VLMs: major clean up 🧼

src/transformers/models/blip_2/modeling_blip_2.py

@@ -2186,29 +2186,14 @@ def forward(
 
         # step 3: use the language model, conditioned on the query outputs and the prompt
         language_model_inputs = self.language_projection(query_output)
-        language_model_attention_mask = torch.ones(
-            language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
-        )
+
         inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
         if attention_mask is None:
             attention_mask = torch.ones_like(input_ids)
 
-        # if the model already has "image_token_index" then the input is expanded to account for image embeds
-        # otherwise we expand manually by concating
-        if getattr(self.config, "image_token_index", None) is not None:
-            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
-            language_model_inputs = language_model_inputs.to(inputs_embeds.device, inputs_embeds.dtype)
-            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, language_model_inputs)
-        else:
-            logger.warning_once(
-                "Expanding inputs for image tokens in BLIP-2 should be done in processing. "
-                "Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your BLIP-2 model. "
-                "Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
-            )
-            inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
-            attention_mask = torch.cat(
-                [language_model_attention_mask, attention_mask.to(language_model_attention_mask.device)], dim=1
-            )
+        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
+        language_model_inputs = language_model_inputs.to(inputs_embeds.device, inputs_embeds.dtype)
+        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, language_model_inputs)
 
         if self.config.use_decoder_only_language_model:
             outputs = self.language_model(
@@ -2304,9 +2289,6 @@ def generate(
         query_output = query_outputs.last_hidden_state
 
         language_model_inputs = self.language_projection(query_output)
-        language_attention_mask = torch.ones(
-            language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
-        )
 
         if input_ids is None:
             start_tokens = [self.config.text_config.bos_token_id]
@@ -2319,36 +2301,15 @@ def generate(
         if attention_mask is None:
             attention_mask = torch.ones_like(input_ids)
 
-        # if the model already has "image_token_index" then the input is expanded to account for image embeds
-        # otherwise we expand manually by concatenating
-        if getattr(self.config, "image_token_index", None) is not None:
-            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
-            inputs_embeds[special_image_mask] = language_model_inputs.flatten()
-        else:
-            logger.warning_once(
-                "Expanding inputs for image tokens in BLIP-2 should be done in processing. "
-                "Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your BLIP-2 model. "
-                "Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
-            )
-            inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
-            attention_mask = torch.cat(
-                [language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1
-            )
-
-            # add image_embeds length to max_length, so that the final max_length in counted only on token embeds
-            # -1 is to account for the prepended BOS after `generate.`
-            # TODO (joao, raushan): refactor `generate` to avoid these operations with VLMs
-            if not self.language_model.config.is_encoder_decoder:
-                generate_kwargs["max_length"] = (
-                    generate_kwargs.get("max_length", 20) + language_model_inputs.shape[1] - 1
-                )
-                generate_kwargs["min_length"] = generate_kwargs.get("min_length", 0) + language_model_inputs.shape[1]
+        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
+        inputs_embeds[special_image_mask] = language_model_inputs.flatten()
 
         inputs = {"inputs_embeds": inputs_embeds, "attention_mask": attention_mask}
         if not self.language_model.config.is_encoder_decoder:
             inputs["input_ids"] = input_ids
 
         outputs = self.language_model.generate(**inputs, **generate_kwargs)
+
         return outputs
 
 

src/transformers/models/blip_2/processing_blip_2.py

@@ -81,6 +81,10 @@ def __init__(self, image_processor, tokenizer, num_query_tokens=None, **kwargs):
             self.image_token = tokenizer.image_token
         self.num_query_tokens = num_query_tokens
 
+        # We'll add the BOS manually as it has to be after image tokens
+        tokenizer.add_bos_token = False
+        self.bos_token = tokenizer.bos_token
+
         super().__init__(image_processor, tokenizer)
 
     def __call__(
@@ -118,49 +122,26 @@ def __call__(
             tokenizer_init_kwargs=self.tokenizer.init_kwargs,
             **kwargs,
         )
+
         # BC for explicit return_tensors
-        if "return_tensors" in output_kwargs["common_kwargs"]:
-            return_tensors = output_kwargs["common_kwargs"].pop("return_tensors", None)
-        else:
-            return_tensors = None
+        return_tensors = output_kwargs["common_kwargs"].pop("return_tensors", None)
         encoding = BatchFeature(tensor_type=return_tensors)
         if text is not None:
             if isinstance(text, str):
                 text = [text]
             elif not isinstance(text, list) and not isinstance(text[0], str):
                 raise ValueError("Invalid input text. Please provide a string, or a list of strings")
 
-            text_encoding = {}
-
-            return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-            _text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
-            output_kwargs["text_kwargs"]["return_tensors"] = return_tensors
-
-            # if we know how many query tokens, expand text inside processor. We need this hacky manipulation
-            # because BLIP expects image tokens to be at the beginning even before BOS token
-            if self.num_query_tokens is not None:
-                image_tokens = self.image_token.content * self.num_query_tokens
-                image_token_encoding = self.tokenizer(
-                    [image_tokens] * len(text), add_special_tokens=False, return_tensors=None
-                )
-                for k in _text_encoding:
-                    text_encoding[k] = [
-                        img_encoding + txt_encoding
-                        for img_encoding, txt_encoding in zip(image_token_encoding[k], _text_encoding[k])
-                    ]
-            else:
-                text_encoding = _text_encoding
-                logger.warning_once(
-                    "Expanding inputs for image tokens in BLIP-2 should be done in processing. "
-                    "Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your BLIP-2 model. "
-                    "Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
-                )
+            # We need this hacky manipulation because BLIP expects image tokens to be at the beginning even before BOS token
+            image_tokens = self.image_token.content * self.num_query_tokens
+            text = [f"{image_tokens}{self.bos_token}{sample}" for sample in text]
+            text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"])
 
             # cast to desired return tensors type
             encoding.update(BatchEncoding(text_encoding, tensor_type=return_tensors))
+
         # add pixel_values encoding. If we also have text_encoding, update image encoding and return it.
         # else, return the text encoding.
-
         if images is not None:
             image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"])
             encoding.update(image_encoding)

src/transformers/models/instructblip/modeling_instructblip.py

@@ -1454,29 +1454,13 @@ def forward(
 
         # step 3: use the language model, conditioned on the query outputs and the prompt
         language_model_inputs = self.language_projection(query_output)
-        language_model_attention_mask = torch.ones(
-            language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
-        )
 
         inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
         if attention_mask is None:
             attention_mask = torch.ones_like(input_ids)
 
-        # if the model already has "image_token_index" then the input is expanded to account for image embeds
-        # otherwise we expand manually by concatenating
-        if getattr(self.config, "image_token_index", None) is not None:
-            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
-            inputs_embeds[special_image_mask] = language_model_inputs.flatten()
-        else:
-            logger.warning_once(
-                "Expanding inputs for image tokens in InstructBLIP should be done in processing. "
-                "Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your InstructBLIP model. "
-                "Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
-            )
-            inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
-            attention_mask = torch.cat(
-                [language_model_attention_mask, attention_mask.to(language_model_attention_mask.device)], dim=1
-            )
+        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
+        inputs_embeds[special_image_mask] = language_model_inputs.flatten()
 
         if self.config.use_decoder_only_language_model:
             outputs = self.language_model(
@@ -1586,9 +1570,6 @@ def generate(
         query_output = query_outputs.last_hidden_state[:, : query_tokens.size(1), :]
 
         language_model_inputs = self.language_projection(query_output)
-        language_attention_mask = torch.ones(
-            language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
-        )
 
         if input_ids is None:
             start_tokens = [self.config.text_config.bos_token_id]
@@ -1602,29 +1583,8 @@ def generate(
 
         inputs_embeds = self.get_input_embeddings()(input_ids)
 
-        # if the model already has "image_token_index" then the input is expanded to account for image embeds
-        # otherwise we expand manually by concatenating
-        if getattr(self.config, "image_token_index", None) is not None:
-            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
-            inputs_embeds[special_image_mask] = language_model_inputs.flatten()
-        else:
-            logger.warning_once(
-                "Expanding inputs for image tokens in InstructBLIP should be done in processing. "
-                "Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your InstructBLIP model. "
-                "Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
-            )
-            inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
-            attention_mask = torch.cat(
-                [language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1
-            )
-
-            # add image_embeds length to max_length, so that the final max_length in counted only on token embeds
-            # -1 is to account for the prepended BOS after `generate.`
-            if not self.language_model.config.is_encoder_decoder:
-                generate_kwargs["max_length"] = (
-                    generate_kwargs.get("max_length", 20) + language_model_inputs.shape[1] - 1
-                )
-                generate_kwargs["min_length"] = generate_kwargs.get("min_length", 0) + language_model_inputs.shape[1]
+        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
+        inputs_embeds[special_image_mask] = language_model_inputs.flatten()
 
         inputs = {"inputs_embeds": inputs_embeds, "attention_mask": attention_mask}
         if not self.language_model.config.is_encoder_decoder:

src/transformers/models/instructblip/processing_instructblip.py

@@ -24,7 +24,6 @@
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import (
     AddedToken,
-    BatchEncoding,
     PreTokenizedInput,
     TextInput,
 )
@@ -84,6 +83,10 @@ def __init__(self, image_processor, tokenizer, qformer_tokenizer, num_query_toke
         else:
             self.image_token = tokenizer.image_token
         self.num_query_tokens = num_query_tokens
+
+        # We'll add the BOS manually as it has to be after image tokens
+        tokenizer.add_bos_token = False
+        self.bos_token = tokenizer.bos_token
         super().__init__(image_processor, tokenizer, qformer_tokenizer)
 
     def __call__(
@@ -125,34 +128,10 @@ def __call__(
             elif not isinstance(text, list) and not isinstance(text[0], str):
                 raise ValueError("Invalid input text. Please provide a string, or a list of strings")
 
-            # we have to concatenate lists - so we keep track of return_tensors here
-            return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-            _text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
-            output_kwargs["text_kwargs"]["return_tensors"] = return_tensors
-            # if we know how many query tokens, expand text inside processor. We need this hacky manipulation
-            # because BLIP expects image tokens to be at the beginning even before BOS token
-            if self.num_query_tokens is not None and images is not None:
-                text_encoding = {}
-                image_tokens = self.image_token.content * self.num_query_tokens
-                image_token_encoding = self.tokenizer(
-                    [image_tokens] * len(text), add_special_tokens=False, return_tensors=None
-                )
-                for k in _text_encoding:
-                    text_encoding[k] = [
-                        img_encoding + txt_encoding
-                        for img_encoding, txt_encoding in zip(image_token_encoding[k], _text_encoding[k])
-                    ]
-            else:
-                text_encoding = _text_encoding
-                if images is not None:
-                    logger.warning_once(
-                        "Expanding inputs for image tokens in InstructBLIP should be done in processing. "
-                        "Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your InstructBLIP model. "
-                        "Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
-                    )
-
-            # cast to desired return tensors type after concatenating
-            text_encoding = BatchEncoding(text_encoding, tensor_type=return_tensors)
+            # We need this hacky manipulation because BLIP expects image tokens to be at the beginning even before BOS token
+            image_tokens = self.image_token.content * self.num_query_tokens
+            text_with_images = [f"{image_tokens}{self.bos_token}{sample}" for sample in text]
+            text_encoding = self.tokenizer(text_with_images, **output_kwargs["text_kwargs"])
 
             encoding.update(text_encoding)
             qformer_text_encoding = self.qformer_tokenizer(text, **output_kwargs["text_kwargs"])