Merge branch 'main' into reenable-fa2-sdpa

src/transformers/modeling_attn_mask_utils.py

@@ -358,7 +358,7 @@ def _prepare_4d_causal_attention_mask_for_sdpa(
     attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
 
     key_value_length = input_shape[-1] + past_key_values_length
-    batch_size, query_length = input_shape
+    _, query_length = input_shape
 
     # torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
     # used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
@@ -369,7 +369,12 @@ def _prepare_4d_causal_attention_mask_for_sdpa(
         or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
     )
 
-    if attention_mask is not None:
+    ignore_causal_mask = False
+
+    if attention_mask is None:
+        if sliding_window is None or key_value_length < sliding_window:
+            ignore_causal_mask = not is_tracing
+    elif sliding_window is None or key_value_length < sliding_window:
         # 4d mask is passed through
         if len(attention_mask.shape) == 4:
             expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
@@ -388,26 +393,17 @@ def _prepare_4d_causal_attention_mask_for_sdpa(
         elif not is_tracing and torch.all(attention_mask == 1):
             if query_length == 1:
                 # For query_length == 1, causal attention and bi-directional attention are the same.
-                attention_mask = None
+                ignore_causal_mask = True
             elif key_value_length == query_length:
-                attention_mask = None
-            else:
-                # Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
-                # may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
-                # Reference: https://github.com/pytorch/pytorch/issues/108108
-                pass
-    elif query_length > 1 and key_value_length != query_length:
-        # See the comment above (https://github.com/pytorch/pytorch/issues/108108).
-        # Ugly: we set it to True here to dispatch in the following controlflow to `to_causal_4d`.
-        attention_mask = True
-    elif is_tracing:
-        raise ValueError(
-            'Attention using SDPA can not be traced with torch.jit.trace when no attention_mask is provided. To solve this issue, please either load your model with the argument `attn_implementation="eager"` or pass an attention_mask input when tracing the model.'
-        )
+                ignore_causal_mask = True
 
-    if attention_mask is None:
+            # Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
+            # may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
+            # Reference: https://github.com/pytorch/pytorch/issues/108108
+
+    if ignore_causal_mask:
         expanded_4d_mask = None
-    elif attention_mask is True:
+    elif attention_mask is None:
         expanded_4d_mask = attn_mask_converter.to_causal_4d(
             input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
         )

src/transformers/models/mistral/modeling_mistral.py

@@ -1006,6 +1006,7 @@ def forward(
                 (batch_size, seq_length),
                 inputs_embeds,
                 past_key_values_length,
+                sliding_window=self.config.sliding_window,
             )
         else:
             # 4d mask is passed through the layers

src/transformers/models/mixtral/modeling_mixtral.py

@@ -1191,6 +1191,7 @@ def forward(
                 (batch_size, seq_length),
                 inputs_embeds,
                 past_key_values_length,
+                sliding_window=self.config.sliding_window,
             )
         else:
             # 4d mask is passed through the layers

src/transformers/models/qwen2/modeling_qwen2.py

@@ -1017,6 +1017,7 @@ def forward(
                 (batch_size, seq_length),
                 inputs_embeds,
                 past_key_values_length,
+                sliding_window=self.config.sliding_window,
             )
         else:
             # 4d mask is passed through the layers

src/transformers/models/qwen2_moe/modeling_qwen2_moe.py

@@ -1183,6 +1183,7 @@ def forward(
                 (batch_size, seq_length),
                 inputs_embeds,
                 past_key_values_length,
+                sliding_window=self.config.sliding_window,
             )
         else:
             # 4d mask is passed through the layers

src/transformers/models/starcoder2/modeling_starcoder2.py

@@ -995,6 +995,7 @@ def forward(
                 (batch_size, seq_length),
                 inputs_embeds,
                 past_key_values_length,
+                sliding_window=self.config.sliding_window,
             )
         else:
             # 4d mask is passed through the layers

tests/test_modeling_common.py

@@ -3877,6 +3877,57 @@ def test_eager_matches_sdpa_generate(self):
 
                 self.assertTrue(torch.allclose(res_eager, res_sdpa))
 
+    @require_torch_sdpa
+    def test_sdpa_matches_eager_sliding_window(self):
+        WINDOW_ATTENTION_MODELS = ["mistral", "mixtral", "qwen2", "qwen_moe", "starcoder2"]
+
+        if len(self.all_generative_model_classes) == 0:
+            self.skipTest(f"No generative model classes for {self.__class__.__name__}")
+
+        for model_class in self.all_generative_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            if config.model_type not in WINDOW_ATTENTION_MODELS:
+                self.skipTest(f"{config.model_type} does not use window attention")
+
+            config.sliding_window = 2
+
+            dummy_input = inputs_dict[model_class.main_input_name]
+            attention_mask = inputs_dict["attention_mask"]
+
+            self.assertTrue(dummy_input.ndim == 2)
+            self.assertTrue(dummy_input.shape[1] > 6)
+
+            with tempfile.TemporaryDirectory() as tmpdir:
+                with torch.device(torch_device):
+                    model_eager = AutoModelForCausalLM.from_config(
+                        config, attn_implementation="eager", torch_dtype=torch.float32
+                    )
+
+                model_eager.save_pretrained(tmpdir)
+
+                with torch.device(torch_device):
+                    model_sdpa = AutoModelForCausalLM.from_pretrained(
+                        tmpdir, attn_implementation="sdpa", torch_dtype=torch.float32
+                    )
+
+                model_eager = model_eager.eval()
+                model_sdpa = model_sdpa.eval()
+
+                with torch.no_grad():
+                    with torch.backends.cuda.sdp_kernel(
+                        enable_flash=False,
+                        enable_math=True,
+                        enable_mem_efficient=False,
+                    ):
+                        res_eager = model_eager(**inputs_dict, return_dict=False)[0]
+                        res_sdpa = model_sdpa(**inputs_dict, return_dict=False)[0]
+
+                # Only non-padding tokens are expected to match.
+                self.assertTrue(
+                    torch.allclose(res_eager[attention_mask == 1], res_sdpa[attention_mask == 1], rtol=1e-3)
+                )
+
     @require_flash_attn
     @require_torch_gpu
     @mark.flash_attn_test