Repository: huggingface/transformers
Pull request: Fix `max_length_q` and `max_length_k` types to `flash_attn_varlen_func`

Also add notes asking users to set `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1` or call `torch._dynamo.config.capture_scalar_outputs = True`, as currently this will cause a graph break.

# What does this PR do?

Fix `max_length_q` and `max_length_k` types to `flash_attn_varlen_func`. Also add notes asking users to set `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1` or call `torch._dynamo.config.capture_scalar_outputs = True`, as currently this will cause a graph break.

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Fixes #35588


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Changed files:
filename: src/transformers/modeling_flash_attention_utils.py
patch: @@ -208,6 +208,8 @@ def _get_unpad_data(attention_mask: torch.Tensor) -> tuple[torch.Tensor, torch.T
     """
     seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
     indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    # NOTE: Similar to the `.item()` in prepare_fa2_from_position_ids, with torch compile,
+    # this might cause a graph break
     max_seqlen_in_batch = seqlens_in_batch.max().item()
     cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
     return (
@@ -342,7 +344,13 @@ def _prepare_flash_attention_from_position_ids(query, key, value, position_ids):
         )
     )
 
-    max_length = position_ids.max() + 1
+    # NOTE: With torch compile, this will cause a graph break if you don't set
+    # `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1` in the environment or call
+    # `torch._dynamo.config.capture_scalar_outputs = True` before doing the forward pass.
+    # This is a limitation of flash attention API, as the function `flash_attn_varlen_func`
+    # requires `max_length_q`, `max_length_k` to be passed as `int` and not `torch.Tensor`.
+    # https://github.com/Dao-AILab/flash-attention/blob/2dd8078adc1d9b74e315ee99718c0dea0de8eeb6/flash_attn/flash_attn_interface.py#L1423-L1424
+    max_length = position_ids.max().item() + 1
 
     return (query, key, value, indices_q, (cu_seq_lens, cu_seq_lens), (max_length, max_length))