[model] Update ernie_vl to adapt new version (#9665)

src/llamafactory/data/mm_plugin.py

@@ -480,21 +480,35 @@ class ErnieVLPlugin(BasePlugin):
         self._validate_input(processor, images, videos, audios)
         self._validate_messages(messages, images, videos, audios)
         messages = deepcopy(messages)
+
+        image_processor: BaseImageProcessor = getattr(processor, "image_processor")
+
+        merge_length: int = getattr(image_processor, "merge_size") ** 2
+        if self.expand_mm_tokens:
+            mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+            image_grid_thw = mm_inputs.get("image_grid_thw", [])
+            video_grid_thw = mm_inputs.get("video_grid_thw", [])
+        else:
+            image_grid_thw = [None] * len(images)
+            video_grid_thw = [None] * len(videos)
+
         image_idx, video_idx = 0, 0
         for message in messages:
             content = message["content"]
-            image_token = self.image_token or "<|image@placeholder|>"
+            image_token = self.image_token or "<|IMAGE_PLACEHOLDER|>"
-            video_token = self.video_token or "<|video@placeholder|>"
+            video_token = self.video_token or "<|VIDEO_PLACEHOLDER|>"
             while IMAGE_PLACEHOLDER in content:
+                image_seqlen = image_grid_thw[image_idx].prod() // merge_length if self.expand_mm_tokens else 1
+                content = content.replace(
+                    IMAGE_PLACEHOLDER, f"Picture {image_idx + 1}:<|IMAGE_START|>{image_token * image_seqlen}<|IMAGE_END|>", 1
+                )
                 image_idx += 1
-                content = content.replace(
-                    IMAGE_PLACEHOLDER, f"Picture {image_idx}:<|IMAGE_START|>{image_token}<|IMAGE_END|>", 1
-                )
             while VIDEO_PLACEHOLDER in content:
-                video_idx += 1
+                video_seqlen = video_grid_thw[video_idx].prod() // merge_length if self.expand_mm_tokens else 1
                 content = content.replace(
-                    VIDEO_PLACEHOLDER, f"Video {video_idx}:<|VIDEO_START|>{video_token}<|VIDEO_END|>", 1
+                    VIDEO_PLACEHOLDER, f"Video {video_idx + 1}:<|VIDEO_START|>{video_token * video_seqlen}<|VIDEO_END|>", 1
                 )
+                video_idx += 1
             message["content"] = content
         return messages
 

src/llamafactory/data/template.py

@@ -981,7 +981,7 @@ register_template(
     replace_eos=True,
     replace_jinja_template=True,
     template_class=ReasoningTemplate,
-    mm_plugin=get_mm_plugin(name="ernie_vl", image_token="<|image@placeholder|>", video_token="<|video@placeholder|>"),
+    mm_plugin=get_mm_plugin(name="ernie_vl", image_token="<|IMAGE_PLACEHOLDER|>", video_token="<|VIDEO_PLACEHOLDER|>"),
 )
 
 

src/llamafactory/model/loader.py

@@ -205,10 +205,6 @@ def load_model(
 
     if not is_trainable:
         model.requires_grad_(False)
-        for param in model.parameters():
-            if param.data.dtype == torch.float32 and model_args.compute_dtype != torch.float32:
-                param.data = param.data.to(model_args.compute_dtype)
-
         model.eval()
     else:
         model.train()

src/llamafactory/model/patcher.py

@@ -158,7 +158,7 @@ def patch_config(
 
     # do not cast data type of the model deepspeed zero3 without qlora
     if not (is_deepspeed_zero3_enabled() and model_args.quantization_bit is None):
-        init_kwargs["torch_dtype"] = model_args.compute_dtype
+        init_kwargs["torch_dtype"] = "auto"
 
         if init_kwargs["low_cpu_mem_usage"] and not is_fsdp_enabled():  # fsdp does not need device map
             if "device_map" not in init_kwargs and model_args.device_map:

src/llamafactory/train/test_utils.py

@@ -84,9 +84,7 @@ def load_reference_model(
         model: AutoModelForCausalLMWithValueHead = AutoModelForCausalLMWithValueHead.from_pretrained(
             model_path, torch_dtype=torch.float16, device_map="auto"
         )
-        if not is_trainable:
+        
-            model.v_head = model.v_head.to(torch.float16)
-
         return model
 
     model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")