support chatglm4v model and llava support img-txt txt calib data when…

llmc/models/__init__.py

@@ -3,6 +3,7 @@
 from .deepseekv2 import DeepseekV2
 from .falcon import Falcon
 from .gemma2 import Gemma2
+from .glm4v import GLM4V
 from .internlm2 import InternLM2
 from .internomni import InternOmni
 from .internvl2 import InternVL2

llmc/models/glm4v.py

@@ -0,0 +1,70 @@
+from loguru import logger
+from PIL import Image
+from transformers import AutoConfig, AutoModelForCausalLM
+
+from llmc.utils.registry_factory import MODEL_REGISTRY
+
+from .chatglm import ChatGLM
+
+
+@MODEL_REGISTRY
+class GLM4V(ChatGLM):
+    def __init__(self, config, device_map=None, use_cache=False):
+        super().__init__(config, device_map, use_cache)
+
+    def build_model(self):
+        self.vlm_model_config = AutoConfig.from_pretrained(
+            self.model_path, trust_remote_code=True
+        )
+        if not self.use_cache:
+            self.vlm_model_config.use_cache = False
+        logger.info(f'self.vlm_model_config : {self.vlm_model_config}')
+        self.vlm_model = AutoModelForCausalLM.from_pretrained(
+            self.model_path,
+            config=self.vlm_model_config,
+            torch_dtype=self.torch_dtype,
+            low_cpu_mem_usage=True,
+            trust_remote_code=True,
+        )
+        self.vision_model = self.vlm_model.transformer.vision
+        self.projector = self.vlm_model.transformer.vision.linear_proj
+        self.model = self.vlm_model
+        self.model_config = self.vlm_model_config
+
+    def batch_process(self, img_qas, calib_or_eval='eval'):
+        assert calib_or_eval == 'calib' or calib_or_eval == 'eval'
+        messages = []
+        answers = []
+        for idx in range(len(img_qas)):
+            img_path = img_qas[idx]['img']
+            if img_path is not None:
+                image = Image.open(img_path).convert('RGB')
+                message = [
+                    {
+                        'role': 'user',
+                        'image': image,
+                        'content': img_qas[idx]['question'],
+                    }
+                ]
+            else:
+                message = [{'role': 'user', 'content': img_qas[idx]['question']}]
+            messages.append(message)
+            answers.append(img_qas[idx]['answer'])
+        inputs = self.tokenizer.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_tensors='pt',
+            return_dict=True,
+            padding=True,
+        )
+        if calib_or_eval == 'calib' and self.config['calib'].get('add_answer', False):
+            raise Exception(
+                'glm4v not support add_answer. '
+                'Maybe you can modify tokenization_chatglm.py in model path.'
+            )
+        if calib_or_eval == 'calib':
+            logger.info(f'Calib data is:\n{inputs}')
+
+        inputs = inputs.to(next(self.vlm_model.parameters()).dtype)
+        return inputs

llmc/models/llava.py

@@ -40,18 +40,28 @@ def batch_process(self, img_qas, calib_or_eval='eval'):
         answers = []
         for idx in range(len(img_qas)):
             img_path = img_qas[idx]['img']
-            image = Image.open(img_path)
-            message = [
-                {
-                    'role': 'user',
-                    'content': [
-                        {'type': 'image'},
-                        {'type': 'text', 'text': img_qas[idx]['question']}
-                    ]
-                }
-            ]
+            if img_path is not None:
+                image = Image.open(img_path)
+                message = [
+                    {
+                        'role': 'user',
+                        'content': [
+                            {'type': 'image'},
+                            {'type': 'text', 'text': img_qas[idx]['question']}
+                        ]
+                    }
+                ]
+                images.append(image)
+            else:
+                message = [
+                    {
+                        'role': 'user',
+                        'content': [
+                            {'type': 'text', 'text': img_qas[idx]['question']}
+                        ]
+                    }
+                ]
             messages.append(message)
-            images.append(image)
             answers.append(img_qas[idx]['answer'])
         texts = [
             self.processor.apply_chat_template(messages[n], add_generation_prompt=True)
@@ -67,7 +77,7 @@ def batch_process(self, img_qas, calib_or_eval='eval'):
 
         inputs = self.processor(
             text=texts,
-            images=images,
+            images=images if len(images) else None,
             padding=True,
             return_tensors='pt'
         ).to(next(self.vlm_model.parameters()).dtype) # noqa

requirements/runtime.txt

@@ -28,3 +28,4 @@ more_itertools
 qtorch
 einops
 qwen-vl-utils
+tiktoken