Prepend bos token to Blip generations (#29642)

* prepend "bos" to blip generation

* minor changes

* Update src/transformers/models/blip_2/modeling_blip_2.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/instructblip/modeling_instructblip.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add generation tester mixin

---------

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/blip_2/modeling_blip_2.py

@@ -1828,8 +1828,10 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
         inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.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]
+            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]
 
         outputs = self.language_model.generate(
@@ -1838,4 +1840,16 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
             **generate_kwargs,
         )
 
+        # this is a temporary workaround to be consistent with other generation models and
+        # have BOS as the first token, even though under the hood we are calling LM with embeds
+        if not self.language_model.config.is_encoder_decoder:
+            bos_tokens = (
+                torch.LongTensor([[self.config.text_config.bos_token_id]])
+                .repeat(batch_size, 1)
+                .to(image_embeds.device)
+            )
+            if not isinstance(outputs, torch.Tensor):
+                outputs.sequences = torch.cat([bos_tokens, outputs.sequences], dim=-1)
+            else:
+                outputs = torch.cat([bos_tokens, outputs], dim=-1)
         return outputs

src/transformers/models/instructblip/modeling_instructblip.py

@@ -1538,8 +1538,9 @@ class InstructBlipForConditionalGeneration(InstructBlipPreTrainedModel):
         inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.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]
+            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]
 
         outputs = self.language_model.generate(
@@ -1548,13 +1549,21 @@ class InstructBlipForConditionalGeneration(InstructBlipPreTrainedModel):
             **generate_kwargs,
         )
 
-        # the InstructBLIP authors used inconsistent tokenizer/model files during training,
+        # this is a temporary workaround to be consistent with other generation models and
-        # with the tokenizer's bos token being set to </s> which has ID=2,
+        # have BOS as the first token, even though under the hood we are calling LM with embeds
-        # whereas the model's text config has bos token id = 0
+        if not self.language_model.config.is_encoder_decoder:
-        if self.config.text_config.architectures[0] == "LLaMAForCausalLM":
+            # the InstructBLIP authors used inconsistent tokenizer/model files during training,
-            if isinstance(outputs, torch.Tensor):
+            # with the tokenizer's bos token being set to </s> which has ID=2,
-                outputs[outputs == 0] = 2
+            # whereas the model's text config has bos token id = 0
+            bos_token_id = (
+                2
+                if self.config.text_config.architectures[0] == "LLaMAForCausalLM"
+                else self.config.text_config.bos_token_id
+            )
+            bos_tokens = torch.LongTensor([[bos_token_id]]).repeat(batch_size, 1).to(image_embeds.device)
+            if not isinstance(outputs, torch.Tensor):
+                outputs.sequences = torch.cat([bos_tokens, outputs.sequences], dim=-1)
             else:
-                outputs.sequences[outputs.sequences == 0] = 2
+                outputs = torch.cat([bos_tokens, outputs], dim=-1)
 
         return outputs

tests/models/blip_2/test_modeling_blip_2.py

@@ -32,6 +32,7 @@ from transformers.testing_utils import (
 )
 from transformers.utils import is_torch_available, is_vision_available
 
+from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
     ModelTesterMixin,
@@ -434,7 +435,7 @@ class Blip2ForConditionalGenerationDecoderOnlyModelTester:
 
 
 @require_torch
-class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, unittest.TestCase):
+class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
     all_model_classes = (Blip2ForConditionalGeneration,) if is_torch_available() else ()
     fx_compatible = False
     test_head_masking = False
@@ -683,7 +684,7 @@ class Blip2ModelTester:
 
 
 @require_torch
-class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixin, unittest.TestCase):
     all_model_classes = (Blip2ForConditionalGeneration, Blip2Model) if is_torch_available() else ()
     pipeline_model_mapping = (
         {
@@ -869,7 +870,8 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
         prompt = "Question: which city is this? Answer:"
         inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
 
-        predictions = model.generate(**inputs)
+        # max_length for BLIP includes prompt length from now on, use max_new_tokens
+        predictions = model.generate(**inputs, max_new_tokens=11)
         generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
 
         # Test output

tests/models/instructblip/test_modeling_instructblip.py

@@ -39,6 +39,7 @@ from transformers.testing_utils import (
 )
 from transformers.utils import is_torch_available, is_vision_available
 
+from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
     ModelTesterMixin,
@@ -452,7 +453,7 @@ class InstructBlipForConditionalGenerationDecoderOnlyModelTester:
 
 
 @require_torch
-class InstructBlipForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, unittest.TestCase):
+class InstructBlipForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
     all_model_classes = (InstructBlipForConditionalGeneration,) if is_torch_available() else ()
     fx_compatible = False
     test_head_masking = False