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Fix fuse_pad_into_pool folding zero-padding into MaxPool #318

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Fix fuse_pad_into_pool folding zero-padding into MaxPool #318

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78 changes: 49 additions & 29 deletions onnxoptimizer/passes/fuse_pad_into_pool.h
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Original file line number Diff line number Diff line change
Expand Up @@ -13,9 +13,15 @@
// After:
// Z = pool(X, Y) with "pads" attribute set
//
// the pass handles the case when Pad is zero-padding the input
// (i.e. mode=constant and constant_value=0)

// The pass only fires when Pad uses mode=constant and the constant value
// matches the value the pool implicitly uses for its own padding:
// - AveragePool (with count_include_pad=1): constant_value = 0
// - MaxPool: constant_value = -inf
// MaxPool ignores its padded elements, which is equivalent to padding with
// -inf. Folding a zero-padding Pad into a MaxPool is therefore incorrect
// (see https://github.com/onnxsim/onnxsim/issues/290).

#include <limits>
#include <numeric>

#include "onnx/defs/tensor_util.h"
Expand Down Expand Up @@ -67,6 +73,17 @@ struct FusePadIntoPool final : public PredicateBasedPass {
}

// Process 'Constant_value'
//
// The Pad can only be fused when it fills the padded region with the same
// value the pool implicitly uses for its own padding:
// - AveragePool (with count_include_pad=1): 0
// - MaxPool: -inf (MaxPool ignores padded elements, which is equivalent
// to padding with -inf). Folding a zero-padding Pad into a MaxPool
// would change the result whenever a window's real values are all
// negative -- see https://github.com/onnxsim/onnxsim/issues/290.
const bool is_maxpool = pool->kind() == Symbol("MaxPool");
const double required_pad_value =
is_maxpool ? -std::numeric_limits<double>::infinity() : double(0);
{
union ConstantValueType {
int32_t i32;
Expand All @@ -79,32 +96,35 @@ struct FusePadIntoPool final : public PredicateBasedPass {
int16_t i16;
} cv;

#define Define_GetConstantValueFromInput(token) \
(GetValueFromInput(pad, 2, cv.token) && cv.token == decltype(cv.token)(0))

do {
if (GetValueFromAttr(pad, kvalue, cv.f64) && cv.f64 == double(0)) {
break;
}
if (pad->inputs().size() >= 3) {
if (pad->input(2)->uniqueName().empty()) {
break;
}
if (Define_GetConstantValueFromInput(i32) ||
Define_GetConstantValueFromInput(i64) ||
Define_GetConstantValueFromInput(f32) ||
Define_GetConstantValueFromInput(f64) ||
Define_GetConstantValueFromInput(ui8) ||
Define_GetConstantValueFromInput(i8) ||
Define_GetConstantValueFromInput(ui16) ||
Define_GetConstantValueFromInput(i16)) {
break;
}
return false;
}
} while (0);

#undef Define_GetConstantValueFromInput
#define Match_ConstantValueFromInput(token) \
(GetValueFromInput(pad, 2, cv.token) && \
static_cast<double>(cv.token) == required_pad_value)

bool pad_value_matches;
if (GetValueFromAttr(pad, kvalue, cv.f64)) {
// Explicit 'value' attribute (opset 10 and below).
pad_value_matches = (cv.f64 == required_pad_value);
} else if (pad->inputs().size() >= 3 &&
!pad->input(2)->uniqueName().empty()) {
// Explicit 'constant_value' input (opset 11 and above).
pad_value_matches = Match_ConstantValueFromInput(i32) ||
Match_ConstantValueFromInput(i64) ||
Match_ConstantValueFromInput(f32) ||
Match_ConstantValueFromInput(f64) ||
Match_ConstantValueFromInput(ui8) ||
Match_ConstantValueFromInput(i8) ||
Match_ConstantValueFromInput(ui16) ||
Match_ConstantValueFromInput(i16);
} else {
// No constant value specified: Pad defaults to 0.
pad_value_matches = (required_pad_value == double(0));
}

#undef Match_ConstantValueFromInput

if (!pad_value_matches) {
return false;
}
}

// check if some values in 'pads' prevents us from fusing it into 'Conv'
Expand Down
192 changes: 187 additions & 5 deletions onnxoptimizer/test/optimizer_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2081,6 +2081,9 @@ def test_fuse_pad_into_avgpool_no_optional_value_opset10(self):
assert list(optimized_model.graph.node[0].attribute[2].ints) == [0, 0, 1, 1]

def test_fuse_pad_into_maxpool_no_optional_value_opset10(self):
# A Pad with the default constant value (0) must NOT be folded into a
# MaxPool: MaxPool pads with -inf, so zero-padding changes the result.
# See https://github.com/onnxsim/onnxsim/issues/290
pad = helper.make_node(
"Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1]
)
Expand All @@ -2098,6 +2101,33 @@ def test_fuse_pad_into_maxpool_no_optional_value_opset10(self):
opset_imports=[helper.make_opsetid("", 10)],
)

assert optimized_model.graph == graph

def test_fuse_pad_into_maxpool_neg_inf_value_opset10(self):
# A Pad with constant value -inf is the correct fill value for MaxPool
# and can be folded into it.
pad = helper.make_node(
"Pad",
["X"],
["P"],
mode="constant",
pads=[0, 0, 0, 0, 0, 0, 1, 1],
value=float("-inf"),
)
max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
graph = helper.make_graph(
[pad, max_pool],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2))],
[helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
)
optimized_model = self._optimized(
graph,
["fuse_pad_into_pool"],
False,
opset_imports=[helper.make_opsetid("", 10)],
)

assert len(list(optimized_model.graph.node)) == 1
assert optimized_model.graph.node[0].op_type == "MaxPool"
assert optimized_model.graph.node[0].attribute[1].name == "pads"
Expand Down Expand Up @@ -2133,6 +2163,9 @@ def test_fuse_pad_into_avgpool_no_optional_value(self):
assert list(optimized_model.graph.node[0].attribute[2].ints) == [0, 0, 1, 1]

def test_fuse_pad_into_maxpool_no_optional_value(self):
# No constant_value input => Pad defaults to 0, which must NOT be folded
# into MaxPool (MaxPool pads with -inf).
# See https://github.com/onnxsim/onnxsim/issues/290
pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
graph = helper.make_graph(
Expand All @@ -2154,10 +2187,7 @@ def test_fuse_pad_into_maxpool_no_optional_value(self):
)
optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

assert len(list(optimized_model.graph.node)) == 1
assert optimized_model.graph.node[0].op_type == "MaxPool"
assert optimized_model.graph.node[0].attribute[1].name == "pads"
assert list(optimized_model.graph.node[0].attribute[1].ints) == [0, 0, 1, 1]
assert optimized_model.graph == graph

def test_fuse_pad_into_avgpool_with_optional_value(self):
pad = helper.make_node("Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant")
Expand Down Expand Up @@ -2196,7 +2226,10 @@ def test_fuse_pad_into_avgpool_with_optional_value(self):
assert optimized_model.graph.node[0].attribute[2].name == "pads"
assert list(optimized_model.graph.node[0].attribute[2].ints) == [0, 0, 1, 1]

def test_fuse_pad_into_maxpool_with_optional_value(self):
def test_fuse_pad_into_maxpool_with_zero_optional_value(self):
# This is the exact case reported in
# https://github.com/onnxsim/onnxsim/issues/290 : a Pad with an explicit
# constant value of 0 must NOT be folded into a MaxPool.
pad = helper.make_node("Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant")
max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
graph = helper.make_graph(
Expand Down Expand Up @@ -2225,6 +2258,39 @@ def test_fuse_pad_into_maxpool_with_optional_value(self):
)
optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

assert optimized_model.graph == graph

def test_fuse_pad_into_maxpool_with_neg_inf_optional_value(self):
# A Pad with constant value -inf is the correct fill value for MaxPool
# and can be folded into it.
pad = helper.make_node("Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant")
max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
graph = helper.make_graph(
[pad, max_pool],
"test",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
],
[helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
[
helper.make_tensor(
"Pads",
TensorProto.INT64,
dims=(8,),
vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
raw=True,
),
helper.make_tensor(
"Constant_value",
TensorProto.FLOAT,
dims=(),
vals=np.array([float("-inf")]).astype(np.float32).tobytes(),
raw=True,
),
],
)
optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

assert len(list(optimized_model.graph.node)) == 1
assert optimized_model.graph.node[0].op_type == "MaxPool"
assert optimized_model.graph.node[0].attribute[1].name == "pads"
Expand Down Expand Up @@ -2317,6 +2383,7 @@ def test_fuse_pad_into_avgpool_1d_opset10(self):
assert list(optimized_model.graph.node[0].attribute[2].ints) == [1, 1]

def test_fuse_pad_into_maxpool_1d_opset10(self):
# Default constant value (0) => must NOT fold into MaxPool.
pad = helper.make_node("Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 1, 0, 0, 1])
max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3])
graph = helper.make_graph(
Expand All @@ -2332,6 +2399,27 @@ def test_fuse_pad_into_maxpool_1d_opset10(self):
opset_imports=[helper.make_opsetid("", 10)],
)

assert optimized_model.graph == graph

def test_fuse_pad_into_maxpool_1d_neg_inf_opset10(self):
pad = helper.make_node(
"Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 1, 0, 0, 1],
value=float("-inf"),
)
max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3])
graph = helper.make_graph(
[pad, max_pool],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1))],
[helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1))],
)
optimized_model = self._optimized(
graph,
["fuse_pad_into_pool"],
False,
opset_imports=[helper.make_opsetid("", 10)],
)

assert len(list(optimized_model.graph.node)) == 1
assert optimized_model.graph.node[0].op_type == "MaxPool"
assert optimized_model.graph.node[0].attribute[1].name == "pads"
Expand Down Expand Up @@ -2367,6 +2455,7 @@ def test_fuse_pad_into_avgpool_1d(self):
assert list(optimized_model.graph.node[0].attribute[2].ints) == [1, 1]

def test_fuse_pad_into_maxpool_1d(self):
# No constant_value input => Pad defaults to 0 => must NOT fold.
pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3])
graph = helper.make_graph(
Expand All @@ -2388,6 +2477,39 @@ def test_fuse_pad_into_maxpool_1d(self):
)
optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

assert optimized_model.graph == graph

def test_fuse_pad_into_maxpool_1d_neg_inf(self):
pad = helper.make_node(
"Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant"
)
max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3])
graph = helper.make_graph(
[pad, max_pool],
"test",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1)),
],
[helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1))],
[
helper.make_tensor(
"Pads",
TensorProto.INT64,
dims=(6,),
vals=np.array([0, 0, 1, 0, 0, 1]).astype(np.int64).tobytes(),
raw=True,
),
helper.make_tensor(
"Constant_value",
TensorProto.FLOAT,
dims=(),
vals=np.array([float("-inf")]).astype(np.float32).tobytes(),
raw=True,
),
],
)
optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

assert len(list(optimized_model.graph.node)) == 1
assert optimized_model.graph.node[0].op_type == "MaxPool"
assert optimized_model.graph.node[0].attribute[1].name == "pads"
Expand Down Expand Up @@ -2424,6 +2546,7 @@ def test_fuse_pad_into_avgpool_existing_avgpool_pad_opset10(self):
assert list(optimized_model.graph.node[0].attribute[2].ints) == [1, 1, 1, 1]

def test_fuse_pad_into_maxpool_existing_maxpool_pad_opset10(self):
# Default constant value (0) => must NOT fold into MaxPool.
pad = helper.make_node(
"Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1]
)
Expand All @@ -2443,6 +2566,29 @@ def test_fuse_pad_into_maxpool_existing_maxpool_pad_opset10(self):
opset_imports=[helper.make_opsetid("", 10)],
)

assert optimized_model.graph == graph

def test_fuse_pad_into_maxpool_existing_maxpool_pad_neg_inf_opset10(self):
pad = helper.make_node(
"Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1],
value=float("-inf"),
)
max_pool = helper.make_node(
"MaxPool", ["P"], ["Z"], kernel_shape=[3, 3], pads=[1, 1, 0, 0]
)
graph = helper.make_graph(
[pad, max_pool],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1, 1))],
[helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
)
optimized_model = self._optimized(
graph,
["fuse_pad_into_pool"],
False,
opset_imports=[helper.make_opsetid("", 10)],
)

assert len(list(optimized_model.graph.node)) == 1
assert optimized_model.graph.node[0].op_type == "MaxPool"
assert optimized_model.graph.node[0].attribute[1].name == "pads"
Expand Down Expand Up @@ -2483,6 +2629,7 @@ def test_fuse_pad_into_avgpool_existing_avgpool_pad(self):
assert list(optimized_model.graph.node[0].attribute[2].ints) == [1, 1, 1, 1]

def test_fuse_pad_into_maxpool_existing_maxpool_pad(self):
# No constant_value input => Pad defaults to 0 => must NOT fold.
pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
max_pool = helper.make_node(
"MaxPool", ["P"], ["Z"], kernel_shape=[3, 3], pads=[1, 1, 0, 0]
Expand All @@ -2506,6 +2653,41 @@ def test_fuse_pad_into_maxpool_existing_maxpool_pad(self):
)
optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

assert optimized_model.graph == graph

def test_fuse_pad_into_maxpool_existing_maxpool_pad_neg_inf(self):
pad = helper.make_node(
"Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant"
)
max_pool = helper.make_node(
"MaxPool", ["P"], ["Z"], kernel_shape=[3, 3], pads=[1, 1, 0, 0]
)
graph = helper.make_graph(
[pad, max_pool],
"test",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1, 1)),
],
[helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
[
helper.make_tensor(
"Pads",
TensorProto.INT64,
dims=(8,),
vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
raw=True,
),
helper.make_tensor(
"Constant_value",
TensorProto.FLOAT,
dims=(),
vals=np.array([float("-inf")]).astype(np.float32).tobytes(),
raw=True,
),
],
)
optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

assert len(list(optimized_model.graph.node)) == 1
assert optimized_model.graph.node[0].op_type == "MaxPool"
assert optimized_model.graph.node[0].attribute[1].name == "pads"
Expand Down
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