81 = multiply(%80, %separable_conv_block_3_bn2_gamma) /* ty=Tensor[(128), float32] */; + %82 = nn.conv2d(%77, %separable_conv_block_3_conv2_weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %83 = expand_dims(%81, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %84 = negative(%separable_conv_block_3_bn2_moving_mean) /* ty=Tensor[(128), float32] */; + %85 = multiply(%84, %81) /* ty=Tensor[(128), float32] */; + %86 = add(%85, %separable_conv_block_3_bn2_beta) /* ty=Tensor[(128), float32] */; + %87 = multiply(%82, %83) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %88 = expand_dims(%86, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %89 = add(%87, %88) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %90 = nn.relu(%89) /* ty=Tensor[(1, 128, 8, 8), float32] */; + %91 = add(%separable_conv_block_4_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %92 = sqrt(%91) /* ty=Tensor[(128), float32] */; + %93 = divide(1f /* ty=float32 */, %92) /* ty=Tensor[(128), float32] */; + %94 = multiply(%93, %separable_conv_block_4_bn1_gamma) /* ty=Tensor[(128), float32] */; + %95 = nn.conv2d(%90, %separable_conv_block_4_weight, strides=[2, 2], padding=[1, 1, 1, 1], groups=128, channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 4, 4), float32] */; + %96 = expand_dims(%94, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %97 = negative(%separable_conv_block_4_bn1_moving_mean) /* ty=Tensor[(128), float32] */; + %98 = multiply(%97, %94) /* ty=Tensor[(128), float32] */; + %99 = add(%98, %separable_conv_block_4_bn1_beta) /* ty=Tensor[(128), float32] */; + %100 = multiply(%95, %96) /* ty=Tensor[(1, 128, 4, 4), float32] */; + %101 = expand_dims(%99, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %102 = add(%100, %101) /* ty=Tensor[(1, 128, 4, 4), float32] */; + %103 = nn.relu(%102) /* ty=Tensor[(1, 128, 4, 4), float32] */; + %104 = add(%separable_conv_block_4_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %105 = sqrt(%104) /* ty=Tensor[(256), float32] */; + %106 = divide(1f /* ty=float32 */, %105) /* ty=Tensor[(256), float32] */; + %107 = multiply(%106, %separable_conv_block_4_bn2_gamma) /* ty=Tensor[(256), float32] */; + %108 = nn.conv2d(%103, %separable_conv_block_4_conv2_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %109 = expand_dims(%107, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %110 = negative(%separable_conv_block_4_bn2_moving_mean) /* ty=Tensor[(256), float32] */; + %111 = multiply(%110, %107) /* ty=Tensor[(256), float32] */; + %112 = add(%111, %separable_conv_block_4_bn2_beta) /* ty=Tensor[(256), float32] */; + %113 = multiply(%108, %109) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %114 = expand_dims(%112, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %115 = add(%113, %114) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %116 = nn.relu(%115) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %117 = add(%separable_conv_block_5_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %118 = sqrt(%117) /* ty=Tensor[(256), float32] */; + %119 = divide(1f /* ty=float32 */, %118) /* ty=Tensor[(256), float32] */; + %120 = multiply(%119, %separable_conv_block_5_bn1_gamma) /* ty=Tensor[(256), float32] */; + %121 = nn.conv2d(%116, %separable_conv_block_5_weight, padding=[1, 1, 1, 1], groups=256, channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %122 = expand_dims(%120, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %123 = negative(%separable_conv_block_5_bn1_moving_mean) /* ty=Tensor[(256), float32] */; + %124 = multiply(%123, %120) /* ty=Tensor[(256), float32] */; + %125 = add(%124, %separable_conv_block_5_bn1_beta) /* ty=Tensor[(256), float32] */; + %126 = multiply(%121, %122) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %127 = expand_dims(%125, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %128 = add(%126, %127) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %129 = nn.relu(%128) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %130 = add(%separable_conv_block_5_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %131 = sqrt(%130) /* ty=Tensor[(256), float32] */; + %132 = divide(1f /* ty=float32 */, %131) /* ty=Tensor[(256), float32] */; + %133 = multiply(%132, %separable_conv_block_5_bn2_gamma) /* ty=Tensor[(256), float32] */; + %134 = nn.conv2d(%129, %separable_conv_block_5_conv2_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %135 = expand_dims(%133, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %136 = negative(%separable_conv_block_5_bn2_moving_mean) /* ty=Tensor[(256), float32] */; + %137 = multiply(%136, %133) /* ty=Tensor[(256), float32] */; + %138 = add(%137, %separable_conv_block_5_bn2_beta) /* ty=Tensor[(256), float32] */; + %139 = multiply(%134, %135) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %140 = expand_dims(%138, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %141 = add(%139, %140) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %142 = nn.relu(%141) /* ty=Tensor[(1, 256, 4, 4), float32] */; + %143 = add(%separable_conv_block_6_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %144 = sqrt(%143) /* ty=Tensor[(256), float32] */; + %145 = divide(1f /* ty=float32 */, %144) /* ty=Tensor[(256), float32] */; + %146 = multiply(%145, %separable_conv_block_6_bn1_gamma) /* ty=Tensor[(256), float32] */; + %147 = nn.conv2d(%142, %separable_conv_block_6_weight, strides=[2, 2], padding=[1, 1, 1, 1], groups=256, channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 2, 2), float32] */; + %148 = expand_dims(%146, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %149 = negative(%separable_conv_block_6_bn1_moving_mean) /* ty=Tensor[(256), float32] */; + %150 = multiply(%149, %146) /* ty=Tensor[(256), float32] */; + %151 = add(%150, %separable_conv_block_6_bn1_beta) /* ty=Tensor[(256), float32] */; + %152 = multiply(%147, %148) /* ty=Tensor[(1, 256, 2, 2), float32] */; + %153 = expand_dims(%151, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %154 = add(%152, %153) /* ty=Tensor[(1, 256, 2, 2), float32] */; + %155 = nn.relu(%154) /* ty=Tensor[(1, 256, 2, 2), float32] */; + %156 = add(%separable_conv_block_6_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %157 = sqrt(%156) /* ty=Tensor[(512), float32] */; + %158 = divide(1f /* ty=float32 */, %157) /* ty=Tensor[(512), float32] */; + %159 = multiply(%158, %separable_conv_block_6_bn2_gamma) /* ty=Tensor[(512), float32] */; + %160 = nn.conv2d(%155, %separable_conv_block_6_conv2_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %161 = expand_dims(%159, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %162 = negative(%separable_conv_block_6_bn2_moving_mean) /* ty=Tensor[(512), float32] */; + %163 = multiply(%162, %159) /* ty=Tensor[(512), float32] */; + %164 = add(%163, %separable_conv_block_6_bn2_beta) /* ty=Tensor[(512), float32] */; + %165 = multiply(%160, %161) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %166 = expand_dims(%164, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %167 = add(%165, %166) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %168 = nn.relu(%167) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %169 = add(%separable_conv_block_7_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %170 = sqrt(%169) /* ty=Tensor[(512), float32] */; + %171 = divide(1f /* ty=float32 */, %170) /* ty=Tensor[(512), float32] */; + %172 = multiply(%171, %separable_conv_block_7_bn1_gamma) /* ty=Tensor[(512), float32] */; + %173 = nn.conv2d(%168, %separable_conv_block_7_weight, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %174 = expand_dims(%172, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %175 = negative(%separable_conv_block_7_bn1_moving_mean) /* ty=Tensor[(512), float32] */; + %176 = multiply(%175, %172) /* ty=Tensor[(512), float32] */; + %177 = add(%176, %separable_conv_block_7_bn1_beta) /* ty=Tensor[(512), float32] */; + %178 = multiply(%173, %174) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %179 = expand_dims(%177, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %180 = add(%178, %179) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %181 = nn.relu(%180) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %182 = add(%separable_conv_block_7_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %183 = sqrt(%182) /* ty=Tensor[(512), float32] */; + %184 = divide(1f /* ty=float32 */, %183) /* ty=Tensor[(512), float32] */; + %185 = multiply(%184, %separable_conv_block_7_bn2_gamma) /* ty=Tensor[(512), float32] */; + %186 = nn.conv2d(%181, %separable_conv_block_7_conv2_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %187 = expand_dims(%185, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %188 = negative(%separable_conv_block_7_bn2_moving_mean) /* ty=Tensor[(512), float32] */; + %189 = multiply(%188, %185) /* ty=Tensor[(512), float32] */; + %190 = add(%189, %separable_conv_block_7_bn2_beta) /* ty=Tensor[(512), float32] */; + %191 = multiply(%186, %187) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %192 = expand_dims(%190, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %193 = add(%191, %192) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %194 = nn.relu(%193) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %195 = add(%separable_conv_block_8_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %196 = sqrt(%195) /* ty=Tensor[(512), float32] */; + %197 = divide(1f /* ty=float32 */, %196) /* ty=Tensor[(512), float32] */; + %198 = multiply(%197, %separable_conv_block_8_bn1_gamma) /* ty=Tensor[(512), float32] */; + %199 = nn.conv2d(%194, %separable_conv_block_8_weight, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %200 = expand_dims(%198, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %201 = negative(%separable_conv_block_8_bn1_moving_mean) /* ty=Tensor[(512), float32] */; + %202 = multiply(%201, %198) /* ty=Tensor[(512), float32] */; + %203 = add(%202, %separable_conv_block_8_bn1_beta) /* ty=Tensor[(512), float32] */; + %204 = multiply(%199, %200) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %205 = expand_dims(%203, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %206 = add(%204, %205) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %207 = nn.relu(%206) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %208 = add(%separable_conv_block_8_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %209 = sqrt(%208) /* ty=Tensor[(512), float32] */; + %210 = divide(1f /* ty=float32 */, %209) /* ty=Tensor[(512), float32] */; + %211 = multiply(%210, %separable_conv_block_8_bn2_gamma) /* ty=Tensor[(512), float32] */; + %212 = nn.conv2d(%207, %separable_conv_block_8_conv2_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %213 = expand_dims(%211, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %214 = negative(%separable_conv_block_8_bn2_moving_mean) /* ty=Tensor[(512), float32] */; + %215 = multiply(%214, %211) /* ty=Tensor[(512), float32] */; + %216 = add(%215, %separable_conv_block_8_bn2_beta) /* ty=Tensor[(512), float32] */; + %217 = multiply(%212, %213) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %218 = expand_dims(%216, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %219 = add(%217, %218) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %220 = nn.relu(%219) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %221 = add(%separable_conv_block_9_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %222 = sqrt(%221) /* ty=Tensor[(512), float32] */; + %223 = divide(1f /* ty=float32 */, %222) /* ty=Tensor[(512), float32] */; + %224 = multiply(%223, %separable_conv_block_9_bn1_gamma) /* ty=Tensor[(512), float32] */; + %225 = nn.conv2d(%220, %separable_conv_block_9_weight, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %226 = expand_dims(%224, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %227 = negative(%separable_conv_block_9_bn1_moving_mean) /* ty=Tensor[(512), float32] */; + %228 = multiply(%227, %224) /* ty=Tensor[(512), float32] */; + %229 = add(%228, %separable_conv_block_9_bn1_beta) /* ty=Tensor[(512), float32] */; + %230 = multiply(%225, %226) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %231 = expand_dims(%229, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %232 = add(%230, %231) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %233 = nn.relu(%232) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %234 = add(%separable_conv_block_9_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %235 = sqrt(%234) /* ty=Tensor[(512), float32] */; + %236 = divide(1f /* ty=float32 */, %235) /* ty=Tensor[(512), float32] */; + %237 = multiply(%236, %separable_conv_block_9_bn2_gamma) /* ty=Tensor[(512), float32] */; + %238 = nn.conv2d(%233, %separable_conv_block_9_conv2_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %239 = expand_dims(%237, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %240 = negative(%separable_conv_block_9_bn2_moving_mean) /* ty=Tensor[(512), float32] */; + %241 = multiply(%240, %237) /* ty=Tensor[(512), float32] */; + %242 = add(%241, %separable_conv_block_9_bn2_beta) /* ty=Tensor[(512), float32] */; + %243 = multiply(%238, %239) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %244 = expand_dims(%242, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %245 = add(%243, %244) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %246 = nn.relu(%245) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %247 = add(%separable_conv_block_10_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %248 = sqrt(%247) /* ty=Tensor[(512), float32] */; + %249 = divide(1f /* ty=float32 */, %248) /* ty=Tensor[(512), float32] */; + %250 = multiply(%249, %separable_conv_block_10_bn1_gamma) /* ty=Tensor[(512), float32] */; + %251 = nn.conv2d(%246, %separable_conv_block_10_weight, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %252 = expand_dims(%250, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %253 = negative(%separable_conv_block_10_bn1_moving_mean) /* ty=Tensor[(512), float32] */; + %254 = multiply(%253, %250) /* ty=Tensor[(512), float32] */; + %255 = add(%254, %separable_conv_block_10_bn1_beta) /* ty=Tensor[(512), float32] */; + %256 = multiply(%251, %252) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %257 = expand_dims(%255, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %258 = add(%256, %257) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %259 = nn.relu(%258) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %260 = add(%separable_conv_block_10_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %261 = sqrt(%260) /* ty=Tensor[(512), float32] */; + %262 = divide(1f /* ty=float32 */, %261) /* ty=Tensor[(512), float32] */; + %263 = multiply(%262, %separable_conv_block_10_bn2_gamma) /* ty=Tensor[(512), float32] */; + %264 = nn.conv2d(%259, %separable_conv_block_10_conv2_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %265 = expand_dims(%263, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %266 = negative(%separable_conv_block_10_bn2_moving_mean) /* ty=Tensor[(512), float32] */; + %267 = multiply(%266, %263) /* ty=Tensor[(512), float32] */; + %268 = add(%267, %separable_conv_block_10_bn2_beta) /* ty=Tensor[(512), float32] */; + %269 = multiply(%264, %265) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %270 = expand_dims(%268, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %271 = add(%269, %270) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %272 = nn.relu(%271) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %273 = add(%separable_conv_block_11_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %274 = sqrt(%273) /* ty=Tensor[(512), float32] */; + %275 = divide(1f /* ty=float32 */, %274) /* ty=Tensor[(512), float32] */; + %276 = multiply(%275, %separable_conv_block_11_bn1_gamma) /* ty=Tensor[(512), float32] */; + %277 = nn.conv2d(%272, %separable_conv_block_11_weight, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %278 = expand_dims(%276, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %279 = negative(%separable_conv_block_11_bn1_moving_mean) /* ty=Tensor[(512), float32] */; + %280 = multiply(%279, %276) /* ty=Tensor[(512), float32] */; + %281 = add(%280, %separable_conv_block_11_bn1_beta) /* ty=Tensor[(512), float32] */; + %282 = multiply(%277, %278) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %283 = expand_dims(%281, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %284 = add(%282, %283) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %285 = nn.relu(%284) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %286 = add(%separable_conv_block_11_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %287 = sqrt(%286) /* ty=Tensor[(512), float32] */; + %288 = divide(1f /* ty=float32 */, %287) /* ty=Tensor[(512), float32] */; + %289 = multiply(%288, %separable_conv_block_11_bn2_gamma) /* ty=Tensor[(512), float32] */; + %290 = nn.conv2d(%285, %separable_conv_block_11_conv2_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %291 = expand_dims(%289, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %292 = negative(%separable_conv_block_11_bn2_moving_mean) /* ty=Tensor[(512), float32] */; + %293 = multiply(%292, %289) /* ty=Tensor[(512), float32] */; + %294 = add(%293, %separable_conv_block_11_bn2_beta) /* ty=Tensor[(512), float32] */; + %295 = multiply(%290, %291) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %296 = expand_dims(%294, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %297 = add(%295, %296) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %298 = nn.relu(%297) /* ty=Tensor[(1, 512, 2, 2), float32] */; + %299 = add(%separable_conv_block_12_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %300 = sqrt(%299) /* ty=Tensor[(512), float32] */; + %301 = divide(1f /* ty=float32 */, %300) /* ty=Tensor[(512), float32] */; + %302 = multiply(%301, %separable_conv_block_12_bn1_gamma) /* ty=Tensor[(512), float32] */; + %303 = nn.conv2d(%298, %separable_conv_block_12_weight, strides=[2, 2], padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 1, 1), float32] */; + %304 = expand_dims(%302, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %305 = negative(%separable_conv_block_12_bn1_moving_mean) /* ty=Tensor[(512), float32] */; + %306 = multiply(%305, %302) /* ty=Tensor[(512), float32] */; + %307 = add(%306, %separable_conv_block_12_bn1_beta) /* ty=Tensor[(512), float32] */; + %308 = multiply(%303, %304) /* ty=Tensor[(1, 512, 1, 1), float32] */; + %309 = expand_dims(%307, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %310 = add(%308, %309) /* ty=Tensor[(1, 512, 1, 1), float32] */; + %311 = nn.relu(%310) /* ty=Tensor[(1, 512, 1, 1), float32] */; + %312 = add(%separable_conv_block_12_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %313 = sqrt(%312) /* ty=Tensor[(1024), float32] */; + %314 = divide(1f /* ty=float32 */, %313) /* ty=Tensor[(1024), float32] */; + %315 = multiply(%314, %separable_conv_block_12_bn2_gamma) /* ty=Tensor[(1024), float32] */; + %316 = nn.conv2d(%311, %separable_conv_block_12_conv2_weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %317 = expand_dims(%315, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] */; + %318 = negative(%separable_conv_block_12_bn2_moving_mean) /* ty=Tensor[(1024), float32] */; + %319 = multiply(%318, %315) /* ty=Tensor[(1024), float32] */; + %320 = add(%319, %separable_conv_block_12_bn2_beta) /* ty=Tensor[(1024), float32] */; + %321 = multiply(%316, %317) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %322 = expand_dims(%320, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] */; + %323 = add(%321, %322) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %324 = nn.relu(%323) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %325 = add(%separable_conv_block_13_bn1_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %326 = sqrt(%325) /* ty=Tensor[(1024), float32] */; + %327 = divide(1f /* ty=float32 */, %326) /* ty=Tensor[(1024), float32] */; + %328 = multiply(%327, %separable_conv_block_13_bn1_gamma) /* ty=Tensor[(1024), float32] */; + %329 = nn.conv2d(%324, %separable_conv_block_13_weight, padding=[1, 1, 1, 1], groups=1024, channels=1024, kernel_size=[3, 3]) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %330 = expand_dims(%328, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] */; + %331 = negative(%separable_conv_block_13_bn1_moving_mean) /* ty=Tensor[(1024), float32] */; + %332 = multiply(%331, %328) /* ty=Tensor[(1024), float32] */; + %333 = add(%332, %separable_conv_block_13_bn1_beta) /* ty=Tensor[(1024), float32] */; + %334 = multiply(%329, %330) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %335 = expand_dims(%333, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] */; + %336 = add(%334, %335) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %337 = nn.relu(%336) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %338 = add(%separable_conv_block_13_bn2_moving_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %339 = sqrt(%338) /* ty=Tensor[(1024), float32] */; + %340 = divide(1f /* ty=float32 */, %339) /* ty=Tensor[(1024), float32] */; + %341 = multiply(%340, %separable_conv_block_13_bn2_gamma) /* ty=Tensor[(1024), float32] */; + %342 = nn.conv2d(%337, %separable_conv_block_13_conv2_weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %343 = expand_dims(%341, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] */; + %344 = negative(%separable_conv_block_13_bn2_moving_mean) /* ty=Tensor[(1024), float32] */; + %345 = multiply(%344, %341) /* ty=Tensor[(1024), float32] */; + %346 = add(%345, %separable_conv_block_13_bn2_beta) /* ty=Tensor[(1024), float32] */; + %347 = multiply(%342, %343) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %348 = expand_dims(%346, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] */; + %349 = add(%347, %348) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %350 = nn.relu(%349) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %351 = nn.global_avg_pool2d(%350) /* ty=Tensor[(1, 1024, 1, 1), float32] */; + %352 = nn.batch_flatten(%351) /* ty=Tensor[(1, 1024), float32] */; + %353 = nn.dense(%352, %fc_weight, units=1000) /* ty=Tensor[(1, 1000), float32] */; + %354 = nn.bias_add(%353, %fc_bias) /* ty=Tensor[(1, 1000), float32] */; + nn.softmax(%354) /* ty=Tensor[(1, 1000), float32] */ +} diff --git a/tests/models/mobilenetv2.py b/tests/models/mobilenetv2.py new file mode 100644 index 0000000..3c299d8 --- /dev/null +++ b/tests/models/mobilenetv2.py @@ -0,0 +1,76 @@ +'''MobileNetV2 in PyTorch. +See the paper "Inverted Residuals and Linear Bottlenecks: +Mobile Networks for Classification, Detection and Segmentation" for more details. +''' +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Block(nn.Module): + '''expand + depthwise + pointwise''' + def __init__(self, in_planes, out_planes, expansion, stride): + super(Block, self).__init__() + self.stride = stride + + planes = expansion * in_planes + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, stride=1, padding=0, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, groups=planes, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False) + self.bn3 = nn.BatchNorm2d(out_planes) + + self.shortcut = nn.Sequential() + if stride == 1 and in_planes != out_planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(out_planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out = out + self.shortcut(x) if self.stride==1 else out + return out + + +class MobileNetV2(nn.Module): + # (expansion, out_planes, num_blocks, stride) + cfg = [(1, 16, 1, 1), + (6, 24, 2, 1), # NOTE: change stride 2 -> 1 for CIFAR10 + (6, 32, 3, 2), + (6, 64, 4, 2), + (6, 96, 3, 1), + (6, 160, 3, 2), + (6, 320, 1, 1)] + + def __init__(self, num_classes=10): + super(MobileNetV2, self).__init__() + # NOTE: change conv1 stride 2 -> 1 for CIFAR10 + self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(32) + self.layers = self._make_layers(in_planes=32) + self.conv2 = nn.Conv2d(320, 1280, kernel_size=1, stride=1, padding=0, bias=False) + self.bn2 = nn.BatchNorm2d(1280) + self.linear = nn.Linear(1280, num_classes) + + def _make_layers(self, in_planes): + layers = [] + for expansion, out_planes, num_blocks, stride in self.cfg: + strides = [stride] + [1]*(num_blocks-1) + for stride in strides: + layers.append(Block(in_planes, out_planes, expansion, stride)) + in_planes = out_planes + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layers(out) + out = F.relu(self.bn2(self.conv2(out))) + # NOTE: change pooling kernel_size 7 -> 4 for CIFAR10 + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out \ No newline at end of file diff --git a/tests/models/mobilenetv2.relay b/tests/models/mobilenetv2.relay new file mode 100644 index 0000000..cdce056 --- /dev/null +++ b/tests/models/mobilenetv2.relay @@ -0,0 +1,760 @@ +#[version = "0.0.5"] +def @main(%input0: Tensor[(1, 3, 32, 32), float32], %conv1_weight: Tensor[(32, 3, 3, 3), float32], %bn1_weight: Tensor[(32), float32], %bn1_bias: Tensor[(32), float32], %bn1_running_mean: Tensor[(32), float32], %bn1_running_var: Tensor[(32), float32], %layers_0_conv1_weight: Tensor[(32, 32, 1, 1), float32], %layers_0_bn1_weight: Tensor[(32), float32], %layers_0_bn1_bias: Tensor[(32), float32], %layers_0_bn1_running_mean: Tensor[(32), float32], %layers_0_bn1_running_var: Tensor[(32), float32], %layers_0_conv2_weight: Tensor[(32, 1, 3, 3), float32], %layers_0_bn2_weight: Tensor[(32), float32], %layers_0_bn2_bias: Tensor[(32), float32], %layers_0_bn2_running_mean: Tensor[(32), float32], %layers_0_bn2_running_var: Tensor[(32), float32], %layers_0_conv3_weight: Tensor[(16, 32, 1, 1), float32], %layers_0_bn3_weight: Tensor[(16), float32], %layers_0_bn3_bias: Tensor[(16), float32], %layers_0_bn3_running_mean: Tensor[(16), float32], %layers_0_bn3_running_var: Tensor[(16), float32], %layers_0_shortcut_0_weight: Tensor[(16, 32, 1, 1), float32], %layers_0_shortcut_1_weight: Tensor[(16), float32], %layers_0_shortcut_1_bias: Tensor[(16), float32], %layers_0_shortcut_1_running_mean: Tensor[(16), float32], %layers_0_shortcut_1_running_var: Tensor[(16), float32], %layers_1_conv1_weight: Tensor[(96, 16, 1, 1), float32], %layers_1_bn1_weight: Tensor[(96), float32], %layers_1_bn1_bias: Tensor[(96), float32], %layers_1_bn1_running_mean: Tensor[(96), float32], %layers_1_bn1_running_var: Tensor[(96), float32], %layers_1_conv2_weight: Tensor[(96, 1, 3, 3), float32], %layers_1_bn2_weight: Tensor[(96), float32], %layers_1_bn2_bias: Tensor[(96), float32], %layers_1_bn2_running_mean: Tensor[(96), float32], %layers_1_bn2_running_var: Tensor[(96), float32], %layers_1_conv3_weight: Tensor[(24, 96, 1, 1), float32], %layers_1_bn3_weight: Tensor[(24), float32], %layers_1_bn3_bias: Tensor[(24), float32], %layers_1_bn3_running_mean: Tensor[(24), float32], %layers_1_bn3_running_var: Tensor[(24), float32], %layers_1_shortcut_0_weight: Tensor[(24, 16, 1, 1), float32], %layers_1_shortcut_1_weight: Tensor[(24), float32], %layers_1_shortcut_1_bias: Tensor[(24), float32], %layers_1_shortcut_1_running_mean: Tensor[(24), float32], %layers_1_shortcut_1_running_var: Tensor[(24), float32], %layers_2_conv1_weight: Tensor[(144, 24, 1, 1), float32], %layers_2_bn1_weight: Tensor[(144), float32], %layers_2_bn1_bias: Tensor[(144), float32], %layers_2_bn1_running_mean: Tensor[(144), float32], %layers_2_bn1_running_var: Tensor[(144), float32], %layers_2_conv2_weight: Tensor[(144, 1, 3, 3), float32], %layers_2_bn2_weight: Tensor[(144), float32], %layers_2_bn2_bias: Tensor[(144), float32], %layers_2_bn2_running_mean: Tensor[(144), float32], %layers_2_bn2_running_var: Tensor[(144), float32], %layers_2_conv3_weight: Tensor[(24, 144, 1, 1), float32], %layers_2_bn3_weight: Tensor[(24), float32], %layers_2_bn3_bias: Tensor[(24), float32], %layers_2_bn3_running_mean: Tensor[(24), float32], %layers_2_bn3_running_var: Tensor[(24), float32], %layers_3_conv1_weight: Tensor[(144, 24, 1, 1), float32], %layers_3_bn1_weight: Tensor[(144), float32], %layers_3_bn1_bias: Tensor[(144), float32], %layers_3_bn1_running_mean: Tensor[(144), float32], %layers_3_bn1_running_var: Tensor[(144), float32], %layers_3_conv2_weight: Tensor[(144, 1, 3, 3), float32], %layers_3_bn2_weight: Tensor[(144), float32], %layers_3_bn2_bias: Tensor[(144), float32], %layers_3_bn2_running_mean: Tensor[(144), float32], %layers_3_bn2_running_var: Tensor[(144), float32], %layers_3_conv3_weight: Tensor[(32, 144, 1, 1), float32], %layers_3_bn3_weight: Tensor[(32), float32], %layers_3_bn3_bias: Tensor[(32), float32], %layers_3_bn3_running_mean: Tensor[(32), float32], %layers_3_bn3_running_var: Tensor[(32), float32], %layers_4_conv1_weight: Tensor[(192, 32, 1, 1), float32], %layers_4_bn1_weight: Tensor[(192), float32], %layers_4_bn1_bias: Tensor[(192), float32], %layers_4_bn1_running_mean: Tensor[(192), float32], %layers_4_bn1_running_var: Tensor[(192), float32], %layers_4_conv2_weight: Tensor[(192, 1, 3, 3), float32], %layers_4_bn2_weight: Tensor[(192), float32], %layers_4_bn2_bias: Tensor[(192), float32], %layers_4_bn2_running_mean: Tensor[(192), float32], %layers_4_bn2_running_var: Tensor[(192), float32], %layers_4_conv3_weight: Tensor[(32, 192, 1, 1), float32], %layers_4_bn3_weight: Tensor[(32), float32], %layers_4_bn3_bias: Tensor[(32), float32], %layers_4_bn3_running_mean: Tensor[(32), float32], %layers_4_bn3_running_var: Tensor[(32), float32], %layers_5_conv1_weight: Tensor[(192, 32, 1, 1), float32], %layers_5_bn1_weight: Tensor[(192), float32], %layers_5_bn1_bias: Tensor[(192), float32], %layers_5_bn1_running_mean: Tensor[(192), float32], %layers_5_bn1_running_var: Tensor[(192), float32], %layers_5_conv2_weight: Tensor[(192, 1, 3, 3), float32], %layers_5_bn2_weight: Tensor[(192), float32], %layers_5_bn2_bias: Tensor[(192), float32], %layers_5_bn2_running_mean: Tensor[(192), float32], %layers_5_bn2_running_var: Tensor[(192), float32], %layers_5_conv3_weight: Tensor[(32, 192, 1, 1), float32], %layers_5_bn3_weight: Tensor[(32), float32], %layers_5_bn3_bias: Tensor[(32), float32], %layers_5_bn3_running_mean: Tensor[(32), float32], %layers_5_bn3_running_var: Tensor[(32), float32], %layers_6_conv1_weight: Tensor[(192, 32, 1, 1), float32], %layers_6_bn1_weight: Tensor[(192), float32], %layers_6_bn1_bias: Tensor[(192), float32], %layers_6_bn1_running_mean: Tensor[(192), float32], %layers_6_bn1_running_var: Tensor[(192), float32], %layers_6_conv2_weight: Tensor[(192, 1, 3, 3), float32], %layers_6_bn2_weight: Tensor[(192), float32], %layers_6_bn2_bias: Tensor[(192), float32], %layers_6_bn2_running_mean: Tensor[(192), float32], %layers_6_bn2_running_var: Tensor[(192), float32], %layers_6_conv3_weight: Tensor[(64, 192, 1, 1), float32], %layers_6_bn3_weight: Tensor[(64), float32], %layers_6_bn3_bias: Tensor[(64), float32], %layers_6_bn3_running_mean: Tensor[(64), float32], %layers_6_bn3_running_var: Tensor[(64), float32], %layers_7_conv1_weight: Tensor[(384, 64, 1, 1), float32], %layers_7_bn1_weight: Tensor[(384), float32], %layers_7_bn1_bias: Tensor[(384), float32], %layers_7_bn1_running_mean: Tensor[(384), float32], %layers_7_bn1_running_var: Tensor[(384), float32], %layers_7_conv2_weight: Tensor[(384, 1, 3, 3), float32], %layers_7_bn2_weight: Tensor[(384), float32], %layers_7_bn2_bias: Tensor[(384), float32], %layers_7_bn2_running_mean: Tensor[(384), float32], %layers_7_bn2_running_var: Tensor[(384), float32], %layers_7_conv3_weight: Tensor[(64, 384, 1, 1), float32], %layers_7_bn3_weight: Tensor[(64), float32], %layers_7_bn3_bias: Tensor[(64), float32], %layers_7_bn3_running_mean: Tensor[(64), float32], %layers_7_bn3_running_var: Tensor[(64), float32], %layers_8_conv1_weight: Tensor[(384, 64, 1, 1), float32], %layers_8_bn1_weight: Tensor[(384), float32], %layers_8_bn1_bias: Tensor[(384), float32], %layers_8_bn1_running_mean: Tensor[(384), float32], %layers_8_bn1_running_var: Tensor[(384), float32], %layers_8_conv2_weight: Tensor[(384, 1, 3, 3), float32], %layers_8_bn2_weight: Tensor[(384), float32], %layers_8_bn2_bias: Tensor[(384), float32], %layers_8_bn2_running_mean: Tensor[(384), float32], %layers_8_bn2_running_var: Tensor[(384), float32], %layers_8_conv3_weight: Tensor[(64, 384, 1, 1), float32], %layers_8_bn3_weight: Tensor[(64), float32], %layers_8_bn3_bias: Tensor[(64), float32], %layers_8_bn3_running_mean: Tensor[(64), float32], %layers_8_bn3_running_var: Tensor[(64), float32], %layers_9_conv1_weight: Tensor[(384, 64, 1, 1), float32], %layers_9_bn1_weight: Tensor[(384), float32], %layers_9_bn1_bias: Tensor[(384), float32], %layers_9_bn1_running_mean: Tensor[(384), float32], %layers_9_bn1_running_var: Tensor[(384), float32], %layers_9_conv2_weight: Tensor[(384, 1, 3, 3), float32], %layers_9_bn2_weight: Tensor[(384), float32], %layers_9_bn2_bias: Tensor[(384), float32], %layers_9_bn2_running_mean: Tensor[(384), float32], %layers_9_bn2_running_var: Tensor[(384), float32], %layers_9_conv3_weight: Tensor[(64, 384, 1, 1), float32], %layers_9_bn3_weight: Tensor[(64), float32], %layers_9_bn3_bias: Tensor[(64), float32], %layers_9_bn3_running_mean: Tensor[(64), float32], %layers_9_bn3_running_var: Tensor[(64), float32], %layers_10_conv1_weight: Tensor[(384, 64, 1, 1), float32], %layers_10_bn1_weight: Tensor[(384), float32], %layers_10_bn1_bias: Tensor[(384), float32], %layers_10_bn1_running_mean: Tensor[(384), float32], %layers_10_bn1_running_var: Tensor[(384), float32], %layers_10_conv2_weight: Tensor[(384, 1, 3, 3), float32], %layers_10_bn2_weight: Tensor[(384), float32], %layers_10_bn2_bias: Tensor[(384), float32], %layers_10_bn2_running_mean: Tensor[(384), float32], %layers_10_bn2_running_var: Tensor[(384), float32], %layers_10_conv3_weight: Tensor[(96, 384, 1, 1), float32], %layers_10_bn3_weight: Tensor[(96), float32], %layers_10_bn3_bias: Tensor[(96), float32], %layers_10_bn3_running_mean: Tensor[(96), float32], %layers_10_bn3_running_var: Tensor[(96), float32], %layers_10_shortcut_0_weight: Tensor[(96, 64, 1, 1), float32], %layers_10_shortcut_1_weight: Tensor[(96), float32], %layers_10_shortcut_1_bias: Tensor[(96), float32], %layers_10_shortcut_1_running_mean: Tensor[(96), float32], %layers_10_shortcut_1_running_var: Tensor[(96), float32], %layers_11_conv1_weight: Tensor[(576, 96, 1, 1), float32], %layers_11_bn1_weight: Tensor[(576), float32], %layers_11_bn1_bias: Tensor[(576), float32], %layers_11_bn1_running_mean: Tensor[(576), float32], %layers_11_bn1_running_var: Tensor[(576), float32], %layers_11_conv2_weight: Tensor[(576, 1, 3, 3), float32], %layers_11_bn2_weight: Tensor[(576), float32], %layers_11_bn2_bias: Tensor[(576), float32], %layers_11_bn2_running_mean: Tensor[(576), float32], %layers_11_bn2_running_var: Tensor[(576), float32], %layers_11_conv3_weight: Tensor[(96, 576, 1, 1), float32], %layers_11_bn3_weight: Tensor[(96), float32], %layers_11_bn3_bias: Tensor[(96), float32], %layers_11_bn3_running_mean: Tensor[(96), float32], %layers_11_bn3_running_var: Tensor[(96), float32], %layers_12_conv1_weight: Tensor[(576, 96, 1, 1), float32], %layers_12_bn1_weight: Tensor[(576), float32], %layers_12_bn1_bias: Tensor[(576), float32], %layers_12_bn1_running_mean: Tensor[(576), float32], %layers_12_bn1_running_var: Tensor[(576), float32], %layers_12_conv2_weight: Tensor[(576, 1, 3, 3), float32], %layers_12_bn2_weight: Tensor[(576), float32], %layers_12_bn2_bias: Tensor[(576), float32], %layers_12_bn2_running_mean: Tensor[(576), float32], %layers_12_bn2_running_var: Tensor[(576), float32], %layers_12_conv3_weight: Tensor[(96, 576, 1, 1), float32], %layers_12_bn3_weight: Tensor[(96), float32], %layers_12_bn3_bias: Tensor[(96), float32], %layers_12_bn3_running_mean: Tensor[(96), float32], %layers_12_bn3_running_var: Tensor[(96), float32], %layers_13_conv1_weight: Tensor[(576, 96, 1, 1), float32], %layers_13_bn1_weight: Tensor[(576), float32], %layers_13_bn1_bias: Tensor[(576), float32], %layers_13_bn1_running_mean: Tensor[(576), float32], %layers_13_bn1_running_var: Tensor[(576), float32], %layers_13_conv2_weight: Tensor[(576, 1, 3, 3), float32], %layers_13_bn2_weight: Tensor[(576), float32], %layers_13_bn2_bias: Tensor[(576), float32], %layers_13_bn2_running_mean: Tensor[(576), float32], %layers_13_bn2_running_var: Tensor[(576), float32], %layers_13_conv3_weight: Tensor[(160, 576, 1, 1), float32], %layers_13_bn3_weight: Tensor[(160), float32], %layers_13_bn3_bias: Tensor[(160), float32], %layers_13_bn3_running_mean: Tensor[(160), float32], %layers_13_bn3_running_var: Tensor[(160), float32], %layers_14_conv1_weight: Tensor[(960, 160, 1, 1), float32], %layers_14_bn1_weight: Tensor[(960), float32], %layers_14_bn1_bias: Tensor[(960), float32], %layers_14_bn1_running_mean: Tensor[(960), float32], %layers_14_bn1_running_var: Tensor[(960), float32], %layers_14_conv2_weight: Tensor[(960, 1, 3, 3), float32], %layers_14_bn2_weight: Tensor[(960), float32], %layers_14_bn2_bias: Tensor[(960), float32], %layers_14_bn2_running_mean: Tensor[(960), float32], %layers_14_bn2_running_var: Tensor[(960), float32], %layers_14_conv3_weight: Tensor[(160, 960, 1, 1), float32], %layers_14_bn3_weight: Tensor[(160), float32], %layers_14_bn3_bias: Tensor[(160), float32], %layers_14_bn3_running_mean: Tensor[(160), float32], %layers_14_bn3_running_var: Tensor[(160), float32], %layers_15_conv1_weight: Tensor[(960, 160, 1, 1), float32], %layers_15_bn1_weight: Tensor[(960), float32], %layers_15_bn1_bias: Tensor[(960), float32], %layers_15_bn1_running_mean: Tensor[(960), float32], %layers_15_bn1_running_var: Tensor[(960), float32], %layers_15_conv2_weight: Tensor[(960, 1, 3, 3), float32], %layers_15_bn2_weight: Tensor[(960), float32], %layers_15_bn2_bias: Tensor[(960), float32], %layers_15_bn2_running_mean: Tensor[(960), float32], %layers_15_bn2_running_var: Tensor[(960), float32], %layers_15_conv3_weight: Tensor[(160, 960, 1, 1), float32], %layers_15_bn3_weight: Tensor[(160), float32], %layers_15_bn3_bias: Tensor[(160), float32], %layers_15_bn3_running_mean: Tensor[(160), float32], %layers_15_bn3_running_var: Tensor[(160), float32], %layers_16_conv1_weight: Tensor[(960, 160, 1, 1), float32], %layers_16_bn1_weight: Tensor[(960), float32], %layers_16_bn1_bias: Tensor[(960), float32], %layers_16_bn1_running_mean: Tensor[(960), float32], %layers_16_bn1_running_var: Tensor[(960), float32], %layers_16_conv2_weight: Tensor[(960, 1, 3, 3), float32], %layers_16_bn2_weight: Tensor[(960), float32], %layers_16_bn2_bias: Tensor[(960), float32], %layers_16_bn2_running_mean: Tensor[(960), float32], %layers_16_bn2_running_var: Tensor[(960), float32], %layers_16_conv3_weight: Tensor[(320, 960, 1, 1), float32], %layers_16_bn3_weight: Tensor[(320), float32], %layers_16_bn3_bias: Tensor[(320), float32], %layers_16_bn3_running_mean: Tensor[(320), float32], %layers_16_bn3_running_var: Tensor[(320), float32], %layers_16_shortcut_0_weight: Tensor[(320, 160, 1, 1), float32], %layers_16_shortcut_1_weight: Tensor[(320), float32], %layers_16_shortcut_1_bias: Tensor[(320), float32], %layers_16_shortcut_1_running_mean: Tensor[(320), float32], %layers_16_shortcut_1_running_var: Tensor[(320), float32], %conv2_weight: Tensor[(1280, 320, 1, 1), float32], %bn2_weight: Tensor[(1280), float32], %bn2_bias: Tensor[(1280), float32], %bn2_running_mean: Tensor[(1280), float32], %bn2_running_var: Tensor[(1280), float32], %linear_weight: Tensor[(10, 1280), float32], %linear_bias: Tensor[(10), float32]) -> Tensor[(1, 10), float32] { + %0 = add(%bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %1 = sqrt(%0) /* ty=Tensor[(32), float32] */; + %2 = divide(1f /* ty=float32 */, %1) /* ty=Tensor[(32), float32] */; + %3 = multiply(%2, %bn1_weight) /* ty=Tensor[(32), float32] */; + %4 = nn.conv2d(%input0, %conv1_weight, padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %5 = expand_dims(%3, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %6 = negative(%bn1_running_mean) /* ty=Tensor[(32), float32] */; + %7 = multiply(%6, %3) /* ty=Tensor[(32), float32] */; + %8 = add(%7, %bn1_bias) /* ty=Tensor[(32), float32] */; + %9 = multiply(%4, %5) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %10 = expand_dims(%8, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %11 = add(%9, %10) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %12 = nn.relu(%11) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %13 = add(%layers_0_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %14 = sqrt(%13) /* ty=Tensor[(32), float32] */; + %15 = divide(1f /* ty=float32 */, %14) /* ty=Tensor[(32), float32] */; + %16 = multiply(%15, %layers_0_bn1_weight) /* ty=Tensor[(32), float32] */; + %17 = nn.conv2d(%12, %layers_0_conv1_weight, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1]) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %18 = expand_dims(%16, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %19 = negative(%layers_0_bn1_running_mean) /* ty=Tensor[(32), float32] */; + %20 = multiply(%19, %16) /* ty=Tensor[(32), float32] */; + %21 = add(%20, %layers_0_bn1_bias) /* ty=Tensor[(32), float32] */; + %22 = multiply(%17, %18) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %23 = expand_dims(%21, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %24 = add(%22, %23) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %25 = nn.relu(%24) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %26 = reshape(%layers_0_conv2_weight, newshape=[32, 1, 3, 3]) /* ty=Tensor[(32, 1, 3, 3), float32] */; + %27 = add(%layers_0_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %28 = sqrt(%27) /* ty=Tensor[(32), float32] */; + %29 = divide(1f /* ty=float32 */, %28) /* ty=Tensor[(32), float32] */; + %30 = multiply(%29, %layers_0_bn2_weight) /* ty=Tensor[(32), float32] */; + %31 = nn.conv2d(%25, %26, padding=[1, 1, 1, 1], groups=32, channels=32, kernel_size=[3, 3]) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %32 = expand_dims(%30, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %33 = negative(%layers_0_bn2_running_mean) /* ty=Tensor[(32), float32] */; + %34 = multiply(%33, %30) /* ty=Tensor[(32), float32] */; + %35 = add(%34, %layers_0_bn2_bias) /* ty=Tensor[(32), float32] */; + %36 = multiply(%31, %32) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %37 = expand_dims(%35, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %38 = add(%36, %37) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %39 = nn.relu(%38) /* ty=Tensor[(1, 32, 32, 32), float32] */; + %40 = add(%layers_0_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %41 = sqrt(%40) /* ty=Tensor[(16), float32] */; + %42 = divide(1f /* ty=float32 */, %41) /* ty=Tensor[(16), float32] */; + %43 = multiply(%42, %layers_0_bn3_weight) /* ty=Tensor[(16), float32] */; + %44 = nn.conv2d(%39, %layers_0_conv3_weight, padding=[0, 0, 0, 0], channels=16, kernel_size=[1, 1]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %45 = expand_dims(%43, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %46 = negative(%layers_0_bn3_running_mean) /* ty=Tensor[(16), float32] */; + %47 = multiply(%46, %43) /* ty=Tensor[(16), float32] */; + %48 = add(%47, %layers_0_bn3_bias) /* ty=Tensor[(16), float32] */; + %49 = multiply(%44, %45) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %50 = expand_dims(%48, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %51 = add(%layers_0_shortcut_1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %52 = sqrt(%51) /* ty=Tensor[(16), float32] */; + %53 = divide(1f /* ty=float32 */, %52) /* ty=Tensor[(16), float32] */; + %54 = multiply(%53, %layers_0_shortcut_1_weight) /* ty=Tensor[(16), float32] */; + %55 = nn.conv2d(%12, %layers_0_shortcut_0_weight, padding=[0, 0, 0, 0], channels=16, kernel_size=[1, 1]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %56 = expand_dims(%54, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %57 = negative(%layers_0_shortcut_1_running_mean) /* ty=Tensor[(16), float32] */; + %58 = multiply(%57, %54) /* ty=Tensor[(16), float32] */; + %59 = add(%58, %layers_0_shortcut_1_bias) /* ty=Tensor[(16), float32] */; + %60 = multiply(%55, %56) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %61 = expand_dims(%59, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %62 = add(%49, %50) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %63 = add(%60, %61) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %64 = add(%62, %63) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %65 = add(%layers_1_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %66 = sqrt(%65) /* ty=Tensor[(96), float32] */; + %67 = divide(1f /* ty=float32 */, %66) /* ty=Tensor[(96), float32] */; + %68 = multiply(%67, %layers_1_bn1_weight) /* ty=Tensor[(96), float32] */; + %69 = nn.conv2d(%64, %layers_1_conv1_weight, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 32, 32), float32] */; + %70 = expand_dims(%68, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %71 = negative(%layers_1_bn1_running_mean) /* ty=Tensor[(96), float32] */; + %72 = multiply(%71, %68) /* ty=Tensor[(96), float32] */; + %73 = add(%72, %layers_1_bn1_bias) /* ty=Tensor[(96), float32] */; + %74 = multiply(%69, %70) /* ty=Tensor[(1, 96, 32, 32), float32] */; + %75 = expand_dims(%73, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %76 = add(%74, %75) /* ty=Tensor[(1, 96, 32, 32), float32] */; + %77 = nn.relu(%76) /* ty=Tensor[(1, 96, 32, 32), float32] */; + %78 = reshape(%layers_1_conv2_weight, newshape=[96, 1, 3, 3]) /* ty=Tensor[(96, 1, 3, 3), float32] */; + %79 = add(%layers_1_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %80 = sqrt(%79) /* ty=Tensor[(96), float32] */; + %81 = divide(1f /* ty=float32 */, %80) /* ty=Tensor[(96), float32] */; + %82 = multiply(%81, %layers_1_bn2_weight) /* ty=Tensor[(96), float32] */; + %83 = nn.conv2d(%77, %78, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 32, 32), float32] */; + %84 = expand_dims(%82, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %85 = negative(%layers_1_bn2_running_mean) /* ty=Tensor[(96), float32] */; + %86 = multiply(%85, %82) /* ty=Tensor[(96), float32] */; + %87 = add(%86, %layers_1_bn2_bias) /* ty=Tensor[(96), float32] */; + %88 = multiply(%83, %84) /* ty=Tensor[(1, 96, 32, 32), float32] */; + %89 = expand_dims(%87, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %90 = add(%88, %89) /* ty=Tensor[(1, 96, 32, 32), float32] */; + %91 = nn.relu(%90) /* ty=Tensor[(1, 96, 32, 32), float32] */; + %92 = add(%layers_1_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(24), float32] */; + %93 = sqrt(%92) /* ty=Tensor[(24), float32] */; + %94 = divide(1f /* ty=float32 */, %93) /* ty=Tensor[(24), float32] */; + %95 = multiply(%94, %layers_1_bn3_weight) /* ty=Tensor[(24), float32] */; + %96 = nn.conv2d(%91, %layers_1_conv3_weight, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1]) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %97 = expand_dims(%95, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %98 = negative(%layers_1_bn3_running_mean) /* ty=Tensor[(24), float32] */; + %99 = multiply(%98, %95) /* ty=Tensor[(24), float32] */; + %100 = add(%99, %layers_1_bn3_bias) /* ty=Tensor[(24), float32] */; + %101 = multiply(%96, %97) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %102 = expand_dims(%100, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %103 = add(%layers_1_shortcut_1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(24), float32] */; + %104 = sqrt(%103) /* ty=Tensor[(24), float32] */; + %105 = divide(1f /* ty=float32 */, %104) /* ty=Tensor[(24), float32] */; + %106 = multiply(%105, %layers_1_shortcut_1_weight) /* ty=Tensor[(24), float32] */; + %107 = nn.conv2d(%64, %layers_1_shortcut_0_weight, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1]) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %108 = expand_dims(%106, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %109 = negative(%layers_1_shortcut_1_running_mean) /* ty=Tensor[(24), float32] */; + %110 = multiply(%109, %106) /* ty=Tensor[(24), float32] */; + %111 = add(%110, %layers_1_shortcut_1_bias) /* ty=Tensor[(24), float32] */; + %112 = multiply(%107, %108) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %113 = expand_dims(%111, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %114 = add(%101, %102) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %115 = add(%112, %113) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %116 = add(%114, %115) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %117 = add(%layers_2_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %118 = sqrt(%117) /* ty=Tensor[(144), float32] */; + %119 = divide(1f /* ty=float32 */, %118) /* ty=Tensor[(144), float32] */; + %120 = multiply(%119, %layers_2_bn1_weight) /* ty=Tensor[(144), float32] */; + %121 = nn.conv2d(%116, %layers_2_conv1_weight, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1]) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %122 = expand_dims(%120, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %123 = negative(%layers_2_bn1_running_mean) /* ty=Tensor[(144), float32] */; + %124 = multiply(%123, %120) /* ty=Tensor[(144), float32] */; + %125 = add(%124, %layers_2_bn1_bias) /* ty=Tensor[(144), float32] */; + %126 = multiply(%121, %122) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %127 = expand_dims(%125, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %128 = add(%126, %127) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %129 = nn.relu(%128) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %130 = reshape(%layers_2_conv2_weight, newshape=[144, 1, 3, 3]) /* ty=Tensor[(144, 1, 3, 3), float32] */; + %131 = add(%layers_2_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %132 = sqrt(%131) /* ty=Tensor[(144), float32] */; + %133 = divide(1f /* ty=float32 */, %132) /* ty=Tensor[(144), float32] */; + %134 = multiply(%133, %layers_2_bn2_weight) /* ty=Tensor[(144), float32] */; + %135 = nn.conv2d(%129, %130, padding=[1, 1, 1, 1], groups=144, channels=144, kernel_size=[3, 3]) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %136 = expand_dims(%134, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %137 = negative(%layers_2_bn2_running_mean) /* ty=Tensor[(144), float32] */; + %138 = multiply(%137, %134) /* ty=Tensor[(144), float32] */; + %139 = add(%138, %layers_2_bn2_bias) /* ty=Tensor[(144), float32] */; + %140 = multiply(%135, %136) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %141 = expand_dims(%139, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %142 = add(%140, %141) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %143 = nn.relu(%142) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %144 = add(%layers_2_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(24), float32] */; + %145 = sqrt(%144) /* ty=Tensor[(24), float32] */; + %146 = divide(1f /* ty=float32 */, %145) /* ty=Tensor[(24), float32] */; + %147 = multiply(%146, %layers_2_bn3_weight) /* ty=Tensor[(24), float32] */; + %148 = nn.conv2d(%143, %layers_2_conv3_weight, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1]) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %149 = expand_dims(%147, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %150 = negative(%layers_2_bn3_running_mean) /* ty=Tensor[(24), float32] */; + %151 = multiply(%150, %147) /* ty=Tensor[(24), float32] */; + %152 = add(%151, %layers_2_bn3_bias) /* ty=Tensor[(24), float32] */; + %153 = multiply(%148, %149) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %154 = expand_dims(%152, axis=1, num_newaxis=2) /* ty=Tensor[(24, 1, 1), float32] */; + %155 = add(%153, %154) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %156 = add(%155, %116) /* ty=Tensor[(1, 24, 32, 32), float32] */; + %157 = add(%layers_3_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %158 = sqrt(%157) /* ty=Tensor[(144), float32] */; + %159 = divide(1f /* ty=float32 */, %158) /* ty=Tensor[(144), float32] */; + %160 = multiply(%159, %layers_3_bn1_weight) /* ty=Tensor[(144), float32] */; + %161 = nn.conv2d(%156, %layers_3_conv1_weight, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1]) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %162 = expand_dims(%160, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %163 = negative(%layers_3_bn1_running_mean) /* ty=Tensor[(144), float32] */; + %164 = multiply(%163, %160) /* ty=Tensor[(144), float32] */; + %165 = add(%164, %layers_3_bn1_bias) /* ty=Tensor[(144), float32] */; + %166 = multiply(%161, %162) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %167 = expand_dims(%165, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %168 = add(%166, %167) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %169 = nn.relu(%168) /* ty=Tensor[(1, 144, 32, 32), float32] */; + %170 = reshape(%layers_3_conv2_weight, newshape=[144, 1, 3, 3]) /* ty=Tensor[(144, 1, 3, 3), float32] */; + %171 = add(%layers_3_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(144), float32] */; + %172 = sqrt(%171) /* ty=Tensor[(144), float32] */; + %173 = divide(1f /* ty=float32 */, %172) /* ty=Tensor[(144), float32] */; + %174 = multiply(%173, %layers_3_bn2_weight) /* ty=Tensor[(144), float32] */; + %175 = nn.conv2d(%169, %170, strides=[2, 2], padding=[1, 1, 1, 1], groups=144, channels=144, kernel_size=[3, 3]) /* ty=Tensor[(1, 144, 16, 16), float32] */; + %176 = expand_dims(%174, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %177 = negative(%layers_3_bn2_running_mean) /* ty=Tensor[(144), float32] */; + %178 = multiply(%177, %174) /* ty=Tensor[(144), float32] */; + %179 = add(%178, %layers_3_bn2_bias) /* ty=Tensor[(144), float32] */; + %180 = multiply(%175, %176) /* ty=Tensor[(1, 144, 16, 16), float32] */; + %181 = expand_dims(%179, axis=1, num_newaxis=2) /* ty=Tensor[(144, 1, 1), float32] */; + %182 = add(%180, %181) /* ty=Tensor[(1, 144, 16, 16), float32] */; + %183 = nn.relu(%182) /* ty=Tensor[(1, 144, 16, 16), float32] */; + %184 = add(%layers_3_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %185 = sqrt(%184) /* ty=Tensor[(32), float32] */; + %186 = divide(1f /* ty=float32 */, %185) /* ty=Tensor[(32), float32] */; + %187 = multiply(%186, %layers_3_bn3_weight) /* ty=Tensor[(32), float32] */; + %188 = nn.conv2d(%183, %layers_3_conv3_weight, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %189 = expand_dims(%187, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %190 = negative(%layers_3_bn3_running_mean) /* ty=Tensor[(32), float32] */; + %191 = multiply(%190, %187) /* ty=Tensor[(32), float32] */; + %192 = add(%191, %layers_3_bn3_bias) /* ty=Tensor[(32), float32] */; + %193 = multiply(%188, %189) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %194 = expand_dims(%192, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %195 = add(%193, %194) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %196 = add(%layers_4_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(192), float32] */; + %197 = sqrt(%196) /* ty=Tensor[(192), float32] */; + %198 = divide(1f /* ty=float32 */, %197) /* ty=Tensor[(192), float32] */; + %199 = multiply(%198, %layers_4_bn1_weight) /* ty=Tensor[(192), float32] */; + %200 = nn.conv2d(%195, %layers_4_conv1_weight, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1]) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %201 = expand_dims(%199, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %202 = negative(%layers_4_bn1_running_mean) /* ty=Tensor[(192), float32] */; + %203 = multiply(%202, %199) /* ty=Tensor[(192), float32] */; + %204 = add(%203, %layers_4_bn1_bias) /* ty=Tensor[(192), float32] */; + %205 = multiply(%200, %201) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %206 = expand_dims(%204, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %207 = add(%205, %206) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %208 = nn.relu(%207) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %209 = reshape(%layers_4_conv2_weight, newshape=[192, 1, 3, 3]) /* ty=Tensor[(192, 1, 3, 3), float32] */; + %210 = add(%layers_4_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(192), float32] */; + %211 = sqrt(%210) /* ty=Tensor[(192), float32] */; + %212 = divide(1f /* ty=float32 */, %211) /* ty=Tensor[(192), float32] */; + %213 = multiply(%212, %layers_4_bn2_weight) /* ty=Tensor[(192), float32] */; + %214 = nn.conv2d(%208, %209, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3]) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %215 = expand_dims(%213, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %216 = negative(%layers_4_bn2_running_mean) /* ty=Tensor[(192), float32] */; + %217 = multiply(%216, %213) /* ty=Tensor[(192), float32] */; + %218 = add(%217, %layers_4_bn2_bias) /* ty=Tensor[(192), float32] */; + %219 = multiply(%214, %215) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %220 = expand_dims(%218, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %221 = add(%219, %220) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %222 = nn.relu(%221) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %223 = add(%layers_4_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %224 = sqrt(%223) /* ty=Tensor[(32), float32] */; + %225 = divide(1f /* ty=float32 */, %224) /* ty=Tensor[(32), float32] */; + %226 = multiply(%225, %layers_4_bn3_weight) /* ty=Tensor[(32), float32] */; + %227 = nn.conv2d(%222, %layers_4_conv3_weight, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %228 = expand_dims(%226, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %229 = negative(%layers_4_bn3_running_mean) /* ty=Tensor[(32), float32] */; + %230 = multiply(%229, %226) /* ty=Tensor[(32), float32] */; + %231 = add(%230, %layers_4_bn3_bias) /* ty=Tensor[(32), float32] */; + %232 = multiply(%227, %228) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %233 = expand_dims(%231, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %234 = add(%232, %233) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %235 = add(%234, %195) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %236 = add(%layers_5_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(192), float32] */; + %237 = sqrt(%236) /* ty=Tensor[(192), float32] */; + %238 = divide(1f /* ty=float32 */, %237) /* ty=Tensor[(192), float32] */; + %239 = multiply(%238, %layers_5_bn1_weight) /* ty=Tensor[(192), float32] */; + %240 = nn.conv2d(%235, %layers_5_conv1_weight, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1]) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %241 = expand_dims(%239, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %242 = negative(%layers_5_bn1_running_mean) /* ty=Tensor[(192), float32] */; + %243 = multiply(%242, %239) /* ty=Tensor[(192), float32] */; + %244 = add(%243, %layers_5_bn1_bias) /* ty=Tensor[(192), float32] */; + %245 = multiply(%240, %241) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %246 = expand_dims(%244, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %247 = add(%245, %246) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %248 = nn.relu(%247) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %249 = reshape(%layers_5_conv2_weight, newshape=[192, 1, 3, 3]) /* ty=Tensor[(192, 1, 3, 3), float32] */; + %250 = add(%layers_5_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(192), float32] */; + %251 = sqrt(%250) /* ty=Tensor[(192), float32] */; + %252 = divide(1f /* ty=float32 */, %251) /* ty=Tensor[(192), float32] */; + %253 = multiply(%252, %layers_5_bn2_weight) /* ty=Tensor[(192), float32] */; + %254 = nn.conv2d(%248, %249, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3]) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %255 = expand_dims(%253, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %256 = negative(%layers_5_bn2_running_mean) /* ty=Tensor[(192), float32] */; + %257 = multiply(%256, %253) /* ty=Tensor[(192), float32] */; + %258 = add(%257, %layers_5_bn2_bias) /* ty=Tensor[(192), float32] */; + %259 = multiply(%254, %255) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %260 = expand_dims(%258, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %261 = add(%259, %260) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %262 = nn.relu(%261) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %263 = add(%layers_5_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %264 = sqrt(%263) /* ty=Tensor[(32), float32] */; + %265 = divide(1f /* ty=float32 */, %264) /* ty=Tensor[(32), float32] */; + %266 = multiply(%265, %layers_5_bn3_weight) /* ty=Tensor[(32), float32] */; + %267 = nn.conv2d(%262, %layers_5_conv3_weight, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %268 = expand_dims(%266, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %269 = negative(%layers_5_bn3_running_mean) /* ty=Tensor[(32), float32] */; + %270 = multiply(%269, %266) /* ty=Tensor[(32), float32] */; + %271 = add(%270, %layers_5_bn3_bias) /* ty=Tensor[(32), float32] */; + %272 = multiply(%267, %268) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %273 = expand_dims(%271, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %274 = add(%272, %273) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %275 = add(%274, %235) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %276 = add(%layers_6_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(192), float32] */; + %277 = sqrt(%276) /* ty=Tensor[(192), float32] */; + %278 = divide(1f /* ty=float32 */, %277) /* ty=Tensor[(192), float32] */; + %279 = multiply(%278, %layers_6_bn1_weight) /* ty=Tensor[(192), float32] */; + %280 = nn.conv2d(%275, %layers_6_conv1_weight, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1]) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %281 = expand_dims(%279, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %282 = negative(%layers_6_bn1_running_mean) /* ty=Tensor[(192), float32] */; + %283 = multiply(%282, %279) /* ty=Tensor[(192), float32] */; + %284 = add(%283, %layers_6_bn1_bias) /* ty=Tensor[(192), float32] */; + %285 = multiply(%280, %281) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %286 = expand_dims(%284, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %287 = add(%285, %286) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %288 = nn.relu(%287) /* ty=Tensor[(1, 192, 16, 16), float32] */; + %289 = reshape(%layers_6_conv2_weight, newshape=[192, 1, 3, 3]) /* ty=Tensor[(192, 1, 3, 3), float32] */; + %290 = add(%layers_6_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(192), float32] */; + %291 = sqrt(%290) /* ty=Tensor[(192), float32] */; + %292 = divide(1f /* ty=float32 */, %291) /* ty=Tensor[(192), float32] */; + %293 = multiply(%292, %layers_6_bn2_weight) /* ty=Tensor[(192), float32] */; + %294 = nn.conv2d(%288, %289, strides=[2, 2], padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3]) /* ty=Tensor[(1, 192, 8, 8), float32] */; + %295 = expand_dims(%293, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %296 = negative(%layers_6_bn2_running_mean) /* ty=Tensor[(192), float32] */; + %297 = multiply(%296, %293) /* ty=Tensor[(192), float32] */; + %298 = add(%297, %layers_6_bn2_bias) /* ty=Tensor[(192), float32] */; + %299 = multiply(%294, %295) /* ty=Tensor[(1, 192, 8, 8), float32] */; + %300 = expand_dims(%298, axis=1, num_newaxis=2) /* ty=Tensor[(192, 1, 1), float32] */; + %301 = add(%299, %300) /* ty=Tensor[(1, 192, 8, 8), float32] */; + %302 = nn.relu(%301) /* ty=Tensor[(1, 192, 8, 8), float32] */; + %303 = add(%layers_6_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %304 = sqrt(%303) /* ty=Tensor[(64), float32] */; + %305 = divide(1f /* ty=float32 */, %304) /* ty=Tensor[(64), float32] */; + %306 = multiply(%305, %layers_6_bn3_weight) /* ty=Tensor[(64), float32] */; + %307 = nn.conv2d(%302, %layers_6_conv3_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %308 = expand_dims(%306, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %309 = negative(%layers_6_bn3_running_mean) /* ty=Tensor[(64), float32] */; + %310 = multiply(%309, %306) /* ty=Tensor[(64), float32] */; + %311 = add(%310, %layers_6_bn3_bias) /* ty=Tensor[(64), float32] */; + %312 = multiply(%307, %308) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %313 = expand_dims(%311, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %314 = add(%312, %313) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %315 = add(%layers_7_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(384), float32] */; + %316 = sqrt(%315) /* ty=Tensor[(384), float32] */; + %317 = divide(1f /* ty=float32 */, %316) /* ty=Tensor[(384), float32] */; + %318 = multiply(%317, %layers_7_bn1_weight) /* ty=Tensor[(384), float32] */; + %319 = nn.conv2d(%314, %layers_7_conv1_weight, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1]) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %320 = expand_dims(%318, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %321 = negative(%layers_7_bn1_running_mean) /* ty=Tensor[(384), float32] */; + %322 = multiply(%321, %318) /* ty=Tensor[(384), float32] */; + %323 = add(%322, %layers_7_bn1_bias) /* ty=Tensor[(384), float32] */; + %324 = multiply(%319, %320) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %325 = expand_dims(%323, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %326 = add(%324, %325) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %327 = nn.relu(%326) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %328 = reshape(%layers_7_conv2_weight, newshape=[384, 1, 3, 3]) /* ty=Tensor[(384, 1, 3, 3), float32] */; + %329 = add(%layers_7_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(384), float32] */; + %330 = sqrt(%329) /* ty=Tensor[(384), float32] */; + %331 = divide(1f /* ty=float32 */, %330) /* ty=Tensor[(384), float32] */; + %332 = multiply(%331, %layers_7_bn2_weight) /* ty=Tensor[(384), float32] */; + %333 = nn.conv2d(%327, %328, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3]) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %334 = expand_dims(%332, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %335 = negative(%layers_7_bn2_running_mean) /* ty=Tensor[(384), float32] */; + %336 = multiply(%335, %332) /* ty=Tensor[(384), float32] */; + %337 = add(%336, %layers_7_bn2_bias) /* ty=Tensor[(384), float32] */; + %338 = multiply(%333, %334) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %339 = expand_dims(%337, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %340 = add(%338, %339) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %341 = nn.relu(%340) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %342 = add(%layers_7_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %343 = sqrt(%342) /* ty=Tensor[(64), float32] */; + %344 = divide(1f /* ty=float32 */, %343) /* ty=Tensor[(64), float32] */; + %345 = multiply(%344, %layers_7_bn3_weight) /* ty=Tensor[(64), float32] */; + %346 = nn.conv2d(%341, %layers_7_conv3_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %347 = expand_dims(%345, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %348 = negative(%layers_7_bn3_running_mean) /* ty=Tensor[(64), float32] */; + %349 = multiply(%348, %345) /* ty=Tensor[(64), float32] */; + %350 = add(%349, %layers_7_bn3_bias) /* ty=Tensor[(64), float32] */; + %351 = multiply(%346, %347) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %352 = expand_dims(%350, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %353 = add(%351, %352) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %354 = add(%353, %314) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %355 = add(%layers_8_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(384), float32] */; + %356 = sqrt(%355) /* ty=Tensor[(384), float32] */; + %357 = divide(1f /* ty=float32 */, %356) /* ty=Tensor[(384), float32] */; + %358 = multiply(%357, %layers_8_bn1_weight) /* ty=Tensor[(384), float32] */; + %359 = nn.conv2d(%354, %layers_8_conv1_weight, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1]) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %360 = expand_dims(%358, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %361 = negative(%layers_8_bn1_running_mean) /* ty=Tensor[(384), float32] */; + %362 = multiply(%361, %358) /* ty=Tensor[(384), float32] */; + %363 = add(%362, %layers_8_bn1_bias) /* ty=Tensor[(384), float32] */; + %364 = multiply(%359, %360) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %365 = expand_dims(%363, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %366 = add(%364, %365) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %367 = nn.relu(%366) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %368 = reshape(%layers_8_conv2_weight, newshape=[384, 1, 3, 3]) /* ty=Tensor[(384, 1, 3, 3), float32] */; + %369 = add(%layers_8_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(384), float32] */; + %370 = sqrt(%369) /* ty=Tensor[(384), float32] */; + %371 = divide(1f /* ty=float32 */, %370) /* ty=Tensor[(384), float32] */; + %372 = multiply(%371, %layers_8_bn2_weight) /* ty=Tensor[(384), float32] */; + %373 = nn.conv2d(%367, %368, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3]) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %374 = expand_dims(%372, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %375 = negative(%layers_8_bn2_running_mean) /* ty=Tensor[(384), float32] */; + %376 = multiply(%375, %372) /* ty=Tensor[(384), float32] */; + %377 = add(%376, %layers_8_bn2_bias) /* ty=Tensor[(384), float32] */; + %378 = multiply(%373, %374) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %379 = expand_dims(%377, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %380 = add(%378, %379) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %381 = nn.relu(%380) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %382 = add(%layers_8_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %383 = sqrt(%382) /* ty=Tensor[(64), float32] */; + %384 = divide(1f /* ty=float32 */, %383) /* ty=Tensor[(64), float32] */; + %385 = multiply(%384, %layers_8_bn3_weight) /* ty=Tensor[(64), float32] */; + %386 = nn.conv2d(%381, %layers_8_conv3_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %387 = expand_dims(%385, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %388 = negative(%layers_8_bn3_running_mean) /* ty=Tensor[(64), float32] */; + %389 = multiply(%388, %385) /* ty=Tensor[(64), float32] */; + %390 = add(%389, %layers_8_bn3_bias) /* ty=Tensor[(64), float32] */; + %391 = multiply(%386, %387) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %392 = expand_dims(%390, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %393 = add(%391, %392) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %394 = add(%393, %354) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %395 = add(%layers_9_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(384), float32] */; + %396 = sqrt(%395) /* ty=Tensor[(384), float32] */; + %397 = divide(1f /* ty=float32 */, %396) /* ty=Tensor[(384), float32] */; + %398 = multiply(%397, %layers_9_bn1_weight) /* ty=Tensor[(384), float32] */; + %399 = nn.conv2d(%394, %layers_9_conv1_weight, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1]) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %400 = expand_dims(%398, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %401 = negative(%layers_9_bn1_running_mean) /* ty=Tensor[(384), float32] */; + %402 = multiply(%401, %398) /* ty=Tensor[(384), float32] */; + %403 = add(%402, %layers_9_bn1_bias) /* ty=Tensor[(384), float32] */; + %404 = multiply(%399, %400) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %405 = expand_dims(%403, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %406 = add(%404, %405) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %407 = nn.relu(%406) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %408 = reshape(%layers_9_conv2_weight, newshape=[384, 1, 3, 3]) /* ty=Tensor[(384, 1, 3, 3), float32] */; + %409 = add(%layers_9_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(384), float32] */; + %410 = sqrt(%409) /* ty=Tensor[(384), float32] */; + %411 = divide(1f /* ty=float32 */, %410) /* ty=Tensor[(384), float32] */; + %412 = multiply(%411, %layers_9_bn2_weight) /* ty=Tensor[(384), float32] */; + %413 = nn.conv2d(%407, %408, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3]) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %414 = expand_dims(%412, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %415 = negative(%layers_9_bn2_running_mean) /* ty=Tensor[(384), float32] */; + %416 = multiply(%415, %412) /* ty=Tensor[(384), float32] */; + %417 = add(%416, %layers_9_bn2_bias) /* ty=Tensor[(384), float32] */; + %418 = multiply(%413, %414) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %419 = expand_dims(%417, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %420 = add(%418, %419) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %421 = nn.relu(%420) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %422 = add(%layers_9_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %423 = sqrt(%422) /* ty=Tensor[(64), float32] */; + %424 = divide(1f /* ty=float32 */, %423) /* ty=Tensor[(64), float32] */; + %425 = multiply(%424, %layers_9_bn3_weight) /* ty=Tensor[(64), float32] */; + %426 = nn.conv2d(%421, %layers_9_conv3_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %427 = expand_dims(%425, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %428 = negative(%layers_9_bn3_running_mean) /* ty=Tensor[(64), float32] */; + %429 = multiply(%428, %425) /* ty=Tensor[(64), float32] */; + %430 = add(%429, %layers_9_bn3_bias) /* ty=Tensor[(64), float32] */; + %431 = multiply(%426, %427) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %432 = expand_dims(%430, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %433 = add(%431, %432) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %434 = add(%433, %394) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %435 = add(%layers_10_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(384), float32] */; + %436 = sqrt(%435) /* ty=Tensor[(384), float32] */; + %437 = divide(1f /* ty=float32 */, %436) /* ty=Tensor[(384), float32] */; + %438 = multiply(%437, %layers_10_bn1_weight) /* ty=Tensor[(384), float32] */; + %439 = nn.conv2d(%434, %layers_10_conv1_weight, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1]) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %440 = expand_dims(%438, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %441 = negative(%layers_10_bn1_running_mean) /* ty=Tensor[(384), float32] */; + %442 = multiply(%441, %438) /* ty=Tensor[(384), float32] */; + %443 = add(%442, %layers_10_bn1_bias) /* ty=Tensor[(384), float32] */; + %444 = multiply(%439, %440) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %445 = expand_dims(%443, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %446 = add(%444, %445) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %447 = nn.relu(%446) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %448 = reshape(%layers_10_conv2_weight, newshape=[384, 1, 3, 3]) /* ty=Tensor[(384, 1, 3, 3), float32] */; + %449 = add(%layers_10_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(384), float32] */; + %450 = sqrt(%449) /* ty=Tensor[(384), float32] */; + %451 = divide(1f /* ty=float32 */, %450) /* ty=Tensor[(384), float32] */; + %452 = multiply(%451, %layers_10_bn2_weight) /* ty=Tensor[(384), float32] */; + %453 = nn.conv2d(%447, %448, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3]) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %454 = expand_dims(%452, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %455 = negative(%layers_10_bn2_running_mean) /* ty=Tensor[(384), float32] */; + %456 = multiply(%455, %452) /* ty=Tensor[(384), float32] */; + %457 = add(%456, %layers_10_bn2_bias) /* ty=Tensor[(384), float32] */; + %458 = multiply(%453, %454) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %459 = expand_dims(%457, axis=1, num_newaxis=2) /* ty=Tensor[(384, 1, 1), float32] */; + %460 = add(%458, %459) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %461 = nn.relu(%460) /* ty=Tensor[(1, 384, 8, 8), float32] */; + %462 = add(%layers_10_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %463 = sqrt(%462) /* ty=Tensor[(96), float32] */; + %464 = divide(1f /* ty=float32 */, %463) /* ty=Tensor[(96), float32] */; + %465 = multiply(%464, %layers_10_bn3_weight) /* ty=Tensor[(96), float32] */; + %466 = nn.conv2d(%461, %layers_10_conv3_weight, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %467 = expand_dims(%465, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %468 = negative(%layers_10_bn3_running_mean) /* ty=Tensor[(96), float32] */; + %469 = multiply(%468, %465) /* ty=Tensor[(96), float32] */; + %470 = add(%469, %layers_10_bn3_bias) /* ty=Tensor[(96), float32] */; + %471 = multiply(%466, %467) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %472 = expand_dims(%470, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %473 = add(%layers_10_shortcut_1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %474 = sqrt(%473) /* ty=Tensor[(96), float32] */; + %475 = divide(1f /* ty=float32 */, %474) /* ty=Tensor[(96), float32] */; + %476 = multiply(%475, %layers_10_shortcut_1_weight) /* ty=Tensor[(96), float32] */; + %477 = nn.conv2d(%434, %layers_10_shortcut_0_weight, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %478 = expand_dims(%476, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %479 = negative(%layers_10_shortcut_1_running_mean) /* ty=Tensor[(96), float32] */; + %480 = multiply(%479, %476) /* ty=Tensor[(96), float32] */; + %481 = add(%480, %layers_10_shortcut_1_bias) /* ty=Tensor[(96), float32] */; + %482 = multiply(%477, %478) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %483 = expand_dims(%481, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %484 = add(%471, %472) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %485 = add(%482, %483) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %486 = add(%484, %485) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %487 = add(%layers_11_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(576), float32] */; + %488 = sqrt(%487) /* ty=Tensor[(576), float32] */; + %489 = divide(1f /* ty=float32 */, %488) /* ty=Tensor[(576), float32] */; + %490 = multiply(%489, %layers_11_bn1_weight) /* ty=Tensor[(576), float32] */; + %491 = nn.conv2d(%486, %layers_11_conv1_weight, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1]) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %492 = expand_dims(%490, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %493 = negative(%layers_11_bn1_running_mean) /* ty=Tensor[(576), float32] */; + %494 = multiply(%493, %490) /* ty=Tensor[(576), float32] */; + %495 = add(%494, %layers_11_bn1_bias) /* ty=Tensor[(576), float32] */; + %496 = multiply(%491, %492) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %497 = expand_dims(%495, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %498 = add(%496, %497) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %499 = nn.relu(%498) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %500 = reshape(%layers_11_conv2_weight, newshape=[576, 1, 3, 3]) /* ty=Tensor[(576, 1, 3, 3), float32] */; + %501 = add(%layers_11_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(576), float32] */; + %502 = sqrt(%501) /* ty=Tensor[(576), float32] */; + %503 = divide(1f /* ty=float32 */, %502) /* ty=Tensor[(576), float32] */; + %504 = multiply(%503, %layers_11_bn2_weight) /* ty=Tensor[(576), float32] */; + %505 = nn.conv2d(%499, %500, padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3]) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %506 = expand_dims(%504, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %507 = negative(%layers_11_bn2_running_mean) /* ty=Tensor[(576), float32] */; + %508 = multiply(%507, %504) /* ty=Tensor[(576), float32] */; + %509 = add(%508, %layers_11_bn2_bias) /* ty=Tensor[(576), float32] */; + %510 = multiply(%505, %506) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %511 = expand_dims(%509, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %512 = add(%510, %511) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %513 = nn.relu(%512) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %514 = add(%layers_11_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %515 = sqrt(%514) /* ty=Tensor[(96), float32] */; + %516 = divide(1f /* ty=float32 */, %515) /* ty=Tensor[(96), float32] */; + %517 = multiply(%516, %layers_11_bn3_weight) /* ty=Tensor[(96), float32] */; + %518 = nn.conv2d(%513, %layers_11_conv3_weight, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %519 = expand_dims(%517, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %520 = negative(%layers_11_bn3_running_mean) /* ty=Tensor[(96), float32] */; + %521 = multiply(%520, %517) /* ty=Tensor[(96), float32] */; + %522 = add(%521, %layers_11_bn3_bias) /* ty=Tensor[(96), float32] */; + %523 = multiply(%518, %519) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %524 = expand_dims(%522, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %525 = add(%523, %524) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %526 = add(%525, %486) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %527 = add(%layers_12_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(576), float32] */; + %528 = sqrt(%527) /* ty=Tensor[(576), float32] */; + %529 = divide(1f /* ty=float32 */, %528) /* ty=Tensor[(576), float32] */; + %530 = multiply(%529, %layers_12_bn1_weight) /* ty=Tensor[(576), float32] */; + %531 = nn.conv2d(%526, %layers_12_conv1_weight, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1]) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %532 = expand_dims(%530, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %533 = negative(%layers_12_bn1_running_mean) /* ty=Tensor[(576), float32] */; + %534 = multiply(%533, %530) /* ty=Tensor[(576), float32] */; + %535 = add(%534, %layers_12_bn1_bias) /* ty=Tensor[(576), float32] */; + %536 = multiply(%531, %532) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %537 = expand_dims(%535, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %538 = add(%536, %537) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %539 = nn.relu(%538) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %540 = reshape(%layers_12_conv2_weight, newshape=[576, 1, 3, 3]) /* ty=Tensor[(576, 1, 3, 3), float32] */; + %541 = add(%layers_12_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(576), float32] */; + %542 = sqrt(%541) /* ty=Tensor[(576), float32] */; + %543 = divide(1f /* ty=float32 */, %542) /* ty=Tensor[(576), float32] */; + %544 = multiply(%543, %layers_12_bn2_weight) /* ty=Tensor[(576), float32] */; + %545 = nn.conv2d(%539, %540, padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3]) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %546 = expand_dims(%544, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %547 = negative(%layers_12_bn2_running_mean) /* ty=Tensor[(576), float32] */; + %548 = multiply(%547, %544) /* ty=Tensor[(576), float32] */; + %549 = add(%548, %layers_12_bn2_bias) /* ty=Tensor[(576), float32] */; + %550 = multiply(%545, %546) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %551 = expand_dims(%549, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %552 = add(%550, %551) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %553 = nn.relu(%552) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %554 = add(%layers_12_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(96), float32] */; + %555 = sqrt(%554) /* ty=Tensor[(96), float32] */; + %556 = divide(1f /* ty=float32 */, %555) /* ty=Tensor[(96), float32] */; + %557 = multiply(%556, %layers_12_bn3_weight) /* ty=Tensor[(96), float32] */; + %558 = nn.conv2d(%553, %layers_12_conv3_weight, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %559 = expand_dims(%557, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %560 = negative(%layers_12_bn3_running_mean) /* ty=Tensor[(96), float32] */; + %561 = multiply(%560, %557) /* ty=Tensor[(96), float32] */; + %562 = add(%561, %layers_12_bn3_bias) /* ty=Tensor[(96), float32] */; + %563 = multiply(%558, %559) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %564 = expand_dims(%562, axis=1, num_newaxis=2) /* ty=Tensor[(96, 1, 1), float32] */; + %565 = add(%563, %564) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %566 = add(%565, %526) /* ty=Tensor[(1, 96, 8, 8), float32] */; + %567 = add(%layers_13_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(576), float32] */; + %568 = sqrt(%567) /* ty=Tensor[(576), float32] */; + %569 = divide(1f /* ty=float32 */, %568) /* ty=Tensor[(576), float32] */; + %570 = multiply(%569, %layers_13_bn1_weight) /* ty=Tensor[(576), float32] */; + %571 = nn.conv2d(%566, %layers_13_conv1_weight, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1]) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %572 = expand_dims(%570, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %573 = negative(%layers_13_bn1_running_mean) /* ty=Tensor[(576), float32] */; + %574 = multiply(%573, %570) /* ty=Tensor[(576), float32] */; + %575 = add(%574, %layers_13_bn1_bias) /* ty=Tensor[(576), float32] */; + %576 = multiply(%571, %572) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %577 = expand_dims(%575, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %578 = add(%576, %577) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %579 = nn.relu(%578) /* ty=Tensor[(1, 576, 8, 8), float32] */; + %580 = reshape(%layers_13_conv2_weight, newshape=[576, 1, 3, 3]) /* ty=Tensor[(576, 1, 3, 3), float32] */; + %581 = add(%layers_13_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(576), float32] */; + %582 = sqrt(%581) /* ty=Tensor[(576), float32] */; + %583 = divide(1f /* ty=float32 */, %582) /* ty=Tensor[(576), float32] */; + %584 = multiply(%583, %layers_13_bn2_weight) /* ty=Tensor[(576), float32] */; + %585 = nn.conv2d(%579, %580, strides=[2, 2], padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3]) /* ty=Tensor[(1, 576, 4, 4), float32] */; + %586 = expand_dims(%584, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %587 = negative(%layers_13_bn2_running_mean) /* ty=Tensor[(576), float32] */; + %588 = multiply(%587, %584) /* ty=Tensor[(576), float32] */; + %589 = add(%588, %layers_13_bn2_bias) /* ty=Tensor[(576), float32] */; + %590 = multiply(%585, %586) /* ty=Tensor[(1, 576, 4, 4), float32] */; + %591 = expand_dims(%589, axis=1, num_newaxis=2) /* ty=Tensor[(576, 1, 1), float32] */; + %592 = add(%590, %591) /* ty=Tensor[(1, 576, 4, 4), float32] */; + %593 = nn.relu(%592) /* ty=Tensor[(1, 576, 4, 4), float32] */; + %594 = add(%layers_13_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(160), float32] */; + %595 = sqrt(%594) /* ty=Tensor[(160), float32] */; + %596 = divide(1f /* ty=float32 */, %595) /* ty=Tensor[(160), float32] */; + %597 = multiply(%596, %layers_13_bn3_weight) /* ty=Tensor[(160), float32] */; + %598 = nn.conv2d(%593, %layers_13_conv3_weight, padding=[0, 0, 0, 0], channels=160, kernel_size=[1, 1]) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %599 = expand_dims(%597, axis=1, num_newaxis=2) /* ty=Tensor[(160, 1, 1), float32] */; + %600 = negative(%layers_13_bn3_running_mean) /* ty=Tensor[(160), float32] */; + %601 = multiply(%600, %597) /* ty=Tensor[(160), float32] */; + %602 = add(%601, %layers_13_bn3_bias) /* ty=Tensor[(160), float32] */; + %603 = multiply(%598, %599) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %604 = expand_dims(%602, axis=1, num_newaxis=2) /* ty=Tensor[(160, 1, 1), float32] */; + %605 = add(%603, %604) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %606 = add(%layers_14_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(960), float32] */; + %607 = sqrt(%606) /* ty=Tensor[(960), float32] */; + %608 = divide(1f /* ty=float32 */, %607) /* ty=Tensor[(960), float32] */; + %609 = multiply(%608, %layers_14_bn1_weight) /* ty=Tensor[(960), float32] */; + %610 = nn.conv2d(%605, %layers_14_conv1_weight, padding=[0, 0, 0, 0], channels=960, kernel_size=[1, 1]) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %611 = expand_dims(%609, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %612 = negative(%layers_14_bn1_running_mean) /* ty=Tensor[(960), float32] */; + %613 = multiply(%612, %609) /* ty=Tensor[(960), float32] */; + %614 = add(%613, %layers_14_bn1_bias) /* ty=Tensor[(960), float32] */; + %615 = multiply(%610, %611) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %616 = expand_dims(%614, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %617 = add(%615, %616) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %618 = nn.relu(%617) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %619 = reshape(%layers_14_conv2_weight, newshape=[960, 1, 3, 3]) /* ty=Tensor[(960, 1, 3, 3), float32] */; + %620 = add(%layers_14_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(960), float32] */; + %621 = sqrt(%620) /* ty=Tensor[(960), float32] */; + %622 = divide(1f /* ty=float32 */, %621) /* ty=Tensor[(960), float32] */; + %623 = multiply(%622, %layers_14_bn2_weight) /* ty=Tensor[(960), float32] */; + %624 = nn.conv2d(%618, %619, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3]) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %625 = expand_dims(%623, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %626 = negative(%layers_14_bn2_running_mean) /* ty=Tensor[(960), float32] */; + %627 = multiply(%626, %623) /* ty=Tensor[(960), float32] */; + %628 = add(%627, %layers_14_bn2_bias) /* ty=Tensor[(960), float32] */; + %629 = multiply(%624, %625) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %630 = expand_dims(%628, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %631 = add(%629, %630) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %632 = nn.relu(%631) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %633 = add(%layers_14_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(160), float32] */; + %634 = sqrt(%633) /* ty=Tensor[(160), float32] */; + %635 = divide(1f /* ty=float32 */, %634) /* ty=Tensor[(160), float32] */; + %636 = multiply(%635, %layers_14_bn3_weight) /* ty=Tensor[(160), float32] */; + %637 = nn.conv2d(%632, %layers_14_conv3_weight, padding=[0, 0, 0, 0], channels=160, kernel_size=[1, 1]) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %638 = expand_dims(%636, axis=1, num_newaxis=2) /* ty=Tensor[(160, 1, 1), float32] */; + %639 = negative(%layers_14_bn3_running_mean) /* ty=Tensor[(160), float32] */; + %640 = multiply(%639, %636) /* ty=Tensor[(160), float32] */; + %641 = add(%640, %layers_14_bn3_bias) /* ty=Tensor[(160), float32] */; + %642 = multiply(%637, %638) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %643 = expand_dims(%641, axis=1, num_newaxis=2) /* ty=Tensor[(160, 1, 1), float32] */; + %644 = add(%642, %643) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %645 = add(%644, %605) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %646 = add(%layers_15_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(960), float32] */; + %647 = sqrt(%646) /* ty=Tensor[(960), float32] */; + %648 = divide(1f /* ty=float32 */, %647) /* ty=Tensor[(960), float32] */; + %649 = multiply(%648, %layers_15_bn1_weight) /* ty=Tensor[(960), float32] */; + %650 = nn.conv2d(%645, %layers_15_conv1_weight, padding=[0, 0, 0, 0], channels=960, kernel_size=[1, 1]) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %651 = expand_dims(%649, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %652 = negative(%layers_15_bn1_running_mean) /* ty=Tensor[(960), float32] */; + %653 = multiply(%652, %649) /* ty=Tensor[(960), float32] */; + %654 = add(%653, %layers_15_bn1_bias) /* ty=Tensor[(960), float32] */; + %655 = multiply(%650, %651) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %656 = expand_dims(%654, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %657 = add(%655, %656) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %658 = nn.relu(%657) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %659 = reshape(%layers_15_conv2_weight, newshape=[960, 1, 3, 3]) /* ty=Tensor[(960, 1, 3, 3), float32] */; + %660 = add(%layers_15_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(960), float32] */; + %661 = sqrt(%660) /* ty=Tensor[(960), float32] */; + %662 = divide(1f /* ty=float32 */, %661) /* ty=Tensor[(960), float32] */; + %663 = multiply(%662, %layers_15_bn2_weight) /* ty=Tensor[(960), float32] */; + %664 = nn.conv2d(%658, %659, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3]) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %665 = expand_dims(%663, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %666 = negative(%layers_15_bn2_running_mean) /* ty=Tensor[(960), float32] */; + %667 = multiply(%666, %663) /* ty=Tensor[(960), float32] */; + %668 = add(%667, %layers_15_bn2_bias) /* ty=Tensor[(960), float32] */; + %669 = multiply(%664, %665) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %670 = expand_dims(%668, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %671 = add(%669, %670) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %672 = nn.relu(%671) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %673 = add(%layers_15_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(160), float32] */; + %674 = sqrt(%673) /* ty=Tensor[(160), float32] */; + %675 = divide(1f /* ty=float32 */, %674) /* ty=Tensor[(160), float32] */; + %676 = multiply(%675, %layers_15_bn3_weight) /* ty=Tensor[(160), float32] */; + %677 = nn.conv2d(%672, %layers_15_conv3_weight, padding=[0, 0, 0, 0], channels=160, kernel_size=[1, 1]) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %678 = expand_dims(%676, axis=1, num_newaxis=2) /* ty=Tensor[(160, 1, 1), float32] */; + %679 = negative(%layers_15_bn3_running_mean) /* ty=Tensor[(160), float32] */; + %680 = multiply(%679, %676) /* ty=Tensor[(160), float32] */; + %681 = add(%680, %layers_15_bn3_bias) /* ty=Tensor[(160), float32] */; + %682 = multiply(%677, %678) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %683 = expand_dims(%681, axis=1, num_newaxis=2) /* ty=Tensor[(160, 1, 1), float32] */; + %684 = add(%682, %683) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %685 = add(%684, %645) /* ty=Tensor[(1, 160, 4, 4), float32] */; + %686 = add(%layers_16_bn1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(960), float32] */; + %687 = sqrt(%686) /* ty=Tensor[(960), float32] */; + %688 = divide(1f /* ty=float32 */, %687) /* ty=Tensor[(960), float32] */; + %689 = multiply(%688, %layers_16_bn1_weight) /* ty=Tensor[(960), float32] */; + %690 = nn.conv2d(%685, %layers_16_conv1_weight, padding=[0, 0, 0, 0], channels=960, kernel_size=[1, 1]) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %691 = expand_dims(%689, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %692 = negative(%layers_16_bn1_running_mean) /* ty=Tensor[(960), float32] */; + %693 = multiply(%692, %689) /* ty=Tensor[(960), float32] */; + %694 = add(%693, %layers_16_bn1_bias) /* ty=Tensor[(960), float32] */; + %695 = multiply(%690, %691) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %696 = expand_dims(%694, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %697 = add(%695, %696) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %698 = nn.relu(%697) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %699 = reshape(%layers_16_conv2_weight, newshape=[960, 1, 3, 3]) /* ty=Tensor[(960, 1, 3, 3), float32] */; + %700 = add(%layers_16_bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(960), float32] */; + %701 = sqrt(%700) /* ty=Tensor[(960), float32] */; + %702 = divide(1f /* ty=float32 */, %701) /* ty=Tensor[(960), float32] */; + %703 = multiply(%702, %layers_16_bn2_weight) /* ty=Tensor[(960), float32] */; + %704 = nn.conv2d(%698, %699, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3]) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %705 = expand_dims(%703, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %706 = negative(%layers_16_bn2_running_mean) /* ty=Tensor[(960), float32] */; + %707 = multiply(%706, %703) /* ty=Tensor[(960), float32] */; + %708 = add(%707, %layers_16_bn2_bias) /* ty=Tensor[(960), float32] */; + %709 = multiply(%704, %705) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %710 = expand_dims(%708, axis=1, num_newaxis=2) /* ty=Tensor[(960, 1, 1), float32] */; + %711 = add(%709, %710) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %712 = nn.relu(%711) /* ty=Tensor[(1, 960, 4, 4), float32] */; + %713 = add(%layers_16_bn3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(320), float32] */; + %714 = sqrt(%713) /* ty=Tensor[(320), float32] */; + %715 = divide(1f /* ty=float32 */, %714) /* ty=Tensor[(320), float32] */; + %716 = multiply(%715, %layers_16_bn3_weight) /* ty=Tensor[(320), float32] */; + %717 = nn.conv2d(%712, %layers_16_conv3_weight, padding=[0, 0, 0, 0], channels=320, kernel_size=[1, 1]) /* ty=Tensor[(1, 320, 4, 4), float32] */; + %718 = expand_dims(%716, axis=1, num_newaxis=2) /* ty=Tensor[(320, 1, 1), float32] */; + %719 = negative(%layers_16_bn3_running_mean) /* ty=Tensor[(320), float32] */; + %720 = multiply(%719, %716) /* ty=Tensor[(320), float32] */; + %721 = add(%720, %layers_16_bn3_bias) /* ty=Tensor[(320), float32] */; + %722 = multiply(%717, %718) /* ty=Tensor[(1, 320, 4, 4), float32] */; + %723 = expand_dims(%721, axis=1, num_newaxis=2) /* ty=Tensor[(320, 1, 1), float32] */; + %724 = add(%layers_16_shortcut_1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(320), float32] */; + %725 = sqrt(%724) /* ty=Tensor[(320), float32] */; + %726 = divide(1f /* ty=float32 */, %725) /* ty=Tensor[(320), float32] */; + %727 = multiply(%726, %layers_16_shortcut_1_weight) /* ty=Tensor[(320), float32] */; + %728 = nn.conv2d(%685, %layers_16_shortcut_0_weight, padding=[0, 0, 0, 0], channels=320, kernel_size=[1, 1]) /* ty=Tensor[(1, 320, 4, 4), float32] */; + %729 = expand_dims(%727, axis=1, num_newaxis=2) /* ty=Tensor[(320, 1, 1), float32] */; + %730 = negative(%layers_16_shortcut_1_running_mean) /* ty=Tensor[(320), float32] */; + %731 = multiply(%730, %727) /* ty=Tensor[(320), float32] */; + %732 = add(%731, %layers_16_shortcut_1_bias) /* ty=Tensor[(320), float32] */; + %733 = multiply(%728, %729) /* ty=Tensor[(1, 320, 4, 4), float32] */; + %734 = expand_dims(%732, axis=1, num_newaxis=2) /* ty=Tensor[(320, 1, 1), float32] */; + %735 = add(%722, %723) /* ty=Tensor[(1, 320, 4, 4), float32] */; + %736 = add(%733, %734) /* ty=Tensor[(1, 320, 4, 4), float32] */; + %737 = add(%735, %736) /* ty=Tensor[(1, 320, 4, 4), float32] */; + %738 = add(%bn2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(1280), float32] */; + %739 = sqrt(%738) /* ty=Tensor[(1280), float32] */; + %740 = divide(1f /* ty=float32 */, %739) /* ty=Tensor[(1280), float32] */; + %741 = multiply(%740, %bn2_weight) /* ty=Tensor[(1280), float32] */; + %742 = nn.conv2d(%737, %conv2_weight, padding=[0, 0, 0, 0], channels=1280, kernel_size=[1, 1]) /* ty=Tensor[(1, 1280, 4, 4), float32] */; + %743 = expand_dims(%741, axis=1, num_newaxis=2) /* ty=Tensor[(1280, 1, 1), float32] */; + %744 = negative(%bn2_running_mean) /* ty=Tensor[(1280), float32] */; + %745 = multiply(%744, %741) /* ty=Tensor[(1280), float32] */; + %746 = add(%745, %bn2_bias) /* ty=Tensor[(1280), float32] */; + %747 = multiply(%742, %743) /* ty=Tensor[(1, 1280, 4, 4), float32] */; + %748 = expand_dims(%746, axis=1, num_newaxis=2) /* ty=Tensor[(1280, 1, 1), float32] */; + %749 = add(%747, %748) /* ty=Tensor[(1, 1280, 4, 4), float32] */; + %750 = nn.relu(%749) /* ty=Tensor[(1, 1280, 4, 4), float32] */; + %751 = nn.avg_pool2d(%750, pool_size=[4, 4], strides=[4, 4], padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 1280, 1, 1), float32] */; + %752 = transpose(%linear_weight, axes=[1, 0]) /* ty=Tensor[(1280, 10), float32] */; + %753 = reshape(%751, newshape=[1, -1]) /* ty=Tensor[(1, 1280), float32] */; + %754 = transpose(%752, axes=[1, 0]) /* ty=Tensor[(10, 1280), float32] */; + %755 = nn.dense(%753, %754, units=10) /* ty=Tensor[(1, 10), float32] */; + add(%755, %linear_bias) /* ty=Tensor[(1, 10), float32] */ +} diff --git a/tests/models/mxnet-resnet.relay b/tests/models/mxnet-resnet.relay new file mode 100644 index 0000000..aa80f30 --- /dev/null +++ b/tests/models/mxnet-resnet.relay @@ -0,0 +1,542 @@ +#[version = "0.0.5"] +def @main(%cifarresnetv20_batchnorm0_running_var: Tensor[(3), float32], %cifarresnetv20_batchnorm0_running_mean: Tensor[(3), float32], %cifarresnetv20_batchnorm0_beta: Tensor[(3), float32], %data: Tensor[(1, 3, 32, 32), float32], %cifarresnetv20_stage1_batchnorm0_running_var: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm0_gamma: Tensor[(16), float32], %cifarresnetv20_conv0_weight: Tensor[(16, 3, 3, 3), float32], %cifarresnetv20_stage1_batchnorm0_running_mean: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm0_beta: Tensor[(16), float32], %cifarresnetv20_stage1_conv0_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv20_stage1_batchnorm1_running_var: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm1_gamma: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm1_running_mean: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm1_beta: Tensor[(16), float32], %cifarresnetv20_stage1_conv1_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv20_stage1_batchnorm2_running_var: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm2_gamma: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm2_running_mean: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm2_beta: Tensor[(16), float32], %cifarresnetv20_stage1_conv2_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv20_stage1_batchnorm3_running_var: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm3_gamma: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm3_running_mean: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm3_beta: Tensor[(16), float32], %cifarresnetv20_stage1_conv3_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv20_stage1_batchnorm4_running_var: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm4_gamma: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm4_running_mean: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm4_beta: Tensor[(16), float32], %cifarresnetv20_stage1_conv4_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv20_stage1_batchnorm5_running_var: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm5_gamma: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm5_running_mean: Tensor[(16), float32], %cifarresnetv20_stage1_batchnorm5_beta: Tensor[(16), float32], %cifarresnetv20_stage1_conv5_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv20_stage2_batchnorm0_running_var: Tensor[(16), float32], %cifarresnetv20_stage2_batchnorm0_gamma: Tensor[(16), float32], %cifarresnetv20_stage2_batchnorm0_running_mean: Tensor[(16), float32], %cifarresnetv20_stage2_batchnorm0_beta: Tensor[(16), float32], %cifarresnetv20_stage2_conv0_weight: Tensor[(32, 16, 3, 3), float32], %cifarresnetv20_stage2_batchnorm1_running_var: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm1_gamma: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm1_running_mean: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm1_beta: Tensor[(32), float32], %cifarresnetv20_stage2_conv1_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv20_stage2_conv2_weight: Tensor[(32, 16, 1, 1), float32], %cifarresnetv20_stage2_batchnorm2_running_var: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm2_gamma: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm2_running_mean: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm2_beta: Tensor[(32), float32], %cifarresnetv20_stage2_conv3_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv20_stage2_batchnorm3_running_var: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm3_gamma: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm3_running_mean: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm3_beta: Tensor[(32), float32], %cifarresnetv20_stage2_conv4_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv20_stage2_batchnorm4_running_var: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm4_gamma: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm4_running_mean: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm4_beta: Tensor[(32), float32], %cifarresnetv20_stage2_conv5_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv20_stage2_batchnorm5_running_var: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm5_gamma: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm5_running_mean: Tensor[(32), float32], %cifarresnetv20_stage2_batchnorm5_beta: Tensor[(32), float32], %cifarresnetv20_stage2_conv6_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv20_stage3_batchnorm0_running_var: Tensor[(32), float32], %cifarresnetv20_stage3_batchnorm0_gamma: Tensor[(32), float32], %cifarresnetv20_stage3_batchnorm0_running_mean: Tensor[(32), float32], %cifarresnetv20_stage3_batchnorm0_beta: Tensor[(32), float32], %cifarresnetv20_stage3_conv0_weight: Tensor[(64, 32, 3, 3), float32], %cifarresnetv20_stage3_batchnorm1_running_var: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm1_gamma: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm1_running_mean: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm1_beta: Tensor[(64), float32], %cifarresnetv20_stage3_conv1_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv20_stage3_conv2_weight: Tensor[(64, 32, 1, 1), float32], %cifarresnetv20_stage3_batchnorm2_running_var: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm2_gamma: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm2_running_mean: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm2_beta: Tensor[(64), float32], %cifarresnetv20_stage3_conv3_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv20_stage3_batchnorm3_running_var: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm3_gamma: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm3_running_mean: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm3_beta: Tensor[(64), float32], %cifarresnetv20_stage3_conv4_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv20_stage3_batchnorm4_running_var: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm4_gamma: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm4_running_mean: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm4_beta: Tensor[(64), float32], %cifarresnetv20_stage3_conv5_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv20_stage3_batchnorm5_running_var: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm5_gamma: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm5_running_mean: Tensor[(64), float32], %cifarresnetv20_stage3_batchnorm5_beta: Tensor[(64), float32], %cifarresnetv20_stage3_conv6_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv20_batchnorm1_running_var: Tensor[(64), float32], %cifarresnetv20_batchnorm1_gamma: Tensor[(64), float32], %cifarresnetv20_batchnorm1_running_mean: Tensor[(64), float32], %cifarresnetv20_batchnorm1_beta: Tensor[(64), float32], %cifarresnetv20_dense0_weight: Tensor[(10, 64), float32], %cifarresnetv20_dense0_bias: Tensor[(10), float32]) -> Tensor[(1, 10), float32] { + let %var_22: Tensor[(3), float32] = add(%cifarresnetv20_batchnorm0_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(3), float32] */; + let %var_23: Tensor[(3), float32] = sqrt(%var_22) /* ty=Tensor[(3), float32] */; + let %var_24: Tensor[(3), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_23) /* ty=Tensor[(3), float32] */; + let %var_25: Tensor[(3, 1), float32] = expand_dims(%var_24, axis=1) /* ty=Tensor[(3, 1), float32] */; + let %var_26: Tensor[(3, 1, 1), float32] = expand_dims(%var_25, axis=1) /* ty=Tensor[(3, 1, 1), float32] */; + let %var_28: Tensor[(3), float32] = negative(%cifarresnetv20_batchnorm0_running_mean) /* ty=Tensor[(3), float32] */; + let %var_30: Tensor[(3), float32] = multiply(%var_28, %var_24) /* ty=Tensor[(3), float32] */; + let %var_31: Tensor[(3), float32] = add(%var_30, %cifarresnetv20_batchnorm0_beta) /* ty=Tensor[(3), float32] */; + let %var_32: Tensor[(3, 1), float32] = expand_dims(%var_31, axis=1) /* ty=Tensor[(3, 1), float32] */; + let %var_33: Tensor[(1, 3, 32, 32), float32] = multiply(%data, %var_26) /* ty=Tensor[(1, 3, 32, 32), float32] */; + let %var_34: Tensor[(3, 1, 1), float32] = expand_dims(%var_32, axis=1) /* ty=Tensor[(3, 1, 1), float32] */; + let %var_36: Tensor[(1, 3, 32, 32), float32] = add(%var_33, %var_34) /* ty=Tensor[(1, 3, 32, 32), float32] */; + let %var_38: Tensor[(16), float32] = add(%cifarresnetv20_stage1_batchnorm0_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + let %var_39: Tensor[(16), float32] = sqrt(%var_38) /* ty=Tensor[(16), float32] */; + let %var_41: Tensor[(16), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_39) /* ty=Tensor[(16), float32] */; + let %var_42: Tensor[(16), float32] = multiply(%var_41, %cifarresnetv20_stage1_batchnorm0_gamma) /* ty=Tensor[(16), float32] */; + let %var_43: Tensor[(16, 1), float32] = expand_dims(%var_42, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_44: Tensor[(1, 16, 32, 32), float32] = nn.conv2d(%var_36, %cifarresnetv20_conv0_weight, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_45: Tensor[(16, 1, 1), float32] = expand_dims(%var_43, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_47: Tensor[(16), float32] = negative(%cifarresnetv20_stage1_batchnorm0_running_mean) /* ty=Tensor[(16), float32] */; + let %var_49: Tensor[(16), float32] = multiply(%var_47, %var_42) /* ty=Tensor[(16), float32] */; + let %var_50: Tensor[(16), float32] = add(%var_49, %cifarresnetv20_stage1_batchnorm0_beta) /* ty=Tensor[(16), float32] */; + let %var_51: Tensor[(16, 1), float32] = expand_dims(%var_50, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_52: Tensor[(1, 16, 32, 32), float32] = multiply(%var_44, %var_45) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_53: Tensor[(16, 1, 1), float32] = expand_dims(%var_51, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_54: Tensor[(1, 16, 32, 32), float32] = add(%var_52, %var_53) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_55: Tensor[(1, 16, 32, 32), float32] = nn.relu(%var_54) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_56: Tensor[(1, 16, 34, 32), float32] = nn.pad(%var_55, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 16, 34, 32), float32] */; + let %var_57: Tensor[(1, 16, 34, 34), float32] = nn.pad(%var_56, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 16, 34, 34), float32] */; + let %var_58: Tensor[(1, 1, 32, 32, 16, 3, 3), float32] = sliding_window(%var_57, axis=1, window_shape=[16, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 32, 32, 16, 3, 3), float32] */; + let %var_59: Tensor[(1, 32, 32, 16, 3, 3), float32] = squeeze(%var_58, axis=[1]) /* ty=Tensor[(1, 32, 32, 16, 3, 3), float32] */; + let %var_60: Tensor[(16, 144), float32] = reshape(%cifarresnetv20_stage1_conv0_weight, newshape=[16, 144]) /* ty=Tensor[(16, 144), float32] */; + let %var_61: Tensor[(1024, 144), float32] = reshape(%var_59, newshape=[1024, 144]) /* ty=Tensor[(1024, 144), float32] */; + let %var_62: Tensor[(16, 1024), float32] = nn.dense(%var_60, %var_61, units=None) /* ty=Tensor[(16, 1024), float32] */; + let %var_63: Tensor[(16, 1, 32, 32), float32] = reshape(%var_62, newshape=[16, 1, 32, 32]) /* ty=Tensor[(16, 1, 32, 32), float32] */; + let %var_65: Tensor[(16), float32] = add(%cifarresnetv20_stage1_batchnorm1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + let %var_66: Tensor[(16), float32] = sqrt(%var_65) /* ty=Tensor[(16), float32] */; + let %var_68: Tensor[(16), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_66) /* ty=Tensor[(16), float32] */; + let %var_69: Tensor[(16), float32] = multiply(%var_68, %cifarresnetv20_stage1_batchnorm1_gamma) /* ty=Tensor[(16), float32] */; + let %var_70: Tensor[(16, 1), float32] = expand_dims(%var_69, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_71: Tensor[(1, 16, 32, 32), float32] = transpose(%var_63, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_72: Tensor[(16, 1, 1), float32] = expand_dims(%var_70, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_74: Tensor[(16), float32] = negative(%cifarresnetv20_stage1_batchnorm1_running_mean) /* ty=Tensor[(16), float32] */; + let %var_76: Tensor[(16), float32] = multiply(%var_74, %var_69) /* ty=Tensor[(16), float32] */; + let %var_77: Tensor[(16), float32] = add(%var_76, %cifarresnetv20_stage1_batchnorm1_beta) /* ty=Tensor[(16), float32] */; + let %var_78: Tensor[(16, 1), float32] = expand_dims(%var_77, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_79: Tensor[(1, 16, 32, 32), float32] = multiply(%var_71, %var_72) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_80: Tensor[(16, 1, 1), float32] = expand_dims(%var_78, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_81: Tensor[(1, 16, 32, 32), float32] = add(%var_79, %var_80) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_82: Tensor[(1, 16, 32, 32), float32] = nn.relu(%var_81) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_83: Tensor[(1, 16, 34, 32), float32] = nn.pad(%var_82, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 16, 34, 32), float32] */; + let %var_84: Tensor[(1, 16, 34, 34), float32] = nn.pad(%var_83, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 16, 34, 34), float32] */; + let %var_85: Tensor[(1, 1, 32, 32, 16, 3, 3), float32] = sliding_window(%var_84, axis=1, window_shape=[16, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 32, 32, 16, 3, 3), float32] */; + let %var_86: Tensor[(1, 32, 32, 16, 3, 3), float32] = squeeze(%var_85, axis=[1]) /* ty=Tensor[(1, 32, 32, 16, 3, 3), float32] */; + let %var_87: Tensor[(16, 144), float32] = reshape(%cifarresnetv20_stage1_conv1_weight, newshape=[16, 144]) /* ty=Tensor[(16, 144), float32] */; + let %var_88: Tensor[(1024, 144), float32] = reshape(%var_86, newshape=[1024, 144]) /* ty=Tensor[(1024, 144), float32] */; + let %var_89: Tensor[(16, 1024), float32] = nn.dense(%var_87, %var_88, units=None) /* ty=Tensor[(16, 1024), float32] */; + let %var_90: Tensor[(16, 1, 32, 32), float32] = reshape(%var_89, newshape=[16, 1, 32, 32]) /* ty=Tensor[(16, 1, 32, 32), float32] */; + let %var_91: Tensor[(1, 16, 32, 32), float32] = transpose(%var_90, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_93: Tensor[(16), float32] = add(%cifarresnetv20_stage1_batchnorm2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + let %var_94: Tensor[(16), float32] = sqrt(%var_93) /* ty=Tensor[(16), float32] */; + let %var_96: Tensor[(16), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_94) /* ty=Tensor[(16), float32] */; + let %var_97: Tensor[(16), float32] = multiply(%var_96, %cifarresnetv20_stage1_batchnorm2_gamma) /* ty=Tensor[(16), float32] */; + let %var_98: Tensor[(16, 1), float32] = expand_dims(%var_97, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_99: Tensor[(1, 16, 32, 32), float32] = add(%var_91, %var_44) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_100: Tensor[(16, 1, 1), float32] = expand_dims(%var_98, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_102: Tensor[(16), float32] = negative(%cifarresnetv20_stage1_batchnorm2_running_mean) /* ty=Tensor[(16), float32] */; + let %var_104: Tensor[(16), float32] = multiply(%var_102, %var_97) /* ty=Tensor[(16), float32] */; + let %var_105: Tensor[(16), float32] = add(%var_104, %cifarresnetv20_stage1_batchnorm2_beta) /* ty=Tensor[(16), float32] */; + let %var_106: Tensor[(16, 1), float32] = expand_dims(%var_105, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_107: Tensor[(1, 16, 32, 32), float32] = multiply(%var_99, %var_100) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_108: Tensor[(16, 1, 1), float32] = expand_dims(%var_106, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_109: Tensor[(1, 16, 32, 32), float32] = add(%var_107, %var_108) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_110: Tensor[(1, 16, 32, 32), float32] = nn.relu(%var_109) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_111: Tensor[(1, 16, 34, 32), float32] = nn.pad(%var_110, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 16, 34, 32), float32] */; + let %var_112: Tensor[(1, 16, 34, 34), float32] = nn.pad(%var_111, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 16, 34, 34), float32] */; + let %var_113: Tensor[(1, 1, 32, 32, 16, 3, 3), float32] = sliding_window(%var_112, axis=1, window_shape=[16, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 32, 32, 16, 3, 3), float32] */; + let %var_114: Tensor[(1, 32, 32, 16, 3, 3), float32] = squeeze(%var_113, axis=[1]) /* ty=Tensor[(1, 32, 32, 16, 3, 3), float32] */; + let %var_115: Tensor[(16, 144), float32] = reshape(%cifarresnetv20_stage1_conv2_weight, newshape=[16, 144]) /* ty=Tensor[(16, 144), float32] */; + let %var_116: Tensor[(1024, 144), float32] = reshape(%var_114, newshape=[1024, 144]) /* ty=Tensor[(1024, 144), float32] */; + let %var_117: Tensor[(16, 1024), float32] = nn.dense(%var_115, %var_116, units=None) /* ty=Tensor[(16, 1024), float32] */; + let %var_118: Tensor[(16, 1, 32, 32), float32] = reshape(%var_117, newshape=[16, 1, 32, 32]) /* ty=Tensor[(16, 1, 32, 32), float32] */; + let %var_120: Tensor[(16), float32] = add(%cifarresnetv20_stage1_batchnorm3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + let %var_121: Tensor[(16), float32] = sqrt(%var_120) /* ty=Tensor[(16), float32] */; + let %var_123: Tensor[(16), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_121) /* ty=Tensor[(16), float32] */; + let %var_124: Tensor[(16), float32] = multiply(%var_123, %cifarresnetv20_stage1_batchnorm3_gamma) /* ty=Tensor[(16), float32] */; + let %var_125: Tensor[(16, 1), float32] = expand_dims(%var_124, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_126: Tensor[(1, 16, 32, 32), float32] = transpose(%var_118, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_127: Tensor[(16, 1, 1), float32] = expand_dims(%var_125, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_129: Tensor[(16), float32] = negative(%cifarresnetv20_stage1_batchnorm3_running_mean) /* ty=Tensor[(16), float32] */; + let %var_131: Tensor[(16), float32] = multiply(%var_129, %var_124) /* ty=Tensor[(16), float32] */; + let %var_132: Tensor[(16), float32] = add(%var_131, %cifarresnetv20_stage1_batchnorm3_beta) /* ty=Tensor[(16), float32] */; + let %var_133: Tensor[(16, 1), float32] = expand_dims(%var_132, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_134: Tensor[(1, 16, 32, 32), float32] = multiply(%var_126, %var_127) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_135: Tensor[(16, 1, 1), float32] = expand_dims(%var_133, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_136: Tensor[(1, 16, 32, 32), float32] = add(%var_134, %var_135) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_137: Tensor[(1, 16, 32, 32), float32] = nn.relu(%var_136) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_138: Tensor[(1, 16, 34, 32), float32] = nn.pad(%var_137, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 16, 34, 32), float32] */; + let %var_139: Tensor[(1, 16, 34, 34), float32] = nn.pad(%var_138, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 16, 34, 34), float32] */; + let %var_140: Tensor[(1, 1, 32, 32, 16, 3, 3), float32] = sliding_window(%var_139, axis=1, window_shape=[16, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 32, 32, 16, 3, 3), float32] */; + let %var_141: Tensor[(1, 32, 32, 16, 3, 3), float32] = squeeze(%var_140, axis=[1]) /* ty=Tensor[(1, 32, 32, 16, 3, 3), float32] */; + let %var_142: Tensor[(16, 144), float32] = reshape(%cifarresnetv20_stage1_conv3_weight, newshape=[16, 144]) /* ty=Tensor[(16, 144), float32] */; + let %var_143: Tensor[(1024, 144), float32] = reshape(%var_141, newshape=[1024, 144]) /* ty=Tensor[(1024, 144), float32] */; + let %var_144: Tensor[(16, 1024), float32] = nn.dense(%var_142, %var_143, units=None) /* ty=Tensor[(16, 1024), float32] */; + let %var_145: Tensor[(16, 1, 32, 32), float32] = reshape(%var_144, newshape=[16, 1, 32, 32]) /* ty=Tensor[(16, 1, 32, 32), float32] */; + let %var_146: Tensor[(1, 16, 32, 32), float32] = transpose(%var_145, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_148: Tensor[(16), float32] = add(%cifarresnetv20_stage1_batchnorm4_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + let %var_149: Tensor[(16), float32] = sqrt(%var_148) /* ty=Tensor[(16), float32] */; + let %var_151: Tensor[(16), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_149) /* ty=Tensor[(16), float32] */; + let %var_152: Tensor[(16), float32] = multiply(%var_151, %cifarresnetv20_stage1_batchnorm4_gamma) /* ty=Tensor[(16), float32] */; + let %var_153: Tensor[(16, 1), float32] = expand_dims(%var_152, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_154: Tensor[(1, 16, 32, 32), float32] = add(%var_146, %var_99) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_155: Tensor[(16, 1, 1), float32] = expand_dims(%var_153, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_157: Tensor[(16), float32] = negative(%cifarresnetv20_stage1_batchnorm4_running_mean) /* ty=Tensor[(16), float32] */; + let %var_159: Tensor[(16), float32] = multiply(%var_157, %var_152) /* ty=Tensor[(16), float32] */; + let %var_160: Tensor[(16), float32] = add(%var_159, %cifarresnetv20_stage1_batchnorm4_beta) /* ty=Tensor[(16), float32] */; + let %var_161: Tensor[(16, 1), float32] = expand_dims(%var_160, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_162: Tensor[(1, 16, 32, 32), float32] = multiply(%var_154, %var_155) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_163: Tensor[(16, 1, 1), float32] = expand_dims(%var_161, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_164: Tensor[(1, 16, 32, 32), float32] = add(%var_162, %var_163) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_165: Tensor[(1, 16, 32, 32), float32] = nn.relu(%var_164) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_166: Tensor[(1, 16, 34, 32), float32] = nn.pad(%var_165, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 16, 34, 32), float32] */; + let %var_167: Tensor[(1, 16, 34, 34), float32] = nn.pad(%var_166, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 16, 34, 34), float32] */; + let %var_168: Tensor[(1, 1, 32, 32, 16, 3, 3), float32] = sliding_window(%var_167, axis=1, window_shape=[16, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 32, 32, 16, 3, 3), float32] */; + let %var_169: Tensor[(1, 32, 32, 16, 3, 3), float32] = squeeze(%var_168, axis=[1]) /* ty=Tensor[(1, 32, 32, 16, 3, 3), float32] */; + let %var_170: Tensor[(16, 144), float32] = reshape(%cifarresnetv20_stage1_conv4_weight, newshape=[16, 144]) /* ty=Tensor[(16, 144), float32] */; + let %var_171: Tensor[(1024, 144), float32] = reshape(%var_169, newshape=[1024, 144]) /* ty=Tensor[(1024, 144), float32] */; + let %var_172: Tensor[(16, 1024), float32] = nn.dense(%var_170, %var_171, units=None) /* ty=Tensor[(16, 1024), float32] */; + let %var_173: Tensor[(16, 1, 32, 32), float32] = reshape(%var_172, newshape=[16, 1, 32, 32]) /* ty=Tensor[(16, 1, 32, 32), float32] */; + let %var_175: Tensor[(16), float32] = add(%cifarresnetv20_stage1_batchnorm5_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + let %var_176: Tensor[(16), float32] = sqrt(%var_175) /* ty=Tensor[(16), float32] */; + let %var_178: Tensor[(16), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_176) /* ty=Tensor[(16), float32] */; + let %var_179: Tensor[(16), float32] = multiply(%var_178, %cifarresnetv20_stage1_batchnorm5_gamma) /* ty=Tensor[(16), float32] */; + let %var_180: Tensor[(16, 1), float32] = expand_dims(%var_179, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_181: Tensor[(1, 16, 32, 32), float32] = transpose(%var_173, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_182: Tensor[(16, 1, 1), float32] = expand_dims(%var_180, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_184: Tensor[(16), float32] = negative(%cifarresnetv20_stage1_batchnorm5_running_mean) /* ty=Tensor[(16), float32] */; + let %var_186: Tensor[(16), float32] = multiply(%var_184, %var_179) /* ty=Tensor[(16), float32] */; + let %var_187: Tensor[(16), float32] = add(%var_186, %cifarresnetv20_stage1_batchnorm5_beta) /* ty=Tensor[(16), float32] */; + let %var_188: Tensor[(16, 1), float32] = expand_dims(%var_187, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_189: Tensor[(1, 16, 32, 32), float32] = multiply(%var_181, %var_182) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_190: Tensor[(16, 1, 1), float32] = expand_dims(%var_188, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_191: Tensor[(1, 16, 32, 32), float32] = add(%var_189, %var_190) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_192: Tensor[(1, 16, 32, 32), float32] = nn.relu(%var_191) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_193: Tensor[(1, 16, 34, 32), float32] = nn.pad(%var_192, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 16, 34, 32), float32] */; + let %var_194: Tensor[(1, 16, 34, 34), float32] = nn.pad(%var_193, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 16, 34, 34), float32] */; + let %var_195: Tensor[(1, 1, 32, 32, 16, 3, 3), float32] = sliding_window(%var_194, axis=1, window_shape=[16, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 32, 32, 16, 3, 3), float32] */; + let %var_196: Tensor[(1, 32, 32, 16, 3, 3), float32] = squeeze(%var_195, axis=[1]) /* ty=Tensor[(1, 32, 32, 16, 3, 3), float32] */; + let %var_197: Tensor[(16, 144), float32] = reshape(%cifarresnetv20_stage1_conv5_weight, newshape=[16, 144]) /* ty=Tensor[(16, 144), float32] */; + let %var_198: Tensor[(1024, 144), float32] = reshape(%var_196, newshape=[1024, 144]) /* ty=Tensor[(1024, 144), float32] */; + let %var_199: Tensor[(16, 1024), float32] = nn.dense(%var_197, %var_198, units=None) /* ty=Tensor[(16, 1024), float32] */; + let %var_200: Tensor[(16, 1, 32, 32), float32] = reshape(%var_199, newshape=[16, 1, 32, 32]) /* ty=Tensor[(16, 1, 32, 32), float32] */; + let %var_201: Tensor[(1, 16, 32, 32), float32] = transpose(%var_200, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_203: Tensor[(16), float32] = add(%cifarresnetv20_stage2_batchnorm0_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + let %var_204: Tensor[(16), float32] = sqrt(%var_203) /* ty=Tensor[(16), float32] */; + let %var_206: Tensor[(16), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_204) /* ty=Tensor[(16), float32] */; + let %var_207: Tensor[(16), float32] = multiply(%var_206, %cifarresnetv20_stage2_batchnorm0_gamma) /* ty=Tensor[(16), float32] */; + let %var_208: Tensor[(16, 1), float32] = expand_dims(%var_207, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_209: Tensor[(1, 16, 32, 32), float32] = add(%var_201, %var_154) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_210: Tensor[(16, 1, 1), float32] = expand_dims(%var_208, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_212: Tensor[(16), float32] = negative(%cifarresnetv20_stage2_batchnorm0_running_mean) /* ty=Tensor[(16), float32] */; + let %var_214: Tensor[(16), float32] = multiply(%var_212, %var_207) /* ty=Tensor[(16), float32] */; + let %var_215: Tensor[(16), float32] = add(%var_214, %cifarresnetv20_stage2_batchnorm0_beta) /* ty=Tensor[(16), float32] */; + let %var_216: Tensor[(16, 1), float32] = expand_dims(%var_215, axis=1) /* ty=Tensor[(16, 1), float32] */; + let %var_217: Tensor[(1, 16, 32, 32), float32] = multiply(%var_209, %var_210) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_218: Tensor[(16, 1, 1), float32] = expand_dims(%var_216, axis=1) /* ty=Tensor[(16, 1, 1), float32] */; + let %var_219: Tensor[(1, 16, 32, 32), float32] = add(%var_217, %var_218) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_220: Tensor[(1, 16, 32, 32), float32] = nn.relu(%var_219) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_221: Tensor[(1, 16, 34, 32), float32] = nn.pad(%var_220, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 16, 34, 32), float32] */; + let %var_222: Tensor[(1, 16, 34, 34), float32] = nn.pad(%var_221, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 16, 34, 34), float32] */; + let %var_223: Tensor[(1, 1, 16, 16, 16, 3, 3), float32] = sliding_window(%var_222, axis=1, window_shape=[16, 3, 3], strides=[1, 2, 2]) /* ty=Tensor[(1, 1, 16, 16, 16, 3, 3), float32] */; + let %var_224: Tensor[(1, 16, 16, 16, 3, 3), float32] = squeeze(%var_223, axis=[1]) /* ty=Tensor[(1, 16, 16, 16, 3, 3), float32] */; + let %var_225: Tensor[(32, 144), float32] = reshape(%cifarresnetv20_stage2_conv0_weight, newshape=[32, 144]) /* ty=Tensor[(32, 144), float32] */; + let %var_226: Tensor[(256, 144), float32] = reshape(%var_224, newshape=[256, 144]) /* ty=Tensor[(256, 144), float32] */; + let %var_227: Tensor[(32, 256), float32] = nn.dense(%var_225, %var_226, units=None) /* ty=Tensor[(32, 256), float32] */; + let %var_228: Tensor[(32, 1, 16, 16), float32] = reshape(%var_227, newshape=[32, 1, 16, 16]) /* ty=Tensor[(32, 1, 16, 16), float32] */; + let %var_230: Tensor[(32), float32] = add(%cifarresnetv20_stage2_batchnorm1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + let %var_231: Tensor[(32), float32] = sqrt(%var_230) /* ty=Tensor[(32), float32] */; + let %var_233: Tensor[(32), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_231) /* ty=Tensor[(32), float32] */; + let %var_234: Tensor[(32), float32] = multiply(%var_233, %cifarresnetv20_stage2_batchnorm1_gamma) /* ty=Tensor[(32), float32] */; + let %var_235: Tensor[(32, 1), float32] = expand_dims(%var_234, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_236: Tensor[(1, 32, 16, 16), float32] = transpose(%var_228, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_237: Tensor[(32, 1, 1), float32] = expand_dims(%var_235, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_239: Tensor[(32), float32] = negative(%cifarresnetv20_stage2_batchnorm1_running_mean) /* ty=Tensor[(32), float32] */; + let %var_241: Tensor[(32), float32] = multiply(%var_239, %var_234) /* ty=Tensor[(32), float32] */; + let %var_242: Tensor[(32), float32] = add(%var_241, %cifarresnetv20_stage2_batchnorm1_beta) /* ty=Tensor[(32), float32] */; + let %var_243: Tensor[(32, 1), float32] = expand_dims(%var_242, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_244: Tensor[(1, 32, 16, 16), float32] = multiply(%var_236, %var_237) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_245: Tensor[(32, 1, 1), float32] = expand_dims(%var_243, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_246: Tensor[(1, 32, 16, 16), float32] = add(%var_244, %var_245) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_247: Tensor[(1, 32, 16, 16), float32] = nn.relu(%var_246) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_248: Tensor[(1, 32, 18, 16), float32] = nn.pad(%var_247, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 32, 18, 16), float32] */; + let %var_249: Tensor[(1, 32, 18, 18), float32] = nn.pad(%var_248, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 32, 18, 18), float32] */; + let %var_250: Tensor[(1, 1, 16, 16, 32, 3, 3), float32] = sliding_window(%var_249, axis=1, window_shape=[32, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 16, 16, 32, 3, 3), float32] */; + let %var_251: Tensor[(1, 16, 16, 32, 3, 3), float32] = squeeze(%var_250, axis=[1]) /* ty=Tensor[(1, 16, 16, 32, 3, 3), float32] */; + let %var_252: Tensor[(32, 288), float32] = reshape(%cifarresnetv20_stage2_conv1_weight, newshape=[32, 288]) /* ty=Tensor[(32, 288), float32] */; + let %var_253: Tensor[(256, 288), float32] = reshape(%var_251, newshape=[256, 288]) /* ty=Tensor[(256, 288), float32] */; + let %var_254: Tensor[(32, 256), float32] = nn.dense(%var_252, %var_253, units=None) /* ty=Tensor[(32, 256), float32] */; + let %var_255: Tensor[(32, 1, 16, 16), float32] = reshape(%var_254, newshape=[32, 1, 16, 16]) /* ty=Tensor[(32, 1, 16, 16), float32] */; + let %var_257: Tensor[(1, 16, 32, 32), float32] = nn.pad(%var_209, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_258: Tensor[(1, 16, 32, 32), float32] = nn.pad(%var_257, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* ty=Tensor[(1, 16, 32, 32), float32] */; + let %var_259: Tensor[(1, 1, 16, 16, 16, 1, 1), float32] = sliding_window(%var_258, axis=1, window_shape=[16, 1, 1], strides=[1, 2, 2]) /* ty=Tensor[(1, 1, 16, 16, 16, 1, 1), float32] */; + let %var_260: Tensor[(1, 16, 16, 16, 1, 1), float32] = squeeze(%var_259, axis=[1]) /* ty=Tensor[(1, 16, 16, 16, 1, 1), float32] */; + let %var_261: Tensor[(32, 16), float32] = reshape(%cifarresnetv20_stage2_conv2_weight, newshape=[32, 16]) /* ty=Tensor[(32, 16), float32] */; + let %var_262: Tensor[(256, 16), float32] = reshape(%var_260, newshape=[256, 16]) /* ty=Tensor[(256, 16), float32] */; + let %var_263: Tensor[(32, 256), float32] = nn.dense(%var_261, %var_262, units=None) /* ty=Tensor[(32, 256), float32] */; + let %var_264: Tensor[(32, 1, 16, 16), float32] = reshape(%var_263, newshape=[32, 1, 16, 16]) /* ty=Tensor[(32, 1, 16, 16), float32] */; + let %var_265: Tensor[(1, 32, 16, 16), float32] = transpose(%var_255, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_266: Tensor[(1, 32, 16, 16), float32] = transpose(%var_264, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_268: Tensor[(32), float32] = add(%cifarresnetv20_stage2_batchnorm2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + let %var_269: Tensor[(32), float32] = sqrt(%var_268) /* ty=Tensor[(32), float32] */; + let %var_271: Tensor[(32), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_269) /* ty=Tensor[(32), float32] */; + let %var_272: Tensor[(32), float32] = multiply(%var_271, %cifarresnetv20_stage2_batchnorm2_gamma) /* ty=Tensor[(32), float32] */; + let %var_273: Tensor[(32, 1), float32] = expand_dims(%var_272, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_274: Tensor[(1, 32, 16, 16), float32] = add(%var_265, %var_266) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_275: Tensor[(32, 1, 1), float32] = expand_dims(%var_273, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_277: Tensor[(32), float32] = negative(%cifarresnetv20_stage2_batchnorm2_running_mean) /* ty=Tensor[(32), float32] */; + let %var_279: Tensor[(32), float32] = multiply(%var_277, %var_272) /* ty=Tensor[(32), float32] */; + let %var_280: Tensor[(32), float32] = add(%var_279, %cifarresnetv20_stage2_batchnorm2_beta) /* ty=Tensor[(32), float32] */; + let %var_281: Tensor[(32, 1), float32] = expand_dims(%var_280, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_282: Tensor[(1, 32, 16, 16), float32] = multiply(%var_274, %var_275) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_283: Tensor[(32, 1, 1), float32] = expand_dims(%var_281, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_284: Tensor[(1, 32, 16, 16), float32] = add(%var_282, %var_283) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_285: Tensor[(1, 32, 16, 16), float32] = nn.relu(%var_284) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_286: Tensor[(1, 32, 18, 16), float32] = nn.pad(%var_285, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 32, 18, 16), float32] */; + let %var_287: Tensor[(1, 32, 18, 18), float32] = nn.pad(%var_286, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 32, 18, 18), float32] */; + let %var_288: Tensor[(1, 1, 16, 16, 32, 3, 3), float32] = sliding_window(%var_287, axis=1, window_shape=[32, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 16, 16, 32, 3, 3), float32] */; + let %var_289: Tensor[(1, 16, 16, 32, 3, 3), float32] = squeeze(%var_288, axis=[1]) /* ty=Tensor[(1, 16, 16, 32, 3, 3), float32] */; + let %var_290: Tensor[(32, 288), float32] = reshape(%cifarresnetv20_stage2_conv3_weight, newshape=[32, 288]) /* ty=Tensor[(32, 288), float32] */; + let %var_291: Tensor[(256, 288), float32] = reshape(%var_289, newshape=[256, 288]) /* ty=Tensor[(256, 288), float32] */; + let %var_292: Tensor[(32, 256), float32] = nn.dense(%var_290, %var_291, units=None) /* ty=Tensor[(32, 256), float32] */; + let %var_293: Tensor[(32, 1, 16, 16), float32] = reshape(%var_292, newshape=[32, 1, 16, 16]) /* ty=Tensor[(32, 1, 16, 16), float32] */; + let %var_295: Tensor[(32), float32] = add(%cifarresnetv20_stage2_batchnorm3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + let %var_296: Tensor[(32), float32] = sqrt(%var_295) /* ty=Tensor[(32), float32] */; + let %var_298: Tensor[(32), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_296) /* ty=Tensor[(32), float32] */; + let %var_299: Tensor[(32), float32] = multiply(%var_298, %cifarresnetv20_stage2_batchnorm3_gamma) /* ty=Tensor[(32), float32] */; + let %var_300: Tensor[(32, 1), float32] = expand_dims(%var_299, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_301: Tensor[(1, 32, 16, 16), float32] = transpose(%var_293, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_302: Tensor[(32, 1, 1), float32] = expand_dims(%var_300, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_304: Tensor[(32), float32] = negative(%cifarresnetv20_stage2_batchnorm3_running_mean) /* ty=Tensor[(32), float32] */; + let %var_306: Tensor[(32), float32] = multiply(%var_304, %var_299) /* ty=Tensor[(32), float32] */; + let %var_307: Tensor[(32), float32] = add(%var_306, %cifarresnetv20_stage2_batchnorm3_beta) /* ty=Tensor[(32), float32] */; + let %var_308: Tensor[(32, 1), float32] = expand_dims(%var_307, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_309: Tensor[(1, 32, 16, 16), float32] = multiply(%var_301, %var_302) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_310: Tensor[(32, 1, 1), float32] = expand_dims(%var_308, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_311: Tensor[(1, 32, 16, 16), float32] = add(%var_309, %var_310) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_312: Tensor[(1, 32, 16, 16), float32] = nn.relu(%var_311) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_313: Tensor[(1, 32, 18, 16), float32] = nn.pad(%var_312, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 32, 18, 16), float32] */; + let %var_314: Tensor[(1, 32, 18, 18), float32] = nn.pad(%var_313, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 32, 18, 18), float32] */; + let %var_315: Tensor[(1, 1, 16, 16, 32, 3, 3), float32] = sliding_window(%var_314, axis=1, window_shape=[32, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 16, 16, 32, 3, 3), float32] */; + let %var_316: Tensor[(1, 16, 16, 32, 3, 3), float32] = squeeze(%var_315, axis=[1]) /* ty=Tensor[(1, 16, 16, 32, 3, 3), float32] */; + let %var_317: Tensor[(32, 288), float32] = reshape(%cifarresnetv20_stage2_conv4_weight, newshape=[32, 288]) /* ty=Tensor[(32, 288), float32] */; + let %var_318: Tensor[(256, 288), float32] = reshape(%var_316, newshape=[256, 288]) /* ty=Tensor[(256, 288), float32] */; + let %var_319: Tensor[(32, 256), float32] = nn.dense(%var_317, %var_318, units=None) /* ty=Tensor[(32, 256), float32] */; + let %var_320: Tensor[(32, 1, 16, 16), float32] = reshape(%var_319, newshape=[32, 1, 16, 16]) /* ty=Tensor[(32, 1, 16, 16), float32] */; + let %var_321: Tensor[(1, 32, 16, 16), float32] = transpose(%var_320, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_323: Tensor[(32), float32] = add(%cifarresnetv20_stage2_batchnorm4_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + let %var_324: Tensor[(32), float32] = sqrt(%var_323) /* ty=Tensor[(32), float32] */; + let %var_326: Tensor[(32), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_324) /* ty=Tensor[(32), float32] */; + let %var_327: Tensor[(32), float32] = multiply(%var_326, %cifarresnetv20_stage2_batchnorm4_gamma) /* ty=Tensor[(32), float32] */; + let %var_328: Tensor[(32, 1), float32] = expand_dims(%var_327, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_329: Tensor[(1, 32, 16, 16), float32] = add(%var_321, %var_274) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_330: Tensor[(32, 1, 1), float32] = expand_dims(%var_328, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_332: Tensor[(32), float32] = negative(%cifarresnetv20_stage2_batchnorm4_running_mean) /* ty=Tensor[(32), float32] */; + let %var_334: Tensor[(32), float32] = multiply(%var_332, %var_327) /* ty=Tensor[(32), float32] */; + let %var_335: Tensor[(32), float32] = add(%var_334, %cifarresnetv20_stage2_batchnorm4_beta) /* ty=Tensor[(32), float32] */; + let %var_336: Tensor[(32, 1), float32] = expand_dims(%var_335, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_337: Tensor[(1, 32, 16, 16), float32] = multiply(%var_329, %var_330) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_338: Tensor[(32, 1, 1), float32] = expand_dims(%var_336, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_339: Tensor[(1, 32, 16, 16), float32] = add(%var_337, %var_338) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_340: Tensor[(1, 32, 16, 16), float32] = nn.relu(%var_339) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_341: Tensor[(1, 32, 18, 16), float32] = nn.pad(%var_340, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 32, 18, 16), float32] */; + let %var_342: Tensor[(1, 32, 18, 18), float32] = nn.pad(%var_341, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 32, 18, 18), float32] */; + let %var_343: Tensor[(1, 1, 16, 16, 32, 3, 3), float32] = sliding_window(%var_342, axis=1, window_shape=[32, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 16, 16, 32, 3, 3), float32] */; + let %var_344: Tensor[(1, 16, 16, 32, 3, 3), float32] = squeeze(%var_343, axis=[1]) /* ty=Tensor[(1, 16, 16, 32, 3, 3), float32] */; + let %var_345: Tensor[(32, 288), float32] = reshape(%cifarresnetv20_stage2_conv5_weight, newshape=[32, 288]) /* ty=Tensor[(32, 288), float32] */; + let %var_346: Tensor[(256, 288), float32] = reshape(%var_344, newshape=[256, 288]) /* ty=Tensor[(256, 288), float32] */; + let %var_347: Tensor[(32, 256), float32] = nn.dense(%var_345, %var_346, units=None) /* ty=Tensor[(32, 256), float32] */; + let %var_348: Tensor[(32, 1, 16, 16), float32] = reshape(%var_347, newshape=[32, 1, 16, 16]) /* ty=Tensor[(32, 1, 16, 16), float32] */; + let %var_350: Tensor[(32), float32] = add(%cifarresnetv20_stage2_batchnorm5_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + let %var_351: Tensor[(32), float32] = sqrt(%var_350) /* ty=Tensor[(32), float32] */; + let %var_353: Tensor[(32), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_351) /* ty=Tensor[(32), float32] */; + let %var_354: Tensor[(32), float32] = multiply(%var_353, %cifarresnetv20_stage2_batchnorm5_gamma) /* ty=Tensor[(32), float32] */; + let %var_355: Tensor[(32, 1), float32] = expand_dims(%var_354, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_356: Tensor[(1, 32, 16, 16), float32] = transpose(%var_348, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_357: Tensor[(32, 1, 1), float32] = expand_dims(%var_355, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_359: Tensor[(32), float32] = negative(%cifarresnetv20_stage2_batchnorm5_running_mean) /* ty=Tensor[(32), float32] */; + let %var_361: Tensor[(32), float32] = multiply(%var_359, %var_354) /* ty=Tensor[(32), float32] */; + let %var_362: Tensor[(32), float32] = add(%var_361, %cifarresnetv20_stage2_batchnorm5_beta) /* ty=Tensor[(32), float32] */; + let %var_363: Tensor[(32, 1), float32] = expand_dims(%var_362, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_364: Tensor[(1, 32, 16, 16), float32] = multiply(%var_356, %var_357) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_365: Tensor[(32, 1, 1), float32] = expand_dims(%var_363, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_366: Tensor[(1, 32, 16, 16), float32] = add(%var_364, %var_365) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_367: Tensor[(1, 32, 16, 16), float32] = nn.relu(%var_366) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_368: Tensor[(1, 32, 18, 16), float32] = nn.pad(%var_367, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 32, 18, 16), float32] */; + let %var_369: Tensor[(1, 32, 18, 18), float32] = nn.pad(%var_368, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 32, 18, 18), float32] */; + let %var_370: Tensor[(1, 1, 16, 16, 32, 3, 3), float32] = sliding_window(%var_369, axis=1, window_shape=[32, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 16, 16, 32, 3, 3), float32] */; + let %var_371: Tensor[(1, 16, 16, 32, 3, 3), float32] = squeeze(%var_370, axis=[1]) /* ty=Tensor[(1, 16, 16, 32, 3, 3), float32] */; + let %var_372: Tensor[(32, 288), float32] = reshape(%cifarresnetv20_stage2_conv6_weight, newshape=[32, 288]) /* ty=Tensor[(32, 288), float32] */; + let %var_373: Tensor[(256, 288), float32] = reshape(%var_371, newshape=[256, 288]) /* ty=Tensor[(256, 288), float32] */; + let %var_374: Tensor[(32, 256), float32] = nn.dense(%var_372, %var_373, units=None) /* ty=Tensor[(32, 256), float32] */; + let %var_375: Tensor[(32, 1, 16, 16), float32] = reshape(%var_374, newshape=[32, 1, 16, 16]) /* ty=Tensor[(32, 1, 16, 16), float32] */; + let %var_376: Tensor[(1, 32, 16, 16), float32] = transpose(%var_375, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_378: Tensor[(32), float32] = add(%cifarresnetv20_stage3_batchnorm0_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + let %var_379: Tensor[(32), float32] = sqrt(%var_378) /* ty=Tensor[(32), float32] */; + let %var_381: Tensor[(32), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_379) /* ty=Tensor[(32), float32] */; + let %var_382: Tensor[(32), float32] = multiply(%var_381, %cifarresnetv20_stage3_batchnorm0_gamma) /* ty=Tensor[(32), float32] */; + let %var_383: Tensor[(32, 1), float32] = expand_dims(%var_382, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_384: Tensor[(1, 32, 16, 16), float32] = add(%var_376, %var_329) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_385: Tensor[(32, 1, 1), float32] = expand_dims(%var_383, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_387: Tensor[(32), float32] = negative(%cifarresnetv20_stage3_batchnorm0_running_mean) /* ty=Tensor[(32), float32] */; + let %var_389: Tensor[(32), float32] = multiply(%var_387, %var_382) /* ty=Tensor[(32), float32] */; + let %var_390: Tensor[(32), float32] = add(%var_389, %cifarresnetv20_stage3_batchnorm0_beta) /* ty=Tensor[(32), float32] */; + let %var_391: Tensor[(32, 1), float32] = expand_dims(%var_390, axis=1) /* ty=Tensor[(32, 1), float32] */; + let %var_392: Tensor[(1, 32, 16, 16), float32] = multiply(%var_384, %var_385) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_393: Tensor[(32, 1, 1), float32] = expand_dims(%var_391, axis=1) /* ty=Tensor[(32, 1, 1), float32] */; + let %var_394: Tensor[(1, 32, 16, 16), float32] = add(%var_392, %var_393) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_395: Tensor[(1, 32, 16, 16), float32] = nn.relu(%var_394) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_396: Tensor[(1, 32, 18, 16), float32] = nn.pad(%var_395, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 32, 18, 16), float32] */; + let %var_397: Tensor[(1, 32, 18, 18), float32] = nn.pad(%var_396, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 32, 18, 18), float32] */; + let %var_398: Tensor[(1, 1, 8, 8, 32, 3, 3), float32] = sliding_window(%var_397, axis=1, window_shape=[32, 3, 3], strides=[1, 2, 2]) /* ty=Tensor[(1, 1, 8, 8, 32, 3, 3), float32] */; + let %var_399: Tensor[(1, 8, 8, 32, 3, 3), float32] = squeeze(%var_398, axis=[1]) /* ty=Tensor[(1, 8, 8, 32, 3, 3), float32] */; + let %var_400: Tensor[(64, 288), float32] = reshape(%cifarresnetv20_stage3_conv0_weight, newshape=[64, 288]) /* ty=Tensor[(64, 288), float32] */; + let %var_401: Tensor[(64, 288), float32] = reshape(%var_399, newshape=[64, 288]) /* ty=Tensor[(64, 288), float32] */; + let %var_402: Tensor[(64, 64), float32] = nn.dense(%var_400, %var_401, units=None) /* ty=Tensor[(64, 64), float32] */; + let %var_403: Tensor[(64, 1, 8, 8), float32] = reshape(%var_402, newshape=[64, 1, 8, 8]) /* ty=Tensor[(64, 1, 8, 8), float32] */; + let %var_405: Tensor[(64), float32] = add(%cifarresnetv20_stage3_batchnorm1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + let %var_406: Tensor[(64), float32] = sqrt(%var_405) /* ty=Tensor[(64), float32] */; + let %var_408: Tensor[(64), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_406) /* ty=Tensor[(64), float32] */; + let %var_409: Tensor[(64), float32] = multiply(%var_408, %cifarresnetv20_stage3_batchnorm1_gamma) /* ty=Tensor[(64), float32] */; + let %var_410: Tensor[(64, 1), float32] = expand_dims(%var_409, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_411: Tensor[(1, 64, 8, 8), float32] = transpose(%var_403, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_412: Tensor[(64, 1, 1), float32] = expand_dims(%var_410, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_414: Tensor[(64), float32] = negative(%cifarresnetv20_stage3_batchnorm1_running_mean) /* ty=Tensor[(64), float32] */; + let %var_416: Tensor[(64), float32] = multiply(%var_414, %var_409) /* ty=Tensor[(64), float32] */; + let %var_417: Tensor[(64), float32] = add(%var_416, %cifarresnetv20_stage3_batchnorm1_beta) /* ty=Tensor[(64), float32] */; + let %var_418: Tensor[(64, 1), float32] = expand_dims(%var_417, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_419: Tensor[(1, 64, 8, 8), float32] = multiply(%var_411, %var_412) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_420: Tensor[(64, 1, 1), float32] = expand_dims(%var_418, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_421: Tensor[(1, 64, 8, 8), float32] = add(%var_419, %var_420) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_422: Tensor[(1, 64, 8, 8), float32] = nn.relu(%var_421) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_423: Tensor[(1, 64, 10, 8), float32] = nn.pad(%var_422, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 64, 10, 8), float32] */; + let %var_424: Tensor[(1, 64, 10, 10), float32] = nn.pad(%var_423, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 64, 10, 10), float32] */; + let %var_425: Tensor[(1, 1, 8, 8, 64, 3, 3), float32] = sliding_window(%var_424, axis=1, window_shape=[64, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 8, 8, 64, 3, 3), float32] */; + let %var_426: Tensor[(1, 8, 8, 64, 3, 3), float32] = squeeze(%var_425, axis=[1]) /* ty=Tensor[(1, 8, 8, 64, 3, 3), float32] */; + let %var_427: Tensor[(64, 576), float32] = reshape(%cifarresnetv20_stage3_conv1_weight, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_428: Tensor[(64, 576), float32] = reshape(%var_426, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_429: Tensor[(64, 64), float32] = nn.dense(%var_427, %var_428, units=None) /* ty=Tensor[(64, 64), float32] */; + let %var_430: Tensor[(64, 1, 8, 8), float32] = reshape(%var_429, newshape=[64, 1, 8, 8]) /* ty=Tensor[(64, 1, 8, 8), float32] */; + let %var_432: Tensor[(1, 32, 16, 16), float32] = nn.pad(%var_384, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_433: Tensor[(1, 32, 16, 16), float32] = nn.pad(%var_432, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* ty=Tensor[(1, 32, 16, 16), float32] */; + let %var_434: Tensor[(1, 1, 8, 8, 32, 1, 1), float32] = sliding_window(%var_433, axis=1, window_shape=[32, 1, 1], strides=[1, 2, 2]) /* ty=Tensor[(1, 1, 8, 8, 32, 1, 1), float32] */; + let %var_435: Tensor[(1, 8, 8, 32, 1, 1), float32] = squeeze(%var_434, axis=[1]) /* ty=Tensor[(1, 8, 8, 32, 1, 1), float32] */; + let %var_436: Tensor[(64, 32), float32] = reshape(%cifarresnetv20_stage3_conv2_weight, newshape=[64, 32]) /* ty=Tensor[(64, 32), float32] */; + let %var_437: Tensor[(64, 32), float32] = reshape(%var_435, newshape=[64, 32]) /* ty=Tensor[(64, 32), float32] */; + let %var_438: Tensor[(64, 64), float32] = nn.dense(%var_436, %var_437, units=None) /* ty=Tensor[(64, 64), float32] */; + let %var_439: Tensor[(64, 1, 8, 8), float32] = reshape(%var_438, newshape=[64, 1, 8, 8]) /* ty=Tensor[(64, 1, 8, 8), float32] */; + let %var_440: Tensor[(1, 64, 8, 8), float32] = transpose(%var_430, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_441: Tensor[(1, 64, 8, 8), float32] = transpose(%var_439, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_443: Tensor[(64), float32] = add(%cifarresnetv20_stage3_batchnorm2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + let %var_444: Tensor[(64), float32] = sqrt(%var_443) /* ty=Tensor[(64), float32] */; + let %var_446: Tensor[(64), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_444) /* ty=Tensor[(64), float32] */; + let %var_447: Tensor[(64), float32] = multiply(%var_446, %cifarresnetv20_stage3_batchnorm2_gamma) /* ty=Tensor[(64), float32] */; + let %var_448: Tensor[(64, 1), float32] = expand_dims(%var_447, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_449: Tensor[(1, 64, 8, 8), float32] = add(%var_440, %var_441) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_450: Tensor[(64, 1, 1), float32] = expand_dims(%var_448, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_452: Tensor[(64), float32] = negative(%cifarresnetv20_stage3_batchnorm2_running_mean) /* ty=Tensor[(64), float32] */; + let %var_454: Tensor[(64), float32] = multiply(%var_452, %var_447) /* ty=Tensor[(64), float32] */; + let %var_455: Tensor[(64), float32] = add(%var_454, %cifarresnetv20_stage3_batchnorm2_beta) /* ty=Tensor[(64), float32] */; + let %var_456: Tensor[(64, 1), float32] = expand_dims(%var_455, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_457: Tensor[(1, 64, 8, 8), float32] = multiply(%var_449, %var_450) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_458: Tensor[(64, 1, 1), float32] = expand_dims(%var_456, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_459: Tensor[(1, 64, 8, 8), float32] = add(%var_457, %var_458) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_460: Tensor[(1, 64, 8, 8), float32] = nn.relu(%var_459) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_461: Tensor[(1, 64, 10, 8), float32] = nn.pad(%var_460, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 64, 10, 8), float32] */; + let %var_462: Tensor[(1, 64, 10, 10), float32] = nn.pad(%var_461, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 64, 10, 10), float32] */; + let %var_463: Tensor[(1, 1, 8, 8, 64, 3, 3), float32] = sliding_window(%var_462, axis=1, window_shape=[64, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 8, 8, 64, 3, 3), float32] */; + let %var_464: Tensor[(1, 8, 8, 64, 3, 3), float32] = squeeze(%var_463, axis=[1]) /* ty=Tensor[(1, 8, 8, 64, 3, 3), float32] */; + let %var_465: Tensor[(64, 576), float32] = reshape(%cifarresnetv20_stage3_conv3_weight, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_466: Tensor[(64, 576), float32] = reshape(%var_464, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_467: Tensor[(64, 64), float32] = nn.dense(%var_465, %var_466, units=None) /* ty=Tensor[(64, 64), float32] */; + let %var_468: Tensor[(64, 1, 8, 8), float32] = reshape(%var_467, newshape=[64, 1, 8, 8]) /* ty=Tensor[(64, 1, 8, 8), float32] */; + let %var_470: Tensor[(64), float32] = add(%cifarresnetv20_stage3_batchnorm3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + let %var_471: Tensor[(64), float32] = sqrt(%var_470) /* ty=Tensor[(64), float32] */; + let %var_473: Tensor[(64), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_471) /* ty=Tensor[(64), float32] */; + let %var_474: Tensor[(64), float32] = multiply(%var_473, %cifarresnetv20_stage3_batchnorm3_gamma) /* ty=Tensor[(64), float32] */; + let %var_475: Tensor[(64, 1), float32] = expand_dims(%var_474, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_476: Tensor[(1, 64, 8, 8), float32] = transpose(%var_468, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_477: Tensor[(64, 1, 1), float32] = expand_dims(%var_475, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_479: Tensor[(64), float32] = negative(%cifarresnetv20_stage3_batchnorm3_running_mean) /* ty=Tensor[(64), float32] */; + let %var_481: Tensor[(64), float32] = multiply(%var_479, %var_474) /* ty=Tensor[(64), float32] */; + let %var_482: Tensor[(64), float32] = add(%var_481, %cifarresnetv20_stage3_batchnorm3_beta) /* ty=Tensor[(64), float32] */; + let %var_483: Tensor[(64, 1), float32] = expand_dims(%var_482, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_484: Tensor[(1, 64, 8, 8), float32] = multiply(%var_476, %var_477) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_485: Tensor[(64, 1, 1), float32] = expand_dims(%var_483, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_486: Tensor[(1, 64, 8, 8), float32] = add(%var_484, %var_485) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_487: Tensor[(1, 64, 8, 8), float32] = nn.relu(%var_486) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_488: Tensor[(1, 64, 10, 8), float32] = nn.pad(%var_487, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 64, 10, 8), float32] */; + let %var_489: Tensor[(1, 64, 10, 10), float32] = nn.pad(%var_488, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 64, 10, 10), float32] */; + let %var_490: Tensor[(1, 1, 8, 8, 64, 3, 3), float32] = sliding_window(%var_489, axis=1, window_shape=[64, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 8, 8, 64, 3, 3), float32] */; + let %var_491: Tensor[(1, 8, 8, 64, 3, 3), float32] = squeeze(%var_490, axis=[1]) /* ty=Tensor[(1, 8, 8, 64, 3, 3), float32] */; + let %var_492: Tensor[(64, 576), float32] = reshape(%cifarresnetv20_stage3_conv4_weight, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_493: Tensor[(64, 576), float32] = reshape(%var_491, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_494: Tensor[(64, 64), float32] = nn.dense(%var_492, %var_493, units=None) /* ty=Tensor[(64, 64), float32] */; + let %var_495: Tensor[(64, 1, 8, 8), float32] = reshape(%var_494, newshape=[64, 1, 8, 8]) /* ty=Tensor[(64, 1, 8, 8), float32] */; + let %var_496: Tensor[(1, 64, 8, 8), float32] = transpose(%var_495, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_498: Tensor[(64), float32] = add(%cifarresnetv20_stage3_batchnorm4_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + let %var_499: Tensor[(64), float32] = sqrt(%var_498) /* ty=Tensor[(64), float32] */; + let %var_501: Tensor[(64), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_499) /* ty=Tensor[(64), float32] */; + let %var_502: Tensor[(64), float32] = multiply(%var_501, %cifarresnetv20_stage3_batchnorm4_gamma) /* ty=Tensor[(64), float32] */; + let %var_503: Tensor[(64, 1), float32] = expand_dims(%var_502, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_504: Tensor[(1, 64, 8, 8), float32] = add(%var_496, %var_449) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_505: Tensor[(64, 1, 1), float32] = expand_dims(%var_503, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_507: Tensor[(64), float32] = negative(%cifarresnetv20_stage3_batchnorm4_running_mean) /* ty=Tensor[(64), float32] */; + let %var_509: Tensor[(64), float32] = multiply(%var_507, %var_502) /* ty=Tensor[(64), float32] */; + let %var_510: Tensor[(64), float32] = add(%var_509, %cifarresnetv20_stage3_batchnorm4_beta) /* ty=Tensor[(64), float32] */; + let %var_511: Tensor[(64, 1), float32] = expand_dims(%var_510, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_512: Tensor[(1, 64, 8, 8), float32] = multiply(%var_504, %var_505) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_513: Tensor[(64, 1, 1), float32] = expand_dims(%var_511, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_514: Tensor[(1, 64, 8, 8), float32] = add(%var_512, %var_513) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_515: Tensor[(1, 64, 8, 8), float32] = nn.relu(%var_514) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_516: Tensor[(1, 64, 10, 8), float32] = nn.pad(%var_515, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 64, 10, 8), float32] */; + let %var_517: Tensor[(1, 64, 10, 10), float32] = nn.pad(%var_516, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 64, 10, 10), float32] */; + let %var_518: Tensor[(1, 1, 8, 8, 64, 3, 3), float32] = sliding_window(%var_517, axis=1, window_shape=[64, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 8, 8, 64, 3, 3), float32] */; + let %var_519: Tensor[(1, 8, 8, 64, 3, 3), float32] = squeeze(%var_518, axis=[1]) /* ty=Tensor[(1, 8, 8, 64, 3, 3), float32] */; + let %var_520: Tensor[(64, 576), float32] = reshape(%cifarresnetv20_stage3_conv5_weight, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_521: Tensor[(64, 576), float32] = reshape(%var_519, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_522: Tensor[(64, 64), float32] = nn.dense(%var_520, %var_521, units=None) /* ty=Tensor[(64, 64), float32] */; + let %var_523: Tensor[(64, 1, 8, 8), float32] = reshape(%var_522, newshape=[64, 1, 8, 8]) /* ty=Tensor[(64, 1, 8, 8), float32] */; + let %var_525: Tensor[(64), float32] = add(%cifarresnetv20_stage3_batchnorm5_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + let %var_526: Tensor[(64), float32] = sqrt(%var_525) /* ty=Tensor[(64), float32] */; + let %var_528: Tensor[(64), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_526) /* ty=Tensor[(64), float32] */; + let %var_529: Tensor[(64), float32] = multiply(%var_528, %cifarresnetv20_stage3_batchnorm5_gamma) /* ty=Tensor[(64), float32] */; + let %var_530: Tensor[(64, 1), float32] = expand_dims(%var_529, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_531: Tensor[(1, 64, 8, 8), float32] = transpose(%var_523, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_532: Tensor[(64, 1, 1), float32] = expand_dims(%var_530, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_534: Tensor[(64), float32] = negative(%cifarresnetv20_stage3_batchnorm5_running_mean) /* ty=Tensor[(64), float32] */; + let %var_536: Tensor[(64), float32] = multiply(%var_534, %var_529) /* ty=Tensor[(64), float32] */; + let %var_537: Tensor[(64), float32] = add(%var_536, %cifarresnetv20_stage3_batchnorm5_beta) /* ty=Tensor[(64), float32] */; + let %var_538: Tensor[(64, 1), float32] = expand_dims(%var_537, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_539: Tensor[(1, 64, 8, 8), float32] = multiply(%var_531, %var_532) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_540: Tensor[(64, 1, 1), float32] = expand_dims(%var_538, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_541: Tensor[(1, 64, 8, 8), float32] = add(%var_539, %var_540) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_542: Tensor[(1, 64, 8, 8), float32] = nn.relu(%var_541) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_543: Tensor[(1, 64, 10, 8), float32] = nn.pad(%var_542, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [1, 1], [0, 0]]) /* ty=Tensor[(1, 64, 10, 8), float32] */; + let %var_544: Tensor[(1, 64, 10, 10), float32] = nn.pad(%var_543, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [1, 1]]) /* ty=Tensor[(1, 64, 10, 10), float32] */; + let %var_545: Tensor[(1, 1, 8, 8, 64, 3, 3), float32] = sliding_window(%var_544, axis=1, window_shape=[64, 3, 3], strides=[1, 1, 1]) /* ty=Tensor[(1, 1, 8, 8, 64, 3, 3), float32] */; + let %var_546: Tensor[(1, 8, 8, 64, 3, 3), float32] = squeeze(%var_545, axis=[1]) /* ty=Tensor[(1, 8, 8, 64, 3, 3), float32] */; + let %var_547: Tensor[(64, 576), float32] = reshape(%cifarresnetv20_stage3_conv6_weight, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_548: Tensor[(64, 576), float32] = reshape(%var_546, newshape=[64, 576]) /* ty=Tensor[(64, 576), float32] */; + let %var_549: Tensor[(64, 64), float32] = nn.dense(%var_547, %var_548, units=None) /* ty=Tensor[(64, 64), float32] */; + let %var_550: Tensor[(64, 1, 8, 8), float32] = reshape(%var_549, newshape=[64, 1, 8, 8]) /* ty=Tensor[(64, 1, 8, 8), float32] */; + let %var_551: Tensor[(1, 64, 8, 8), float32] = transpose(%var_550, axes=[1, 0, 2, 3]) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_553: Tensor[(64), float32] = add(%cifarresnetv20_batchnorm1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + let %var_554: Tensor[(64), float32] = sqrt(%var_553) /* ty=Tensor[(64), float32] */; + let %var_556: Tensor[(64), float32] = divide(meta[relay.Constant][0] /* ty=Tensor[(1), float32] */, %var_554) /* ty=Tensor[(64), float32] */; + let %var_557: Tensor[(64), float32] = multiply(%var_556, %cifarresnetv20_batchnorm1_gamma) /* ty=Tensor[(64), float32] */; + let %var_558: Tensor[(64, 1), float32] = expand_dims(%var_557, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_559: Tensor[(1, 64, 8, 8), float32] = add(%var_551, %var_504) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_560: Tensor[(64, 1, 1), float32] = expand_dims(%var_558, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_562: Tensor[(64), float32] = negative(%cifarresnetv20_batchnorm1_running_mean) /* ty=Tensor[(64), float32] */; + let %var_564: Tensor[(64), float32] = multiply(%var_562, %var_557) /* ty=Tensor[(64), float32] */; + let %var_565: Tensor[(64), float32] = add(%var_564, %cifarresnetv20_batchnorm1_beta) /* ty=Tensor[(64), float32] */; + let %var_566: Tensor[(64, 1), float32] = expand_dims(%var_565, axis=1) /* ty=Tensor[(64, 1), float32] */; + let %var_567: Tensor[(1, 64, 8, 8), float32] = multiply(%var_559, %var_560) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_568: Tensor[(64, 1, 1), float32] = expand_dims(%var_566, axis=1) /* ty=Tensor[(64, 1, 1), float32] */; + let %var_569: Tensor[(1, 64, 8, 8), float32] = add(%var_567, %var_568) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_570: Tensor[(1, 64, 8, 8), float32] = nn.relu(%var_569) /* ty=Tensor[(1, 64, 8, 8), float32] */; + let %var_571: Tensor[(1, 64, 1, 1), float32] = nn.global_avg_pool2d(%var_570) /* ty=Tensor[(1, 64, 1, 1), float32] */; + let %var_572: Tensor[(1, 64), float32] = reshape(%var_571, newshape=[1, 64]) /* ty=Tensor[(1, 64), float32] */; + let %var_574: Tensor[(1, 64), float32] = reshape(%var_572, newshape=[1, 64]) /* ty=Tensor[(1, 64), float32] */; + let %var_576: Tensor[(1, 10), float32] = nn.dense(%var_574, %cifarresnetv20_dense0_weight, units=None) /* ty=Tensor[(1, 10), float32] */; + nn.bias_add(%var_576, %cifarresnetv20_dense0_bias) /* ty=Tensor[(1, 10), float32] */ +} + +#[metadata] +{ + "root": 1, + "nodes": [ + { + "type_key": "" + }, + { + "type_key": "Map", + "keys": [ + "relay.Constant" + ], + "data": [2] + }, + { + "type_key": "Array", + "data": [3] + }, + { + "type_key": "relay.Constant", + "attrs": { + "_checked_type_": "6", + "data": "0", + "span": "0", + "virtual_device_": "4" + } + }, + { + "type_key": "VirtualDevice", + "attrs": { + "device_type_int": "-1", + "memory_scope": "5", + "target": "0", + "virtual_device_id": "-1" + } + }, + { + "type_key": "runtime.String" + }, + { + "type_key": "relay.TensorType", + "attrs": { + "dtype": "float32", + "shape": "7", + "span": "0" + } + }, + { + "type_key": "Array", + "data": [8] + }, + { + "type_key": "IntImm", + "attrs": { + "dtype": "int32", + "span": "0", + "value": "1" + } + } + ], + "b64ndarrays": [ + "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAAAQAAAAIgAQABAAAAAAAAAAQAAAAAAAAAAACAPw==" + ], + "attrs": {"tvm_version": "0.9.dev0"} +} \ No newline at end of file diff --git a/tests/models/qmobilenet.relay b/tests/models/qmobilenet.relay new file mode 100644 index 0000000..f742627 --- /dev/null +++ b/tests/models/qmobilenet.relay @@ -0,0 +1,10850 @@ +#[version = "0.0.5"] +def @main(%input0: Tensor[(1, 3, 32, 32), float32]) -> Tensor[(1, 10), float32] { + %0 = multiply(%input0, 16f /* ty=float32 */) /* from_string */ /* ty=Tensor[(1, 3, 32, 32), float32] */; + %1 = round(%0) /* from_string */ /* ty=Tensor[(1, 3, 32, 32), float32] */; + %2 = clip(%1, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 3, 32, 32), float32] */; + %3 = cast(%2, dtype="int8") /* from_string */ /* ty=Tensor[(1, 3, 32, 32), int8] */; + %4 = nn.conv2d(%3, meta[relay.Constant][0] /* ty=Tensor[(32, 3, 3, 3), int8] */, padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %5 = add(%4, 64 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %6 = right_shift(%5, 7 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %7 = clip(%6, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %8 = cast(%7, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int8] */; + %9 = cast(%8, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %10 = multiply(%9, meta[relay.Constant][1] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %11 = add(%10, meta[relay.Constant][2] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %12 = nn.relu(%11) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %13 = add(%12, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %14 = right_shift(%13, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %15 = clip(%14, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %16 = cast(%15, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int8] */; + %17 = nn.conv2d(%16, meta[relay.Constant][3] /* ty=Tensor[(32, 32, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %18 = add(%17, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %19 = right_shift(%18, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %20 = clip(%19, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %21 = cast(%20, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int8] */; + %22 = cast(%21, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %23 = multiply(%22, meta[relay.Constant][4] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %24 = add(%23, meta[relay.Constant][5] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %25 = nn.relu(%24) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %26 = add(%25, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %27 = right_shift(%26, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %28 = clip(%27, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %29 = cast(%28, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int8] */; + %30 = nn.conv2d(%29, meta[relay.Constant][6] /* ty=Tensor[(32, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=32, channels=32, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %31 = add(%30, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %32 = right_shift(%31, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %33 = clip(%32, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %34 = cast(%33, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int8] */; + %35 = cast(%34, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %36 = multiply(%35, meta[relay.Constant][7] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %37 = left_shift(%36, 1 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %38 = add(%37, meta[relay.Constant][8] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %39 = nn.relu(%38) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %40 = add(%39, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %41 = right_shift(%40, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %42 = clip(%41, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int32] */; + %43 = cast(%42, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int8] */; + %44 = nn.conv2d(%43, meta[relay.Constant][9] /* ty=Tensor[(16, 32, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=16, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %45 = add(%44, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %46 = right_shift(%45, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %47 = clip(%46, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %48 = cast(%47, dtype="int8") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int8] */; + %49 = cast(%48, dtype="int32") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %50 = multiply(%49, meta[relay.Constant][10] /* ty=Tensor[(16, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %51 = add(%50, meta[relay.Constant][11] /* ty=Tensor[(16, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %52 = add(%51, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %53 = right_shift(%52, 3 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %54 = clip(%53, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %55 = cast(%54, dtype="int8") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int8] */; + %56 = cast(%15, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 32, 32), int8] */; + %57 = nn.conv2d(%56, meta[relay.Constant][12] /* ty=Tensor[(16, 32, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=16, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %58 = add(%57, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %59 = right_shift(%58, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %60 = clip(%59, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %61 = cast(%60, dtype="int8") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int8] */; + %62 = cast(%61, dtype="int32") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %63 = multiply(%62, meta[relay.Constant][13] /* ty=Tensor[(16, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %64 = add(%63, meta[relay.Constant][14] /* ty=Tensor[(16, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %65 = add(%64, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %66 = right_shift(%65, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %67 = clip(%66, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %68 = cast(%67, dtype="int8") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int8] */; + %69 = cast(%55, dtype="int32") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %70 = cast(%68, dtype="int32") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %71 = add(%69, %70) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %72 = clip(%71, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int32] */; + %73 = cast(%72, dtype="int8") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int8] */; + %74 = nn.conv2d(%73, meta[relay.Constant][15] /* ty=Tensor[(96, 16, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %75 = add(%74, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %76 = right_shift(%75, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %77 = clip(%76, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %78 = cast(%77, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int8] */; + %79 = cast(%78, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %80 = multiply(%79, meta[relay.Constant][16] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %81 = add(%80, meta[relay.Constant][17] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %82 = nn.relu(%81) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %83 = add(%82, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %84 = right_shift(%83, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %85 = clip(%84, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %86 = cast(%85, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int8] */; + %87 = nn.conv2d(%86, meta[relay.Constant][18] /* ty=Tensor[(96, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %88 = add(%87, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %89 = right_shift(%88, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %90 = clip(%89, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %91 = cast(%90, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int8] */; + %92 = cast(%91, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %93 = multiply(%92, meta[relay.Constant][19] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %94 = left_shift(%93, 1 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %95 = add(%94, meta[relay.Constant][20] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %96 = nn.relu(%95) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %97 = add(%96, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %98 = right_shift(%97, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %99 = clip(%98, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int32] */; + %100 = cast(%99, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 32, 32), int8] */; + %101 = nn.conv2d(%100, meta[relay.Constant][21] /* ty=Tensor[(24, 96, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %102 = add(%101, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %103 = right_shift(%102, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %104 = clip(%103, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %105 = cast(%104, dtype="int8") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int8] */; + %106 = cast(%105, dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %107 = multiply(%106, meta[relay.Constant][22] /* ty=Tensor[(24, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %108 = add(%107, meta[relay.Constant][23] /* ty=Tensor[(24, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %109 = add(%108, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %110 = right_shift(%109, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %111 = clip(%110, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %112 = cast(%111, dtype="int8") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int8] */; + %113 = cast(%72, dtype="int8") /* from_string */ /* ty=Tensor[(1, 16, 32, 32), int8] */; + %114 = nn.conv2d(%113, meta[relay.Constant][24] /* ty=Tensor[(24, 16, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %115 = add(%114, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %116 = right_shift(%115, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %117 = clip(%116, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %118 = cast(%117, dtype="int8") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int8] */; + %119 = cast(%118, dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %120 = multiply(%119, meta[relay.Constant][25] /* ty=Tensor[(24, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %121 = left_shift(%120, 18 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %122 = add(%121, meta[relay.Constant][26] /* ty=Tensor[(24, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %123 = add(%122, 4194304 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %124 = right_shift(%123, 23 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %125 = clip(%124, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %126 = cast(%125, dtype="int8") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int8] */; + %127 = cast(%112, dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %128 = cast(%126, dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %129 = add(%127, %128) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %130 = clip(%129, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %131 = cast(%130, dtype="int8") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int8] */; + %132 = nn.conv2d(%131, meta[relay.Constant][27] /* ty=Tensor[(144, 24, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %133 = add(%132, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %134 = right_shift(%133, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %135 = clip(%134, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %136 = cast(%135, dtype="int8") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int8] */; + %137 = cast(%136, dtype="int32") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %138 = multiply(%137, meta[relay.Constant][28] /* ty=Tensor[(144, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %139 = add(%138, meta[relay.Constant][29] /* ty=Tensor[(144, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %140 = nn.relu(%139) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %141 = add(%140, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %142 = right_shift(%141, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %143 = clip(%142, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %144 = cast(%143, dtype="int8") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int8] */; + %145 = nn.conv2d(%144, meta[relay.Constant][30] /* ty=Tensor[(144, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=144, channels=144, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %146 = add(%145, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %147 = right_shift(%146, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %148 = clip(%147, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %149 = cast(%148, dtype="int8") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int8] */; + %150 = cast(%149, dtype="int32") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %151 = multiply(%150, meta[relay.Constant][31] /* ty=Tensor[(144, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %152 = left_shift(%151, 3 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %153 = add(%152, meta[relay.Constant][32] /* ty=Tensor[(144, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %154 = nn.relu(%153) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %155 = add(%154, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %156 = right_shift(%155, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %157 = clip(%156, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %158 = cast(%157, dtype="int8") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int8] */; + %159 = nn.conv2d(%158, meta[relay.Constant][33] /* ty=Tensor[(24, 144, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %160 = add(%159, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %161 = right_shift(%160, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %162 = clip(%161, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %163 = cast(%162, dtype="int8") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int8] */; + %164 = cast(%163, dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %165 = multiply(%164, meta[relay.Constant][34] /* ty=Tensor[(24, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %166 = add(%165, meta[relay.Constant][35] /* ty=Tensor[(24, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %167 = add(%166, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %168 = right_shift(%167, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %169 = clip(%168, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %170 = cast(%169, dtype="int8") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int8] */; + %171 = cast(%130, dtype="int8") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int8] */; + %172 = cast(%170, dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %173 = cast(%171, dtype="int32") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %174 = add(%172, %173) /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int32] */; + %175 = cast(%174, dtype="int8") /* from_string */ /* ty=Tensor[(1, 24, 32, 32), int8] */; + %176 = nn.conv2d(%175, meta[relay.Constant][36] /* ty=Tensor[(144, 24, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %177 = add(%176, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %178 = right_shift(%177, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %179 = clip(%178, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %180 = cast(%179, dtype="int8") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int8] */; + %181 = cast(%180, dtype="int32") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %182 = multiply(%181, meta[relay.Constant][37] /* ty=Tensor[(144, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %183 = add(%182, meta[relay.Constant][38] /* ty=Tensor[(144, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %184 = nn.relu(%183) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %185 = add(%184, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %186 = right_shift(%185, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %187 = clip(%186, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int32] */; + %188 = cast(%187, dtype="int8") /* from_string */ /* ty=Tensor[(1, 144, 32, 32), int8] */; + %189 = nn.conv2d(%188, meta[relay.Constant][39] /* ty=Tensor[(144, 1, 3, 3), int8] */, strides=[2, 2], padding=[1, 1, 1, 1], groups=144, channels=144, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %190 = add(%189, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %191 = right_shift(%190, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %192 = clip(%191, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %193 = cast(%192, dtype="int8") /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int8] */; + %194 = cast(%193, dtype="int32") /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %195 = multiply(%194, meta[relay.Constant][40] /* ty=Tensor[(144, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %196 = left_shift(%195, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %197 = add(%196, meta[relay.Constant][41] /* ty=Tensor[(144, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %198 = nn.relu(%197) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %199 = add(%198, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %200 = right_shift(%199, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %201 = clip(%200, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int32] */; + %202 = cast(%201, dtype="int8") /* from_string */ /* ty=Tensor[(1, 144, 16, 16), int8] */; + %203 = nn.conv2d(%202, meta[relay.Constant][42] /* ty=Tensor[(32, 144, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %204 = add(%203, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %205 = right_shift(%204, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %206 = clip(%205, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %207 = cast(%206, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %208 = cast(%207, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %209 = multiply(%208, meta[relay.Constant][43] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %210 = add(%209, meta[relay.Constant][44] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %211 = add(%210, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %212 = right_shift(%211, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %213 = clip(%212, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %214 = cast(%213, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %215 = nn.conv2d(%214, meta[relay.Constant][45] /* ty=Tensor[(192, 32, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %216 = add(%215, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %217 = right_shift(%216, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %218 = clip(%217, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %219 = cast(%218, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %220 = cast(%219, dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %221 = multiply(%220, meta[relay.Constant][46] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %222 = add(%221, meta[relay.Constant][47] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %223 = nn.relu(%222) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %224 = add(%223, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %225 = right_shift(%224, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %226 = clip(%225, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %227 = cast(%226, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %228 = nn.conv2d(%227, meta[relay.Constant][48] /* ty=Tensor[(192, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %229 = add(%228, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %230 = right_shift(%229, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %231 = clip(%230, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %232 = cast(%231, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %233 = cast(%232, dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %234 = multiply(%233, meta[relay.Constant][49] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %235 = left_shift(%234, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %236 = add(%235, meta[relay.Constant][50] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %237 = nn.relu(%236) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %238 = add(%237, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %239 = right_shift(%238, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %240 = clip(%239, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %241 = cast(%240, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %242 = nn.conv2d(%241, meta[relay.Constant][51] /* ty=Tensor[(32, 192, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %243 = add(%242, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %244 = right_shift(%243, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %245 = clip(%244, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %246 = cast(%245, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %247 = cast(%246, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %248 = multiply(%247, meta[relay.Constant][52] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %249 = add(%248, meta[relay.Constant][53] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %250 = add(%249, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %251 = right_shift(%250, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %252 = clip(%251, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %253 = cast(%252, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %254 = cast(%213, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %255 = cast(%253, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %256 = cast(%254, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %257 = add(%255, %256) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %258 = clip(%257, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %259 = cast(%258, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %260 = nn.conv2d(%259, meta[relay.Constant][54] /* ty=Tensor[(192, 32, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %261 = add(%260, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %262 = right_shift(%261, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %263 = clip(%262, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %264 = cast(%263, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %265 = cast(%264, dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %266 = multiply(%265, meta[relay.Constant][55] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %267 = add(%266, meta[relay.Constant][56] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %268 = nn.relu(%267) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %269 = add(%268, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %270 = right_shift(%269, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %271 = clip(%270, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %272 = cast(%271, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %273 = nn.conv2d(%272, meta[relay.Constant][57] /* ty=Tensor[(192, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %274 = add(%273, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %275 = right_shift(%274, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %276 = clip(%275, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %277 = cast(%276, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %278 = cast(%277, dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %279 = multiply(%278, meta[relay.Constant][58] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %280 = left_shift(%279, 3 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %281 = add(%280, meta[relay.Constant][59] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %282 = nn.relu(%281) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %283 = add(%282, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %284 = right_shift(%283, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %285 = clip(%284, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %286 = cast(%285, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %287 = nn.conv2d(%286, meta[relay.Constant][60] /* ty=Tensor[(32, 192, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %288 = add(%287, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %289 = right_shift(%288, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %290 = clip(%289, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %291 = cast(%290, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %292 = cast(%291, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %293 = multiply(%292, meta[relay.Constant][61] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %294 = add(%293, meta[relay.Constant][62] /* ty=Tensor[(32, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %295 = add(%294, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %296 = right_shift(%295, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %297 = clip(%296, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %298 = cast(%297, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %299 = cast(%258, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %300 = cast(%298, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %301 = cast(%299, dtype="int32") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %302 = add(%300, %301) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int32] */; + %303 = cast(%302, dtype="int8") /* from_string */ /* ty=Tensor[(1, 32, 16, 16), int8] */; + %304 = nn.conv2d(%303, meta[relay.Constant][63] /* ty=Tensor[(192, 32, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %305 = add(%304, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %306 = right_shift(%305, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %307 = clip(%306, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %308 = cast(%307, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %309 = cast(%308, dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %310 = multiply(%309, meta[relay.Constant][64] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %311 = add(%310, meta[relay.Constant][65] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %312 = nn.relu(%311) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %313 = add(%312, 64 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %314 = right_shift(%313, 7 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %315 = clip(%314, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int32] */; + %316 = cast(%315, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 16, 16), int8] */; + %317 = nn.conv2d(%316, meta[relay.Constant][66] /* ty=Tensor[(192, 1, 3, 3), int8] */, strides=[2, 2], padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %318 = add(%317, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %319 = right_shift(%318, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %320 = clip(%319, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %321 = cast(%320, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int8] */; + %322 = cast(%321, dtype="int32") /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %323 = multiply(%322, meta[relay.Constant][67] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %324 = add(%323, meta[relay.Constant][68] /* ty=Tensor[(192, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %325 = nn.relu(%324) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %326 = add(%325, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %327 = right_shift(%326, 3 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %328 = clip(%327, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int32] */; + %329 = cast(%328, dtype="int8") /* from_string */ /* ty=Tensor[(1, 192, 8, 8), int8] */; + %330 = nn.conv2d(%329, meta[relay.Constant][69] /* ty=Tensor[(64, 192, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %331 = add(%330, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %332 = right_shift(%331, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %333 = clip(%332, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %334 = cast(%333, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %335 = cast(%334, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %336 = multiply(%335, meta[relay.Constant][70] /* ty=Tensor[(64, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %337 = add(%336, meta[relay.Constant][71] /* ty=Tensor[(64, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %338 = add(%337, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %339 = right_shift(%338, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %340 = clip(%339, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %341 = cast(%340, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %342 = nn.conv2d(%341, meta[relay.Constant][72] /* ty=Tensor[(384, 64, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %343 = add(%342, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %344 = right_shift(%343, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %345 = clip(%344, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %346 = cast(%345, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %347 = cast(%346, dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %348 = multiply(%347, meta[relay.Constant][73] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %349 = add(%348, meta[relay.Constant][74] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %350 = nn.relu(%349) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %351 = add(%350, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %352 = right_shift(%351, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %353 = clip(%352, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %354 = cast(%353, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %355 = nn.conv2d(%354, meta[relay.Constant][75] /* ty=Tensor[(384, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %356 = add(%355, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %357 = right_shift(%356, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %358 = clip(%357, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %359 = cast(%358, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %360 = cast(%359, dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %361 = multiply(%360, meta[relay.Constant][76] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %362 = left_shift(%361, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %363 = add(%362, meta[relay.Constant][77] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %364 = nn.relu(%363) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %365 = add(%364, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %366 = right_shift(%365, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %367 = clip(%366, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %368 = cast(%367, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %369 = nn.conv2d(%368, meta[relay.Constant][78] /* ty=Tensor[(64, 384, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %370 = add(%369, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %371 = right_shift(%370, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %372 = clip(%371, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %373 = cast(%372, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %374 = cast(%373, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %375 = multiply(%374, meta[relay.Constant][79] /* ty=Tensor[(64, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %376 = add(%375, meta[relay.Constant][80] /* ty=Tensor[(64, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %377 = add(%376, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %378 = right_shift(%377, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %379 = clip(%378, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %380 = cast(%379, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %381 = cast(%340, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %382 = cast(%380, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %383 = cast(%381, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %384 = add(%382, %383) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %385 = clip(%384, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %386 = cast(%385, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %387 = nn.conv2d(%386, meta[relay.Constant][81] /* ty=Tensor[(384, 64, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %388 = add(%387, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %389 = right_shift(%388, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %390 = clip(%389, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %391 = cast(%390, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %392 = cast(%391, dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %393 = multiply(%392, meta[relay.Constant][82] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %394 = add(%393, meta[relay.Constant][83] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %395 = nn.relu(%394) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %396 = add(%395, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %397 = right_shift(%396, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %398 = clip(%397, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %399 = cast(%398, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %400 = nn.conv2d(%399, meta[relay.Constant][84] /* ty=Tensor[(384, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %401 = add(%400, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %402 = right_shift(%401, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %403 = clip(%402, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %404 = cast(%403, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %405 = cast(%404, dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %406 = multiply(%405, meta[relay.Constant][85] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %407 = left_shift(%406, 1 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %408 = add(%407, meta[relay.Constant][86] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %409 = nn.relu(%408) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %410 = add(%409, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %411 = right_shift(%410, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %412 = clip(%411, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %413 = cast(%412, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %414 = nn.conv2d(%413, meta[relay.Constant][87] /* ty=Tensor[(64, 384, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %415 = add(%414, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %416 = right_shift(%415, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %417 = clip(%416, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %418 = cast(%417, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %419 = cast(%418, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %420 = multiply(%419, meta[relay.Constant][88] /* ty=Tensor[(64, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %421 = add(%420, meta[relay.Constant][89] /* ty=Tensor[(64, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %422 = add(%421, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %423 = right_shift(%422, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %424 = clip(%423, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %425 = cast(%424, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %426 = cast(%385, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %427 = cast(%425, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %428 = cast(%426, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %429 = add(%427, %428) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %430 = clip(%429, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %431 = cast(%430, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %432 = nn.conv2d(%431, meta[relay.Constant][90] /* ty=Tensor[(384, 64, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %433 = add(%432, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %434 = right_shift(%433, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %435 = clip(%434, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %436 = cast(%435, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %437 = cast(%436, dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %438 = multiply(%437, meta[relay.Constant][91] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %439 = add(%438, meta[relay.Constant][92] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %440 = nn.relu(%439) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %441 = add(%440, 64 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %442 = right_shift(%441, 7 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %443 = clip(%442, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %444 = cast(%443, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %445 = nn.conv2d(%444, meta[relay.Constant][93] /* ty=Tensor[(384, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %446 = add(%445, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %447 = right_shift(%446, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %448 = clip(%447, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %449 = cast(%448, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %450 = cast(%449, dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %451 = multiply(%450, meta[relay.Constant][94] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %452 = left_shift(%451, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %453 = add(%452, meta[relay.Constant][95] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %454 = nn.relu(%453) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %455 = add(%454, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %456 = right_shift(%455, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %457 = clip(%456, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %458 = cast(%457, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %459 = nn.conv2d(%458, meta[relay.Constant][96] /* ty=Tensor[(64, 384, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %460 = add(%459, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %461 = right_shift(%460, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %462 = clip(%461, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %463 = cast(%462, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %464 = cast(%463, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %465 = multiply(%464, meta[relay.Constant][97] /* ty=Tensor[(64, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %466 = left_shift(%465, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %467 = add(%466, meta[relay.Constant][98] /* ty=Tensor[(64, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %468 = add(%467, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %469 = right_shift(%468, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %470 = clip(%469, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %471 = cast(%470, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %472 = cast(%430, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %473 = cast(%471, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %474 = cast(%472, dtype="int32") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %475 = add(%473, %474) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %476 = clip(%475, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int32] */; + %477 = cast(%476, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %478 = nn.conv2d(%477, meta[relay.Constant][99] /* ty=Tensor[(384, 64, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %479 = add(%478, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %480 = right_shift(%479, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %481 = clip(%480, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %482 = cast(%481, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %483 = cast(%482, dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %484 = multiply(%483, meta[relay.Constant][100] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %485 = add(%484, meta[relay.Constant][101] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %486 = nn.relu(%485) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %487 = add(%486, 64 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %488 = right_shift(%487, 7 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %489 = clip(%488, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %490 = cast(%489, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %491 = nn.conv2d(%490, meta[relay.Constant][102] /* ty=Tensor[(384, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %492 = add(%491, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %493 = right_shift(%492, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %494 = clip(%493, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %495 = cast(%494, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %496 = cast(%495, dtype="int32") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %497 = multiply(%496, meta[relay.Constant][103] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %498 = left_shift(%497, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %499 = add(%498, meta[relay.Constant][104] /* ty=Tensor[(384, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %500 = nn.relu(%499) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %501 = add(%500, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %502 = right_shift(%501, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %503 = clip(%502, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int32] */; + %504 = cast(%503, dtype="int8") /* from_string */ /* ty=Tensor[(1, 384, 8, 8), int8] */; + %505 = nn.conv2d(%504, meta[relay.Constant][105] /* ty=Tensor[(96, 384, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %506 = add(%505, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %507 = right_shift(%506, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %508 = clip(%507, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %509 = cast(%508, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %510 = cast(%509, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %511 = multiply(%510, meta[relay.Constant][106] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %512 = add(%511, meta[relay.Constant][107] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %513 = add(%512, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %514 = right_shift(%513, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %515 = clip(%514, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %516 = cast(%515, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %517 = cast(%476, dtype="int8") /* from_string */ /* ty=Tensor[(1, 64, 8, 8), int8] */; + %518 = nn.conv2d(%517, meta[relay.Constant][108] /* ty=Tensor[(96, 64, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %519 = add(%518, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %520 = right_shift(%519, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %521 = clip(%520, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %522 = cast(%521, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %523 = cast(%522, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %524 = multiply(%523, meta[relay.Constant][109] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %525 = left_shift(%524, 19 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %526 = add(%525, meta[relay.Constant][110] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %527 = add(%526, 16777216 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %528 = right_shift(%527, 25 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %529 = clip(%528, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %530 = cast(%529, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %531 = cast(%516, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %532 = cast(%530, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %533 = add(%531, %532) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %534 = clip(%533, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %535 = cast(%534, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %536 = nn.conv2d(%535, meta[relay.Constant][111] /* ty=Tensor[(576, 96, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %537 = add(%536, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %538 = right_shift(%537, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %539 = clip(%538, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %540 = cast(%539, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %541 = cast(%540, dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %542 = multiply(%541, meta[relay.Constant][112] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %543 = add(%542, meta[relay.Constant][113] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %544 = nn.relu(%543) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %545 = add(%544, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %546 = right_shift(%545, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %547 = clip(%546, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %548 = cast(%547, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %549 = nn.conv2d(%548, meta[relay.Constant][114] /* ty=Tensor[(576, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %550 = add(%549, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %551 = right_shift(%550, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %552 = clip(%551, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %553 = cast(%552, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %554 = cast(%553, dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %555 = multiply(%554, meta[relay.Constant][115] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %556 = left_shift(%555, 1 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %557 = add(%556, meta[relay.Constant][116] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %558 = nn.relu(%557) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %559 = add(%558, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %560 = right_shift(%559, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %561 = clip(%560, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %562 = cast(%561, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %563 = nn.conv2d(%562, meta[relay.Constant][117] /* ty=Tensor[(96, 576, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %564 = add(%563, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %565 = right_shift(%564, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %566 = clip(%565, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %567 = cast(%566, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %568 = cast(%567, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %569 = multiply(%568, meta[relay.Constant][118] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %570 = add(%569, meta[relay.Constant][119] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %571 = add(%570, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %572 = right_shift(%571, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %573 = clip(%572, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %574 = cast(%573, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %575 = cast(%534, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %576 = cast(%574, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %577 = cast(%575, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %578 = add(%576, %577) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %579 = clip(%578, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %580 = cast(%579, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %581 = nn.conv2d(%580, meta[relay.Constant][120] /* ty=Tensor[(576, 96, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %582 = add(%581, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %583 = right_shift(%582, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %584 = clip(%583, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %585 = cast(%584, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %586 = cast(%585, dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %587 = multiply(%586, meta[relay.Constant][121] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %588 = add(%587, meta[relay.Constant][122] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %589 = nn.relu(%588) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %590 = add(%589, 64 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %591 = right_shift(%590, 7 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %592 = clip(%591, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %593 = cast(%592, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %594 = nn.conv2d(%593, meta[relay.Constant][123] /* ty=Tensor[(576, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %595 = add(%594, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %596 = right_shift(%595, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %597 = clip(%596, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %598 = cast(%597, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %599 = cast(%598, dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %600 = multiply(%599, meta[relay.Constant][124] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %601 = left_shift(%600, 1 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %602 = add(%601, meta[relay.Constant][125] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %603 = nn.relu(%602) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %604 = add(%603, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %605 = right_shift(%604, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %606 = clip(%605, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %607 = cast(%606, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %608 = nn.conv2d(%607, meta[relay.Constant][126] /* ty=Tensor[(96, 576, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %609 = add(%608, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %610 = right_shift(%609, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %611 = clip(%610, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %612 = cast(%611, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %613 = cast(%612, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %614 = multiply(%613, meta[relay.Constant][127] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %615 = add(%614, meta[relay.Constant][128] /* ty=Tensor[(96, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %616 = add(%615, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %617 = right_shift(%616, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %618 = clip(%617, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %619 = cast(%618, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %620 = cast(%579, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %621 = cast(%619, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %622 = cast(%620, dtype="int32") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %623 = add(%621, %622) /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int32] */; + %624 = cast(%623, dtype="int8") /* from_string */ /* ty=Tensor[(1, 96, 8, 8), int8] */; + %625 = nn.conv2d(%624, meta[relay.Constant][129] /* ty=Tensor[(576, 96, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %626 = add(%625, 128 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %627 = right_shift(%626, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %628 = clip(%627, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %629 = cast(%628, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %630 = cast(%629, dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %631 = multiply(%630, meta[relay.Constant][130] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %632 = add(%631, meta[relay.Constant][131] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %633 = nn.relu(%632) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %634 = add(%633, 64 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %635 = right_shift(%634, 7 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %636 = clip(%635, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int32] */; + %637 = cast(%636, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 8, 8), int8] */; + %638 = nn.conv2d(%637, meta[relay.Constant][132] /* ty=Tensor[(576, 1, 3, 3), int8] */, strides=[2, 2], padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %639 = add(%638, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %640 = right_shift(%639, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %641 = clip(%640, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %642 = cast(%641, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int8] */; + %643 = cast(%642, dtype="int32") /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %644 = multiply(%643, meta[relay.Constant][133] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %645 = add(%644, meta[relay.Constant][134] /* ty=Tensor[(576, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %646 = nn.relu(%645) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %647 = add(%646, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %648 = right_shift(%647, 3 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %649 = clip(%648, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int32] */; + %650 = cast(%649, dtype="int8") /* from_string */ /* ty=Tensor[(1, 576, 4, 4), int8] */; + %651 = nn.conv2d(%650, meta[relay.Constant][135] /* ty=Tensor[(160, 576, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=160, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %652 = add(%651, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %653 = right_shift(%652, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %654 = clip(%653, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %655 = cast(%654, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %656 = cast(%655, dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %657 = multiply(%656, meta[relay.Constant][136] /* ty=Tensor[(160, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %658 = add(%657, meta[relay.Constant][137] /* ty=Tensor[(160, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %659 = add(%658, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %660 = right_shift(%659, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %661 = clip(%660, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %662 = cast(%661, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %663 = nn.conv2d(%662, meta[relay.Constant][138] /* ty=Tensor[(960, 160, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=960, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %664 = add(%663, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %665 = right_shift(%664, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %666 = clip(%665, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %667 = cast(%666, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %668 = cast(%667, dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %669 = multiply(%668, meta[relay.Constant][139] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %670 = add(%669, meta[relay.Constant][140] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %671 = nn.relu(%670) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %672 = add(%671, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %673 = right_shift(%672, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %674 = clip(%673, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %675 = cast(%674, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %676 = nn.conv2d(%675, meta[relay.Constant][141] /* ty=Tensor[(960, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %677 = add(%676, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %678 = right_shift(%677, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %679 = clip(%678, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %680 = cast(%679, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %681 = cast(%680, dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %682 = multiply(%681, meta[relay.Constant][142] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %683 = left_shift(%682, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %684 = add(%683, meta[relay.Constant][143] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %685 = nn.relu(%684) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %686 = add(%685, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %687 = right_shift(%686, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %688 = clip(%687, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %689 = cast(%688, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %690 = nn.conv2d(%689, meta[relay.Constant][144] /* ty=Tensor[(160, 960, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=160, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %691 = add(%690, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %692 = right_shift(%691, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %693 = clip(%692, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %694 = cast(%693, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %695 = cast(%694, dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %696 = multiply(%695, meta[relay.Constant][145] /* ty=Tensor[(160, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %697 = add(%696, meta[relay.Constant][146] /* ty=Tensor[(160, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %698 = add(%697, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %699 = right_shift(%698, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %700 = clip(%699, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %701 = cast(%700, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %702 = cast(%661, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %703 = cast(%701, dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %704 = cast(%702, dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %705 = add(%703, %704) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %706 = clip(%705, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %707 = cast(%706, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %708 = nn.conv2d(%707, meta[relay.Constant][147] /* ty=Tensor[(960, 160, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=960, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %709 = add(%708, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %710 = right_shift(%709, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %711 = clip(%710, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %712 = cast(%711, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %713 = cast(%712, dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %714 = multiply(%713, meta[relay.Constant][148] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %715 = add(%714, meta[relay.Constant][149] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %716 = nn.relu(%715) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %717 = add(%716, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %718 = right_shift(%717, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %719 = clip(%718, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %720 = cast(%719, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %721 = nn.conv2d(%720, meta[relay.Constant][150] /* ty=Tensor[(960, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %722 = add(%721, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %723 = right_shift(%722, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %724 = clip(%723, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %725 = cast(%724, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %726 = cast(%725, dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %727 = multiply(%726, meta[relay.Constant][151] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %728 = left_shift(%727, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %729 = add(%728, meta[relay.Constant][152] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %730 = nn.relu(%729) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %731 = add(%730, 8 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %732 = right_shift(%731, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %733 = clip(%732, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %734 = cast(%733, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %735 = nn.conv2d(%734, meta[relay.Constant][153] /* ty=Tensor[(160, 960, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=160, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %736 = add(%735, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %737 = right_shift(%736, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %738 = clip(%737, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %739 = cast(%738, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %740 = cast(%739, dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %741 = multiply(%740, meta[relay.Constant][154] /* ty=Tensor[(160, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %742 = left_shift(%741, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %743 = add(%742, meta[relay.Constant][155] /* ty=Tensor[(160, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %744 = add(%743, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %745 = right_shift(%744, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %746 = clip(%745, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %747 = cast(%746, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %748 = cast(%706, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %749 = cast(%747, dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %750 = cast(%748, dtype="int32") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %751 = add(%749, %750) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %752 = clip(%751, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int32] */; + %753 = cast(%752, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %754 = nn.conv2d(%753, meta[relay.Constant][156] /* ty=Tensor[(960, 160, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=960, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %755 = add(%754, 256 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %756 = right_shift(%755, 9 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %757 = clip(%756, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %758 = cast(%757, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %759 = cast(%758, dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %760 = multiply(%759, meta[relay.Constant][157] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %761 = add(%760, meta[relay.Constant][158] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %762 = nn.relu(%761) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %763 = add(%762, 32 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %764 = right_shift(%763, 6 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %765 = clip(%764, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %766 = cast(%765, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %767 = nn.conv2d(%766, meta[relay.Constant][159] /* ty=Tensor[(960, 1, 3, 3), int8] */, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %768 = add(%767, 512 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %769 = right_shift(%768, 10 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %770 = clip(%769, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %771 = cast(%770, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %772 = cast(%771, dtype="int32") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %773 = multiply(%772, meta[relay.Constant][160] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %774 = left_shift(%773, 4 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %775 = add(%774, meta[relay.Constant][161] /* ty=Tensor[(960, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %776 = nn.relu(%775) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %777 = add(%776, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %778 = right_shift(%777, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %779 = clip(%778, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int32] */; + %780 = cast(%779, dtype="int8") /* from_string */ /* ty=Tensor[(1, 960, 4, 4), int8] */; + %781 = nn.conv2d(%780, meta[relay.Constant][162] /* ty=Tensor[(320, 960, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=320, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %782 = add(%781, 512 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %783 = right_shift(%782, 10 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %784 = clip(%783, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %785 = cast(%784, dtype="int8") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int8] */; + %786 = cast(%785, dtype="int32") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %787 = multiply(%786, meta[relay.Constant][163] /* ty=Tensor[(320, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %788 = left_shift(%787, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %789 = add(%788, meta[relay.Constant][164] /* ty=Tensor[(320, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %790 = add(%789, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %791 = right_shift(%790, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %792 = clip(%791, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %793 = cast(%792, dtype="int8") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int8] */; + %794 = cast(%752, dtype="int8") /* from_string */ /* ty=Tensor[(1, 160, 4, 4), int8] */; + %795 = nn.conv2d(%794, meta[relay.Constant][165] /* ty=Tensor[(320, 160, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=320, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %796 = add(%795, 1024 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %797 = right_shift(%796, 11 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %798 = clip(%797, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %799 = cast(%798, dtype="int8") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int8] */; + %800 = cast(%799, dtype="int32") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %801 = multiply(%800, meta[relay.Constant][166] /* ty=Tensor[(320, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %802 = left_shift(%801, 22 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %803 = add(%802, meta[relay.Constant][167] /* ty=Tensor[(320, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %804 = add(%803, 67108864 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %805 = right_shift(%804, 27 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %806 = clip(%805, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %807 = cast(%806, dtype="int8") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int8] */; + %808 = cast(%793, dtype="int32") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %809 = cast(%807, dtype="int32") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %810 = add(%808, %809) /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int32] */; + %811 = cast(%810, dtype="int8") /* from_string */ /* ty=Tensor[(1, 320, 4, 4), int8] */; + %812 = nn.conv2d(%811, meta[relay.Constant][168] /* ty=Tensor[(1280, 320, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=1280, kernel_size=[1, 1], out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %813 = add(%812, 1024 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %814 = right_shift(%813, 11 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %815 = clip(%814, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %816 = cast(%815, dtype="int8") /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int8] */; + %817 = cast(%816, dtype="int32") /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %818 = multiply(%817, meta[relay.Constant][169] /* ty=Tensor[(1280, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %819 = left_shift(%818, 2 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %820 = add(%819, meta[relay.Constant][170] /* ty=Tensor[(1280, 1, 1), int32] */) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %821 = nn.relu(%820) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %822 = add(%821, 16 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %823 = right_shift(%822, 5 /* ty=int32 */) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %824 = clip(%823, a_min=-127f, a_max=127f) /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %825 = cast(%824, dtype="int8") /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int8] */; + %826 = cast(%825, dtype="int32") /* from_string */ /* ty=Tensor[(1, 1280, 4, 4), int32] */; + %827 = nn.avg_pool2d(%826, pool_size=[4, 4], strides=[4, 4], padding=[0, 0, 0, 0]) /* from_string */ /* ty=Tensor[(1, 1280, 1, 1), int32] */; + %828 = reshape(%827, newshape=[1, -1]) /* from_string */ /* ty=Tensor[(1, 1280), int32] */; + %829 = cast(%828, dtype="int8") /* from_string */ /* ty=Tensor[(1, 1280), int8] */; + %830 = nn.dense(%829, meta[relay.Constant][171] /* ty=Tensor[(10, 1280), int8] */, units=10, out_dtype="int32") /* from_string */ /* ty=Tensor[(1, 10), int32] */; + %831 = add(%830, meta[relay.Constant][172] /* ty=Tensor[(10), int32] */) /* from_string */ /* ty=Tensor[(1, 10), int32] */; + %832 = cast(%831, dtype="float32") /* from_string */ /* ty=Tensor[(1, 10), float32] */; + multiply(%832, 0.000244141f /* ty=float32 */) /* from_string */ /* ty=Tensor[(1, 10), float32] */ +} + +#[metadata] +{ + "root": 1, + "nodes": [ + { + "type_key": "" + }, + { + "type_key": "Map", + "keys": [ + "relay.Constant" + ], + "data": [2] + }, + { + "type_key": "Array", + "data": [ + 3, + 12, + 19, + 26, + 34, + 41, + 48, + 55, + 62, + 69, + 77, + 84, + 91, + 99, + 106, + 113, + 121, + 128, + 135, + 142, + 149, + 156, + 164, + 171, + 178, + 186, + 193, + 200, + 208, + 215, + 222, + 229, 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", + "P6G0lvBAXt0AAAAAAAAAAAEAAAAAAAAAAQAAAAAgAQAKAAAAAAAAACgAAAAAAAAA+v////r///8sAAAAqgAAADYAAACe////1P////r////M////yv///w==" + ], + "attrs": {"tvm_version": "0.8.dev0"} +} \ No newline at end of file diff --git a/tests/models/qnn-mobilenet.relay b/tests/models/qnn-mobilenet.relay new file mode 100644 index 0000000..e53b835 --- /dev/null +++ b/tests/models/qnn-mobilenet.relay @@ -0,0 +1,176 @@ +#[version = "0.0.5"] +def @main(%input: Tensor[(1, 224, 224, 3), uint8], %v_param_1: Tensor[(3, 3, 3, 32), uint8], %v_param_2: Tensor[(32), int32], %v_param_3: Tensor[(3, 3, 32, 1), uint8], %v_param_4: Tensor[(32), int32], %v_param_5: Tensor[(1, 1, 32, 16), uint8], %v_param_6: Tensor[(16), int32], %v_param_7: Tensor[(1, 1, 16, 96), uint8], %v_param_8: Tensor[(96), int32], %v_param_9: Tensor[(3, 3, 96, 1), uint8], %v_param_10: Tensor[(96), int32], %v_param_11: Tensor[(1, 1, 96, 24), uint8], %v_param_12: Tensor[(24), int32], %v_param_13: Tensor[(1, 1, 24, 144), uint8], %v_param_14: Tensor[(144), int32], %v_param_15: Tensor[(3, 3, 144, 1), uint8], %v_param_16: Tensor[(144), int32], %v_param_17: Tensor[(1, 1, 144, 24), uint8], %v_param_18: Tensor[(24), int32], %v_param_19: Tensor[(1, 1, 24, 144), uint8], %v_param_20: Tensor[(144), int32], %v_param_21: Tensor[(3, 3, 144, 1), uint8], %v_param_22: Tensor[(144), int32], %v_param_23: Tensor[(1, 1, 144, 32), uint8], %v_param_24: Tensor[(32), int32], %v_param_25: Tensor[(1, 1, 32, 192), uint8], %v_param_26: Tensor[(192), int32], %v_param_27: Tensor[(3, 3, 192, 1), uint8], %v_param_28: Tensor[(192), int32], %v_param_29: Tensor[(1, 1, 192, 32), uint8], %v_param_30: Tensor[(32), int32], %v_param_31: Tensor[(1, 1, 32, 192), uint8], %v_param_32: Tensor[(192), int32], %v_param_33: Tensor[(3, 3, 192, 1), uint8], %v_param_34: Tensor[(192), int32], %v_param_35: Tensor[(1, 1, 192, 32), uint8], %v_param_36: Tensor[(32), int32], %v_param_37: Tensor[(1, 1, 32, 192), uint8], %v_param_38: Tensor[(192), int32], %v_param_39: Tensor[(3, 3, 192, 1), uint8], %v_param_40: Tensor[(192), int32], %v_param_41: Tensor[(1, 1, 192, 64), uint8], %v_param_42: Tensor[(64), int32], %v_param_43: Tensor[(1, 1, 64, 384), uint8], %v_param_44: Tensor[(384), int32], %v_param_45: Tensor[(3, 3, 384, 1), uint8], %v_param_46: Tensor[(384), int32], %v_param_47: Tensor[(1, 1, 384, 64), uint8], %v_param_48: Tensor[(64), int32], %v_param_49: Tensor[(1, 1, 64, 384), uint8], %v_param_50: Tensor[(384), int32], %v_param_51: Tensor[(3, 3, 384, 1), uint8], %v_param_52: Tensor[(384), int32], %v_param_53: Tensor[(1, 1, 384, 64), uint8], %v_param_54: Tensor[(64), int32], %v_param_55: Tensor[(1, 1, 64, 384), uint8], %v_param_56: Tensor[(384), int32], %v_param_57: Tensor[(3, 3, 384, 1), uint8], %v_param_58: Tensor[(384), int32], %v_param_59: Tensor[(1, 1, 384, 64), uint8], %v_param_60: Tensor[(64), int32], %v_param_61: Tensor[(1, 1, 64, 384), uint8], %v_param_62: Tensor[(384), int32], %v_param_63: Tensor[(3, 3, 384, 1), uint8], %v_param_64: Tensor[(384), int32], %v_param_65: Tensor[(1, 1, 384, 96), uint8], %v_param_66: Tensor[(96), int32], %v_param_67: Tensor[(1, 1, 96, 576), uint8], %v_param_68: Tensor[(576), int32], %v_param_69: Tensor[(3, 3, 576, 1), uint8], %v_param_70: Tensor[(576), int32], %v_param_71: Tensor[(1, 1, 576, 96), uint8], %v_param_72: Tensor[(96), int32], %v_param_73: Tensor[(1, 1, 96, 576), uint8], %v_param_74: Tensor[(576), int32], %v_param_75: Tensor[(3, 3, 576, 1), uint8], %v_param_76: Tensor[(576), int32], %v_param_77: Tensor[(1, 1, 576, 96), uint8], %v_param_78: Tensor[(96), int32], %v_param_79: Tensor[(1, 1, 96, 576), uint8], %v_param_80: Tensor[(576), int32], %v_param_81: Tensor[(3, 3, 576, 1), uint8], %v_param_82: Tensor[(576), int32], %v_param_83: Tensor[(1, 1, 576, 160), uint8], %v_param_84: Tensor[(160), int32], %v_param_85: Tensor[(1, 1, 160, 960), uint8], %v_param_86: Tensor[(960), int32], %v_param_87: Tensor[(3, 3, 960, 1), uint8], %v_param_88: Tensor[(960), int32], %v_param_89: Tensor[(1, 1, 960, 160), uint8], %v_param_90: Tensor[(160), int32], %v_param_91: Tensor[(1, 1, 160, 960), uint8], %v_param_92: Tensor[(960), int32], %v_param_93: Tensor[(3, 3, 960, 1), uint8], %v_param_94: Tensor[(960), int32], %v_param_95: Tensor[(1, 1, 960, 160), uint8], %v_param_96: Tensor[(160), int32], %v_param_97: Tensor[(1, 1, 160, 960), uint8], %v_param_98: Tensor[(960), int32], %v_param_99: Tensor[(3, 3, 960, 1), uint8], %v_param_100: Tensor[(960), int32], %v_param_101: Tensor[(1, 1, 960, 320), uint8], %v_param_102: Tensor[(320), int32], %v_param_103: Tensor[(1, 1, 320, 1280), uint8], %v_param_104: Tensor[(1280), int32], %v_param_105: Tensor[(1, 1, 1280, 1001), uint8], %v_param_106: Tensor[(1001), int32]) { + %0 = qnn.conv2d(%input, %v_param_1, 128, 122, 0.0078125f, 0.0339689f, strides=[2, 2], padding=[0, 0, 1, 1], channels=32, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %1 = nn.bias_add(%0, %v_param_2, axis=3); + %2 = qnn.requantize(%1, 0.000265382f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %3 = qnn.conv2d(%2, %v_param_3, 0, 165, 0.0235285f, 0.343696f, padding=[1, 1, 1, 1], groups=32, channels=32, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %4 = nn.bias_add(%3, %v_param_4, axis=3); + %5 = qnn.requantize(%4, 0.00808663f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %6 = qnn.conv2d(%5, %v_param_5, 0, 140, 0.0235285f, 0.0373718f, padding=[0, 0, 0, 0], channels=16, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %7 = nn.bias_add(%6, %v_param_6, axis=3); + %8 = qnn.requantize(%7, 0.0008793f, 0, 0.354413f, 129, axis=3, out_dtype="uint8"); + %9 = qnn.conv2d(%8, %v_param_7, 129, 127, 0.354413f, 0.00975851f, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %10 = nn.bias_add(%9, %v_param_8, axis=3); + %11 = qnn.requantize(%10, 0.00345855f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %12 = qnn.conv2d(%11, %v_param_9, 0, 109, 0.0235285f, 0.0209691f, strides=[2, 2], padding=[0, 0, 1, 1], groups=96, channels=96, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %13 = nn.bias_add(%12, %v_param_10, axis=3); + %14 = qnn.requantize(%13, 0.000493371f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %15 = qnn.conv2d(%14, %v_param_11, 0, 156, 0.0235285f, 0.022536f, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %16 = nn.bias_add(%15, %v_param_12, axis=3); + %17 = qnn.requantize(%16, 0.000530238f, 0, 0.275834f, 119, axis=3, out_dtype="uint8"); + %18 = qnn.conv2d(%17, %v_param_13, 119, 144, 0.275834f, 0.0036557f, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %19 = nn.bias_add(%18, %v_param_14, axis=3); + %20 = qnn.requantize(%19, 0.00100837f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %21 = qnn.conv2d(%20, %v_param_15, 0, 52, 0.0235285f, 0.169819f, padding=[1, 1, 1, 1], groups=144, channels=144, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %22 = nn.bias_add(%21, %v_param_16, axis=3); + %23 = qnn.requantize(%22, 0.00399559f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %24 = qnn.conv2d(%23, %v_param_17, 0, 122, 0.0235285f, 0.0274089f, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %25 = nn.bias_add(%24, %v_param_18, axis=3); + %26 = qnn.requantize(%25, 0.000644889f, 0, 0.401493f, 136, axis=3, out_dtype="uint8"); + %27 = qnn.add(%26, %17, 0.401493f, 136, 0.275834f, 119, 0.432169f, 133); + %28 = qnn.conv2d(%27, %v_param_19, 133, 104, 0.432169f, 0.00299887f, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %29 = nn.bias_add(%28, %v_param_20, axis=3); + %30 = qnn.requantize(%29, 0.00129602f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %31 = qnn.conv2d(%30, %v_param_21, 0, 143, 0.0235285f, 0.0172029f, strides=[2, 2], padding=[0, 0, 1, 1], groups=144, channels=144, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %32 = nn.bias_add(%31, %v_param_22, axis=3); + %33 = qnn.requantize(%32, 0.000404757f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %34 = qnn.conv2d(%33, %v_param_23, 0, 111, 0.0235285f, 0.0168447f, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %35 = nn.bias_add(%34, %v_param_24, axis=3); + %36 = qnn.requantize(%35, 0.00039633f, 0, 0.218362f, 127, axis=3, out_dtype="uint8"); + %37 = qnn.conv2d(%36, %v_param_25, 127, 128, 0.218362f, 0.00192442f, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %38 = nn.bias_add(%37, %v_param_26, axis=3); + %39 = qnn.requantize(%38, 0.000420222f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %40 = qnn.conv2d(%39, %v_param_27, 0, 118, 0.0235285f, 0.0652507f, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %41 = nn.bias_add(%40, %v_param_28, axis=3); + %42 = qnn.requantize(%41, 0.00153525f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %43 = qnn.conv2d(%42, %v_param_29, 0, 146, 0.0235285f, 0.0190629f, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %44 = nn.bias_add(%43, %v_param_30, axis=3); + %45 = qnn.requantize(%44, 0.000448521f, 0, 0.227942f, 121, axis=3, out_dtype="uint8"); + %46 = qnn.add(%45, %36, 0.227942f, 121, 0.218362f, 127, 0.25969f, 130); + %47 = qnn.conv2d(%46, %v_param_31, 130, 135, 0.25969f, 0.00136492f, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %48 = nn.bias_add(%47, %v_param_32, axis=3); + %49 = qnn.requantize(%48, 0.000354455f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %50 = qnn.conv2d(%49, %v_param_33, 0, 95, 0.0235285f, 0.0790978f, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %51 = nn.bias_add(%50, %v_param_34, axis=3); + %52 = qnn.requantize(%51, 0.00186105f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %53 = qnn.conv2d(%52, %v_param_35, 0, 128, 0.0235285f, 0.0182931f, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %54 = nn.bias_add(%53, %v_param_36, axis=3); + %55 = qnn.requantize(%54, 0.000430409f, 0, 0.257749f, 124, axis=3, out_dtype="uint8"); + %56 = qnn.add(%55, %46, 0.257749f, 124, 0.25969f, 130, 0.331715f, 124); + %57 = qnn.conv2d(%56, %v_param_37, 124, 127, 0.331715f, 0.00191704f, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %58 = nn.bias_add(%57, %v_param_38, axis=3); + %59 = qnn.requantize(%58, 0.000635912f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %60 = qnn.conv2d(%59, %v_param_39, 0, 127, 0.0235285f, 0.0100879f, strides=[2, 2], padding=[0, 0, 1, 1], groups=192, channels=192, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %61 = nn.bias_add(%60, %v_param_40, axis=3); + %62 = qnn.requantize(%61, 0.000237353f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %63 = qnn.conv2d(%62, %v_param_41, 0, 147, 0.0235285f, 0.0146013f, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %64 = nn.bias_add(%63, %v_param_42, axis=3); + %65 = qnn.requantize(%64, 0.000343546f, 0, 0.185405f, 126, axis=3, out_dtype="uint8"); + %66 = qnn.conv2d(%65, %v_param_43, 126, 125, 0.185405f, 0.00155389f, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %67 = nn.bias_add(%66, %v_param_44, axis=3); + %68 = qnn.requantize(%67, 0.0002881f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %69 = qnn.conv2d(%68, %v_param_45, 0, 110, 0.0235285f, 0.0609271f, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %70 = nn.bias_add(%69, %v_param_46, axis=3); + %71 = qnn.requantize(%70, 0.00143352f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %72 = qnn.conv2d(%71, %v_param_47, 0, 124, 0.0235285f, 0.0167829f, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %73 = nn.bias_add(%72, %v_param_48, axis=3); + %74 = qnn.requantize(%73, 0.000394877f, 0, 0.172635f, 109, axis=3, out_dtype="uint8"); + %75 = qnn.add(%74, %65, 0.172635f, 109, 0.185405f, 126, 0.18911f, 122); + %76 = qnn.conv2d(%75, %v_param_49, 122, 134, 0.18911f, 0.0014703f, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %77 = nn.bias_add(%76, %v_param_50, axis=3); + %78 = qnn.requantize(%77, 0.000278048f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %79 = qnn.conv2d(%78, %v_param_51, 0, 133, 0.0235285f, 0.0524078f, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %80 = nn.bias_add(%79, %v_param_52, axis=3); + %81 = qnn.requantize(%80, 0.00123308f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %82 = qnn.conv2d(%81, %v_param_53, 0, 125, 0.0235285f, 0.0128983f, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %83 = nn.bias_add(%82, %v_param_54, axis=3); + %84 = qnn.requantize(%83, 0.000303476f, 0, 0.147155f, 123, axis=3, out_dtype="uint8"); + %85 = qnn.add(%84, %75, 0.147155f, 123, 0.18911f, 122, 0.199681f, 124); + %86 = qnn.conv2d(%85, %v_param_55, 124, 127, 0.199681f, 0.00137335f, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %87 = nn.bias_add(%86, %v_param_56, axis=3); + %88 = qnn.requantize(%87, 0.000274232f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %89 = qnn.conv2d(%88, %v_param_57, 0, 155, 0.0235285f, 0.0407789f, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %90 = nn.bias_add(%89, %v_param_58, axis=3); + %91 = qnn.requantize(%90, 0.000959465f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %92 = qnn.conv2d(%91, %v_param_59, 0, 144, 0.0235285f, 0.0195615f, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %93 = nn.bias_add(%92, %v_param_60, axis=3); + %94 = qnn.requantize(%93, 0.000460252f, 0, 0.156276f, 122, axis=3, out_dtype="uint8"); + %95 = qnn.add(%94, %85, 0.156276f, 122, 0.199681f, 124, 0.220273f, 120); + %96 = qnn.conv2d(%95, %v_param_61, 120, 131, 0.220273f, 0.00162825f, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %97 = nn.bias_add(%96, %v_param_62, axis=3); + %98 = qnn.requantize(%97, 0.00035866f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %99 = qnn.conv2d(%98, %v_param_63, 0, 143, 0.0235285f, 0.0311078f, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %100 = nn.bias_add(%99, %v_param_64, axis=3); + %101 = qnn.requantize(%100, 0.00073192f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %102 = qnn.conv2d(%101, %v_param_65, 0, 129, 0.0235285f, 0.00743631f, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %103 = nn.bias_add(%102, %v_param_66, axis=3); + %104 = qnn.requantize(%103, 0.000174965f, 0, 0.170611f, 129, axis=3, out_dtype="uint8"); + %105 = qnn.conv2d(%104, %v_param_67, 129, 134, 0.170611f, 0.00163099f, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %106 = nn.bias_add(%105, %v_param_68, axis=3); + %107 = qnn.requantize(%106, 0.000278264f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %108 = qnn.conv2d(%107, %v_param_69, 0, 66, 0.0235285f, 0.0708081f, padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %109 = nn.bias_add(%108, %v_param_70, axis=3); + %110 = qnn.requantize(%109, 0.00166601f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %111 = qnn.conv2d(%110, %v_param_71, 0, 136, 0.0235285f, 0.00838223f, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %112 = nn.bias_add(%111, %v_param_72, axis=3); + %113 = qnn.requantize(%112, 0.000197221f, 0, 0.123328f, 127, axis=3, out_dtype="uint8"); + %114 = qnn.add(%113, %104, 0.123328f, 127, 0.170611f, 129, 0.176158f, 127); + %115 = qnn.conv2d(%114, %v_param_73, 127, 138, 0.176158f, 0.00182588f, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %116 = nn.bias_add(%115, %v_param_74, axis=3); + %117 = qnn.requantize(%116, 0.000321643f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %118 = qnn.conv2d(%117, %v_param_75, 0, 159, 0.0235285f, 0.0744879f, padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %119 = nn.bias_add(%118, %v_param_76, axis=3); + %120 = qnn.requantize(%119, 0.00175259f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %121 = qnn.conv2d(%120, %v_param_77, 0, 154, 0.0235285f, 0.0239826f, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %122 = nn.bias_add(%121, %v_param_78, axis=3); + %123 = qnn.requantize(%122, 0.000564274f, 0, 0.186196f, 127, axis=3, out_dtype="uint8"); + %124 = qnn.add(%123, %114, 0.186196f, 127, 0.176158f, 127, 0.233401f, 126); + %125 = qnn.conv2d(%124, %v_param_79, 126, 123, 0.233401f, 0.0013828f, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %126 = nn.bias_add(%125, %v_param_80, axis=3); + %127 = qnn.requantize(%126, 0.000322747f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %128 = qnn.conv2d(%127, %v_param_81, 0, 92, 0.0235285f, 0.0152579f, strides=[2, 2], padding=[0, 0, 1, 1], groups=576, channels=576, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %129 = nn.bias_add(%128, %v_param_82, axis=3); + %130 = qnn.requantize(%129, 0.000358996f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %131 = qnn.conv2d(%130, %v_param_83, 0, 140, 0.0235285f, 0.00944795f, padding=[0, 0, 0, 0], channels=160, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %132 = nn.bias_add(%131, %v_param_84, axis=3); + %133 = qnn.requantize(%132, 0.000222296f, 0, 0.132378f, 132, axis=3, out_dtype="uint8"); + %134 = qnn.conv2d(%133, %v_param_85, 132, 135, 0.132378f, 0.00202221f, padding=[0, 0, 0, 0], channels=960, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %135 = nn.bias_add(%134, %v_param_86, axis=3); + %136 = qnn.requantize(%135, 0.000267696f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %137 = qnn.conv2d(%136, %v_param_87, 0, 147, 0.0235285f, 0.0416675f, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %138 = nn.bias_add(%137, %v_param_88, axis=3); + %139 = qnn.requantize(%138, 0.000980373f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %140 = qnn.conv2d(%139, %v_param_89, 0, 139, 0.0235285f, 0.0078987f, padding=[0, 0, 0, 0], channels=160, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %141 = nn.bias_add(%140, %v_param_90, axis=3); + %142 = qnn.requantize(%141, 0.000185844f, 0, 0.100457f, 129, axis=3, out_dtype="uint8"); + %143 = qnn.add(%142, %133, 0.100457f, 129, 0.132378f, 132, 0.15071f, 134); + %144 = qnn.conv2d(%143, %v_param_91, 134, 127, 0.15071f, 0.00159444f, padding=[0, 0, 0, 0], channels=960, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %145 = nn.bias_add(%144, %v_param_92, axis=3); + %146 = qnn.requantize(%145, 0.000240298f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %147 = qnn.conv2d(%146, %v_param_93, 0, 102, 0.0235285f, 0.0428194f, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %148 = nn.bias_add(%147, %v_param_94, axis=3); + %149 = qnn.requantize(%148, 0.00100747f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %150 = qnn.conv2d(%149, %v_param_95, 0, 131, 0.0235285f, 0.0369741f, padding=[0, 0, 0, 0], channels=160, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %151 = nn.bias_add(%150, %v_param_96, axis=3); + %152 = qnn.requantize(%151, 0.000869944f, 0, 0.169606f, 133, axis=3, out_dtype="uint8"); + %153 = qnn.add(%152, %143, 0.169606f, 133, 0.15071f, 134, 0.210051f, 131); + %154 = qnn.conv2d(%153, %v_param_97, 131, 135, 0.210051f, 0.00204683f, padding=[0, 0, 0, 0], channels=960, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %155 = nn.bias_add(%154, %v_param_98, axis=3); + %156 = qnn.requantize(%155, 0.000429939f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %157 = qnn.conv2d(%156, %v_param_99, 0, 201, 0.0235285f, 0.164563f, padding=[1, 1, 1, 1], groups=960, channels=960, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32"); + %158 = nn.bias_add(%157, %v_param_100, axis=3); + %159 = qnn.requantize(%158, 0.00387191f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %160 = qnn.conv2d(%159, %v_param_101, 0, 111, 0.0235285f, 0.00800929f, padding=[0, 0, 0, 0], channels=320, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %161 = nn.bias_add(%160, %v_param_102, axis=3); + %162 = qnn.requantize(%161, 0.000188446f, 0, 0.116945f, 130, axis=3, out_dtype="uint8"); + %163 = qnn.conv2d(%162, %v_param_103, 130, 125, 0.116945f, 0.00516707f, padding=[0, 0, 0, 0], channels=1280, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %164 = nn.bias_add(%163, %v_param_104, axis=3); + %165 = qnn.requantize(%164, 0.000604263f, 0, 0.0235285f, 0, axis=3, out_dtype="uint8"); + %166 = cast(%165, dtype="int32"); + %167 = nn.avg_pool2d(%166, pool_size=[7, 7], padding=[0, 0, 0, 0], layout="NHWC"); + %168 = cast(%167, dtype="uint8"); + %169 = qnn.conv2d(%168, %v_param_105, 0, 113, 0.0235285f, 0.00169108f, padding=[0, 0, 0, 0], channels=1001, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32"); + %170 = nn.bias_add(%169, %v_param_106, axis=3); + %171 = qnn.requantize(%170, 3.97886e-05f, 0, 0.0988925f, 58, axis=3, out_dtype="uint8"); + reshape(%171, newshape=[1, 1001]) +} diff --git a/tests/models/resmlp-depth-4.relay b/tests/models/resmlp-depth-4.relay new file mode 100644 index 0000000..f940128 --- /dev/null +++ b/tests/models/resmlp-depth-4.relay @@ -0,0 +1,459 @@ +#[version = "0.0.5"] +type List[A] { + Cons(A, List[A]), + Nil, +} + +type tensor_int32_t { + tensor_nil_int32, + tensor0_int32(int32), + tensor1_int32(Tensor[(?), int32]), + tensor2_int32(Tensor[(?, ?), int32]), + tensor3_int32(Tensor[(?, ?, ?), int32]), + tensor4_int32(Tensor[(?, ?, ?, ?), int32]), + tensor5_int32(Tensor[(?, ?, ?, ?, ?), int32]), + tensor6_int32(Tensor[(?, ?, ?, ?, ?, ?), int32]), +} + +type tensor_int64_t { + tensor_nil_int64, + tensor0_int64(int64), + tensor1_int64(Tensor[(?), int64]), + tensor2_int64(Tensor[(?, ?), int64]), + tensor3_int64(Tensor[(?, ?, ?), int64]), + tensor4_int64(Tensor[(?, ?, ?, ?), int64]), + tensor5_int64(Tensor[(?, ?, ?, ?, ?), int64]), + tensor6_int64(Tensor[(?, ?, ?, ?, ?, ?), int64]), +} + +type tensor_int8_t { + tensor_nil_int8, + tensor0_int8(int8), + tensor1_int8(Tensor[(?), int8]), + tensor2_int8(Tensor[(?, ?), int8]), + tensor3_int8(Tensor[(?, ?, ?), int8]), + tensor4_int8(Tensor[(?, ?, ?, ?), int8]), + tensor5_int8(Tensor[(?, ?, ?, ?, ?), int8]), + tensor6_int8(Tensor[(?, ?, ?, ?, ?, ?), int8]), +} + +type tensor_uint8_t { + tensor_nil_uint8, + tensor0_uint8(uint8), + tensor1_uint8(Tensor[(?), uint8]), + tensor2_uint8(Tensor[(?, ?), uint8]), + tensor3_uint8(Tensor[(?, ?, ?), uint8]), + tensor4_uint8(Tensor[(?, ?, ?, ?), uint8]), + tensor5_uint8(Tensor[(?, ?, ?, ?, ?), uint8]), + tensor6_uint8(Tensor[(?, ?, ?, ?, ?, ?), uint8]), +} + +type tensor_float64_t { + tensor_nil_float64, + tensor0_float64(float64), + tensor1_float64(Tensor[(?), float64]), + tensor2_float64(Tensor[(?, ?), float64]), + tensor3_float64(Tensor[(?, ?, ?), float64]), + tensor4_float64(Tensor[(?, ?, ?, ?), float64]), + tensor5_float64(Tensor[(?, ?, ?, ?, ?), float64]), + tensor6_float64(Tensor[(?, ?, ?, ?, ?, ?), float64]), +} + +type tensor_uint16_t { + tensor_nil_uint16, + tensor0_uint16(uint16), + tensor1_uint16(Tensor[(?), uint16]), + tensor2_uint16(Tensor[(?, ?), uint16]), + tensor3_uint16(Tensor[(?, ?, ?), uint16]), + tensor4_uint16(Tensor[(?, ?, ?, ?), uint16]), + tensor5_uint16(Tensor[(?, ?, ?, ?, ?), uint16]), + tensor6_uint16(Tensor[(?, ?, ?, ?, ?, ?), uint16]), +} + +type tensor_int16_t { + tensor_nil_int16, + tensor0_int16(int16), + tensor1_int16(Tensor[(?), int16]), + tensor2_int16(Tensor[(?, ?), int16]), + tensor3_int16(Tensor[(?, ?, ?), int16]), + tensor4_int16(Tensor[(?, ?, ?, ?), int16]), + tensor5_int16(Tensor[(?, ?, ?, ?, ?), int16]), + tensor6_int16(Tensor[(?, ?, ?, ?, ?, ?), int16]), +} + +type Tree[A] { + Rose(A, List[Tree[A]]), +} + +type tensor_float32_t { + tensor_nil_float32, + tensor0_float32(float32), + tensor1_float32(Tensor[(?), float32]), + tensor2_float32(Tensor[(?, ?), float32]), + tensor3_float32(Tensor[(?, ?, ?), float32]), + tensor4_float32(Tensor[(?, ?, ?, ?), float32]), + tensor5_float32(Tensor[(?, ?, ?, ?, ?), float32]), + tensor6_float32(Tensor[(?, ?, ?, ?, ?, ?), float32]), +} + +type tensor_float16_t { + tensor_nil_float16, + tensor0_float16(float16), + tensor1_float16(Tensor[(?), float16]), + tensor2_float16(Tensor[(?, ?), float16]), + tensor3_float16(Tensor[(?, ?, ?), float16]), + tensor4_float16(Tensor[(?, ?, ?, ?), float16]), + tensor5_float16(Tensor[(?, ?, ?, ?, ?), float16]), + tensor6_float16(Tensor[(?, ?, ?, ?, ?, ?), float16]), +} + +type Option[A] { + Some(A), + None, +} + +def @main(%input0: Tensor[(1, 3, 32, 32), float32], %v1_weight: Tensor[(64, 768), float32], %v1_bias: Tensor[(64), float32], %v2_0_affine_g: Tensor[(1, 1, 64), float32], %v2_0_affine_b: Tensor[(1, 1, 64), float32], %v2_0_fn_weight: Tensor[(4, 4, 1), float32], %v2_0_fn_bias: Tensor[(4), float32], %v2_0_scale: Tensor[(1, 1, 64), float32], %v2_1_affine_g: Tensor[(1, 1, 64), float32], %v2_1_affine_b: Tensor[(1, 1, 64), float32], %v2_1_fn_0_weight: Tensor[(256, 64), float32], %v2_1_fn_0_bias: Tensor[(256), float32], %v2_1_fn_2_weight: Tensor[(64, 256), float32], %v2_1_fn_2_bias: Tensor[(64), float32], %v2_1_scale: Tensor[(1, 1, 64), float32], %v3_0_affine_g: Tensor[(1, 1, 64), float32], %v3_0_affine_b: Tensor[(1, 1, 64), float32], %v3_0_fn_weight: Tensor[(4, 4, 1), float32], %v3_0_fn_bias: Tensor[(4), float32], %v3_0_scale: Tensor[(1, 1, 64), float32], %v3_1_affine_g: Tensor[(1, 1, 64), float32], %v3_1_affine_b: Tensor[(1, 1, 64), float32], %v3_1_fn_0_weight: Tensor[(256, 64), float32], %v3_1_fn_0_bias: Tensor[(256), float32], %v3_1_fn_2_weight: Tensor[(64, 256), float32], %v3_1_fn_2_bias: Tensor[(64), float32], %v3_1_scale: Tensor[(1, 1, 64), float32], %v4_0_affine_g: Tensor[(1, 1, 64), float32], %v4_0_affine_b: Tensor[(1, 1, 64), float32], %v4_0_fn_weight: Tensor[(4, 4, 1), float32], %v4_0_fn_bias: Tensor[(4), float32], %v4_0_scale: Tensor[(1, 1, 64), float32], %v4_1_affine_g: Tensor[(1, 1, 64), float32], %v4_1_affine_b: Tensor[(1, 1, 64), float32], %v4_1_fn_0_weight: Tensor[(256, 64), float32], %v4_1_fn_0_bias: Tensor[(256), float32], %v4_1_fn_2_weight: Tensor[(64, 256), float32], %v4_1_fn_2_bias: Tensor[(64), float32], %v4_1_scale: Tensor[(1, 1, 64), float32], %v5_0_affine_g: Tensor[(1, 1, 64), float32], %v5_0_affine_b: Tensor[(1, 1, 64), float32], %v5_0_fn_weight: Tensor[(4, 4, 1), float32], %v5_0_fn_bias: Tensor[(4), float32], %v5_0_scale: Tensor[(1, 1, 64), float32], %v5_1_affine_g: Tensor[(1, 1, 64), float32], %v5_1_affine_b: Tensor[(1, 1, 64), float32], %v5_1_fn_0_weight: Tensor[(256, 64), float32], %v5_1_fn_0_bias: Tensor[(256), float32], %v5_1_fn_2_weight: Tensor[(64, 256), float32], %v5_1_fn_2_bias: Tensor[(64), float32], %v5_1_scale: Tensor[(1, 1, 64), float32], %v6_0_affine_g: Tensor[(1, 1, 64), float32], %v6_0_affine_b: Tensor[(1, 1, 64), float32], %v6_0_fn_weight: Tensor[(4, 4, 1), float32], %v6_0_fn_bias: Tensor[(4), float32], %v6_0_scale: Tensor[(1, 1, 64), float32], %v6_1_affine_g: Tensor[(1, 1, 64), float32], %v6_1_affine_b: Tensor[(1, 1, 64), float32], %v6_1_fn_0_weight: Tensor[(256, 64), float32], %v6_1_fn_0_bias: Tensor[(256), float32], %v6_1_fn_2_weight: Tensor[(64, 256), float32], %v6_1_fn_2_bias: Tensor[(64), float32], %v6_1_scale: Tensor[(1, 1, 64), float32], %v7_0_affine_g: Tensor[(1, 1, 64), float32], %v7_0_affine_b: Tensor[(1, 1, 64), float32], %v7_0_fn_weight: Tensor[(4, 4, 1), float32], %v7_0_fn_bias: Tensor[(4), float32], %v7_0_scale: Tensor[(1, 1, 64), float32], %v7_1_affine_g: Tensor[(1, 1, 64), float32], %v7_1_affine_b: Tensor[(1, 1, 64), float32], %v7_1_fn_0_weight: Tensor[(256, 64), float32], %v7_1_fn_0_bias: Tensor[(256), float32], %v7_1_fn_2_weight: Tensor[(64, 256), float32], %v7_1_fn_2_bias: Tensor[(64), float32], %v7_1_scale: Tensor[(1, 1, 64), float32], %v8_0_affine_g: Tensor[(1, 1, 64), float32], %v8_0_affine_b: Tensor[(1, 1, 64), float32], %v8_0_fn_weight: Tensor[(4, 4, 1), float32], %v8_0_fn_bias: Tensor[(4), float32], %v8_0_scale: Tensor[(1, 1, 64), float32], %v8_1_affine_g: Tensor[(1, 1, 64), float32], %v8_1_affine_b: Tensor[(1, 1, 64), float32], %v8_1_fn_0_weight: Tensor[(256, 64), float32], %v8_1_fn_0_bias: Tensor[(256), float32], %v8_1_fn_2_weight: Tensor[(64, 256), float32], %v8_1_fn_2_bias: Tensor[(64), float32], %v8_1_scale: Tensor[(1, 1, 64), float32], %v9_0_affine_g: Tensor[(1, 1, 64), float32], %v9_0_affine_b: Tensor[(1, 1, 64), float32], %v9_0_fn_weight: Tensor[(4, 4, 1), float32], %v9_0_fn_bias: Tensor[(4), float32], %v9_0_scale: Tensor[(1, 1, 64), float32], %v9_1_affine_g: Tensor[(1, 1, 64), float32], %v9_1_affine_b: Tensor[(1, 1, 64), float32], %v9_1_fn_0_weight: Tensor[(256, 64), float32], %v9_1_fn_0_bias: Tensor[(256), float32], %v9_1_fn_2_weight: Tensor[(64, 256), float32], %v9_1_fn_2_bias: Tensor[(64), float32], %v9_1_scale: Tensor[(1, 1, 64), float32], %v10_0_affine_g: Tensor[(1, 1, 64), float32], %v10_0_affine_b: Tensor[(1, 1, 64), float32], %v10_0_fn_weight: Tensor[(4, 4, 1), float32], %v10_0_fn_bias: Tensor[(4), float32], %v10_0_scale: Tensor[(1, 1, 64), float32], %v10_1_affine_g: Tensor[(1, 1, 64), float32], %v10_1_affine_b: Tensor[(1, 1, 64), float32], %v10_1_fn_0_weight: Tensor[(256, 64), float32], %v10_1_fn_0_bias: Tensor[(256), float32], %v10_1_fn_2_weight: Tensor[(64, 256), float32], %v10_1_fn_2_bias: Tensor[(64), float32], %v10_1_scale: Tensor[(1, 1, 64), float32], %v11_0_affine_g: Tensor[(1, 1, 64), float32], %v11_0_affine_b: Tensor[(1, 1, 64), float32], %v11_0_fn_weight: Tensor[(4, 4, 1), float32], %v11_0_fn_bias: Tensor[(4), float32], %v11_0_scale: Tensor[(1, 1, 64), float32], %v11_1_affine_g: Tensor[(1, 1, 64), float32], %v11_1_affine_b: Tensor[(1, 1, 64), float32], %v11_1_fn_0_weight: Tensor[(256, 64), float32], %v11_1_fn_0_bias: Tensor[(256), float32], %v11_1_fn_2_weight: Tensor[(64, 256), float32], %v11_1_fn_2_bias: Tensor[(64), float32], %v11_1_scale: Tensor[(1, 1, 64), float32], %v12_0_affine_g: Tensor[(1, 1, 64), float32], %v12_0_affine_b: Tensor[(1, 1, 64), float32], %v12_0_fn_weight: Tensor[(4, 4, 1), float32], %v12_0_fn_bias: Tensor[(4), float32], %v12_0_scale: Tensor[(1, 1, 64), float32], %v12_1_affine_g: Tensor[(1, 1, 64), float32], %v12_1_affine_b: Tensor[(1, 1, 64), float32], %v12_1_fn_0_weight: Tensor[(256, 64), float32], %v12_1_fn_0_bias: Tensor[(256), float32], %v12_1_fn_2_weight: Tensor[(64, 256), float32], %v12_1_fn_2_bias: Tensor[(64), float32], %v12_1_scale: Tensor[(1, 1, 64), float32], %v13_0_affine_g: Tensor[(1, 1, 64), float32], %v13_0_affine_b: Tensor[(1, 1, 64), float32], %v13_0_fn_weight: Tensor[(4, 4, 1), float32], %v13_0_fn_bias: Tensor[(4), float32], %v13_0_scale: Tensor[(1, 1, 64), float32], %v13_1_affine_g: Tensor[(1, 1, 64), float32], %v13_1_affine_b: Tensor[(1, 1, 64), float32], %v13_1_fn_0_weight: Tensor[(256, 64), float32], %v13_1_fn_0_bias: Tensor[(256), float32], %v13_1_fn_2_weight: Tensor[(64, 256), float32], %v13_1_fn_2_bias: Tensor[(64), float32], %v13_1_scale: Tensor[(1, 1, 64), float32], %v14_g: Tensor[(1, 1, 64), float32], %v14_b: Tensor[(1, 1, 64), float32], %v16_weight: Tensor[(32, 64), float32], %v16_bias: Tensor[(32), float32]) { + %0 = reshape(%input0, newshape=[1, 3, 2, 16, 2, 16]); + %1 = transpose(%0, axes=[0, 2, 4, 3, 5, 1]); + %2 = reshape(%1, newshape=[1, 4, 768]); + %3 = transpose(%v1_weight, axes=[1, 0]); + %4 = reshape(%2, newshape=[-1, 768]); + %5 = transpose(%3, axes=[1, 0]); + %6 = nn.dense(%4, %5, units=None); + %7 = reshape(%6, newshape=[1, 4, 64]); + %8 = add(%7, %v1_bias); + %9 = multiply(%8, %v2_0_affine_g); + %10 = add(%9, %v2_0_affine_b); + %11 = nn.conv1d(%10, %v2_0_fn_weight, channels=4, kernel_size=[1]); + %12 = nn.bias_add(%11, %v2_0_fn_bias); + %13 = multiply(%12, %v2_0_scale); + %14 = add(%13, %8); + %15 = multiply(%14, %v2_1_affine_g); + %16 = add(%15, %v2_1_affine_b); + %17 = transpose(%v2_1_fn_0_weight, axes=[1, 0]); + %18 = reshape(%16, newshape=[-1, 64]); + %19 = transpose(%17, axes=[1, 0]); + %20 = nn.dense(%18, %19, units=None); + %21 = reshape(%20, newshape=[1, 4, 256]); + %22 = add(%21, %v2_1_fn_0_bias); + %23 = multiply(%22, 0.707107f); + %24 = erf(%23); + %25 = multiply(%24, 0.5f); + %26 = add(0.5f, %25); + %27 = multiply(%22, %26); + %28 = transpose(%v2_1_fn_2_weight, axes=[1, 0]); + %29 = reshape(%27, newshape=[-1, 256]); + %30 = transpose(%28, axes=[1, 0]); + %31 = nn.dense(%29, %30, units=None); + %32 = reshape(%31, newshape=[1, 4, 64]); + %33 = add(%32, %v2_1_fn_2_bias); + %34 = multiply(%33, %v2_1_scale); + %35 = add(%34, %14); + %36 = multiply(%35, %v3_0_affine_g); + %37 = add(%36, %v3_0_affine_b); + %38 = nn.conv1d(%37, %v3_0_fn_weight, channels=4, kernel_size=[1]); + %39 = nn.bias_add(%38, %v3_0_fn_bias); + %40 = multiply(%39, %v3_0_scale); + %41 = add(%40, %35); + %42 = multiply(%41, %v3_1_affine_g); + %43 = add(%42, %v3_1_affine_b); + %44 = transpose(%v3_1_fn_0_weight, axes=[1, 0]); + %45 = reshape(%43, newshape=[-1, 64]); + %46 = transpose(%44, axes=[1, 0]); + %47 = nn.dense(%45, %46, units=None); + %48 = reshape(%47, newshape=[1, 4, 256]); + %49 = add(%48, %v3_1_fn_0_bias); + %50 = multiply(%49, 0.707107f); + %51 = erf(%50); + %52 = multiply(%51, 0.5f); + %53 = add(0.5f, %52); + %54 = multiply(%49, %53); + %55 = transpose(%v3_1_fn_2_weight, axes=[1, 0]); + %56 = reshape(%54, newshape=[-1, 256]); + %57 = transpose(%55, axes=[1, 0]); + %58 = nn.dense(%56, %57, units=None); + %59 = reshape(%58, newshape=[1, 4, 64]); + %60 = add(%59, %v3_1_fn_2_bias); + %61 = multiply(%60, %v3_1_scale); + %62 = add(%61, %41); + %63 = multiply(%62, %v4_0_affine_g); + %64 = add(%63, %v4_0_affine_b); + %65 = nn.conv1d(%64, %v4_0_fn_weight, channels=4, kernel_size=[1]); + %66 = nn.bias_add(%65, %v4_0_fn_bias); + %67 = multiply(%66, %v4_0_scale); + %68 = add(%67, %62); + %69 = multiply(%68, %v4_1_affine_g); + %70 = add(%69, %v4_1_affine_b); + %71 = transpose(%v4_1_fn_0_weight, axes=[1, 0]); + %72 = reshape(%70, newshape=[-1, 64]); + %73 = transpose(%71, axes=[1, 0]); + %74 = nn.dense(%72, %73, units=None); + %75 = reshape(%74, newshape=[1, 4, 256]); + %76 = add(%75, %v4_1_fn_0_bias); + %77 = multiply(%76, 0.707107f); + %78 = erf(%77); + %79 = multiply(%78, 0.5f); + %80 = add(0.5f, %79); + %81 = multiply(%76, %80); + %82 = transpose(%v4_1_fn_2_weight, axes=[1, 0]); + %83 = reshape(%81, newshape=[-1, 256]); + %84 = transpose(%82, axes=[1, 0]); + %85 = nn.dense(%83, %84, units=None); + %86 = reshape(%85, newshape=[1, 4, 64]); + %87 = add(%86, %v4_1_fn_2_bias); + %88 = multiply(%87, %v4_1_scale); + %89 = add(%88, %68); + %90 = multiply(%89, %v5_0_affine_g); + %91 = add(%90, %v5_0_affine_b); + %92 = nn.conv1d(%91, %v5_0_fn_weight, channels=4, kernel_size=[1]); + %93 = nn.bias_add(%92, %v5_0_fn_bias); + %94 = multiply(%93, %v5_0_scale); + %95 = add(%94, %89); + %96 = multiply(%95, %v5_1_affine_g); + %97 = add(%96, %v5_1_affine_b); + %98 = transpose(%v5_1_fn_0_weight, axes=[1, 0]); + %99 = reshape(%97, newshape=[-1, 64]); + %100 = transpose(%98, axes=[1, 0]); + %101 = nn.dense(%99, %100, units=None); + %102 = reshape(%101, newshape=[1, 4, 256]); + %103 = add(%102, %v5_1_fn_0_bias); + %104 = multiply(%103, 0.707107f); + %105 = erf(%104); + %106 = multiply(%105, 0.5f); + %107 = add(0.5f, %106); + %108 = multiply(%103, %107); + %109 = transpose(%v5_1_fn_2_weight, axes=[1, 0]); + %110 = reshape(%108, newshape=[-1, 256]); + %111 = transpose(%109, axes=[1, 0]); + %112 = nn.dense(%110, %111, units=None); + %113 = reshape(%112, newshape=[1, 4, 64]); + %114 = add(%113, %v5_1_fn_2_bias); + %115 = multiply(%114, %v5_1_scale); + %116 = add(%115, %95); + %117 = multiply(%116, %v6_0_affine_g); + %118 = add(%117, %v6_0_affine_b); + %119 = nn.conv1d(%118, %v6_0_fn_weight, channels=4, kernel_size=[1]); + %120 = nn.bias_add(%119, %v6_0_fn_bias); + %121 = multiply(%120, %v6_0_scale); + %122 = add(%121, %116); + %123 = multiply(%122, %v6_1_affine_g); + %124 = add(%123, %v6_1_affine_b); + %125 = transpose(%v6_1_fn_0_weight, axes=[1, 0]); + %126 = reshape(%124, newshape=[-1, 64]); + %127 = transpose(%125, axes=[1, 0]); + %128 = nn.dense(%126, %127, units=None); + %129 = reshape(%128, newshape=[1, 4, 256]); + %130 = add(%129, %v6_1_fn_0_bias); + %131 = multiply(%130, 0.707107f); + %132 = erf(%131); + %133 = multiply(%132, 0.5f); + %134 = add(0.5f, %133); + %135 = multiply(%130, %134); + %136 = transpose(%v6_1_fn_2_weight, axes=[1, 0]); + %137 = reshape(%135, newshape=[-1, 256]); + %138 = transpose(%136, axes=[1, 0]); + %139 = nn.dense(%137, %138, units=None); + %140 = reshape(%139, newshape=[1, 4, 64]); + %141 = add(%140, %v6_1_fn_2_bias); + %142 = multiply(%141, %v6_1_scale); + %143 = add(%142, %122); + %144 = multiply(%143, %v7_0_affine_g); + %145 = add(%144, %v7_0_affine_b); + %146 = nn.conv1d(%145, %v7_0_fn_weight, channels=4, kernel_size=[1]); + %147 = nn.bias_add(%146, %v7_0_fn_bias); + %148 = multiply(%147, %v7_0_scale); + %149 = add(%148, %143); + %150 = multiply(%149, %v7_1_affine_g); + %151 = add(%150, %v7_1_affine_b); + %152 = transpose(%v7_1_fn_0_weight, axes=[1, 0]); + %153 = reshape(%151, newshape=[-1, 64]); + %154 = transpose(%152, axes=[1, 0]); + %155 = nn.dense(%153, %154, units=None); + %156 = reshape(%155, newshape=[1, 4, 256]); + %157 = add(%156, %v7_1_fn_0_bias); + %158 = multiply(%157, 0.707107f); + %159 = erf(%158); + %160 = multiply(%159, 0.5f); + %161 = add(0.5f, %160); + %162 = multiply(%157, %161); + %163 = transpose(%v7_1_fn_2_weight, axes=[1, 0]); + %164 = reshape(%162, newshape=[-1, 256]); + %165 = transpose(%163, axes=[1, 0]); + %166 = nn.dense(%164, %165, units=None); + %167 = reshape(%166, newshape=[1, 4, 64]); + %168 = add(%167, %v7_1_fn_2_bias); + %169 = multiply(%168, %v7_1_scale); + %170 = add(%169, %149); + %171 = multiply(%170, %v8_0_affine_g); + %172 = add(%171, %v8_0_affine_b); + %173 = nn.conv1d(%172, %v8_0_fn_weight, channels=4, kernel_size=[1]); + %174 = nn.bias_add(%173, %v8_0_fn_bias); + %175 = multiply(%174, %v8_0_scale); + %176 = add(%175, %170); + %177 = multiply(%176, %v8_1_affine_g); + %178 = add(%177, %v8_1_affine_b); + %179 = transpose(%v8_1_fn_0_weight, axes=[1, 0]); + %180 = reshape(%178, newshape=[-1, 64]); + %181 = transpose(%179, axes=[1, 0]); + %182 = nn.dense(%180, %181, units=None); + %183 = reshape(%182, newshape=[1, 4, 256]); + %184 = add(%183, %v8_1_fn_0_bias); + %185 = multiply(%184, 0.707107f); + %186 = erf(%185); + %187 = multiply(%186, 0.5f); + %188 = add(0.5f, %187); + %189 = multiply(%184, %188); + %190 = transpose(%v8_1_fn_2_weight, axes=[1, 0]); + %191 = reshape(%189, newshape=[-1, 256]); + %192 = transpose(%190, axes=[1, 0]); + %193 = nn.dense(%191, %192, units=None); + %194 = reshape(%193, newshape=[1, 4, 64]); + %195 = add(%194, %v8_1_fn_2_bias); + %196 = multiply(%195, %v8_1_scale); + %197 = add(%196, %176); + %198 = multiply(%197, %v9_0_affine_g); + %199 = add(%198, %v9_0_affine_b); + %200 = nn.conv1d(%199, %v9_0_fn_weight, channels=4, kernel_size=[1]); + %201 = nn.bias_add(%200, %v9_0_fn_bias); + %202 = multiply(%201, %v9_0_scale); + %203 = add(%202, %197); + %204 = multiply(%203, %v9_1_affine_g); + %205 = add(%204, %v9_1_affine_b); + %206 = transpose(%v9_1_fn_0_weight, axes=[1, 0]); + %207 = reshape(%205, newshape=[-1, 64]); + %208 = transpose(%206, axes=[1, 0]); + %209 = nn.dense(%207, %208, units=None); + %210 = reshape(%209, newshape=[1, 4, 256]); + %211 = add(%210, %v9_1_fn_0_bias); + %212 = multiply(%211, 0.707107f); + %213 = erf(%212); + %214 = multiply(%213, 0.5f); + %215 = add(0.5f, %214); + %216 = multiply(%211, %215); + %217 = transpose(%v9_1_fn_2_weight, axes=[1, 0]); + %218 = reshape(%216, newshape=[-1, 256]); + %219 = transpose(%217, axes=[1, 0]); + %220 = nn.dense(%218, %219, units=None); + %221 = reshape(%220, newshape=[1, 4, 64]); + %222 = add(%221, %v9_1_fn_2_bias); + %223 = multiply(%222, %v9_1_scale); + %224 = add(%223, %203); + %225 = multiply(%224, %v10_0_affine_g); + %226 = add(%225, %v10_0_affine_b); + %227 = nn.conv1d(%226, %v10_0_fn_weight, channels=4, kernel_size=[1]); + %228 = nn.bias_add(%227, %v10_0_fn_bias); + %229 = multiply(%228, %v10_0_scale); + %230 = add(%229, %224); + %231 = multiply(%230, %v10_1_affine_g); + %232 = add(%231, %v10_1_affine_b); + %233 = transpose(%v10_1_fn_0_weight, axes=[1, 0]); + %234 = reshape(%232, newshape=[-1, 64]); + %235 = transpose(%233, axes=[1, 0]); + %236 = nn.dense(%234, %235, units=None); + %237 = reshape(%236, newshape=[1, 4, 256]); + %238 = add(%237, %v10_1_fn_0_bias); + %239 = multiply(%238, 0.707107f); + %240 = erf(%239); + %241 = multiply(%240, 0.5f); + %242 = add(0.5f, %241); + %243 = multiply(%238, %242); + %244 = transpose(%v10_1_fn_2_weight, axes=[1, 0]); + %245 = reshape(%243, newshape=[-1, 256]); + %246 = transpose(%244, axes=[1, 0]); + %247 = nn.dense(%245, %246, units=None); + %248 = reshape(%247, newshape=[1, 4, 64]); + %249 = add(%248, %v10_1_fn_2_bias); + %250 = multiply(%249, %v10_1_scale); + %251 = add(%250, %230); + %252 = multiply(%251, %v11_0_affine_g); + %253 = add(%252, %v11_0_affine_b); + %254 = nn.conv1d(%253, %v11_0_fn_weight, channels=4, kernel_size=[1]); + %255 = nn.bias_add(%254, %v11_0_fn_bias); + %256 = multiply(%255, %v11_0_scale); + %257 = add(%256, %251); + %258 = multiply(%257, %v11_1_affine_g); + %259 = add(%258, %v11_1_affine_b); + %260 = transpose(%v11_1_fn_0_weight, axes=[1, 0]); + %261 = reshape(%259, newshape=[-1, 64]); + %262 = transpose(%260, axes=[1, 0]); + %263 = nn.dense(%261, %262, units=None); + %264 = reshape(%263, newshape=[1, 4, 256]); + %265 = add(%264, %v11_1_fn_0_bias); + %266 = multiply(%265, 0.707107f); + %267 = erf(%266); + %268 = multiply(%267, 0.5f); + %269 = add(0.5f, %268); + %270 = multiply(%265, %269); + %271 = transpose(%v11_1_fn_2_weight, axes=[1, 0]); + %272 = reshape(%270, newshape=[-1, 256]); + %273 = transpose(%271, axes=[1, 0]); + %274 = nn.dense(%272, %273, units=None); + %275 = reshape(%274, newshape=[1, 4, 64]); + %276 = add(%275, %v11_1_fn_2_bias); + %277 = multiply(%276, %v11_1_scale); + %278 = add(%277, %257); + %279 = multiply(%278, %v12_0_affine_g); + %280 = add(%279, %v12_0_affine_b); + %281 = nn.conv1d(%280, %v12_0_fn_weight, channels=4, kernel_size=[1]); + %282 = nn.bias_add(%281, %v12_0_fn_bias); + %283 = multiply(%282, %v12_0_scale); + %284 = add(%283, %278); + %285 = multiply(%284, %v12_1_affine_g); + %286 = add(%285, %v12_1_affine_b); + %287 = transpose(%v12_1_fn_0_weight, axes=[1, 0]); + %288 = reshape(%286, newshape=[-1, 64]); + %289 = transpose(%287, axes=[1, 0]); + %290 = nn.dense(%288, %289, units=None); + %291 = reshape(%290, newshape=[1, 4, 256]); + %292 = add(%291, %v12_1_fn_0_bias); + %293 = multiply(%292, 0.707107f); + %294 = erf(%293); + %295 = multiply(%294, 0.5f); + %296 = add(0.5f, %295); + %297 = multiply(%292, %296); + %298 = transpose(%v12_1_fn_2_weight, axes=[1, 0]); + %299 = reshape(%297, newshape=[-1, 256]); + %300 = transpose(%298, axes=[1, 0]); + %301 = nn.dense(%299, %300, units=None); + %302 = reshape(%301, newshape=[1, 4, 64]); + %303 = add(%302, %v12_1_fn_2_bias); + %304 = multiply(%303, %v12_1_scale); + %305 = add(%304, %284); + %306 = multiply(%305, %v13_0_affine_g); + %307 = add(%306, %v13_0_affine_b); + %308 = nn.conv1d(%307, %v13_0_fn_weight, channels=4, kernel_size=[1]); + %309 = nn.bias_add(%308, %v13_0_fn_bias); + %310 = multiply(%309, %v13_0_scale); + %311 = add(%310, %305); + %312 = multiply(%311, %v13_1_affine_g); + %313 = add(%312, %v13_1_affine_b); + %314 = transpose(%v13_1_fn_0_weight, axes=[1, 0]); + %315 = reshape(%313, newshape=[-1, 64]); + %316 = transpose(%314, axes=[1, 0]); + %317 = nn.dense(%315, %316, units=None); + %318 = reshape(%317, newshape=[1, 4, 256]); + %319 = add(%318, %v13_1_fn_0_bias); + %320 = multiply(%319, 0.707107f); + %321 = erf(%320); + %322 = multiply(%321, 0.5f); + %323 = add(0.5f, %322); + %324 = multiply(%319, %323); + %325 = transpose(%v13_1_fn_2_weight, axes=[1, 0]); + %326 = reshape(%324, newshape=[-1, 256]); + %327 = transpose(%325, axes=[1, 0]); + %328 = nn.dense(%326, %327, units=None); + %329 = reshape(%328, newshape=[1, 4, 64]); + %330 = add(%329, %v13_1_fn_2_bias); + %331 = multiply(%330, %v13_1_scale); + %332 = add(%331, %311); + %333 = multiply(%332, %v14_g); + %334 = add(%333, %v14_b); + %335 = reshape(%334, newshape=[1, 4, 64]); + %336 = mean(%335, axis=[1]); + %337 = transpose(%336, axes=[0, 1]); + %338 = transpose(%v16_weight, axes=[1, 0]); + %339 = reshape(%337, newshape=[1, 64]); + %340 = transpose(%338, axes=[1, 0]); + %341 = nn.dense(%339, %340, units=32); + add(%341, %v16_bias) +} diff --git a/tests/resmlp.py b/tests/models/resmlp.py similarity index 64% rename from tests/resmlp.py rename to tests/models/resmlp.py index e4ae248..a6be29e 100644 --- a/tests/resmlp.py +++ b/tests/models/resmlp.py @@ -1,5 +1,6 @@ # Res-MLP implementation taken from https://github.com/lucidrains/res-mlp-pytorch/blob/7a5b5276cd9270ad8131f77dfe4e6f56fe65fb3f/res_mlp_pytorch/res_mlp_pytorch.py import torch +import argparse from torch import nn, einsum from einops.layers.torch import Rearrange, Reduce @@ -59,4 +60,32 @@ def ResMLP(*, image_size, patch_size, dim, depth, num_classes, expansion_factor= Affine(dim), Reduce("b n c -> b c", "mean"), nn.Linear(dim, num_classes) - ) \ No newline at end of file + ) + +def main(depth): + import os + import tvm + from tvm import relay + from tvm.relay import ExprMutator + from utils import RenameMutator + model = ResMLP( + image_size = 32, + patch_size = 16, + dim = 64, + depth = 12, + num_classes = 32) + inputs = [torch.randn(1, 3, 32, 32)] + input_names = ["input{}".format(idx) for idx, inp in enumerate(inputs)] + input_shapes = list(zip(input_names, [inp.shape for inp in inputs])) + trace = torch.jit.trace(model, [input.clone() for input in inputs]) + mod, _ = relay.frontend.from_pytorch(trace, input_shapes, {}) + mod['main'] = RenameMutator({'.': '_'}).visit(mod['main']) + with open(os.path.join(os.environ['FLEXMATCH_HOME'], + 'tests', 'models', f'resmlp-depth-{depth}.relay'), 'w') as fp: + fp.write(mod.astext()) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--depth', dest='depth', type=int, required=True, help='Depth of ResMLP model') + args = parser.parse_args() + main(args.depth) \ No newline at end of file diff --git a/tests/models/resmlp.relay b/tests/models/resmlp.relay index 1510d44..fdce370 100644 --- a/tests/models/resmlp.relay +++ b/tests/models/resmlp.relay @@ -1,49 +1,459 @@ #[version = "0.0.5"] -def @main(%input0: Tensor[(1, 3, 32, 32), float32], %v1_weight: Tensor[(64, 768), float32], %v1_bias: Tensor[(64), float32], %v2_0_affine_g: Tensor[(1, 1, 64), float32], %v2_0_affine_b: Tensor[(1, 1, 64), float32], %v2_0_fn_weight: Tensor[(4, 4, 1), float32], %v2_0_fn_bias: Tensor[(4), float32], %v2_0_scale: Tensor[(1, 1, 64), float32], %v2_1_affine_g: Tensor[(1, 1, 64), float32], %v2_1_affine_b: Tensor[(1, 1, 64), float32], %v2_1_fn_0_weight: Tensor[(256, 64), float32], %v2_1_fn_0_bias: Tensor[(256), float32], %v2_1_fn_2_weight: Tensor[(64, 256), float32], %v2_1_fn_2_bias: Tensor[(64), float32], %v2_1_scale: Tensor[(1, 1, 64), float32], %v3_g: Tensor[(1, 1, 64), float32], %v3_b: Tensor[(1, 1, 64), float32], %v5_weight: Tensor[(32, 64), float32], %v5_bias: Tensor[(32), float32]) -> Tensor[(1, 32), float32] { - %0 = reshape(%input0, newshape=[1, 3, 2, 16, 2, 16]) /* ty=Tensor[(1, 3, 2, 16, 2, 16), float32] */; - %1 = transpose(%0, axes=[0, 2, 4, 3, 5, 1]) /* ty=Tensor[(1, 2, 2, 16, 16, 3), float32] */; - %2 = reshape(%1, newshape=[1, 4, 768]) /* ty=Tensor[(1, 4, 768), float32] */; - %3 = transpose(%v1_weight, axes=[1, 0]) /* ty=Tensor[(768, 64), float32] */; - %4 = reshape(%2, newshape=[-1, 768]) /* ty=Tensor[(4, 768), float32] */; - %5 = transpose(%3, axes=[1, 0]) /* ty=Tensor[(64, 768), float32] */; - %6 = nn.dense(%4, %5, units=None) /* ty=Tensor[(4, 64), float32] */; - %7 = nn.bias_add(%6, %v1_bias); - %8 = reshape(%7, newshape=[1, 4, 64]) /* ty=Tensor[(1, 4, 64), float32] */; - %9 = multiply(%8, %v2_0_affine_g) /* ty=Tensor[(1, 4, 64), float32] */; - %10 = add(%9, %v2_0_affine_b) /* ty=Tensor[(1, 4, 64), float32] */; - %11 = nn.conv1d(%10, %v2_0_fn_weight, channels=4, kernel_size=[1]) /* ty=Tensor[(1, 4, 64), float32] */; - %12 = nn.bias_add(%11, %v2_0_fn_bias) /* ty=Tensor[(1, 4, 64), float32] */; - %13 = multiply(%12, %v2_0_scale) /* ty=Tensor[(1, 4, 64), float32] */; - %14 = add(%13, %8) /* ty=Tensor[(1, 4, 64), float32] */; - %15 = multiply(%14, %v2_1_affine_g) /* ty=Tensor[(1, 4, 64), float32] */; - %16 = add(%15, %v2_1_affine_b) /* ty=Tensor[(1, 4, 64), float32] */; - %17 = transpose(%v2_1_fn_0_weight, axes=[1, 0]) /* ty=Tensor[(64, 256), float32] */; - %18 = reshape(%16, newshape=[-1, 64]) /* ty=Tensor[(4, 64), float32] */; - %19 = transpose(%17, axes=[1, 0]) /* ty=Tensor[(256, 64), float32] */; - %20 = nn.dense(%18, %19, units=None) /* ty=Tensor[(4, 256), float32] */; - %21 = reshape(%20, newshape=[1, 4, 256]) /* ty=Tensor[(1, 4, 256), float32] */; - %22 = add(%21, %v2_1_fn_0_bias) /* ty=Tensor[(1, 4, 256), float32] */; - %23 = multiply(%22, 0.707107f /* ty=float32 */) /* ty=Tensor[(1, 4, 256), float32] */; - %24 = erf(%23) /* ty=Tensor[(1, 4, 256), float32] */; - %25 = multiply(%24, 0.5f /* ty=float32 */) /* ty=Tensor[(1, 4, 256), float32] */; - %26 = add(0.5f /* ty=float32 */, %25) /* ty=Tensor[(1, 4, 256), float32] */; - %27 = multiply(%22, %26) /* ty=Tensor[(1, 4, 256), float32] */; - %28 = transpose(%v2_1_fn_2_weight, axes=[1, 0]) /* ty=Tensor[(256, 64), float32] */; - %29 = reshape(%27, newshape=[-1, 256]) /* ty=Tensor[(4, 256), float32] */; - %30 = transpose(%28, axes=[1, 0]) /* ty=Tensor[(64, 256), float32] */; - %31 = nn.dense(%29, %30, units=None) /* ty=Tensor[(4, 64), float32] */; - %32 = reshape(%31, newshape=[1, 4, 64]) /* ty=Tensor[(1, 4, 64), float32] */; - %33 = add(%32, %v2_1_fn_2_bias) /* ty=Tensor[(1, 4, 64), float32] */; - %34 = multiply(%33, %v2_1_scale) /* ty=Tensor[(1, 4, 64), float32] */; - %35 = add(%34, %14) /* ty=Tensor[(1, 4, 64), float32] */; - %36 = multiply(%35, %v3_g) /* ty=Tensor[(1, 4, 64), float32] */; - %37 = add(%36, %v3_b) /* ty=Tensor[(1, 4, 64), float32] */; - %38 = reshape(%37, newshape=[1, 4, 64]) /* ty=Tensor[(1, 4, 64), float32] */; - %39 = mean(%38, axis=[1]) /* ty=Tensor[(1, 64), float32] */; - %40 = transpose(%39, axes=[0, 1]) /* ty=Tensor[(1, 64), float32] */; - %41 = transpose(%v5_weight, axes=[1, 0]) /* ty=Tensor[(64, 32), float32] */; - %42 = reshape(%40, newshape=[1, 64]) /* ty=Tensor[(1, 64), float32] */; - %43 = transpose(%41, axes=[1, 0]) /* ty=Tensor[(32, 64), float32] */; - %44 = nn.dense(%42, %43, units=32) /* ty=Tensor[(1, 32), float32] */; - add(%44, %v5_bias) /* ty=Tensor[(1, 32), float32] */ +type tensor_int32_t { + tensor_nil_int32, + tensor0_int32(int32), + tensor1_int32(Tensor[(?), int32]), + tensor2_int32(Tensor[(?, ?), int32]), + tensor3_int32(Tensor[(?, ?, ?), int32]), + tensor4_int32(Tensor[(?, ?, ?, ?), int32]), + tensor5_int32(Tensor[(?, ?, ?, ?, ?), int32]), + tensor6_int32(Tensor[(?, ?, ?, ?, ?, ?), int32]), +} + +type tensor_uint8_t { + tensor_nil_uint8, + tensor0_uint8(uint8), + tensor1_uint8(Tensor[(?), uint8]), + tensor2_uint8(Tensor[(?, ?), uint8]), + tensor3_uint8(Tensor[(?, ?, ?), uint8]), + tensor4_uint8(Tensor[(?, ?, ?, ?), uint8]), + tensor5_uint8(Tensor[(?, ?, ?, ?, ?), uint8]), + tensor6_uint8(Tensor[(?, ?, ?, ?, ?, ?), uint8]), +} + +type tensor_float32_t { + tensor_nil_float32, + tensor0_float32(float32), + tensor1_float32(Tensor[(?), float32]), + tensor2_float32(Tensor[(?, ?), float32]), + tensor3_float32(Tensor[(?, ?, ?), float32]), + tensor4_float32(Tensor[(?, ?, ?, ?), float32]), + tensor5_float32(Tensor[(?, ?, ?, ?, ?), float32]), + tensor6_float32(Tensor[(?, ?, ?, ?, ?, ?), float32]), +} + +type tensor_int8_t { + tensor_nil_int8, + tensor0_int8(int8), + tensor1_int8(Tensor[(?), int8]), + tensor2_int8(Tensor[(?, ?), int8]), + tensor3_int8(Tensor[(?, ?, ?), int8]), + tensor4_int8(Tensor[(?, ?, ?, ?), int8]), + tensor5_int8(Tensor[(?, ?, ?, ?, ?), int8]), + tensor6_int8(Tensor[(?, ?, ?, ?, ?, ?), int8]), +} + +type tensor_float16_t { + tensor_nil_float16, + tensor0_float16(float16), + tensor1_float16(Tensor[(?), float16]), + tensor2_float16(Tensor[(?, ?), float16]), + tensor3_float16(Tensor[(?, ?, ?), float16]), + tensor4_float16(Tensor[(?, ?, ?, ?), float16]), + tensor5_float16(Tensor[(?, ?, ?, ?, ?), float16]), + tensor6_float16(Tensor[(?, ?, ?, ?, ?, ?), float16]), +} + +type List[A] { + Cons(A, List[A]), + Nil, +} + +type tensor_int16_t { + tensor_nil_int16, + tensor0_int16(int16), + tensor1_int16(Tensor[(?), int16]), + tensor2_int16(Tensor[(?, ?), int16]), + tensor3_int16(Tensor[(?, ?, ?), int16]), + tensor4_int16(Tensor[(?, ?, ?, ?), int16]), + tensor5_int16(Tensor[(?, ?, ?, ?, ?), int16]), + tensor6_int16(Tensor[(?, ?, ?, ?, ?, ?), int16]), +} + +type Option[A] { + Some(A), + None, +} + +type Tree[A] { + Rose(A, List[Tree[A]]), +} + +type tensor_int64_t { + tensor_nil_int64, + tensor0_int64(int64), + tensor1_int64(Tensor[(?), int64]), + tensor2_int64(Tensor[(?, ?), int64]), + tensor3_int64(Tensor[(?, ?, ?), int64]), + tensor4_int64(Tensor[(?, ?, ?, ?), int64]), + tensor5_int64(Tensor[(?, ?, ?, ?, ?), int64]), + tensor6_int64(Tensor[(?, ?, ?, ?, ?, ?), int64]), +} + +type tensor_uint16_t { + tensor_nil_uint16, + tensor0_uint16(uint16), + tensor1_uint16(Tensor[(?), uint16]), + tensor2_uint16(Tensor[(?, ?), uint16]), + tensor3_uint16(Tensor[(?, ?, ?), uint16]), + tensor4_uint16(Tensor[(?, ?, ?, ?), uint16]), + tensor5_uint16(Tensor[(?, ?, ?, ?, ?), uint16]), + tensor6_uint16(Tensor[(?, ?, ?, ?, ?, ?), uint16]), +} + +type tensor_float64_t { + tensor_nil_float64, + tensor0_float64(float64), + tensor1_float64(Tensor[(?), float64]), + tensor2_float64(Tensor[(?, ?), float64]), + tensor3_float64(Tensor[(?, ?, ?), float64]), + tensor4_float64(Tensor[(?, ?, ?, ?), float64]), + tensor5_float64(Tensor[(?, ?, ?, ?, ?), float64]), + tensor6_float64(Tensor[(?, ?, ?, ?, ?, ?), float64]), +} + +def @main(%input0: Tensor[(1, 3, 32, 32), float32], %v1_weight: Tensor[(64, 768), float32], %v1_bias: Tensor[(64), float32], %v2_0_affine_g: Tensor[(1, 1, 64), float32], %v2_0_affine_b: Tensor[(1, 1, 64), float32], %v2_0_fn_weight: Tensor[(4, 4, 1), float32], %v2_0_fn_bias: Tensor[(4), float32], %v2_0_scale: Tensor[(1, 1, 64), float32], %v2_1_affine_g: Tensor[(1, 1, 64), float32], %v2_1_affine_b: Tensor[(1, 1, 64), float32], %v2_1_fn_0_weight: Tensor[(256, 64), float32], %v2_1_fn_0_bias: Tensor[(256), float32], %v2_1_fn_2_weight: Tensor[(64, 256), float32], %v2_1_fn_2_bias: Tensor[(64), float32], %v2_1_scale: Tensor[(1, 1, 64), float32], %v3_0_affine_g: Tensor[(1, 1, 64), float32], %v3_0_affine_b: Tensor[(1, 1, 64), float32], %v3_0_fn_weight: Tensor[(4, 4, 1), float32], %v3_0_fn_bias: Tensor[(4), float32], %v3_0_scale: Tensor[(1, 1, 64), float32], %v3_1_affine_g: Tensor[(1, 1, 64), float32], %v3_1_affine_b: Tensor[(1, 1, 64), float32], %v3_1_fn_0_weight: Tensor[(256, 64), float32], %v3_1_fn_0_bias: Tensor[(256), float32], %v3_1_fn_2_weight: Tensor[(64, 256), float32], %v3_1_fn_2_bias: Tensor[(64), float32], %v3_1_scale: Tensor[(1, 1, 64), float32], %v4_0_affine_g: Tensor[(1, 1, 64), float32], %v4_0_affine_b: Tensor[(1, 1, 64), float32], %v4_0_fn_weight: Tensor[(4, 4, 1), float32], %v4_0_fn_bias: Tensor[(4), float32], %v4_0_scale: Tensor[(1, 1, 64), float32], %v4_1_affine_g: Tensor[(1, 1, 64), float32], %v4_1_affine_b: Tensor[(1, 1, 64), float32], %v4_1_fn_0_weight: Tensor[(256, 64), float32], %v4_1_fn_0_bias: Tensor[(256), float32], %v4_1_fn_2_weight: Tensor[(64, 256), float32], %v4_1_fn_2_bias: Tensor[(64), float32], %v4_1_scale: Tensor[(1, 1, 64), float32], %v5_0_affine_g: Tensor[(1, 1, 64), float32], %v5_0_affine_b: Tensor[(1, 1, 64), float32], %v5_0_fn_weight: Tensor[(4, 4, 1), float32], %v5_0_fn_bias: Tensor[(4), float32], %v5_0_scale: Tensor[(1, 1, 64), float32], %v5_1_affine_g: Tensor[(1, 1, 64), float32], %v5_1_affine_b: Tensor[(1, 1, 64), float32], %v5_1_fn_0_weight: Tensor[(256, 64), float32], %v5_1_fn_0_bias: Tensor[(256), float32], %v5_1_fn_2_weight: Tensor[(64, 256), float32], %v5_1_fn_2_bias: Tensor[(64), float32], %v5_1_scale: Tensor[(1, 1, 64), float32], %v6_0_affine_g: Tensor[(1, 1, 64), float32], %v6_0_affine_b: Tensor[(1, 1, 64), float32], %v6_0_fn_weight: Tensor[(4, 4, 1), float32], %v6_0_fn_bias: Tensor[(4), float32], %v6_0_scale: Tensor[(1, 1, 64), float32], %v6_1_affine_g: Tensor[(1, 1, 64), float32], %v6_1_affine_b: Tensor[(1, 1, 64), float32], %v6_1_fn_0_weight: Tensor[(256, 64), float32], %v6_1_fn_0_bias: Tensor[(256), float32], %v6_1_fn_2_weight: Tensor[(64, 256), float32], %v6_1_fn_2_bias: Tensor[(64), float32], %v6_1_scale: Tensor[(1, 1, 64), float32], %v7_0_affine_g: Tensor[(1, 1, 64), float32], %v7_0_affine_b: Tensor[(1, 1, 64), float32], %v7_0_fn_weight: Tensor[(4, 4, 1), float32], %v7_0_fn_bias: Tensor[(4), float32], %v7_0_scale: Tensor[(1, 1, 64), float32], %v7_1_affine_g: Tensor[(1, 1, 64), float32], %v7_1_affine_b: Tensor[(1, 1, 64), float32], %v7_1_fn_0_weight: Tensor[(256, 64), float32], %v7_1_fn_0_bias: Tensor[(256), float32], %v7_1_fn_2_weight: Tensor[(64, 256), float32], %v7_1_fn_2_bias: Tensor[(64), float32], %v7_1_scale: Tensor[(1, 1, 64), float32], %v8_0_affine_g: Tensor[(1, 1, 64), float32], %v8_0_affine_b: Tensor[(1, 1, 64), float32], %v8_0_fn_weight: Tensor[(4, 4, 1), float32], %v8_0_fn_bias: Tensor[(4), float32], %v8_0_scale: Tensor[(1, 1, 64), float32], %v8_1_affine_g: Tensor[(1, 1, 64), float32], %v8_1_affine_b: Tensor[(1, 1, 64), float32], %v8_1_fn_0_weight: Tensor[(256, 64), float32], %v8_1_fn_0_bias: Tensor[(256), float32], %v8_1_fn_2_weight: Tensor[(64, 256), float32], %v8_1_fn_2_bias: Tensor[(64), float32], %v8_1_scale: Tensor[(1, 1, 64), float32], %v9_0_affine_g: Tensor[(1, 1, 64), float32], %v9_0_affine_b: Tensor[(1, 1, 64), float32], %v9_0_fn_weight: Tensor[(4, 4, 1), float32], %v9_0_fn_bias: Tensor[(4), float32], %v9_0_scale: Tensor[(1, 1, 64), float32], %v9_1_affine_g: Tensor[(1, 1, 64), float32], %v9_1_affine_b: Tensor[(1, 1, 64), float32], %v9_1_fn_0_weight: Tensor[(256, 64), float32], %v9_1_fn_0_bias: Tensor[(256), float32], %v9_1_fn_2_weight: Tensor[(64, 256), float32], %v9_1_fn_2_bias: Tensor[(64), float32], %v9_1_scale: Tensor[(1, 1, 64), float32], %v10_0_affine_g: Tensor[(1, 1, 64), float32], %v10_0_affine_b: Tensor[(1, 1, 64), float32], %v10_0_fn_weight: Tensor[(4, 4, 1), float32], %v10_0_fn_bias: Tensor[(4), float32], %v10_0_scale: Tensor[(1, 1, 64), float32], %v10_1_affine_g: Tensor[(1, 1, 64), float32], %v10_1_affine_b: Tensor[(1, 1, 64), float32], %v10_1_fn_0_weight: Tensor[(256, 64), float32], %v10_1_fn_0_bias: Tensor[(256), float32], %v10_1_fn_2_weight: Tensor[(64, 256), float32], %v10_1_fn_2_bias: Tensor[(64), float32], %v10_1_scale: Tensor[(1, 1, 64), float32], %v11_0_affine_g: Tensor[(1, 1, 64), float32], %v11_0_affine_b: Tensor[(1, 1, 64), float32], %v11_0_fn_weight: Tensor[(4, 4, 1), float32], %v11_0_fn_bias: Tensor[(4), float32], %v11_0_scale: Tensor[(1, 1, 64), float32], %v11_1_affine_g: Tensor[(1, 1, 64), float32], %v11_1_affine_b: Tensor[(1, 1, 64), float32], %v11_1_fn_0_weight: Tensor[(256, 64), float32], %v11_1_fn_0_bias: Tensor[(256), float32], %v11_1_fn_2_weight: Tensor[(64, 256), float32], %v11_1_fn_2_bias: Tensor[(64), float32], %v11_1_scale: Tensor[(1, 1, 64), float32], %v12_0_affine_g: Tensor[(1, 1, 64), float32], %v12_0_affine_b: Tensor[(1, 1, 64), float32], %v12_0_fn_weight: Tensor[(4, 4, 1), float32], %v12_0_fn_bias: Tensor[(4), float32], %v12_0_scale: Tensor[(1, 1, 64), float32], %v12_1_affine_g: Tensor[(1, 1, 64), float32], %v12_1_affine_b: Tensor[(1, 1, 64), float32], %v12_1_fn_0_weight: Tensor[(256, 64), float32], %v12_1_fn_0_bias: Tensor[(256), float32], %v12_1_fn_2_weight: Tensor[(64, 256), float32], %v12_1_fn_2_bias: Tensor[(64), float32], %v12_1_scale: Tensor[(1, 1, 64), float32], %v13_0_affine_g: Tensor[(1, 1, 64), float32], %v13_0_affine_b: Tensor[(1, 1, 64), float32], %v13_0_fn_weight: Tensor[(4, 4, 1), float32], %v13_0_fn_bias: Tensor[(4), float32], %v13_0_scale: Tensor[(1, 1, 64), float32], %v13_1_affine_g: Tensor[(1, 1, 64), float32], %v13_1_affine_b: Tensor[(1, 1, 64), float32], %v13_1_fn_0_weight: Tensor[(256, 64), float32], %v13_1_fn_0_bias: Tensor[(256), float32], %v13_1_fn_2_weight: Tensor[(64, 256), float32], %v13_1_fn_2_bias: Tensor[(64), float32], %v13_1_scale: Tensor[(1, 1, 64), float32], %v14_g: Tensor[(1, 1, 64), float32], %v14_b: Tensor[(1, 1, 64), float32], %v16_weight: Tensor[(32, 64), float32], %v16_bias: Tensor[(32), float32]) { + %0 = reshape(%input0, newshape=[1, 3, 2, 16, 2, 16]); + %1 = transpose(%0, axes=[0, 2, 4, 3, 5, 1]); + %2 = reshape(%1, newshape=[1, 4, 768]); + %3 = transpose(%v1_weight, axes=[1, 0]); + %4 = reshape(%2, newshape=[-1, 768]); + %5 = transpose(%3, axes=[1, 0]); + %6 = nn.dense(%4, %5, units=None); + %7 = reshape(%6, newshape=[1, 4, 64]); + %8 = add(%7, %v1_bias); + %9 = multiply(%8, %v2_0_affine_g); + %10 = add(%9, %v2_0_affine_b); + %11 = nn.conv1d(%10, %v2_0_fn_weight, channels=4, kernel_size=[1]); + %12 = nn.bias_add(%11, %v2_0_fn_bias); + %13 = multiply(%12, %v2_0_scale); + %14 = add(%13, %8); + %15 = multiply(%14, %v2_1_affine_g); + %16 = add(%15, %v2_1_affine_b); + %17 = transpose(%v2_1_fn_0_weight, axes=[1, 0]); + %18 = reshape(%16, newshape=[-1, 64]); + %19 = transpose(%17, axes=[1, 0]); + %20 = nn.dense(%18, %19, units=None); + %21 = reshape(%20, newshape=[1, 4, 256]); + %22 = add(%21, %v2_1_fn_0_bias); + %23 = multiply(%22, 0.707107f); + %24 = erf(%23); + %25 = multiply(%24, 0.5f); + %26 = add(0.5f, %25); + %27 = multiply(%22, %26); + %28 = transpose(%v2_1_fn_2_weight, axes=[1, 0]); + %29 = reshape(%27, newshape=[-1, 256]); + %30 = transpose(%28, axes=[1, 0]); + %31 = nn.dense(%29, %30, units=None); + %32 = reshape(%31, newshape=[1, 4, 64]); + %33 = add(%32, %v2_1_fn_2_bias); + %34 = multiply(%33, %v2_1_scale); + %35 = add(%34, %14); + %36 = multiply(%35, %v3_0_affine_g); + %37 = add(%36, %v3_0_affine_b); + %38 = nn.conv1d(%37, %v3_0_fn_weight, channels=4, kernel_size=[1]); + %39 = nn.bias_add(%38, %v3_0_fn_bias); + %40 = multiply(%39, %v3_0_scale); + %41 = add(%40, %35); + %42 = multiply(%41, %v3_1_affine_g); + %43 = add(%42, %v3_1_affine_b); + %44 = transpose(%v3_1_fn_0_weight, axes=[1, 0]); + %45 = reshape(%43, newshape=[-1, 64]); + %46 = transpose(%44, axes=[1, 0]); + %47 = nn.dense(%45, %46, units=None); + %48 = reshape(%47, newshape=[1, 4, 256]); + %49 = add(%48, %v3_1_fn_0_bias); + %50 = multiply(%49, 0.707107f); + %51 = erf(%50); + %52 = multiply(%51, 0.5f); + %53 = add(0.5f, %52); + %54 = multiply(%49, %53); + %55 = transpose(%v3_1_fn_2_weight, axes=[1, 0]); + %56 = reshape(%54, newshape=[-1, 256]); + %57 = transpose(%55, axes=[1, 0]); + %58 = nn.dense(%56, %57, units=None); + %59 = reshape(%58, newshape=[1, 4, 64]); + %60 = add(%59, %v3_1_fn_2_bias); + %61 = multiply(%60, %v3_1_scale); + %62 = add(%61, %41); + %63 = multiply(%62, %v4_0_affine_g); + %64 = add(%63, %v4_0_affine_b); + %65 = nn.conv1d(%64, %v4_0_fn_weight, channels=4, kernel_size=[1]); + %66 = nn.bias_add(%65, %v4_0_fn_bias); + %67 = multiply(%66, %v4_0_scale); + %68 = add(%67, %62); + %69 = multiply(%68, %v4_1_affine_g); + %70 = add(%69, %v4_1_affine_b); + %71 = transpose(%v4_1_fn_0_weight, axes=[1, 0]); + %72 = reshape(%70, newshape=[-1, 64]); + %73 = transpose(%71, axes=[1, 0]); + %74 = nn.dense(%72, %73, units=None); + %75 = reshape(%74, newshape=[1, 4, 256]); + %76 = add(%75, %v4_1_fn_0_bias); + %77 = multiply(%76, 0.707107f); + %78 = erf(%77); + %79 = multiply(%78, 0.5f); + %80 = add(0.5f, %79); + %81 = multiply(%76, %80); + %82 = transpose(%v4_1_fn_2_weight, axes=[1, 0]); + %83 = reshape(%81, newshape=[-1, 256]); + %84 = transpose(%82, axes=[1, 0]); + %85 = nn.dense(%83, %84, units=None); + %86 = reshape(%85, newshape=[1, 4, 64]); + %87 = add(%86, %v4_1_fn_2_bias); + %88 = multiply(%87, %v4_1_scale); + %89 = add(%88, %68); + %90 = multiply(%89, %v5_0_affine_g); + %91 = add(%90, %v5_0_affine_b); + %92 = nn.conv1d(%91, %v5_0_fn_weight, channels=4, kernel_size=[1]); + %93 = nn.bias_add(%92, %v5_0_fn_bias); + %94 = multiply(%93, %v5_0_scale); + %95 = add(%94, %89); + %96 = multiply(%95, %v5_1_affine_g); + %97 = add(%96, %v5_1_affine_b); + %98 = transpose(%v5_1_fn_0_weight, axes=[1, 0]); + %99 = reshape(%97, newshape=[-1, 64]); + %100 = transpose(%98, axes=[1, 0]); + %101 = nn.dense(%99, %100, units=None); + %102 = reshape(%101, newshape=[1, 4, 256]); + %103 = add(%102, %v5_1_fn_0_bias); + %104 = multiply(%103, 0.707107f); + %105 = erf(%104); + %106 = multiply(%105, 0.5f); + %107 = add(0.5f, %106); + %108 = multiply(%103, %107); + %109 = transpose(%v5_1_fn_2_weight, axes=[1, 0]); + %110 = reshape(%108, newshape=[-1, 256]); + %111 = transpose(%109, axes=[1, 0]); + %112 = nn.dense(%110, %111, units=None); + %113 = reshape(%112, newshape=[1, 4, 64]); + %114 = add(%113, %v5_1_fn_2_bias); + %115 = multiply(%114, %v5_1_scale); + %116 = add(%115, %95); + %117 = multiply(%116, %v6_0_affine_g); + %118 = add(%117, %v6_0_affine_b); + %119 = nn.conv1d(%118, %v6_0_fn_weight, channels=4, kernel_size=[1]); + %120 = nn.bias_add(%119, %v6_0_fn_bias); + %121 = multiply(%120, %v6_0_scale); + %122 = add(%121, %116); + %123 = multiply(%122, %v6_1_affine_g); + %124 = add(%123, %v6_1_affine_b); + %125 = transpose(%v6_1_fn_0_weight, axes=[1, 0]); + %126 = reshape(%124, newshape=[-1, 64]); + %127 = transpose(%125, axes=[1, 0]); + %128 = nn.dense(%126, %127, units=None); + %129 = reshape(%128, newshape=[1, 4, 256]); + %130 = add(%129, %v6_1_fn_0_bias); + %131 = multiply(%130, 0.707107f); + %132 = erf(%131); + %133 = multiply(%132, 0.5f); + %134 = add(0.5f, %133); + %135 = multiply(%130, %134); + %136 = transpose(%v6_1_fn_2_weight, axes=[1, 0]); + %137 = reshape(%135, newshape=[-1, 256]); + %138 = transpose(%136, axes=[1, 0]); + %139 = nn.dense(%137, %138, units=None); + %140 = reshape(%139, newshape=[1, 4, 64]); + %141 = add(%140, %v6_1_fn_2_bias); + %142 = multiply(%141, %v6_1_scale); + %143 = add(%142, %122); + %144 = multiply(%143, %v7_0_affine_g); + %145 = add(%144, %v7_0_affine_b); + %146 = nn.conv1d(%145, %v7_0_fn_weight, channels=4, kernel_size=[1]); + %147 = nn.bias_add(%146, %v7_0_fn_bias); + %148 = multiply(%147, %v7_0_scale); + %149 = add(%148, %143); + %150 = multiply(%149, %v7_1_affine_g); + %151 = add(%150, %v7_1_affine_b); + %152 = transpose(%v7_1_fn_0_weight, axes=[1, 0]); + %153 = reshape(%151, newshape=[-1, 64]); + %154 = transpose(%152, axes=[1, 0]); + %155 = nn.dense(%153, %154, units=None); + %156 = reshape(%155, newshape=[1, 4, 256]); + %157 = add(%156, %v7_1_fn_0_bias); + %158 = multiply(%157, 0.707107f); + %159 = erf(%158); + %160 = multiply(%159, 0.5f); + %161 = add(0.5f, %160); + %162 = multiply(%157, %161); + %163 = transpose(%v7_1_fn_2_weight, axes=[1, 0]); + %164 = reshape(%162, newshape=[-1, 256]); + %165 = transpose(%163, axes=[1, 0]); + %166 = nn.dense(%164, %165, units=None); + %167 = reshape(%166, newshape=[1, 4, 64]); + %168 = add(%167, %v7_1_fn_2_bias); + %169 = multiply(%168, %v7_1_scale); + %170 = add(%169, %149); + %171 = multiply(%170, %v8_0_affine_g); + %172 = add(%171, %v8_0_affine_b); + %173 = nn.conv1d(%172, %v8_0_fn_weight, channels=4, kernel_size=[1]); + %174 = nn.bias_add(%173, %v8_0_fn_bias); + %175 = multiply(%174, %v8_0_scale); + %176 = add(%175, %170); + %177 = multiply(%176, %v8_1_affine_g); + %178 = add(%177, %v8_1_affine_b); + %179 = transpose(%v8_1_fn_0_weight, axes=[1, 0]); + %180 = reshape(%178, newshape=[-1, 64]); + %181 = transpose(%179, axes=[1, 0]); + %182 = nn.dense(%180, %181, units=None); + %183 = reshape(%182, newshape=[1, 4, 256]); + %184 = add(%183, %v8_1_fn_0_bias); + %185 = multiply(%184, 0.707107f); + %186 = erf(%185); + %187 = multiply(%186, 0.5f); + %188 = add(0.5f, %187); + %189 = multiply(%184, %188); + %190 = transpose(%v8_1_fn_2_weight, axes=[1, 0]); + %191 = reshape(%189, newshape=[-1, 256]); + %192 = transpose(%190, axes=[1, 0]); + %193 = nn.dense(%191, %192, units=None); + %194 = reshape(%193, newshape=[1, 4, 64]); + %195 = add(%194, %v8_1_fn_2_bias); + %196 = multiply(%195, %v8_1_scale); + %197 = add(%196, %176); + %198 = multiply(%197, %v9_0_affine_g); + %199 = add(%198, %v9_0_affine_b); + %200 = nn.conv1d(%199, %v9_0_fn_weight, channels=4, kernel_size=[1]); + %201 = nn.bias_add(%200, %v9_0_fn_bias); + %202 = multiply(%201, %v9_0_scale); + %203 = add(%202, %197); + %204 = multiply(%203, %v9_1_affine_g); + %205 = add(%204, %v9_1_affine_b); + %206 = transpose(%v9_1_fn_0_weight, axes=[1, 0]); + %207 = reshape(%205, newshape=[-1, 64]); + %208 = transpose(%206, axes=[1, 0]); + %209 = nn.dense(%207, %208, units=None); + %210 = reshape(%209, newshape=[1, 4, 256]); + %211 = add(%210, %v9_1_fn_0_bias); + %212 = multiply(%211, 0.707107f); + %213 = erf(%212); + %214 = multiply(%213, 0.5f); + %215 = add(0.5f, %214); + %216 = multiply(%211, %215); + %217 = transpose(%v9_1_fn_2_weight, axes=[1, 0]); + %218 = reshape(%216, newshape=[-1, 256]); + %219 = transpose(%217, axes=[1, 0]); + %220 = nn.dense(%218, %219, units=None); + %221 = reshape(%220, newshape=[1, 4, 64]); + %222 = add(%221, %v9_1_fn_2_bias); + %223 = multiply(%222, %v9_1_scale); + %224 = add(%223, %203); + %225 = multiply(%224, %v10_0_affine_g); + %226 = add(%225, %v10_0_affine_b); + %227 = nn.conv1d(%226, %v10_0_fn_weight, channels=4, kernel_size=[1]); + %228 = nn.bias_add(%227, %v10_0_fn_bias); + %229 = multiply(%228, %v10_0_scale); + %230 = add(%229, %224); + %231 = multiply(%230, %v10_1_affine_g); + %232 = add(%231, %v10_1_affine_b); + %233 = transpose(%v10_1_fn_0_weight, axes=[1, 0]); + %234 = reshape(%232, newshape=[-1, 64]); + %235 = transpose(%233, axes=[1, 0]); + %236 = nn.dense(%234, %235, units=None); + %237 = reshape(%236, newshape=[1, 4, 256]); + %238 = add(%237, %v10_1_fn_0_bias); + %239 = multiply(%238, 0.707107f); + %240 = erf(%239); + %241 = multiply(%240, 0.5f); + %242 = add(0.5f, %241); + %243 = multiply(%238, %242); + %244 = transpose(%v10_1_fn_2_weight, axes=[1, 0]); + %245 = reshape(%243, newshape=[-1, 256]); + %246 = transpose(%244, axes=[1, 0]); + %247 = nn.dense(%245, %246, units=None); + %248 = reshape(%247, newshape=[1, 4, 64]); + %249 = add(%248, %v10_1_fn_2_bias); + %250 = multiply(%249, %v10_1_scale); + %251 = add(%250, %230); + %252 = multiply(%251, %v11_0_affine_g); + %253 = add(%252, %v11_0_affine_b); + %254 = nn.conv1d(%253, %v11_0_fn_weight, channels=4, kernel_size=[1]); + %255 = nn.bias_add(%254, %v11_0_fn_bias); + %256 = multiply(%255, %v11_0_scale); + %257 = add(%256, %251); + %258 = multiply(%257, %v11_1_affine_g); + %259 = add(%258, %v11_1_affine_b); + %260 = transpose(%v11_1_fn_0_weight, axes=[1, 0]); + %261 = reshape(%259, newshape=[-1, 64]); + %262 = transpose(%260, axes=[1, 0]); + %263 = nn.dense(%261, %262, units=None); + %264 = reshape(%263, newshape=[1, 4, 256]); + %265 = add(%264, %v11_1_fn_0_bias); + %266 = multiply(%265, 0.707107f); + %267 = erf(%266); + %268 = multiply(%267, 0.5f); + %269 = add(0.5f, %268); + %270 = multiply(%265, %269); + %271 = transpose(%v11_1_fn_2_weight, axes=[1, 0]); + %272 = reshape(%270, newshape=[-1, 256]); + %273 = transpose(%271, axes=[1, 0]); + %274 = nn.dense(%272, %273, units=None); + %275 = reshape(%274, newshape=[1, 4, 64]); + %276 = add(%275, %v11_1_fn_2_bias); + %277 = multiply(%276, %v11_1_scale); + %278 = add(%277, %257); + %279 = multiply(%278, %v12_0_affine_g); + %280 = add(%279, %v12_0_affine_b); + %281 = nn.conv1d(%280, %v12_0_fn_weight, channels=4, kernel_size=[1]); + %282 = nn.bias_add(%281, %v12_0_fn_bias); + %283 = multiply(%282, %v12_0_scale); + %284 = add(%283, %278); + %285 = multiply(%284, %v12_1_affine_g); + %286 = add(%285, %v12_1_affine_b); + %287 = transpose(%v12_1_fn_0_weight, axes=[1, 0]); + %288 = reshape(%286, newshape=[-1, 64]); + %289 = transpose(%287, axes=[1, 0]); + %290 = nn.dense(%288, %289, units=None); + %291 = reshape(%290, newshape=[1, 4, 256]); + %292 = add(%291, %v12_1_fn_0_bias); + %293 = multiply(%292, 0.707107f); + %294 = erf(%293); + %295 = multiply(%294, 0.5f); + %296 = add(0.5f, %295); + %297 = multiply(%292, %296); + %298 = transpose(%v12_1_fn_2_weight, axes=[1, 0]); + %299 = reshape(%297, newshape=[-1, 256]); + %300 = transpose(%298, axes=[1, 0]); + %301 = nn.dense(%299, %300, units=None); + %302 = reshape(%301, newshape=[1, 4, 64]); + %303 = add(%302, %v12_1_fn_2_bias); + %304 = multiply(%303, %v12_1_scale); + %305 = add(%304, %284); + %306 = multiply(%305, %v13_0_affine_g); + %307 = add(%306, %v13_0_affine_b); + %308 = nn.conv1d(%307, %v13_0_fn_weight, channels=4, kernel_size=[1]); + %309 = nn.bias_add(%308, %v13_0_fn_bias); + %310 = multiply(%309, %v13_0_scale); + %311 = add(%310, %305); + %312 = multiply(%311, %v13_1_affine_g); + %313 = add(%312, %v13_1_affine_b); + %314 = transpose(%v13_1_fn_0_weight, axes=[1, 0]); + %315 = reshape(%313, newshape=[-1, 64]); + %316 = transpose(%314, axes=[1, 0]); + %317 = nn.dense(%315, %316, units=None); + %318 = reshape(%317, newshape=[1, 4, 256]); + %319 = add(%318, %v13_1_fn_0_bias); + %320 = multiply(%319, 0.707107f); + %321 = erf(%320); + %322 = multiply(%321, 0.5f); + %323 = add(0.5f, %322); + %324 = multiply(%319, %323); + %325 = transpose(%v13_1_fn_2_weight, axes=[1, 0]); + %326 = reshape(%324, newshape=[-1, 256]); + %327 = transpose(%325, axes=[1, 0]); + %328 = nn.dense(%326, %327, units=None); + %329 = reshape(%328, newshape=[1, 4, 64]); + %330 = add(%329, %v13_1_fn_2_bias); + %331 = multiply(%330, %v13_1_scale); + %332 = add(%331, %311); + %333 = multiply(%332, %v14_g); + %334 = add(%333, %v14_b); + %335 = reshape(%334, newshape=[1, 4, 64]); + %336 = mean(%335, axis=[1]); + %337 = transpose(%336, axes=[0, 1]); + %338 = transpose(%v16_weight, axes=[1, 0]); + %339 = reshape(%337, newshape=[1, 64]); + %340 = transpose(%338, axes=[1, 0]); + %341 = nn.dense(%339, %340, units=32); + add(%341, %v16_bias) } diff --git a/tests/resnet.py b/tests/models/resnet.py similarity index 87% rename from tests/resnet.py rename to tests/models/resnet.py index 7e25d45..af73b21 100644 --- a/tests/resnet.py +++ b/tests/models/resnet.py @@ -3,7 +3,7 @@ import tvm.relay import tvm.relay.testing -def main(batch, num_classes, num_layers, image_shape=(3, 32, 32)): +def main(batch, num_classes, num_layers, image_shape=(3, 224, 224)): model = tvm.relay.testing.resnet.get_net(batch, num_classes, num_layers, image_shape=image_shape) mod = tvm.ir.IRModule.from_expr(model.body) mod = tvm.relay.transform.InferType()(mod) diff --git a/tests/models/resnet18.relay b/tests/models/resnet18.relay new file mode 100644 index 0000000..414617f --- /dev/null +++ b/tests/models/resnet18.relay @@ -0,0 +1,264 @@ +#[version = "0.0.5"] +def @main(%data: Tensor[(1, 3, 224, 224), float32], %bn_data_gamma: Tensor[(3), float32], %bn_data_beta: Tensor[(3), float32], %bn_data_moving_mean: Tensor[(3), float32], %bn_data_moving_var: Tensor[(3), float32], %conv0_weight: Tensor[(64, 3, 7, 7), float32], %bn0_gamma: Tensor[(64), float32], %bn0_beta: Tensor[(64), float32], %bn0_moving_mean: Tensor[(64), float32], %bn0_moving_var: Tensor[(64), float32], %stage1_unit1_bn1_gamma: Tensor[(64), float32], %stage1_unit1_bn1_beta: Tensor[(64), float32], %stage1_unit1_bn1_moving_mean: Tensor[(64), float32], %stage1_unit1_bn1_moving_var: Tensor[(64), float32], %stage1_unit1_conv1_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit1_bn2_gamma: Tensor[(64), float32], %stage1_unit1_bn2_beta: Tensor[(64), float32], %stage1_unit1_bn2_moving_mean: Tensor[(64), float32], %stage1_unit1_bn2_moving_var: Tensor[(64), float32], %stage1_unit1_conv2_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit1_sc_weight: Tensor[(64, 64, 1, 1), float32], %stage1_unit2_bn1_gamma: Tensor[(64), float32], %stage1_unit2_bn1_beta: Tensor[(64), float32], %stage1_unit2_bn1_moving_mean: Tensor[(64), float32], %stage1_unit2_bn1_moving_var: Tensor[(64), float32], %stage1_unit2_conv1_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit2_bn2_gamma: Tensor[(64), float32], %stage1_unit2_bn2_beta: Tensor[(64), float32], %stage1_unit2_bn2_moving_mean: Tensor[(64), float32], %stage1_unit2_bn2_moving_var: Tensor[(64), float32], %stage1_unit2_conv2_weight: Tensor[(64, 64, 3, 3), float32], %stage2_unit1_bn1_gamma: Tensor[(64), float32], %stage2_unit1_bn1_beta: Tensor[(64), float32], %stage2_unit1_bn1_moving_mean: Tensor[(64), float32], %stage2_unit1_bn1_moving_var: Tensor[(64), float32], %stage2_unit1_conv1_weight: Tensor[(128, 64, 3, 3), float32], %stage2_unit1_bn2_gamma: Tensor[(128), float32], %stage2_unit1_bn2_beta: Tensor[(128), float32], %stage2_unit1_bn2_moving_mean: Tensor[(128), float32], %stage2_unit1_bn2_moving_var: Tensor[(128), float32], %stage2_unit1_conv2_weight: Tensor[(128, 128, 3, 3), float32], %stage2_unit1_sc_weight: Tensor[(128, 64, 1, 1), float32], %stage2_unit2_bn1_gamma: Tensor[(128), float32], %stage2_unit2_bn1_beta: Tensor[(128), float32], %stage2_unit2_bn1_moving_mean: Tensor[(128), float32], %stage2_unit2_bn1_moving_var: Tensor[(128), float32], %stage2_unit2_conv1_weight: Tensor[(128, 128, 3, 3), float32], %stage2_unit2_bn2_gamma: Tensor[(128), float32], %stage2_unit2_bn2_beta: Tensor[(128), float32], %stage2_unit2_bn2_moving_mean: Tensor[(128), float32], %stage2_unit2_bn2_moving_var: Tensor[(128), float32], %stage2_unit2_conv2_weight: Tensor[(128, 128, 3, 3), float32], %stage3_unit1_bn1_gamma: Tensor[(128), float32], %stage3_unit1_bn1_beta: Tensor[(128), float32], %stage3_unit1_bn1_moving_mean: Tensor[(128), float32], %stage3_unit1_bn1_moving_var: Tensor[(128), float32], %stage3_unit1_conv1_weight: Tensor[(256, 128, 3, 3), float32], %stage3_unit1_bn2_gamma: Tensor[(256), float32], %stage3_unit1_bn2_beta: Tensor[(256), float32], %stage3_unit1_bn2_moving_mean: Tensor[(256), float32], %stage3_unit1_bn2_moving_var: Tensor[(256), float32], %stage3_unit1_conv2_weight: Tensor[(256, 256, 3, 3), float32], %stage3_unit1_sc_weight: Tensor[(256, 128, 1, 1), float32], %stage3_unit2_bn1_gamma: Tensor[(256), float32], %stage3_unit2_bn1_beta: Tensor[(256), float32], %stage3_unit2_bn1_moving_mean: Tensor[(256), float32], %stage3_unit2_bn1_moving_var: Tensor[(256), float32], %stage3_unit2_conv1_weight: Tensor[(256, 256, 3, 3), float32], %stage3_unit2_bn2_gamma: Tensor[(256), float32], %stage3_unit2_bn2_beta: Tensor[(256), float32], %stage3_unit2_bn2_moving_mean: Tensor[(256), float32], %stage3_unit2_bn2_moving_var: Tensor[(256), float32], %stage3_unit2_conv2_weight: Tensor[(256, 256, 3, 3), float32], %stage4_unit1_bn1_gamma: Tensor[(256), float32], %stage4_unit1_bn1_beta: Tensor[(256), float32], %stage4_unit1_bn1_moving_mean: Tensor[(256), float32], %stage4_unit1_bn1_moving_var: Tensor[(256), float32], %stage4_unit1_conv1_weight: Tensor[(512, 256, 3, 3), float32], %stage4_unit1_bn2_gamma: Tensor[(512), float32], %stage4_unit1_bn2_beta: Tensor[(512), float32], %stage4_unit1_bn2_moving_mean: Tensor[(512), float32], %stage4_unit1_bn2_moving_var: Tensor[(512), float32], %stage4_unit1_conv2_weight: Tensor[(512, 512, 3, 3), float32], %stage4_unit1_sc_weight: Tensor[(512, 256, 1, 1), float32], %stage4_unit2_bn1_gamma: Tensor[(512), float32], %stage4_unit2_bn1_beta: Tensor[(512), float32], %stage4_unit2_bn1_moving_mean: Tensor[(512), float32], %stage4_unit2_bn1_moving_var: Tensor[(512), float32], %stage4_unit2_conv1_weight: Tensor[(512, 512, 3, 3), float32], %stage4_unit2_bn2_gamma: Tensor[(512), float32], %stage4_unit2_bn2_beta: Tensor[(512), float32], %stage4_unit2_bn2_moving_mean: Tensor[(512), float32], %stage4_unit2_bn2_moving_var: Tensor[(512), float32], %stage4_unit2_conv2_weight: Tensor[(512, 512, 3, 3), float32], %bn1_gamma: Tensor[(512), float32], %bn1_beta: Tensor[(512), float32], %bn1_moving_mean: Tensor[(512), float32], %bn1_moving_var: Tensor[(512), float32], %fc1_weight: Tensor[(32, 512), float32], %fc1_bias: Tensor[(32), float32]) -> Tensor[(1, 32), float32] { + %0 = add(%bn_data_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(3), float32] */; + %1 = sqrt(%0) /* ty=Tensor[(3), float32] */; + %2 = divide(1f /* ty=float32 */, %1) /* ty=Tensor[(3), float32] */; + %3 = expand_dims(%2, axis=1, num_newaxis=2) /* ty=Tensor[(3, 1, 1), float32] */; + %4 = negative(%bn_data_moving_mean) /* ty=Tensor[(3), float32] */; + %5 = multiply(%4, %2) /* ty=Tensor[(3), float32] */; + %6 = add(%5, %bn_data_beta) /* ty=Tensor[(3), float32] */; + %7 = multiply(%data, %3) /* ty=Tensor[(1, 3, 224, 224), float32] */; + %8 = expand_dims(%6, axis=1, num_newaxis=2) /* ty=Tensor[(3, 1, 1), float32] */; + %9 = add(%7, %8) /* ty=Tensor[(1, 3, 224, 224), float32] */; + %10 = add(%bn0_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %11 = sqrt(%10) /* ty=Tensor[(64), float32] */; + %12 = divide(1f /* ty=float32 */, %11) /* ty=Tensor[(64), float32] */; + %13 = multiply(%12, %bn0_gamma) /* ty=Tensor[(64), float32] */; + %14 = nn.conv2d(%9, %conv0_weight, strides=[2, 2], padding=[3, 3, 3, 3], channels=64, kernel_size=[7, 7]) /* ty=Tensor[(1, 64, 112, 112), float32] */; + %15 = expand_dims(%13, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %16 = negative(%bn0_moving_mean) /* ty=Tensor[(64), float32] */; + %17 = multiply(%16, %13) /* ty=Tensor[(64), float32] */; + %18 = add(%17, %bn0_beta) /* ty=Tensor[(64), float32] */; + %19 = multiply(%14, %15) /* ty=Tensor[(1, 64, 112, 112), float32] */; + %20 = expand_dims(%18, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %21 = add(%19, %20) /* ty=Tensor[(1, 64, 112, 112), float32] */; + %22 = nn.relu(%21) /* ty=Tensor[(1, 64, 112, 112), float32] */; + %23 = add(%stage1_unit1_bn1_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %24 = sqrt(%23) /* ty=Tensor[(64), float32] */; + %25 = divide(1f /* ty=float32 */, %24) /* ty=Tensor[(64), float32] */; + %26 = multiply(%25, %stage1_unit1_bn1_gamma) /* ty=Tensor[(64), float32] */; + %27 = nn.max_pool2d(%22, pool_size=[3, 3], strides=[2, 2], padding=[1, 1, 1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %28 = expand_dims(%26, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %29 = negative(%stage1_unit1_bn1_moving_mean) /* ty=Tensor[(64), float32] */; + %30 = multiply(%29, %26) /* ty=Tensor[(64), float32] */; + %31 = add(%30, %stage1_unit1_bn1_beta) /* ty=Tensor[(64), float32] */; + %32 = multiply(%27, %28) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %33 = expand_dims(%31, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %34 = add(%32, %33) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %35 = nn.relu(%34) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %36 = add(%stage1_unit1_bn2_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %37 = sqrt(%36) /* ty=Tensor[(64), float32] */; + %38 = divide(1f /* ty=float32 */, %37) /* ty=Tensor[(64), float32] */; + %39 = multiply(%38, %stage1_unit1_bn2_gamma) /* ty=Tensor[(64), float32] */; + %40 = nn.conv2d(%35, %stage1_unit1_conv1_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %41 = expand_dims(%39, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %42 = negative(%stage1_unit1_bn2_moving_mean) /* ty=Tensor[(64), float32] */; + %43 = multiply(%42, %39) /* ty=Tensor[(64), float32] */; + %44 = add(%43, %stage1_unit1_bn2_beta) /* ty=Tensor[(64), float32] */; + %45 = multiply(%40, %41) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %46 = expand_dims(%44, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %47 = add(%45, %46) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %48 = nn.relu(%47) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %49 = nn.conv2d(%48, %stage1_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %50 = nn.conv2d(%35, %stage1_unit1_sc_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %51 = add(%stage1_unit2_bn1_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %52 = sqrt(%51) /* ty=Tensor[(64), float32] */; + %53 = divide(1f /* ty=float32 */, %52) /* ty=Tensor[(64), float32] */; + %54 = multiply(%53, %stage1_unit2_bn1_gamma) /* ty=Tensor[(64), float32] */; + %55 = add(%49, %50) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %56 = expand_dims(%54, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %57 = negative(%stage1_unit2_bn1_moving_mean) /* ty=Tensor[(64), float32] */; + %58 = multiply(%57, %54) /* ty=Tensor[(64), float32] */; + %59 = add(%58, %stage1_unit2_bn1_beta) /* ty=Tensor[(64), float32] */; + %60 = multiply(%55, %56) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %61 = expand_dims(%59, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %62 = add(%60, %61) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %63 = nn.relu(%62) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %64 = add(%stage1_unit2_bn2_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %65 = sqrt(%64) /* ty=Tensor[(64), float32] */; + %66 = divide(1f /* ty=float32 */, %65) /* ty=Tensor[(64), float32] */; + %67 = multiply(%66, %stage1_unit2_bn2_gamma) /* ty=Tensor[(64), float32] */; + %68 = nn.conv2d(%63, %stage1_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %69 = expand_dims(%67, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %70 = negative(%stage1_unit2_bn2_moving_mean) /* ty=Tensor[(64), float32] */; + %71 = multiply(%70, %67) /* ty=Tensor[(64), float32] */; + %72 = add(%71, %stage1_unit2_bn2_beta) /* ty=Tensor[(64), float32] */; + %73 = multiply(%68, %69) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %74 = expand_dims(%72, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %75 = add(%73, %74) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %76 = nn.relu(%75) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %77 = nn.conv2d(%76, %stage1_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %78 = add(%stage2_unit1_bn1_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %79 = sqrt(%78) /* ty=Tensor[(64), float32] */; + %80 = divide(1f /* ty=float32 */, %79) /* ty=Tensor[(64), float32] */; + %81 = multiply(%80, %stage2_unit1_bn1_gamma) /* ty=Tensor[(64), float32] */; + %82 = add(%77, %55) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %83 = expand_dims(%81, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %84 = negative(%stage2_unit1_bn1_moving_mean) /* ty=Tensor[(64), float32] */; + %85 = multiply(%84, %81) /* ty=Tensor[(64), float32] */; + %86 = add(%85, %stage2_unit1_bn1_beta) /* ty=Tensor[(64), float32] */; + %87 = multiply(%82, %83) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %88 = expand_dims(%86, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %89 = add(%87, %88) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %90 = nn.relu(%89) /* ty=Tensor[(1, 64, 56, 56), float32] */; + %91 = add(%stage2_unit1_bn2_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %92 = sqrt(%91) /* ty=Tensor[(128), float32] */; + %93 = divide(1f /* ty=float32 */, %92) /* ty=Tensor[(128), float32] */; + %94 = multiply(%93, %stage2_unit1_bn2_gamma) /* ty=Tensor[(128), float32] */; + %95 = nn.conv2d(%90, %stage2_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %96 = expand_dims(%94, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %97 = negative(%stage2_unit1_bn2_moving_mean) /* ty=Tensor[(128), float32] */; + %98 = multiply(%97, %94) /* ty=Tensor[(128), float32] */; + %99 = add(%98, %stage2_unit1_bn2_beta) /* ty=Tensor[(128), float32] */; + %100 = multiply(%95, %96) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %101 = expand_dims(%99, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %102 = add(%100, %101) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %103 = nn.relu(%102) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %104 = nn.conv2d(%103, %stage2_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %105 = nn.conv2d(%90, %stage2_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %106 = add(%stage2_unit2_bn1_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %107 = sqrt(%106) /* ty=Tensor[(128), float32] */; + %108 = divide(1f /* ty=float32 */, %107) /* ty=Tensor[(128), float32] */; + %109 = multiply(%108, %stage2_unit2_bn1_gamma) /* ty=Tensor[(128), float32] */; + %110 = add(%104, %105) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %111 = expand_dims(%109, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %112 = negative(%stage2_unit2_bn1_moving_mean) /* ty=Tensor[(128), float32] */; + %113 = multiply(%112, %109) /* ty=Tensor[(128), float32] */; + %114 = add(%113, %stage2_unit2_bn1_beta) /* ty=Tensor[(128), float32] */; + %115 = multiply(%110, %111) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %116 = expand_dims(%114, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %117 = add(%115, %116) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %118 = nn.relu(%117) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %119 = add(%stage2_unit2_bn2_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %120 = sqrt(%119) /* ty=Tensor[(128), float32] */; + %121 = divide(1f /* ty=float32 */, %120) /* ty=Tensor[(128), float32] */; + %122 = multiply(%121, %stage2_unit2_bn2_gamma) /* ty=Tensor[(128), float32] */; + %123 = nn.conv2d(%118, %stage2_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %124 = expand_dims(%122, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %125 = negative(%stage2_unit2_bn2_moving_mean) /* ty=Tensor[(128), float32] */; + %126 = multiply(%125, %122) /* ty=Tensor[(128), float32] */; + %127 = add(%126, %stage2_unit2_bn2_beta) /* ty=Tensor[(128), float32] */; + %128 = multiply(%123, %124) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %129 = expand_dims(%127, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %130 = add(%128, %129) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %131 = nn.relu(%130) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %132 = nn.conv2d(%131, %stage2_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %133 = add(%stage3_unit1_bn1_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %134 = sqrt(%133) /* ty=Tensor[(128), float32] */; + %135 = divide(1f /* ty=float32 */, %134) /* ty=Tensor[(128), float32] */; + %136 = multiply(%135, %stage3_unit1_bn1_gamma) /* ty=Tensor[(128), float32] */; + %137 = add(%132, %110) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %138 = expand_dims(%136, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %139 = negative(%stage3_unit1_bn1_moving_mean) /* ty=Tensor[(128), float32] */; + %140 = multiply(%139, %136) /* ty=Tensor[(128), float32] */; + %141 = add(%140, %stage3_unit1_bn1_beta) /* ty=Tensor[(128), float32] */; + %142 = multiply(%137, %138) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %143 = expand_dims(%141, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] */; + %144 = add(%142, %143) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %145 = nn.relu(%144) /* ty=Tensor[(1, 128, 28, 28), float32] */; + %146 = add(%stage3_unit1_bn2_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %147 = sqrt(%146) /* ty=Tensor[(256), float32] */; + %148 = divide(1f /* ty=float32 */, %147) /* ty=Tensor[(256), float32] */; + %149 = multiply(%148, %stage3_unit1_bn2_gamma) /* ty=Tensor[(256), float32] */; + %150 = nn.conv2d(%145, %stage3_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %151 = expand_dims(%149, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %152 = negative(%stage3_unit1_bn2_moving_mean) /* ty=Tensor[(256), float32] */; + %153 = multiply(%152, %149) /* ty=Tensor[(256), float32] */; + %154 = add(%153, %stage3_unit1_bn2_beta) /* ty=Tensor[(256), float32] */; + %155 = multiply(%150, %151) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %156 = expand_dims(%154, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %157 = add(%155, %156) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %158 = nn.relu(%157) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %159 = nn.conv2d(%158, %stage3_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %160 = nn.conv2d(%145, %stage3_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %161 = add(%stage3_unit2_bn1_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %162 = sqrt(%161) /* ty=Tensor[(256), float32] */; + %163 = divide(1f /* ty=float32 */, %162) /* ty=Tensor[(256), float32] */; + %164 = multiply(%163, %stage3_unit2_bn1_gamma) /* ty=Tensor[(256), float32] */; + %165 = add(%159, %160) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %166 = expand_dims(%164, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %167 = negative(%stage3_unit2_bn1_moving_mean) /* ty=Tensor[(256), float32] */; + %168 = multiply(%167, %164) /* ty=Tensor[(256), float32] */; + %169 = add(%168, %stage3_unit2_bn1_beta) /* ty=Tensor[(256), float32] */; + %170 = multiply(%165, %166) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %171 = expand_dims(%169, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %172 = add(%170, %171) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %173 = nn.relu(%172) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %174 = add(%stage3_unit2_bn2_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %175 = sqrt(%174) /* ty=Tensor[(256), float32] */; + %176 = divide(1f /* ty=float32 */, %175) /* ty=Tensor[(256), float32] */; + %177 = multiply(%176, %stage3_unit2_bn2_gamma) /* ty=Tensor[(256), float32] */; + %178 = nn.conv2d(%173, %stage3_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %179 = expand_dims(%177, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %180 = negative(%stage3_unit2_bn2_moving_mean) /* ty=Tensor[(256), float32] */; + %181 = multiply(%180, %177) /* ty=Tensor[(256), float32] */; + %182 = add(%181, %stage3_unit2_bn2_beta) /* ty=Tensor[(256), float32] */; + %183 = multiply(%178, %179) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %184 = expand_dims(%182, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %185 = add(%183, %184) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %186 = nn.relu(%185) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %187 = nn.conv2d(%186, %stage3_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %188 = add(%stage4_unit1_bn1_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %189 = sqrt(%188) /* ty=Tensor[(256), float32] */; + %190 = divide(1f /* ty=float32 */, %189) /* ty=Tensor[(256), float32] */; + %191 = multiply(%190, %stage4_unit1_bn1_gamma) /* ty=Tensor[(256), float32] */; + %192 = add(%187, %165) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %193 = expand_dims(%191, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %194 = negative(%stage4_unit1_bn1_moving_mean) /* ty=Tensor[(256), float32] */; + %195 = multiply(%194, %191) /* ty=Tensor[(256), float32] */; + %196 = add(%195, %stage4_unit1_bn1_beta) /* ty=Tensor[(256), float32] */; + %197 = multiply(%192, %193) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %198 = expand_dims(%196, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] */; + %199 = add(%197, %198) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %200 = nn.relu(%199) /* ty=Tensor[(1, 256, 14, 14), float32] */; + %201 = add(%stage4_unit1_bn2_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %202 = sqrt(%201) /* ty=Tensor[(512), float32] */; + %203 = divide(1f /* ty=float32 */, %202) /* ty=Tensor[(512), float32] */; + %204 = multiply(%203, %stage4_unit1_bn2_gamma) /* ty=Tensor[(512), float32] */; + %205 = nn.conv2d(%200, %stage4_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %206 = expand_dims(%204, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %207 = negative(%stage4_unit1_bn2_moving_mean) /* ty=Tensor[(512), float32] */; + %208 = multiply(%207, %204) /* ty=Tensor[(512), float32] */; + %209 = add(%208, %stage4_unit1_bn2_beta) /* ty=Tensor[(512), float32] */; + %210 = multiply(%205, %206) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %211 = expand_dims(%209, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %212 = add(%210, %211) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %213 = nn.relu(%212) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %214 = nn.conv2d(%213, %stage4_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %215 = nn.conv2d(%200, %stage4_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %216 = add(%stage4_unit2_bn1_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %217 = sqrt(%216) /* ty=Tensor[(512), float32] */; + %218 = divide(1f /* ty=float32 */, %217) /* ty=Tensor[(512), float32] */; + %219 = multiply(%218, %stage4_unit2_bn1_gamma) /* ty=Tensor[(512), float32] */; + %220 = add(%214, %215) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %221 = expand_dims(%219, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %222 = negative(%stage4_unit2_bn1_moving_mean) /* ty=Tensor[(512), float32] */; + %223 = multiply(%222, %219) /* ty=Tensor[(512), float32] */; + %224 = add(%223, %stage4_unit2_bn1_beta) /* ty=Tensor[(512), float32] */; + %225 = multiply(%220, %221) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %226 = expand_dims(%224, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %227 = add(%225, %226) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %228 = nn.relu(%227) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %229 = add(%stage4_unit2_bn2_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %230 = sqrt(%229) /* ty=Tensor[(512), float32] */; + %231 = divide(1f /* ty=float32 */, %230) /* ty=Tensor[(512), float32] */; + %232 = multiply(%231, %stage4_unit2_bn2_gamma) /* ty=Tensor[(512), float32] */; + %233 = nn.conv2d(%228, %stage4_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %234 = expand_dims(%232, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %235 = negative(%stage4_unit2_bn2_moving_mean) /* ty=Tensor[(512), float32] */; + %236 = multiply(%235, %232) /* ty=Tensor[(512), float32] */; + %237 = add(%236, %stage4_unit2_bn2_beta) /* ty=Tensor[(512), float32] */; + %238 = multiply(%233, %234) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %239 = expand_dims(%237, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %240 = add(%238, %239) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %241 = nn.relu(%240) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %242 = nn.conv2d(%241, %stage4_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %243 = add(%bn1_moving_var, 2e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %244 = sqrt(%243) /* ty=Tensor[(512), float32] */; + %245 = divide(1f /* ty=float32 */, %244) /* ty=Tensor[(512), float32] */; + %246 = multiply(%245, %bn1_gamma) /* ty=Tensor[(512), float32] */; + %247 = add(%242, %220) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %248 = expand_dims(%246, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %249 = negative(%bn1_moving_mean) /* ty=Tensor[(512), float32] */; + %250 = multiply(%249, %246) /* ty=Tensor[(512), float32] */; + %251 = add(%250, %bn1_beta) /* ty=Tensor[(512), float32] */; + %252 = multiply(%247, %248) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %253 = expand_dims(%251, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] */; + %254 = add(%252, %253) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %255 = nn.relu(%254) /* ty=Tensor[(1, 512, 7, 7), float32] */; + %256 = nn.global_avg_pool2d(%255) /* ty=Tensor[(1, 512, 1, 1), float32] */; + %257 = nn.batch_flatten(%256) /* ty=Tensor[(1, 512), float32] */; + %258 = nn.dense(%257, %fc1_weight, units=32) /* ty=Tensor[(1, 32), float32] */; + %259 = nn.bias_add(%258, %fc1_bias, axis=-1) /* ty=Tensor[(1, 32), float32] */; + nn.softmax(%259) /* ty=Tensor[(1, 32), float32] */ +} diff --git a/tests/models/resnet20.relay b/tests/models/resnet20.relay new file mode 100644 index 0000000..1643939 --- /dev/null +++ b/tests/models/resnet20.relay @@ -0,0 +1,297 @@ +#[version = "0.0.5"] +def @main(%data: Tensor[(1, 3, 32, 32), float32], %cifarresnetv11_conv0_weight: Tensor[(16, 3, 3, 3), float32], %cifarresnetv11_batchnorm0_gamma: Tensor[(16), float32], %cifarresnetv11_batchnorm0_beta: Tensor[(16), float32], %cifarresnetv11_batchnorm0_running_mean: Tensor[(16), float32], %cifarresnetv11_batchnorm0_running_var: Tensor[(16), float32], %cifarresnetv11_stage1_conv0_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv11_stage1_batchnorm0_gamma: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm0_beta: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm0_running_mean: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm0_running_var: Tensor[(16), float32], %cifarresnetv11_stage1_conv1_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv11_stage1_batchnorm1_gamma: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm1_beta: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm1_running_mean: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm1_running_var: Tensor[(16), float32], %cifarresnetv11_stage1_conv2_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv11_stage1_batchnorm2_gamma: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm2_beta: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm2_running_mean: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm2_running_var: Tensor[(16), float32], %cifarresnetv11_stage1_conv3_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv11_stage1_batchnorm3_gamma: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm3_beta: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm3_running_mean: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm3_running_var: Tensor[(16), float32], %cifarresnetv11_stage1_conv4_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv11_stage1_batchnorm4_gamma: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm4_beta: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm4_running_mean: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm4_running_var: Tensor[(16), float32], %cifarresnetv11_stage1_conv5_weight: Tensor[(16, 16, 3, 3), float32], %cifarresnetv11_stage1_batchnorm5_gamma: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm5_beta: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm5_running_mean: Tensor[(16), float32], %cifarresnetv11_stage1_batchnorm5_running_var: Tensor[(16), float32], %cifarresnetv11_stage2_conv2_weight: Tensor[(32, 16, 1, 1), float32], %cifarresnetv11_stage2_batchnorm2_gamma: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm2_beta: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm2_running_mean: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm2_running_var: Tensor[(32), float32], %cifarresnetv11_stage2_conv0_weight: Tensor[(32, 16, 3, 3), float32], %cifarresnetv11_stage2_batchnorm0_gamma: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm0_beta: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm0_running_mean: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm0_running_var: Tensor[(32), float32], %cifarresnetv11_stage2_conv1_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv11_stage2_batchnorm1_gamma: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm1_beta: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm1_running_mean: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm1_running_var: Tensor[(32), float32], %cifarresnetv11_stage2_conv3_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv11_stage2_batchnorm3_gamma: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm3_beta: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm3_running_mean: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm3_running_var: Tensor[(32), float32], %cifarresnetv11_stage2_conv4_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv11_stage2_batchnorm4_gamma: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm4_beta: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm4_running_mean: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm4_running_var: Tensor[(32), float32], %cifarresnetv11_stage2_conv5_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv11_stage2_batchnorm5_gamma: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm5_beta: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm5_running_mean: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm5_running_var: Tensor[(32), float32], %cifarresnetv11_stage2_conv6_weight: Tensor[(32, 32, 3, 3), float32], %cifarresnetv11_stage2_batchnorm6_gamma: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm6_beta: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm6_running_mean: Tensor[(32), float32], %cifarresnetv11_stage2_batchnorm6_running_var: Tensor[(32), float32], %cifarresnetv11_stage3_conv2_weight: Tensor[(64, 32, 1, 1), float32], %cifarresnetv11_stage3_batchnorm2_gamma: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm2_beta: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm2_running_mean: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm2_running_var: Tensor[(64), float32], %cifarresnetv11_stage3_conv0_weight: Tensor[(64, 32, 3, 3), float32], %cifarresnetv11_stage3_batchnorm0_gamma: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm0_beta: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm0_running_mean: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm0_running_var: Tensor[(64), float32], %cifarresnetv11_stage3_conv1_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv11_stage3_batchnorm1_gamma: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm1_beta: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm1_running_mean: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm1_running_var: Tensor[(64), float32], %cifarresnetv11_stage3_conv3_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv11_stage3_batchnorm3_gamma: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm3_beta: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm3_running_mean: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm3_running_var: Tensor[(64), float32], %cifarresnetv11_stage3_conv4_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv11_stage3_batchnorm4_gamma: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm4_beta: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm4_running_mean: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm4_running_var: Tensor[(64), float32], %cifarresnetv11_stage3_conv5_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv11_stage3_batchnorm5_gamma: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm5_beta: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm5_running_mean: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm5_running_var: Tensor[(64), float32], %cifarresnetv11_stage3_conv6_weight: Tensor[(64, 64, 3, 3), float32], %cifarresnetv11_stage3_batchnorm6_gamma: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm6_beta: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm6_running_mean: Tensor[(64), float32], %cifarresnetv11_stage3_batchnorm6_running_var: Tensor[(64), float32], %cifarresnetv11_dense0_weight: Tensor[(10, 64), float32], %cifarresnetv11_dense0_bias: Tensor[(10), float32]) -> Tensor[(1, 10), float32] { + %0 = add(%cifarresnetv11_batchnorm0_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %1 = sqrt(%0) /* ty=Tensor[(16), float32] */; + %2 = divide(1f /* ty=float32 */, %1) /* ty=Tensor[(16), float32] */; + %3 = multiply(%2, %cifarresnetv11_batchnorm0_gamma) /* ty=Tensor[(16), float32] */; + %4 = nn.conv2d(%data, %cifarresnetv11_conv0_weight, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %5 = expand_dims(%3, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %6 = negative(%cifarresnetv11_batchnorm0_running_mean) /* ty=Tensor[(16), float32] */; + %7 = multiply(%6, %3) /* ty=Tensor[(16), float32] */; + %8 = add(%7, %cifarresnetv11_batchnorm0_beta) /* ty=Tensor[(16), float32] */; + %9 = multiply(%4, %5) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %10 = expand_dims(%8, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %11 = add(%cifarresnetv11_batchnorm0_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %12 = sqrt(%11) /* ty=Tensor[(16), float32] */; + %13 = divide(1f /* ty=float32 */, %12) /* ty=Tensor[(16), float32] */; + %14 = multiply(%13, %cifarresnetv11_batchnorm0_gamma) /* ty=Tensor[(16), float32] */; + %15 = expand_dims(%14, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %16 = negative(%cifarresnetv11_batchnorm0_running_mean) /* ty=Tensor[(16), float32] */; + %17 = multiply(%16, %14) /* ty=Tensor[(16), float32] */; + %18 = add(%17, %cifarresnetv11_batchnorm0_beta) /* ty=Tensor[(16), float32] */; + %19 = multiply(%4, %15) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %20 = expand_dims(%18, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %21 = add(%19, %20) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %22 = add(%cifarresnetv11_stage1_batchnorm0_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %23 = sqrt(%22) /* ty=Tensor[(16), float32] */; + %24 = divide(1f /* ty=float32 */, %23) /* ty=Tensor[(16), float32] */; + %25 = multiply(%24, %cifarresnetv11_stage1_batchnorm0_gamma) /* ty=Tensor[(16), float32] */; + %26 = nn.conv2d(%21, %cifarresnetv11_stage1_conv0_weight, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %27 = expand_dims(%25, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %28 = negative(%cifarresnetv11_stage1_batchnorm0_running_mean) /* ty=Tensor[(16), float32] */; + %29 = multiply(%28, %25) /* ty=Tensor[(16), float32] */; + %30 = add(%29, %cifarresnetv11_stage1_batchnorm0_beta) /* ty=Tensor[(16), float32] */; + %31 = multiply(%26, %27) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %32 = expand_dims(%30, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %33 = add(%31, %32) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %34 = nn.relu(%33) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %35 = add(%cifarresnetv11_stage1_batchnorm1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %36 = sqrt(%35) /* ty=Tensor[(16), float32] */; + %37 = divide(1f /* ty=float32 */, %36) /* ty=Tensor[(16), float32] */; + %38 = multiply(%37, %cifarresnetv11_stage1_batchnorm1_gamma) /* ty=Tensor[(16), float32] */; + %39 = nn.conv2d(%34, %cifarresnetv11_stage1_conv1_weight, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %40 = expand_dims(%38, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %41 = negative(%cifarresnetv11_stage1_batchnorm1_running_mean) /* ty=Tensor[(16), float32] */; + %42 = multiply(%41, %38) /* ty=Tensor[(16), float32] */; + %43 = add(%42, %cifarresnetv11_stage1_batchnorm1_beta) /* ty=Tensor[(16), float32] */; + %44 = multiply(%39, %40) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %45 = expand_dims(%43, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %46 = add(%9, %10) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %47 = add(%44, %45) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %48 = add(%46, %47) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %49 = nn.relu(%48) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %50 = add(%cifarresnetv11_stage1_batchnorm2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %51 = sqrt(%50) /* ty=Tensor[(16), float32] */; + %52 = divide(1f /* ty=float32 */, %51) /* ty=Tensor[(16), float32] */; + %53 = multiply(%52, %cifarresnetv11_stage1_batchnorm2_gamma) /* ty=Tensor[(16), float32] */; + %54 = nn.conv2d(%49, %cifarresnetv11_stage1_conv2_weight, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %55 = expand_dims(%53, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %56 = negative(%cifarresnetv11_stage1_batchnorm2_running_mean) /* ty=Tensor[(16), float32] */; + %57 = multiply(%56, %53) /* ty=Tensor[(16), float32] */; + %58 = add(%57, %cifarresnetv11_stage1_batchnorm2_beta) /* ty=Tensor[(16), float32] */; + %59 = multiply(%54, %55) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %60 = expand_dims(%58, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %61 = add(%59, %60) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %62 = nn.relu(%61) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %63 = add(%cifarresnetv11_stage1_batchnorm3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %64 = sqrt(%63) /* ty=Tensor[(16), float32] */; + %65 = divide(1f /* ty=float32 */, %64) /* ty=Tensor[(16), float32] */; + %66 = multiply(%65, %cifarresnetv11_stage1_batchnorm3_gamma) /* ty=Tensor[(16), float32] */; + %67 = nn.conv2d(%62, %cifarresnetv11_stage1_conv3_weight, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %68 = expand_dims(%66, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %69 = negative(%cifarresnetv11_stage1_batchnorm3_running_mean) /* ty=Tensor[(16), float32] */; + %70 = multiply(%69, %66) /* ty=Tensor[(16), float32] */; + %71 = add(%70, %cifarresnetv11_stage1_batchnorm3_beta) /* ty=Tensor[(16), float32] */; + %72 = multiply(%67, %68) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %73 = expand_dims(%71, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %74 = add(%72, %73) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %75 = add(%49, %74) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %76 = nn.relu(%75) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %77 = add(%cifarresnetv11_stage1_batchnorm4_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %78 = sqrt(%77) /* ty=Tensor[(16), float32] */; + %79 = divide(1f /* ty=float32 */, %78) /* ty=Tensor[(16), float32] */; + %80 = multiply(%79, %cifarresnetv11_stage1_batchnorm4_gamma) /* ty=Tensor[(16), float32] */; + %81 = nn.conv2d(%76, %cifarresnetv11_stage1_conv4_weight, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %82 = expand_dims(%80, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %83 = negative(%cifarresnetv11_stage1_batchnorm4_running_mean) /* ty=Tensor[(16), float32] */; + %84 = multiply(%83, %80) /* ty=Tensor[(16), float32] */; + %85 = add(%84, %cifarresnetv11_stage1_batchnorm4_beta) /* ty=Tensor[(16), float32] */; + %86 = multiply(%81, %82) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %87 = expand_dims(%85, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %88 = add(%86, %87) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %89 = nn.relu(%88) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %90 = add(%cifarresnetv11_stage1_batchnorm5_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(16), float32] */; + %91 = sqrt(%90) /* ty=Tensor[(16), float32] */; + %92 = divide(1f /* ty=float32 */, %91) /* ty=Tensor[(16), float32] */; + %93 = multiply(%92, %cifarresnetv11_stage1_batchnorm5_gamma) /* ty=Tensor[(16), float32] */; + %94 = nn.conv2d(%89, %cifarresnetv11_stage1_conv5_weight, padding=[1, 1, 1, 1], channels=16, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %95 = expand_dims(%93, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %96 = negative(%cifarresnetv11_stage1_batchnorm5_running_mean) /* ty=Tensor[(16), float32] */; + %97 = multiply(%96, %93) /* ty=Tensor[(16), float32] */; + %98 = add(%97, %cifarresnetv11_stage1_batchnorm5_beta) /* ty=Tensor[(16), float32] */; + %99 = multiply(%94, %95) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %100 = expand_dims(%98, axis=1, num_newaxis=2) /* ty=Tensor[(16, 1, 1), float32] */; + %101 = add(%99, %100) /* ty=Tensor[(1, 16, 32, 32), float32] */; + %102 = add(%76, %101) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %103 = nn.relu(%102) /* from_string */ /* ty=Tensor[(1, 16, 32, 32), float32] */; + %104 = add(%cifarresnetv11_stage2_batchnorm2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %105 = sqrt(%104) /* ty=Tensor[(32), float32] */; + %106 = divide(1f /* ty=float32 */, %105) /* ty=Tensor[(32), float32] */; + %107 = multiply(%106, %cifarresnetv11_stage2_batchnorm2_gamma) /* ty=Tensor[(32), float32] */; + %108 = nn.conv2d(%103, %cifarresnetv11_stage2_conv2_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %109 = expand_dims(%107, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %110 = negative(%cifarresnetv11_stage2_batchnorm2_running_mean) /* ty=Tensor[(32), float32] */; + %111 = multiply(%110, %107) /* ty=Tensor[(32), float32] */; + %112 = add(%111, %cifarresnetv11_stage2_batchnorm2_beta) /* ty=Tensor[(32), float32] */; + %113 = multiply(%108, %109) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %114 = expand_dims(%112, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %115 = add(%cifarresnetv11_stage2_batchnorm0_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %116 = sqrt(%115) /* ty=Tensor[(32), float32] */; + %117 = divide(1f /* ty=float32 */, %116) /* ty=Tensor[(32), float32] */; + %118 = multiply(%117, %cifarresnetv11_stage2_batchnorm0_gamma) /* ty=Tensor[(32), float32] */; + %119 = nn.conv2d(%103, %cifarresnetv11_stage2_conv0_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %120 = expand_dims(%118, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %121 = negative(%cifarresnetv11_stage2_batchnorm0_running_mean) /* ty=Tensor[(32), float32] */; + %122 = multiply(%121, %118) /* ty=Tensor[(32), float32] */; + %123 = add(%122, %cifarresnetv11_stage2_batchnorm0_beta) /* ty=Tensor[(32), float32] */; + %124 = multiply(%119, %120) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %125 = expand_dims(%123, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %126 = add(%124, %125) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %127 = nn.relu(%126) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %128 = add(%cifarresnetv11_stage2_batchnorm1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %129 = sqrt(%128) /* ty=Tensor[(32), float32] */; + %130 = divide(1f /* ty=float32 */, %129) /* ty=Tensor[(32), float32] */; + %131 = multiply(%130, %cifarresnetv11_stage2_batchnorm1_gamma) /* ty=Tensor[(32), float32] */; + %132 = nn.conv2d(%127, %cifarresnetv11_stage2_conv1_weight, padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %133 = expand_dims(%131, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %134 = negative(%cifarresnetv11_stage2_batchnorm1_running_mean) /* ty=Tensor[(32), float32] */; + %135 = multiply(%134, %131) /* ty=Tensor[(32), float32] */; + %136 = add(%135, %cifarresnetv11_stage2_batchnorm1_beta) /* ty=Tensor[(32), float32] */; + %137 = multiply(%132, %133) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %138 = expand_dims(%136, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %139 = add(%113, %114) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %140 = add(%137, %138) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %141 = add(%139, %140) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %142 = nn.relu(%141) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %143 = add(%cifarresnetv11_stage2_batchnorm3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %144 = sqrt(%143) /* ty=Tensor[(32), float32] */; + %145 = divide(1f /* ty=float32 */, %144) /* ty=Tensor[(32), float32] */; + %146 = multiply(%145, %cifarresnetv11_stage2_batchnorm3_gamma) /* ty=Tensor[(32), float32] */; + %147 = nn.conv2d(%142, %cifarresnetv11_stage2_conv3_weight, padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %148 = expand_dims(%146, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %149 = negative(%cifarresnetv11_stage2_batchnorm3_running_mean) /* ty=Tensor[(32), float32] */; + %150 = multiply(%149, %146) /* ty=Tensor[(32), float32] */; + %151 = add(%150, %cifarresnetv11_stage2_batchnorm3_beta) /* ty=Tensor[(32), float32] */; + %152 = multiply(%147, %148) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %153 = expand_dims(%151, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %154 = add(%152, %153) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %155 = nn.relu(%154) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %156 = add(%cifarresnetv11_stage2_batchnorm4_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %157 = sqrt(%156) /* ty=Tensor[(32), float32] */; + %158 = divide(1f /* ty=float32 */, %157) /* ty=Tensor[(32), float32] */; + %159 = multiply(%158, %cifarresnetv11_stage2_batchnorm4_gamma) /* ty=Tensor[(32), float32] */; + %160 = nn.conv2d(%155, %cifarresnetv11_stage2_conv4_weight, padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %161 = expand_dims(%159, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %162 = negative(%cifarresnetv11_stage2_batchnorm4_running_mean) /* ty=Tensor[(32), float32] */; + %163 = multiply(%162, %159) /* ty=Tensor[(32), float32] */; + %164 = add(%163, %cifarresnetv11_stage2_batchnorm4_beta) /* ty=Tensor[(32), float32] */; + %165 = multiply(%160, %161) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %166 = expand_dims(%164, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %167 = add(%165, %166) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %168 = add(%142, %167) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %169 = nn.relu(%168) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %170 = add(%cifarresnetv11_stage2_batchnorm5_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %171 = sqrt(%170) /* ty=Tensor[(32), float32] */; + %172 = divide(1f /* ty=float32 */, %171) /* ty=Tensor[(32), float32] */; + %173 = multiply(%172, %cifarresnetv11_stage2_batchnorm5_gamma) /* ty=Tensor[(32), float32] */; + %174 = nn.conv2d(%169, %cifarresnetv11_stage2_conv5_weight, padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %175 = expand_dims(%173, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %176 = negative(%cifarresnetv11_stage2_batchnorm5_running_mean) /* ty=Tensor[(32), float32] */; + %177 = multiply(%176, %173) /* ty=Tensor[(32), float32] */; + %178 = add(%177, %cifarresnetv11_stage2_batchnorm5_beta) /* ty=Tensor[(32), float32] */; + %179 = multiply(%174, %175) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %180 = expand_dims(%178, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %181 = add(%179, %180) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %182 = nn.relu(%181) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %183 = add(%cifarresnetv11_stage2_batchnorm6_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(32), float32] */; + %184 = sqrt(%183) /* ty=Tensor[(32), float32] */; + %185 = divide(1f /* ty=float32 */, %184) /* ty=Tensor[(32), float32] */; + %186 = multiply(%185, %cifarresnetv11_stage2_batchnorm6_gamma) /* ty=Tensor[(32), float32] */; + %187 = nn.conv2d(%182, %cifarresnetv11_stage2_conv6_weight, padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %188 = expand_dims(%186, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %189 = negative(%cifarresnetv11_stage2_batchnorm6_running_mean) /* ty=Tensor[(32), float32] */; + %190 = multiply(%189, %186) /* ty=Tensor[(32), float32] */; + %191 = add(%190, %cifarresnetv11_stage2_batchnorm6_beta) /* ty=Tensor[(32), float32] */; + %192 = multiply(%187, %188) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %193 = expand_dims(%191, axis=1, num_newaxis=2) /* ty=Tensor[(32, 1, 1), float32] */; + %194 = add(%192, %193) /* ty=Tensor[(1, 32, 16, 16), float32] */; + %195 = add(%169, %194) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %196 = nn.relu(%195) /* from_string */ /* ty=Tensor[(1, 32, 16, 16), float32] */; + %197 = add(%cifarresnetv11_stage3_batchnorm2_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %198 = sqrt(%197) /* ty=Tensor[(64), float32] */; + %199 = divide(1f /* ty=float32 */, %198) /* ty=Tensor[(64), float32] */; + %200 = multiply(%199, %cifarresnetv11_stage3_batchnorm2_gamma) /* ty=Tensor[(64), float32] */; + %201 = nn.conv2d(%196, %cifarresnetv11_stage3_conv2_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %202 = expand_dims(%200, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %203 = negative(%cifarresnetv11_stage3_batchnorm2_running_mean) /* ty=Tensor[(64), float32] */; + %204 = multiply(%203, %200) /* ty=Tensor[(64), float32] */; + %205 = add(%204, %cifarresnetv11_stage3_batchnorm2_beta) /* ty=Tensor[(64), float32] */; + %206 = multiply(%201, %202) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %207 = expand_dims(%205, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %208 = add(%cifarresnetv11_stage3_batchnorm0_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %209 = sqrt(%208) /* ty=Tensor[(64), float32] */; + %210 = divide(1f /* ty=float32 */, %209) /* ty=Tensor[(64), float32] */; + %211 = multiply(%210, %cifarresnetv11_stage3_batchnorm0_gamma) /* ty=Tensor[(64), float32] */; + %212 = nn.conv2d(%196, %cifarresnetv11_stage3_conv0_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %213 = expand_dims(%211, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %214 = negative(%cifarresnetv11_stage3_batchnorm0_running_mean) /* ty=Tensor[(64), float32] */; + %215 = multiply(%214, %211) /* ty=Tensor[(64), float32] */; + %216 = add(%215, %cifarresnetv11_stage3_batchnorm0_beta) /* ty=Tensor[(64), float32] */; + %217 = multiply(%212, %213) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %218 = expand_dims(%216, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %219 = add(%217, %218) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %220 = nn.relu(%219) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %221 = add(%cifarresnetv11_stage3_batchnorm1_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %222 = sqrt(%221) /* ty=Tensor[(64), float32] */; + %223 = divide(1f /* ty=float32 */, %222) /* ty=Tensor[(64), float32] */; + %224 = multiply(%223, %cifarresnetv11_stage3_batchnorm1_gamma) /* ty=Tensor[(64), float32] */; + %225 = nn.conv2d(%220, %cifarresnetv11_stage3_conv1_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %226 = expand_dims(%224, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %227 = negative(%cifarresnetv11_stage3_batchnorm1_running_mean) /* ty=Tensor[(64), float32] */; + %228 = multiply(%227, %224) /* ty=Tensor[(64), float32] */; + %229 = add(%228, %cifarresnetv11_stage3_batchnorm1_beta) /* ty=Tensor[(64), float32] */; + %230 = multiply(%225, %226) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %231 = expand_dims(%229, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %232 = add(%206, %207) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %233 = add(%230, %231) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %234 = add(%232, %233) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %235 = nn.relu(%234) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %236 = add(%cifarresnetv11_stage3_batchnorm3_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %237 = sqrt(%236) /* ty=Tensor[(64), float32] */; + %238 = divide(1f /* ty=float32 */, %237) /* ty=Tensor[(64), float32] */; + %239 = multiply(%238, %cifarresnetv11_stage3_batchnorm3_gamma) /* ty=Tensor[(64), float32] */; + %240 = nn.conv2d(%235, %cifarresnetv11_stage3_conv3_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %241 = expand_dims(%239, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %242 = negative(%cifarresnetv11_stage3_batchnorm3_running_mean) /* ty=Tensor[(64), float32] */; + %243 = multiply(%242, %239) /* ty=Tensor[(64), float32] */; + %244 = add(%243, %cifarresnetv11_stage3_batchnorm3_beta) /* ty=Tensor[(64), float32] */; + %245 = multiply(%240, %241) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %246 = expand_dims(%244, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %247 = add(%245, %246) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %248 = nn.relu(%247) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %249 = add(%cifarresnetv11_stage3_batchnorm4_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %250 = sqrt(%249) /* ty=Tensor[(64), float32] */; + %251 = divide(1f /* ty=float32 */, %250) /* ty=Tensor[(64), float32] */; + %252 = multiply(%251, %cifarresnetv11_stage3_batchnorm4_gamma) /* ty=Tensor[(64), float32] */; + %253 = nn.conv2d(%248, %cifarresnetv11_stage3_conv4_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %254 = expand_dims(%252, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %255 = negative(%cifarresnetv11_stage3_batchnorm4_running_mean) /* ty=Tensor[(64), float32] */; + %256 = multiply(%255, %252) /* ty=Tensor[(64), float32] */; + %257 = add(%256, %cifarresnetv11_stage3_batchnorm4_beta) /* ty=Tensor[(64), float32] */; + %258 = multiply(%253, %254) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %259 = expand_dims(%257, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %260 = add(%258, %259) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %261 = add(%235, %260) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %262 = nn.relu(%261) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %263 = add(%cifarresnetv11_stage3_batchnorm5_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %264 = sqrt(%263) /* ty=Tensor[(64), float32] */; + %265 = divide(1f /* ty=float32 */, %264) /* ty=Tensor[(64), float32] */; + %266 = multiply(%265, %cifarresnetv11_stage3_batchnorm5_gamma) /* ty=Tensor[(64), float32] */; + %267 = nn.conv2d(%262, %cifarresnetv11_stage3_conv5_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %268 = expand_dims(%266, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %269 = negative(%cifarresnetv11_stage3_batchnorm5_running_mean) /* ty=Tensor[(64), float32] */; + %270 = multiply(%269, %266) /* ty=Tensor[(64), float32] */; + %271 = add(%270, %cifarresnetv11_stage3_batchnorm5_beta) /* ty=Tensor[(64), float32] */; + %272 = multiply(%267, %268) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %273 = expand_dims(%271, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %274 = add(%272, %273) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %275 = nn.relu(%274) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %276 = add(%cifarresnetv11_stage3_batchnorm6_running_var, 1e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %277 = sqrt(%276) /* ty=Tensor[(64), float32] */; + %278 = divide(1f /* ty=float32 */, %277) /* ty=Tensor[(64), float32] */; + %279 = multiply(%278, %cifarresnetv11_stage3_batchnorm6_gamma) /* ty=Tensor[(64), float32] */; + %280 = nn.conv2d(%275, %cifarresnetv11_stage3_conv6_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %281 = expand_dims(%279, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %282 = negative(%cifarresnetv11_stage3_batchnorm6_running_mean) /* ty=Tensor[(64), float32] */; + %283 = multiply(%282, %279) /* ty=Tensor[(64), float32] */; + %284 = add(%283, %cifarresnetv11_stage3_batchnorm6_beta) /* ty=Tensor[(64), float32] */; + %285 = multiply(%280, %281) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %286 = expand_dims(%284, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] */; + %287 = add(%285, %286) /* ty=Tensor[(1, 64, 8, 8), float32] */; + %288 = add(%262, %287) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %289 = nn.relu(%288) /* from_string */ /* ty=Tensor[(1, 64, 8, 8), float32] */; + %290 = nn.global_avg_pool2d(%289) /* from_string */ /* ty=Tensor[(1, 64, 1, 1), float32] */; + %291 = nn.batch_flatten(%290) /* from_string */ /* ty=Tensor[(1, 64), float32] */; + %292 = nn.dense(%291, %cifarresnetv11_dense0_weight, units=10) /* from_string */ /* ty=Tensor[(1, 10), float32] */; + nn.bias_add(%292, %cifarresnetv11_dense0_bias, axis=-1) /* from_string */ /* ty=Tensor[(1, 10), float32] */ +} diff --git a/tests/models/resnet50_simplifyinference_from_onnx.relay b/tests/models/resnet50_simplifyinference_from_onnx.relay new file mode 100644 index 0000000..14f76d1 --- /dev/null +++ b/tests/models/resnet50_simplifyinference_from_onnx.relay @@ -0,0 +1,199 @@ +#[version = "0.0.5"] + +def @main(%input_tensor_0: Tensor[(1, 3, 224, 224), float32] /* ty=Tensor[(1, 3, 224, 224), float32] span=from_string:8:18 */, %resnet_model_conv2d_44_kernel_read__251__cf__251_0: Tensor[(512, 1024, 1, 1), float32] /* ty=Tensor[(512, 1024, 1, 1), float32] span=from_string:154:26 */, %resnet_model_conv2d_43_Conv2D_bn_offset_0: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:163:28 */, %resnet_model_conv2d_43_kernel_read__250__cf__250_0: Tensor[(2048, 1024, 1, 1), float32] /* ty=Tensor[(2048, 1024, 1, 1), float32] span=from_string:161:26 */, %resnet_model_conv2d_42_Conv2D_bn_offset_0: Tensor[(1024), float32] /* ty=Tensor[(1024), float32] span=from_string:151:28 */, %resnet_model_conv2d_42_kernel_read__249__cf__249_0: Tensor[(1024, 256, 1, 1), float32] /* ty=Tensor[(1024, 256, 1, 1), float32] span=from_string:150:26 */, %resnet_model_conv2d_41_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:148:28 */, %resnet_model_conv2d_41_kernel_read__248__cf__248_0: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] span=from_string:147:26 */, %resnet_model_conv2d_40_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:145:28 */, %resnet_model_conv2d_40_kernel_read__247__cf__247_0: Tensor[(256, 1024, 1, 1), float32] /* ty=Tensor[(256, 1024, 1, 1), float32] span=from_string:144:26 */, %resnet_model_conv2d_39_Conv2D_bn_offset_0: Tensor[(1024), float32] /* ty=Tensor[(1024), float32] span=from_string:141:28 */, %resnet_model_conv2d_39_kernel_read__245__cf__245_0: Tensor[(1024, 256, 1, 1), float32] /* ty=Tensor[(1024, 256, 1, 1), float32] span=from_string:140:26 */, %resnet_model_conv2d_38_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:138:28 */, %resnet_model_conv2d_38_kernel_read__244__cf__244_0: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] span=from_string:137:26 */, %resnet_model_conv2d_37_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:135:28 */, %resnet_model_conv2d_37_kernel_read__243__cf__243_0: Tensor[(256, 1024, 1, 1), float32] /* ty=Tensor[(256, 1024, 1, 1), float32] span=from_string:134:26 */, %resnet_model_conv2d_36_Conv2D_bn_offset_0: Tensor[(1024), float32] /* ty=Tensor[(1024), float32] span=from_string:131:28 */, %resnet_model_conv2d_36_kernel_read__242__cf__242_0: Tensor[(1024, 256, 1, 1), float32] /* ty=Tensor[(1024, 256, 1, 1), float32] span=from_string:130:26 */, %resnet_model_conv2d_35_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:128:28 */, %resnet_model_conv2d_35_kernel_read__241__cf__241_0: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] span=from_string:127:26 */, %resnet_model_conv2d_34_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:125:28 */, %resnet_model_conv2d_34_kernel_read__240__cf__240_0: Tensor[(256, 1024, 1, 1), float32] /* ty=Tensor[(256, 1024, 1, 1), float32] span=from_string:124:26 */, %resnet_model_dense_kernel_read__266__cf__266_0: Tensor[(2048, 1001), float32] /* ty=Tensor[(2048, 1001), float32] span=from_string:189:20 */, %resnet_model_conv2d_33_Conv2D_bn_offset_0: Tensor[(1024), float32] /* ty=Tensor[(1024), float32] span=from_string:121:28 */, %resnet_model_dense_bias_read__265__cf__265_0: Tensor[(1001), float32] /* ty=Tensor[(1001), float32] span=from_string:191:20 */, %resnet_model_conv2d_kernel_read__212__cf__212_0: Tensor[(64, 3, 7, 7), float32] /* ty=Tensor[(64, 3, 7, 7), float32] span=from_string:8:37 */, %resnet_model_conv2d_33_kernel_read__239__cf__239_0: Tensor[(1024, 256, 1, 1), float32] /* ty=Tensor[(1024, 256, 1, 1), float32] span=from_string:120:26 */, %resnet_model_conv2d_21_Conv2D_bn_offset_0: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:83:26 */, %resnet_model_conv2d_21_kernel_read__226__cf__226_0: Tensor[(128, 512, 1, 1), float32] /* ty=Tensor[(128, 512, 1, 1), float32] span=from_string:82:24 */, %resnet_model_conv2d_20_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:79:26 */, %resnet_model_conv2d_20_kernel_read__225__cf__225_0: Tensor[(512, 128, 1, 1), float32] /* ty=Tensor[(512, 128, 1, 1), float32] span=from_string:78:24 */, %resnet_model_conv2d_19_Conv2D_bn_offset_0: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:76:26 */, %resnet_model_conv2d_19_kernel_read__223__cf__223_0: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] span=from_string:75:24 */, %resnet_model_conv2d_18_Conv2D_bn_offset_0: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:73:26 */, %resnet_model_conv2d_18_kernel_read__222__cf__222_0: Tensor[(128, 512, 1, 1), float32] /* ty=Tensor[(128, 512, 1, 1), float32] span=from_string:72:24 */, %resnet_model_conv2d_17_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:69:26 */, %resnet_model_conv2d_17_kernel_read__221__cf__221_0: Tensor[(512, 128, 1, 1), float32] /* ty=Tensor[(512, 128, 1, 1), float32] span=from_string:68:24 */, %resnet_model_conv2d_16_Conv2D_bn_offset_0: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:66:26 */, %resnet_model_conv2d_16_kernel_read__220__cf__220_0: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] span=from_string:65:24 */, %resnet_model_conv2d_15_Conv2D_bn_offset_0: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:63:26 */, %resnet_model_conv2d_15_kernel_read__219__cf__219_0: Tensor[(128, 512, 1, 1), float32] /* ty=Tensor[(128, 512, 1, 1), float32] span=from_string:62:24 */, %resnet_model_conv2d_14_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:58:26 */, %resnet_model_conv2d_14_kernel_read__218__cf__218_0: Tensor[(512, 128, 1, 1), float32] /* ty=Tensor[(512, 128, 1, 1), float32] span=from_string:56:24 */, %resnet_model_conv2d_13_Conv2D_bn_offset_0: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:54:26 */, %resnet_model_conv2d_13_kernel_read__217__cf__217_0: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] span=from_string:53:24 */, %resnet_model_conv2d_12_Conv2D_bn_offset_0: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:51:26 */, %resnet_model_conv2d_12_kernel_read__216__cf__216_0: Tensor[(128, 256, 1, 1), float32] /* ty=Tensor[(128, 256, 1, 1), float32] span=from_string:50:24 */, %resnet_model_conv2d_11_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:59:26 */, %reshape__269__270: Tensor[(4), int64] /* ty=Tensor[(4), int64] */, %resnet_model_conv2d_11_kernel_read__215__cf__215_0: Tensor[(512, 256, 1, 1), float32] /* ty=Tensor[(512, 256, 1, 1), float32] span=from_string:57:24 */, %resnet_model_conv2d_32_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:118:28 */, %resnet_model_conv2d_32_kernel_read__238__cf__238_0: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] span=from_string:117:26 */, %resnet_model_conv2d_31_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:115:28 */, %resnet_model_conv2d_31_kernel_read__237__cf__237_0: Tensor[(256, 1024, 1, 1), float32] /* ty=Tensor[(256, 1024, 1, 1), float32] span=from_string:114:26 */, %resnet_model_conv2d_30_Conv2D_bn_offset_0: Tensor[(1024), float32] /* ty=Tensor[(1024), float32] span=from_string:111:28 */, %resnet_model_conv2d_30_kernel_read__236__cf__236_0: Tensor[(1024, 256, 1, 1), float32] /* ty=Tensor[(1024, 256, 1, 1), float32] span=from_string:110:26 */, %resnet_model_conv2d_29_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:108:28 */, %resnet_model_conv2d_29_kernel_read__234__cf__234_0: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] span=from_string:107:26 */, %resnet_model_conv2d_28_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:105:28 */, %resnet_model_conv2d_28_kernel_read__233__cf__233_0: Tensor[(256, 1024, 1, 1), float32] /* ty=Tensor[(256, 1024, 1, 1), float32] span=from_string:104:25 */, %resnet_model_conv2d_27_Conv2D_bn_offset_0: Tensor[(1024), float32] /* ty=Tensor[(1024), float32] span=from_string:100:26 */, %resnet_model_conv2d_27_kernel_read__232__cf__232_0: Tensor[(1024, 256, 1, 1), float32] /* ty=Tensor[(1024, 256, 1, 1), float32] span=from_string:98:24 */, %resnet_model_conv2d_26_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:96:26 */, %resnet_model_conv2d_26_kernel_read__231__cf__231_0: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] span=from_string:95:24 */, %resnet_model_conv2d_25_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:93:26 */, %resnet_model_conv2d_25_kernel_read__230__cf__230_0: Tensor[(256, 512, 1, 1), float32] /* ty=Tensor[(256, 512, 1, 1), float32] span=from_string:92:24 */, %resnet_model_conv2d_24_Conv2D_bn_offset_0: Tensor[(1024), float32] /* ty=Tensor[(1024), float32] span=from_string:101:26 */, %resnet_model_conv2d_24_kernel_read__229__cf__229_0: Tensor[(1024, 512, 1, 1), float32] /* ty=Tensor[(1024, 512, 1, 1), float32] span=from_string:99:24 */, %resnet_model_conv2d_23_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:89:26 */, %resnet_model_conv2d_23_kernel_read__228__cf__228_0: Tensor[(512, 128, 1, 1), float32] /* ty=Tensor[(512, 128, 1, 1), float32] span=from_string:88:24 */, %resnet_model_conv2d_22_Conv2D_bn_offset_0: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:86:26 */, %resnet_model_conv2d_22_kernel_read__227__cf__227_0: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] span=from_string:85:24 */, %resnet_model_batch_normalization_moving_variance_read__3__cf__3_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:4:12 */, %resnet_model_conv2d_10_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:47:26 */, %resnet_model_batch_normalization_moving_mean_read__2__cf__2_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:10:17 */, %resnet_model_conv2d_10_kernel_read__214__cf__214_0: Tensor[(256, 64, 1, 1), float32] /* ty=Tensor[(256, 64, 1, 1), float32] span=from_string:46:24 */, %resnet_model_batch_normalization_gamma_read__1__cf__1_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:7:22 */, %resnet_model_conv2d_9_Conv2D_bn_offset_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:44:26 */, %resnet_model_batch_normalization_beta_read__0__cf__0_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:12:16 */, %resnet_model_conv2d_9_kernel_read__264__cf__264_0: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] span=from_string:43:24 */, %resnet_model_conv2d_8_Conv2D_bn_offset_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:41:26 */, %resnet_model_conv2d_8_kernel_read__263__cf__263_0: Tensor[(64, 256, 1, 1), float32] /* ty=Tensor[(64, 256, 1, 1), float32] span=from_string:40:24 */, %resnet_model_conv2d_7_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:37:26 */, %resnet_model_conv2d_52_Conv2D_bn_offset_0: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:183:28 */, %resnet_model_conv2d_7_kernel_read__262__cf__262_0: Tensor[(256, 64, 1, 1), float32] /* ty=Tensor[(256, 64, 1, 1), float32] span=from_string:36:24 */, %resnet_model_conv2d_52_kernel_read__260__cf__260_0: Tensor[(2048, 512, 1, 1), float32] /* ty=Tensor[(2048, 512, 1, 1), float32] span=from_string:182:26 */, %resnet_model_conv2d_6_Conv2D_bn_offset_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:34:26 */, %resnet_model_conv2d_51_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:180:28 */, %resnet_model_conv2d_6_kernel_read__261__cf__261_0: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] span=from_string:33:24 */, %resnet_model_conv2d_51_kernel_read__259__cf__259_0: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] span=from_string:179:26 */, %resnet_model_conv2d_5_Conv2D_bn_offset_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:31:26 */, %resnet_model_conv2d_50_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:177:28 */, %resnet_model_conv2d_5_kernel_read__257__cf__257_0: Tensor[(64, 256, 1, 1), float32] /* ty=Tensor[(64, 256, 1, 1), float32] span=from_string:30:24 */, %resnet_model_conv2d_50_kernel_read__258__cf__258_0: Tensor[(512, 2048, 1, 1), float32] /* ty=Tensor[(512, 2048, 1, 1), float32] span=from_string:176:26 */, %resnet_model_conv2d_4_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:26:26 */, %resnet_model_conv2d_49_Conv2D_bn_offset_0: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:173:28 */, %resnet_model_conv2d_4_kernel_read__246__cf__246_0: Tensor[(256, 64, 1, 1), float32] /* ty=Tensor[(256, 64, 1, 1), float32] span=from_string:24:24 */, %resnet_model_conv2d_49_kernel_read__256__cf__256_0: Tensor[(2048, 512, 1, 1), float32] /* ty=Tensor[(2048, 512, 1, 1), float32] span=from_string:172:26 */, %resnet_model_conv2d_3_Conv2D_bn_offset_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:22:26 */, %resnet_model_conv2d_48_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:170:28 */, %resnet_model_conv2d_3_kernel_read__235__cf__235_0: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] span=from_string:21:24 */, %resnet_model_conv2d_48_kernel_read__255__cf__255_0: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] span=from_string:169:26 */, %resnet_model_conv2d_2_Conv2D_bn_offset_0: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:19:26 */, %resnet_model_conv2d_47_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:167:28 */, %resnet_model_conv2d_2_kernel_read__224__cf__224_0: Tensor[(64, 64, 1, 1), float32] /* ty=Tensor[(64, 64, 1, 1), float32] span=from_string:18:24 */, %resnet_model_conv2d_47_kernel_read__254__cf__254_0: Tensor[(512, 2048, 1, 1), float32] /* ty=Tensor[(512, 2048, 1, 1), float32] span=from_string:166:26 */, %resnet_model_conv2d_1_Conv2D_bn_offset_0: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:27:26 */, %resnet_model_conv2d_46_Conv2D_bn_offset_0: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:162:28 */, %resnet_model_conv2d_1_kernel_read__213__cf__213_0: Tensor[(256, 64, 1, 1), float32] /* ty=Tensor[(256, 64, 1, 1), float32] span=from_string:25:24 */, %resnet_model_conv2d_46_kernel_read__253__cf__253_0: Tensor[(2048, 512, 1, 1), float32] /* ty=Tensor[(2048, 512, 1, 1), float32] span=from_string:160:26 */, %resnet_model_conv2d_45_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:158:28 */, %resnet_model_conv2d_45_kernel_read__252__cf__252_0: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] span=from_string:157:26 */, %resnet_model_conv2d_44_Conv2D_bn_offset_0: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:155:28 */) -> (Tensor[(1), int64], Tensor[(1, 1001), float32]) { + %0 = add(%resnet_model_batch_normalization_moving_variance_read__3__cf__3_0, 1.001e-05f /* ty=float32 span=from_string:4:90 */) /* ty=Tensor[(64), float32] span=from_string:5:13 */; + %1 = sqrt(%0) /* ty=Tensor[(64), float32] span=from_string:6:36 */; + %2 = divide(1f /* ty=float32 span=from_string:6:17 */, %1) /* ty=Tensor[(64), float32] span=from_string:7:18 */; + %3 = multiply(%2, %resnet_model_batch_normalization_gamma_read__1__cf__1_0) /* ty=Tensor[(64), float32] span=from_string:11:22 */; + %4 = nn.conv2d(%input_tensor_0, %resnet_model_conv2d_kernel_read__212__cf__212_0, strides=[2, 2], padding=[3, 3, 3, 3], channels=64, kernel_size=[7, 7]) /* ty=Tensor[(1, 64, 112, 112), float32] span=from_string:13:18 */; + %5 = expand_dims(%3, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:13:22 */; + %6 = negative(%resnet_model_batch_normalization_moving_mean_read__2__cf__2_0) /* ty=Tensor[(64), float32] span=from_string:11:18 */; + %7 = multiply(%6, %3) /* ty=Tensor[(64), float32] span=from_string:12:12 */; + %8 = add(%7, %resnet_model_batch_normalization_beta_read__0__cf__0_0) /* ty=Tensor[(64), float32] span=from_string:14:21 */; + %9 = multiply(%4, %5) /* ty=Tensor[(1, 64, 112, 112), float32] span=from_string:15:13 */; + %10 = expand_dims(%8, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:15:17 */; + %11 = add(%9, %10) /* ty=Tensor[(1, 64, 112, 112), float32] span=from_string:16:17 */; + %12 = nn.relu(%11) /* ty=Tensor[(1, 64, 112, 112), float32] span=from_string:17:24 */; + %13 = nn.max_pool2d(%12, pool_size=[3, 3], strides=[2, 2], padding=[0, 0, 1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:25:19 */; + %14 = nn.conv2d(%13, %resnet_model_conv2d_2_kernel_read__224__cf__224_0, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:19:21 */; + %15 = nn.bias_add(%14, %resnet_model_conv2d_2_Conv2D_bn_offset_0) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:20:17 */; + %16 = nn.relu(%15) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:21:19 */; + %17 = nn.conv2d(%16, %resnet_model_conv2d_3_kernel_read__235__cf__235_0, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:22:21 */; + %18 = nn.bias_add(%17, %resnet_model_conv2d_3_Conv2D_bn_offset_0) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:23:17 */; + %19 = nn.relu(%18) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:24:19 */; + %20 = nn.conv2d(%19, %resnet_model_conv2d_4_kernel_read__246__cf__246_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:26:21 */; + %21 = nn.conv2d(%13, %resnet_model_conv2d_1_kernel_read__213__cf__213_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:27:21 */; + %22 = nn.bias_add(%20, %resnet_model_conv2d_4_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:28:13 */; + %23 = nn.bias_add(%21, %resnet_model_conv2d_1_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:28:18 */; + %24 = add(%22, %23) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:29:17 */; + %25 = nn.relu(%24) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:38:18 */; + %26 = nn.conv2d(%25, %resnet_model_conv2d_5_kernel_read__257__cf__257_0, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:31:21 */; + %27 = nn.bias_add(%26, %resnet_model_conv2d_5_Conv2D_bn_offset_0) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:32:17 */; + %28 = nn.relu(%27) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:33:19 */; + %29 = nn.conv2d(%28, %resnet_model_conv2d_6_kernel_read__261__cf__261_0, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:34:21 */; + %30 = nn.bias_add(%29, %resnet_model_conv2d_6_Conv2D_bn_offset_0) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:35:17 */; + %31 = nn.relu(%30) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:36:19 */; + %32 = nn.conv2d(%31, %resnet_model_conv2d_7_kernel_read__262__cf__262_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:37:21 */; + %33 = nn.bias_add(%32, %resnet_model_conv2d_7_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:38:13 */; + %34 = add(%33, %25) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:39:17 */; + %35 = nn.relu(%34) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:48:18 */; + %36 = nn.conv2d(%35, %resnet_model_conv2d_8_kernel_read__263__cf__263_0, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:41:21 */; + %37 = nn.bias_add(%36, %resnet_model_conv2d_8_Conv2D_bn_offset_0) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:42:17 */; + %38 = nn.relu(%37) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:43:19 */; + %39 = nn.conv2d(%38, %resnet_model_conv2d_9_kernel_read__264__cf__264_0, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:44:21 */; + %40 = nn.bias_add(%39, %resnet_model_conv2d_9_Conv2D_bn_offset_0) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:45:17 */; + %41 = nn.relu(%40) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:46:19 */; + %42 = nn.conv2d(%41, %resnet_model_conv2d_10_kernel_read__214__cf__214_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:47:21 */; + %43 = nn.bias_add(%42, %resnet_model_conv2d_10_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:48:13 */; + %44 = add(%43, %35) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:49:17 */; + %45 = nn.relu(%44) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:57:19 */; + %46 = nn.conv2d(%45, %resnet_model_conv2d_12_kernel_read__216__cf__216_0, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 56, 56), float32] span=from_string:51:21 */; + %47 = nn.bias_add(%46, %resnet_model_conv2d_12_Conv2D_bn_offset_0) /* ty=Tensor[(1, 128, 56, 56), float32] span=from_string:52:17 */; + %48 = nn.relu(%47) /* ty=Tensor[(1, 128, 56, 56), float32] span=from_string:53:19 */; + %49 = nn.conv2d(%48, %resnet_model_conv2d_13_kernel_read__217__cf__217_0, strides=[2, 2], padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:54:21 */; + %50 = nn.bias_add(%49, %resnet_model_conv2d_13_Conv2D_bn_offset_0) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:55:17 */; + %51 = nn.relu(%50) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:56:19 */; + %52 = nn.conv2d(%51, %resnet_model_conv2d_14_kernel_read__218__cf__218_0, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:58:21 */; + %53 = nn.conv2d(%45, %resnet_model_conv2d_11_kernel_read__215__cf__215_0, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:59:21 */; + %54 = nn.bias_add(%52, %resnet_model_conv2d_14_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:60:13 */; + %55 = nn.bias_add(%53, %resnet_model_conv2d_11_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:60:18 */; + %56 = add(%54, %55) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:61:17 */; + %57 = nn.relu(%56) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:70:18 */; + %58 = nn.conv2d(%57, %resnet_model_conv2d_15_kernel_read__219__cf__219_0, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:63:21 */; + %59 = nn.bias_add(%58, %resnet_model_conv2d_15_Conv2D_bn_offset_0) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:64:17 */; + %60 = nn.relu(%59) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:65:19 */; + %61 = nn.conv2d(%60, %resnet_model_conv2d_16_kernel_read__220__cf__220_0, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:66:21 */; + %62 = nn.bias_add(%61, %resnet_model_conv2d_16_Conv2D_bn_offset_0) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:67:17 */; + %63 = nn.relu(%62) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:68:19 */; + %64 = nn.conv2d(%63, %resnet_model_conv2d_17_kernel_read__221__cf__221_0, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:69:21 */; + %65 = nn.bias_add(%64, %resnet_model_conv2d_17_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:70:13 */; + %66 = add(%65, %57) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:71:17 */; + %67 = nn.relu(%66) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:80:18 */; + %68 = nn.conv2d(%67, %resnet_model_conv2d_18_kernel_read__222__cf__222_0, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:73:21 */; + %69 = nn.bias_add(%68, %resnet_model_conv2d_18_Conv2D_bn_offset_0) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:74:17 */; + %70 = nn.relu(%69) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:75:19 */; + %71 = nn.conv2d(%70, %resnet_model_conv2d_19_kernel_read__223__cf__223_0, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:76:21 */; + %72 = nn.bias_add(%71, %resnet_model_conv2d_19_Conv2D_bn_offset_0) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:77:17 */; + %73 = nn.relu(%72) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:78:19 */; + %74 = nn.conv2d(%73, %resnet_model_conv2d_20_kernel_read__225__cf__225_0, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:79:21 */; + %75 = nn.bias_add(%74, %resnet_model_conv2d_20_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:80:13 */; + %76 = add(%75, %67) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:81:17 */; + %77 = nn.relu(%76) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:90:18 */; + %78 = nn.conv2d(%77, %resnet_model_conv2d_21_kernel_read__226__cf__226_0, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:83:21 */; + %79 = nn.bias_add(%78, %resnet_model_conv2d_21_Conv2D_bn_offset_0) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:84:17 */; + %80 = nn.relu(%79) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:85:19 */; + %81 = nn.conv2d(%80, %resnet_model_conv2d_22_kernel_read__227__cf__227_0, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:86:21 */; + %82 = nn.bias_add(%81, %resnet_model_conv2d_22_Conv2D_bn_offset_0) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:87:17 */; + %83 = nn.relu(%82) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:88:19 */; + %84 = nn.conv2d(%83, %resnet_model_conv2d_23_kernel_read__228__cf__228_0, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:89:21 */; + %85 = nn.bias_add(%84, %resnet_model_conv2d_23_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:90:13 */; + %86 = add(%85, %77) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:91:17 */; + %87 = nn.relu(%86) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:99:19 */; + %88 = nn.conv2d(%87, %resnet_model_conv2d_25_kernel_read__230__cf__230_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 28, 28), float32] span=from_string:93:21 */; + %89 = nn.bias_add(%88, %resnet_model_conv2d_25_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 28, 28), float32] span=from_string:94:17 */; + %90 = nn.relu(%89) /* ty=Tensor[(1, 256, 28, 28), float32] span=from_string:95:19 */; + %91 = nn.conv2d(%90, %resnet_model_conv2d_26_kernel_read__231__cf__231_0, strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:96:21 */; + %92 = nn.bias_add(%91, %resnet_model_conv2d_26_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:97:17 */; + %93 = nn.relu(%92) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:98:19 */; + %94 = nn.conv2d(%93, %resnet_model_conv2d_27_kernel_read__232__cf__232_0, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:100:21 */; + %95 = nn.conv2d(%87, %resnet_model_conv2d_24_kernel_read__229__cf__229_0, strides=[2, 2], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:101:21 */; + %96 = nn.bias_add(%94, %resnet_model_conv2d_27_Conv2D_bn_offset_0) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:102:13 */; + %97 = nn.bias_add(%95, %resnet_model_conv2d_24_Conv2D_bn_offset_0) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:102:18 */; + %98 = add(%96, %97) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:103:17 */; + %99 = nn.relu(%98) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:112:20 */; + %100 = nn.conv2d(%99, %resnet_model_conv2d_28_kernel_read__233__cf__233_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:105:22 */; + %101 = nn.bias_add(%100, %resnet_model_conv2d_28_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:106:18 */; + %102 = nn.relu(%101) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:107:20 */; + %103 = nn.conv2d(%102, %resnet_model_conv2d_29_kernel_read__234__cf__234_0, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:108:22 */; + %104 = nn.bias_add(%103, %resnet_model_conv2d_29_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:109:18 */; + %105 = nn.relu(%104) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:110:20 */; + %106 = nn.conv2d(%105, %resnet_model_conv2d_30_kernel_read__236__cf__236_0, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:111:22 */; + %107 = nn.bias_add(%106, %resnet_model_conv2d_30_Conv2D_bn_offset_0) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:112:14 */; + %108 = add(%107, %99) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:113:18 */; + %109 = nn.relu(%108) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:122:20 */; + %110 = nn.conv2d(%109, %resnet_model_conv2d_31_kernel_read__237__cf__237_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:115:22 */; + %111 = nn.bias_add(%110, %resnet_model_conv2d_31_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:116:18 */; + %112 = nn.relu(%111) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:117:20 */; + %113 = nn.conv2d(%112, %resnet_model_conv2d_32_kernel_read__238__cf__238_0, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:118:22 */; + %114 = nn.bias_add(%113, %resnet_model_conv2d_32_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:119:18 */; + %115 = nn.relu(%114) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:120:20 */; + %116 = nn.conv2d(%115, %resnet_model_conv2d_33_kernel_read__239__cf__239_0, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:121:22 */; + %117 = nn.bias_add(%116, %resnet_model_conv2d_33_Conv2D_bn_offset_0) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:122:14 */; + %118 = add(%117, %109) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:123:18 */; + %119 = nn.relu(%118) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:132:20 */; + %120 = nn.conv2d(%119, %resnet_model_conv2d_34_kernel_read__240__cf__240_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:125:22 */; + %121 = nn.bias_add(%120, %resnet_model_conv2d_34_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:126:18 */; + %122 = nn.relu(%121) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:127:20 */; + %123 = nn.conv2d(%122, %resnet_model_conv2d_35_kernel_read__241__cf__241_0, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:128:22 */; + %124 = nn.bias_add(%123, %resnet_model_conv2d_35_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:129:18 */; + %125 = nn.relu(%124) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:130:20 */; + %126 = nn.conv2d(%125, %resnet_model_conv2d_36_kernel_read__242__cf__242_0, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:131:22 */; + %127 = nn.bias_add(%126, %resnet_model_conv2d_36_Conv2D_bn_offset_0) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:132:14 */; + %128 = add(%127, %119) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:133:18 */; + %129 = nn.relu(%128) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:142:20 */; + %130 = nn.conv2d(%129, %resnet_model_conv2d_37_kernel_read__243__cf__243_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:135:22 */; + %131 = nn.bias_add(%130, %resnet_model_conv2d_37_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:136:18 */; + %132 = nn.relu(%131) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:137:20 */; + %133 = nn.conv2d(%132, %resnet_model_conv2d_38_kernel_read__244__cf__244_0, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:138:22 */; + %134 = nn.bias_add(%133, %resnet_model_conv2d_38_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:139:18 */; + %135 = nn.relu(%134) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:140:20 */; + %136 = nn.conv2d(%135, %resnet_model_conv2d_39_kernel_read__245__cf__245_0, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:141:22 */; + %137 = nn.bias_add(%136, %resnet_model_conv2d_39_Conv2D_bn_offset_0) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:142:14 */; + %138 = add(%137, %129) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:143:18 */; + %139 = nn.relu(%138) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:152:20 */; + %140 = nn.conv2d(%139, %resnet_model_conv2d_40_kernel_read__247__cf__247_0, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:145:22 */; + %141 = nn.bias_add(%140, %resnet_model_conv2d_40_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:146:18 */; + %142 = nn.relu(%141) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:147:20 */; + %143 = nn.conv2d(%142, %resnet_model_conv2d_41_kernel_read__248__cf__248_0, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:148:22 */; + %144 = nn.bias_add(%143, %resnet_model_conv2d_41_Conv2D_bn_offset_0) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:149:18 */; + %145 = nn.relu(%144) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:150:20 */; + %146 = nn.conv2d(%145, %resnet_model_conv2d_42_kernel_read__249__cf__249_0, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:151:22 */; + %147 = nn.bias_add(%146, %resnet_model_conv2d_42_Conv2D_bn_offset_0) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:152:14 */; + %148 = add(%147, %139) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:153:18 */; + %149 = nn.relu(%148) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:161:20 */; + %150 = nn.conv2d(%149, %resnet_model_conv2d_44_kernel_read__251__cf__251_0, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 14, 14), float32] span=from_string:155:22 */; + %151 = nn.bias_add(%150, %resnet_model_conv2d_44_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 14, 14), float32] span=from_string:156:18 */; + %152 = nn.relu(%151) /* ty=Tensor[(1, 512, 14, 14), float32] span=from_string:157:20 */; + %153 = nn.conv2d(%152, %resnet_model_conv2d_45_kernel_read__252__cf__252_0, strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:158:22 */; + %154 = nn.bias_add(%153, %resnet_model_conv2d_45_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:159:18 */; + %155 = nn.relu(%154) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:160:20 */; + %156 = nn.conv2d(%155, %resnet_model_conv2d_46_kernel_read__253__cf__253_0, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:162:22 */; + %157 = nn.conv2d(%149, %resnet_model_conv2d_43_kernel_read__250__cf__250_0, strides=[2, 2], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:163:22 */; + %158 = nn.bias_add(%156, %resnet_model_conv2d_46_Conv2D_bn_offset_0) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:164:14 */; + %159 = nn.bias_add(%157, %resnet_model_conv2d_43_Conv2D_bn_offset_0) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:164:20 */; + %160 = add(%158, %159) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:165:18 */; + %161 = nn.relu(%160) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:174:20 */; + %162 = nn.conv2d(%161, %resnet_model_conv2d_47_kernel_read__254__cf__254_0, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:167:22 */; + %163 = nn.bias_add(%162, %resnet_model_conv2d_47_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:168:18 */; + %164 = nn.relu(%163) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:169:20 */; + %165 = nn.conv2d(%164, %resnet_model_conv2d_48_kernel_read__255__cf__255_0, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:170:22 */; + %166 = nn.bias_add(%165, %resnet_model_conv2d_48_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:171:18 */; + %167 = nn.relu(%166) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:172:20 */; + %168 = nn.conv2d(%167, %resnet_model_conv2d_49_kernel_read__256__cf__256_0, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:173:22 */; + %169 = nn.bias_add(%168, %resnet_model_conv2d_49_Conv2D_bn_offset_0) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:174:14 */; + %170 = add(%169, %161) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:175:18 */; + %171 = nn.relu(%170) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:184:20 */; + %172 = nn.conv2d(%171, %resnet_model_conv2d_50_kernel_read__258__cf__258_0, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:177:22 */; + %173 = nn.bias_add(%172, %resnet_model_conv2d_50_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:178:18 */; + %174 = nn.relu(%173) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:179:20 */; + %175 = nn.conv2d(%174, %resnet_model_conv2d_51_kernel_read__259__cf__259_0, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:180:22 */; + %176 = nn.bias_add(%175, %resnet_model_conv2d_51_Conv2D_bn_offset_0) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:181:18 */; + %177 = nn.relu(%176) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:182:20 */; + %178 = nn.conv2d(%177, %resnet_model_conv2d_52_kernel_read__260__cf__260_0, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:183:22 */; + %179 = nn.bias_add(%178, %resnet_model_conv2d_52_Conv2D_bn_offset_0) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:184:14 */; + %180 = add(%179, %171) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:185:18 */; + %181 = nn.relu(%180) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:186:17 */; + %182 = mean(%181, axis=[2, 3], keepdims=True) /* ty=Tensor[(1, 2048, 1, 1), float32] span=from_string:187:18 */; + %183 = reshape(%182, newshape=[-1, 1, 1, 2048]) /* ty=Tensor[(1, 1, 1, 2048), float32] span=from_string:188:18 */; + %184 = squeeze(%183, axis=[1, 2]) /* ty=Tensor[(1, 2048), float32] span=from_string:190:19 */; + %185 = transpose(%resnet_model_dense_kernel_read__266__cf__266_0, axes=[1, 0]) /* ty=Tensor[(1001, 2048), float32] span=from_string:190:25 */; + %186 = nn.dense(%184, %185, units=None, out_dtype="float32") /* ty=Tensor[(1, 1001), float32] span=from_string:191:14 */; + %187 = add(%186, %resnet_model_dense_bias_read__265__cf__265_0) /* ty=Tensor[(1, 1001), float32] span=from_string:192:15 */; + %188 = copy(%187) /* ty=Tensor[(1, 1001), float32] span=from_string:195:21 */; + %189 = argmax(%188, axis=[1]) /* ty=Tensor[(1), int32] span=from_string:194:15 */; + %190 = cast(%189, dtype="int64") /* ty=Tensor[(1), int64] span=from_string:196:15 */; + %191 = nn.softmax(%188, axis=1) /* ty=Tensor[(1, 1001), float32] span=from_string:197:15 */; + %192 = copy(%190) /* ty=Tensor[(1), int64] span=from_string:198:4 */; + %193 = copy(%191) /* ty=Tensor[(1, 1001), float32] span=from_string:198:10 */; + (%192, %193) /* ty=(Tensor[(1), int64], Tensor[(1, 1001), float32]) span=from_string:4:3 */ +} \ No newline at end of file diff --git a/tests/models/resnet50_simplifyinference_from_pytorch.relay b/tests/models/resnet50_simplifyinference_from_pytorch.relay new file mode 100644 index 0000000..d610a6b --- /dev/null +++ b/tests/models/resnet50_simplifyinference_from_pytorch.relay @@ -0,0 +1,713 @@ +#[version = "0.0.5"] + +def @main(%image: Tensor[(1, 3, 224, 224), float32] /* ty=Tensor[(1, 3, 224, 224), float32] span=from_string:8:18 */, %conv1_weight: Tensor[(64, 3, 7, 7), float32] /* ty=Tensor[(64, 3, 7, 7), float32] span=from_string:8:27 */, %bn1_weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:7:22 */, %bn1_bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:12:16 */, %bn1_running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:10:17 */, %bn1_running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:4:12 */, %layer1_0_conv1_weight: Tensor[(64, 64, 1, 1), float32] /* ty=Tensor[(64, 64, 1, 1), float32] span=from_string:22:24 */, %layer1_0_bn1_weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:21:24 */, %layer1_0_bn1_bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:26:18 */, %layer1_0_bn1_running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:24:18 */, %layer1_0_bn1_running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:18:13 */, %layer1_0_conv2_weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] span=from_string:35:24 */, %layer1_0_bn2_weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:34:24 */, %layer1_0_bn2_bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:39:18 */, %layer1_0_bn2_running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:37:18 */, %layer1_0_bn2_running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:31:13 */, %layer1_0_conv3_weight: Tensor[(256, 64, 1, 1), float32] /* ty=Tensor[(256, 64, 1, 1), float32] span=from_string:48:24 */, %layer1_0_bn3_weight: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:47:24 */, %layer1_0_bn3_bias: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:52:18 */, %layer1_0_bn3_running_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:50:18 */, %layer1_0_bn3_running_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:44:13 */, %layer1_0_downsample_0_weight: Tensor[(256, 64, 1, 1), float32] /* ty=Tensor[(256, 64, 1, 1), float32] span=from_string:59:24 */, %layer1_0_downsample_1_weight: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:58:24 */, %layer1_0_downsample_1_bias: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:63:18 */, %layer1_0_downsample_1_running_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:61:18 */, %layer1_0_downsample_1_running_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:55:13 */, %layer1_1_conv1_weight: Tensor[(64, 256, 1, 1), float32] /* ty=Tensor[(64, 256, 1, 1), float32] span=from_string:74:24 */, %layer1_1_bn1_weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:73:24 */, %layer1_1_bn1_bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:78:18 */, %layer1_1_bn1_running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:76:18 */, %layer1_1_bn1_running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:70:13 */, %layer1_1_conv2_weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] span=from_string:87:24 */, %layer1_1_bn2_weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:86:24 */, %layer1_1_bn2_bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:91:18 */, %layer1_1_bn2_running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:89:18 */, %layer1_1_bn2_running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:83:13 */, %layer1_1_conv3_weight: Tensor[(256, 64, 1, 1), float32] /* ty=Tensor[(256, 64, 1, 1), float32] span=from_string:100:24 */, %layer1_1_bn3_weight: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:99:24 */, %layer1_1_bn3_bias: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:104:19 */, %layer1_1_bn3_running_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:102:18 */, %layer1_1_bn3_running_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:96:13 */, %layer1_2_conv1_weight: Tensor[(64, 256, 1, 1), float32] /* ty=Tensor[(64, 256, 1, 1), float32] span=from_string:114:26 */, %layer1_2_bn1_weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:113:26 */, %layer1_2_bn1_bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:118:20 */, %layer1_2_bn1_running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:116:19 */, %layer1_2_bn1_running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:110:14 */, %layer1_2_conv2_weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] span=from_string:127:26 */, %layer1_2_bn2_weight: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:126:26 */, %layer1_2_bn2_bias: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:131:20 */, %layer1_2_bn2_running_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:129:19 */, %layer1_2_bn2_running_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] span=from_string:123:14 */, %layer1_2_conv3_weight: Tensor[(256, 64, 1, 1), float32] /* ty=Tensor[(256, 64, 1, 1), float32] span=from_string:140:26 */, %layer1_2_bn3_weight: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:139:26 */, %layer1_2_bn3_bias: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:144:20 */, %layer1_2_bn3_running_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:142:19 */, %layer1_2_bn3_running_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] span=from_string:136:14 */, %layer2_0_conv1_weight: Tensor[(128, 256, 1, 1), float32] /* ty=Tensor[(128, 256, 1, 1), float32] span=from_string:154:26 */, %layer2_0_bn1_weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:153:26 */, %layer2_0_bn1_bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:158:20 */, %layer2_0_bn1_running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:156:19 */, %layer2_0_bn1_running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:150:14 */, %layer2_0_conv2_weight: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] span=from_string:167:26 */, %layer2_0_bn2_weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:166:26 */, %layer2_0_bn2_bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:171:20 */, %layer2_0_bn2_running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:169:19 */, %layer2_0_bn2_running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:163:14 */, %layer2_0_conv3_weight: Tensor[(512, 128, 1, 1), float32] /* ty=Tensor[(512, 128, 1, 1), float32] span=from_string:180:26 */, %layer2_0_bn3_weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:179:26 */, %layer2_0_bn3_bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:184:20 */, %layer2_0_bn3_running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:182:19 */, %layer2_0_bn3_running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:176:14 */, %layer2_0_downsample_0_weight: Tensor[(512, 256, 1, 1), float32] /* ty=Tensor[(512, 256, 1, 1), float32] span=from_string:191:26 */, %layer2_0_downsample_1_weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:190:26 */, %layer2_0_downsample_1_bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:195:20 */, %layer2_0_downsample_1_running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:193:19 */, %layer2_0_downsample_1_running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:187:14 */, %layer2_1_conv1_weight: Tensor[(128, 512, 1, 1), float32] /* ty=Tensor[(128, 512, 1, 1), float32] span=from_string:206:26 */, %layer2_1_bn1_weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:205:26 */, %layer2_1_bn1_bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:210:20 */, %layer2_1_bn1_running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:208:19 */, %layer2_1_bn1_running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:202:14 */, %layer2_1_conv2_weight: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] span=from_string:219:26 */, %layer2_1_bn2_weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:218:26 */, %layer2_1_bn2_bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:223:20 */, %layer2_1_bn2_running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:221:19 */, %layer2_1_bn2_running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:215:14 */, %layer2_1_conv3_weight: Tensor[(512, 128, 1, 1), float32] /* ty=Tensor[(512, 128, 1, 1), float32] span=from_string:232:26 */, %layer2_1_bn3_weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:231:26 */, %layer2_1_bn3_bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:236:20 */, %layer2_1_bn3_running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:234:19 */, %layer2_1_bn3_running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:228:14 */, %layer2_2_conv1_weight: Tensor[(128, 512, 1, 1), float32] /* ty=Tensor[(128, 512, 1, 1), float32] span=from_string:246:26 */, %layer2_2_bn1_weight: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:245:26 */, %layer2_2_bn1_bias: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:250:20 */, %layer2_2_bn1_running_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:248:19 */, %layer2_2_bn1_running_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] span=from_string:242:14 */, 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%layer4_1_bn3_running_mean: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:658:19 */, %layer4_1_bn3_running_var: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:652:14 */, %layer4_2_conv1_weight: Tensor[(512, 2048, 1, 1), float32] /* ty=Tensor[(512, 2048, 1, 1), float32] span=from_string:670:26 */, %layer4_2_bn1_weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:669:26 */, %layer4_2_bn1_bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:674:20 */, %layer4_2_bn1_running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:672:19 */, %layer4_2_bn1_running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:666:14 */, %layer4_2_conv2_weight: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] span=from_string:683:26 */, %layer4_2_bn2_weight: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:682:26 */, %layer4_2_bn2_bias: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:687:20 */, %layer4_2_bn2_running_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:685:19 */, %layer4_2_bn2_running_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] span=from_string:679:14 */, %layer4_2_conv3_weight: Tensor[(2048, 512, 1, 1), float32] /* ty=Tensor[(2048, 512, 1, 1), float32] span=from_string:696:26 */, %layer4_2_bn3_weight: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:695:26 */, %layer4_2_bn3_bias: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:700:20 */, %layer4_2_bn3_running_mean: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:698:19 */, %layer4_2_bn3_running_var: Tensor[(2048), float32] /* ty=Tensor[(2048), float32] span=from_string:692:14 */, %fc_weight: Tensor[(1000, 2048), float32] /* ty=Tensor[(1000, 2048), float32] span=from_string:708:20 */, %fc_bias: Tensor[(1000), float32] /* ty=Tensor[(1000), float32] span=from_string:712:13 */) -> Tensor[(1, 1000), float32] { + %0 = add(%bn1_running_var, 1e-05f /* ty=float32 span=from_string:4:36 */) /* ty=Tensor[(64), float32] span=from_string:5:13 */; + %1 = sqrt(%0) /* ty=Tensor[(64), float32] span=from_string:6:36 */; + %2 = divide(1f /* ty=float32 span=from_string:6:17 */, %1) /* ty=Tensor[(64), float32] span=from_string:7:18 */; + %3 = multiply(%2, %bn1_weight) /* ty=Tensor[(64), float32] span=from_string:11:22 */; + %4 = nn.conv2d(%image, %conv1_weight, strides=[2, 2], padding=[3, 3, 3, 3], channels=64, kernel_size=[7, 7]) /* ty=Tensor[(1, 64, 112, 112), float32] span=from_string:13:18 */; + %5 = expand_dims(%3, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:13:22 */; + %6 = negative(%bn1_running_mean) /* ty=Tensor[(64), float32] span=from_string:11:18 */; + %7 = multiply(%6, %3) /* ty=Tensor[(64), float32] span=from_string:12:12 */; + %8 = add(%7, %bn1_bias) /* ty=Tensor[(64), float32] span=from_string:14:21 */; + %9 = multiply(%4, %5) /* ty=Tensor[(1, 64, 112, 112), float32] span=from_string:15:13 */; + %10 = expand_dims(%8, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:15:17 */; + %11 = add(%9, %10) /* ty=Tensor[(1, 64, 112, 112), float32] span=from_string:16:17 */; + %12 = nn.relu(%11) /* ty=Tensor[(1, 64, 112, 112), float32] span=from_string:17:24 */; + %13 = nn.max_pool2d(%12, pool_size=[3, 3], strides=[2, 2], padding=[1, 1, 1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:59:19 */; + %14 = add(%layer1_0_bn1_running_var, 1e-05f /* ty=float32 span=from_string:18:46 */) /* ty=Tensor[(64), float32] span=from_string:19:14 */; + %15 = sqrt(%14) /* ty=Tensor[(64), float32] span=from_string:20:37 */; + %16 = divide(1f /* ty=float32 span=from_string:20:18 */, %15) /* ty=Tensor[(64), float32] span=from_string:21:19 */; + %17 = multiply(%16, %layer1_0_bn1_weight) /* ty=Tensor[(64), float32] span=from_string:25:24 */; + %18 = nn.conv2d(%13, %layer1_0_conv1_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:27:19 */; + %19 = expand_dims(%17, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:27:24 */; + %20 = negative(%layer1_0_bn1_running_mean) /* ty=Tensor[(64), float32] span=from_string:25:19 */; + %21 = multiply(%20, %17) /* ty=Tensor[(64), float32] span=from_string:26:13 */; + %22 = add(%21, %layer1_0_bn1_bias) /* ty=Tensor[(64), float32] span=from_string:28:21 */; + %23 = multiply(%18, %19) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:29:13 */; + %24 = expand_dims(%22, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:29:18 */; + %25 = add(%23, %24) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:30:17 */; + %26 = nn.relu(%25) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:35:19 */; + %27 = add(%layer1_0_bn2_running_var, 1e-05f /* ty=float32 span=from_string:31:46 */) /* ty=Tensor[(64), float32] span=from_string:32:14 */; + %28 = sqrt(%27) /* ty=Tensor[(64), float32] span=from_string:33:37 */; + %29 = divide(1f /* ty=float32 span=from_string:33:18 */, %28) /* ty=Tensor[(64), float32] span=from_string:34:19 */; + %30 = multiply(%29, %layer1_0_bn2_weight) /* ty=Tensor[(64), float32] span=from_string:38:24 */; + %31 = nn.conv2d(%26, %layer1_0_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:40:19 */; + %32 = expand_dims(%30, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:40:24 */; + %33 = negative(%layer1_0_bn2_running_mean) /* ty=Tensor[(64), float32] span=from_string:38:19 */; + %34 = multiply(%33, %30) /* ty=Tensor[(64), float32] span=from_string:39:13 */; + %35 = add(%34, %layer1_0_bn2_bias) /* ty=Tensor[(64), float32] span=from_string:41:21 */; + %36 = multiply(%31, %32) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:42:13 */; + %37 = expand_dims(%35, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:42:18 */; + %38 = add(%36, %37) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:43:17 */; + %39 = nn.relu(%38) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:48:19 */; + %40 = add(%layer1_0_bn3_running_var, 1e-05f /* ty=float32 span=from_string:44:46 */) /* ty=Tensor[(256), float32] span=from_string:45:14 */; + %41 = sqrt(%40) /* ty=Tensor[(256), float32] span=from_string:46:37 */; + %42 = divide(1f /* ty=float32 span=from_string:46:18 */, %41) /* ty=Tensor[(256), float32] span=from_string:47:19 */; + %43 = multiply(%42, %layer1_0_bn3_weight) /* ty=Tensor[(256), float32] span=from_string:51:24 */; + %44 = nn.conv2d(%39, %layer1_0_conv3_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:53:19 */; + %45 = expand_dims(%43, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:53:24 */; + %46 = negative(%layer1_0_bn3_running_mean) /* ty=Tensor[(256), float32] span=from_string:51:19 */; + %47 = multiply(%46, %43) /* ty=Tensor[(256), float32] span=from_string:52:13 */; + %48 = add(%47, %layer1_0_bn3_bias) /* ty=Tensor[(256), float32] span=from_string:54:21 */; + %49 = multiply(%44, %45) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:66:13 */; + %50 = expand_dims(%48, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:66:18 */; + %51 = add(%layer1_0_downsample_1_running_var, 1e-05f /* ty=float32 span=from_string:55:55 */) /* ty=Tensor[(256), float32] span=from_string:56:14 */; + %52 = sqrt(%51) /* ty=Tensor[(256), float32] span=from_string:57:37 */; + %53 = divide(1f /* ty=float32 span=from_string:57:18 */, %52) /* ty=Tensor[(256), float32] span=from_string:58:19 */; + %54 = multiply(%53, %layer1_0_downsample_1_weight) /* ty=Tensor[(256), float32] span=from_string:62:24 */; + %55 = nn.conv2d(%13, %layer1_0_downsample_0_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:64:19 */; + %56 = expand_dims(%54, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:64:24 */; + %57 = negative(%layer1_0_downsample_1_running_mean) /* ty=Tensor[(256), float32] span=from_string:62:19 */; + %58 = multiply(%57, %54) /* ty=Tensor[(256), float32] span=from_string:63:13 */; + %59 = add(%58, %layer1_0_downsample_1_bias) /* ty=Tensor[(256), float32] span=from_string:65:21 */; + %60 = multiply(%55, %56) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:67:13 */; + %61 = expand_dims(%59, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:67:18 */; + %62 = add(%49, %50) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:68:13 */; + %63 = add(%60, %61) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:68:18 */; + %64 = add(%62, %63) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:69:17 */; + %65 = nn.relu(%64) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:108:20 */; + %66 = add(%layer1_1_bn1_running_var, 1e-05f /* ty=float32 span=from_string:70:46 */) /* ty=Tensor[(64), float32] span=from_string:71:14 */; + %67 = sqrt(%66) /* ty=Tensor[(64), float32] span=from_string:72:37 */; + %68 = divide(1f /* ty=float32 span=from_string:72:18 */, %67) /* ty=Tensor[(64), float32] span=from_string:73:19 */; + %69 = multiply(%68, %layer1_1_bn1_weight) /* ty=Tensor[(64), float32] span=from_string:77:24 */; + %70 = nn.conv2d(%65, %layer1_1_conv1_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:79:19 */; + %71 = expand_dims(%69, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:79:24 */; + %72 = negative(%layer1_1_bn1_running_mean) /* ty=Tensor[(64), float32] span=from_string:77:19 */; + %73 = multiply(%72, %69) /* ty=Tensor[(64), float32] span=from_string:78:13 */; + %74 = add(%73, %layer1_1_bn1_bias) /* ty=Tensor[(64), float32] span=from_string:80:21 */; + %75 = multiply(%70, %71) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:81:13 */; + %76 = expand_dims(%74, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:81:18 */; + %77 = add(%75, %76) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:82:17 */; + %78 = nn.relu(%77) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:87:19 */; + %79 = add(%layer1_1_bn2_running_var, 1e-05f /* ty=float32 span=from_string:83:46 */) /* ty=Tensor[(64), float32] span=from_string:84:14 */; + %80 = sqrt(%79) /* ty=Tensor[(64), float32] span=from_string:85:37 */; + %81 = divide(1f /* ty=float32 span=from_string:85:18 */, %80) /* ty=Tensor[(64), float32] span=from_string:86:19 */; + %82 = multiply(%81, %layer1_1_bn2_weight) /* ty=Tensor[(64), float32] span=from_string:90:24 */; + %83 = nn.conv2d(%78, %layer1_1_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:92:19 */; + %84 = expand_dims(%82, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:92:24 */; + %85 = negative(%layer1_1_bn2_running_mean) /* ty=Tensor[(64), float32] span=from_string:90:19 */; + %86 = multiply(%85, %82) /* ty=Tensor[(64), float32] span=from_string:91:13 */; + %87 = add(%86, %layer1_1_bn2_bias) /* ty=Tensor[(64), float32] span=from_string:93:21 */; + %88 = multiply(%83, %84) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:94:13 */; + %89 = expand_dims(%87, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:94:18 */; + %90 = add(%88, %89) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:95:17 */; + %91 = nn.relu(%90) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:100:19 */; + %92 = add(%layer1_1_bn3_running_var, 1e-05f /* ty=float32 span=from_string:96:46 */) /* ty=Tensor[(256), float32] span=from_string:97:14 */; + %93 = sqrt(%92) /* ty=Tensor[(256), float32] span=from_string:98:37 */; + %94 = divide(1f /* ty=float32 span=from_string:98:18 */, %93) /* ty=Tensor[(256), float32] span=from_string:99:19 */; + %95 = multiply(%94, %layer1_1_bn3_weight) /* ty=Tensor[(256), float32] span=from_string:103:24 */; + %96 = nn.conv2d(%91, %layer1_1_conv3_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:105:20 */; + %97 = expand_dims(%95, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:105:25 */; + %98 = negative(%layer1_1_bn3_running_mean) /* ty=Tensor[(256), float32] span=from_string:103:19 */; + %99 = multiply(%98, %95) /* ty=Tensor[(256), float32] span=from_string:104:14 */; + %100 = add(%99, %layer1_1_bn3_bias) /* ty=Tensor[(256), float32] span=from_string:106:22 */; + %101 = multiply(%96, %97) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:107:14 */; + %102 = expand_dims(%100, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:107:20 */; + %103 = add(%101, %102) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:108:14 */; + %104 = add(%103, %65) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:109:18 */; + %105 = nn.relu(%104) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:148:20 */; + %106 = add(%layer1_2_bn1_running_var, 1e-05f /* ty=float32 span=from_string:110:47 */) /* ty=Tensor[(64), float32] span=from_string:111:15 */; + %107 = sqrt(%106) /* ty=Tensor[(64), float32] span=from_string:112:38 */; + %108 = divide(1f /* ty=float32 span=from_string:112:19 */, %107) /* ty=Tensor[(64), float32] span=from_string:113:20 */; + %109 = multiply(%108, %layer1_2_bn1_weight) /* ty=Tensor[(64), float32] span=from_string:117:26 */; + %110 = nn.conv2d(%105, %layer1_2_conv1_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:119:20 */; + %111 = expand_dims(%109, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:119:26 */; + %112 = negative(%layer1_2_bn1_running_mean) /* ty=Tensor[(64), float32] span=from_string:117:20 */; + %113 = multiply(%112, %109) /* ty=Tensor[(64), float32] span=from_string:118:14 */; + %114 = add(%113, %layer1_2_bn1_bias) /* ty=Tensor[(64), float32] span=from_string:120:22 */; + %115 = multiply(%110, %111) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:121:14 */; + %116 = expand_dims(%114, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:121:20 */; + %117 = add(%115, %116) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:122:18 */; + %118 = nn.relu(%117) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:127:20 */; + %119 = add(%layer1_2_bn2_running_var, 1e-05f /* ty=float32 span=from_string:123:47 */) /* ty=Tensor[(64), float32] span=from_string:124:15 */; + %120 = sqrt(%119) /* ty=Tensor[(64), float32] span=from_string:125:38 */; + %121 = divide(1f /* ty=float32 span=from_string:125:19 */, %120) /* ty=Tensor[(64), float32] span=from_string:126:20 */; + %122 = multiply(%121, %layer1_2_bn2_weight) /* ty=Tensor[(64), float32] span=from_string:130:26 */; + %123 = nn.conv2d(%118, %layer1_2_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:132:20 */; + %124 = expand_dims(%122, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:132:26 */; + %125 = negative(%layer1_2_bn2_running_mean) /* ty=Tensor[(64), float32] span=from_string:130:20 */; + %126 = multiply(%125, %122) /* ty=Tensor[(64), float32] span=from_string:131:14 */; + %127 = add(%126, %layer1_2_bn2_bias) /* ty=Tensor[(64), float32] span=from_string:133:22 */; + %128 = multiply(%123, %124) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:134:14 */; + %129 = expand_dims(%127, axis=1, num_newaxis=2) /* ty=Tensor[(64, 1, 1), float32] span=from_string:134:20 */; + %130 = add(%128, %129) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:135:18 */; + %131 = nn.relu(%130) /* ty=Tensor[(1, 64, 56, 56), float32] span=from_string:140:20 */; + %132 = add(%layer1_2_bn3_running_var, 1e-05f /* ty=float32 span=from_string:136:47 */) /* ty=Tensor[(256), float32] span=from_string:137:15 */; + %133 = sqrt(%132) /* ty=Tensor[(256), float32] span=from_string:138:38 */; + %134 = divide(1f /* ty=float32 span=from_string:138:19 */, %133) /* ty=Tensor[(256), float32] span=from_string:139:20 */; + %135 = multiply(%134, %layer1_2_bn3_weight) /* ty=Tensor[(256), float32] span=from_string:143:26 */; + %136 = nn.conv2d(%131, %layer1_2_conv3_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:145:20 */; + %137 = expand_dims(%135, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:145:26 */; + %138 = negative(%layer1_2_bn3_running_mean) /* ty=Tensor[(256), float32] span=from_string:143:20 */; + %139 = multiply(%138, %135) /* ty=Tensor[(256), float32] span=from_string:144:14 */; + %140 = add(%139, %layer1_2_bn3_bias) /* ty=Tensor[(256), float32] span=from_string:146:22 */; + %141 = multiply(%136, %137) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:147:14 */; + %142 = expand_dims(%140, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:147:20 */; + %143 = add(%141, %142) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:148:14 */; + %144 = add(%143, %105) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:149:18 */; + %145 = nn.relu(%144) /* ty=Tensor[(1, 256, 56, 56), float32] span=from_string:191:20 */; + %146 = add(%layer2_0_bn1_running_var, 1e-05f /* ty=float32 span=from_string:150:47 */) /* ty=Tensor[(128), float32] span=from_string:151:15 */; + %147 = sqrt(%146) /* ty=Tensor[(128), float32] span=from_string:152:38 */; + %148 = divide(1f /* ty=float32 span=from_string:152:19 */, %147) /* ty=Tensor[(128), float32] span=from_string:153:20 */; + %149 = multiply(%148, %layer2_0_bn1_weight) /* ty=Tensor[(128), float32] span=from_string:157:26 */; + %150 = nn.conv2d(%145, %layer2_0_conv1_weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 56, 56), float32] span=from_string:159:20 */; + %151 = expand_dims(%149, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:159:26 */; + %152 = negative(%layer2_0_bn1_running_mean) /* ty=Tensor[(128), float32] span=from_string:157:20 */; + %153 = multiply(%152, %149) /* ty=Tensor[(128), float32] span=from_string:158:14 */; + %154 = add(%153, %layer2_0_bn1_bias) /* ty=Tensor[(128), float32] span=from_string:160:22 */; + %155 = multiply(%150, %151) /* ty=Tensor[(1, 128, 56, 56), float32] span=from_string:161:14 */; + %156 = expand_dims(%154, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:161:20 */; + %157 = add(%155, %156) /* ty=Tensor[(1, 128, 56, 56), float32] span=from_string:162:18 */; + %158 = nn.relu(%157) /* ty=Tensor[(1, 128, 56, 56), float32] span=from_string:167:20 */; + %159 = add(%layer2_0_bn2_running_var, 1e-05f /* ty=float32 span=from_string:163:47 */) /* ty=Tensor[(128), float32] span=from_string:164:15 */; + %160 = sqrt(%159) /* ty=Tensor[(128), float32] span=from_string:165:38 */; + %161 = divide(1f /* ty=float32 span=from_string:165:19 */, %160) /* ty=Tensor[(128), float32] span=from_string:166:20 */; + %162 = multiply(%161, %layer2_0_bn2_weight) /* ty=Tensor[(128), float32] span=from_string:170:26 */; + %163 = nn.conv2d(%158, %layer2_0_conv2_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:172:20 */; + %164 = expand_dims(%162, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:172:26 */; + %165 = negative(%layer2_0_bn2_running_mean) /* ty=Tensor[(128), float32] span=from_string:170:20 */; + %166 = multiply(%165, %162) /* ty=Tensor[(128), float32] span=from_string:171:14 */; + %167 = add(%166, %layer2_0_bn2_bias) /* ty=Tensor[(128), float32] span=from_string:173:22 */; + %168 = multiply(%163, %164) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:174:14 */; + %169 = expand_dims(%167, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:174:20 */; + %170 = add(%168, %169) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:175:18 */; + %171 = nn.relu(%170) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:180:20 */; + %172 = add(%layer2_0_bn3_running_var, 1e-05f /* ty=float32 span=from_string:176:47 */) /* ty=Tensor[(512), float32] span=from_string:177:15 */; + %173 = sqrt(%172) /* ty=Tensor[(512), float32] span=from_string:178:38 */; + %174 = divide(1f /* ty=float32 span=from_string:178:19 */, %173) /* ty=Tensor[(512), float32] span=from_string:179:20 */; + %175 = multiply(%174, %layer2_0_bn3_weight) /* ty=Tensor[(512), float32] span=from_string:183:26 */; + %176 = nn.conv2d(%171, %layer2_0_conv3_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:185:20 */; + %177 = expand_dims(%175, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:185:26 */; + %178 = negative(%layer2_0_bn3_running_mean) /* ty=Tensor[(512), float32] span=from_string:183:20 */; + %179 = multiply(%178, %175) /* ty=Tensor[(512), float32] span=from_string:184:14 */; + %180 = add(%179, %layer2_0_bn3_bias) /* ty=Tensor[(512), float32] span=from_string:186:22 */; + %181 = multiply(%176, %177) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:198:14 */; + %182 = expand_dims(%180, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:198:20 */; + %183 = add(%layer2_0_downsample_1_running_var, 1e-05f /* ty=float32 span=from_string:187:56 */) /* ty=Tensor[(512), float32] span=from_string:188:15 */; + %184 = sqrt(%183) /* ty=Tensor[(512), float32] span=from_string:189:38 */; + %185 = divide(1f /* ty=float32 span=from_string:189:19 */, %184) /* ty=Tensor[(512), float32] span=from_string:190:20 */; + %186 = multiply(%185, %layer2_0_downsample_1_weight) /* ty=Tensor[(512), float32] span=from_string:194:26 */; + %187 = nn.conv2d(%145, %layer2_0_downsample_0_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:196:20 */; + %188 = expand_dims(%186, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:196:26 */; + %189 = negative(%layer2_0_downsample_1_running_mean) /* ty=Tensor[(512), float32] span=from_string:194:20 */; + %190 = multiply(%189, %186) /* ty=Tensor[(512), float32] span=from_string:195:14 */; + %191 = add(%190, %layer2_0_downsample_1_bias) /* ty=Tensor[(512), float32] span=from_string:197:22 */; + %192 = multiply(%187, %188) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:199:14 */; + %193 = expand_dims(%191, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:199:20 */; + %194 = add(%181, %182) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:200:14 */; + %195 = add(%192, %193) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:200:20 */; + %196 = add(%194, %195) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:201:18 */; + %197 = nn.relu(%196) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:240:20 */; + %198 = add(%layer2_1_bn1_running_var, 1e-05f /* ty=float32 span=from_string:202:47 */) /* ty=Tensor[(128), float32] span=from_string:203:15 */; + %199 = sqrt(%198) /* ty=Tensor[(128), float32] span=from_string:204:38 */; + %200 = divide(1f /* ty=float32 span=from_string:204:19 */, %199) /* ty=Tensor[(128), float32] span=from_string:205:20 */; + %201 = multiply(%200, %layer2_1_bn1_weight) /* ty=Tensor[(128), float32] span=from_string:209:26 */; + %202 = nn.conv2d(%197, %layer2_1_conv1_weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:211:20 */; + %203 = expand_dims(%201, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:211:26 */; + %204 = negative(%layer2_1_bn1_running_mean) /* ty=Tensor[(128), float32] span=from_string:209:20 */; + %205 = multiply(%204, %201) /* ty=Tensor[(128), float32] span=from_string:210:14 */; + %206 = add(%205, %layer2_1_bn1_bias) /* ty=Tensor[(128), float32] span=from_string:212:22 */; + %207 = multiply(%202, %203) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:213:14 */; + %208 = expand_dims(%206, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:213:20 */; + %209 = add(%207, %208) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:214:18 */; + %210 = nn.relu(%209) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:219:20 */; + %211 = add(%layer2_1_bn2_running_var, 1e-05f /* ty=float32 span=from_string:215:47 */) /* ty=Tensor[(128), float32] span=from_string:216:15 */; + %212 = sqrt(%211) /* ty=Tensor[(128), float32] span=from_string:217:38 */; + %213 = divide(1f /* ty=float32 span=from_string:217:19 */, %212) /* ty=Tensor[(128), float32] span=from_string:218:20 */; + %214 = multiply(%213, %layer2_1_bn2_weight) /* ty=Tensor[(128), float32] span=from_string:222:26 */; + %215 = nn.conv2d(%210, %layer2_1_conv2_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:224:20 */; + %216 = expand_dims(%214, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:224:26 */; + %217 = negative(%layer2_1_bn2_running_mean) /* ty=Tensor[(128), float32] span=from_string:222:20 */; + %218 = multiply(%217, %214) /* ty=Tensor[(128), float32] span=from_string:223:14 */; + %219 = add(%218, %layer2_1_bn2_bias) /* ty=Tensor[(128), float32] span=from_string:225:22 */; + %220 = multiply(%215, %216) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:226:14 */; + %221 = expand_dims(%219, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:226:20 */; + %222 = add(%220, %221) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:227:18 */; + %223 = nn.relu(%222) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:232:20 */; + %224 = add(%layer2_1_bn3_running_var, 1e-05f /* ty=float32 span=from_string:228:47 */) /* ty=Tensor[(512), float32] span=from_string:229:15 */; + %225 = sqrt(%224) /* ty=Tensor[(512), float32] span=from_string:230:38 */; + %226 = divide(1f /* ty=float32 span=from_string:230:19 */, %225) /* ty=Tensor[(512), float32] span=from_string:231:20 */; + %227 = multiply(%226, %layer2_1_bn3_weight) /* ty=Tensor[(512), float32] span=from_string:235:26 */; + %228 = nn.conv2d(%223, %layer2_1_conv3_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:237:20 */; + %229 = expand_dims(%227, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:237:26 */; + %230 = negative(%layer2_1_bn3_running_mean) /* ty=Tensor[(512), float32] span=from_string:235:20 */; + %231 = multiply(%230, %227) /* ty=Tensor[(512), float32] span=from_string:236:14 */; + %232 = add(%231, %layer2_1_bn3_bias) /* ty=Tensor[(512), float32] span=from_string:238:22 */; + %233 = multiply(%228, %229) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:239:14 */; + %234 = expand_dims(%232, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:239:20 */; + %235 = add(%233, %234) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:240:14 */; + %236 = add(%235, %197) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:241:18 */; + %237 = nn.relu(%236) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:280:20 */; + %238 = add(%layer2_2_bn1_running_var, 1e-05f /* ty=float32 span=from_string:242:47 */) /* ty=Tensor[(128), float32] span=from_string:243:15 */; + %239 = sqrt(%238) /* ty=Tensor[(128), float32] span=from_string:244:38 */; + %240 = divide(1f /* ty=float32 span=from_string:244:19 */, %239) /* ty=Tensor[(128), float32] span=from_string:245:20 */; + %241 = multiply(%240, %layer2_2_bn1_weight) /* ty=Tensor[(128), float32] span=from_string:249:26 */; + %242 = nn.conv2d(%237, %layer2_2_conv1_weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:251:20 */; + %243 = expand_dims(%241, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:251:26 */; + %244 = negative(%layer2_2_bn1_running_mean) /* ty=Tensor[(128), float32] span=from_string:249:20 */; + %245 = multiply(%244, %241) /* ty=Tensor[(128), float32] span=from_string:250:14 */; + %246 = add(%245, %layer2_2_bn1_bias) /* ty=Tensor[(128), float32] span=from_string:252:22 */; + %247 = multiply(%242, %243) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:253:14 */; + %248 = expand_dims(%246, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:253:20 */; + %249 = add(%247, %248) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:254:18 */; + %250 = nn.relu(%249) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:259:20 */; + %251 = add(%layer2_2_bn2_running_var, 1e-05f /* ty=float32 span=from_string:255:47 */) /* ty=Tensor[(128), float32] span=from_string:256:15 */; + %252 = sqrt(%251) /* ty=Tensor[(128), float32] span=from_string:257:38 */; + %253 = divide(1f /* ty=float32 span=from_string:257:19 */, %252) /* ty=Tensor[(128), float32] span=from_string:258:20 */; + %254 = multiply(%253, %layer2_2_bn2_weight) /* ty=Tensor[(128), float32] span=from_string:262:26 */; + %255 = nn.conv2d(%250, %layer2_2_conv2_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:264:20 */; + %256 = expand_dims(%254, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:264:26 */; + %257 = negative(%layer2_2_bn2_running_mean) /* ty=Tensor[(128), float32] span=from_string:262:20 */; + %258 = multiply(%257, %254) /* ty=Tensor[(128), float32] span=from_string:263:14 */; + %259 = add(%258, %layer2_2_bn2_bias) /* ty=Tensor[(128), float32] span=from_string:265:22 */; + %260 = multiply(%255, %256) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:266:14 */; + %261 = expand_dims(%259, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:266:20 */; + %262 = add(%260, %261) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:267:18 */; + %263 = nn.relu(%262) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:272:20 */; + %264 = add(%layer2_2_bn3_running_var, 1e-05f /* ty=float32 span=from_string:268:47 */) /* ty=Tensor[(512), float32] span=from_string:269:15 */; + %265 = sqrt(%264) /* ty=Tensor[(512), float32] span=from_string:270:38 */; + %266 = divide(1f /* ty=float32 span=from_string:270:19 */, %265) /* ty=Tensor[(512), float32] span=from_string:271:20 */; + %267 = multiply(%266, %layer2_2_bn3_weight) /* ty=Tensor[(512), float32] span=from_string:275:26 */; + %268 = nn.conv2d(%263, %layer2_2_conv3_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:277:20 */; + %269 = expand_dims(%267, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:277:26 */; + %270 = negative(%layer2_2_bn3_running_mean) /* ty=Tensor[(512), float32] span=from_string:275:20 */; + %271 = multiply(%270, %267) /* ty=Tensor[(512), float32] span=from_string:276:14 */; + %272 = add(%271, %layer2_2_bn3_bias) /* ty=Tensor[(512), float32] span=from_string:278:22 */; + %273 = multiply(%268, %269) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:279:14 */; + %274 = expand_dims(%272, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:279:20 */; + %275 = add(%273, %274) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:280:14 */; + %276 = add(%275, %237) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:281:18 */; + %277 = nn.relu(%276) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:320:20 */; + %278 = add(%layer2_3_bn1_running_var, 1e-05f /* ty=float32 span=from_string:282:47 */) /* ty=Tensor[(128), float32] span=from_string:283:15 */; + %279 = sqrt(%278) /* ty=Tensor[(128), float32] span=from_string:284:38 */; + %280 = divide(1f /* ty=float32 span=from_string:284:19 */, %279) /* ty=Tensor[(128), float32] span=from_string:285:20 */; + %281 = multiply(%280, %layer2_3_bn1_weight) /* ty=Tensor[(128), float32] span=from_string:289:26 */; + %282 = nn.conv2d(%277, %layer2_3_conv1_weight, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:291:20 */; + %283 = expand_dims(%281, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:291:26 */; + %284 = negative(%layer2_3_bn1_running_mean) /* ty=Tensor[(128), float32] span=from_string:289:20 */; + %285 = multiply(%284, %281) /* ty=Tensor[(128), float32] span=from_string:290:14 */; + %286 = add(%285, %layer2_3_bn1_bias) /* ty=Tensor[(128), float32] span=from_string:292:22 */; + %287 = multiply(%282, %283) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:293:14 */; + %288 = expand_dims(%286, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:293:20 */; + %289 = add(%287, %288) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:294:18 */; + %290 = nn.relu(%289) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:299:20 */; + %291 = add(%layer2_3_bn2_running_var, 1e-05f /* ty=float32 span=from_string:295:47 */) /* ty=Tensor[(128), float32] span=from_string:296:15 */; + %292 = sqrt(%291) /* ty=Tensor[(128), float32] span=from_string:297:38 */; + %293 = divide(1f /* ty=float32 span=from_string:297:19 */, %292) /* ty=Tensor[(128), float32] span=from_string:298:20 */; + %294 = multiply(%293, %layer2_3_bn2_weight) /* ty=Tensor[(128), float32] span=from_string:302:26 */; + %295 = nn.conv2d(%290, %layer2_3_conv2_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:304:20 */; + %296 = expand_dims(%294, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:304:26 */; + %297 = negative(%layer2_3_bn2_running_mean) /* ty=Tensor[(128), float32] span=from_string:302:20 */; + %298 = multiply(%297, %294) /* ty=Tensor[(128), float32] span=from_string:303:14 */; + %299 = add(%298, %layer2_3_bn2_bias) /* ty=Tensor[(128), float32] span=from_string:305:22 */; + %300 = multiply(%295, %296) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:306:14 */; + %301 = expand_dims(%299, axis=1, num_newaxis=2) /* ty=Tensor[(128, 1, 1), float32] span=from_string:306:20 */; + %302 = add(%300, %301) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:307:18 */; + %303 = nn.relu(%302) /* ty=Tensor[(1, 128, 28, 28), float32] span=from_string:312:20 */; + %304 = add(%layer2_3_bn3_running_var, 1e-05f /* ty=float32 span=from_string:308:47 */) /* ty=Tensor[(512), float32] span=from_string:309:15 */; + %305 = sqrt(%304) /* ty=Tensor[(512), float32] span=from_string:310:38 */; + %306 = divide(1f /* ty=float32 span=from_string:310:19 */, %305) /* ty=Tensor[(512), float32] span=from_string:311:20 */; + %307 = multiply(%306, %layer2_3_bn3_weight) /* ty=Tensor[(512), float32] span=from_string:315:26 */; + %308 = nn.conv2d(%303, %layer2_3_conv3_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:317:20 */; + %309 = expand_dims(%307, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:317:26 */; + %310 = negative(%layer2_3_bn3_running_mean) /* ty=Tensor[(512), float32] span=from_string:315:20 */; + %311 = multiply(%310, %307) /* ty=Tensor[(512), float32] span=from_string:316:14 */; + %312 = add(%311, %layer2_3_bn3_bias) /* ty=Tensor[(512), float32] span=from_string:318:22 */; + %313 = multiply(%308, %309) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:319:14 */; + %314 = expand_dims(%312, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:319:20 */; + %315 = add(%313, %314) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:320:14 */; + %316 = add(%315, %277) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:321:18 */; + %317 = nn.relu(%316) /* ty=Tensor[(1, 512, 28, 28), float32] span=from_string:363:20 */; + %318 = add(%layer3_0_bn1_running_var, 1e-05f /* ty=float32 span=from_string:322:47 */) /* ty=Tensor[(256), float32] span=from_string:323:15 */; + %319 = sqrt(%318) /* ty=Tensor[(256), float32] span=from_string:324:38 */; + %320 = divide(1f /* ty=float32 span=from_string:324:19 */, %319) /* ty=Tensor[(256), float32] span=from_string:325:20 */; + %321 = multiply(%320, %layer3_0_bn1_weight) /* ty=Tensor[(256), float32] span=from_string:329:26 */; + %322 = nn.conv2d(%317, %layer3_0_conv1_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 28, 28), float32] span=from_string:331:20 */; + %323 = expand_dims(%321, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:331:26 */; + %324 = negative(%layer3_0_bn1_running_mean) /* ty=Tensor[(256), float32] span=from_string:329:20 */; + %325 = multiply(%324, %321) /* ty=Tensor[(256), float32] span=from_string:330:14 */; + %326 = add(%325, %layer3_0_bn1_bias) /* ty=Tensor[(256), float32] span=from_string:332:22 */; + %327 = multiply(%322, %323) /* ty=Tensor[(1, 256, 28, 28), float32] span=from_string:333:14 */; + %328 = expand_dims(%326, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:333:20 */; + %329 = add(%327, %328) /* ty=Tensor[(1, 256, 28, 28), float32] span=from_string:334:18 */; + %330 = nn.relu(%329) /* ty=Tensor[(1, 256, 28, 28), float32] span=from_string:339:20 */; + %331 = add(%layer3_0_bn2_running_var, 1e-05f /* ty=float32 span=from_string:335:47 */) /* ty=Tensor[(256), float32] span=from_string:336:15 */; + %332 = sqrt(%331) /* ty=Tensor[(256), float32] span=from_string:337:38 */; + %333 = divide(1f /* ty=float32 span=from_string:337:19 */, %332) /* ty=Tensor[(256), float32] span=from_string:338:20 */; + %334 = multiply(%333, %layer3_0_bn2_weight) /* ty=Tensor[(256), float32] span=from_string:342:26 */; + %335 = nn.conv2d(%330, %layer3_0_conv2_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:344:20 */; + %336 = expand_dims(%334, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:344:26 */; + %337 = negative(%layer3_0_bn2_running_mean) /* ty=Tensor[(256), float32] span=from_string:342:20 */; + %338 = multiply(%337, %334) /* ty=Tensor[(256), float32] span=from_string:343:14 */; + %339 = add(%338, %layer3_0_bn2_bias) /* ty=Tensor[(256), float32] span=from_string:345:22 */; + %340 = multiply(%335, %336) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:346:14 */; + %341 = expand_dims(%339, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:346:20 */; + %342 = add(%340, %341) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:347:18 */; + %343 = nn.relu(%342) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:352:20 */; + %344 = add(%layer3_0_bn3_running_var, 1e-05f /* ty=float32 span=from_string:348:47 */) /* ty=Tensor[(1024), float32] span=from_string:349:15 */; + %345 = sqrt(%344) /* ty=Tensor[(1024), float32] span=from_string:350:38 */; + %346 = divide(1f /* ty=float32 span=from_string:350:19 */, %345) /* ty=Tensor[(1024), float32] span=from_string:351:20 */; + %347 = multiply(%346, %layer3_0_bn3_weight) /* ty=Tensor[(1024), float32] span=from_string:355:26 */; + %348 = nn.conv2d(%343, %layer3_0_conv3_weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:357:20 */; + %349 = expand_dims(%347, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:357:26 */; + %350 = negative(%layer3_0_bn3_running_mean) /* ty=Tensor[(1024), float32] span=from_string:355:20 */; + %351 = multiply(%350, %347) /* ty=Tensor[(1024), float32] span=from_string:356:14 */; + %352 = add(%351, %layer3_0_bn3_bias) /* ty=Tensor[(1024), float32] span=from_string:358:22 */; + %353 = multiply(%348, %349) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:370:14 */; + %354 = expand_dims(%352, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:370:20 */; + %355 = add(%layer3_0_downsample_1_running_var, 1e-05f /* ty=float32 span=from_string:359:56 */) /* ty=Tensor[(1024), float32] span=from_string:360:15 */; + %356 = sqrt(%355) /* ty=Tensor[(1024), float32] span=from_string:361:38 */; + %357 = divide(1f /* ty=float32 span=from_string:361:19 */, %356) /* ty=Tensor[(1024), float32] span=from_string:362:20 */; + %358 = multiply(%357, %layer3_0_downsample_1_weight) /* ty=Tensor[(1024), float32] span=from_string:366:26 */; + %359 = nn.conv2d(%317, %layer3_0_downsample_0_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:368:20 */; + %360 = expand_dims(%358, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:368:26 */; + %361 = negative(%layer3_0_downsample_1_running_mean) /* ty=Tensor[(1024), float32] span=from_string:366:20 */; + %362 = multiply(%361, %358) /* ty=Tensor[(1024), float32] span=from_string:367:14 */; + %363 = add(%362, %layer3_0_downsample_1_bias) /* ty=Tensor[(1024), float32] span=from_string:369:22 */; + %364 = multiply(%359, %360) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:371:14 */; + %365 = expand_dims(%363, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:371:20 */; + %366 = add(%353, %354) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:372:14 */; + %367 = add(%364, %365) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:372:20 */; + %368 = add(%366, %367) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:373:18 */; + %369 = nn.relu(%368) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:412:20 */; + %370 = add(%layer3_1_bn1_running_var, 1e-05f /* ty=float32 span=from_string:374:47 */) /* ty=Tensor[(256), float32] span=from_string:375:15 */; + %371 = sqrt(%370) /* ty=Tensor[(256), float32] span=from_string:376:38 */; + %372 = divide(1f /* ty=float32 span=from_string:376:19 */, %371) /* ty=Tensor[(256), float32] span=from_string:377:20 */; + %373 = multiply(%372, %layer3_1_bn1_weight) /* ty=Tensor[(256), float32] span=from_string:381:26 */; + %374 = nn.conv2d(%369, %layer3_1_conv1_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:383:20 */; + %375 = expand_dims(%373, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:383:26 */; + %376 = negative(%layer3_1_bn1_running_mean) /* ty=Tensor[(256), float32] span=from_string:381:20 */; + %377 = multiply(%376, %373) /* ty=Tensor[(256), float32] span=from_string:382:14 */; + %378 = add(%377, %layer3_1_bn1_bias) /* ty=Tensor[(256), float32] span=from_string:384:22 */; + %379 = multiply(%374, %375) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:385:14 */; + %380 = expand_dims(%378, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:385:20 */; + %381 = add(%379, %380) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:386:18 */; + %382 = nn.relu(%381) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:391:20 */; + %383 = add(%layer3_1_bn2_running_var, 1e-05f /* ty=float32 span=from_string:387:47 */) /* ty=Tensor[(256), float32] span=from_string:388:15 */; + %384 = sqrt(%383) /* ty=Tensor[(256), float32] span=from_string:389:38 */; + %385 = divide(1f /* ty=float32 span=from_string:389:19 */, %384) /* ty=Tensor[(256), float32] span=from_string:390:20 */; + %386 = multiply(%385, %layer3_1_bn2_weight) /* ty=Tensor[(256), float32] span=from_string:394:26 */; + %387 = nn.conv2d(%382, %layer3_1_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:396:20 */; + %388 = expand_dims(%386, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:396:26 */; + %389 = negative(%layer3_1_bn2_running_mean) /* ty=Tensor[(256), float32] span=from_string:394:20 */; + %390 = multiply(%389, %386) /* ty=Tensor[(256), float32] span=from_string:395:14 */; + %391 = add(%390, %layer3_1_bn2_bias) /* ty=Tensor[(256), float32] span=from_string:397:22 */; + %392 = multiply(%387, %388) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:398:14 */; + %393 = expand_dims(%391, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:398:20 */; + %394 = add(%392, %393) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:399:18 */; + %395 = nn.relu(%394) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:404:20 */; + %396 = add(%layer3_1_bn3_running_var, 1e-05f /* ty=float32 span=from_string:400:47 */) /* ty=Tensor[(1024), float32] span=from_string:401:15 */; + %397 = sqrt(%396) /* ty=Tensor[(1024), float32] span=from_string:402:38 */; + %398 = divide(1f /* ty=float32 span=from_string:402:19 */, %397) /* ty=Tensor[(1024), float32] span=from_string:403:20 */; + %399 = multiply(%398, %layer3_1_bn3_weight) /* ty=Tensor[(1024), float32] span=from_string:407:26 */; + %400 = nn.conv2d(%395, %layer3_1_conv3_weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:409:20 */; + %401 = expand_dims(%399, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:409:26 */; + %402 = negative(%layer3_1_bn3_running_mean) /* ty=Tensor[(1024), float32] span=from_string:407:20 */; + %403 = multiply(%402, %399) /* ty=Tensor[(1024), float32] span=from_string:408:14 */; + %404 = add(%403, %layer3_1_bn3_bias) /* ty=Tensor[(1024), float32] span=from_string:410:22 */; + %405 = multiply(%400, %401) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:411:14 */; + %406 = expand_dims(%404, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:411:20 */; + %407 = add(%405, %406) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:412:14 */; + %408 = add(%407, %369) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:413:18 */; + %409 = nn.relu(%408) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:452:20 */; + %410 = add(%layer3_2_bn1_running_var, 1e-05f /* ty=float32 span=from_string:414:47 */) /* ty=Tensor[(256), float32] span=from_string:415:15 */; + %411 = sqrt(%410) /* ty=Tensor[(256), float32] span=from_string:416:38 */; + %412 = divide(1f /* ty=float32 span=from_string:416:19 */, %411) /* ty=Tensor[(256), float32] span=from_string:417:20 */; + %413 = multiply(%412, %layer3_2_bn1_weight) /* ty=Tensor[(256), float32] span=from_string:421:26 */; + %414 = nn.conv2d(%409, %layer3_2_conv1_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:423:20 */; + %415 = expand_dims(%413, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:423:26 */; + %416 = negative(%layer3_2_bn1_running_mean) /* ty=Tensor[(256), float32] span=from_string:421:20 */; + %417 = multiply(%416, %413) /* ty=Tensor[(256), float32] span=from_string:422:14 */; + %418 = add(%417, %layer3_2_bn1_bias) /* ty=Tensor[(256), float32] span=from_string:424:22 */; + %419 = multiply(%414, %415) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:425:14 */; + %420 = expand_dims(%418, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:425:20 */; + %421 = add(%419, %420) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:426:18 */; + %422 = nn.relu(%421) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:431:20 */; + %423 = add(%layer3_2_bn2_running_var, 1e-05f /* ty=float32 span=from_string:427:47 */) /* ty=Tensor[(256), float32] span=from_string:428:15 */; + %424 = sqrt(%423) /* ty=Tensor[(256), float32] span=from_string:429:38 */; + %425 = divide(1f /* ty=float32 span=from_string:429:19 */, %424) /* ty=Tensor[(256), float32] span=from_string:430:20 */; + %426 = multiply(%425, %layer3_2_bn2_weight) /* ty=Tensor[(256), float32] span=from_string:434:26 */; + %427 = nn.conv2d(%422, %layer3_2_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:436:20 */; + %428 = expand_dims(%426, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:436:26 */; + %429 = negative(%layer3_2_bn2_running_mean) /* ty=Tensor[(256), float32] span=from_string:434:20 */; + %430 = multiply(%429, %426) /* ty=Tensor[(256), float32] span=from_string:435:14 */; + %431 = add(%430, %layer3_2_bn2_bias) /* ty=Tensor[(256), float32] span=from_string:437:22 */; + %432 = multiply(%427, %428) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:438:14 */; + %433 = expand_dims(%431, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:438:20 */; + %434 = add(%432, %433) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:439:18 */; + %435 = nn.relu(%434) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:444:20 */; + %436 = add(%layer3_2_bn3_running_var, 1e-05f /* ty=float32 span=from_string:440:47 */) /* ty=Tensor[(1024), float32] span=from_string:441:15 */; + %437 = sqrt(%436) /* ty=Tensor[(1024), float32] span=from_string:442:38 */; + %438 = divide(1f /* ty=float32 span=from_string:442:19 */, %437) /* ty=Tensor[(1024), float32] span=from_string:443:20 */; + %439 = multiply(%438, %layer3_2_bn3_weight) /* ty=Tensor[(1024), float32] span=from_string:447:26 */; + %440 = nn.conv2d(%435, %layer3_2_conv3_weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:449:20 */; + %441 = expand_dims(%439, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:449:26 */; + %442 = negative(%layer3_2_bn3_running_mean) /* ty=Tensor[(1024), float32] span=from_string:447:20 */; + %443 = multiply(%442, %439) /* ty=Tensor[(1024), float32] span=from_string:448:14 */; + %444 = add(%443, %layer3_2_bn3_bias) /* ty=Tensor[(1024), float32] span=from_string:450:22 */; + %445 = multiply(%440, %441) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:451:14 */; + %446 = expand_dims(%444, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:451:20 */; + %447 = add(%445, %446) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:452:14 */; + %448 = add(%447, %409) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:453:18 */; + %449 = nn.relu(%448) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:492:20 */; + %450 = add(%layer3_3_bn1_running_var, 1e-05f /* ty=float32 span=from_string:454:47 */) /* ty=Tensor[(256), float32] span=from_string:455:15 */; + %451 = sqrt(%450) /* ty=Tensor[(256), float32] span=from_string:456:38 */; + %452 = divide(1f /* ty=float32 span=from_string:456:19 */, %451) /* ty=Tensor[(256), float32] span=from_string:457:20 */; + %453 = multiply(%452, %layer3_3_bn1_weight) /* ty=Tensor[(256), float32] span=from_string:461:26 */; + %454 = nn.conv2d(%449, %layer3_3_conv1_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:463:20 */; + %455 = expand_dims(%453, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:463:26 */; + %456 = negative(%layer3_3_bn1_running_mean) /* ty=Tensor[(256), float32] span=from_string:461:20 */; + %457 = multiply(%456, %453) /* ty=Tensor[(256), float32] span=from_string:462:14 */; + %458 = add(%457, %layer3_3_bn1_bias) /* ty=Tensor[(256), float32] span=from_string:464:22 */; + %459 = multiply(%454, %455) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:465:14 */; + %460 = expand_dims(%458, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:465:20 */; + %461 = add(%459, %460) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:466:18 */; + %462 = nn.relu(%461) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:471:20 */; + %463 = add(%layer3_3_bn2_running_var, 1e-05f /* ty=float32 span=from_string:467:47 */) /* ty=Tensor[(256), float32] span=from_string:468:15 */; + %464 = sqrt(%463) /* ty=Tensor[(256), float32] span=from_string:469:38 */; + %465 = divide(1f /* ty=float32 span=from_string:469:19 */, %464) /* ty=Tensor[(256), float32] span=from_string:470:20 */; + %466 = multiply(%465, %layer3_3_bn2_weight) /* ty=Tensor[(256), float32] span=from_string:474:26 */; + %467 = nn.conv2d(%462, %layer3_3_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:476:20 */; + %468 = expand_dims(%466, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:476:26 */; + %469 = negative(%layer3_3_bn2_running_mean) /* ty=Tensor[(256), float32] span=from_string:474:20 */; + %470 = multiply(%469, %466) /* ty=Tensor[(256), float32] span=from_string:475:14 */; + %471 = add(%470, %layer3_3_bn2_bias) /* ty=Tensor[(256), float32] span=from_string:477:22 */; + %472 = multiply(%467, %468) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:478:14 */; + %473 = expand_dims(%471, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:478:20 */; + %474 = add(%472, %473) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:479:18 */; + %475 = nn.relu(%474) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:484:20 */; + %476 = add(%layer3_3_bn3_running_var, 1e-05f /* ty=float32 span=from_string:480:47 */) /* ty=Tensor[(1024), float32] span=from_string:481:15 */; + %477 = sqrt(%476) /* ty=Tensor[(1024), float32] span=from_string:482:38 */; + %478 = divide(1f /* ty=float32 span=from_string:482:19 */, %477) /* ty=Tensor[(1024), float32] span=from_string:483:20 */; + %479 = multiply(%478, %layer3_3_bn3_weight) /* ty=Tensor[(1024), float32] span=from_string:487:26 */; + %480 = nn.conv2d(%475, %layer3_3_conv3_weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:489:20 */; + %481 = expand_dims(%479, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:489:26 */; + %482 = negative(%layer3_3_bn3_running_mean) /* ty=Tensor[(1024), float32] span=from_string:487:20 */; + %483 = multiply(%482, %479) /* ty=Tensor[(1024), float32] span=from_string:488:14 */; + %484 = add(%483, %layer3_3_bn3_bias) /* ty=Tensor[(1024), float32] span=from_string:490:22 */; + %485 = multiply(%480, %481) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:491:14 */; + %486 = expand_dims(%484, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:491:20 */; + %487 = add(%485, %486) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:492:14 */; + %488 = add(%487, %449) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:493:18 */; + %489 = nn.relu(%488) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:532:20 */; + %490 = add(%layer3_4_bn1_running_var, 1e-05f /* ty=float32 span=from_string:494:47 */) /* ty=Tensor[(256), float32] span=from_string:495:15 */; + %491 = sqrt(%490) /* ty=Tensor[(256), float32] span=from_string:496:38 */; + %492 = divide(1f /* ty=float32 span=from_string:496:19 */, %491) /* ty=Tensor[(256), float32] span=from_string:497:20 */; + %493 = multiply(%492, %layer3_4_bn1_weight) /* ty=Tensor[(256), float32] span=from_string:501:26 */; + %494 = nn.conv2d(%489, %layer3_4_conv1_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:503:20 */; + %495 = expand_dims(%493, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:503:26 */; + %496 = negative(%layer3_4_bn1_running_mean) /* ty=Tensor[(256), float32] span=from_string:501:20 */; + %497 = multiply(%496, %493) /* ty=Tensor[(256), float32] span=from_string:502:14 */; + %498 = add(%497, %layer3_4_bn1_bias) /* ty=Tensor[(256), float32] span=from_string:504:22 */; + %499 = multiply(%494, %495) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:505:14 */; + %500 = expand_dims(%498, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:505:20 */; + %501 = add(%499, %500) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:506:18 */; + %502 = nn.relu(%501) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:511:20 */; + %503 = add(%layer3_4_bn2_running_var, 1e-05f /* ty=float32 span=from_string:507:47 */) /* ty=Tensor[(256), float32] span=from_string:508:15 */; + %504 = sqrt(%503) /* ty=Tensor[(256), float32] span=from_string:509:38 */; + %505 = divide(1f /* ty=float32 span=from_string:509:19 */, %504) /* ty=Tensor[(256), float32] span=from_string:510:20 */; + %506 = multiply(%505, %layer3_4_bn2_weight) /* ty=Tensor[(256), float32] span=from_string:514:26 */; + %507 = nn.conv2d(%502, %layer3_4_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:516:20 */; + %508 = expand_dims(%506, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:516:26 */; + %509 = negative(%layer3_4_bn2_running_mean) /* ty=Tensor[(256), float32] span=from_string:514:20 */; + %510 = multiply(%509, %506) /* ty=Tensor[(256), float32] span=from_string:515:14 */; + %511 = add(%510, %layer3_4_bn2_bias) /* ty=Tensor[(256), float32] span=from_string:517:22 */; + %512 = multiply(%507, %508) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:518:14 */; + %513 = expand_dims(%511, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:518:20 */; + %514 = add(%512, %513) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:519:18 */; + %515 = nn.relu(%514) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:524:20 */; + %516 = add(%layer3_4_bn3_running_var, 1e-05f /* ty=float32 span=from_string:520:47 */) /* ty=Tensor[(1024), float32] span=from_string:521:15 */; + %517 = sqrt(%516) /* ty=Tensor[(1024), float32] span=from_string:522:38 */; + %518 = divide(1f /* ty=float32 span=from_string:522:19 */, %517) /* ty=Tensor[(1024), float32] span=from_string:523:20 */; + %519 = multiply(%518, %layer3_4_bn3_weight) /* ty=Tensor[(1024), float32] span=from_string:527:26 */; + %520 = nn.conv2d(%515, %layer3_4_conv3_weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:529:20 */; + %521 = expand_dims(%519, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:529:26 */; + %522 = negative(%layer3_4_bn3_running_mean) /* ty=Tensor[(1024), float32] span=from_string:527:20 */; + %523 = multiply(%522, %519) /* ty=Tensor[(1024), float32] span=from_string:528:14 */; + %524 = add(%523, %layer3_4_bn3_bias) /* ty=Tensor[(1024), float32] span=from_string:530:22 */; + %525 = multiply(%520, %521) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:531:14 */; + %526 = expand_dims(%524, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:531:20 */; + %527 = add(%525, %526) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:532:14 */; + %528 = add(%527, %489) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:533:18 */; + %529 = nn.relu(%528) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:572:20 */; + %530 = add(%layer3_5_bn1_running_var, 1e-05f /* ty=float32 span=from_string:534:47 */) /* ty=Tensor[(256), float32] span=from_string:535:15 */; + %531 = sqrt(%530) /* ty=Tensor[(256), float32] span=from_string:536:38 */; + %532 = divide(1f /* ty=float32 span=from_string:536:19 */, %531) /* ty=Tensor[(256), float32] span=from_string:537:20 */; + %533 = multiply(%532, %layer3_5_bn1_weight) /* ty=Tensor[(256), float32] span=from_string:541:26 */; + %534 = nn.conv2d(%529, %layer3_5_conv1_weight, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:543:20 */; + %535 = expand_dims(%533, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:543:26 */; + %536 = negative(%layer3_5_bn1_running_mean) /* ty=Tensor[(256), float32] span=from_string:541:20 */; + %537 = multiply(%536, %533) /* ty=Tensor[(256), float32] span=from_string:542:14 */; + %538 = add(%537, %layer3_5_bn1_bias) /* ty=Tensor[(256), float32] span=from_string:544:22 */; + %539 = multiply(%534, %535) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:545:14 */; + %540 = expand_dims(%538, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:545:20 */; + %541 = add(%539, %540) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:546:18 */; + %542 = nn.relu(%541) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:551:20 */; + %543 = add(%layer3_5_bn2_running_var, 1e-05f /* ty=float32 span=from_string:547:47 */) /* ty=Tensor[(256), float32] span=from_string:548:15 */; + %544 = sqrt(%543) /* ty=Tensor[(256), float32] span=from_string:549:38 */; + %545 = divide(1f /* ty=float32 span=from_string:549:19 */, %544) /* ty=Tensor[(256), float32] span=from_string:550:20 */; + %546 = multiply(%545, %layer3_5_bn2_weight) /* ty=Tensor[(256), float32] span=from_string:554:26 */; + %547 = nn.conv2d(%542, %layer3_5_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:556:20 */; + %548 = expand_dims(%546, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:556:26 */; + %549 = negative(%layer3_5_bn2_running_mean) /* ty=Tensor[(256), float32] span=from_string:554:20 */; + %550 = multiply(%549, %546) /* ty=Tensor[(256), float32] span=from_string:555:14 */; + %551 = add(%550, %layer3_5_bn2_bias) /* ty=Tensor[(256), float32] span=from_string:557:22 */; + %552 = multiply(%547, %548) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:558:14 */; + %553 = expand_dims(%551, axis=1, num_newaxis=2) /* ty=Tensor[(256, 1, 1), float32] span=from_string:558:20 */; + %554 = add(%552, %553) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:559:18 */; + %555 = nn.relu(%554) /* ty=Tensor[(1, 256, 14, 14), float32] span=from_string:564:20 */; + %556 = add(%layer3_5_bn3_running_var, 1e-05f /* ty=float32 span=from_string:560:47 */) /* ty=Tensor[(1024), float32] span=from_string:561:15 */; + %557 = sqrt(%556) /* ty=Tensor[(1024), float32] span=from_string:562:38 */; + %558 = divide(1f /* ty=float32 span=from_string:562:19 */, %557) /* ty=Tensor[(1024), float32] span=from_string:563:20 */; + %559 = multiply(%558, %layer3_5_bn3_weight) /* ty=Tensor[(1024), float32] span=from_string:567:26 */; + %560 = nn.conv2d(%555, %layer3_5_conv3_weight, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1]) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:569:20 */; + %561 = expand_dims(%559, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:569:26 */; + %562 = negative(%layer3_5_bn3_running_mean) /* ty=Tensor[(1024), float32] span=from_string:567:20 */; + %563 = multiply(%562, %559) /* ty=Tensor[(1024), float32] span=from_string:568:14 */; + %564 = add(%563, %layer3_5_bn3_bias) /* ty=Tensor[(1024), float32] span=from_string:570:22 */; + %565 = multiply(%560, %561) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:571:14 */; + %566 = expand_dims(%564, axis=1, num_newaxis=2) /* ty=Tensor[(1024, 1, 1), float32] span=from_string:571:20 */; + %567 = add(%565, %566) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:572:14 */; + %568 = add(%567, %529) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:573:18 */; + %569 = nn.relu(%568) /* ty=Tensor[(1, 1024, 14, 14), float32] span=from_string:615:20 */; + %570 = add(%layer4_0_bn1_running_var, 1e-05f /* ty=float32 span=from_string:574:47 */) /* ty=Tensor[(512), float32] span=from_string:575:15 */; + %571 = sqrt(%570) /* ty=Tensor[(512), float32] span=from_string:576:38 */; + %572 = divide(1f /* ty=float32 span=from_string:576:19 */, %571) /* ty=Tensor[(512), float32] span=from_string:577:20 */; + %573 = multiply(%572, %layer4_0_bn1_weight) /* ty=Tensor[(512), float32] span=from_string:581:26 */; + %574 = nn.conv2d(%569, %layer4_0_conv1_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 14, 14), float32] span=from_string:583:20 */; + %575 = expand_dims(%573, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:583:26 */; + %576 = negative(%layer4_0_bn1_running_mean) /* ty=Tensor[(512), float32] span=from_string:581:20 */; + %577 = multiply(%576, %573) /* ty=Tensor[(512), float32] span=from_string:582:14 */; + %578 = add(%577, %layer4_0_bn1_bias) /* ty=Tensor[(512), float32] span=from_string:584:22 */; + %579 = multiply(%574, %575) /* ty=Tensor[(1, 512, 14, 14), float32] span=from_string:585:14 */; + %580 = expand_dims(%578, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:585:20 */; + %581 = add(%579, %580) /* ty=Tensor[(1, 512, 14, 14), float32] span=from_string:586:18 */; + %582 = nn.relu(%581) /* ty=Tensor[(1, 512, 14, 14), float32] span=from_string:591:20 */; + %583 = add(%layer4_0_bn2_running_var, 1e-05f /* ty=float32 span=from_string:587:47 */) /* ty=Tensor[(512), float32] span=from_string:588:15 */; + %584 = sqrt(%583) /* ty=Tensor[(512), float32] span=from_string:589:38 */; + %585 = divide(1f /* ty=float32 span=from_string:589:19 */, %584) /* ty=Tensor[(512), float32] span=from_string:590:20 */; + %586 = multiply(%585, %layer4_0_bn2_weight) /* ty=Tensor[(512), float32] span=from_string:594:26 */; + %587 = nn.conv2d(%582, %layer4_0_conv2_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:596:20 */; + %588 = expand_dims(%586, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:596:26 */; + %589 = negative(%layer4_0_bn2_running_mean) /* ty=Tensor[(512), float32] span=from_string:594:20 */; + %590 = multiply(%589, %586) /* ty=Tensor[(512), float32] span=from_string:595:14 */; + %591 = add(%590, %layer4_0_bn2_bias) /* ty=Tensor[(512), float32] span=from_string:597:22 */; + %592 = multiply(%587, %588) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:598:14 */; + %593 = expand_dims(%591, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:598:20 */; + %594 = add(%592, %593) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:599:18 */; + %595 = nn.relu(%594) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:604:20 */; + %596 = add(%layer4_0_bn3_running_var, 1e-05f /* ty=float32 span=from_string:600:47 */) /* ty=Tensor[(2048), float32] span=from_string:601:15 */; + %597 = sqrt(%596) /* ty=Tensor[(2048), float32] span=from_string:602:38 */; + %598 = divide(1f /* ty=float32 span=from_string:602:19 */, %597) /* ty=Tensor[(2048), float32] span=from_string:603:20 */; + %599 = multiply(%598, %layer4_0_bn3_weight) /* ty=Tensor[(2048), float32] span=from_string:607:26 */; + %600 = nn.conv2d(%595, %layer4_0_conv3_weight, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:609:20 */; + %601 = expand_dims(%599, axis=1, num_newaxis=2) /* ty=Tensor[(2048, 1, 1), float32] span=from_string:609:26 */; + %602 = negative(%layer4_0_bn3_running_mean) /* ty=Tensor[(2048), float32] span=from_string:607:20 */; + %603 = multiply(%602, %599) /* ty=Tensor[(2048), float32] span=from_string:608:14 */; + %604 = add(%603, %layer4_0_bn3_bias) /* ty=Tensor[(2048), float32] span=from_string:610:22 */; + %605 = multiply(%600, %601) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:622:14 */; + %606 = expand_dims(%604, axis=1, num_newaxis=2) /* ty=Tensor[(2048, 1, 1), float32] span=from_string:622:20 */; + %607 = add(%layer4_0_downsample_1_running_var, 1e-05f /* ty=float32 span=from_string:611:56 */) /* ty=Tensor[(2048), float32] span=from_string:612:15 */; + %608 = sqrt(%607) /* ty=Tensor[(2048), float32] span=from_string:613:38 */; + %609 = divide(1f /* ty=float32 span=from_string:613:19 */, %608) /* ty=Tensor[(2048), float32] span=from_string:614:20 */; + %610 = multiply(%609, %layer4_0_downsample_1_weight) /* ty=Tensor[(2048), float32] span=from_string:618:26 */; + %611 = nn.conv2d(%569, %layer4_0_downsample_0_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:620:20 */; + %612 = expand_dims(%610, axis=1, num_newaxis=2) /* ty=Tensor[(2048, 1, 1), float32] span=from_string:620:26 */; + %613 = negative(%layer4_0_downsample_1_running_mean) /* ty=Tensor[(2048), float32] span=from_string:618:20 */; + %614 = multiply(%613, %610) /* ty=Tensor[(2048), float32] span=from_string:619:14 */; + %615 = add(%614, %layer4_0_downsample_1_bias) /* ty=Tensor[(2048), float32] span=from_string:621:22 */; + %616 = multiply(%611, %612) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:623:14 */; + %617 = expand_dims(%615, axis=1, num_newaxis=2) /* ty=Tensor[(2048, 1, 1), float32] span=from_string:623:20 */; + %618 = add(%605, %606) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:624:14 */; + %619 = add(%616, %617) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:624:20 */; + %620 = add(%618, %619) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:625:18 */; + %621 = nn.relu(%620) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:664:20 */; + %622 = add(%layer4_1_bn1_running_var, 1e-05f /* ty=float32 span=from_string:626:47 */) /* ty=Tensor[(512), float32] span=from_string:627:15 */; + %623 = sqrt(%622) /* ty=Tensor[(512), float32] span=from_string:628:38 */; + %624 = divide(1f /* ty=float32 span=from_string:628:19 */, %623) /* ty=Tensor[(512), float32] span=from_string:629:20 */; + %625 = multiply(%624, %layer4_1_bn1_weight) /* ty=Tensor[(512), float32] span=from_string:633:26 */; + %626 = nn.conv2d(%621, %layer4_1_conv1_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:635:20 */; + %627 = expand_dims(%625, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:635:26 */; + %628 = negative(%layer4_1_bn1_running_mean) /* ty=Tensor[(512), float32] span=from_string:633:20 */; + %629 = multiply(%628, %625) /* ty=Tensor[(512), float32] span=from_string:634:14 */; + %630 = add(%629, %layer4_1_bn1_bias) /* ty=Tensor[(512), float32] span=from_string:636:22 */; + %631 = multiply(%626, %627) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:637:14 */; + %632 = expand_dims(%630, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:637:20 */; + %633 = add(%631, %632) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:638:18 */; + %634 = nn.relu(%633) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:643:20 */; + %635 = add(%layer4_1_bn2_running_var, 1e-05f /* ty=float32 span=from_string:639:47 */) /* ty=Tensor[(512), float32] span=from_string:640:15 */; + %636 = sqrt(%635) /* ty=Tensor[(512), float32] span=from_string:641:38 */; + %637 = divide(1f /* ty=float32 span=from_string:641:19 */, %636) /* ty=Tensor[(512), float32] span=from_string:642:20 */; + %638 = multiply(%637, %layer4_1_bn2_weight) /* ty=Tensor[(512), float32] span=from_string:646:26 */; + %639 = nn.conv2d(%634, %layer4_1_conv2_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:648:20 */; + %640 = expand_dims(%638, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:648:26 */; + %641 = negative(%layer4_1_bn2_running_mean) /* ty=Tensor[(512), float32] span=from_string:646:20 */; + %642 = multiply(%641, %638) /* ty=Tensor[(512), float32] span=from_string:647:14 */; + %643 = add(%642, %layer4_1_bn2_bias) /* ty=Tensor[(512), float32] span=from_string:649:22 */; + %644 = multiply(%639, %640) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:650:14 */; + %645 = expand_dims(%643, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:650:20 */; + %646 = add(%644, %645) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:651:18 */; + %647 = nn.relu(%646) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:656:20 */; + %648 = add(%layer4_1_bn3_running_var, 1e-05f /* ty=float32 span=from_string:652:47 */) /* ty=Tensor[(2048), float32] span=from_string:653:15 */; + %649 = sqrt(%648) /* ty=Tensor[(2048), float32] span=from_string:654:38 */; + %650 = divide(1f /* ty=float32 span=from_string:654:19 */, %649) /* ty=Tensor[(2048), float32] span=from_string:655:20 */; + %651 = multiply(%650, %layer4_1_bn3_weight) /* ty=Tensor[(2048), float32] span=from_string:659:26 */; + %652 = nn.conv2d(%647, %layer4_1_conv3_weight, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:661:20 */; + %653 = expand_dims(%651, axis=1, num_newaxis=2) /* ty=Tensor[(2048, 1, 1), float32] span=from_string:661:26 */; + %654 = negative(%layer4_1_bn3_running_mean) /* ty=Tensor[(2048), float32] span=from_string:659:20 */; + %655 = multiply(%654, %651) /* ty=Tensor[(2048), float32] span=from_string:660:14 */; + %656 = add(%655, %layer4_1_bn3_bias) /* ty=Tensor[(2048), float32] span=from_string:662:22 */; + %657 = multiply(%652, %653) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:663:14 */; + %658 = expand_dims(%656, axis=1, num_newaxis=2) /* ty=Tensor[(2048, 1, 1), float32] span=from_string:663:20 */; + %659 = add(%657, %658) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:664:14 */; + %660 = add(%659, %621) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:665:18 */; + %661 = nn.relu(%660) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:704:20 */; + %662 = add(%layer4_2_bn1_running_var, 1e-05f /* ty=float32 span=from_string:666:47 */) /* ty=Tensor[(512), float32] span=from_string:667:15 */; + %663 = sqrt(%662) /* ty=Tensor[(512), float32] span=from_string:668:38 */; + %664 = divide(1f /* ty=float32 span=from_string:668:19 */, %663) /* ty=Tensor[(512), float32] span=from_string:669:20 */; + %665 = multiply(%664, %layer4_2_bn1_weight) /* ty=Tensor[(512), float32] span=from_string:673:26 */; + %666 = nn.conv2d(%661, %layer4_2_conv1_weight, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:675:20 */; + %667 = expand_dims(%665, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:675:26 */; + %668 = negative(%layer4_2_bn1_running_mean) /* ty=Tensor[(512), float32] span=from_string:673:20 */; + %669 = multiply(%668, %665) /* ty=Tensor[(512), float32] span=from_string:674:14 */; + %670 = add(%669, %layer4_2_bn1_bias) /* ty=Tensor[(512), float32] span=from_string:676:22 */; + %671 = multiply(%666, %667) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:677:14 */; + %672 = expand_dims(%670, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:677:20 */; + %673 = add(%671, %672) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:678:18 */; + %674 = nn.relu(%673) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:683:20 */; + %675 = add(%layer4_2_bn2_running_var, 1e-05f /* ty=float32 span=from_string:679:47 */) /* ty=Tensor[(512), float32] span=from_string:680:15 */; + %676 = sqrt(%675) /* ty=Tensor[(512), float32] span=from_string:681:38 */; + %677 = divide(1f /* ty=float32 span=from_string:681:19 */, %676) /* ty=Tensor[(512), float32] span=from_string:682:20 */; + %678 = multiply(%677, %layer4_2_bn2_weight) /* ty=Tensor[(512), float32] span=from_string:686:26 */; + %679 = nn.conv2d(%674, %layer4_2_conv2_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:688:20 */; + %680 = expand_dims(%678, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:688:26 */; + %681 = negative(%layer4_2_bn2_running_mean) /* ty=Tensor[(512), float32] span=from_string:686:20 */; + %682 = multiply(%681, %678) /* ty=Tensor[(512), float32] span=from_string:687:14 */; + %683 = add(%682, %layer4_2_bn2_bias) /* ty=Tensor[(512), float32] span=from_string:689:22 */; + %684 = multiply(%679, %680) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:690:14 */; + %685 = expand_dims(%683, axis=1, num_newaxis=2) /* ty=Tensor[(512, 1, 1), float32] span=from_string:690:20 */; + %686 = add(%684, %685) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:691:18 */; + %687 = nn.relu(%686) /* ty=Tensor[(1, 512, 7, 7), float32] span=from_string:696:20 */; + %688 = add(%layer4_2_bn3_running_var, 1e-05f /* ty=float32 span=from_string:692:47 */) /* ty=Tensor[(2048), float32] span=from_string:693:15 */; + %689 = sqrt(%688) /* ty=Tensor[(2048), float32] span=from_string:694:38 */; + %690 = divide(1f /* ty=float32 span=from_string:694:19 */, %689) /* ty=Tensor[(2048), float32] span=from_string:695:20 */; + %691 = multiply(%690, %layer4_2_bn3_weight) /* ty=Tensor[(2048), float32] span=from_string:699:26 */; + %692 = nn.conv2d(%687, %layer4_2_conv3_weight, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1]) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:701:20 */; + %693 = expand_dims(%691, axis=1, num_newaxis=2) /* ty=Tensor[(2048, 1, 1), float32] span=from_string:701:26 */; + %694 = negative(%layer4_2_bn3_running_mean) /* ty=Tensor[(2048), float32] span=from_string:699:20 */; + %695 = multiply(%694, %691) /* ty=Tensor[(2048), float32] span=from_string:700:14 */; + %696 = add(%695, %layer4_2_bn3_bias) /* ty=Tensor[(2048), float32] span=from_string:702:22 */; + %697 = multiply(%692, %693) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:703:14 */; + %698 = expand_dims(%696, axis=1, num_newaxis=2) /* ty=Tensor[(2048, 1, 1), float32] span=from_string:703:20 */; + %699 = add(%697, %698) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:704:14 */; + %700 = add(%699, %661) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:705:18 */; + %701 = nn.relu(%700) /* ty=Tensor[(1, 2048, 7, 7), float32] span=from_string:706:33 */; + %702 = nn.adaptive_avg_pool2d(%701, output_size=[1, 1]) /* ty=Tensor[(1, 2048, 1, 1), float32] span=from_string:707:18 */; + %703 = reshape(%702, newshape=[0, -1, 1, 1]) /* ty=Tensor[(1, 2048, 1, 1), float32] span=from_string:709:18 */; + %704 = transpose(%fc_weight, axes=[1, 0]) /* ty=Tensor[(2048, 1000), float32] span=from_string:710:20 */; + %705 = squeeze(%703, axis=[2, 3]) /* ty=Tensor[(1, 2048), float32] span=from_string:711:19 */; + %706 = transpose(%704, axes=[1, 0]) /* ty=Tensor[(1000, 2048), float32] span=from_string:711:25 */; + %707 = nn.dense(%705, %706, units=1000) /* ty=Tensor[(1, 1000), float32] span=from_string:712:7 */; + add(%707, %fc_bias) /* ty=Tensor[(1, 1000), float32] span=from_string:4:3 */ +} \ No newline at end of file diff --git a/tests/models/resnet50_simplifyinference_from_tf.relay b/tests/models/resnet50_simplifyinference_from_tf.relay new file mode 100644 index 0000000..2adefb8 --- /dev/null +++ b/tests/models/resnet50_simplifyinference_from_tf.relay @@ -0,0 +1,613 @@ +#[version="0.0.5"] + +def @main(%input_tensor: Tensor[(1, 224, 224, 3), float32], %resnet_model_conv2d_kernel: Tensor[(7, 7, 3, 64), float32], %resnet_model_batch_normalization_gamma: Tensor[(64), float32], %resnet_model_batch_normalization_beta: Tensor[(64), float32], %resnet_model_batch_normalization_moving_mean: Tensor[(64), float32], %resnet_model_batch_normalization_moving_variance: Tensor[(64), float32], %resnet_model_conv2d_2_kernel: Tensor[(1, 1, 64, 64), float32], %resnet_model_batch_normalization_2_gamma: Tensor[(64), float32], %resnet_model_batch_normalization_2_beta: Tensor[(64), float32], %resnet_model_batch_normalization_2_moving_mean: Tensor[(64), float32], %resnet_model_batch_normalization_2_moving_variance: Tensor[(64), float32], %resnet_model_conv2d_3_kernel: Tensor[(3, 3, 64, 64), float32], %resnet_model_batch_normalization_3_gamma: Tensor[(64), float32], %resnet_model_batch_normalization_3_beta: Tensor[(64), float32], %resnet_model_batch_normalization_3_moving_mean: Tensor[(64), float32], %resnet_model_batch_normalization_3_moving_variance: Tensor[(64), float32], %resnet_model_conv2d_4_kernel: Tensor[(1, 1, 64, 256), float32], %resnet_model_batch_normalization_4_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_4_beta: Tensor[(256), float32], %resnet_model_batch_normalization_4_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_4_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_1_kernel: Tensor[(1, 1, 64, 256), float32], %resnet_model_batch_normalization_1_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_1_beta: Tensor[(256), float32], %resnet_model_batch_normalization_1_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_1_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_5_kernel: Tensor[(1, 1, 256, 64), float32], %resnet_model_batch_normalization_5_gamma: Tensor[(64), float32], %resnet_model_batch_normalization_5_beta: Tensor[(64), float32], %resnet_model_batch_normalization_5_moving_mean: Tensor[(64), float32], %resnet_model_batch_normalization_5_moving_variance: Tensor[(64), float32], %resnet_model_conv2d_6_kernel: Tensor[(3, 3, 64, 64), float32], %resnet_model_batch_normalization_6_gamma: Tensor[(64), float32], %resnet_model_batch_normalization_6_beta: Tensor[(64), float32], %resnet_model_batch_normalization_6_moving_mean: Tensor[(64), float32], %resnet_model_batch_normalization_6_moving_variance: Tensor[(64), float32], %resnet_model_conv2d_7_kernel: Tensor[(1, 1, 64, 256), float32], %resnet_model_batch_normalization_7_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_7_beta: Tensor[(256), float32], %resnet_model_batch_normalization_7_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_7_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_8_kernel: Tensor[(1, 1, 256, 64), float32], %resnet_model_batch_normalization_8_gamma: Tensor[(64), float32], %resnet_model_batch_normalization_8_beta: Tensor[(64), float32], %resnet_model_batch_normalization_8_moving_mean: Tensor[(64), float32], %resnet_model_batch_normalization_8_moving_variance: Tensor[(64), float32], %resnet_model_conv2d_9_kernel: Tensor[(3, 3, 64, 64), float32], %resnet_model_batch_normalization_9_gamma: Tensor[(64), float32], %resnet_model_batch_normalization_9_beta: Tensor[(64), float32], %resnet_model_batch_normalization_9_moving_mean: Tensor[(64), float32], %resnet_model_batch_normalization_9_moving_variance: Tensor[(64), float32], %resnet_model_conv2d_10_kernel: Tensor[(1, 1, 64, 256), float32], %resnet_model_batch_normalization_10_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_10_beta: Tensor[(256), float32], %resnet_model_batch_normalization_10_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_10_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_12_kernel: Tensor[(1, 1, 256, 128), float32], %resnet_model_batch_normalization_12_gamma: Tensor[(128), float32], %resnet_model_batch_normalization_12_beta: Tensor[(128), float32], %resnet_model_batch_normalization_12_moving_mean: Tensor[(128), float32], %resnet_model_batch_normalization_12_moving_variance: Tensor[(128), float32], %resnet_model_conv2d_13_kernel: Tensor[(3, 3, 128, 128), float32], %resnet_model_batch_normalization_13_gamma: Tensor[(128), float32], %resnet_model_batch_normalization_13_beta: Tensor[(128), float32], %resnet_model_batch_normalization_13_moving_mean: Tensor[(128), float32], %resnet_model_batch_normalization_13_moving_variance: Tensor[(128), float32], %resnet_model_conv2d_14_kernel: Tensor[(1, 1, 128, 512), float32], %resnet_model_batch_normalization_14_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_14_beta: Tensor[(512), float32], %resnet_model_batch_normalization_14_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_14_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_11_kernel: Tensor[(1, 1, 256, 512), float32], %resnet_model_batch_normalization_11_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_11_beta: Tensor[(512), float32], %resnet_model_batch_normalization_11_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_11_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_15_kernel: Tensor[(1, 1, 512, 128), float32], %resnet_model_batch_normalization_15_gamma: Tensor[(128), float32], %resnet_model_batch_normalization_15_beta: Tensor[(128), float32], %resnet_model_batch_normalization_15_moving_mean: Tensor[(128), float32], %resnet_model_batch_normalization_15_moving_variance: Tensor[(128), float32], %resnet_model_conv2d_16_kernel: Tensor[(3, 3, 128, 128), float32], %resnet_model_batch_normalization_16_gamma: Tensor[(128), float32], %resnet_model_batch_normalization_16_beta: Tensor[(128), float32], %resnet_model_batch_normalization_16_moving_mean: Tensor[(128), float32], %resnet_model_batch_normalization_16_moving_variance: Tensor[(128), float32], %resnet_model_conv2d_17_kernel: Tensor[(1, 1, 128, 512), float32], %resnet_model_batch_normalization_17_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_17_beta: Tensor[(512), float32], %resnet_model_batch_normalization_17_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_17_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_18_kernel: Tensor[(1, 1, 512, 128), float32], %resnet_model_batch_normalization_18_gamma: Tensor[(128), float32], %resnet_model_batch_normalization_18_beta: Tensor[(128), float32], %resnet_model_batch_normalization_18_moving_mean: Tensor[(128), float32], %resnet_model_batch_normalization_18_moving_variance: Tensor[(128), float32], %resnet_model_conv2d_19_kernel: Tensor[(3, 3, 128, 128), float32], %resnet_model_batch_normalization_19_gamma: Tensor[(128), float32], %resnet_model_batch_normalization_19_beta: Tensor[(128), float32], %resnet_model_batch_normalization_19_moving_mean: Tensor[(128), float32], %resnet_model_batch_normalization_19_moving_variance: Tensor[(128), float32], %resnet_model_conv2d_20_kernel: Tensor[(1, 1, 128, 512), float32], %resnet_model_batch_normalization_20_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_20_beta: Tensor[(512), float32], %resnet_model_batch_normalization_20_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_20_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_21_kernel: Tensor[(1, 1, 512, 128), float32], %resnet_model_batch_normalization_21_gamma: Tensor[(128), float32], %resnet_model_batch_normalization_21_beta: Tensor[(128), float32], %resnet_model_batch_normalization_21_moving_mean: Tensor[(128), float32], %resnet_model_batch_normalization_21_moving_variance: Tensor[(128), float32], %resnet_model_conv2d_22_kernel: Tensor[(3, 3, 128, 128), float32], %resnet_model_batch_normalization_22_gamma: Tensor[(128), float32], %resnet_model_batch_normalization_22_beta: Tensor[(128), float32], %resnet_model_batch_normalization_22_moving_mean: Tensor[(128), float32], %resnet_model_batch_normalization_22_moving_variance: Tensor[(128), float32], %resnet_model_conv2d_23_kernel: Tensor[(1, 1, 128, 512), float32], %resnet_model_batch_normalization_23_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_23_beta: Tensor[(512), float32], %resnet_model_batch_normalization_23_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_23_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_25_kernel: Tensor[(1, 1, 512, 256), float32], %resnet_model_batch_normalization_25_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_25_beta: Tensor[(256), float32], %resnet_model_batch_normalization_25_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_25_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_26_kernel: Tensor[(3, 3, 256, 256), float32], %resnet_model_batch_normalization_26_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_26_beta: Tensor[(256), float32], %resnet_model_batch_normalization_26_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_26_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_27_kernel: Tensor[(1, 1, 256, 1024), float32], %resnet_model_batch_normalization_27_gamma: Tensor[(1024), float32], %resnet_model_batch_normalization_27_beta: Tensor[(1024), float32], %resnet_model_batch_normalization_27_moving_mean: Tensor[(1024), float32], %resnet_model_batch_normalization_27_moving_variance: Tensor[(1024), float32], %resnet_model_conv2d_24_kernel: Tensor[(1, 1, 512, 1024), float32], %resnet_model_batch_normalization_24_gamma: Tensor[(1024), float32], %resnet_model_batch_normalization_24_beta: Tensor[(1024), float32], %resnet_model_batch_normalization_24_moving_mean: Tensor[(1024), float32], %resnet_model_batch_normalization_24_moving_variance: Tensor[(1024), float32], %resnet_model_conv2d_28_kernel: Tensor[(1, 1, 1024, 256), float32], %resnet_model_batch_normalization_28_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_28_beta: Tensor[(256), float32], %resnet_model_batch_normalization_28_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_28_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_29_kernel: Tensor[(3, 3, 256, 256), float32], %resnet_model_batch_normalization_29_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_29_beta: Tensor[(256), float32], %resnet_model_batch_normalization_29_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_29_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_30_kernel: Tensor[(1, 1, 256, 1024), float32], %resnet_model_batch_normalization_30_gamma: Tensor[(1024), float32], %resnet_model_batch_normalization_30_beta: Tensor[(1024), float32], %resnet_model_batch_normalization_30_moving_mean: Tensor[(1024), float32], %resnet_model_batch_normalization_30_moving_variance: Tensor[(1024), float32], %resnet_model_conv2d_31_kernel: Tensor[(1, 1, 1024, 256), float32], %resnet_model_batch_normalization_31_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_31_beta: Tensor[(256), float32], %resnet_model_batch_normalization_31_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_31_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_32_kernel: Tensor[(3, 3, 256, 256), float32], %resnet_model_batch_normalization_32_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_32_beta: Tensor[(256), float32], %resnet_model_batch_normalization_32_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_32_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_33_kernel: Tensor[(1, 1, 256, 1024), float32], %resnet_model_batch_normalization_33_gamma: Tensor[(1024), float32], %resnet_model_batch_normalization_33_beta: Tensor[(1024), float32], %resnet_model_batch_normalization_33_moving_mean: Tensor[(1024), float32], %resnet_model_batch_normalization_33_moving_variance: Tensor[(1024), float32], %resnet_model_conv2d_34_kernel: Tensor[(1, 1, 1024, 256), float32], %resnet_model_batch_normalization_34_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_34_beta: Tensor[(256), float32], %resnet_model_batch_normalization_34_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_34_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_35_kernel: Tensor[(3, 3, 256, 256), float32], %resnet_model_batch_normalization_35_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_35_beta: Tensor[(256), float32], %resnet_model_batch_normalization_35_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_35_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_36_kernel: Tensor[(1, 1, 256, 1024), float32], %resnet_model_batch_normalization_36_gamma: Tensor[(1024), float32], %resnet_model_batch_normalization_36_beta: Tensor[(1024), float32], %resnet_model_batch_normalization_36_moving_mean: Tensor[(1024), float32], %resnet_model_batch_normalization_36_moving_variance: Tensor[(1024), float32], %resnet_model_conv2d_37_kernel: Tensor[(1, 1, 1024, 256), float32], %resnet_model_batch_normalization_37_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_37_beta: Tensor[(256), float32], %resnet_model_batch_normalization_37_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_37_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_38_kernel: Tensor[(3, 3, 256, 256), float32], %resnet_model_batch_normalization_38_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_38_beta: Tensor[(256), float32], %resnet_model_batch_normalization_38_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_38_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_39_kernel: Tensor[(1, 1, 256, 1024), float32], %resnet_model_batch_normalization_39_gamma: Tensor[(1024), float32], %resnet_model_batch_normalization_39_beta: Tensor[(1024), float32], %resnet_model_batch_normalization_39_moving_mean: Tensor[(1024), float32], %resnet_model_batch_normalization_39_moving_variance: Tensor[(1024), float32], %resnet_model_conv2d_40_kernel: Tensor[(1, 1, 1024, 256), float32], %resnet_model_batch_normalization_40_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_40_beta: Tensor[(256), float32], %resnet_model_batch_normalization_40_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_40_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_41_kernel: Tensor[(3, 3, 256, 256), float32], %resnet_model_batch_normalization_41_gamma: Tensor[(256), float32], %resnet_model_batch_normalization_41_beta: Tensor[(256), float32], %resnet_model_batch_normalization_41_moving_mean: Tensor[(256), float32], %resnet_model_batch_normalization_41_moving_variance: Tensor[(256), float32], %resnet_model_conv2d_42_kernel: Tensor[(1, 1, 256, 1024), float32], %resnet_model_batch_normalization_42_gamma: Tensor[(1024), float32], %resnet_model_batch_normalization_42_beta: Tensor[(1024), float32], %resnet_model_batch_normalization_42_moving_mean: Tensor[(1024), float32], %resnet_model_batch_normalization_42_moving_variance: Tensor[(1024), float32], %resnet_model_conv2d_44_kernel: Tensor[(1, 1, 1024, 512), float32], %resnet_model_batch_normalization_44_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_44_beta: Tensor[(512), float32], %resnet_model_batch_normalization_44_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_44_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_45_kernel: Tensor[(3, 3, 512, 512), float32], %resnet_model_batch_normalization_45_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_45_beta: Tensor[(512), float32], %resnet_model_batch_normalization_45_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_45_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_46_kernel: Tensor[(1, 1, 512, 2048), float32], %resnet_model_batch_normalization_46_gamma: Tensor[(2048), float32], %resnet_model_batch_normalization_46_beta: Tensor[(2048), float32], %resnet_model_batch_normalization_46_moving_mean: Tensor[(2048), float32], %resnet_model_batch_normalization_46_moving_variance: Tensor[(2048), float32], %resnet_model_conv2d_43_kernel: Tensor[(1, 1, 1024, 2048), float32], %resnet_model_batch_normalization_43_gamma: Tensor[(2048), float32], %resnet_model_batch_normalization_43_beta: Tensor[(2048), float32], %resnet_model_batch_normalization_43_moving_mean: Tensor[(2048), float32], %resnet_model_batch_normalization_43_moving_variance: Tensor[(2048), float32], %resnet_model_conv2d_47_kernel: Tensor[(1, 1, 2048, 512), float32], %resnet_model_batch_normalization_47_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_47_beta: Tensor[(512), float32], %resnet_model_batch_normalization_47_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_47_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_48_kernel: Tensor[(3, 3, 512, 512), float32], %resnet_model_batch_normalization_48_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_48_beta: Tensor[(512), float32], %resnet_model_batch_normalization_48_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_48_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_49_kernel: Tensor[(1, 1, 512, 2048), float32], %resnet_model_batch_normalization_49_gamma: Tensor[(2048), float32], %resnet_model_batch_normalization_49_beta: Tensor[(2048), float32], %resnet_model_batch_normalization_49_moving_mean: Tensor[(2048), float32], %resnet_model_batch_normalization_49_moving_variance: Tensor[(2048), float32], %resnet_model_conv2d_50_kernel: Tensor[(1, 1, 2048, 512), float32], %resnet_model_batch_normalization_50_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_50_beta: Tensor[(512), float32], %resnet_model_batch_normalization_50_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_50_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_51_kernel: Tensor[(3, 3, 512, 512), float32], %resnet_model_batch_normalization_51_gamma: Tensor[(512), float32], %resnet_model_batch_normalization_51_beta: Tensor[(512), float32], %resnet_model_batch_normalization_51_moving_mean: Tensor[(512), float32], %resnet_model_batch_normalization_51_moving_variance: Tensor[(512), float32], %resnet_model_conv2d_52_kernel: Tensor[(1, 1, 512, 2048), float32], %resnet_model_batch_normalization_52_gamma: Tensor[(2048), float32], %resnet_model_batch_normalization_52_beta: Tensor[(2048), float32], %resnet_model_batch_normalization_52_moving_mean: Tensor[(2048), float32], %resnet_model_batch_normalization_52_moving_variance: Tensor[(2048), float32], %resnet_model_dense_kernel: Tensor[(2048, 1001), float32], %resnet_model_dense_bias: Tensor[(1001), float32]) -> Tensor[(1, 1001), float32] { + %10 = nn.pad(%input_tensor, 0 /* ty=int32 */, pad_width=[[0, 0], [3, 3], [3, 3], [0, 0]]) /* resnet_model/Pad */ /* ty=Tensor[(1, 230, 230, 3), float32] */; + %11 = add(%resnet_model_batch_normalization_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %12 = sqrt(%11) /* ty=Tensor[(64), float32] */; + %13 = divide(1f /* ty=float32 */, %12) /* ty=Tensor[(64), float32] */; + %14 = nn.conv2d(%10, %resnet_model_conv2d_kernel, strides=[2, 2], padding=[0, 0, 0, 0], channels=64, kernel_size=[7, 7], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d/Conv2D */ /* ty=Tensor[(1, 112, 112, 64), float32] */; + %15 = multiply(%13, %resnet_model_batch_normalization_gamma) /* ty=Tensor[(64), float32] */; + %16 = negative(%resnet_model_batch_normalization_moving_mean) /* ty=Tensor[(64), float32] */; + %17 = multiply(%16, %15) /* ty=Tensor[(64), float32] */; + %18 = multiply(%14, %15) /* ty=Tensor[(1, 112, 112, 64), float32] */; + %19 = add(%17, %resnet_model_batch_normalization_beta) /* ty=Tensor[(64), float32] */; + %20 = add(%18, %19) /* ty=Tensor[(1, 112, 112, 64), float32] */; + %21 = nn.relu(%20) /* resnet_model/Relu */ /* ty=Tensor[(1, 112, 112, 64), float32] */; + %22 = nn.max_pool2d(%21, pool_size=[3, 3], strides=[2, 2], padding=[0, 0, 1, 1], layout="NHWC") /* resnet_model/max_pooling2d/MaxPool */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %23 = add(%resnet_model_batch_normalization_2_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %24 = sqrt(%23) /* ty=Tensor[(64), float32] */; + %25 = divide(1f /* ty=float32 */, %24) /* ty=Tensor[(64), float32] */; + %26 = nn.conv2d(%22, %resnet_model_conv2d_2_kernel, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_2/Conv2D */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %27 = multiply(%25, %resnet_model_batch_normalization_2_gamma) /* ty=Tensor[(64), float32] */; + %28 = negative(%resnet_model_batch_normalization_2_moving_mean) /* ty=Tensor[(64), float32] */; + %29 = multiply(%28, %27) /* ty=Tensor[(64), float32] */; + %30 = multiply(%26, %27) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %31 = add(%29, %resnet_model_batch_normalization_2_beta) /* ty=Tensor[(64), float32] */; + %32 = add(%30, %31) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %33 = nn.relu(%32) /* resnet_model/Relu_1 */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %34 = add(%resnet_model_batch_normalization_3_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %35 = sqrt(%34) /* ty=Tensor[(64), float32] */; + %36 = divide(1f /* ty=float32 */, %35) /* ty=Tensor[(64), float32] */; + %37 = nn.conv2d(%33, %resnet_model_conv2d_3_kernel, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_3/Conv2D */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %38 = multiply(%36, %resnet_model_batch_normalization_3_gamma) /* ty=Tensor[(64), float32] */; + %39 = negative(%resnet_model_batch_normalization_3_moving_mean) /* ty=Tensor[(64), float32] */; + %40 = multiply(%39, %38) /* ty=Tensor[(64), float32] */; + %41 = multiply(%37, %38) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %42 = add(%40, %resnet_model_batch_normalization_3_beta) /* ty=Tensor[(64), float32] */; + %43 = add(%41, %42) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %44 = nn.relu(%43) /* resnet_model/Relu_2 */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %45 = add(%resnet_model_batch_normalization_4_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %46 = sqrt(%45) /* ty=Tensor[(256), float32] */; + %47 = divide(1f /* ty=float32 */, %46) /* ty=Tensor[(256), float32] */; + %48 = nn.conv2d(%44, %resnet_model_conv2d_4_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_4/Conv2D */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %49 = multiply(%47, %resnet_model_batch_normalization_4_gamma) /* ty=Tensor[(256), float32] */; + %50 = negative(%resnet_model_batch_normalization_4_moving_mean) /* ty=Tensor[(256), float32] */; + %51 = multiply(%50, %49) /* ty=Tensor[(256), float32] */; + %52 = multiply(%48, %49) /* ty=Tensor[(1, 56, 56, 256), float32] */; + %53 = add(%51, %resnet_model_batch_normalization_4_beta) /* ty=Tensor[(256), float32] */; + %54 = add(%resnet_model_batch_normalization_1_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %55 = sqrt(%54) /* ty=Tensor[(256), float32] */; + %56 = divide(1f /* ty=float32 */, %55) /* ty=Tensor[(256), float32] */; + %57 = nn.conv2d(%22, %resnet_model_conv2d_1_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_1/Conv2D */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %58 = multiply(%56, %resnet_model_batch_normalization_1_gamma) /* ty=Tensor[(256), float32] */; + %59 = negative(%resnet_model_batch_normalization_1_moving_mean) /* ty=Tensor[(256), float32] */; + %60 = multiply(%59, %58) /* ty=Tensor[(256), float32] */; + %61 = multiply(%57, %58) /* ty=Tensor[(1, 56, 56, 256), float32] */; + %62 = add(%60, %resnet_model_batch_normalization_1_beta) /* ty=Tensor[(256), float32] */; + %63 = add(%52, %53) /* ty=Tensor[(1, 56, 56, 256), float32] */; + %64 = add(%61, %62) /* ty=Tensor[(1, 56, 56, 256), float32] */; + %65 = add(%63, %64) /* resnet_model/add */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %66 = nn.relu(%65) /* resnet_model/Relu_3 */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %67 = add(%resnet_model_batch_normalization_5_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %68 = sqrt(%67) /* ty=Tensor[(64), float32] */; + %69 = divide(1f /* ty=float32 */, %68) /* ty=Tensor[(64), float32] */; + %70 = nn.conv2d(%66, %resnet_model_conv2d_5_kernel, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_5/Conv2D */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %71 = multiply(%69, %resnet_model_batch_normalization_5_gamma) /* ty=Tensor[(64), float32] */; + %72 = negative(%resnet_model_batch_normalization_5_moving_mean) /* ty=Tensor[(64), float32] */; + %73 = multiply(%72, %71) /* ty=Tensor[(64), float32] */; + %74 = multiply(%70, %71) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %75 = add(%73, %resnet_model_batch_normalization_5_beta) /* ty=Tensor[(64), float32] */; + %76 = add(%74, %75) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %77 = nn.relu(%76) /* resnet_model/Relu_4 */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %78 = add(%resnet_model_batch_normalization_6_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %79 = sqrt(%78) /* ty=Tensor[(64), float32] */; + %80 = divide(1f /* ty=float32 */, %79) /* ty=Tensor[(64), float32] */; + %81 = nn.conv2d(%77, %resnet_model_conv2d_6_kernel, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_6/Conv2D */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %82 = multiply(%80, %resnet_model_batch_normalization_6_gamma) /* ty=Tensor[(64), float32] */; + %83 = negative(%resnet_model_batch_normalization_6_moving_mean) /* ty=Tensor[(64), float32] */; + %84 = multiply(%83, %82) /* ty=Tensor[(64), float32] */; + %85 = multiply(%81, %82) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %86 = add(%84, %resnet_model_batch_normalization_6_beta) /* ty=Tensor[(64), float32] */; + %87 = add(%85, %86) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %88 = nn.relu(%87) /* resnet_model/Relu_5 */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %89 = add(%resnet_model_batch_normalization_7_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %90 = sqrt(%89) /* ty=Tensor[(256), float32] */; + %91 = divide(1f /* ty=float32 */, %90) /* ty=Tensor[(256), float32] */; + %92 = nn.conv2d(%88, %resnet_model_conv2d_7_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_7/Conv2D */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %93 = multiply(%91, %resnet_model_batch_normalization_7_gamma) /* ty=Tensor[(256), float32] */; + %94 = negative(%resnet_model_batch_normalization_7_moving_mean) /* ty=Tensor[(256), float32] */; + %95 = multiply(%94, %93) /* ty=Tensor[(256), float32] */; + %96 = multiply(%92, %93) /* ty=Tensor[(1, 56, 56, 256), float32] */; + %97 = add(%95, %resnet_model_batch_normalization_7_beta) /* ty=Tensor[(256), float32] */; + %98 = add(%96, %97) /* ty=Tensor[(1, 56, 56, 256), float32] */; + %99 = add(%98, %66) /* resnet_model/add_1 */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %100 = nn.relu(%99) /* resnet_model/Relu_6 */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %101 = add(%resnet_model_batch_normalization_8_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %102 = sqrt(%101) /* ty=Tensor[(64), float32] */; + %103 = divide(1f /* ty=float32 */, %102) /* ty=Tensor[(64), float32] */; + %104 = nn.conv2d(%100, %resnet_model_conv2d_8_kernel, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_8/Conv2D */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %105 = multiply(%103, %resnet_model_batch_normalization_8_gamma) /* ty=Tensor[(64), float32] */; + %106 = negative(%resnet_model_batch_normalization_8_moving_mean) /* ty=Tensor[(64), float32] */; + %107 = multiply(%106, %105) /* ty=Tensor[(64), float32] */; + %108 = multiply(%104, %105) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %109 = add(%107, %resnet_model_batch_normalization_8_beta) /* ty=Tensor[(64), float32] */; + %110 = add(%108, %109) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %111 = nn.relu(%110) /* resnet_model/Relu_7 */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %112 = add(%resnet_model_batch_normalization_9_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(64), float32] */; + %113 = sqrt(%112) /* ty=Tensor[(64), float32] */; + %114 = divide(1f /* ty=float32 */, %113) /* ty=Tensor[(64), float32] */; + %115 = nn.conv2d(%111, %resnet_model_conv2d_9_kernel, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_9/Conv2D */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %116 = multiply(%114, %resnet_model_batch_normalization_9_gamma) /* ty=Tensor[(64), float32] */; + %117 = negative(%resnet_model_batch_normalization_9_moving_mean) /* ty=Tensor[(64), float32] */; + %118 = multiply(%117, %116) /* ty=Tensor[(64), float32] */; + %119 = multiply(%115, %116) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %120 = add(%118, %resnet_model_batch_normalization_9_beta) /* ty=Tensor[(64), float32] */; + %121 = add(%119, %120) /* ty=Tensor[(1, 56, 56, 64), float32] */; + %122 = nn.relu(%121) /* resnet_model/Relu_8 */ /* ty=Tensor[(1, 56, 56, 64), float32] */; + %123 = add(%resnet_model_batch_normalization_10_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %124 = sqrt(%123) /* ty=Tensor[(256), float32] */; + %125 = divide(1f /* ty=float32 */, %124) /* ty=Tensor[(256), float32] */; + %126 = nn.conv2d(%122, %resnet_model_conv2d_10_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_10/Conv2D */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %127 = multiply(%125, %resnet_model_batch_normalization_10_gamma) /* ty=Tensor[(256), float32] */; + %128 = negative(%resnet_model_batch_normalization_10_moving_mean) /* ty=Tensor[(256), float32] */; + %129 = multiply(%128, %127) /* ty=Tensor[(256), float32] */; + %130 = multiply(%126, %127) /* ty=Tensor[(1, 56, 56, 256), float32] */; + %131 = add(%129, %resnet_model_batch_normalization_10_beta) /* ty=Tensor[(256), float32] */; + %132 = add(%130, %131) /* ty=Tensor[(1, 56, 56, 256), float32] */; + %133 = add(%132, %100) /* resnet_model/add_2 */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %134 = nn.relu(%133) /* resnet_model/Relu_9 */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %135 = add(%resnet_model_batch_normalization_12_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %136 = sqrt(%135) /* ty=Tensor[(128), float32] */; + %137 = divide(1f /* ty=float32 */, %136) /* ty=Tensor[(128), float32] */; + %138 = nn.conv2d(%134, %resnet_model_conv2d_12_kernel, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_12/Conv2D */ /* ty=Tensor[(1, 56, 56, 128), float32] */; + %139 = multiply(%137, %resnet_model_batch_normalization_12_gamma) /* ty=Tensor[(128), float32] */; + %140 = negative(%resnet_model_batch_normalization_12_moving_mean) /* ty=Tensor[(128), float32] */; + %141 = multiply(%140, %139) /* ty=Tensor[(128), float32] */; + %142 = multiply(%138, %139) /* ty=Tensor[(1, 56, 56, 128), float32] */; + %143 = add(%141, %resnet_model_batch_normalization_12_beta) /* ty=Tensor[(128), float32] */; + %144 = add(%142, %143) /* ty=Tensor[(1, 56, 56, 128), float32] */; + %145 = nn.relu(%144) /* resnet_model/Relu_10 */ /* ty=Tensor[(1, 56, 56, 128), float32] */; + %146 = nn.pad(%145, 0 /* ty=int32 */, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]) /* resnet_model/Pad_2 */ /* ty=Tensor[(1, 58, 58, 128), float32] */; + %147 = add(%resnet_model_batch_normalization_13_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %148 = sqrt(%147) /* ty=Tensor[(128), float32] */; + %149 = divide(1f /* ty=float32 */, %148) /* ty=Tensor[(128), float32] */; + %150 = nn.conv2d(%146, %resnet_model_conv2d_13_kernel, strides=[2, 2], padding=[0, 0, 0, 0], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_13/Conv2D */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %151 = multiply(%149, %resnet_model_batch_normalization_13_gamma) /* ty=Tensor[(128), float32] */; + %152 = negative(%resnet_model_batch_normalization_13_moving_mean) /* ty=Tensor[(128), float32] */; + %153 = multiply(%152, %151) /* ty=Tensor[(128), float32] */; + %154 = multiply(%150, %151) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %155 = add(%153, %resnet_model_batch_normalization_13_beta) /* ty=Tensor[(128), float32] */; + %156 = add(%154, %155) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %157 = nn.relu(%156) /* resnet_model/Relu_11 */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %158 = add(%resnet_model_batch_normalization_14_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %159 = sqrt(%158) /* ty=Tensor[(512), float32] */; + %160 = divide(1f /* ty=float32 */, %159) /* ty=Tensor[(512), float32] */; + %161 = nn.conv2d(%157, %resnet_model_conv2d_14_kernel, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_14/Conv2D */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %162 = multiply(%160, %resnet_model_batch_normalization_14_gamma) /* ty=Tensor[(512), float32] */; + %163 = negative(%resnet_model_batch_normalization_14_moving_mean) /* ty=Tensor[(512), float32] */; + %164 = multiply(%163, %162) /* ty=Tensor[(512), float32] */; + %165 = multiply(%161, %162) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %166 = add(%164, %resnet_model_batch_normalization_14_beta) /* ty=Tensor[(512), float32] */; + %167 = nn.pad(%134, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* resnet_model/Pad_1 */ /* ty=Tensor[(1, 56, 56, 256), float32] */; + %168 = add(%resnet_model_batch_normalization_11_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %169 = sqrt(%168) /* ty=Tensor[(512), float32] */; + %170 = divide(1f /* ty=float32 */, %169) /* ty=Tensor[(512), float32] */; + %171 = nn.conv2d(%167, %resnet_model_conv2d_11_kernel, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_11/Conv2D */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %172 = multiply(%170, %resnet_model_batch_normalization_11_gamma) /* ty=Tensor[(512), float32] */; + %173 = negative(%resnet_model_batch_normalization_11_moving_mean) /* ty=Tensor[(512), float32] */; + %174 = multiply(%173, %172) /* ty=Tensor[(512), float32] */; + %175 = multiply(%171, %172) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %176 = add(%174, %resnet_model_batch_normalization_11_beta) /* ty=Tensor[(512), float32] */; + %177 = add(%165, %166) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %178 = add(%175, %176) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %179 = add(%177, %178) /* resnet_model/add_3 */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %180 = nn.relu(%179) /* resnet_model/Relu_12 */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %181 = add(%resnet_model_batch_normalization_15_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %182 = sqrt(%181) /* ty=Tensor[(128), float32] */; + %183 = divide(1f /* ty=float32 */, %182) /* ty=Tensor[(128), float32] */; + %184 = nn.conv2d(%180, %resnet_model_conv2d_15_kernel, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_15/Conv2D */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %185 = multiply(%183, %resnet_model_batch_normalization_15_gamma) /* ty=Tensor[(128), float32] */; + %186 = negative(%resnet_model_batch_normalization_15_moving_mean) /* ty=Tensor[(128), float32] */; + %187 = multiply(%186, %185) /* ty=Tensor[(128), float32] */; + %188 = multiply(%184, %185) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %189 = add(%187, %resnet_model_batch_normalization_15_beta) /* ty=Tensor[(128), float32] */; + %190 = add(%188, %189) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %191 = nn.relu(%190) /* resnet_model/Relu_13 */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %192 = add(%resnet_model_batch_normalization_16_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %193 = sqrt(%192) /* ty=Tensor[(128), float32] */; + %194 = divide(1f /* ty=float32 */, %193) /* ty=Tensor[(128), float32] */; + %195 = nn.conv2d(%191, %resnet_model_conv2d_16_kernel, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_16/Conv2D */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %196 = multiply(%194, %resnet_model_batch_normalization_16_gamma) /* ty=Tensor[(128), float32] */; + %197 = negative(%resnet_model_batch_normalization_16_moving_mean) /* ty=Tensor[(128), float32] */; + %198 = multiply(%197, %196) /* ty=Tensor[(128), float32] */; + %199 = multiply(%195, %196) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %200 = add(%198, %resnet_model_batch_normalization_16_beta) /* ty=Tensor[(128), float32] */; + %201 = add(%199, %200) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %202 = nn.relu(%201) /* resnet_model/Relu_14 */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %203 = add(%resnet_model_batch_normalization_17_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %204 = sqrt(%203) /* ty=Tensor[(512), float32] */; + %205 = divide(1f /* ty=float32 */, %204) /* ty=Tensor[(512), float32] */; + %206 = nn.conv2d(%202, %resnet_model_conv2d_17_kernel, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_17/Conv2D */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %207 = multiply(%205, %resnet_model_batch_normalization_17_gamma) /* ty=Tensor[(512), float32] */; + %208 = negative(%resnet_model_batch_normalization_17_moving_mean) /* ty=Tensor[(512), float32] */; + %209 = multiply(%208, %207) /* ty=Tensor[(512), float32] */; + %210 = multiply(%206, %207) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %211 = add(%209, %resnet_model_batch_normalization_17_beta) /* ty=Tensor[(512), float32] */; + %212 = add(%210, %211) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %213 = add(%212, %180) /* resnet_model/add_4 */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %214 = nn.relu(%213) /* resnet_model/Relu_15 */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %215 = add(%resnet_model_batch_normalization_18_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %216 = sqrt(%215) /* ty=Tensor[(128), float32] */; + %217 = divide(1f /* ty=float32 */, %216) /* ty=Tensor[(128), float32] */; + %218 = nn.conv2d(%214, %resnet_model_conv2d_18_kernel, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_18/Conv2D */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %219 = multiply(%217, %resnet_model_batch_normalization_18_gamma) /* ty=Tensor[(128), float32] */; + %220 = negative(%resnet_model_batch_normalization_18_moving_mean) /* ty=Tensor[(128), float32] */; + %221 = multiply(%220, %219) /* ty=Tensor[(128), float32] */; + %222 = multiply(%218, %219) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %223 = add(%221, %resnet_model_batch_normalization_18_beta) /* ty=Tensor[(128), float32] */; + %224 = add(%222, %223) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %225 = nn.relu(%224) /* resnet_model/Relu_16 */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %226 = add(%resnet_model_batch_normalization_19_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %227 = sqrt(%226) /* ty=Tensor[(128), float32] */; + %228 = divide(1f /* ty=float32 */, %227) /* ty=Tensor[(128), float32] */; + %229 = nn.conv2d(%225, %resnet_model_conv2d_19_kernel, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_19/Conv2D */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %230 = multiply(%228, %resnet_model_batch_normalization_19_gamma) /* ty=Tensor[(128), float32] */; + %231 = negative(%resnet_model_batch_normalization_19_moving_mean) /* ty=Tensor[(128), float32] */; + %232 = multiply(%231, %230) /* ty=Tensor[(128), float32] */; + %233 = multiply(%229, %230) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %234 = add(%232, %resnet_model_batch_normalization_19_beta) /* ty=Tensor[(128), float32] */; + %235 = add(%233, %234) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %236 = nn.relu(%235) /* resnet_model/Relu_17 */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %237 = add(%resnet_model_batch_normalization_20_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %238 = sqrt(%237) /* ty=Tensor[(512), float32] */; + %239 = divide(1f /* ty=float32 */, %238) /* ty=Tensor[(512), float32] */; + %240 = nn.conv2d(%236, %resnet_model_conv2d_20_kernel, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_20/Conv2D */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %241 = multiply(%239, %resnet_model_batch_normalization_20_gamma) /* ty=Tensor[(512), float32] */; + %242 = negative(%resnet_model_batch_normalization_20_moving_mean) /* ty=Tensor[(512), float32] */; + %243 = multiply(%242, %241) /* ty=Tensor[(512), float32] */; + %244 = multiply(%240, %241) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %245 = add(%243, %resnet_model_batch_normalization_20_beta) /* ty=Tensor[(512), float32] */; + %246 = add(%244, %245) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %247 = add(%246, %214) /* resnet_model/add_5 */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %248 = nn.relu(%247) /* resnet_model/Relu_18 */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %249 = add(%resnet_model_batch_normalization_21_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %250 = sqrt(%249) /* ty=Tensor[(128), float32] */; + %251 = divide(1f /* ty=float32 */, %250) /* ty=Tensor[(128), float32] */; + %252 = nn.conv2d(%248, %resnet_model_conv2d_21_kernel, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_21/Conv2D */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %253 = multiply(%251, %resnet_model_batch_normalization_21_gamma) /* ty=Tensor[(128), float32] */; + %254 = negative(%resnet_model_batch_normalization_21_moving_mean) /* ty=Tensor[(128), float32] */; + %255 = multiply(%254, %253) /* ty=Tensor[(128), float32] */; + %256 = multiply(%252, %253) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %257 = add(%255, %resnet_model_batch_normalization_21_beta) /* ty=Tensor[(128), float32] */; + %258 = add(%256, %257) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %259 = nn.relu(%258) /* resnet_model/Relu_19 */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %260 = add(%resnet_model_batch_normalization_22_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(128), float32] */; + %261 = sqrt(%260) /* ty=Tensor[(128), float32] */; + %262 = divide(1f /* ty=float32 */, %261) /* ty=Tensor[(128), float32] */; + %263 = nn.conv2d(%259, %resnet_model_conv2d_22_kernel, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_22/Conv2D */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %264 = multiply(%262, %resnet_model_batch_normalization_22_gamma) /* ty=Tensor[(128), float32] */; + %265 = negative(%resnet_model_batch_normalization_22_moving_mean) /* ty=Tensor[(128), float32] */; + %266 = multiply(%265, %264) /* ty=Tensor[(128), float32] */; + %267 = multiply(%263, %264) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %268 = add(%266, %resnet_model_batch_normalization_22_beta) /* ty=Tensor[(128), float32] */; + %269 = add(%267, %268) /* ty=Tensor[(1, 28, 28, 128), float32] */; + %270 = nn.relu(%269) /* resnet_model/Relu_20 */ /* ty=Tensor[(1, 28, 28, 128), float32] */; + %271 = add(%resnet_model_batch_normalization_23_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %272 = sqrt(%271) /* ty=Tensor[(512), float32] */; + %273 = divide(1f /* ty=float32 */, %272) /* ty=Tensor[(512), float32] */; + %274 = nn.conv2d(%270, %resnet_model_conv2d_23_kernel, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_23/Conv2D */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %275 = multiply(%273, %resnet_model_batch_normalization_23_gamma) /* ty=Tensor[(512), float32] */; + %276 = negative(%resnet_model_batch_normalization_23_moving_mean) /* ty=Tensor[(512), float32] */; + %277 = multiply(%276, %275) /* ty=Tensor[(512), float32] */; + %278 = multiply(%274, %275) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %279 = add(%277, %resnet_model_batch_normalization_23_beta) /* ty=Tensor[(512), float32] */; + %280 = add(%278, %279) /* ty=Tensor[(1, 28, 28, 512), float32] */; + %281 = add(%280, %248) /* resnet_model/add_6 */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %282 = nn.relu(%281) /* resnet_model/Relu_21 */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %283 = add(%resnet_model_batch_normalization_25_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %284 = sqrt(%283) /* ty=Tensor[(256), float32] */; + %285 = divide(1f /* ty=float32 */, %284) /* ty=Tensor[(256), float32] */; + %286 = nn.conv2d(%282, %resnet_model_conv2d_25_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_25/Conv2D */ /* ty=Tensor[(1, 28, 28, 256), float32] */; + %287 = multiply(%285, %resnet_model_batch_normalization_25_gamma) /* ty=Tensor[(256), float32] */; + %288 = negative(%resnet_model_batch_normalization_25_moving_mean) /* ty=Tensor[(256), float32] */; + %289 = multiply(%288, %287) /* ty=Tensor[(256), float32] */; + %290 = multiply(%286, %287) /* ty=Tensor[(1, 28, 28, 256), float32] */; + %291 = add(%289, %resnet_model_batch_normalization_25_beta) /* ty=Tensor[(256), float32] */; + %292 = add(%290, %291) /* ty=Tensor[(1, 28, 28, 256), float32] */; + %293 = nn.relu(%292) /* resnet_model/Relu_22 */ /* ty=Tensor[(1, 28, 28, 256), float32] */; + %294 = nn.pad(%293, 0 /* ty=int32 */, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]) /* resnet_model/Pad_4 */ /* ty=Tensor[(1, 30, 30, 256), float32] */; + %295 = add(%resnet_model_batch_normalization_26_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %296 = sqrt(%295) /* ty=Tensor[(256), float32] */; + %297 = divide(1f /* ty=float32 */, %296) /* ty=Tensor[(256), float32] */; + %298 = nn.conv2d(%294, %resnet_model_conv2d_26_kernel, strides=[2, 2], padding=[0, 0, 0, 0], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_26/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %299 = multiply(%297, %resnet_model_batch_normalization_26_gamma) /* ty=Tensor[(256), float32] */; + %300 = negative(%resnet_model_batch_normalization_26_moving_mean) /* ty=Tensor[(256), float32] */; + %301 = multiply(%300, %299) /* ty=Tensor[(256), float32] */; + %302 = multiply(%298, %299) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %303 = add(%301, %resnet_model_batch_normalization_26_beta) /* ty=Tensor[(256), float32] */; + %304 = add(%302, %303) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %305 = nn.relu(%304) /* resnet_model/Relu_23 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %306 = add(%resnet_model_batch_normalization_27_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %307 = sqrt(%306) /* ty=Tensor[(1024), float32] */; + %308 = divide(1f /* ty=float32 */, %307) /* ty=Tensor[(1024), float32] */; + %309 = nn.conv2d(%305, %resnet_model_conv2d_27_kernel, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_27/Conv2D */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %310 = multiply(%308, %resnet_model_batch_normalization_27_gamma) /* ty=Tensor[(1024), float32] */; + %311 = negative(%resnet_model_batch_normalization_27_moving_mean) /* ty=Tensor[(1024), float32] */; + %312 = multiply(%311, %310) /* ty=Tensor[(1024), float32] */; + %313 = multiply(%309, %310) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %314 = add(%312, %resnet_model_batch_normalization_27_beta) /* ty=Tensor[(1024), float32] */; + %315 = nn.pad(%282, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* resnet_model/Pad_3 */ /* ty=Tensor[(1, 28, 28, 512), float32] */; + %316 = add(%resnet_model_batch_normalization_24_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %317 = sqrt(%316) /* ty=Tensor[(1024), float32] */; + %318 = divide(1f /* ty=float32 */, %317) /* ty=Tensor[(1024), float32] */; + %319 = nn.conv2d(%315, %resnet_model_conv2d_24_kernel, strides=[2, 2], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_24/Conv2D */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %320 = multiply(%318, %resnet_model_batch_normalization_24_gamma) /* ty=Tensor[(1024), float32] */; + %321 = negative(%resnet_model_batch_normalization_24_moving_mean) /* ty=Tensor[(1024), float32] */; + %322 = multiply(%321, %320) /* ty=Tensor[(1024), float32] */; + %323 = multiply(%319, %320) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %324 = add(%322, %resnet_model_batch_normalization_24_beta) /* ty=Tensor[(1024), float32] */; + %325 = add(%313, %314) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %326 = add(%323, %324) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %327 = add(%325, %326) /* resnet_model/add_7 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %328 = nn.relu(%327) /* resnet_model/Relu_24 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %329 = add(%resnet_model_batch_normalization_28_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %330 = sqrt(%329) /* ty=Tensor[(256), float32] */; + %331 = divide(1f /* ty=float32 */, %330) /* ty=Tensor[(256), float32] */; + %332 = nn.conv2d(%328, %resnet_model_conv2d_28_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_28/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %333 = multiply(%331, %resnet_model_batch_normalization_28_gamma) /* ty=Tensor[(256), float32] */; + %334 = negative(%resnet_model_batch_normalization_28_moving_mean) /* ty=Tensor[(256), float32] */; + %335 = multiply(%334, %333) /* ty=Tensor[(256), float32] */; + %336 = multiply(%332, %333) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %337 = add(%335, %resnet_model_batch_normalization_28_beta) /* ty=Tensor[(256), float32] */; + %338 = add(%336, %337) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %339 = nn.relu(%338) /* resnet_model/Relu_25 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %340 = add(%resnet_model_batch_normalization_29_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %341 = sqrt(%340) /* ty=Tensor[(256), float32] */; + %342 = divide(1f /* ty=float32 */, %341) /* ty=Tensor[(256), float32] */; + %343 = nn.conv2d(%339, %resnet_model_conv2d_29_kernel, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_29/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %344 = multiply(%342, %resnet_model_batch_normalization_29_gamma) /* ty=Tensor[(256), float32] */; + %345 = negative(%resnet_model_batch_normalization_29_moving_mean) /* ty=Tensor[(256), float32] */; + %346 = multiply(%345, %344) /* ty=Tensor[(256), float32] */; + %347 = multiply(%343, %344) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %348 = add(%346, %resnet_model_batch_normalization_29_beta) /* ty=Tensor[(256), float32] */; + %349 = add(%347, %348) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %350 = nn.relu(%349) /* resnet_model/Relu_26 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %351 = add(%resnet_model_batch_normalization_30_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %352 = sqrt(%351) /* ty=Tensor[(1024), float32] */; + %353 = divide(1f /* ty=float32 */, %352) /* ty=Tensor[(1024), float32] */; + %354 = nn.conv2d(%350, %resnet_model_conv2d_30_kernel, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_30/Conv2D */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %355 = multiply(%353, %resnet_model_batch_normalization_30_gamma) /* ty=Tensor[(1024), float32] */; + %356 = negative(%resnet_model_batch_normalization_30_moving_mean) /* ty=Tensor[(1024), float32] */; + %357 = multiply(%356, %355) /* ty=Tensor[(1024), float32] */; + %358 = multiply(%354, %355) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %359 = add(%357, %resnet_model_batch_normalization_30_beta) /* ty=Tensor[(1024), float32] */; + %360 = add(%358, %359) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %361 = add(%360, %328) /* resnet_model/add_8 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %362 = nn.relu(%361) /* resnet_model/Relu_27 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %363 = add(%resnet_model_batch_normalization_31_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %364 = sqrt(%363) /* ty=Tensor[(256), float32] */; + %365 = divide(1f /* ty=float32 */, %364) /* ty=Tensor[(256), float32] */; + %366 = nn.conv2d(%362, %resnet_model_conv2d_31_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_31/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %367 = multiply(%365, %resnet_model_batch_normalization_31_gamma) /* ty=Tensor[(256), float32] */; + %368 = negative(%resnet_model_batch_normalization_31_moving_mean) /* ty=Tensor[(256), float32] */; + %369 = multiply(%368, %367) /* ty=Tensor[(256), float32] */; + %370 = multiply(%366, %367) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %371 = add(%369, %resnet_model_batch_normalization_31_beta) /* ty=Tensor[(256), float32] */; + %372 = add(%370, %371) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %373 = nn.relu(%372) /* resnet_model/Relu_28 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %374 = add(%resnet_model_batch_normalization_32_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %375 = sqrt(%374) /* ty=Tensor[(256), float32] */; + %376 = divide(1f /* ty=float32 */, %375) /* ty=Tensor[(256), float32] */; + %377 = nn.conv2d(%373, %resnet_model_conv2d_32_kernel, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_32/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %378 = multiply(%376, %resnet_model_batch_normalization_32_gamma) /* ty=Tensor[(256), float32] */; + %379 = negative(%resnet_model_batch_normalization_32_moving_mean) /* ty=Tensor[(256), float32] */; + %380 = multiply(%379, %378) /* ty=Tensor[(256), float32] */; + %381 = multiply(%377, %378) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %382 = add(%380, %resnet_model_batch_normalization_32_beta) /* ty=Tensor[(256), float32] */; + %383 = add(%381, %382) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %384 = nn.relu(%383) /* resnet_model/Relu_29 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %385 = add(%resnet_model_batch_normalization_33_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %386 = sqrt(%385) /* ty=Tensor[(1024), float32] */; + %387 = divide(1f /* ty=float32 */, %386) /* ty=Tensor[(1024), float32] */; + %388 = nn.conv2d(%384, %resnet_model_conv2d_33_kernel, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_33/Conv2D */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %389 = multiply(%387, %resnet_model_batch_normalization_33_gamma) /* ty=Tensor[(1024), float32] */; + %390 = negative(%resnet_model_batch_normalization_33_moving_mean) /* ty=Tensor[(1024), float32] */; + %391 = multiply(%390, %389) /* ty=Tensor[(1024), float32] */; + %392 = multiply(%388, %389) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %393 = add(%391, %resnet_model_batch_normalization_33_beta) /* ty=Tensor[(1024), float32] */; + %394 = add(%392, %393) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %395 = add(%394, %362) /* resnet_model/add_9 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %396 = nn.relu(%395) /* resnet_model/Relu_30 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %397 = add(%resnet_model_batch_normalization_34_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %398 = sqrt(%397) /* ty=Tensor[(256), float32] */; + %399 = divide(1f /* ty=float32 */, %398) /* ty=Tensor[(256), float32] */; + %400 = nn.conv2d(%396, %resnet_model_conv2d_34_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_34/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %401 = multiply(%399, %resnet_model_batch_normalization_34_gamma) /* ty=Tensor[(256), float32] */; + %402 = negative(%resnet_model_batch_normalization_34_moving_mean) /* ty=Tensor[(256), float32] */; + %403 = multiply(%402, %401) /* ty=Tensor[(256), float32] */; + %404 = multiply(%400, %401) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %405 = add(%403, %resnet_model_batch_normalization_34_beta) /* ty=Tensor[(256), float32] */; + %406 = add(%404, %405) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %407 = nn.relu(%406) /* resnet_model/Relu_31 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %408 = add(%resnet_model_batch_normalization_35_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %409 = sqrt(%408) /* ty=Tensor[(256), float32] */; + %410 = divide(1f /* ty=float32 */, %409) /* ty=Tensor[(256), float32] */; + %411 = nn.conv2d(%407, %resnet_model_conv2d_35_kernel, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_35/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %412 = multiply(%410, %resnet_model_batch_normalization_35_gamma) /* ty=Tensor[(256), float32] */; + %413 = negative(%resnet_model_batch_normalization_35_moving_mean) /* ty=Tensor[(256), float32] */; + %414 = multiply(%413, %412) /* ty=Tensor[(256), float32] */; + %415 = multiply(%411, %412) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %416 = add(%414, %resnet_model_batch_normalization_35_beta) /* ty=Tensor[(256), float32] */; + %417 = add(%415, %416) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %418 = nn.relu(%417) /* resnet_model/Relu_32 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %419 = add(%resnet_model_batch_normalization_36_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %420 = sqrt(%419) /* ty=Tensor[(1024), float32] */; + %421 = divide(1f /* ty=float32 */, %420) /* ty=Tensor[(1024), float32] */; + %422 = nn.conv2d(%418, %resnet_model_conv2d_36_kernel, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_36/Conv2D */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %423 = multiply(%421, %resnet_model_batch_normalization_36_gamma) /* ty=Tensor[(1024), float32] */; + %424 = negative(%resnet_model_batch_normalization_36_moving_mean) /* ty=Tensor[(1024), float32] */; + %425 = multiply(%424, %423) /* ty=Tensor[(1024), float32] */; + %426 = multiply(%422, %423) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %427 = add(%425, %resnet_model_batch_normalization_36_beta) /* ty=Tensor[(1024), float32] */; + %428 = add(%426, %427) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %429 = add(%428, %396) /* resnet_model/add_10 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %430 = nn.relu(%429) /* resnet_model/Relu_33 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %431 = add(%resnet_model_batch_normalization_37_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %432 = sqrt(%431) /* ty=Tensor[(256), float32] */; + %433 = divide(1f /* ty=float32 */, %432) /* ty=Tensor[(256), float32] */; + %434 = nn.conv2d(%430, %resnet_model_conv2d_37_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_37/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %435 = multiply(%433, %resnet_model_batch_normalization_37_gamma) /* ty=Tensor[(256), float32] */; + %436 = negative(%resnet_model_batch_normalization_37_moving_mean) /* ty=Tensor[(256), float32] */; + %437 = multiply(%436, %435) /* ty=Tensor[(256), float32] */; + %438 = multiply(%434, %435) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %439 = add(%437, %resnet_model_batch_normalization_37_beta) /* ty=Tensor[(256), float32] */; + %440 = add(%438, %439) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %441 = nn.relu(%440) /* resnet_model/Relu_34 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %442 = add(%resnet_model_batch_normalization_38_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %443 = sqrt(%442) /* ty=Tensor[(256), float32] */; + %444 = divide(1f /* ty=float32 */, %443) /* ty=Tensor[(256), float32] */; + %445 = nn.conv2d(%441, %resnet_model_conv2d_38_kernel, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_38/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %446 = multiply(%444, %resnet_model_batch_normalization_38_gamma) /* ty=Tensor[(256), float32] */; + %447 = negative(%resnet_model_batch_normalization_38_moving_mean) /* ty=Tensor[(256), float32] */; + %448 = multiply(%447, %446) /* ty=Tensor[(256), float32] */; + %449 = multiply(%445, %446) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %450 = add(%448, %resnet_model_batch_normalization_38_beta) /* ty=Tensor[(256), float32] */; + %451 = add(%449, %450) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %452 = nn.relu(%451) /* resnet_model/Relu_35 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %453 = add(%resnet_model_batch_normalization_39_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %454 = sqrt(%453) /* ty=Tensor[(1024), float32] */; + %455 = divide(1f /* ty=float32 */, %454) /* ty=Tensor[(1024), float32] */; + %456 = nn.conv2d(%452, %resnet_model_conv2d_39_kernel, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_39/Conv2D */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %457 = multiply(%455, %resnet_model_batch_normalization_39_gamma) /* ty=Tensor[(1024), float32] */; + %458 = negative(%resnet_model_batch_normalization_39_moving_mean) /* ty=Tensor[(1024), float32] */; + %459 = multiply(%458, %457) /* ty=Tensor[(1024), float32] */; + %460 = multiply(%456, %457) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %461 = add(%459, %resnet_model_batch_normalization_39_beta) /* ty=Tensor[(1024), float32] */; + %462 = add(%460, %461) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %463 = add(%462, %430) /* resnet_model/add_11 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %464 = nn.relu(%463) /* resnet_model/Relu_36 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %465 = add(%resnet_model_batch_normalization_40_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %466 = sqrt(%465) /* ty=Tensor[(256), float32] */; + %467 = divide(1f /* ty=float32 */, %466) /* ty=Tensor[(256), float32] */; + %468 = nn.conv2d(%464, %resnet_model_conv2d_40_kernel, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_40/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %469 = multiply(%467, %resnet_model_batch_normalization_40_gamma) /* ty=Tensor[(256), float32] */; + %470 = negative(%resnet_model_batch_normalization_40_moving_mean) /* ty=Tensor[(256), float32] */; + %471 = multiply(%470, %469) /* ty=Tensor[(256), float32] */; + %472 = multiply(%468, %469) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %473 = add(%471, %resnet_model_batch_normalization_40_beta) /* ty=Tensor[(256), float32] */; + %474 = add(%472, %473) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %475 = nn.relu(%474) /* resnet_model/Relu_37 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %476 = add(%resnet_model_batch_normalization_41_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(256), float32] */; + %477 = sqrt(%476) /* ty=Tensor[(256), float32] */; + %478 = divide(1f /* ty=float32 */, %477) /* ty=Tensor[(256), float32] */; + %479 = nn.conv2d(%475, %resnet_model_conv2d_41_kernel, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_41/Conv2D */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %480 = multiply(%478, %resnet_model_batch_normalization_41_gamma) /* ty=Tensor[(256), float32] */; + %481 = negative(%resnet_model_batch_normalization_41_moving_mean) /* ty=Tensor[(256), float32] */; + %482 = multiply(%481, %480) /* ty=Tensor[(256), float32] */; + %483 = multiply(%479, %480) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %484 = add(%482, %resnet_model_batch_normalization_41_beta) /* ty=Tensor[(256), float32] */; + %485 = add(%483, %484) /* ty=Tensor[(1, 14, 14, 256), float32] */; + %486 = nn.relu(%485) /* resnet_model/Relu_38 */ /* ty=Tensor[(1, 14, 14, 256), float32] */; + %487 = add(%resnet_model_batch_normalization_42_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(1024), float32] */; + %488 = sqrt(%487) /* ty=Tensor[(1024), float32] */; + %489 = divide(1f /* ty=float32 */, %488) /* ty=Tensor[(1024), float32] */; + %490 = nn.conv2d(%486, %resnet_model_conv2d_42_kernel, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_42/Conv2D */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %491 = multiply(%489, %resnet_model_batch_normalization_42_gamma) /* ty=Tensor[(1024), float32] */; + %492 = negative(%resnet_model_batch_normalization_42_moving_mean) /* ty=Tensor[(1024), float32] */; + %493 = multiply(%492, %491) /* ty=Tensor[(1024), float32] */; + %494 = multiply(%490, %491) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %495 = add(%493, %resnet_model_batch_normalization_42_beta) /* ty=Tensor[(1024), float32] */; + %496 = add(%494, %495) /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %497 = add(%496, %464) /* resnet_model/add_12 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %498 = nn.relu(%497) /* resnet_model/Relu_39 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %499 = add(%resnet_model_batch_normalization_44_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %500 = sqrt(%499) /* ty=Tensor[(512), float32] */; + %501 = divide(1f /* ty=float32 */, %500) /* ty=Tensor[(512), float32] */; + %502 = nn.conv2d(%498, %resnet_model_conv2d_44_kernel, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_44/Conv2D */ /* ty=Tensor[(1, 14, 14, 512), float32] */; + %503 = multiply(%501, %resnet_model_batch_normalization_44_gamma) /* ty=Tensor[(512), float32] */; + %504 = negative(%resnet_model_batch_normalization_44_moving_mean) /* ty=Tensor[(512), float32] */; + %505 = multiply(%504, %503) /* ty=Tensor[(512), float32] */; + %506 = multiply(%502, %503) /* ty=Tensor[(1, 14, 14, 512), float32] */; + %507 = add(%505, %resnet_model_batch_normalization_44_beta) /* ty=Tensor[(512), float32] */; + %508 = add(%506, %507) /* ty=Tensor[(1, 14, 14, 512), float32] */; + %509 = nn.relu(%508) /* resnet_model/Relu_40 */ /* ty=Tensor[(1, 14, 14, 512), float32] */; + %510 = nn.pad(%509, 0 /* ty=int32 */, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]) /* resnet_model/Pad_6 */ /* ty=Tensor[(1, 16, 16, 512), float32] */; + %511 = add(%resnet_model_batch_normalization_45_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %512 = sqrt(%511) /* ty=Tensor[(512), float32] */; + %513 = divide(1f /* ty=float32 */, %512) /* ty=Tensor[(512), float32] */; + %514 = nn.conv2d(%510, %resnet_model_conv2d_45_kernel, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_45/Conv2D */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %515 = multiply(%513, %resnet_model_batch_normalization_45_gamma) /* ty=Tensor[(512), float32] */; + %516 = negative(%resnet_model_batch_normalization_45_moving_mean) /* ty=Tensor[(512), float32] */; + %517 = multiply(%516, %515) /* ty=Tensor[(512), float32] */; + %518 = multiply(%514, %515) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %519 = add(%517, %resnet_model_batch_normalization_45_beta) /* ty=Tensor[(512), float32] */; + %520 = add(%518, %519) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %521 = nn.relu(%520) /* resnet_model/Relu_41 */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %522 = add(%resnet_model_batch_normalization_46_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(2048), float32] */; + %523 = sqrt(%522) /* ty=Tensor[(2048), float32] */; + %524 = divide(1f /* ty=float32 */, %523) /* ty=Tensor[(2048), float32] */; + %525 = nn.conv2d(%521, %resnet_model_conv2d_46_kernel, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_46/Conv2D */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %526 = multiply(%524, %resnet_model_batch_normalization_46_gamma) /* ty=Tensor[(2048), float32] */; + %527 = negative(%resnet_model_batch_normalization_46_moving_mean) /* ty=Tensor[(2048), float32] */; + %528 = multiply(%527, %526) /* ty=Tensor[(2048), float32] */; + %529 = multiply(%525, %526) /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %530 = add(%528, %resnet_model_batch_normalization_46_beta) /* ty=Tensor[(2048), float32] */; + %531 = nn.pad(%498, 0 /* ty=int32 */, pad_width=[[0, 0], [0, 0], [0, 0], [0, 0]]) /* resnet_model/Pad_5 */ /* ty=Tensor[(1, 14, 14, 1024), float32] */; + %532 = add(%resnet_model_batch_normalization_43_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(2048), float32] */; + %533 = sqrt(%532) /* ty=Tensor[(2048), float32] */; + %534 = divide(1f /* ty=float32 */, %533) /* ty=Tensor[(2048), float32] */; + %535 = nn.conv2d(%531, %resnet_model_conv2d_43_kernel, strides=[2, 2], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_43/Conv2D */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %536 = multiply(%534, %resnet_model_batch_normalization_43_gamma) /* ty=Tensor[(2048), float32] */; + %537 = negative(%resnet_model_batch_normalization_43_moving_mean) /* ty=Tensor[(2048), float32] */; + %538 = multiply(%537, %536) /* ty=Tensor[(2048), float32] */; + %539 = multiply(%535, %536) /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %540 = add(%538, %resnet_model_batch_normalization_43_beta) /* ty=Tensor[(2048), float32] */; + %541 = add(%529, %530) /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %542 = add(%539, %540) /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %543 = add(%541, %542) /* resnet_model/add_13 */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %544 = nn.relu(%543) /* resnet_model/Relu_42 */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %545 = add(%resnet_model_batch_normalization_47_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %546 = sqrt(%545) /* ty=Tensor[(512), float32] */; + %547 = divide(1f /* ty=float32 */, %546) /* ty=Tensor[(512), float32] */; + %548 = nn.conv2d(%544, %resnet_model_conv2d_47_kernel, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_47/Conv2D */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %549 = multiply(%547, %resnet_model_batch_normalization_47_gamma) /* ty=Tensor[(512), float32] */; + %550 = negative(%resnet_model_batch_normalization_47_moving_mean) /* ty=Tensor[(512), float32] */; + %551 = multiply(%550, %549) /* ty=Tensor[(512), float32] */; + %552 = multiply(%548, %549) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %553 = add(%551, %resnet_model_batch_normalization_47_beta) /* ty=Tensor[(512), float32] */; + %554 = add(%552, %553) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %555 = nn.relu(%554) /* resnet_model/Relu_43 */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %556 = add(%resnet_model_batch_normalization_48_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %557 = sqrt(%556) /* ty=Tensor[(512), float32] */; + %558 = divide(1f /* ty=float32 */, %557) /* ty=Tensor[(512), float32] */; + %559 = nn.conv2d(%555, %resnet_model_conv2d_48_kernel, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_48/Conv2D */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %560 = multiply(%558, %resnet_model_batch_normalization_48_gamma) /* ty=Tensor[(512), float32] */; + %561 = negative(%resnet_model_batch_normalization_48_moving_mean) /* ty=Tensor[(512), float32] */; + %562 = multiply(%561, %560) /* ty=Tensor[(512), float32] */; + %563 = multiply(%559, %560) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %564 = add(%562, %resnet_model_batch_normalization_48_beta) /* ty=Tensor[(512), float32] */; + %565 = add(%563, %564) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %566 = nn.relu(%565) /* resnet_model/Relu_44 */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %567 = add(%resnet_model_batch_normalization_49_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(2048), float32] */; + %568 = sqrt(%567) /* ty=Tensor[(2048), float32] */; + %569 = divide(1f /* ty=float32 */, %568) /* ty=Tensor[(2048), float32] */; + %570 = nn.conv2d(%566, %resnet_model_conv2d_49_kernel, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_49/Conv2D */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %571 = multiply(%569, %resnet_model_batch_normalization_49_gamma) /* ty=Tensor[(2048), float32] */; + %572 = negative(%resnet_model_batch_normalization_49_moving_mean) /* ty=Tensor[(2048), float32] */; + %573 = multiply(%572, %571) /* ty=Tensor[(2048), float32] */; + %574 = multiply(%570, %571) /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %575 = add(%573, %resnet_model_batch_normalization_49_beta) /* ty=Tensor[(2048), float32] */; + %576 = add(%574, %575) /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %577 = add(%576, %544) /* resnet_model/add_14 */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %578 = nn.relu(%577) /* resnet_model/Relu_45 */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %579 = add(%resnet_model_batch_normalization_50_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %580 = sqrt(%579) /* ty=Tensor[(512), float32] */; + %581 = divide(1f /* ty=float32 */, %580) /* ty=Tensor[(512), float32] */; + %582 = nn.conv2d(%578, %resnet_model_conv2d_50_kernel, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_50/Conv2D */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %583 = multiply(%581, %resnet_model_batch_normalization_50_gamma) /* ty=Tensor[(512), float32] */; + %584 = negative(%resnet_model_batch_normalization_50_moving_mean) /* ty=Tensor[(512), float32] */; + %585 = multiply(%584, %583) /* ty=Tensor[(512), float32] */; + %586 = multiply(%582, %583) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %587 = add(%585, %resnet_model_batch_normalization_50_beta) /* ty=Tensor[(512), float32] */; + %588 = add(%586, %587) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %589 = nn.relu(%588) /* resnet_model/Relu_46 */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %590 = add(%resnet_model_batch_normalization_51_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(512), float32] */; + %591 = sqrt(%590) /* ty=Tensor[(512), float32] */; + %592 = divide(1f /* ty=float32 */, %591) /* ty=Tensor[(512), float32] */; + %593 = nn.conv2d(%589, %resnet_model_conv2d_51_kernel, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_51/Conv2D */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %594 = multiply(%592, %resnet_model_batch_normalization_51_gamma) /* ty=Tensor[(512), float32] */; + %595 = negative(%resnet_model_batch_normalization_51_moving_mean) /* ty=Tensor[(512), float32] */; + %596 = multiply(%595, %594) /* ty=Tensor[(512), float32] */; + %597 = multiply(%593, %594) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %598 = add(%596, %resnet_model_batch_normalization_51_beta) /* ty=Tensor[(512), float32] */; + %599 = add(%597, %598) /* ty=Tensor[(1, 7, 7, 512), float32] */; + %600 = nn.relu(%599) /* resnet_model/Relu_47 */ /* ty=Tensor[(1, 7, 7, 512), float32] */; + %601 = add(%resnet_model_batch_normalization_52_moving_variance, 1.001e-05f /* ty=float32 */) /* ty=Tensor[(2048), float32] */; + %602 = sqrt(%601) /* ty=Tensor[(2048), float32] */; + %603 = divide(1f /* ty=float32 */, %602) /* ty=Tensor[(2048), float32] */; + %604 = nn.conv2d(%600, %resnet_model_conv2d_52_kernel, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* resnet_model/conv2d_52/Conv2D */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %605 = multiply(%603, %resnet_model_batch_normalization_52_gamma) /* ty=Tensor[(2048), float32] */; + %606 = negative(%resnet_model_batch_normalization_52_moving_mean) /* ty=Tensor[(2048), float32] */; + %607 = multiply(%606, %605) /* ty=Tensor[(2048), float32] */; + %608 = multiply(%604, %605) /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %609 = add(%607, %resnet_model_batch_normalization_52_beta) /* ty=Tensor[(2048), float32] */; + %610 = add(%608, %609) /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %611 = add(%610, %578) /* resnet_model/add_15 */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %612 = nn.relu(%611) /* resnet_model/Relu_48 */ /* ty=Tensor[(1, 7, 7, 2048), float32] */; + %613 = mean(%612, axis=[1, 2], keepdims=True) /* resnet_model/Mean */ /* ty=Tensor[(1, 1, 1, 2048), float32] */; + %614 = squeeze(%613, axis=[1, 2]) /* resnet_model/Squeeze */ /* ty=Tensor[(1, 2048), float32] */; + %615 = transpose(%resnet_model_dense_kernel, axes=[1, 0]) /* ty=Tensor[(1001, 2048), float32] */; + %616 = nn.dense(%614, %615, units=1001) /* resnet_model/dense/MatMul */ /* ty=Tensor[(1, 1001), float32] */; + %617 = add(%616, %resnet_model_dense_bias) /* resnet_model/dense/BiasAdd */ /* ty=Tensor[(1, 1001), float32] */; + nn.softmax(%617) /* softmax_tensor */ /* ty=Tensor[(1, 1001), float32] */ +} diff --git a/tests/models/transformer.relay b/tests/models/transformer.relay new file mode 100644 index 0000000..cf85f3d --- /dev/null +++ b/tests/models/transformer.relay @@ -0,0 +1,1252 @@ +#[version = "0.0.5"] +type tensor_uint8_t { + tensor_nil_uint8, + tensor0_uint8(uint8), + tensor1_uint8(Tensor[(?), uint8]), + tensor2_uint8(Tensor[(?, ?), uint8]), + tensor3_uint8(Tensor[(?, ?, ?), uint8]), + tensor4_uint8(Tensor[(?, ?, ?, ?), uint8]), + tensor5_uint8(Tensor[(?, ?, ?, ?, ?), uint8]), + tensor6_uint8(Tensor[(?, ?, ?, ?, ?, ?), uint8]), +} + +type tensor_int64_t { + tensor_nil_int64, + tensor0_int64(int64), + tensor1_int64(Tensor[(?), int64]), + tensor2_int64(Tensor[(?, ?), int64]), + tensor3_int64(Tensor[(?, ?, ?), int64]), + tensor4_int64(Tensor[(?, ?, ?, ?), int64]), + tensor5_int64(Tensor[(?, ?, ?, ?, ?), int64]), + tensor6_int64(Tensor[(?, ?, ?, ?, ?, ?), int64]), +} + +type tensor_int32_t { + tensor_nil_int32, + tensor0_int32(int32), + tensor1_int32(Tensor[(?), int32]), + tensor2_int32(Tensor[(?, ?), int32]), + tensor3_int32(Tensor[(?, ?, ?), int32]), + tensor4_int32(Tensor[(?, ?, ?, ?), int32]), + tensor5_int32(Tensor[(?, ?, ?, ?, ?), int32]), + tensor6_int32(Tensor[(?, ?, ?, ?, ?, ?), int32]), +} + +type tensor_int8_t { + tensor_nil_int8, + tensor0_int8(int8), + tensor1_int8(Tensor[(?), int8]), + tensor2_int8(Tensor[(?, ?), int8]), + tensor3_int8(Tensor[(?, ?, ?), int8]), + tensor4_int8(Tensor[(?, ?, ?, ?), int8]), + tensor5_int8(Tensor[(?, ?, ?, ?, ?), int8]), + tensor6_int8(Tensor[(?, ?, ?, ?, ?, ?), int8]), +} + +type Option[A] { + Some(A), + None, +} + +type tensor_float64_t { + tensor_nil_float64, + tensor0_float64(float64), + tensor1_float64(Tensor[(?), float64]), + tensor2_float64(Tensor[(?, ?), float64]), + tensor3_float64(Tensor[(?, ?, ?), float64]), + tensor4_float64(Tensor[(?, ?, ?, ?), float64]), + tensor5_float64(Tensor[(?, ?, ?, ?, ?), float64]), + tensor6_float64(Tensor[(?, ?, ?, ?, ?, ?), float64]), +} + +type tensor_float16_t { + tensor_nil_float16, + tensor0_float16(float16), + tensor1_float16(Tensor[(?), float16]), + tensor2_float16(Tensor[(?, ?), float16]), + tensor3_float16(Tensor[(?, ?, ?), float16]), + tensor4_float16(Tensor[(?, ?, ?, ?), float16]), + tensor5_float16(Tensor[(?, ?, ?, ?, ?), float16]), + tensor6_float16(Tensor[(?, ?, ?, ?, ?, ?), float16]), +} + +type tensor_float32_t { + tensor_nil_float32, + tensor0_float32(float32), + tensor1_float32(Tensor[(?), float32]), + tensor2_float32(Tensor[(?, ?), float32]), + tensor3_float32(Tensor[(?, ?, ?), float32]), + tensor4_float32(Tensor[(?, ?, ?, ?), float32]), + tensor5_float32(Tensor[(?, ?, ?, ?, ?), float32]), + tensor6_float32(Tensor[(?, ?, ?, ?, ?, ?), float32]), +} + +type List[A] { + Cons(A, List[A]), + Nil, +} + +type tensor_int16_t { + tensor_nil_int16, + tensor0_int16(int16), + tensor1_int16(Tensor[(?), int16]), + tensor2_int16(Tensor[(?, ?), int16]), + tensor3_int16(Tensor[(?, ?, ?), int16]), + tensor4_int16(Tensor[(?, ?, ?, ?), int16]), + tensor5_int16(Tensor[(?, ?, ?, ?, ?), int16]), + tensor6_int16(Tensor[(?, ?, ?, ?, ?, ?), int16]), +} + +type Tree[A] { + Rose(A, List[Tree[A]]), +} + +type tensor_uint16_t { + tensor_nil_uint16, + tensor0_uint16(uint16), + tensor1_uint16(Tensor[(?), uint16]), + tensor2_uint16(Tensor[(?, ?), uint16]), + tensor3_uint16(Tensor[(?, ?, ?), uint16]), + tensor4_uint16(Tensor[(?, ?, ?, ?), uint16]), + tensor5_uint16(Tensor[(?, ?, ?, ?, ?), uint16]), + tensor6_uint16(Tensor[(?, ?, ?, ?, ?, ?), uint16]), +} + +def @main(%input_1: Tensor[(20, 32, 256), float32], %input_0: Tensor[(10, 32, 256), float32], %decoder_layers_0_self_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_0_self_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_0_self_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_0_self_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_0_norm1_weight: Tensor[(256), float32], %decoder_layers_0_norm1_bias: Tensor[(256), float32], %decoder_layers_0_multihead_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_0_multihead_attn_in_proj_bias: Tensor[(768), float32], %encoder_layers_0_self_attn_in_proj_weight: Tensor[(768, 256), float32], %encoder_layers_0_self_attn_in_proj_bias: Tensor[(768), float32], %encoder_layers_0_self_attn_out_proj_weight: Tensor[(256, 256), float32], %encoder_layers_0_self_attn_out_proj_bias: Tensor[(256), float32], %encoder_layers_0_norm1_weight: Tensor[(256), float32], %encoder_layers_0_norm1_bias: Tensor[(256), float32], %encoder_layers_0_linear1_weight: Tensor[(2048, 256), float32], %encoder_layers_0_linear1_bias: Tensor[(2048), float32], %encoder_layers_0_linear2_weight: Tensor[(256, 2048), float32], %encoder_layers_0_linear2_bias: Tensor[(256), float32], %encoder_layers_0_norm2_weight: Tensor[(256), float32], %encoder_layers_0_norm2_bias: Tensor[(256), float32], %encoder_layers_1_self_attn_in_proj_weight: Tensor[(768, 256), float32], %encoder_layers_1_self_attn_in_proj_bias: Tensor[(768), float32], %encoder_layers_1_self_attn_out_proj_weight: Tensor[(256, 256), float32], %encoder_layers_1_self_attn_out_proj_bias: Tensor[(256), float32], %encoder_layers_1_norm1_weight: Tensor[(256), float32], %encoder_layers_1_norm1_bias: Tensor[(256), float32], %encoder_layers_1_linear1_weight: Tensor[(2048, 256), float32], %encoder_layers_1_linear1_bias: Tensor[(2048), float32], %encoder_layers_1_linear2_weight: Tensor[(256, 2048), float32], %encoder_layers_1_linear2_bias: Tensor[(256), float32], %encoder_layers_1_norm2_weight: Tensor[(256), float32], %encoder_layers_1_norm2_bias: Tensor[(256), float32], %encoder_layers_2_self_attn_in_proj_weight: Tensor[(768, 256), float32], %encoder_layers_2_self_attn_in_proj_bias: Tensor[(768), float32], %encoder_layers_2_self_attn_out_proj_weight: Tensor[(256, 256), float32], %encoder_layers_2_self_attn_out_proj_bias: Tensor[(256), float32], %encoder_layers_2_norm1_weight: Tensor[(256), float32], %encoder_layers_2_norm1_bias: Tensor[(256), float32], %encoder_layers_2_linear1_weight: Tensor[(2048, 256), float32], %encoder_layers_2_linear1_bias: Tensor[(2048), float32], %encoder_layers_2_linear2_weight: Tensor[(256, 2048), float32], %encoder_layers_2_linear2_bias: Tensor[(256), float32], %encoder_layers_2_norm2_weight: Tensor[(256), float32], %encoder_layers_2_norm2_bias: Tensor[(256), float32], %encoder_layers_3_self_attn_in_proj_weight: Tensor[(768, 256), float32], %encoder_layers_3_self_attn_in_proj_bias: Tensor[(768), float32], %encoder_layers_3_self_attn_out_proj_weight: Tensor[(256, 256), float32], %encoder_layers_3_self_attn_out_proj_bias: Tensor[(256), float32], %encoder_layers_3_norm1_weight: Tensor[(256), float32], %encoder_layers_3_norm1_bias: Tensor[(256), float32], %encoder_layers_3_linear1_weight: Tensor[(2048, 256), float32], %encoder_layers_3_linear1_bias: Tensor[(2048), float32], %encoder_layers_3_linear2_weight: Tensor[(256, 2048), float32], %encoder_layers_3_linear2_bias: Tensor[(256), float32], %encoder_layers_3_norm2_weight: Tensor[(256), float32], %encoder_layers_3_norm2_bias: Tensor[(256), float32], %encoder_layers_4_self_attn_in_proj_weight: Tensor[(768, 256), float32], %encoder_layers_4_self_attn_in_proj_bias: Tensor[(768), float32], %encoder_layers_4_self_attn_out_proj_weight: Tensor[(256, 256), float32], %encoder_layers_4_self_attn_out_proj_bias: Tensor[(256), float32], %encoder_layers_4_norm1_weight: Tensor[(256), float32], %encoder_layers_4_norm1_bias: Tensor[(256), float32], %encoder_layers_4_linear1_weight: Tensor[(2048, 256), float32], %encoder_layers_4_linear1_bias: Tensor[(2048), float32], %encoder_layers_4_linear2_weight: Tensor[(256, 2048), float32], %encoder_layers_4_linear2_bias: Tensor[(256), float32], %encoder_layers_4_norm2_weight: Tensor[(256), float32], %encoder_layers_4_norm2_bias: Tensor[(256), float32], %encoder_layers_5_self_attn_in_proj_weight: Tensor[(768, 256), float32], %encoder_layers_5_self_attn_in_proj_bias: Tensor[(768), float32], %encoder_layers_5_self_attn_out_proj_weight: Tensor[(256, 256), float32], %encoder_layers_5_self_attn_out_proj_bias: Tensor[(256), float32], %encoder_layers_5_norm1_weight: Tensor[(256), float32], %encoder_layers_5_norm1_bias: Tensor[(256), float32], %encoder_layers_5_linear1_weight: Tensor[(2048, 256), float32], %encoder_layers_5_linear1_bias: Tensor[(2048), float32], %encoder_layers_5_linear2_weight: Tensor[(256, 2048), float32], %encoder_layers_5_linear2_bias: Tensor[(256), float32], %encoder_layers_5_norm2_weight: Tensor[(256), float32], %encoder_layers_5_norm2_bias: Tensor[(256), float32], %encoder_norm_weight: Tensor[(256), float32], %encoder_norm_bias: Tensor[(256), float32], %decoder_layers_0_multihead_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_0_multihead_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_0_norm2_weight: Tensor[(256), float32], %decoder_layers_0_norm2_bias: Tensor[(256), float32], %decoder_layers_0_linear1_weight: Tensor[(2048, 256), float32], %decoder_layers_0_linear1_bias: Tensor[(2048), float32], %decoder_layers_0_linear2_weight: Tensor[(256, 2048), float32], %decoder_layers_0_linear2_bias: Tensor[(256), float32], %decoder_layers_0_norm3_weight: Tensor[(256), float32], %decoder_layers_0_norm3_bias: Tensor[(256), float32], %decoder_layers_1_self_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_1_self_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_1_self_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_1_self_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_1_norm1_weight: Tensor[(256), float32], %decoder_layers_1_norm1_bias: Tensor[(256), float32], %decoder_layers_1_multihead_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_1_multihead_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_1_multihead_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_1_multihead_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_1_norm2_weight: Tensor[(256), float32], %decoder_layers_1_norm2_bias: Tensor[(256), float32], %decoder_layers_1_linear1_weight: Tensor[(2048, 256), float32], %decoder_layers_1_linear1_bias: Tensor[(2048), float32], %decoder_layers_1_linear2_weight: Tensor[(256, 2048), float32], %decoder_layers_1_linear2_bias: Tensor[(256), float32], %decoder_layers_1_norm3_weight: Tensor[(256), float32], %decoder_layers_1_norm3_bias: Tensor[(256), float32], %decoder_layers_2_self_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_2_self_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_2_self_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_2_self_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_2_norm1_weight: Tensor[(256), float32], %decoder_layers_2_norm1_bias: Tensor[(256), float32], %decoder_layers_2_multihead_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_2_multihead_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_2_multihead_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_2_multihead_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_2_norm2_weight: Tensor[(256), float32], %decoder_layers_2_norm2_bias: Tensor[(256), float32], %decoder_layers_2_linear1_weight: Tensor[(2048, 256), float32], %decoder_layers_2_linear1_bias: Tensor[(2048), float32], %decoder_layers_2_linear2_weight: Tensor[(256, 2048), float32], %decoder_layers_2_linear2_bias: Tensor[(256), float32], %decoder_layers_2_norm3_weight: Tensor[(256), float32], %decoder_layers_2_norm3_bias: Tensor[(256), float32], %decoder_layers_3_self_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_3_self_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_3_self_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_3_self_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_3_norm1_weight: Tensor[(256), float32], %decoder_layers_3_norm1_bias: Tensor[(256), float32], %decoder_layers_3_multihead_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_3_multihead_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_3_multihead_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_3_multihead_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_3_norm2_weight: Tensor[(256), float32], %decoder_layers_3_norm2_bias: Tensor[(256), float32], %decoder_layers_3_linear1_weight: Tensor[(2048, 256), float32], %decoder_layers_3_linear1_bias: Tensor[(2048), float32], %decoder_layers_3_linear2_weight: Tensor[(256, 2048), float32], %decoder_layers_3_linear2_bias: Tensor[(256), float32], %decoder_layers_3_norm3_weight: Tensor[(256), float32], %decoder_layers_3_norm3_bias: Tensor[(256), float32], %decoder_layers_4_self_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_4_self_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_4_self_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_4_self_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_4_norm1_weight: Tensor[(256), float32], %decoder_layers_4_norm1_bias: Tensor[(256), float32], %decoder_layers_4_multihead_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_4_multihead_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_4_multihead_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_4_multihead_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_4_norm2_weight: Tensor[(256), float32], %decoder_layers_4_norm2_bias: Tensor[(256), float32], %decoder_layers_4_linear1_weight: Tensor[(2048, 256), float32], %decoder_layers_4_linear1_bias: Tensor[(2048), float32], %decoder_layers_4_linear2_weight: Tensor[(256, 2048), float32], %decoder_layers_4_linear2_bias: Tensor[(256), float32], %decoder_layers_4_norm3_weight: Tensor[(256), float32], %decoder_layers_4_norm3_bias: Tensor[(256), float32], %decoder_layers_5_self_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_5_self_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_5_self_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_5_self_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_5_norm1_weight: Tensor[(256), float32], %decoder_layers_5_norm1_bias: Tensor[(256), float32], %decoder_layers_5_multihead_attn_in_proj_weight: Tensor[(768, 256), float32], %decoder_layers_5_multihead_attn_in_proj_bias: Tensor[(768), float32], %decoder_layers_5_multihead_attn_out_proj_weight: Tensor[(256, 256), float32], %decoder_layers_5_multihead_attn_out_proj_bias: Tensor[(256), float32], %decoder_layers_5_norm2_weight: Tensor[(256), float32], %decoder_layers_5_norm2_bias: Tensor[(256), float32], %decoder_layers_5_linear1_weight: Tensor[(2048, 256), float32], %decoder_layers_5_linear1_bias: Tensor[(2048), float32], %decoder_layers_5_linear2_weight: Tensor[(256, 2048), float32], %decoder_layers_5_linear2_bias: Tensor[(256), float32], %decoder_layers_5_norm3_weight: Tensor[(256), float32], %decoder_layers_5_norm3_bias: Tensor[(256), float32], %decoder_norm_weight: Tensor[(256), float32], %decoder_norm_bias: Tensor[(256), float32]) { + %0 = transpose(%decoder_layers_0_self_attn_in_proj_weight, axes=[1, 0]); + %1 = reshape(%input_1, newshape=[-1, 256]); + %2 = transpose(%0, axes=[1, 0]); + %3 = nn.dense(%1, %2, units=None); + %4 = reshape(%3, newshape=[20, 32, 768]); + %5 = add(%4, %decoder_layers_0_self_attn_in_proj_bias); + %6 = strided_slice(%5, begin=[0, 0, 0], end=[20, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %7 = multiply(%6, 0.176777f); + %8 = reshape(%7, newshape=[20, 256, 32]); + %9 = strided_slice(%5, begin=[0, 0, 256], end=[20, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %10 = reshape(%9, newshape=[-1, 256, 32]); + %11 = transpose(%10, axes=[1, 0, 2]); + %12 = transpose(%11, axes=[0, 2, 1]); + %13 = transpose(%8, axes=[1, 0, 2]); + %14 = transpose(%12, axes=[0, 2, 1]); + %15 = nn.batch_matmul(%13, %14, meta[relay.attrs.BatchMatmulAttrs][0]); + %16 = nn.softmax(%15); + %17 = nn.dropout(%16, rate=0.1f); + %18 = strided_slice(%5, begin=[0, 0, 512], end=[20, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %19 = reshape(%18, newshape=[-1, 256, 32]); + %20 = transpose(%19, axes=[1, 0, 2]); + %21 = %17.0; + %22 = transpose(%20, axes=[0, 2, 1]); + %23 = nn.batch_matmul(%21, %22, meta[relay.attrs.BatchMatmulAttrs][1]); + %24 = transpose(%23, axes=[1, 0, 2]); + %25 = reshape(%24, newshape=[20, 32, 256]); + %26 = transpose(%decoder_layers_0_self_attn_out_proj_weight, axes=[1, 0]); + %27 = reshape(%25, newshape=[-1, 256]); + %28 = transpose(%26, axes=[1, 0]); + %29 = nn.dense(%27, %28, units=None); + %30 = reshape(%29, newshape=[20, 32, 256]); + %31 = add(%30, %decoder_layers_0_self_attn_out_proj_bias); + %32 = nn.dropout(%31, rate=0.1f); + %33 = %32.0; + %34 = add(%input_1, %33); + %35 = nn.layer_norm(%34, %decoder_layers_0_norm1_weight, %decoder_layers_0_norm1_bias); + %36 = strided_slice(%decoder_layers_0_multihead_attn_in_proj_weight, begin=[0, 0], end=[256, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %37 = transpose(%36, axes=[1, 0]); + %38 = reshape(%35, newshape=[-1, 256]); + %39 = transpose(%37, axes=[1, 0]); + %40 = nn.dense(%38, %39, units=None); + %41 = reshape(%40, newshape=[20, 32, 256]); + %42 = strided_slice(%decoder_layers_0_multihead_attn_in_proj_bias, begin=[0], end=[256], strides=[1], axes=[0], slice_mode="end"); + %43 = add(%41, %42); + %44 = multiply(%43, 0.176777f); + %45 = reshape(%44, newshape=[20, 256, 32]); + %46 = transpose(%encoder_layers_0_self_attn_in_proj_weight, axes=[1, 0]); + %47 = reshape(%input_0, newshape=[-1, 256]); + %48 = transpose(%46, axes=[1, 0]); + %49 = nn.dense(%47, %48, units=None); + %50 = reshape(%49, newshape=[10, 32, 768]); + %51 = add(%50, %encoder_layers_0_self_attn_in_proj_bias); + %52 = strided_slice(%51, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %53 = multiply(%52, 0.176777f); + %54 = reshape(%53, newshape=[10, 256, 32]); + %55 = strided_slice(%51, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %56 = reshape(%55, newshape=[-1, 256, 32]); + %57 = transpose(%56, axes=[1, 0, 2]); + %58 = transpose(%57, axes=[0, 2, 1]); + %59 = transpose(%54, axes=[1, 0, 2]); + %60 = transpose(%58, axes=[0, 2, 1]); + %61 = nn.batch_matmul(%59, %60, meta[relay.attrs.BatchMatmulAttrs][2]); + %62 = nn.softmax(%61); + %63 = nn.dropout(%62, rate=0.1f); + %64 = strided_slice(%51, begin=[0, 0, 512], end=[10, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %65 = reshape(%64, newshape=[-1, 256, 32]); + %66 = transpose(%65, axes=[1, 0, 2]); + %67 = %63.0; + %68 = transpose(%66, axes=[0, 2, 1]); + %69 = nn.batch_matmul(%67, %68, meta[relay.attrs.BatchMatmulAttrs][3]); + %70 = transpose(%69, axes=[1, 0, 2]); + %71 = reshape(%70, newshape=[10, 32, 256]); + %72 = transpose(%encoder_layers_0_self_attn_out_proj_weight, axes=[1, 0]); + %73 = reshape(%71, newshape=[-1, 256]); + %74 = transpose(%72, axes=[1, 0]); + %75 = nn.dense(%73, %74, units=None); + %76 = reshape(%75, newshape=[10, 32, 256]); + %77 = add(%76, %encoder_layers_0_self_attn_out_proj_bias); + %78 = nn.dropout(%77, rate=0.1f); + %79 = %78.0; + %80 = add(%input_0, %79); + %81 = nn.layer_norm(%80, %encoder_layers_0_norm1_weight, %encoder_layers_0_norm1_bias); + %82 = transpose(%encoder_layers_0_linear1_weight, axes=[1, 0]); + %83 = reshape(%81, newshape=[-1, 256]); + %84 = transpose(%82, axes=[1, 0]); + %85 = nn.dense(%83, %84, units=None); + %86 = reshape(%85, newshape=[10, 32, 2048]); + %87 = add(%86, %encoder_layers_0_linear1_bias); + %88 = nn.relu(%87); + %89 = nn.dropout(%88, rate=0.1f); + %90 = %89.0; + %91 = transpose(%encoder_layers_0_linear2_weight, axes=[1, 0]); + %92 = reshape(%90, newshape=[-1, 2048]); + %93 = transpose(%91, axes=[1, 0]); + %94 = nn.dense(%92, %93, units=None); + %95 = reshape(%94, newshape=[10, 32, 256]); + %96 = add(%95, %encoder_layers_0_linear2_bias); + %97 = nn.dropout(%96, rate=0.1f); + %98 = %97.0; + %99 = add(%81, %98); + %100 = nn.layer_norm(%99, %encoder_layers_0_norm2_weight, %encoder_layers_0_norm2_bias); + %101 = transpose(%encoder_layers_1_self_attn_in_proj_weight, axes=[1, 0]); + %102 = reshape(%100, newshape=[-1, 256]); + %103 = transpose(%101, axes=[1, 0]); + %104 = nn.dense(%102, %103, units=None); + %105 = reshape(%104, newshape=[10, 32, 768]); + %106 = add(%105, %encoder_layers_1_self_attn_in_proj_bias); + %107 = strided_slice(%106, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %108 = multiply(%107, 0.176777f); + %109 = reshape(%108, newshape=[10, 256, 32]); + %110 = strided_slice(%106, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %111 = reshape(%110, newshape=[-1, 256, 32]); + %112 = transpose(%111, axes=[1, 0, 2]); + %113 = transpose(%112, axes=[0, 2, 1]); + %114 = transpose(%109, axes=[1, 0, 2]); + %115 = transpose(%113, axes=[0, 2, 1]); + %116 = nn.batch_matmul(%114, %115, meta[relay.attrs.BatchMatmulAttrs][4]); + %117 = nn.softmax(%116); + %118 = nn.dropout(%117, rate=0.1f); + %119 = strided_slice(%106, begin=[0, 0, 512], end=[10, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %120 = reshape(%119, newshape=[-1, 256, 32]); + %121 = transpose(%120, axes=[1, 0, 2]); + %122 = %118.0; + %123 = transpose(%121, axes=[0, 2, 1]); + %124 = nn.batch_matmul(%122, %123, meta[relay.attrs.BatchMatmulAttrs][5]); + %125 = transpose(%124, axes=[1, 0, 2]); + %126 = reshape(%125, newshape=[10, 32, 256]); + %127 = transpose(%encoder_layers_1_self_attn_out_proj_weight, axes=[1, 0]); + %128 = reshape(%126, newshape=[-1, 256]); + %129 = transpose(%127, axes=[1, 0]); + %130 = nn.dense(%128, %129, units=None); + %131 = reshape(%130, newshape=[10, 32, 256]); + %132 = add(%131, %encoder_layers_1_self_attn_out_proj_bias); + %133 = nn.dropout(%132, rate=0.1f); + %134 = %133.0; + %135 = add(%100, %134); + %136 = nn.layer_norm(%135, %encoder_layers_1_norm1_weight, %encoder_layers_1_norm1_bias); + %137 = transpose(%encoder_layers_1_linear1_weight, axes=[1, 0]); + %138 = reshape(%136, newshape=[-1, 256]); + %139 = transpose(%137, axes=[1, 0]); + %140 = nn.dense(%138, %139, units=None); + %141 = reshape(%140, newshape=[10, 32, 2048]); + %142 = add(%141, %encoder_layers_1_linear1_bias); + %143 = nn.relu(%142); + %144 = nn.dropout(%143, rate=0.1f); + %145 = %144.0; + %146 = transpose(%encoder_layers_1_linear2_weight, axes=[1, 0]); + %147 = reshape(%145, newshape=[-1, 2048]); + %148 = transpose(%146, axes=[1, 0]); + %149 = nn.dense(%147, %148, units=None); + %150 = reshape(%149, newshape=[10, 32, 256]); + %151 = add(%150, %encoder_layers_1_linear2_bias); + %152 = nn.dropout(%151, rate=0.1f); + %153 = %152.0; + %154 = add(%136, %153); + %155 = nn.layer_norm(%154, %encoder_layers_1_norm2_weight, %encoder_layers_1_norm2_bias); + %156 = transpose(%encoder_layers_2_self_attn_in_proj_weight, axes=[1, 0]); + %157 = reshape(%155, newshape=[-1, 256]); + %158 = transpose(%156, axes=[1, 0]); + %159 = nn.dense(%157, %158, units=None); + %160 = reshape(%159, newshape=[10, 32, 768]); + %161 = add(%160, %encoder_layers_2_self_attn_in_proj_bias); + %162 = strided_slice(%161, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %163 = multiply(%162, 0.176777f); + %164 = reshape(%163, newshape=[10, 256, 32]); + %165 = strided_slice(%161, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %166 = reshape(%165, newshape=[-1, 256, 32]); + %167 = transpose(%166, axes=[1, 0, 2]); + %168 = transpose(%167, axes=[0, 2, 1]); + %169 = transpose(%164, axes=[1, 0, 2]); + %170 = transpose(%168, axes=[0, 2, 1]); + %171 = nn.batch_matmul(%169, %170, meta[relay.attrs.BatchMatmulAttrs][6]); + %172 = nn.softmax(%171); + %173 = nn.dropout(%172, rate=0.1f); + %174 = strided_slice(%161, begin=[0, 0, 512], end=[10, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %175 = reshape(%174, newshape=[-1, 256, 32]); + %176 = transpose(%175, axes=[1, 0, 2]); + %177 = %173.0; + %178 = transpose(%176, axes=[0, 2, 1]); + %179 = nn.batch_matmul(%177, %178, meta[relay.attrs.BatchMatmulAttrs][7]); + %180 = transpose(%179, axes=[1, 0, 2]); + %181 = reshape(%180, newshape=[10, 32, 256]); + %182 = transpose(%encoder_layers_2_self_attn_out_proj_weight, axes=[1, 0]); + %183 = reshape(%181, newshape=[-1, 256]); + %184 = transpose(%182, axes=[1, 0]); + %185 = nn.dense(%183, %184, units=None); + %186 = reshape(%185, newshape=[10, 32, 256]); + %187 = add(%186, %encoder_layers_2_self_attn_out_proj_bias); + %188 = nn.dropout(%187, rate=0.1f); + %189 = %188.0; + %190 = add(%155, %189); + %191 = nn.layer_norm(%190, %encoder_layers_2_norm1_weight, %encoder_layers_2_norm1_bias); + %192 = transpose(%encoder_layers_2_linear1_weight, axes=[1, 0]); + %193 = reshape(%191, newshape=[-1, 256]); + %194 = transpose(%192, axes=[1, 0]); + %195 = nn.dense(%193, %194, units=None); + %196 = reshape(%195, newshape=[10, 32, 2048]); + %197 = add(%196, %encoder_layers_2_linear1_bias); + %198 = nn.relu(%197); + %199 = nn.dropout(%198, rate=0.1f); + %200 = %199.0; + %201 = transpose(%encoder_layers_2_linear2_weight, axes=[1, 0]); + %202 = reshape(%200, newshape=[-1, 2048]); + %203 = transpose(%201, axes=[1, 0]); + %204 = nn.dense(%202, %203, units=None); + %205 = reshape(%204, newshape=[10, 32, 256]); + %206 = add(%205, %encoder_layers_2_linear2_bias); + %207 = nn.dropout(%206, rate=0.1f); + %208 = %207.0; + %209 = add(%191, %208); + %210 = nn.layer_norm(%209, %encoder_layers_2_norm2_weight, %encoder_layers_2_norm2_bias); + %211 = transpose(%encoder_layers_3_self_attn_in_proj_weight, axes=[1, 0]); + %212 = reshape(%210, newshape=[-1, 256]); + %213 = transpose(%211, axes=[1, 0]); + %214 = nn.dense(%212, %213, units=None); + %215 = reshape(%214, newshape=[10, 32, 768]); + %216 = add(%215, %encoder_layers_3_self_attn_in_proj_bias); + %217 = strided_slice(%216, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %218 = multiply(%217, 0.176777f); + %219 = reshape(%218, newshape=[10, 256, 32]); + %220 = strided_slice(%216, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %221 = reshape(%220, newshape=[-1, 256, 32]); + %222 = transpose(%221, axes=[1, 0, 2]); + %223 = transpose(%222, axes=[0, 2, 1]); + %224 = transpose(%219, axes=[1, 0, 2]); + %225 = transpose(%223, axes=[0, 2, 1]); + %226 = nn.batch_matmul(%224, %225, meta[relay.attrs.BatchMatmulAttrs][8]); + %227 = nn.softmax(%226); + %228 = nn.dropout(%227, rate=0.1f); + %229 = strided_slice(%216, begin=[0, 0, 512], end=[10, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %230 = reshape(%229, newshape=[-1, 256, 32]); + %231 = transpose(%230, axes=[1, 0, 2]); + %232 = %228.0; + %233 = transpose(%231, axes=[0, 2, 1]); + %234 = nn.batch_matmul(%232, %233, meta[relay.attrs.BatchMatmulAttrs][9]); + %235 = transpose(%234, axes=[1, 0, 2]); + %236 = reshape(%235, newshape=[10, 32, 256]); + %237 = transpose(%encoder_layers_3_self_attn_out_proj_weight, axes=[1, 0]); + %238 = reshape(%236, newshape=[-1, 256]); + %239 = transpose(%237, axes=[1, 0]); + %240 = nn.dense(%238, %239, units=None); + %241 = reshape(%240, newshape=[10, 32, 256]); + %242 = add(%241, %encoder_layers_3_self_attn_out_proj_bias); + %243 = nn.dropout(%242, rate=0.1f); + %244 = %243.0; + %245 = add(%210, %244); + %246 = nn.layer_norm(%245, %encoder_layers_3_norm1_weight, %encoder_layers_3_norm1_bias); + %247 = transpose(%encoder_layers_3_linear1_weight, axes=[1, 0]); + %248 = reshape(%246, newshape=[-1, 256]); + %249 = transpose(%247, axes=[1, 0]); + %250 = nn.dense(%248, %249, units=None); + %251 = reshape(%250, newshape=[10, 32, 2048]); + %252 = add(%251, %encoder_layers_3_linear1_bias); + %253 = nn.relu(%252); + %254 = nn.dropout(%253, rate=0.1f); + %255 = %254.0; + %256 = transpose(%encoder_layers_3_linear2_weight, axes=[1, 0]); + %257 = reshape(%255, newshape=[-1, 2048]); + %258 = transpose(%256, axes=[1, 0]); + %259 = nn.dense(%257, %258, units=None); + %260 = reshape(%259, newshape=[10, 32, 256]); + %261 = add(%260, %encoder_layers_3_linear2_bias); + %262 = nn.dropout(%261, rate=0.1f); + %263 = %262.0; + %264 = add(%246, %263); + %265 = nn.layer_norm(%264, %encoder_layers_3_norm2_weight, %encoder_layers_3_norm2_bias); + %266 = transpose(%encoder_layers_4_self_attn_in_proj_weight, axes=[1, 0]); + %267 = reshape(%265, newshape=[-1, 256]); + %268 = transpose(%266, axes=[1, 0]); + %269 = nn.dense(%267, %268, units=None); + %270 = reshape(%269, newshape=[10, 32, 768]); + %271 = add(%270, %encoder_layers_4_self_attn_in_proj_bias); + %272 = strided_slice(%271, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %273 = multiply(%272, 0.176777f); + %274 = reshape(%273, newshape=[10, 256, 32]); + %275 = strided_slice(%271, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %276 = reshape(%275, newshape=[-1, 256, 32]); + %277 = transpose(%276, axes=[1, 0, 2]); + %278 = transpose(%277, axes=[0, 2, 1]); + %279 = transpose(%274, axes=[1, 0, 2]); + %280 = transpose(%278, axes=[0, 2, 1]); + %281 = nn.batch_matmul(%279, %280, meta[relay.attrs.BatchMatmulAttrs][10]); + %282 = nn.softmax(%281); + %283 = nn.dropout(%282, rate=0.1f); + %284 = strided_slice(%271, begin=[0, 0, 512], end=[10, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %285 = reshape(%284, newshape=[-1, 256, 32]); + %286 = transpose(%285, axes=[1, 0, 2]); + %287 = %283.0; + %288 = transpose(%286, axes=[0, 2, 1]); + %289 = nn.batch_matmul(%287, %288, meta[relay.attrs.BatchMatmulAttrs][11]); + %290 = transpose(%289, axes=[1, 0, 2]); + %291 = reshape(%290, newshape=[10, 32, 256]); + %292 = transpose(%encoder_layers_4_self_attn_out_proj_weight, axes=[1, 0]); + %293 = reshape(%291, newshape=[-1, 256]); + %294 = transpose(%292, axes=[1, 0]); + %295 = nn.dense(%293, %294, units=None); + %296 = reshape(%295, newshape=[10, 32, 256]); + %297 = add(%296, %encoder_layers_4_self_attn_out_proj_bias); + %298 = nn.dropout(%297, rate=0.1f); + %299 = %298.0; + %300 = add(%265, %299); + %301 = nn.layer_norm(%300, %encoder_layers_4_norm1_weight, %encoder_layers_4_norm1_bias); + %302 = transpose(%encoder_layers_4_linear1_weight, axes=[1, 0]); + %303 = reshape(%301, newshape=[-1, 256]); + %304 = transpose(%302, axes=[1, 0]); + %305 = nn.dense(%303, %304, units=None); + %306 = reshape(%305, newshape=[10, 32, 2048]); + %307 = add(%306, %encoder_layers_4_linear1_bias); + %308 = nn.relu(%307); + %309 = nn.dropout(%308, rate=0.1f); + %310 = %309.0; + %311 = transpose(%encoder_layers_4_linear2_weight, axes=[1, 0]); + %312 = reshape(%310, newshape=[-1, 2048]); + %313 = transpose(%311, axes=[1, 0]); + %314 = nn.dense(%312, %313, units=None); + %315 = reshape(%314, newshape=[10, 32, 256]); + %316 = add(%315, %encoder_layers_4_linear2_bias); + %317 = nn.dropout(%316, rate=0.1f); + %318 = %317.0; + %319 = add(%301, %318); + %320 = nn.layer_norm(%319, %encoder_layers_4_norm2_weight, %encoder_layers_4_norm2_bias); + %321 = transpose(%encoder_layers_5_self_attn_in_proj_weight, axes=[1, 0]); + %322 = reshape(%320, newshape=[-1, 256]); + %323 = transpose(%321, axes=[1, 0]); + %324 = nn.dense(%322, %323, units=None); + %325 = reshape(%324, newshape=[10, 32, 768]); + %326 = add(%325, %encoder_layers_5_self_attn_in_proj_bias); + %327 = strided_slice(%326, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %328 = multiply(%327, 0.176777f); + %329 = reshape(%328, newshape=[10, 256, 32]); + %330 = strided_slice(%326, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %331 = reshape(%330, newshape=[-1, 256, 32]); + %332 = transpose(%331, axes=[1, 0, 2]); + %333 = transpose(%332, axes=[0, 2, 1]); + %334 = transpose(%329, axes=[1, 0, 2]); + %335 = transpose(%333, axes=[0, 2, 1]); + %336 = nn.batch_matmul(%334, %335, meta[relay.attrs.BatchMatmulAttrs][12]); + %337 = nn.softmax(%336); + %338 = nn.dropout(%337, rate=0.1f); + %339 = strided_slice(%326, begin=[0, 0, 512], end=[10, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %340 = reshape(%339, newshape=[-1, 256, 32]); + %341 = transpose(%340, axes=[1, 0, 2]); + %342 = %338.0; + %343 = transpose(%341, axes=[0, 2, 1]); + %344 = nn.batch_matmul(%342, %343, meta[relay.attrs.BatchMatmulAttrs][13]); + %345 = transpose(%344, axes=[1, 0, 2]); + %346 = reshape(%345, newshape=[10, 32, 256]); + %347 = transpose(%encoder_layers_5_self_attn_out_proj_weight, axes=[1, 0]); + %348 = reshape(%346, newshape=[-1, 256]); + %349 = transpose(%347, axes=[1, 0]); + %350 = nn.dense(%348, %349, units=None); + %351 = reshape(%350, newshape=[10, 32, 256]); + %352 = add(%351, %encoder_layers_5_self_attn_out_proj_bias); + %353 = nn.dropout(%352, rate=0.1f); + %354 = %353.0; + %355 = add(%320, %354); + %356 = nn.layer_norm(%355, %encoder_layers_5_norm1_weight, %encoder_layers_5_norm1_bias); + %357 = transpose(%encoder_layers_5_linear1_weight, axes=[1, 0]); + %358 = reshape(%356, newshape=[-1, 256]); + %359 = transpose(%357, axes=[1, 0]); + %360 = nn.dense(%358, %359, units=None); + %361 = reshape(%360, newshape=[10, 32, 2048]); + %362 = add(%361, %encoder_layers_5_linear1_bias); + %363 = nn.relu(%362); + %364 = nn.dropout(%363, rate=0.1f); + %365 = %364.0; + %366 = transpose(%encoder_layers_5_linear2_weight, axes=[1, 0]); + %367 = reshape(%365, newshape=[-1, 2048]); + %368 = transpose(%366, axes=[1, 0]); + %369 = nn.dense(%367, %368, units=None); + %370 = reshape(%369, newshape=[10, 32, 256]); + %371 = add(%370, %encoder_layers_5_linear2_bias); + %372 = nn.dropout(%371, rate=0.1f); + %373 = %372.0; + %374 = add(%356, %373); + %375 = nn.layer_norm(%374, %encoder_layers_5_norm2_weight, %encoder_layers_5_norm2_bias); + %376 = nn.layer_norm(%375, %encoder_norm_weight, %encoder_norm_bias); + %377 = strided_slice(%decoder_layers_0_multihead_attn_in_proj_weight, begin=[256, 0], end=[768, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %378 = transpose(%377, axes=[1, 0]); + %379 = reshape(%376, newshape=[-1, 256]); + %380 = transpose(%378, axes=[1, 0]); + %381 = nn.dense(%379, %380, units=None); + %382 = reshape(%381, newshape=[10, 32, 512]); + %383 = strided_slice(%decoder_layers_0_multihead_attn_in_proj_bias, begin=[256], end=[768], strides=[1], axes=[0], slice_mode="end"); + %384 = add(%382, %383); + %385 = strided_slice(%384, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %386 = reshape(%385, newshape=[-1, 256, 32]); + %387 = transpose(%386, axes=[1, 0, 2]); + %388 = transpose(%387, axes=[0, 2, 1]); + %389 = transpose(%45, axes=[1, 0, 2]); + %390 = transpose(%388, axes=[0, 2, 1]); + %391 = nn.batch_matmul(%389, %390, meta[relay.attrs.BatchMatmulAttrs][14]); + %392 = nn.softmax(%391); + %393 = nn.dropout(%392, rate=0.1f); + %394 = strided_slice(%384, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %395 = reshape(%394, newshape=[-1, 256, 32]); + %396 = transpose(%395, axes=[1, 0, 2]); + %397 = %393.0; + %398 = transpose(%396, axes=[0, 2, 1]); + %399 = nn.batch_matmul(%397, %398, meta[relay.attrs.BatchMatmulAttrs][15]); + %400 = transpose(%399, axes=[1, 0, 2]); + %401 = reshape(%400, newshape=[20, 32, 256]); + %402 = transpose(%decoder_layers_0_multihead_attn_out_proj_weight, axes=[1, 0]); + %403 = reshape(%401, newshape=[-1, 256]); + %404 = transpose(%402, axes=[1, 0]); + %405 = nn.dense(%403, %404, units=None); + %406 = reshape(%405, newshape=[20, 32, 256]); + %407 = add(%406, %decoder_layers_0_multihead_attn_out_proj_bias); + %408 = nn.dropout(%407, rate=0.1f); + %409 = %408.0; + %410 = add(%35, %409); + %411 = nn.layer_norm(%410, %decoder_layers_0_norm2_weight, %decoder_layers_0_norm2_bias); + %412 = transpose(%decoder_layers_0_linear1_weight, axes=[1, 0]); + %413 = reshape(%411, newshape=[-1, 256]); + %414 = transpose(%412, axes=[1, 0]); + %415 = nn.dense(%413, %414, units=None); + %416 = reshape(%415, newshape=[20, 32, 2048]); + %417 = add(%416, %decoder_layers_0_linear1_bias); + %418 = nn.relu(%417); + %419 = nn.dropout(%418, rate=0.1f); + %420 = %419.0; + %421 = transpose(%decoder_layers_0_linear2_weight, axes=[1, 0]); + %422 = reshape(%420, newshape=[-1, 2048]); + %423 = transpose(%421, axes=[1, 0]); + %424 = nn.dense(%422, %423, units=None); + %425 = reshape(%424, newshape=[20, 32, 256]); + %426 = add(%425, %decoder_layers_0_linear2_bias); + %427 = nn.dropout(%426, rate=0.1f); + %428 = %427.0; + %429 = add(%411, %428); + %430 = nn.layer_norm(%429, %decoder_layers_0_norm3_weight, %decoder_layers_0_norm3_bias); + %431 = transpose(%decoder_layers_1_self_attn_in_proj_weight, axes=[1, 0]); + %432 = reshape(%430, newshape=[-1, 256]); + %433 = transpose(%431, axes=[1, 0]); + %434 = nn.dense(%432, %433, units=None); + %435 = reshape(%434, newshape=[20, 32, 768]); + %436 = add(%435, %decoder_layers_1_self_attn_in_proj_bias); + %437 = strided_slice(%436, begin=[0, 0, 0], end=[20, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %438 = multiply(%437, 0.176777f); + %439 = reshape(%438, newshape=[20, 256, 32]); + %440 = strided_slice(%436, begin=[0, 0, 256], end=[20, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %441 = reshape(%440, newshape=[-1, 256, 32]); + %442 = transpose(%441, axes=[1, 0, 2]); + %443 = transpose(%442, axes=[0, 2, 1]); + %444 = transpose(%439, axes=[1, 0, 2]); + %445 = transpose(%443, axes=[0, 2, 1]); + %446 = nn.batch_matmul(%444, %445, meta[relay.attrs.BatchMatmulAttrs][16]); + %447 = nn.softmax(%446); + %448 = nn.dropout(%447, rate=0.1f); + %449 = strided_slice(%436, begin=[0, 0, 512], end=[20, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %450 = reshape(%449, newshape=[-1, 256, 32]); + %451 = transpose(%450, axes=[1, 0, 2]); + %452 = %448.0; + %453 = transpose(%451, axes=[0, 2, 1]); + %454 = nn.batch_matmul(%452, %453, meta[relay.attrs.BatchMatmulAttrs][17]); + %455 = transpose(%454, axes=[1, 0, 2]); + %456 = reshape(%455, newshape=[20, 32, 256]); + %457 = transpose(%decoder_layers_1_self_attn_out_proj_weight, axes=[1, 0]); + %458 = reshape(%456, newshape=[-1, 256]); + %459 = transpose(%457, axes=[1, 0]); + %460 = nn.dense(%458, %459, units=None); + %461 = reshape(%460, newshape=[20, 32, 256]); + %462 = add(%461, %decoder_layers_1_self_attn_out_proj_bias); + %463 = nn.dropout(%462, rate=0.1f); + %464 = %463.0; + %465 = add(%430, %464); + %466 = nn.layer_norm(%465, %decoder_layers_1_norm1_weight, %decoder_layers_1_norm1_bias); + %467 = strided_slice(%decoder_layers_1_multihead_attn_in_proj_weight, begin=[0, 0], end=[256, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %468 = transpose(%467, axes=[1, 0]); + %469 = reshape(%466, newshape=[-1, 256]); + %470 = transpose(%468, axes=[1, 0]); + %471 = nn.dense(%469, %470, units=None); + %472 = reshape(%471, newshape=[20, 32, 256]); + %473 = strided_slice(%decoder_layers_1_multihead_attn_in_proj_bias, begin=[0], end=[256], strides=[1], axes=[0], slice_mode="end"); + %474 = add(%472, %473); + %475 = multiply(%474, 0.176777f); + %476 = reshape(%475, newshape=[20, 256, 32]); + %477 = strided_slice(%decoder_layers_1_multihead_attn_in_proj_weight, begin=[256, 0], end=[768, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %478 = transpose(%477, axes=[1, 0]); + %479 = reshape(%376, newshape=[-1, 256]); + %480 = transpose(%478, axes=[1, 0]); + %481 = nn.dense(%479, %480, units=None); + %482 = reshape(%481, newshape=[10, 32, 512]); + %483 = strided_slice(%decoder_layers_1_multihead_attn_in_proj_bias, begin=[256], end=[768], strides=[1], axes=[0], slice_mode="end"); + %484 = add(%482, %483); + %485 = strided_slice(%484, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %486 = reshape(%485, newshape=[-1, 256, 32]); + %487 = transpose(%486, axes=[1, 0, 2]); + %488 = transpose(%487, axes=[0, 2, 1]); + %489 = transpose(%476, axes=[1, 0, 2]); + %490 = transpose(%488, axes=[0, 2, 1]); + %491 = nn.batch_matmul(%489, %490, meta[relay.attrs.BatchMatmulAttrs][18]); + %492 = nn.softmax(%491); + %493 = nn.dropout(%492, rate=0.1f); + %494 = strided_slice(%484, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %495 = reshape(%494, newshape=[-1, 256, 32]); + %496 = transpose(%495, axes=[1, 0, 2]); + %497 = %493.0; + %498 = transpose(%496, axes=[0, 2, 1]); + %499 = nn.batch_matmul(%497, %498, meta[relay.attrs.BatchMatmulAttrs][19]); + %500 = transpose(%499, axes=[1, 0, 2]); + %501 = reshape(%500, newshape=[20, 32, 256]); + %502 = transpose(%decoder_layers_1_multihead_attn_out_proj_weight, axes=[1, 0]); + %503 = reshape(%501, newshape=[-1, 256]); + %504 = transpose(%502, axes=[1, 0]); + %505 = nn.dense(%503, %504, units=None); + %506 = reshape(%505, newshape=[20, 32, 256]); + %507 = add(%506, %decoder_layers_1_multihead_attn_out_proj_bias); + %508 = nn.dropout(%507, rate=0.1f); + %509 = %508.0; + %510 = add(%466, %509); + %511 = nn.layer_norm(%510, %decoder_layers_1_norm2_weight, %decoder_layers_1_norm2_bias); + %512 = transpose(%decoder_layers_1_linear1_weight, axes=[1, 0]); + %513 = reshape(%511, newshape=[-1, 256]); + %514 = transpose(%512, axes=[1, 0]); + %515 = nn.dense(%513, %514, units=None); + %516 = reshape(%515, newshape=[20, 32, 2048]); + %517 = add(%516, %decoder_layers_1_linear1_bias); + %518 = nn.relu(%517); + %519 = nn.dropout(%518, rate=0.1f); + %520 = %519.0; + %521 = transpose(%decoder_layers_1_linear2_weight, axes=[1, 0]); + %522 = reshape(%520, newshape=[-1, 2048]); + %523 = transpose(%521, axes=[1, 0]); + %524 = nn.dense(%522, %523, units=None); + %525 = reshape(%524, newshape=[20, 32, 256]); + %526 = add(%525, %decoder_layers_1_linear2_bias); + %527 = nn.dropout(%526, rate=0.1f); + %528 = %527.0; + %529 = add(%511, %528); + %530 = nn.layer_norm(%529, %decoder_layers_1_norm3_weight, %decoder_layers_1_norm3_bias); + %531 = transpose(%decoder_layers_2_self_attn_in_proj_weight, axes=[1, 0]); + %532 = reshape(%530, newshape=[-1, 256]); + %533 = transpose(%531, axes=[1, 0]); + %534 = nn.dense(%532, %533, units=None); + %535 = reshape(%534, newshape=[20, 32, 768]); + %536 = add(%535, %decoder_layers_2_self_attn_in_proj_bias); + %537 = strided_slice(%536, begin=[0, 0, 0], end=[20, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %538 = multiply(%537, 0.176777f); + %539 = reshape(%538, newshape=[20, 256, 32]); + %540 = strided_slice(%536, begin=[0, 0, 256], end=[20, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %541 = reshape(%540, newshape=[-1, 256, 32]); + %542 = transpose(%541, axes=[1, 0, 2]); + %543 = transpose(%542, axes=[0, 2, 1]); + %544 = transpose(%539, axes=[1, 0, 2]); + %545 = transpose(%543, axes=[0, 2, 1]); + %546 = nn.batch_matmul(%544, %545, meta[relay.attrs.BatchMatmulAttrs][20]); + %547 = nn.softmax(%546); + %548 = nn.dropout(%547, rate=0.1f); + %549 = strided_slice(%536, begin=[0, 0, 512], end=[20, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %550 = reshape(%549, newshape=[-1, 256, 32]); + %551 = transpose(%550, axes=[1, 0, 2]); + %552 = %548.0; + %553 = transpose(%551, axes=[0, 2, 1]); + %554 = nn.batch_matmul(%552, %553, meta[relay.attrs.BatchMatmulAttrs][21]); + %555 = transpose(%554, axes=[1, 0, 2]); + %556 = reshape(%555, newshape=[20, 32, 256]); + %557 = transpose(%decoder_layers_2_self_attn_out_proj_weight, axes=[1, 0]); + %558 = reshape(%556, newshape=[-1, 256]); + %559 = transpose(%557, axes=[1, 0]); + %560 = nn.dense(%558, %559, units=None); + %561 = reshape(%560, newshape=[20, 32, 256]); + %562 = add(%561, %decoder_layers_2_self_attn_out_proj_bias); + %563 = nn.dropout(%562, rate=0.1f); + %564 = %563.0; + %565 = add(%530, %564); + %566 = nn.layer_norm(%565, %decoder_layers_2_norm1_weight, %decoder_layers_2_norm1_bias); + %567 = strided_slice(%decoder_layers_2_multihead_attn_in_proj_weight, begin=[0, 0], end=[256, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %568 = transpose(%567, axes=[1, 0]); + %569 = reshape(%566, newshape=[-1, 256]); + %570 = transpose(%568, axes=[1, 0]); + %571 = nn.dense(%569, %570, units=None); + %572 = reshape(%571, newshape=[20, 32, 256]); + %573 = strided_slice(%decoder_layers_2_multihead_attn_in_proj_bias, begin=[0], end=[256], strides=[1], axes=[0], slice_mode="end"); + %574 = add(%572, %573); + %575 = multiply(%574, 0.176777f); + %576 = reshape(%575, newshape=[20, 256, 32]); + %577 = strided_slice(%decoder_layers_2_multihead_attn_in_proj_weight, begin=[256, 0], end=[768, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %578 = transpose(%577, axes=[1, 0]); + %579 = reshape(%376, newshape=[-1, 256]); + %580 = transpose(%578, axes=[1, 0]); + %581 = nn.dense(%579, %580, units=None); + %582 = reshape(%581, newshape=[10, 32, 512]); + %583 = strided_slice(%decoder_layers_2_multihead_attn_in_proj_bias, begin=[256], end=[768], strides=[1], axes=[0], slice_mode="end"); + %584 = add(%582, %583); + %585 = strided_slice(%584, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %586 = reshape(%585, newshape=[-1, 256, 32]); + %587 = transpose(%586, axes=[1, 0, 2]); + %588 = transpose(%587, axes=[0, 2, 1]); + %589 = transpose(%576, axes=[1, 0, 2]); + %590 = transpose(%588, axes=[0, 2, 1]); + %591 = nn.batch_matmul(%589, %590, meta[relay.attrs.BatchMatmulAttrs][22]); + %592 = nn.softmax(%591); + %593 = nn.dropout(%592, rate=0.1f); + %594 = strided_slice(%584, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %595 = reshape(%594, newshape=[-1, 256, 32]); + %596 = transpose(%595, axes=[1, 0, 2]); + %597 = %593.0; + %598 = transpose(%596, axes=[0, 2, 1]); + %599 = nn.batch_matmul(%597, %598, meta[relay.attrs.BatchMatmulAttrs][23]); + %600 = transpose(%599, axes=[1, 0, 2]); + %601 = reshape(%600, newshape=[20, 32, 256]); + %602 = transpose(%decoder_layers_2_multihead_attn_out_proj_weight, axes=[1, 0]); + %603 = reshape(%601, newshape=[-1, 256]); + %604 = transpose(%602, axes=[1, 0]); + %605 = nn.dense(%603, %604, units=None); + %606 = reshape(%605, newshape=[20, 32, 256]); + %607 = add(%606, %decoder_layers_2_multihead_attn_out_proj_bias); + %608 = nn.dropout(%607, rate=0.1f); + %609 = %608.0; + %610 = add(%566, %609); + %611 = nn.layer_norm(%610, %decoder_layers_2_norm2_weight, %decoder_layers_2_norm2_bias); + %612 = transpose(%decoder_layers_2_linear1_weight, axes=[1, 0]); + %613 = reshape(%611, newshape=[-1, 256]); + %614 = transpose(%612, axes=[1, 0]); + %615 = nn.dense(%613, %614, units=None); + %616 = reshape(%615, newshape=[20, 32, 2048]); + %617 = add(%616, %decoder_layers_2_linear1_bias); + %618 = nn.relu(%617); + %619 = nn.dropout(%618, rate=0.1f); + %620 = %619.0; + %621 = transpose(%decoder_layers_2_linear2_weight, axes=[1, 0]); + %622 = reshape(%620, newshape=[-1, 2048]); + %623 = transpose(%621, axes=[1, 0]); + %624 = nn.dense(%622, %623, units=None); + %625 = reshape(%624, newshape=[20, 32, 256]); + %626 = add(%625, %decoder_layers_2_linear2_bias); + %627 = nn.dropout(%626, rate=0.1f); + %628 = %627.0; + %629 = add(%611, %628); + %630 = nn.layer_norm(%629, %decoder_layers_2_norm3_weight, %decoder_layers_2_norm3_bias); + %631 = transpose(%decoder_layers_3_self_attn_in_proj_weight, axes=[1, 0]); + %632 = reshape(%630, newshape=[-1, 256]); + %633 = transpose(%631, axes=[1, 0]); + %634 = nn.dense(%632, %633, units=None); + %635 = reshape(%634, newshape=[20, 32, 768]); + %636 = add(%635, %decoder_layers_3_self_attn_in_proj_bias); + %637 = strided_slice(%636, begin=[0, 0, 0], end=[20, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %638 = multiply(%637, 0.176777f); + %639 = reshape(%638, newshape=[20, 256, 32]); + %640 = strided_slice(%636, begin=[0, 0, 256], end=[20, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %641 = reshape(%640, newshape=[-1, 256, 32]); + %642 = transpose(%641, axes=[1, 0, 2]); + %643 = transpose(%642, axes=[0, 2, 1]); + %644 = transpose(%639, axes=[1, 0, 2]); + %645 = transpose(%643, axes=[0, 2, 1]); + %646 = nn.batch_matmul(%644, %645, meta[relay.attrs.BatchMatmulAttrs][24]); + %647 = nn.softmax(%646); + %648 = nn.dropout(%647, rate=0.1f); + %649 = strided_slice(%636, begin=[0, 0, 512], end=[20, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %650 = reshape(%649, newshape=[-1, 256, 32]); + %651 = transpose(%650, axes=[1, 0, 2]); + %652 = %648.0; + %653 = transpose(%651, axes=[0, 2, 1]); + %654 = nn.batch_matmul(%652, %653, meta[relay.attrs.BatchMatmulAttrs][25]); + %655 = transpose(%654, axes=[1, 0, 2]); + %656 = reshape(%655, newshape=[20, 32, 256]); + %657 = transpose(%decoder_layers_3_self_attn_out_proj_weight, axes=[1, 0]); + %658 = reshape(%656, newshape=[-1, 256]); + %659 = transpose(%657, axes=[1, 0]); + %660 = nn.dense(%658, %659, units=None); + %661 = reshape(%660, newshape=[20, 32, 256]); + %662 = add(%661, %decoder_layers_3_self_attn_out_proj_bias); + %663 = nn.dropout(%662, rate=0.1f); + %664 = %663.0; + %665 = add(%630, %664); + %666 = nn.layer_norm(%665, %decoder_layers_3_norm1_weight, %decoder_layers_3_norm1_bias); + %667 = strided_slice(%decoder_layers_3_multihead_attn_in_proj_weight, begin=[0, 0], end=[256, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %668 = transpose(%667, axes=[1, 0]); + %669 = reshape(%666, newshape=[-1, 256]); + %670 = transpose(%668, axes=[1, 0]); + %671 = nn.dense(%669, %670, units=None); + %672 = reshape(%671, newshape=[20, 32, 256]); + %673 = strided_slice(%decoder_layers_3_multihead_attn_in_proj_bias, begin=[0], end=[256], strides=[1], axes=[0], slice_mode="end"); + %674 = add(%672, %673); + %675 = multiply(%674, 0.176777f); + %676 = reshape(%675, newshape=[20, 256, 32]); + %677 = strided_slice(%decoder_layers_3_multihead_attn_in_proj_weight, begin=[256, 0], end=[768, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %678 = transpose(%677, axes=[1, 0]); + %679 = reshape(%376, newshape=[-1, 256]); + %680 = transpose(%678, axes=[1, 0]); + %681 = nn.dense(%679, %680, units=None); + %682 = reshape(%681, newshape=[10, 32, 512]); + %683 = strided_slice(%decoder_layers_3_multihead_attn_in_proj_bias, begin=[256], end=[768], strides=[1], axes=[0], slice_mode="end"); + %684 = add(%682, %683); + %685 = strided_slice(%684, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %686 = reshape(%685, newshape=[-1, 256, 32]); + %687 = transpose(%686, axes=[1, 0, 2]); + %688 = transpose(%687, axes=[0, 2, 1]); + %689 = transpose(%676, axes=[1, 0, 2]); + %690 = transpose(%688, axes=[0, 2, 1]); + %691 = nn.batch_matmul(%689, %690, meta[relay.attrs.BatchMatmulAttrs][26]); + %692 = nn.softmax(%691); + %693 = nn.dropout(%692, rate=0.1f); + %694 = strided_slice(%684, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %695 = reshape(%694, newshape=[-1, 256, 32]); + %696 = transpose(%695, axes=[1, 0, 2]); + %697 = %693.0; + %698 = transpose(%696, axes=[0, 2, 1]); + %699 = nn.batch_matmul(%697, %698, meta[relay.attrs.BatchMatmulAttrs][27]); + %700 = transpose(%699, axes=[1, 0, 2]); + %701 = reshape(%700, newshape=[20, 32, 256]); + %702 = transpose(%decoder_layers_3_multihead_attn_out_proj_weight, axes=[1, 0]); + %703 = reshape(%701, newshape=[-1, 256]); + %704 = transpose(%702, axes=[1, 0]); + %705 = nn.dense(%703, %704, units=None); + %706 = reshape(%705, newshape=[20, 32, 256]); + %707 = add(%706, %decoder_layers_3_multihead_attn_out_proj_bias); + %708 = nn.dropout(%707, rate=0.1f); + %709 = %708.0; + %710 = add(%666, %709); + %711 = nn.layer_norm(%710, %decoder_layers_3_norm2_weight, %decoder_layers_3_norm2_bias); + %712 = transpose(%decoder_layers_3_linear1_weight, axes=[1, 0]); + %713 = reshape(%711, newshape=[-1, 256]); + %714 = transpose(%712, axes=[1, 0]); + %715 = nn.dense(%713, %714, units=None); + %716 = reshape(%715, newshape=[20, 32, 2048]); + %717 = add(%716, %decoder_layers_3_linear1_bias); + %718 = nn.relu(%717); + %719 = nn.dropout(%718, rate=0.1f); + %720 = %719.0; + %721 = transpose(%decoder_layers_3_linear2_weight, axes=[1, 0]); + %722 = reshape(%720, newshape=[-1, 2048]); + %723 = transpose(%721, axes=[1, 0]); + %724 = nn.dense(%722, %723, units=None); + %725 = reshape(%724, newshape=[20, 32, 256]); + %726 = add(%725, %decoder_layers_3_linear2_bias); + %727 = nn.dropout(%726, rate=0.1f); + %728 = %727.0; + %729 = add(%711, %728); + %730 = nn.layer_norm(%729, %decoder_layers_3_norm3_weight, %decoder_layers_3_norm3_bias); + %731 = transpose(%decoder_layers_4_self_attn_in_proj_weight, axes=[1, 0]); + %732 = reshape(%730, newshape=[-1, 256]); + %733 = transpose(%731, axes=[1, 0]); + %734 = nn.dense(%732, %733, units=None); + %735 = reshape(%734, newshape=[20, 32, 768]); + %736 = add(%735, %decoder_layers_4_self_attn_in_proj_bias); + %737 = strided_slice(%736, begin=[0, 0, 0], end=[20, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %738 = multiply(%737, 0.176777f); + %739 = reshape(%738, newshape=[20, 256, 32]); + %740 = strided_slice(%736, begin=[0, 0, 256], end=[20, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %741 = reshape(%740, newshape=[-1, 256, 32]); + %742 = transpose(%741, axes=[1, 0, 2]); + %743 = transpose(%742, axes=[0, 2, 1]); + %744 = transpose(%739, axes=[1, 0, 2]); + %745 = transpose(%743, axes=[0, 2, 1]); + %746 = nn.batch_matmul(%744, %745, meta[relay.attrs.BatchMatmulAttrs][28]); + %747 = nn.softmax(%746); + %748 = nn.dropout(%747, rate=0.1f); + %749 = strided_slice(%736, begin=[0, 0, 512], end=[20, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %750 = reshape(%749, newshape=[-1, 256, 32]); + %751 = transpose(%750, axes=[1, 0, 2]); + %752 = %748.0; + %753 = transpose(%751, axes=[0, 2, 1]); + %754 = nn.batch_matmul(%752, %753, meta[relay.attrs.BatchMatmulAttrs][29]); + %755 = transpose(%754, axes=[1, 0, 2]); + %756 = reshape(%755, newshape=[20, 32, 256]); + %757 = transpose(%decoder_layers_4_self_attn_out_proj_weight, axes=[1, 0]); + %758 = reshape(%756, newshape=[-1, 256]); + %759 = transpose(%757, axes=[1, 0]); + %760 = nn.dense(%758, %759, units=None); + %761 = reshape(%760, newshape=[20, 32, 256]); + %762 = add(%761, %decoder_layers_4_self_attn_out_proj_bias); + %763 = nn.dropout(%762, rate=0.1f); + %764 = %763.0; + %765 = add(%730, %764); + %766 = nn.layer_norm(%765, %decoder_layers_4_norm1_weight, %decoder_layers_4_norm1_bias); + %767 = strided_slice(%decoder_layers_4_multihead_attn_in_proj_weight, begin=[0, 0], end=[256, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %768 = transpose(%767, axes=[1, 0]); + %769 = reshape(%766, newshape=[-1, 256]); + %770 = transpose(%768, axes=[1, 0]); + %771 = nn.dense(%769, %770, units=None); + %772 = reshape(%771, newshape=[20, 32, 256]); + %773 = strided_slice(%decoder_layers_4_multihead_attn_in_proj_bias, begin=[0], end=[256], strides=[1], axes=[0], slice_mode="end"); + %774 = add(%772, %773); + %775 = multiply(%774, 0.176777f); + %776 = reshape(%775, newshape=[20, 256, 32]); + %777 = strided_slice(%decoder_layers_4_multihead_attn_in_proj_weight, begin=[256, 0], end=[768, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %778 = transpose(%777, axes=[1, 0]); + %779 = reshape(%376, newshape=[-1, 256]); + %780 = transpose(%778, axes=[1, 0]); + %781 = nn.dense(%779, %780, units=None); + %782 = reshape(%781, newshape=[10, 32, 512]); + %783 = strided_slice(%decoder_layers_4_multihead_attn_in_proj_bias, begin=[256], end=[768], strides=[1], axes=[0], slice_mode="end"); + %784 = add(%782, %783); + %785 = strided_slice(%784, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %786 = reshape(%785, newshape=[-1, 256, 32]); + %787 = transpose(%786, axes=[1, 0, 2]); + %788 = transpose(%787, axes=[0, 2, 1]); + %789 = transpose(%776, axes=[1, 0, 2]); + %790 = transpose(%788, axes=[0, 2, 1]); + %791 = nn.batch_matmul(%789, %790, meta[relay.attrs.BatchMatmulAttrs][30]); + %792 = nn.softmax(%791); + %793 = nn.dropout(%792, rate=0.1f); + %794 = strided_slice(%784, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %795 = reshape(%794, newshape=[-1, 256, 32]); + %796 = transpose(%795, axes=[1, 0, 2]); + %797 = %793.0; + %798 = transpose(%796, axes=[0, 2, 1]); + %799 = nn.batch_matmul(%797, %798, meta[relay.attrs.BatchMatmulAttrs][31]); + %800 = transpose(%799, axes=[1, 0, 2]); + %801 = reshape(%800, newshape=[20, 32, 256]); + %802 = transpose(%decoder_layers_4_multihead_attn_out_proj_weight, axes=[1, 0]); + %803 = reshape(%801, newshape=[-1, 256]); + %804 = transpose(%802, axes=[1, 0]); + %805 = nn.dense(%803, %804, units=None); + %806 = reshape(%805, newshape=[20, 32, 256]); + %807 = add(%806, %decoder_layers_4_multihead_attn_out_proj_bias); + %808 = nn.dropout(%807, rate=0.1f); + %809 = %808.0; + %810 = add(%766, %809); + %811 = nn.layer_norm(%810, %decoder_layers_4_norm2_weight, %decoder_layers_4_norm2_bias); + %812 = transpose(%decoder_layers_4_linear1_weight, axes=[1, 0]); + %813 = reshape(%811, newshape=[-1, 256]); + %814 = transpose(%812, axes=[1, 0]); + %815 = nn.dense(%813, %814, units=None); + %816 = reshape(%815, newshape=[20, 32, 2048]); + %817 = add(%816, %decoder_layers_4_linear1_bias); + %818 = nn.relu(%817); + %819 = nn.dropout(%818, rate=0.1f); + %820 = %819.0; + %821 = transpose(%decoder_layers_4_linear2_weight, axes=[1, 0]); + %822 = reshape(%820, newshape=[-1, 2048]); + %823 = transpose(%821, axes=[1, 0]); + %824 = nn.dense(%822, %823, units=None); + %825 = reshape(%824, newshape=[20, 32, 256]); + %826 = add(%825, %decoder_layers_4_linear2_bias); + %827 = nn.dropout(%826, rate=0.1f); + %828 = %827.0; + %829 = add(%811, %828); + %830 = nn.layer_norm(%829, %decoder_layers_4_norm3_weight, %decoder_layers_4_norm3_bias); + %831 = transpose(%decoder_layers_5_self_attn_in_proj_weight, axes=[1, 0]); + %832 = reshape(%830, newshape=[-1, 256]); + %833 = transpose(%831, axes=[1, 0]); + %834 = nn.dense(%832, %833, units=None); + %835 = reshape(%834, newshape=[20, 32, 768]); + %836 = add(%835, %decoder_layers_5_self_attn_in_proj_bias); + %837 = strided_slice(%836, begin=[0, 0, 0], end=[20, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %838 = multiply(%837, 0.176777f); + %839 = reshape(%838, newshape=[20, 256, 32]); + %840 = strided_slice(%836, begin=[0, 0, 256], end=[20, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %841 = reshape(%840, newshape=[-1, 256, 32]); + %842 = transpose(%841, axes=[1, 0, 2]); + %843 = transpose(%842, axes=[0, 2, 1]); + %844 = transpose(%839, axes=[1, 0, 2]); + %845 = transpose(%843, axes=[0, 2, 1]); + %846 = nn.batch_matmul(%844, %845, meta[relay.attrs.BatchMatmulAttrs][32]); + %847 = nn.softmax(%846); + %848 = nn.dropout(%847, rate=0.1f); + %849 = strided_slice(%836, begin=[0, 0, 512], end=[20, 32, 768], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %850 = reshape(%849, newshape=[-1, 256, 32]); + %851 = transpose(%850, axes=[1, 0, 2]); + %852 = %848.0; + %853 = transpose(%851, axes=[0, 2, 1]); + %854 = nn.batch_matmul(%852, %853, meta[relay.attrs.BatchMatmulAttrs][33]); + %855 = transpose(%854, axes=[1, 0, 2]); + %856 = reshape(%855, newshape=[20, 32, 256]); + %857 = transpose(%decoder_layers_5_self_attn_out_proj_weight, axes=[1, 0]); + %858 = reshape(%856, newshape=[-1, 256]); + %859 = transpose(%857, axes=[1, 0]); + %860 = nn.dense(%858, %859, units=None); + %861 = reshape(%860, newshape=[20, 32, 256]); + %862 = add(%861, %decoder_layers_5_self_attn_out_proj_bias); + %863 = nn.dropout(%862, rate=0.1f); + %864 = %863.0; + %865 = add(%830, %864); + %866 = nn.layer_norm(%865, %decoder_layers_5_norm1_weight, %decoder_layers_5_norm1_bias); + %867 = strided_slice(%decoder_layers_5_multihead_attn_in_proj_weight, begin=[0, 0], end=[256, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %868 = transpose(%867, axes=[1, 0]); + %869 = reshape(%866, newshape=[-1, 256]); + %870 = transpose(%868, axes=[1, 0]); + %871 = nn.dense(%869, %870, units=None); + %872 = reshape(%871, newshape=[20, 32, 256]); + %873 = strided_slice(%decoder_layers_5_multihead_attn_in_proj_bias, begin=[0], end=[256], strides=[1], axes=[0], slice_mode="end"); + %874 = add(%872, %873); + %875 = multiply(%874, 0.176777f); + %876 = reshape(%875, newshape=[20, 256, 32]); + %877 = strided_slice(%decoder_layers_5_multihead_attn_in_proj_weight, begin=[256, 0], end=[768, 256], strides=[1, 1], axes=[0, 1], slice_mode="end"); + %878 = transpose(%877, axes=[1, 0]); + %879 = reshape(%376, newshape=[-1, 256]); + %880 = transpose(%878, axes=[1, 0]); + %881 = nn.dense(%879, %880, units=None); + %882 = reshape(%881, newshape=[10, 32, 512]); + %883 = strided_slice(%decoder_layers_5_multihead_attn_in_proj_bias, begin=[256], end=[768], strides=[1], axes=[0], slice_mode="end"); + %884 = add(%882, %883); + %885 = strided_slice(%884, begin=[0, 0, 0], end=[10, 32, 256], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %886 = reshape(%885, newshape=[-1, 256, 32]); + %887 = transpose(%886, axes=[1, 0, 2]); + %888 = transpose(%887, axes=[0, 2, 1]); + %889 = transpose(%876, axes=[1, 0, 2]); + %890 = transpose(%888, axes=[0, 2, 1]); + %891 = nn.batch_matmul(%889, %890, meta[relay.attrs.BatchMatmulAttrs][34]); + %892 = nn.softmax(%891); + %893 = nn.dropout(%892, rate=0.1f); + %894 = strided_slice(%884, begin=[0, 0, 256], end=[10, 32, 512], strides=[1, 1, 1], axes=[0, 1, 2], slice_mode="end"); + %895 = reshape(%894, newshape=[-1, 256, 32]); + %896 = transpose(%895, axes=[1, 0, 2]); + %897 = %893.0; + %898 = transpose(%896, axes=[0, 2, 1]); + %899 = nn.batch_matmul(%897, %898, meta[relay.attrs.BatchMatmulAttrs][35]); + %900 = transpose(%899, axes=[1, 0, 2]); + %901 = reshape(%900, newshape=[20, 32, 256]); + %902 = transpose(%decoder_layers_5_multihead_attn_out_proj_weight, axes=[1, 0]); + %903 = reshape(%901, newshape=[-1, 256]); + %904 = transpose(%902, axes=[1, 0]); + %905 = nn.dense(%903, %904, units=None); + %906 = reshape(%905, newshape=[20, 32, 256]); + %907 = add(%906, %decoder_layers_5_multihead_attn_out_proj_bias); + %908 = nn.dropout(%907, rate=0.1f); + %909 = %908.0; + %910 = add(%866, %909); + %911 = nn.layer_norm(%910, %decoder_layers_5_norm2_weight, %decoder_layers_5_norm2_bias); + %912 = transpose(%decoder_layers_5_linear1_weight, axes=[1, 0]); + %913 = reshape(%911, newshape=[-1, 256]); + %914 = transpose(%912, axes=[1, 0]); + %915 = nn.dense(%913, %914, units=None); + %916 = reshape(%915, newshape=[20, 32, 2048]); + %917 = add(%916, %decoder_layers_5_linear1_bias); + %918 = nn.relu(%917); + %919 = nn.dropout(%918, rate=0.1f); + %920 = %919.0; + %921 = transpose(%decoder_layers_5_linear2_weight, axes=[1, 0]); + %922 = reshape(%920, newshape=[-1, 2048]); + %923 = transpose(%921, axes=[1, 0]); + %924 = nn.dense(%922, %923, units=None); + %925 = reshape(%924, newshape=[20, 32, 256]); + %926 = add(%925, %decoder_layers_5_linear2_bias); + %927 = nn.dropout(%926, rate=0.1f); + %928 = %927.0; + %929 = add(%911, %928); + %930 = nn.layer_norm(%929, %decoder_layers_5_norm3_weight, %decoder_layers_5_norm3_bias); + nn.layer_norm(%930, %decoder_norm_weight, %decoder_norm_bias) +} + +#[metadata] +{ + "root": 1, + "nodes": [ + { + "type_key": "" + }, + { + "type_key": "Map", + "keys": [ + "relay.attrs.BatchMatmulAttrs" + ], + "data": [2] + }, + { + "type_key": "Array", + "data": [ + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38 + ] + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a": "0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + }, + { + "type_key": "relay.attrs.BatchMatmulAttrs", + "attrs": {"out_dtype": "", "transpose_a":"0", "transpose_b":"1"} + } + ], + "b64ndarrays": [], + "attrs": {"tvm_version": "0.8.dev0"} +} \ No newline at end of file diff --git a/tests/models/utils.py b/tests/models/utils.py new file mode 100644 index 0000000..71e5cf8 --- /dev/null +++ b/tests/models/utils.py @@ -0,0 +1,66 @@ +import tvm +from tvm import relay + +class RenameMutator(relay.ExprMutator): + def __init__(self, rws): + super().__init__() + self.var_map = dict() + self.rws = rws + + def visit_var(self, var): + if var in self.var_map: + return self.var_map[var] + else: + new_name = var.name_hint + for (f, t) in self.rws.items(): + new_name = new_name.replace(f, t) + if new_name != var.name_hint: + new_var = relay.Var(new_name, type_annotation=var.type_annotation) + self.var_map[var] = new_var + return new_var + else: + self.var_map[var] = var + return var + +import tvm +from tvm import relay +from tvm.relay.expr_functor import ExprMutator + +class LetInliner(ExprMutator): + def __init__(self): + super().__init__() + self.let_map = {} + + def visit_var(self, var): + if var in self.let_map: + return self.let_map[var] + return var + + def visit_let(self, let): + # don't deal with functions + if isinstance(let.value, relay.Function): + return super().visit_let(let) + self.let_map[let.var] = self.visit(let.value) + return self.visit(let.body) + +class AlterDense(relay.ExprMutator): + def __init__(self): + super().__init__() + + def visit_call(self, call): + args = [self.visit(x) for x in call.args] + if call.op.name == 'nn.dense' and isinstance(args[0], relay.Constant): + return relay.transpose(relay.Call(call.op, [args[1], args[0]], call.attrs, call.type_args, call.span)) + else: + return relay.Call(call.op, args, call.attrs, call.type_args, call.span) + +class RemoveAnnotations(relay.ExprMutator): + def __init__(self): + super().__init__() + + def visit_call(self, call): + args = [self.visit(x) for x in call.args] + if call.op.name == 'annotation.stop_fusion': + return args[0] + else: + return relay.Call(call.op, args, call.attrs, call.type_args, call.span) \ No newline at end of file diff --git a/tests/qdense.relay b/tests/qdense.relay new file mode 100644 index 0000000..7950809 --- /dev/null +++ b/tests/qdense.relay @@ -0,0 +1,40 @@ +#[version = "0.0.5"] +def @main(%data: Tensor[(2, 2), float32], %weights: Tensor[(2, 2), float32]) -> Tensor[(2, 2), float32] { + %7 = max(%data) /* ty=float32 */; + %8 = min(%data) /* ty=float32 */; + %9 = divide(%7, 127f /* ty=float32 */) /* ty=float32 */; + %10 = divide(%8, -127f /* ty=float32 */) /* ty=float32 */; + %11 = maximum(%9, %10) /* ty=float32 */; + %12 = divide(%data, %11) /* ty=Tensor[(2, 2), float32] */; + %13 = round(%12) /* ty=Tensor[(2, 2), float32] */; + %14 = max(%weights) /* ty=float32 */; + %15 = min(%weights) /* ty=float32 */; + %16 = divide(%14, 127f /* ty=float32 */) /* ty=float32 */; + %17 = divide(%15, -127f /* ty=float32 */) /* ty=float32 */; + %18 = maximum(%16, %17) /* ty=float32 */; + %19 = divide(%weights, %18) /* ty=Tensor[(2, 2), float32] */; + %20 = round(%19) /* ty=Tensor[(2, 2), float32] */; + %21 = nn.dense(%data, %weights, units=None) /* ty=Tensor[(2, 2), float32] */; + %22 = max(%21) /* ty=float32 */; + %23 = min(%21) /* ty=float32 */; + %24 = divide(%22, 127f /* ty=float32 */) /* ty=float32 */; + %25 = divide(%23, -127f /* ty=float32 */) /* ty=float32 */; + %26 = cast(%13, dtype="int8") /* ty=Tensor[(2, 2), int8] */; + %27 = cast(%20, dtype="int8") /* ty=Tensor[(2, 2), int8] */; + %28 = maximum(%24, %25) /* ty=float32 */; + %29 = fn (%outer_arg_0: Tensor[(2, 2), int8], %outer_arg_1: Tensor[(2, 2), int8], %outer_arg_2: float32, %outer_arg_3: float32, %outer_arg_4: float32, Compiler="ilavta", Primitive=1, global_symbol="ilavta.dense_0") -> Tensor[(2, 2), int8] { + %6 = fn (%data1: Tensor[(2, 2), int8], %weights1: Tensor[(2, 2), int8], %s_data: float32, %s_w: float32, %s_act: float32, Composite="ilavta.dense") -> Tensor[(2, 2), int8] { + %0 = nn.dense(%data1, %weights1, units=None, out_dtype="int32") /* ty=Tensor[(2, 2), int32] */; + %1 = multiply(%s_data, %s_w) /* ty=float32 */; + %2 = cast(%0, dtype="float32") /* ty=Tensor[(2, 2), float32] */; + %3 = divide(%1, %s_act) /* ty=float32 */; + %4 = multiply(%2, %3) /* ty=Tensor[(2, 2), float32] */; + %5 = clip(%4, a_min=-127f, a_max=127f) /* ty=Tensor[(2, 2), float32] */; + cast(%5, dtype="int8") /* ty=Tensor[(2, 2), int8] */ + }; + %6(%outer_arg_0, %outer_arg_1, %outer_arg_2, %outer_arg_3, %outer_arg_4) /* ty=Tensor[(2, 2), int8] */ + }; + %30 = %29(%26, %27, %11, %18, %28) /* ty=Tensor[(2, 2), int8] */; + %31 = cast(%30, dtype="float32") /* ty=Tensor[(2, 2), float32] */; + multiply(%31, %28) /* ty=Tensor[(2, 2), float32] */ +} diff --git a/tests/run_comparison.py b/tests/run_comparison.py index 0dfba1e..fa26e62 100644 --- a/tests/run_comparison.py +++ b/tests/run_comparison.py @@ -5,6 +5,7 @@ from tvm import relay from tvm.ir import transform from tvm.contrib import graph_executor +from tvm.relay import ExprMutator def get_inputs(src): with open(src, 'r') as fp: @@ -12,36 +13,64 @@ def get_inputs(src): mod = tvm.parser.fromtext(relay_src) mod = relay.transform.InferType()(mod) inputs = dict() + # inputs = [] for var in mod['main'].params: shape = var.type_annotation.shape name_hint = var.name_hint - inputs[name_hint] = np.random.rand(*[int(x) for x in shape]).astype('float32') + inputs[name_hint] = np.random.rand(*[int(x) for x in shape]).astype('float32') / 100000.0 + # inputs.append(np.random.rand(*[int(x) for x in shape]).astype('float32')) return inputs -def run_file(src, **params): +class LetInliner(ExprMutator): + def __init__(self): + super().__init__() + self.let_map = {} + + def visit_var(self, var): + if var in self.let_map: + return self.let_map[var] + return var + + def visit_let(self, let): + # don't deal with functions + if isinstance(let.value, relay.Function): + return super().visit_let(let) + self.let_map[let.var] = self.visit(let.value) + return self.visit(let.body) + +def run_file(src, params): print(f'Compiling & Running: {src}') with open(src, 'r') as fp: relay_src = fp.read() start = time.time() mod = tvm.parser.fromtext(relay_src) + # This line is necessary to validate some models.....and it causes some + #models to fail. Needs more investigation. + #mod = tvm.ir.IRModule.from_expr(LetInliner().visit(mod["main"])) + mod = relay.transform.SimplifyInference()(mod) mod = relay.transform.InferType()(mod) - with tvm.transform.PassContext(opt_level=0): - for target, dev in tvm.testing.enabled_targets(): - relay_graph, lib, params = relay.build(mod, target=target, params=params) - end = time.time() - print(f'compile time: {end - start}') - relay_model = graph_executor.create(relay_graph, lib, dev) - relay_model.set_input(**params) - start = time.time() - relay_model.run() - end = time.time() - print(f'run time: {end - start}') - return relay_model.get_output(0) + inputs = [params[x.name_hint] for x in mod['main'].params] + for target, dev in tvm.testing.enabled_targets(): + # relay_graph, lib, params = relay.build(mod, target=target, params=params) + executor = relay.create_executor('vm', mod=mod, device=dev, target=target).evaluate() + end = time.time() + print(f'compile time: {end - start}') + # relay_model = graph_executor.create(relay_graph, lib, dev) + # relay_model.set_input(**params) + start = time.time() + # relay_model.run() + result = executor(*inputs) + end = time.time() + print(f'run time: {end - start}') + # return relay_model.get_output(0) + return result def main(lhs_src, rhs_src): inputs = get_inputs(lhs_src) - lhs_res = run_file(lhs_src, **inputs) - rhs_res = run_file(rhs_src, **inputs) + lhs_res = run_file(lhs_src, inputs) + rhs_res = run_file(rhs_src, inputs) + print(lhs_res) + print(rhs_res) tvm.testing.assert_allclose(lhs_res.asnumpy(), rhs_res.asnumpy()) if __name__ == '__main__': diff --git a/tests/run_eqsat.py b/tests/run_eqsat.py index b982337..2743b2e 100644 --- a/tests/run_eqsat.py +++ b/tests/run_eqsat.py @@ -1,34 +1,51 @@ import os import subprocess +import argparse import sys -def main(relay_file, output_filename, *configs): +def main(relay_file, output_filename, configs, use_ilp): home_dir = os.environ.get('FLEXMATCH_HOME') + if home_dir is None: + print('FLEXMATCH_HOME not set, skipping...') + return cur_dir = os.getcwd() relay_file = os.path.join(cur_dir, relay_file) + model_rewrite_file = os.path.join(cur_dir, f'{output_filename}-rewritten.json') + analysis_data_file = os.path.join(cur_dir, f'{output_filename}-data.json') configs = list(map(lambda x: f'{x}.json', configs)) - if home_dir: - for config in configs: - if not os.path.isfile(os.path.join(home_dir, 'configs', config)): - raise Exception(f'{config} is not a valid config json') - os.chdir(os.path.join(home_dir, 'flexmatch')) - os.system('cargo build') - cmd = './target/debug/flexmatch {} {} {} {}'.format( - relay_file, - os.path.join(cur_dir, f'{output_filename}-rewritten.json'), - os.path.join(cur_dir, f'{output_filename}-data.json'), - ' '.join(configs) - ) - os.system(cmd) + for config in configs: + if not os.path.isfile(os.path.join(home_dir, 'configs', config)): + raise Exception(f'{config} is not a valid config json') + cmd = './target/debug/flexmatch {} {} {} {}'.format( + relay_file, + os.path.join(cur_dir, f'{output_filename}-rewritten.json'), + os.path.join(cur_dir, f'{output_filename}-data.json'), + ' '.join(configs) + ) + try: + subprocess.run(cwd=os.path.join(home_dir, 'flexmatch'), + args=['./target/debug/flexmatch', + relay_file, + model_rewrite_file, + analysis_data_file] + + configs + + (['--ilp'] if use_ilp else []), + stdout=sys.stdout.buffer, + check=True) + except subprocess.CalledProcessError as e: + print('Error caught when running EqSat ({}):\n{}', e.returncode, str(e)) + return + else: print('Output file written to: ', - os.path.join(cur_dir, f'{output_filename}-rewritten.json'), - os.path.join(cur_dir, f'{output_filename}-data.json')) - else: - print('FLEXMATCH_HOME not set, skipping...') + model_rewrite_file, + analysis_data_file) if __name__ == '__main__': - if len(sys.argv) < 4: - print(f'run_eqsat.py relay_src output_file configs+') - exit(0) - main(*sys.argv[1:]) \ No newline at end of file + parser = argparse.ArgumentParser() + parser.add_argument('--relay-file', dest='relay_file', type=str, help='Relay source file', required=True) + parser.add_argument('--output-file', dest='output_file', type=str, help='Output file name of extracted model and analysis data (they share the same name)', required=True) + parser.add_argument('--configs', dest='configs', nargs='+', help='Equality Saturation Configs', required=True) + parser.add_argument('--use-ilp', dest='use_ilp', action='store_true') + args = parser.parse_args() + main(args.relay_file, args.output_file, args.configs, args.use_ilp) \ No newline at end of file diff --git a/tests/test_model_on_vta.py b/tests/test_model_on_vta.py new file mode 100644 index 0000000..0fa3f34 --- /dev/null +++ b/tests/test_model_on_vta.py @@ -0,0 +1,159 @@ +import logging +import os +import tqdm +import tvm +import numpy as np +import torch +from tvm import relay +from tvm.contrib import graph_executor +from tvm.runtime.ndarray import cpu +from models.mobilenetv2 import MobileNetV2 +import torchvision +import torchvision.transforms as transforms +import models +import test_vta_quantization as quant_utils + +# Data prep code taken from https://github.com/kuangliu/pytorch-cifar/blob/master/main.py +transform_train = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), +]) + +transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), +]) +# trainset = torchvision.datasets.CIFAR10('./data', download=True, transform=transform_train) +# trainloader = DataLoader(trainset, batch_size=4, shuffle=True) +testset = torchvision.datasets.CIFAR10( + root='./data', train=False, download=True, transform=transform_test) +testloader = torch.utils.data.DataLoader( + testset, batch_size=1, shuffle=True, num_workers=2) + +classes = ('plane', 'car', 'bird', 'cat', 'deer', + 'dog', 'frog', 'horse', 'ship', 'truck') + +def get_relay_model(param_file, input_shape=(1, 3, 32, 32)): + params = torch.load(param_file) + prefix = "module." + params = {(k[len(prefix):] if k.startswith(prefix) else k): v for k, v in params.items()} + model = MobileNetV2() + model.load_state_dict(params) + trace = torch.jit.trace(model, torch.randn(*input_shape)) + inputs = [('input0', input_shape)] + return relay.frontend.from_pytorch(trace, inputs) + +def test_relay_model(mod, params): + print(mod) + with tvm.transform.PassContext(opt_level=3): + relay_graph, lib, params = relay.build(mod, params=params, target='llvm') + graph_rt = graph_executor.create(relay_graph, lib, device=cpu(0)) + graph_rt.set_input(**params) + total = 0 + correct = 0 + for idx, (inp, targets) in enumerate(testloader): + graph_rt.set_input('input0', inp.numpy().astype('float32')) + graph_rt.run() + output = graph_rt.get_output(0).asnumpy() + prediected = np.argmax(output, axis=1) + total += targets.size(0) + correct += np.sum(np.equal(prediected, targets.numpy())) + if idx % 100 == 0: + print(f'Batch #{idx}, Accuracy: {correct / total}') + if idx > 2000: + break + print(f'Final accuracy: {correct / total}') + +def get_cali_data(): + logging.info('Calibration:') + total = len(testloader) // 100 + for idx, (inp, _) in enumerate(tqdm.tqdm(testloader, total=total)): + if idx > total: + break + yield {'input0': inp.cpu().numpy()} + +def bind_params(func, params): + """Bind the params to the expression.""" + name_dict = {} + for arg in func.params: + name = arg.name_hint + if name in name_dict: + name_dict[name] = None + else: + name_dict[name] = arg + bind_dict = {} + for k, v in params.items(): + if k not in name_dict: + continue + arg = name_dict[k] + if arg is None: + raise ValueError("Multiple args in the function have name %s" % k) + bind_dict[arg] = relay.expr.const(v) + return relay.expr.bind(func, bind_dict) + +def run_with_relay_quantization(mod, params, run=True): + BASE_CFG = { + "skip_conv_layers": [], + "skip_dense_layers": False, + "dtype_input": "int8", + "dtype_weight": "int8", + "dtype_activation": "int32", + } + mod['main'] = models.utils.LetInliner().visit(mod['main']) + mod['main'] = bind_params(mod['main'], params) + mod = relay.transform.InferType()(mod) + mod = relay.transform.FoldConstant()(mod) + mod['main'] = models.utils.AlterDense().visit(mod['main']) + with relay.quantize.qconfig(**BASE_CFG, weight_scale='power2', calibration_mode='kl_divergence', skip_dense_layer=False): + qmod = relay.quantize.quantize(mod, params=params, dataset=list(get_cali_data())) + if run: + test_relay_model(qmod, None) + return qmod + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--save-model', required=False, dest='save_model', action='store_true') + parser.add_argument('--relay-model', required=False, dest='relay_model') + parser.add_argument('--quantize', required=False, dest='quantize', action='store_true') + parser.add_argument('--layerwise', required=False, dest='layerwise_debug', action='store_true') + parser.add_argument('--params', required=True, dest='params') + parser.add_argument('--calibrate', required=False, dest='calibrate', action='store_true') + args = parser.parse_args() + # param_file = 'params/final_mobilenet_cifar10_400_epochs.pth' + param_file = args.params + if args.save_model: + mod, params = get_relay_model(param_file) + mod = relay.transform.InferType()(mod) + mod = relay.transform.SimplifyInference()(mod) + mod['main'] = models.RenameMutator({'.': '_'}).visit(mod['main']) + mod = relay.transform.InferType()(mod) + with open(os.path.join('./models/mobilenetv2.relay'), 'w') as fp: + fp.write(mod.astext()) + exit(0) + else: + params = params = torch.load(param_file) + prefix = "module." + params = {(k[len(prefix):].replace('.', '_') if k.startswith(prefix) else k): v.cpu().numpy() for k, v in params.items()} + if not args.relay_model: + raise Exception('relay model not set') + with open(args.relay_model, 'r') as fp: + relay_src = fp.read() + mod = tvm.parser.fromtext(relay_src) + calibration_data = [] + if args.quantize: + # run_with_relay_quantization(mod, params) + if args.calibrate: + cali_dataset = get_cali_data() + calibration_data = quant_utils.calibrate(mod, params, cali_dataset, ['nn.dense']) + mod = tvm.parser.fromtext(relay_src) + expr = quant_utils.VTAQuantize(calibration_data, ['nn.dense']).visit(mod['main'].body) + mod = tvm.ir.IRModule.from_expr(expr) + mod = relay.transform.InferType()(mod) + if args.layerwise_debug: + inp = [{'input0': next(enumerate(testloader))[1][0], **params}] + quant_utils.lockstep_layerwise(mod, calibration_data, args.relay_model, [{'input0': inp} for inp in map(lambda x: x[0][0], zip(testloader, range(1)))], params=params) + else: + test_relay_model(mod, params) \ No newline at end of file diff --git a/tests/test_qdense.py b/tests/test_qdense.py new file mode 100644 index 0000000..6bccc23 --- /dev/null +++ b/tests/test_qdense.py @@ -0,0 +1,91 @@ +import numpy as np +import tvm +import math +from tvm import relay +import tvm.testing +import time + +params = { + 'data': np.random.randn(2, 2).astype('float32'), + 'weights': np.random.randn(2, 2).astype('float32'), +} + +def approximate(x): + eps = 1e-7 + n = 1 + d = 1 + f = n / d + while math.fabs(f - x) > eps: + if f < x: + n += 1 + else: + d += 1 + n = round(x * d) + f = n / d + nbits_up = int(math.ceil(math.log(d) / math.log(2))) + nbits_down = int(math.floor(math.log(d) / math.log(2))) + print('Now::', n, d, nbits_up, nbits_down) + round_up_d = 1 << nbits_up + round_down_d = 1 << nbits_down + nbits = nbits_up + if round_up_d - d > d - round_down_d: + round_up_d = round_down_d + nbits = nbits_down + fact = round_up_d / d + n *= fact + n = round(n) + return n, nbits + +def quantize_uniform(x, num_bits=8): + qmin = -2.**(num_bits-1) + 1 + qmax = 2.**(num_bits-1) - 1 + scale_p = x.max() / qmax + scale_n = x.min() / qmin + scale = max(scale_p, scale_n) + #print ("integer scaling factor is: ", scale) + q_x = (x / scale).round() + #print ("integer-quantized weights: ", q_x) + + return q_x, scale + +def run_file(src, params): + print(f'Compiling & Running: {src}') + with open(src, 'r') as fp: + relay_src = fp.read() + start = time.time() + mod = tvm.parser.fromtext(relay_src) + mod = relay.transform.InferType()(mod) + inputs = [params[x.name_hint] for x in mod['main'].params] + for target, dev in tvm.testing.enabled_targets(): + # relay_graph, lib, params = relay.build(mod, target=target, params=params) + executor = relay.create_executor('vm', mod=mod, device=dev, target=target).evaluate() + end = time.time() + print(f'compile time: {end - start}') + # relay_model = graph_executor.create(relay_graph, lib, dev) + # relay_model.set_input(**params) + start = time.time() + # relay_model.run() + result = executor(*inputs) + end = time.time() + print(f'run time: {end - start}') + # return relay_model.get_output(0) + qd, S_data = quantize_uniform(inputs[0]) + qw, S_w = quantize_uniform(inputs[1]) + _, S_act = quantize_uniform(np.matmul(inputs[0], np.transpose(inputs[1]))) + qd = qd.astype('int32') + qw = qw.astype('int32') + qact = np.matmul(qd, np.transpose(qw)) + factor, nbits = approximate(float(S_data * S_w / S_act)) + acc_buf = ((qact * factor) >> nbits) + acc_buf = np.minimum(np.maximum(acc_buf, -127), 127) + print(acc_buf) + output_buf = (acc_buf & 0b11111111).astype('int8') + print('Scale: ', S_data * S_w / S_act) + print('Approximate', approximate(S_data * S_w / S_act)) + print('Approx Result:', output_buf.astype('float32') * S_act) + print() + return result +import numpy as np +print(params) +print(run_file('qdense.relay', params)) +print(np.matmul(params['data'], np.transpose(params['weights']))) diff --git a/tests/test_vta_quantization.py b/tests/test_vta_quantization.py new file mode 100644 index 0000000..774e936 --- /dev/null +++ b/tests/test_vta_quantization.py @@ -0,0 +1,286 @@ +import math +import torch +import tqdm +import tvm +import logging +import numpy as np +from tvm import relay +from tvm.contrib import graph_executor +from tvm.relay import * +from tvm.relay import nn +from tvm.runtime.ndarray import cpu +import sys + +sys.setrecursionlimit(65535) + +def round_up(x: Expr): + # return cast(left_shift(const(2, 'int32'), cast(relay.log2(relay.ceil(x)), 'int32')), 'float32') + return power(const(2, 'float32'), ceil(log2(x))) + +def bound(e): + return relay.max(e) - relay.min(e) + +def quantize_dense(data, weights, nbits, max_val): + # weights should already be quantized prior to runtime + act = nn.dense(data, weights, out_dtype='int32') + # Quantize back to int8, this is what happen on VTA + # (S_act / R_act * MAX_INT8) is a power of 2 + # implement in terms of right_shift + act = right_shift(act, nbits) + act = clip(act, -max_val, max_val) + return cast(act, 'int8') + +# def zero_point(data, scale, max_val): +# data_zp = const(0, 'float32') - relay.min(data) / scale +# return cast(clip(data_zp, 0, max_val), 'int8') +class VTAQuantize(relay.ExprMutator): + def __init__(self, calibration_data=[], ops=[], layerwise=False, nbits=8): + super().__init__() + self.calibration = calibration_data.copy() + self.ops = ops + self.MAX_VALUE = 2 ** (nbits - 1) - 1 + self.MIN_VALUE = -(2 ** (nbits - 1)) + 1 + self.counter = 0 + self.layerwise = layerwise + self.layers = [] + self.bindings = {} + self.nbits = nbits + + def get_dtype(self, x): + if isinstance(x, relay.Var): + return x.type_annotation.dtype + if isinstance(x, relay.Constant): + return x.data.dtype + if isinstance(x, relay.Call): + return x.checked_type.dtype + else: + raise Exception(f'cannot get type for {x}') + + def visit_var(self, var): + return self.bindings.get(var, var) + + def visit_let(self, let): + new_val = self.visit(let.value) + self.bindings[let.var] = new_val + new_body = self.visit(let.body) + return new_body + + def quantize(self, data): + scale_p = relay.max(data) / const(self.MAX_VALUE, 'float32') + scale_n = relay.min(data) / const(self.MIN_VALUE, 'float32') + scale = relay.maximum(scale_p, scale_n) + # return scale, scale + qdata = relay.round(data / scale) + return cast(qdata, 'int8'), scale + + def dequantize_uniform(self, data, scale): + # Need to implement in terms of * and >> + return clip(cast(data, 'float32') * scale, self.MIN_VALUE, self.MAX_VALUE) + + def visit_call(self, call): + op = call.op + if op.name not in self.ops: + return relay.Call(call.op, list(map(self.visit, call.args)), call.attrs, type_args=call.type_args, span=call.span) + args = [self.visit(x) for x in call.args] + data = args[0] + weights = args[1] + qdata, S_data = self.quantize(data) + qweight, S_w = self.quantize(weights) + if len(self.calibration): + S_ref = const(self.calibration[self.counter][1], 'float32') + self.counter += 1 + else: + # debug use + _, S_ref = self.quantize(nn.dense(data, weights)) + # return qdata + qact = nn.dense(qdata, qweight, out_dtype='int32') + S_act = S_data * S_w / S_ref + expr = self.dequantize_uniform(qact, S_act) + expr = cast(expr, 'int8') + # return qdata + expr = relay.multiply(cast(expr, 'float32'), S_ref) + if self.layerwise: + self.layers.append(expr) + return expr + # if not self.layerwise: + # if len(self.calibration): + # R_act = const(self.calibration[self.counter][1], 'float32') + # self.counter += 1 + # else: + # raise Exception('incomplete calibration data') + # else: + # R_act = round_up(bound(nn.dense(data, weights))) + ''' + quant_range = const(self.MAX_VALUE, 'float32') + data_range = bound(data) + weights_range = bound(weights) + rounded_up_data_range = round_up(data_range) + rounded_up_w_range = round_up(weights_range) + S_data = data_range / quant_range + S_w = weights_range / quant_range + S_act = S_data * S_w + q_data = cast(clip(data / rounded_up_data_range * quant_range, -self.MAX_VALUE + 1, self.MAX_VALUE - 1), 'int8') + q_weights = cast(clip(weights / rounded_up_w_range * quant_range, -self.MAX_VALUE + 1, self.MAX_VALUE - 1), 'int8') + # NOTE: R_act need to be estimzated using a calibration set + factor = S_act / R_act * quant_range + # nbits = -cast(floor(log2(factor)), 'int32') + nbits = const(self.nbits, 'int32') + S_data_inv = rounded_up_data_range / quant_range + S_w_inv = rounded_up_w_range / quant_range + expr = quantize_dense(q_data, q_weights, nbits, self.MAX_VALUE - 1) + expr = left_shift(cast(expr, 'int32'), nbits) + expr = cast(expr, 'float32') + expr = multiply(expr, S_data_inv * S_w_inv) + ''' + # if self.layerwise: + # self.layers.append(expr) + # return expr + +class CalibrationMutator(): + def __init__(self, ops): + class Counter(relay.ExprMutator): + def __init__(self, ops): + super().__init__() + self.aggregate = [] + self.aggregate_names = [] + self.bindings = {} + self.ops = ops + + def reset(self): + self.aggregate = [] + self.aggregate_names = [] + + def visit_call(self, call): + op = call.op + args = [self.visit(x) for x in call.args] + args = list(map(lambda x: self.bindings.get(x, x) if isinstance(x, Var) else x, args)) + if op.name in self.ops: + self.aggregate_names.append(op.name) + self.aggregate.append(Call(op, args, call.attrs, call.type_args, call.span)) + return Call(op, args, call.attrs, call.type_args, call.span) + + def visit_var(self, var): + if var in self.bindings: + return self.bindings[var] + return var + + def visit_let(self, let): + new_var = self.visit(let.var) + new_val = self.visit(let.value) + self.bindings[new_var] = new_val + new_body = self.visit(let.body) + return Let(new_var, new_val, new_body) + + def visit(self, expr): + return super().visit(expr) + + self.counter = Counter(ops) + + def calibrate_mode(self, mod): + self.counter.reset() + expr = mod['main'] + self.counter.visit(expr) + return tvm.ir.IRModule.from_expr(Tuple(self.counter.aggregate)), self.counter.aggregate_names + +def get_test_workload(): + data = relay.var('data', type_annotation=relay.TensorType((4, 4))) + weights_1 = relay.const(np.random.random((2, 4)) * 1) + weights_2 = relay.const(np.random.random((3, 2)) * 1) + a = relay.multiply(data, relay.const(4.0)) + b = relay.nn.dense(a, weights_1) + c = relay.nn.relu(b) + d = relay.nn.dense(c, weights_2) + e = relay.nn.relu(d) + f = relay.nn.dense(e, relay.const(np.random.random((4, 3)) * 2)) + g = relay.nn.relu(f) + return tvm.ir.IRModule.from_expr(g) + +def get_model(src): + # Prepare a model + with open(src) as fp: + src = fp.read() + return tvm.parser.fromtext(src) + +def get_inputs(mod, scale): + mod = relay.transform.InferType()(mod) + inputs = dict() + for var in mod['main'].params: + shape = var.type_annotation.shape + name_hint = var.name_hint + inputs[name_hint] = np.random.rand(*[int(x) for x in shape]).astype('float32') * scale + return inputs + +def run_mod(mod, inputs, params, num_ops): + with tvm.transform.PassContext(opt_level=3): + relay_graph, lib, params = relay.build(mod, params=params, target='llvm') + graph_rt = graph_executor.create(relay_graph, lib, device=cpu(0)) + graph_rt.set_input(**params) + graph_rt.run(**inputs) + result = [graph_rt.get_output(i).asnumpy() for i in range(num_ops)] + return result + +def lockstep_layerwise(mod, calibration, src, inputs, params): + ref_mod, ops = CalibrationMutator(['nn.dense']).calibrate_mode(get_model(src)) + num_ops = len(ops) + quantizer = VTAQuantize(calibration, ops=['nn.dense'], layerwise=True) + quantizer.visit(get_model(src)['main'].body) + expr = Tuple(quantizer.layers) + qmod_layerwise = tvm.ir.IRModule.from_expr(expr) + qmod_layerwise = relay.transform.InferType()(qmod_layerwise) + for inp in inputs: + ref_result = run_mod(ref_mod, inp, params, num_ops) + qmod_layer_results = run_mod(qmod_layerwise, inp, params, num_ops) + assert(len(ref_result) == len(qmod_layer_results)) + for (i, (ref, quant)) in enumerate(zip(ref_result, qmod_layer_results)): + print(f'Ref: {ref}', '\n', f'Quant: {quant}') + print(f'Layer {i} ({ops[i]}) relative error:\n', np.abs(ref - quant) / ref) + +def calibrate(mod, params, repr_dataset, ops): + def quantize(data): + scale_p = np.max(data) / 127 + scale_n = np.min(data) / -127 + return float(scale_p) if scale_p > scale_n else float(scale_n) + logging.info('Starting calibration') + calibrate_mod, agg_ops = CalibrationMutator(ops).calibrate_mode(mod) + quantizer = VTAQuantize(nbits=8) + with tvm.transform.PassContext(opt_level=3): + relay_graph, lib, params = relay.build(calibrate_mod, params=params, target='llvm') + graph_rt = graph_executor.create(relay_graph, lib, device=cpu(0)) + graph_rt.set_input(**params) + calibration_data = [list()] * len(agg_ops) + for datum in tqdm.tqdm(repr_dataset): + graph_rt.run(**datum) + result = [graph_rt.get_output(i).asnumpy() for i in range(len(agg_ops))] + for i, res in zip(range(len(calibration_data)), reversed(result)): + calibration_data[i].append(quantize(res)) + return list(zip(agg_ops, map(np.mean, calibration_data))) + +def main(src): + mod = get_test_workload() + inputs = [get_inputs(mod, 0.1) for _ in range(10)] + ref_output = [run_mod(mod, inp).asnumpy() for inp in inputs] + cali_dataset = inputs[0:len(inputs)] + mod = relay.transform.InferType()(mod) + calibrations = calibrate(mod, {}, cali_dataset, ['nn.dense']) + ref = run_mod(mod, inputs[0]).asnumpy() + expr = VTAQuantize(calibrations, ['nn.dense'], nbits=8).visit(mod['main'].body) + qmod = tvm.ir.IRModule.from_expr(expr) + qmod = relay.transform.InferType()(qmod) + qmod = relay.transform.EliminateCommonSubexpr()(qmod) + res = run_mod(qmod, inputs[0]).asnumpy() + # print(f'quantized with nbits={8}:\n{res}') + # print(f'rel error: {(ref - res) / ref}') + for i, inp in enumerate(inputs): + quant_result = run_mod(qmod, inp).asnumpy() + print('==============================================') + print(f'ref:\n{ref_output[i]}') + print(f'quant:\n{quant_result}') + print(f'Input {i} difference:\n{np.abs(ref_output[i] - quant_result) / ref_output[i]}') + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('model', type=str, help='Name of the model under ./models') + args = parser.parse_args() + main(args.model) diff --git a/tests/validate_compilation.py b/tests/validate_compilation.py new file mode 100644 index 0000000..545ae49 --- /dev/null +++ b/tests/validate_compilation.py @@ -0,0 +1,112 @@ +import subprocess +import sys +import argparse +import numpy +import tvm +import test_model_on_vta as utils +from tvm.relay.testing import op_summary + +DEFAULT_CONFIGS = { + 'max_pool2d': ['flexasr-maxpool'], + 'resnet18': ['hlscnn-conv2d'], + 'mobilenet': ['hlscnn-conv2d', 'linear-rewrites'], + 'mobilenetv2': ['im2col', 'vta-dense'], + 'resmlp': ['linear-rewrites'], + 'efficientnet': ['hlscnn-conv2d'] +} + +def run_eqsat(model, configs, use_ilp): + try: + subprocess.run(['python3', + 'run_eqsat.py', + '--relay-file', + f'./models/{model}.relay', + f'--output-file', + model, + '--configs'] + configs + (['--use-ilp'] if use_ilp else []), + stdout=sys.stdout.buffer, + check=True) + return (f'{model}-rewritten.json', f'{model}-data.json') + except subprocess.CalledProcessError as e: + print(f'Error while running eqsat:\n {e}') + exit(1) + +def compile_model(model, model_json, data_json, configs, debug): + try: + subprocess.run(['python3', 'compile_model.py', + f'./models/{model}.relay', + f'{model}-rewritten.relay', + model_json, + data_json] + configs + (['--debug'] if debug else []), + stdout=sys.stdout.buffer, + stderr=sys.stderr.buffer, + check=True) + return f'{model}-rewritten.relay' + except subprocess.CalledProcessError as e: + print(f'Error while compiling:\n {e}') + exit(1) + +def run_comparison(model, compiled): + try: + subprocess.run(['python3', 'run_comparison.py', + f'./models/{model}.relay', + compiled], stdout=sys.stdout.buffer, stderr=sys.stderr.buffer, check=True) + except subprocess.CalledProcessError as e: + print(f'Error while running comparison:\n {e}') + exit(1) + print('Passed!') + +def quantize_model(mod): + params = { + k: numpy.random.randn(*v) / 1000.0 + for (k, v) in map(lambda x: (x.name_hint, x.type_annotation.shape), mod['main'].params[1:]) + } + return utils.run_with_relay_quantization(mod, params, run=False) + +def get_offload_stats(model, quantize): + with open(model) as fp: + src = fp.read() + mod = tvm.parser.fromtext(src) + if quantize: + mod = quantize_model(mod) + print(f'ALL overloads: {op_summary.count_all_overloads(mod)}') + print(f'ALL Ops: {op_summary.count_all_ops(mod)}') + print(f'ALL Ops in overloads = {op_summary.count_all_ops_in_overloads(mod)} * #ops per pattern') + +def run_model(model, configs, use_ilp, debug, get_stats, quantize): + print(f'Step 1: Run EqSat with {" ".join(configs)}') + (model_json, data_json) = run_eqsat(model, configs, use_ilp) + print(f'Step 2: Compiling back to Relay with {model_json} and {data_json}') + compiled = compile_model(model, model_json, data_json, configs, debug) + if get_stats: + print('Vanilla relay:') + get_offload_stats(f'./models/{model}.relay', quantize) + print('EqSat model:') + get_offload_stats(compiled, quantize) + else: + print('Step 3: Running numeric comparisons') + run_comparison(model, compiled) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('model', type=str, help='Name of the model to run') + parser.add_argument('--configs', nargs='+', dest='configs', required=False, help='configs pass to eqsat') + parser.add_argument('--default', required=False, dest='use_default', action='store_true', help='use pre-set config') + parser.add_argument('--use-ilp', required=False, dest='use_ilp', action='store_true', help='use ILP extraction') + parser.add_argument('--debug', required=False, dest='debug', action='store_true', help='use debug function to substitute accelerator calls') + parser.add_argument('--get-stats', required=False, dest='get_stats', action='store_true') + parser.add_argument('--quantize', required=False, dest='quantize', action='store_true') + args = parser.parse_args() + if args.use_default: + if args.configs: + print('Warning: overwriting configs with defaults') + args.configs = DEFAULT_CONFIGS[args.model] + if not args.configs: + print('No config specified, skipping...') + exit(0) + run_model(args.model, + args.configs if not args.use_default else DEFAULT_CONFIGS[args.model], + args.use_ilp, + args.debug, + args.get_stats, + args.quantize) diff --git a/tests/vta_tsim_conv2d.py b/tests/vta_tsim_conv2d.py new file mode 100644 index 0000000..fb1ebf8 --- /dev/null +++ b/tests/vta_tsim_conv2d.py @@ -0,0 +1,106 @@ +import tvm +from tvm import relay + +from tvm import autotvm +import numpy as np +import vta +from vta.testing import simulator +from vta.top import graph_pack + +from tvm import rpc +from tvm.contrib import utils, graph_executor + +import argparse +parser = argparse.ArgumentParser() +parser.add_argument('n', type=int, default=1) +parser.add_argument('c', type=int, default=32) +parser.add_argument('h', type=int, default=64) +parser.add_argument('w', type=int, default=16) +parser.add_argument('o', type=int, default=16) +parser.add_argument('kh', type=int, default=1) +parser.add_argument('kw', type=int, default=1) +parser.add_argument('padding_top', type=int, default=0) +parser.add_argument('padding_left', type=int, default=0) +parser.add_argument('padding_bottom', type=int, default=0) +parser.add_argument('padding_right', type=int, default=0) +args = parser.parse_args() +N, C, H, W, O, kH, kW = args.n, args.c, args.h, args.w, args.o, args.kh, args.kw +padding = (args.padding_top, args.padding_left, args.padding_bottom, args.padding_right) + +# N, C, H, W, O, kH, kW = 1, 64, 8, 8, 64, 3, 3 + +# Notes: +''' +1. Make TVM with both lib_fsim and lib_tsim on +2. Make lib (run `make lib`) in vta-hw/hardware/chisel +3. run this script +''' + +env = vta.get_env() +target = tvm.target.vta(model='sim') + +remote = rpc.LocalSession() +ctx = remote.ext_dev(0) + +def get_conv2d(n, c, o, h, w, kH, kW, padding): + data = relay.var('data', shape=(n, c, h, w)) + weight = relay.var('weight', shape=(o, c, kH, kW)) + data_1 = relay.nn.max_pool2d(data) + return relay.nn.adaptive_avg_pool2d(relay.nn.conv2d(data_1, weight, kernel_size=(kH, kW), channels=o, padding=padding)) + +with autotvm.tophub.context(target): + + dtype_dict = {'data': 'float32', 'weight': 'float32'} + shape_dict = {'data': (N, C, H, W), 'weight': (O, C, kH, kW)} + + params = { + # 'data': tvm.nd.array(np.random.uniform(size=(N, C, H, W)).astype('float32')), + 'weight': tvm.nd.array(np.random.uniform(size=(O, C, kH, kW)).astype('float32')) + } + + e = get_conv2d(N, C, O, H, W, kH, kW, padding) + model = tvm.IRModule.from_expr(e) + model = relay.transform.InferType()(model) + with tvm.transform.PassContext(opt_level=3): + with relay.quantize.qconfig(global_scale=8.0, skip_conv_layers=[]): + relay_prog = relay.quantize.quantize(model, params=params) + # Perform graph packing and constant folding for VTA target + # do device annotation if target is intelfocl or sim + relay_prog = graph_pack( + relay_prog["main"], + env.BATCH, + env.BLOCK_OUT, + env.WGT_WIDTH, + start_name="nn.max_pool2d", + stop_name="nn.adaptive_avg_pool2d", + device_annot=(env.TARGET == "intelfocl"), + ) + + with vta.build_config( + opt_level=3, disabled_pass={"AlterOpLayout", "tir.CommonSubexprElimTIR"} + ): + graph, lib, params = relay.build( + relay_prog, target=tvm.target.Target(target, host=env.target_host), params=params + ) + + + temp = utils.tempdir() + lib.export_library(temp.relpath("graphlib.tar")) + remote.upload(temp.relpath("graphlib.tar")) + lib = remote.load_module("graphlib.tar") + + # Graph runtime + m = graph_executor.create(graph, lib, ctx) + m.set_input(**params) + m.set_input('data', tvm.nd.array(np.random.uniform(size=(N, C, H, W)).astype('float32'))) + num = 1 # number of times we run module for a single measurement + rep = 10 # number of measurements (we derive std dev from this) + timer = m.module.time_evaluator("run", ctx, number=num, repeat=rep) + simulator.clear_stats() + timer() + sim_stats = simulator.stats() + for k, v in sim_stats.items(): + # Since we execute the workload many times, we need to normalize stats + # Note that there is always one warm up run + # Therefore we divide the overall stats by (num * rep + 1) + print(v // (num * rep + 1)) diff --git a/tests/vta_tsim_dense.py b/tests/vta_tsim_dense.py new file mode 100644 index 0000000..86198fb --- /dev/null +++ b/tests/vta_tsim_dense.py @@ -0,0 +1,223 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Testing topi gemm operator for VTA""" + +import os +import json +from collections import namedtuple + +import numpy as np + +import tvm +from tvm import te +from tvm import autotvm +from tvm.contrib import utils +from tvm.contrib.pickle_memoize import memoize +from tvm import topi +import tvm.topi.testing +import vta +from vta import program_fpga, reconfig_runtime +import vta.testing +from vta.testing import simulator + +# FIXME: we need a custom clip operator to circumvent a pattern detection limitation +@tvm.te.tag_scope(tag=topi.tag.ELEMWISE) +def my_clip(x, a_min, a_max): + """Unlike topi's current clip, put min and max into two stages.""" + const_min = tvm.tir.const(a_min, x.dtype) + const_max = tvm.tir.const(a_max, x.dtype) + x = te.compute(x.shape, lambda *i: tvm.te.min(x(*i), const_max), name="clipA") + x = te.compute(x.shape, lambda *i: tvm.te.max(x(*i), const_min), name="clipB") + return x + + +def run_gemm( + env, + remote, + target, + batch_size, + in_feat, + out_feat, + check_correctness=True, + print_ir=False, + number=1, + samples=10, +): + + # Perform packing only if we are targeting the accelerator + if "arm_cpu" in target.keys: + data_pack = False + elif "vta" in target.keys: + data_pack = True + + # Derive shapes depending upon packing + a_shape = (batch_size, in_feat) + w_shape = (out_feat, in_feat) + if data_pack: + data_shape = (batch_size // env.BATCH, in_feat // env.BLOCK_IN, env.BATCH, env.BLOCK_IN) + kernel_shape = ( + out_feat // env.BLOCK_OUT, + in_feat // env.BLOCK_IN, + env.BLOCK_OUT, + env.BLOCK_IN, + ) + fcompute = vta.top.dense_packed + fschedule = vta.top.schedule_dense_packed + else: + data_shape = a_shape + kernel_shape = w_shape + fcompute = topi.x86.dense_nopack + fschedule = topi.x86.schedule_dense_nopack + data = te.placeholder(data_shape, name="data", dtype=env.inp_dtype) + kernel = te.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype) + + # Define base computation schedule + with target: + res = fcompute(data, kernel, None, env.acc_dtype) + res = topi.right_shift(res, 8) + res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1) + res = topi.cast(res, env.out_dtype) + # Derive base schedule + s = fschedule([res]) + if print_ir: + print(vta.lower(s, [data, kernel, res], simple_mode=True)) + + # Derive number of ops + num_ops = 2 * batch_size * in_feat * out_feat + + # @memoize("vta.tests.test_benchmark_topi.dense.verify") + def get_ref_data(): + # derive min max for act, wgt types (max non inclusive) + a_min, a_max = 0 - (1 << (env.INP_WIDTH - 1)), (1 << (env.INP_WIDTH - 1)) + w_min, w_max = 0 - (1 << (env.WGT_WIDTH - 1)), (1 << (env.WGT_WIDTH - 1)) + a_np = np.random.randint(a_min, a_max, size=a_shape).astype(data.dtype) + w_np = np.random.randint(w_min, w_max, size=w_shape).astype(kernel.dtype) + + r_np = np.dot(a_np.astype(env.acc_dtype), w_np.T.astype(env.acc_dtype)).astype( + env.acc_dtype + ) + return a_np, w_np, r_np + + # Data in original format + data_np, kernel_np, res_ref = get_ref_data() + if data_pack: + data_np = data_np.reshape( + batch_size // env.BATCH, env.BATCH, in_feat // env.BLOCK_IN, env.BLOCK_IN + ).transpose((0, 2, 1, 3)) + kernel_np = kernel_np.reshape( + out_feat // env.BLOCK_OUT, env.BLOCK_OUT, in_feat // env.BLOCK_IN, env.BLOCK_IN + ).transpose((0, 2, 1, 3)) + + # Build + if "vta" in target.keys: + mod = vta.build( + s, + [data, kernel, res], + target=tvm.target.Target(target, host=env.target_host), + name="dense", + ) + else: + mod = tvm.build( + s, + [data, kernel, res], + target=tvm.target.Target(target, host=env.target_host), + name="dense", + ) + temp = utils.tempdir() + mod.save(temp.relpath("dense.o")) + remote.upload(temp.relpath("dense.o")) + f = remote.load_module("dense.o") + dev = remote.device(str(target)) + + res_np = np.zeros(topi.utils.get_const_tuple(res.shape)).astype(res.dtype) + data_arr = tvm.nd.array(data_np, dev) + kernel_arr = tvm.nd.array(kernel_np, dev) + res_arr = tvm.nd.array(res_np, dev) + time_f = f.time_evaluator("dense", dev, number=number, repeat=samples) + + # In vta sim mode, collect simulator runtime statistics + stats = {} + cost = None + if env.TARGET in ["sim", "tsim"]: + # Check if we're in local RPC mode (allows us to rebuild the + # runtime on the fly when varying the VTA designs) + local_rpc = int(os.environ.get("VTA_LOCAL_SIM_RPC", "0")) + if local_rpc: + if env.TARGET == "sim": + remote.get_function("vta.simulator.profiler_clear")() + else: + remote.get_function("vta.tsim.profiler_clear")() + cost = time_f(data_arr, kernel_arr, res_arr) + if env.TARGET == "sim": + stats = json.loads(remote.get_function("vta.simulator.profiler_status")()) + else: + stats = json.loads(remote.get_function("vta.tsim.profiler_status")()) + else: + simulator.clear_stats() + cost = time_f(data_arr, kernel_arr, res_arr) + stats = simulator.stats() + else: + cost = time_f(data_arr, kernel_arr, res_arr) + + if 'cycle_count' in stats: + print(stats['cycle_count'] // (number * samples + 1)) + + # Check correctness + correct = False + if check_correctness: + res_orig = res_arr.numpy() + if data_pack: + res_orig = res_orig.reshape(batch_size, out_feat) + res_ref = res_ref >> 8 + res_ref = np.clip(res_ref, 0, (1 << env.OUT_WIDTH - 1) - 1) + res_ref = res_ref.astype(env.out_dtype) + correct = np.allclose(res_orig, res_ref) + + gops = (num_ops / cost.mean) / float(10 ** 9) + status = "PASSED" if correct else "FAILED" + if "arm_cpu" in target.keys: + device = "CPU" + elif "vta" in target.keys: + device = "VTA" + # print("%s DENSE TEST %s: Time cost = %g sec/op, %g GOPS" % (device, status, cost.mean, gops)) + + return correct, cost, stats + + +def test_gemm(device="vta", batch=128, in_feat=128, out_feat=128): + def _run(env, remote): + if device == "vta": + target = env.target + if env.TARGET not in ["sim", "tsim"]: + assert tvm.runtime.enabled("rpc") + program_fpga(remote, bitstream=None) + reconfig_runtime(remote) + with autotvm.tophub.context(target): # load pre-tuned schedule parameters + run_gemm(env, remote, target, batch, in_feat, out_feat) + + vta.testing.run(_run) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("batch", type=int) + parser.add_argument("in_feat", type=int) + parser.add_argument("out_feat", type=int) + args = parser.parse_args() + test_gemm("vta", args.batch, args.in_feat, args.out_feat)