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Unblock Llama2 ONNX export w/ sdpa by falling back to manual impl #28823

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Unblock Llama2 ONNX export w/ sdpa by falling back to manual impl #28823

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20 changes: 16 additions & 4 deletions src/transformers/models/llama/modeling_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -673,12 +673,22 @@ def forward(
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
_jit_tracing = torch.jit.is_tracing()
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This means that we call torch.jit.is_tracing as many times as there are layers.

_fallback_to_manual = _jit_tracing or output_attentions
# TODO: Improve these warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
if _jit_tracing:
logger.warning_once(
"LlamaModel is using LlamaSdpaAttention, but SDPA can not be traced with torch.jit.trace when no attention_mask is provided. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)

if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)

if _fallback_to_manual:
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
Expand Down Expand Up @@ -1029,9 +1039,11 @@ def forward(
if self._use_flash_attention_2:
# 2d mask is passed through the layers
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
elif self._use_sdpa and not output_attentions:
# output_attentions=True can not be supported when using SDPA, and we fall back on
# the manual implementation that requires a 4D causal mask in all cases.
elif self._use_sdpa and not output_attentions and not torch.jit.is_tracing():
# NOTE: Fallback to eager attention in any of the following cases:
# - output_attentions=True can not be supported when using SDPA, and we fall back on
# the manual implementation that requires a 4D causal mask in all cases.
# - Attention using SDPA can not be traced with torch.jit.trace when no attention_mask is provided.
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
attention_mask,
(batch_size, seq_length),
Expand Down