fix red check-copies (#33964)

src/transformers/models/paligemma/modeling_paligemma.py

@@ -46,7 +46,7 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "PaliGemmaConfig"
 
 
-# Adapted from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
+# Adapted from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
 # But Paligemma has no causal mask on prefix
 def _prepare_4d_causal_attention_mask_with_cache_position(
     attention_mask: torch.Tensor,

src/transformers/models/vipllava/modeling_vipllava.py

@@ -283,7 +283,7 @@ class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel, GenerationMixin)
         return model_embeds
 
     # Ignore copy
-    def get_image_features(self, pixel_values: torch.FloatTensor, vision_feature_layers: list[int]):
+    def get_image_features(self, pixel_values: torch.FloatTensor, vision_feature_layers: List[int]):
         image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
 
         # For VIP-llava, the image features are computed this way

src/transformers/models/zamba/modeling_zamba.py

@@ -77,60 +77,6 @@ logger = logging.get_logger(__name__)
 _CONFIG_FOR_DOC = "ZambaConfig"
 
 
-# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
-def _prepare_4d_causal_attention_mask_with_cache_position(
-    attention_mask: torch.Tensor,
-    sequence_length: int,
-    target_length: int,
-    dtype: torch.dtype,
-    device: torch.device,
-    min_dtype: float,
-    cache_position: torch.Tensor,
-    batch_size: int,
-):
-    """
-    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
-    `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
-
-    Args:
-        attention_mask (`torch.Tensor`):
-            A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
-        sequence_length (`int`):
-            The sequence length being processed.
-        target_length (`int`):
-            The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
-        dtype (`torch.dtype`):
-            The dtype to use for the 4D attention mask.
-        device (`torch.device`):
-            The device to plcae the 4D attention mask on.
-        min_dtype (`float`):
-            The minimum value representable with the dtype `dtype`.
-        cache_position (`torch.Tensor`):
-            Indices depicting the position of the input sequence tokens in the sequence.
-        batch_size (`torch.Tensor`):
-            Batch size.
-    """
-    if attention_mask is not None and attention_mask.dim() == 4:
-        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
-        causal_mask = attention_mask
-    else:
-        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
-        if sequence_length != 1:
-            causal_mask = torch.triu(causal_mask, diagonal=1)
-        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
-        if attention_mask is not None:
-            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-            mask_length = attention_mask.shape[-1]
-            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-            padding_mask = padding_mask == 0
-            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                padding_mask, min_dtype
-            )
-
-    return causal_mask
-
-
 # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Zamba
 class ZambaRMSNorm(nn.Module):
     def __init__(self, hidden_size, eps=1e-6):

tests/models/phimoe/test_modeling_phimoe.py

@@ -154,7 +154,7 @@ class PhimoeModelTester:
 
         input_mask = None
         if self.use_input_mask:
-            input_mask = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device)
+            input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device))
 
         token_type_ids = None
         if self.use_token_type_ids: