[cleanup] vestiges of causal mask (#29806)

nit

src/transformers/models/cohere/modeling_cohere.py

@@ -825,12 +825,6 @@ def __init__(self, config: CohereConfig):
         self.norm = CohereLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.gradient_checkpointing = False
 
-        # Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
-        # NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_position_embeddings`.
-        causal_mask = torch.full(
-            (config.max_position_embeddings, config.max_position_embeddings), fill_value=True, dtype=torch.bool
-        )
-        self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/gemma/modeling_gemma.py

@@ -719,10 +719,6 @@ def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] =
                 "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
             )
 
-        if max_cache_len > self.model.causal_mask.shape[-1] or self.device != self.model.causal_mask.device:
-            causal_mask = torch.full((max_cache_len, max_cache_len), fill_value=1, device=self.device)
-            self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
-
         for layer in self.model.layers:
             weights = layer.self_attn.o_proj.weight
             layer.self_attn.past_key_value = cache_cls(