tests, feat(attention): GQA/MQA and decode-phase support via IAttentionLayer with HLO tests#4197
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yizhuoz004 wants to merge 2 commits intopytorch:mainfrom
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tests, feat(attention): GQA/MQA and decode-phase support via IAttentionLayer with HLO tests#4197yizhuoz004 wants to merge 2 commits intopytorch:mainfrom
yizhuoz004 wants to merge 2 commits intopytorch:mainfrom
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… documentation - Add tests/py/dynamo/hlo/test_attention.py covering all SDPA kernel variants (flash, efficient, GQA/MQA, decode-phase, bool/float masks) with LLM-realistic shapes and multiple dtypes - Add tests/py/dynamo/hlo/__init__.py to make hlo/ a Python package - Extend harness.py DispatchTestCase.run_test() with attn_bias_is_causal param to thread through CompilationSettings for efficient-attention bias tests - File-level docstring documents three known IAttentionLayer bugs (large causal seq, GQA, decode-phase) and explains why affected tests use decompose_attention=True Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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This is a duplicate definition of line 42
- Extend flash attention validator to accept GQA shapes (Hq != Hkv): IAttentionLayer natively handles non-equal head counts without K/V expansion. Requires Hq divisible by Hkv and matching batch/head_dim. - Add decode-phase support (seq_q != seq_k) to all three attention validators; only the seq dimension is skipped in shape checks. - Document why GQA is not supported in the efficient attention validator: PyTorch's eager kernel rejects Hq != Hkv, so no reference output exists; GQA models dispatch to flash attention (FP16) or decompose via matmul+_safe_softmax (FP32) and never produce this op with GQA shapes. - Restructure test_attention.py: merge five SDPA classes into TestSDPA, expand TestFlashAttention with test_decode and test_gqa methods, add TestEfficientAttention.test_with_bias_decode; trim redundant cases and remove BUG-1 inline annotations (kept only in module docstring). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Description
Extends the TRT attention converter to support GQA/MQA and decode-phase attention, and adds a comprehensive HLO-level test suite.
Converter changes (aten_ops_converters.py, force_causal_efficient_attention.py):
New test suite (tests/py/dynamo/hlo/test_attention.py):
Fixes # (issue)
Type of change
Please delete options that are not relevant and/or add your own.
Checklist: