Revert "Fix quality Olmo + SDPA (#30302)"

This reverts commit ec92f98.

src/transformers/models/olmo/modeling_olmo.py

@@ -653,7 +653,6 @@ def forward(
             value_states,
             attn_mask=causal_mask,
             dropout_p=self.attention_dropout if self.training else 0.0,
-            is_causal=causal_mask is None and q_len > 1,
         )
 
         attn_output = attn_output.transpose(1, 2).contiguous()
@@ -971,7 +970,9 @@ def forward(
         if position_ids is None:
             position_ids = cache_position.unsqueeze(0)
 
-        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_seen_tokens)
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_seen_tokens + inputs_embeds.shape[1]
+        )
 
         # embed positions
         hidden_states = inputs_embeds
@@ -1035,42 +1036,25 @@ def forward(
             attentions=all_self_attns,
         )
 
+    # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+    # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+    # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+    # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
     # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_seen_tokens: int,
-    ):
-        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
-        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
-        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
-        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
-
+    def _update_causal_mask(self, attention_mask, input_tensor, cache_position, current_length):
         if self.config._attn_implementation == "flash_attention_2":
             if attention_mask is not None and 0.0 in attention_mask:
                 return attention_mask
             return None
 
-        if self.config._attn_implementation == "sdpa":
-            # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument,
-            # in order to dispatch on Flash Attention 2.
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens
-            ):
-                return None
-
         dtype, device = input_tensor.dtype, input_tensor.device
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if hasattr(getattr(self.layers[0], "self_attn", {}), "past_key_value"):  # static cache
             target_length = self.config.max_position_embeddings
         else:  # dynamic cache
             target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
+                attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else current_length + 1
             )
 
         causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)