[Cohere2Vision] remove unused arg (#40103)

* remove unused arg

* remove the arg from test as well

Author: zucchini-nlp

src/transformers/models/cohere2_vision/modeling_cohere2_vision.py

@@ -174,19 +174,13 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.language_model
 
-    def get_image_features(
-        self,
-        pixel_values: torch.FloatTensor,
-        image_num_patches: torch.Tensor,
-    ):
+    def get_image_features(self, pixel_values: torch.FloatTensor):
         """
         Obtains image last hidden states from the vision tower and apply multimodal projection.
 
         Args:
             pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
                The tensors corresponding to the input images.
-            image_num_patches (`torch.Tensor` of shape `(num_images)`)
-                Number of patches for each image.
         Returns:
             image_features (List[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches
             and are of shape `(num_patches, image_length, embed_dim)`).
@@ -227,7 +221,6 @@ def forward(
         self,
         input_ids: torch.LongTensor = None,
         pixel_values: torch.FloatTensor = None,
-        image_num_patches: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,
@@ -236,18 +229,14 @@ def forward(
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
     ) -> Union[tuple, Cohere2VisionModelOutputWithPast]:
-        r"""
-        image_num_patches (`torch.Tensor` of shape `(num_images,)`):
-            Number of patches per input image.
-        """
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
         if inputs_embeds is None:
             inputs_embeds = self.get_input_embeddings()(input_ids)
 
         if pixel_values is not None:
-            image_features = self.get_image_features(pixel_values, image_num_patches=image_num_patches)
+            image_features = self.get_image_features(pixel_values)
             image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
             special_image_mask = self.get_placeholder_mask(
                 input_ids, inputs_embeds=inputs_embeds, image_features=image_features
@@ -303,15 +292,8 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.model.get_decoder()
 
-    def get_image_features(
-        self,
-        pixel_values: torch.FloatTensor,
-        image_num_patches: torch.Tensor,
-    ):
-        return self.model.get_image_features(
-            pixel_values=pixel_values,
-            image_num_patches=image_num_patches,
-        )
+    def get_image_features(self, pixel_values: torch.FloatTensor):
+        return self.model.get_image_features(pixel_values=pixel_values)
 
     # Make modules available throught conditional class for BC
     @property
@@ -332,7 +314,6 @@ def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
         pixel_values: Optional[torch.FloatTensor] = None,
-        image_num_patches: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,
@@ -345,8 +326,6 @@ def forward(
         **kwargs: Unpack[TransformersKwargs],
     ) -> Union[tuple, Cohere2VisionCausalLMOutputWithPast]:
         r"""
-        image_num_patches (`torch.Tensor` of shape `(num_images,)`):
-            Number of patches per input image.
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
             config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
@@ -384,7 +363,6 @@ def forward(
         outputs = self.model(
             input_ids=input_ids,
             pixel_values=pixel_values,
-            image_num_patches=image_num_patches,
             attention_mask=attention_mask,
             position_ids=position_ids,
             past_key_values=past_key_values,

src/transformers/models/cohere2_vision/modular_cohere2_vision.py

@@ -94,19 +94,13 @@ class Cohere2VisionCausalLMOutputWithPast(AyaVisionCausalLMOutputWithPast):
 class Cohere2VisionModel(AyaVisionModel):
     _checkpoint_conversion_mapping = {}
 
-    def get_image_features(
-        self,
-        pixel_values: torch.FloatTensor,
-        image_num_patches: torch.Tensor,
-    ):
+    def get_image_features(self, pixel_values: torch.FloatTensor):
         """
         Obtains image last hidden states from the vision tower and apply multimodal projection.
 
         Args:
             pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
                The tensors corresponding to the input images.
-            image_num_patches (`torch.Tensor` of shape `(num_images)`)
-                Number of patches for each image.
         Returns:
             image_features (List[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches
             and are of shape `(num_patches, image_length, embed_dim)`).
@@ -123,7 +117,6 @@ def forward(
         self,
         input_ids: torch.LongTensor = None,
         pixel_values: torch.FloatTensor = None,
-        image_num_patches: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,
@@ -132,18 +125,14 @@ def forward(
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
     ) -> Union[tuple, Cohere2VisionModelOutputWithPast]:
-        r"""
-        image_num_patches (`torch.Tensor` of shape `(num_images,)`):
-            Number of patches per input image.
-        """
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
         if inputs_embeds is None:
             inputs_embeds = self.get_input_embeddings()(input_ids)
 
         if pixel_values is not None:
-            image_features = self.get_image_features(pixel_values, image_num_patches=image_num_patches)
+            image_features = self.get_image_features(pixel_values)
             image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
             special_image_mask = self.get_placeholder_mask(
                 input_ids, inputs_embeds=inputs_embeds, image_features=image_features
@@ -172,23 +161,15 @@ def forward(
 class Cohere2VisionForConditionalGeneration(AyaVisionForConditionalGeneration):
     _checkpoint_conversion_mapping = {}
 
-    def get_image_features(
-        self,
-        pixel_values: torch.FloatTensor,
-        image_num_patches: torch.Tensor,
-    ):
-        return self.model.get_image_features(
-            pixel_values=pixel_values,
-            image_num_patches=image_num_patches,
-        )
+    def get_image_features(self, pixel_values: torch.FloatTensor):
+        return self.model.get_image_features(pixel_values=pixel_values)
 
     @check_model_inputs
     @auto_docstring
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
         pixel_values: Optional[torch.FloatTensor] = None,
-        image_num_patches: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,
@@ -201,8 +182,6 @@ def forward(
         **kwargs: Unpack[TransformersKwargs],
     ) -> Union[tuple, Cohere2VisionCausalLMOutputWithPast]:
         r"""
-        image_num_patches (`torch.Tensor` of shape `(num_images,)`):
-            Number of patches per input image.
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
             config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
@@ -240,7 +219,6 @@ def forward(
         outputs = self.model(
             input_ids=input_ids,
             pixel_values=pixel_values,
-            image_num_patches=image_num_patches,
             attention_mask=attention_mask,
             position_ids=position_ids,
             past_key_values=past_key_values,

tests/models/cohere2_vision/test_modeling_cohere2_vision.py

@@ -120,13 +120,12 @@ def get_config(self):
     def prepare_config_and_inputs(self):
         config = self.get_config()
         pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
-        image_num_patches = torch.tensor([1] * self.batch_size).to(torch_device)
 
-        return config, pixel_values, image_num_patches
+        return config, pixel_values
 
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
-        config, pixel_values, image_num_patches = config_and_inputs
+        config, pixel_values = config_and_inputs
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
         attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
         input_ids[input_ids == self.image_token_id] = self.pad_token_id
@@ -136,7 +135,6 @@ def prepare_config_and_inputs_for_common(self):
             "pixel_values": pixel_values,
             "input_ids": input_ids,
             "attention_mask": attention_mask,
-            "image_num_patches": image_num_patches,
         }
         return config, inputs_dict