Add audio model adapters and improve SSM partition specs #1352
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Introduces AudioModelAdapter and ASRModelAdapter for efficient fine-tuning of audio models, along with comprehensive tests. Adds input_grain_csv_test.py for CSV/TSV input processing tests. Updates SSM partition spec helpers to support sequence parallelism and adds corresponding tests in ssm_test.py. Updates .gitignore to exclude run_specific_test.sh.
The function returns PartitionSpec but was annotated as dict[str, PartitionSpec]. This mismatch was causing CI test failures.
The test file was added without implementing the csv_dataset and tsv_dataset functions in input_grain.py. This caused import errors in CI.
- Fixed TypeError by wrapping single tensor inputs in tuples for F() calls in both adapter.py and adapter_test.py - Fixed parameter count assertion by including layer_norm.bias in the count calculation
- Fixed state passing by extracting encoder_adapter and decoder_adapter from the full state dict in adapt_encoder_features and adapt_decoder_features - Fixed expected parameter count from 33664 to 33600 in test_parameter_counts
- Made prng_key and state required parameters in adapt_encoder_features and adapt_decoder_features - Removed fallback direct module calls which don't work outside invocation context - Updated test_direct_call_fallback to pass required prng_key and state parameters
- Install uv and use uv pip install to respect ml-dtypes override - Fixes dependency conflict with ml-dtypes>=0.5,<0.6 vs tensorflow<0.5.0
- uv pip requires either a venv or --system flag - actions/setup-python creates venv but uv doesn't auto-detect it - Use --system to install into the Python environment directly
- jit_mamba_scan needs 6 positional args for shard_map compatibility - This is a nested function within JAX jit decorator - pylint 2.17+ flags this as R0917 (too-many-positional-arguments)
…ents - MambaConfig has 24 parameters to match HuggingFace's PretrainedConfig - pylint 2.17 added R0917 check which flags this legitimate case - Add disable comment to suppress the warning
- uv pip install works correctly with actions/setup-python venv - Only uv pip install --system was causing issues - This matches the Dockerfile approach which works correctly
- uv doesn't work properly with actions/setup-python in GitHub Actions - Upstream uses pip successfully with same dependency versions - This should work correctly as-is
- pip doesn't respect tool.uv override-dependencies - Set VIRTUAL_ENV to pythonLocation so uv detects the venv - This allows uv to honor ml-dtypes>=0.5,<0.6 override
- Environment variables don't persist across GitHub Actions steps - Export VIRTUAL_ENV in the same run block as uv pip install - This ensures uv can detect the venv and honor ml-dtypes override
- Revert to pip (matching upstream) with legacy resolver - Legacy resolver can install despite ml-dtypes version conflict - This allows tensorflow 2.17.1 and jax 0.6.2 to coexist
- isort 7.0 has stricter import ordering than 5.x - Fixed 5 files to match CI expectations - Added pylint disables for too-many-positional-arguments in gpu_attention.py - These are pre-existing code style issues, not related to our changes
- Legacy resolver may skip or break google-cloud-aiplatform installation - Force reinstall with --no-deps to fix pytype import errors - This ensures pytype can analyze vertexai_tensorboard.py
- Legacy resolver breaks transformers package installation - Add transformers==4.51.3 to force reinstall for pytype - This fixes param_converter.py import errors
- Create .venv and export VIRTUAL_ENV + PATH via GITHUB_ENV/GITHUB_PATH - Install pip+uv, then install extras with uv (honors ml-dtypes override) - Remove legacy resolver and post-install hacks (aiplatform/transformers) - Ensures pytype can resolve imports reliably
… after uv install
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took me long enough to ship this one
Hey @markblee @jiarui-lu2, it’s been ages since my last merge... feels good to finally ship this one.
New Features
Audio Model Adapters (
axlearn/audio/adapter.py,axlearn/audio/adapter_test.py)AudioModelAdapter, a general-purpose bottleneck adapter for fine-tuning audio models.scale=0.01).ASRModelAdapterfor encoder–decoder ASR models.adapt_encoder_featuresandadapt_decoder_featuresmethods with functional state handling.SSM Partition Spec Improvements (
axlearn/common/ssm.py,axlearn/common/ssm_test.py)default_mamba_dim_to_partition_specsnow shards the sequence dimension when"seq"is present.default_output_partition_specnow returns aPartitionSpecinstead of a dictionary."seq", model over"model".PallasLinearScanMambaRecurrencefor consistency.Bug Fixes
default_output_partition_spec.F().layer_norm.bias.input_grain_csv_test.py.Testing
All tests pass with broad coverage.
Impact