add pixtral template

Former-commit-id: 7b3336dd97e06a11ec52433ef36980aefdbb45ba

src/llamafactory/data/mm_plugin.py

@@ -24,6 +24,7 @@ if TYPE_CHECKING:
     from av.stream import Stream
     from transformers import PreTrainedTokenizer, ProcessorMixin
     from transformers.image_processing_utils import BaseImageProcessor
+    from transformers.processing_utils import _validate_images_text_input_order, ProcessingKwargs
 
     class EncodedImage(TypedDict):
         path: Optional[str]
@@ -324,11 +325,65 @@ class PaliGemmaPlugin(BasePlugin):
         return mm_inputs
 
 class PixtralPlugin(BasePlugin):
-    #TODO preprocess according to Pixtral hf
-    from transformers import LlavaForConditionalGeneration
     @override
-    def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
-        pass
+    def process_messages(
+        self,
+        messages: Sequence[Dict[str, str]],
+        images: Sequence["ImageInput"],
+        videos: Sequence["VideoInput"],
+        processor: Optional["ProcessorMixin"],
+    ) -> List[Dict[str, str]]:
+        patch_size = processor.patch_size
+        image_token = processor.image_token
+        image_break_token = processor.image_break_token
+        image_end_token = processor.image_end_token
+
+        self._validate_input(images, videos)
+        num_image_tokens = 0
+        image_input_sizes = self._get_mm_inputs(images, videos, processor)["image_sizes"]
+        messages = deepcopy(messages)
+        print(image_input_sizes[0], messages)
+        for message in messages:
+            content = message["content"]
+            img_id = 0
+            while IMAGE_PLACEHOLDER in content:
+                # only support one image for one time?
+                image_size =  image_input_sizes[0][0]
+                height, width = image_size
+                num_height_tokens = height // patch_size
+                num_width_tokens = width // patch_size
+                replace_tokens = [
+                    [image_token] * num_width_tokens + [image_break_token]
+                ] * num_height_tokens
+                # Flatten list
+                replace_tokens = [item for sublist in replace_tokens for item in sublist]
+                replace_tokens[-1] = image_end_token
+                replace_str = "".join(replace_tokens)
+                content.replace(IMAGE_PLACEHOLDER, replace_str, 1)
+
+                img_id += 1
+                num_image_tokens += 1
+
+            message["content"] = content
+
+        if len(images) != num_image_tokens:
+            raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
+
+        return messages
+    
+    @override
+    def get_mm_inputs(
+        self,
+        images: Sequence["ImageInput"],
+        videos: Sequence["VideoInput"],
+        imglens: Sequence[int],
+        vidlens: Sequence[int],
+        seqlens: Sequence[int],
+        processor: Optional["ProcessorMixin"],
+    ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
+        
+        self._validate_input(images, videos)
+        return self._get_mm_inputs(images, videos, processor)
 
 class Qwen2vlPlugin(BasePlugin):
     @override
@@ -428,6 +483,7 @@ PLUGINS = {
     "llava": LlavaPlugin,
     "paligemma": PaliGemmaPlugin,
     "qwen2_vl": Qwen2vlPlugin,
+    "pixtral": PixtralPlugin,
 }
 
 

src/llamafactory/model/loader.py

@@ -119,43 +119,6 @@ def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
     Loads model config.
     """
     init_kwargs = _get_init_kwargs(model_args)
-    if "pixtral" in model_args.model_name_or_path:
-        from transformers import PretrainedConfig
-
-        class PixtralVisionConfig(PretrainedConfig):
-            model_type = "pixtral"
-
-            def __init__(
-                self,
-                hidden_size=1024,
-                intermediate_size=4096,
-                num_hidden_layers=24,
-                num_attention_heads=16,
-                num_channels=3,
-                image_size=1024,
-                patch_size=16,
-                hidden_act="gelu",
-                attention_dropout=0.0,
-                rope_theta=10000.0,
-                tie_word_embeddings=False,
-                **kwargs,
-            ):
-                super().__init__(**kwargs)
-
-                self.hidden_size = hidden_size
-                self.intermediate_size = intermediate_size
-                self.num_hidden_layers = num_hidden_layers
-                self.num_attention_heads = num_attention_heads
-                self.num_channels = num_channels
-                self.patch_size = patch_size
-                self.image_size = image_size
-                self.attention_dropout = attention_dropout
-                self.hidden_act = hidden_act
-                self.rope_theta = rope_theta
-                self.tie_word_embeddings = tie_word_embeddings
-                self.head_dim = hidden_size // num_attention_heads
-                
-        return PixtralVisionConfig()
     
     return AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)