fix 2

src/transformers/integrations/sdpa_attention.py

@@ -1,4 +1,4 @@
-from typing import Optional, Tuple
+from typing import Optional, Tuple, TypedDict
 
 import torch
 
@@ -62,3 +62,17 @@ def sdpa_attention_forward(
     attn_output = attn_output.transpose(1, 2).contiguous()
 
     return attn_output, None
+
+
+class SdpaAttentionKwargs(TypedDict, total=False):
+    """
+    Keyword arguments for sdpa Attention.
+
+    Attributes:
+        is_causal (`bool`, *optional*)
+            The value for the argument `is_causal` that is passed to `torch.nn.functional.scaled_dot_product_attention`.
+            An error is thrown if both attention_mask and is_causal are set. If `None`, it is inferred in
+            `sdpa_attention_forward`.
+    """
+
+    is_causal: Optional[bool]

src/transformers/modeling_utils.py

@@ -30,7 +30,7 @@ from contextlib import contextmanager
 from dataclasses import dataclass
 from functools import partial, wraps
 from threading import Thread
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, TypeVar, Union
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, TypedDict, TypeVar, Union
 from zipfile import is_zipfile
 
 import torch
@@ -48,8 +48,9 @@ from .generation import CompileConfig, GenerationConfig, GenerationMixin
 from .integrations import PeftAdapterMixin, deepspeed_config, is_deepspeed_zero3_enabled
 from .integrations.flash_attention import flash_attention_forward
 from .integrations.flex_attention import flex_attention_forward
-from .integrations.sdpa_attention import sdpa_attention_forward
+from .integrations.sdpa_attention import sdpa_attention_forward, SdpaAttentionKwargs
 from .loss.loss_utils import LOSS_MAPPING
+from .modeling_flash_attention_utils import FlashAttentionKwargs
 from .pytorch_utils import (  # noqa: F401
     Conv1D,
     apply_chunking_to_forward,
@@ -5702,3 +5703,6 @@ ALL_ATTENTION_FUNCTIONS.update(
         "sdpa": sdpa_attention_forward,
     }
 )
+
+
+AttentionKwargs = Union[FlashAttentionKwargs, SdpaAttentionKwargs]

src/transformers/models/llama/modeling_llama.py

@@ -27,7 +27,6 @@ from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
 from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
-from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
@@ -36,7 +35,7 @@ from ...modeling_outputs import (
     TokenClassifierOutput,
 )
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
-from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, AttentionKwargs, PreTrainedModel
 from ...processing_utils import Unpack
 from ...pytorch_utils import ALL_LAYERNORM_LAYERS
 from ...utils import (
@@ -262,7 +261,7 @@ class LlamaAttention(nn.Module):
         attention_mask: Optional[torch.Tensor],
         past_key_value: Optional[Cache] = None,
         cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
+        **kwargs: Unpack[AttentionKwargs],
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, self.head_dim)
@@ -528,7 +527,7 @@ class LlamaModel(LlamaPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
-        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+        **flash_attn_kwargs: Unpack[AttentionKwargs],
     ) -> Union[Tuple, BaseModelOutputWithPast]:
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (