82 lines
3 KiB
Python
82 lines
3 KiB
Python
from typing import Optional
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import torch
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from ..modeling_flash_attention_utils import _flash_attention_forward, flash_attn_supports_top_left_mask
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from ..utils import logging
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logger = logging.get_logger(__name__)
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_use_top_left_mask = flash_attn_supports_top_left_mask()
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def flash_attention_forward(
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module: torch.nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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dropout: float = 0.0,
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scaling: Optional[float] = None,
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sliding_window: Optional[int] = None,
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softcap: Optional[float] = None,
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**kwargs,
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) -> tuple[torch.Tensor, None]:
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if kwargs.get("output_attentions", False) or kwargs.get("head_mask", None) is not None:
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logger.warning_once(
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"`flash_attention_2` does not support `output_attentions=True` or `head_mask`."
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" Please set your attention to `eager` if you want any of these features."
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)
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# This is before the transpose
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seq_len = query.shape[2]
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if any(dim == 0 for dim in query.shape):
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raise ValueError(
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"Tensor query has shape with a zero dimension.\n"
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"FlashAttention does not support inputs with dim=0.\n"
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"Please check your input shapes or use SDPA instead."
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)
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# FA2 uses non-transposed inputs
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query = query.transpose(1, 2)
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key = key.transpose(1, 2)
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value = value.transpose(1, 2)
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in the correct dtype just to be sure everything works as expected.
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# This might slowdown training & inference so it is recommended to not cast the LayerNorms
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# in fp32. (usually our RMSNorm modules handle it correctly)
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target_dtype = None
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if query.dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = torch.get_autocast_gpu_dtype()
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# Handle the case where the model is quantized
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elif hasattr(module.config, "_pre_quantization_dtype"):
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target_dtype = module.config._pre_quantization_dtype
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else:
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target_dtype = next(layer for layer in module.modules() if isinstance(layer, torch.nn.Linear)).weight.dtype
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# FA2 always relies on the value set in the module, so remove it if present in kwargs to avoid passing it twice
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kwargs.pop("is_causal", None)
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attn_output = _flash_attention_forward(
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query,
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key,
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value,
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attention_mask,
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query_length=seq_len,
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is_causal=module.is_causal,
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dropout=dropout,
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softmax_scale=scaling,
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sliding_window=sliding_window,
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softcap=softcap,
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use_top_left_mask=_use_top_left_mask,
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target_dtype=target_dtype,
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attn_implementation=module.config._attn_implementation,
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layer_idx=module.layer_idx if hasattr(module, "layer_idx") else None,
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**kwargs,
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)
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return attn_output, None
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