[Fix] Fix performance issue of visualglm. (#424)

* [Fix] Visualglm performance fixed.

* [Fix] Hide ckpt path.

configs/multimodal/visualglm/visualglm_6b_coco_caption.py

@@ -32,7 +32,7 @@ visualglm_coco_caption_model = dict(
     type='visualglm',
     pretrained_path='/path/to/visualglm',  # or Huggingface repo id
     is_caption_task=True,
-    prompt_constructor=dict(type=VisualGLMBasePromptConstructor, system_prompt='A photo of'),
+    prompt_constructor=dict(type=VisualGLMBasePromptConstructor, system_prompt='Describe the image.'),
     post_processor=dict(type=VisualGLMBasePostProcessor)
 )
 

configs/multimodal/visualglm/visualglm_6b_flickr30k.py

@@ -33,7 +33,7 @@ visualglm_flickr30k_model = dict(
     type='visualglm',
     pretrained_path='/path/to/visualglm',  # or Huggingface repo id
     is_caption_task=True,
-    prompt_constructor=dict(type=VisualGLMBasePromptConstructor, system_prompt='A photo of'),
+    prompt_constructor=dict(type=VisualGLMBasePromptConstructor, system_prompt='Describe the image.'),
     post_processor=dict(type=VisualGLMBasePostProcessor)
 )
 

opencompass/multimodal/models/visualglm/post_processor.py

@@ -9,9 +9,8 @@ class VisualGLMBasePostProcessor:
     def __init__(self) -> None:
         pass
 
-    def __call__(self, output_token: torch.tensor, tokenizer: Any,
+    def __call__(self, output_token: torch.tensor, tokenizer: Any) -> str:
-                 input_len: int) -> str:
+        return tokenizer.decode(output_token)
-        return tokenizer.decode(output_token[input_len:])
 
 
 class VisualGLMVSRPostProcessor(VisualGLMBasePostProcessor):
@@ -20,9 +19,8 @@ class VisualGLMVSRPostProcessor(VisualGLMBasePostProcessor):
     def __init__(self) -> None:
         super().__init__()
 
-    def __call__(self, output_token: torch.tensor, tokenizer: Any,
+    def __call__(self, output_token: torch.tensor, tokenizer: Any) -> str:
-                 input_len: int) -> str:
+        output_text = tokenizer.decode(output_token)
-        output_text = tokenizer.decode(output_token[input_len:])
         if 'yes' in output_text.lower():
             return 'yes'
         elif 'no' in output_text.lower():

opencompass/multimodal/models/visualglm/prompt_constructor.py

@@ -1,24 +1,16 @@
-import torch
-
-
 class VisualGLMMMBenchPromptConstructor:
     """MMBench prompt constructor for VisualGLM.
 
-    The overall prompt will be formulated as
-    "system_prompt"+"human_prompt"+"image_prompt"+question+"assistant+prompt".
     Args:
         system_prompt (str): System prompt. (Default: '')
         human_prompt (str): Human prompt. (Default: 'Q:')
-        image_prompt (str): Image prompt. (Default: '<img></img>')
         assistant_prompt (str): Assistant prompt. (Default: 'A:')
     """
 
     def __init__(self,
                  system_prompt: str = '',
                  human_prompt: str = 'Q:',
-                 image_prompt: str = '<img></img>',
                  assistant_prompt: str = 'A:') -> None:
-        self.image_prompt = image_prompt
         self.system_prompt = system_prompt
         self.human_prompt = human_prompt
         self.assistant_prompt = assistant_prompt
@@ -33,26 +25,18 @@ class VisualGLMMMBenchPromptConstructor:
             A tuple containing images, prompt, data_samples and image_position.
         """
 
-        images = batch.pop('inputs')
+        assert len(batch['inputs']) == 1
-        images = torch.stack(images, dim=0)
+        image = batch.pop('inputs')[0].unsqueeze(0)
+        data_sample = batch.pop('data_samples')[0]
+        img_prompt = '<img></img>'
+        if data_sample.get('context') is not None:
+            prompt = img_prompt + self.system_prompt + self.human_prompt + data_sample.context + ' ' + data_sample.question + ' ' + data_sample.options  # noqa
+        else:
+            prompt = img_prompt + self.system_prompt + self.human_prompt + data_sample.question + ' ' + data_sample.options  # noqa
+        prompt += self.assistant_prompt
+        image_position = prompt.rfind('<img>') + 5
 
-        data_samples = batch.pop('data_samples')
+        return image, prompt, data_sample, image_position
-        questions = [sample.get('question') for sample in data_samples]
-        options = [sample.get('options') for sample in data_samples]
-        contexts = [sample.get('context') for sample in data_samples]
-        contexts = [c if c else '' for c in contexts]
-
-        # generate text prompt
-        prompt = [
-            '{}{}{}{}{}{}{}'.format(self.system_prompt, self.image_prompt,
-                                    self.human_prompt, context, question,
-                                    option, self.assistant_prompt)
-            for context, question, option in zip(contexts, questions, options)
-        ]
-
-        image_position = 5
-
-        return images, prompt, data_samples, image_position
 
 
 class VisualGLMBasePromptConstructor:
@@ -61,10 +45,17 @@ class VisualGLMBasePromptConstructor:
     The prompt will concat <img> and the given system prompt.
     Args:
         system_prompt (str): System prompt. (Default: '')
+        human_prompt (str): Human prompt. (Default: 'Q:')
+        assistant_prompt (str): Assistant prompt. (Default: 'A:')
     """
 
-    def __init__(self, system_prompt='') -> None:
+    def __init__(self,
+                 system_prompt: str = '',
+                 human_prompt: str = 'Q:',
+                 assistant_prompt: str = 'A:') -> None:
         self.prompt = system_prompt
+        self.human_prompt = human_prompt
+        self.assistant_prompt = assistant_prompt
 
     def __call__(self, batch: dict) -> tuple:
         """Construct prompt.
@@ -76,16 +67,16 @@ class VisualGLMBasePromptConstructor:
             A tuple containing images, prompt, data_samples and image_position.
         """
 
-        images = batch.pop('inputs')
+        assert len(batch['inputs']) == 1
-        images = torch.stack(images, dim=0)
+        image = batch.pop('inputs')[0].unsqueeze(0)
-        data_samples = batch.pop('data_samples')
+        data_sample = batch.pop('data_samples')[0]
 
         # generate text prompt
-        prompt = ['<img></img>' + self.prompt for i in range(images.shape[0])]
+        prompt = '<img></img>' + self.human_prompt + self.prompt + self.assistant_prompt  # noqa
 
-        image_position = 5
+        image_position = prompt.rfind('<img>') + 5
 
-        return images, prompt, data_samples, image_position
+        return image, prompt, data_sample, image_position
 
 
 class VisualGLMVQAPromptConstructor(VisualGLMBasePromptConstructor):
@@ -94,10 +85,15 @@ class VisualGLMVQAPromptConstructor(VisualGLMBasePromptConstructor):
     The prompt will concat <img>, the question and the system prompt.
     Args:
         system_prompt (str): System prompt. (Default: '')
+        human_prompt (str): Human prompt. (Default: 'Q:')
+        assistant_prompt (str): Assistant prompt. (Default: 'A:')
     """
 
-    def __init__(self, system_prompt='') -> None:
+    def __init__(self,
-        super().__init__(system_prompt)
+                 system_prompt='',
+                 human_prompt: str = 'Q:',
+                 assistant_prompt: str = 'A:') -> None:
+        super().__init__(system_prompt, human_prompt, assistant_prompt)
 
     def __call__(self, batch: dict) -> tuple:
         """Construct prompt.
@@ -109,19 +105,18 @@ class VisualGLMVQAPromptConstructor(VisualGLMBasePromptConstructor):
             A tuple containing images, prompt, data_samples and image_position.
         """
 
-        images = batch.pop('inputs')
+        assert len(batch['inputs']) == 1
-        images = torch.stack(images, dim=0)
+        image = batch.pop('inputs')[0].unsqueeze(0)
-        data_samples = batch.pop('data_samples')
+        data_sample = batch.pop('data_samples')[0]
-        questions = [sample.get('question') for sample in data_samples]
 
         # generate text prompt
-        prompt = [
+        question = data_sample.get('question')
-            '<img></img>Q:{} {}\nA:'.format(question, self.prompt)
+        prompt = '<img></img>' + self.human_prompt + question + self.prompt
-            for question in questions
+        prompt += '\n' + self.assistant_prompt
-        ]
-        image_position = 5
 
-        return images, prompt, data_samples, image_position
+        image_position = prompt.rfind('<img>') + 5
+
+        return image, prompt, data_sample, image_position
 
 
 class VisualGLMScienceQAPromptConstructor(VisualGLMBasePromptConstructor):
@@ -130,12 +125,17 @@ class VisualGLMScienceQAPromptConstructor(VisualGLMBasePromptConstructor):
     The prompt will concat image and all terms in a question.
     Args:
         system_prompt (str): System prompt. (Default: '')
+        human_prompt (str): Human prompt. (Default: 'Q:')
+        assistant_prompt (str): Assistant prompt. (Default: 'A:')
     """
 
     choice_mapping = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F'}
 
-    def __init__(self, system_prompt='') -> None:
+    def __init__(self,
-        super().__init__(system_prompt)
+                 system_prompt='',
+                 human_prompt: str = 'Q:',
+                 assistant_prompt: str = 'A:') -> None:
+        super().__init__(system_prompt, human_prompt, assistant_prompt)
 
     def __call__(self, batch: dict) -> tuple:
         """Construct prompt.
@@ -147,33 +147,24 @@ class VisualGLMScienceQAPromptConstructor(VisualGLMBasePromptConstructor):
             A tuple containing images, prompt, data_samples and image_position.
         """
 
-        images = batch.pop('inputs')
+        assert len(batch['inputs']) == 1
-        images = torch.stack(images, dim=0)
+        image = batch.pop('inputs')[0].unsqueeze(0)
-        data_samples = batch.pop('data_samples')
+        data_sample = batch.pop('data_samples')[0]
-        questions = [
+
-            'Q: ' + sample.get('question') + '\n' for sample in data_samples
+        questions = 'Question: ' + data_sample.get('question')
-        ]
+        choices = data_sample.get('choices')
-        choices = [sample.get('choices') for sample in data_samples]
-        choices = [[
-            f'({self.choice_mapping[i]}) ' + item
-            for i, item in enumerate(choice)
-        ] for choice in choices]
         choices = [
-            'Choices: ' + ' '.join(choice) + '\n' for choice in choices
+            f'({self.choice_mapping[i]}) ' + item
-        ]  # noqa
+            for i, item in enumerate(choices)
-        contexts = [
+        ]
-            'Context: ' + data_sample.get('hint') + '\n'
+        choices = 'Choices: ' + ' '.join(choices) + '\n'
-            for data_sample in data_samples
+        contexts = 'Context: ' + data_sample.get('hint') + '\n'
-        ]  # noqa
 
         # generate text prompt
-        prompt = [
+        prompt = '<img></img>' + self.human_prompt + contexts + questions + choices + self.prompt + self.assistant_prompt  # noqa
-            '<img></img>' + context + question + choice + self.prompt
+        image_position = prompt.rfind('<img>') + 5
-            for context, question, choice in zip(contexts, questions, choices)
-        ]
-        image_position = 5
 
-        return images, prompt, data_samples, image_position
+        return image, prompt, data_sample, image_position
 
 
 class VisualGLMIconQAPromptConstructor(VisualGLMBasePromptConstructor):
@@ -182,10 +173,15 @@ class VisualGLMIconQAPromptConstructor(VisualGLMBasePromptConstructor):
     The prompt will concat <img>, the question and the system prompt.
     Args:
         system_prompt (str): System prompt. (Default: '')
+        human_prompt (str): Human prompt. (Default: 'Q:')
+        assistant_prompt (str): Assistant prompt. (Default: 'A:')
     """
 
-    def __init__(self, system_prompt='') -> None:
+    def __init__(self,
-        super().__init__(system_prompt)
+                 system_prompt='',
+                 human_prompt: str = 'Q:',
+                 assistant_prompt: str = 'A:') -> None:
+        super().__init__(system_prompt, human_prompt, assistant_prompt)
 
     def __call__(self, batch: dict) -> tuple:
         """Construct prompt.
@@ -197,22 +193,16 @@ class VisualGLMIconQAPromptConstructor(VisualGLMBasePromptConstructor):
             A tuple containing images, prompt, data_samples and image_position.
         """
 
-        images = batch.pop('inputs')
+        assert len(batch['inputs']) == 1
-        images = torch.stack(images, dim=0)
+        image = batch.pop('inputs')[0].unsqueeze(0)
-        data_samples = batch.pop('data_samples')
+        data_sample = batch.pop('data_samples')[0]
-        questions = [
+
-            'Q: ' + sample.get('question') + '\n' for sample in data_samples
+        questions = data_sample.get('question') + '\n'
-        ]
+        choices = data_sample.get('choices')
-        choices = [sample.get('choices') for sample in data_samples]
+        choices = 'Options: ' + ', '.join(choices) + '.\n'
-        choices = [
-            'Options: ' + ', '.join(choice) + '.\n' for choice in choices
-        ]  # noqa
 
         # generate text prompt
-        prompt = [
+        prompt = '<img></img>' + self.human_prompt + questions + choices + self.prompt + self.assistant_prompt  # noqa
-            '<img></img>' + question + choice + self.prompt
+        image_position = prompt.rfind('<img>') + 5
-            for question, choice in zip(questions, choices)
-        ]
-        image_position = 5
 
-        return images, prompt, data_samples, image_position
+        return image, prompt, data_sample, image_position

opencompass/multimodal/models/visualglm/visualglm.py

@@ -43,39 +43,31 @@ class VisualGLM(nn.Module):
         if gen_kwargs:
             self.gen_kwargs = gen_kwargs
         else:
-            self.gen_kwargs = dict(
+            self.gen_kwargs = dict(max_length=1024,
-                max_new_tokens=30,
+                                   min_length=100,
-                num_beams=1,
+                                   do_sample=True,
-                do_sample=False,
+                                   temperature=0.8,
-                repetition_penalty=1.0,
+                                   top_p=0.4,
-                length_penalty=-1.0,
+                                   top_k=100,
-            )
+                                   repetition_penalty=1.2)
 
         self.is_caption_task = is_caption_task
 
-    def encode_by_tokenizer(self, multi_prompts, image_position):
+    def encode_by_tokenizer(self, prompt, image_position):
-        input_ids = []
+
-        max_seq_length = 0
-        for prompt in multi_prompts:
         input0 = self.tokenizer.encode(prompt[:image_position],
                                        add_special_tokens=False)
-            input1 = [self.tokenizer.pad_token_id] * self.model.image_length
+        input1 = [self.tokenizer.unk_token_id] * self.model.image_length
         input2 = self.tokenizer.encode(prompt[image_position:],
                                        add_special_tokens=False)
         input_all = sum([input0, input1, input2], [])
-            input_all = self.tokenizer.build_inputs_with_special_tokens(
+        input_all = self.tokenizer.build_inputs_with_special_tokens(input_all)
-                input_all)
+        input_all = torch.tensor(input_all, dtype=torch.long).to(get_device())
-            max_seq_length = max(max_seq_length, len(input_all))
+        input_all = input_all.unsqueeze(0)
-            input_ids.append(input_all)
+
         pre_image_len = len(input0)
 
-        # padding
+        return input_all, pre_image_len
-        for i, _ in enumerate(input_ids):
-            pad_len = max_seq_length - len(input_ids[i])
-            input_ids[i] = [self.tokenizer.pad_token_id
-                            ] * pad_len + input_ids[i]
-
-        return input_ids, pre_image_len
 
     def generate(self, batch):
         # process input
@@ -87,26 +79,24 @@ class VisualGLM(nn.Module):
         input_all, pre_image_len = self.encode_by_tokenizer(
             prompt, image_position)
 
-        input_all = torch.tensor(input_all, dtype=torch.long).to(get_device())
-
         # build input param
         inputs = {
             'input_ids': input_all,
             'pre_image_length': pre_image_len,
             'images': image
         }
+
         # generate answer
         outputs = self.model.generate(**inputs, **self.gen_kwargs)
 
         # format output
-        outputs = outputs.tolist()
+        outputs = outputs.tolist()[0][input_all.shape[1]:]
-        for i, sample in enumerate(data_sample):
+        answer = self.post_processor(outputs, self.tokenizer)
-            answer = self.post_processor(outputs[i], self.tokenizer,
+
-                                         input_all.shape[1])
         if self.is_caption_task:
-                data_sample[i].pred_caption = answer
+            data_sample.pred_caption = answer
         else:
-                data_sample[i].pred_answer = answer
+            data_sample.pred_answer = answer
 
         return data_sample