Add Llama4 support (#5092)

Co-authored-by: Cheng Wan <cwan39@gatech.edu>
Co-authored-by: fzyzcjy <ch271828n@outlook.com>
Co-authored-by: ispobock <ispobaoke@163.com>

python/sglang/srt/managers/multimodal_processors/mllama4.py

@@ -0,0 +1,161 @@
+from typing import List, Mapping, Optional, Tuple, Union
+
+import torch
+from PIL import Image
+from transformers import Llama4Processor
+from transformers.image_utils import SizeDict
+from transformers.models.llama4.image_processing_llama4 import (
+    find_supported_resolutions,
+    get_best_fit,
+)
+
+from sglang.srt.managers.multimodal_processors.base_processor import (
+    BaseMultimodalProcessor,
+    MultimodalSpecialTokens,
+)
+from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
+from sglang.srt.models.mllama4 import Llama4ForConditionalGeneration
+from sglang.srt.utils import load_image
+
+
+class Mllama4ImageProcessor(BaseMultimodalProcessor):
+    models = [Llama4ForConditionalGeneration]
+
+    def __init__(self, hf_config, server_args, _processor):
+        super().__init__(hf_config, server_args, _processor)
+        self.vision_config = hf_config.vision_config
+        self.text_config = hf_config.text_config
+        self.multimodal_tokens = MultimodalSpecialTokens(
+            image_token=_processor.image_token
+        )
+
+    async def process_mm_data_async(
+        self,
+        image_data: List[Union[str, bytes]],
+        input_text,
+        max_req_input_len=None,
+        *args,
+        **kwargs,
+    ):
+        if not image_data:
+            return None
+
+        if isinstance(input_text, list):
+            assert len(input_text) and isinstance(input_text[0], int)
+            input_text = self._processor.tokenizer.decode(input_text)
+
+        # Process images and text using the base processor's load_mm_data method
+        processed_data = self.load_mm_data(
+            prompt=input_text,
+            multimodal_tokens=self.multimodal_tokens,
+            max_req_input_len=max_req_input_len or 4096,
+            image_data=image_data,
+            return_text=True,
+        )
+
+        # Process the images using the processor
+        processor = Llama4Processor.from_pretrained(
+            self.server_args.model_path, **kwargs
+        )
+
+        # Process the prompt and images
+        image_inputs = processor(
+            text=processed_data.input_text,
+            images=processed_data.images,
+            return_tensors="pt",
+        )
+
+        # Handle image resolutions and aspect ratios
+        if "pixel_values" in image_inputs:
+            image_processor = processor.image_processor
+            tokenizer = self._processor.tokenizer
+
+            # Calculate tile size and find supported resolutions
+            tile_size = self.vision_config.image_size
+            max_num_tiles = getattr(self.vision_config, "max_patches", 1)
+
+            possible_resolutions = find_supported_resolutions(
+                max_num_chunks=max_num_tiles,
+                patch_size=SizeDict(height=tile_size, width=tile_size),
+            )
+
+            # Find best fit for each image
+            best_fit_sizes = [
+                get_best_fit(
+                    (image.size[1], image.size[0]),  # (height, width)
+                    torch.tensor(possible_resolutions),
+                    resize_to_max_canvas=image_processor.resize_to_max_canvas,
+                )
+                for image in processed_data.images
+            ]
+
+            # Calculate aspect ratios and patches per image
+            aspect_ratios = [
+                (image_size[0] // tile_size, image_size[1] // tile_size)
+                for image_size in best_fit_sizes
+            ]
+
+            patches_per_image = [
+                1 if r_h * r_w == 1 else 1 + r_h * r_w for (r_h, r_w) in aspect_ratios
+            ]
+
+            # Add to image_inputs
+            image_inputs["aspect_ratios"] = aspect_ratios
+            image_inputs["patches_per_image"] = torch.tensor(patches_per_image)
+
+            # Process embed_is_patch
+            vocab = tokenizer.get_vocab()
+            patch_id = vocab.get(processor.img_patch_token, -1)
+            image_end_id = vocab.get(processor.end_of_img_token, -1)
+
+            if patch_id != -1 and image_end_id != -1:
+                input_ids = image_inputs["input_ids"].view(-1)
+
+                # Remove BOS token if present
+                if input_ids.size(0) > 0 and input_ids[0] == tokenizer.bos_token_id:
+                    input_ids = input_ids[1:]
+
+                # Find image end indices and split input_ids
+                image_end_indices = (input_ids == image_end_id).nonzero().view(-1)
+
+                if image_end_indices.size(0) > 0:
+                    # Split at image boundaries
+                    split_indices = (image_end_indices + 1)[:-1]
+                    split_input_ids = torch.tensor_split(input_ids, split_indices)
+                    split_input_ids = [x for x in split_input_ids if x.numel() > 0]
+
+                    # Create embed_is_patch for each image
+                    embed_is_patch = []
+                    for per_image_input_ids in split_input_ids:
+                        embed_is_patch.append(per_image_input_ids == patch_id)
+
+                    image_inputs["embed_is_patch"] = embed_is_patch
+
+        # Convert to the format expected by SGLang
+        image_inputs["input_ids"] = image_inputs["input_ids"].tolist()[0]
+
+        # Add metadata for image processing
+        image_inputs["mm_items"] = [
+            MultimodalDataItem(
+                pixel_values=image_inputs["pixel_values"],
+                modality=Modality.IMAGE,
+                # Add additional metadata needed for Llama4 vision processing
+                embed_is_patch=image_inputs.get("embed_is_patch", None),
+                aspect_ratios=image_inputs.get("aspect_ratios", None),
+                patches_per_image=image_inputs.get("patches_per_image", None),
+            )
+        ]
+
+        return image_inputs
+
+    def get_patch_per_chunk(self):
+        """Calculate patches per chunk based on vision config"""
+        image_size = self.vision_config.image_size
+        patch_size = self.vision_config.patch_size
+
+        assert (
+            image_size % patch_size == 0
+        ), f"chunk size {image_size} should be multiple of patch_size {patch_size}"
+
+        ds_ratio = int(round(1.0 / (self.vision_config.pixel_shuffle_ratio**2)))
+        return (image_size // patch_size) ** 2 // ds_ratio