FIX [`Gemma`] Fix bad rebase with transformers main (#29170)

fix bad rebase

src/transformers/models/gemma/modeling_gemma.py

@@ -124,7 +124,7 @@ def rotate_half(x):
 
 
 # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 
     Args:
@@ -132,9 +132,8 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
         k (`torch.Tensor`): The key tensor.
         cos (`torch.Tensor`): The cosine part of the rotary embedding.
         sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
         unsqueeze_dim (`int`, *optional*, defaults to 1):
             The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
             sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
@@ -940,6 +939,10 @@ class GemmaModel(GemmaPreTrainedModel):
             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
     def _update_causal_mask(self, attention_mask, input_tensor):
         if self.config._attn_implementation == "flash_attention_2":
             if attention_mask is not None and 0.0 in attention_mask:
@@ -955,16 +958,8 @@ class GemmaModel(GemmaPreTrainedModel):
             causal_mask = torch.full((2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]), fill_value=1)
             self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
 
-        if hasattr(self, "causal_mask"):  # we use the current dtype to avoid any overflows
-            causal_mask = (
-                self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype) * torch.finfo(dtype).min
-            )
-        else:
-            mask = torch.full(
-                (self.config.max_position_embeddings, self.config.max_position_embeddings),
-                fill_value=torch.finfo(dtype).min,
-            )
-            causal_mask = torch.triu(mask, diagonal=1)
+        # We use the current dtype to avoid any overflows
+        causal_mask = self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype) * torch.finfo(dtype).min
 
         causal_mask = causal_mask.to(dtype=dtype, device=device)
         if attention_mask is not None and attention_mask.dim() == 2:
@@ -1146,29 +1141,32 @@ class GemmaForCausalLM(GemmaPreTrainedModel):
             if past_key_values:
                 position_ids = position_ids[:, -input_ids.shape[1] :]
 
-        if past_key_value := getattr(self.model.layers[0].self_attn, "past_key_value", None):
+        if getattr(self.model.layers[0].self_attn, "past_key_value", None) is not None:
             # generation with static cache
-            past_length = past_key_value.get_seq_length()
+            cache_position = kwargs.get("cache_position", None)
+            if cache_position is None:
+                past_length = 0
+            else:
+                past_length = cache_position[-1] + 1
             input_ids = input_ids[:, past_length:]
             position_ids = position_ids[:, past_length:]
 
         # TODO @gante we should only keep a `cache_position` in generate, and do +=1.
         # same goes for position ids. Could also help with continued generation.
-        cache_position = kwargs.get("cache_position", None)
-        if cache_position is None:
-            cache_position = torch.arange(
-                past_length, past_length + position_ids.shape[-1], device=position_ids.device
-            )
+        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
 
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
         if inputs_embeds is not None and past_key_values is None:
             model_inputs = {"inputs_embeds": inputs_embeds}
         else:
-            model_inputs = {"input_ids": input_ids}
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {"input_ids": input_ids.contiguous()}
 
         model_inputs.update(
             {
-                "position_ids": position_ids,
+                "position_ids": position_ids.contiguous(),
                 "cache_position": cache_position,
                 "past_key_values": past_key_values,
                 "use_cache": kwargs.get("use_cache"),