model(vlm): pixtral (#5084)

python/sglang/srt/models/llava.py

@@ -15,7 +15,8 @@
 
 import math
 import re
-from typing import Iterable, List, Optional, Tuple
+from functools import lru_cache
+from typing import Dict, Iterable, List, Optional, Tuple, Type, Union
 
 import numpy as np
 import torch
@@ -28,10 +29,18 @@ from transformers import (
     Qwen2Config,
     SiglipVisionModel,
 )
+from transformers.models.auto.modeling_auto import AutoModel, AutoModelForCausalLM
 from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
 
+# leave till last and symbol only in case circular import
+import sglang.srt.models as sgl_models
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
-from sglang.srt.managers.schedule_batch import Modality, MultimodalInputs
+from sglang.srt.managers.mm_utils import general_mm_embed_routine
+from sglang.srt.managers.schedule_batch import (
+    Modality,
+    MultimodalDataItem,
+    MultimodalInputs,
+)
 from sglang.srt.mm_utils import (
     get_anyres_image_grid_shape,
     unpad_image,
@@ -42,7 +51,7 @@ from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.models.llama import LlamaForCausalLM
 from sglang.srt.models.mistral import MistralForCausalLM
 from sglang.srt.models.qwen2 import Qwen2ForCausalLM
-from sglang.srt.utils import add_prefix, flatten_nested_list
+from sglang.srt.utils import add_prefix, flatten_nested_list, logger
 
 
 class LlavaBaseForCausalLM(nn.Module):
@@ -114,7 +123,16 @@ class LlavaBaseForCausalLM(nn.Module):
         image_inputs.image_offsets = offset_list
         return input_ids
 
-    def encode_images(self, pixel_values: torch.Tensor) -> torch.Tensor:
+    def encode_images(
+        self, pixel_values: Union[torch.Tensor, List[torch.Tensor]]
+    ) -> torch.Tensor:
+        """
+        encode images by vision tower and multimodal projector
+        Args:
+            pixel_values: torch.Tensor or List[torch.Tensor]: each tensor for an input image
+        Returns:
+            torch.Tensor: encoded image features from the input image; if multiple, flattened by seq_len axis
+        """
         image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
         # NOTE: This is not memory efficient. (output_hidden_states=True) will save all the hidden stated.
 
@@ -583,4 +601,229 @@ class LlavaMistralForCausalLM(LlavaBaseForCausalLM):
             )
 
 
-EntryClass = [LlavaLlamaForCausalLM, LlavaQwenForCausalLM, LlavaMistralForCausalLM]
+class LlavaForConditionalGeneration(LlavaBaseForCausalLM):
+    """
+    An adaptor class to enable support for multiple mmlm such as mistral-community/pixtral-12b
+    It follows the structure of (vision_tower, multi_modal_projector, language_model)
+
+    Once a model config is loaded, text_config and vision_config will be extracted, and
+    LlavaForConditionalGeneration will load the language_model and vision_tower models
+    according to config.
+    """
+
+    MULTIMODAL_PROJECTOR_TYPE = LlavaMultiModalProjector
+
+    def pad_input_ids(self, input_ids: List[int], image_inputs: MultimodalInputs):
+        if hasattr(self.vision_tower, "pad_input_ids"):
+            return self.vision_tower.pad_input_ids(input_ids, image_inputs)
+        else:
+            return super().pad_input_ids(input_ids, image_inputs)
+
+    def _get_sgl_model_cls(self, config, auto_model_type: Type[AutoModel] = AutoModel):
+        """
+        Get the SGLang model implementation class according to config.
+
+        Args:
+            config: The config object of the model.
+            auto_model_type: The type of the auto model.
+
+        Returns:
+            The SGLang model implementation class.
+        """
+        config_cls_name = config.__class__.__name__
+        arch_name_mapping = self._config_cls_name_to_arch_name_mapping(auto_model_type)
+        if arch := arch_name_mapping.get(config_cls_name):
+            if isinstance(arch, tuple):
+                arch = arch[0]
+                logger.warning(
+                    f"Multiple {auto_model_type.__name__} models found for submodule config `{config_cls_name}`, defaulting to [0]: {arch.__name__}"
+                )
+            try:
+                return sgl_models.registry.ModelRegistry.resolve_model_cls(arch)[0]
+            except Exception as e:
+                raise ValueError(
+                    f"{auto_model_type.__name__} found a corresponding model `{arch}` for config class `{config_cls_name}`, but failed to load it from SGLang ModelRegistry. \n{e}"
+                )
+        else:
+            raise ValueError(
+                f"{auto_model_type.__name__} cannot find a corresponding model for config class `{config_cls_name}`"
+            )
+
+    @lru_cache
+    def _config_cls_name_to_arch_name_mapping(
+        self, auto_model_type: Type[AutoModel]
+    ) -> Dict[str, str]:
+        mapping = {}
+        for config_cls, archs in auto_model_type._model_mapping.items():
+            if isinstance(archs, tuple):
+                mapping[config_cls.__name__] = tuple(arch.__name__ for arch in archs)
+            else:
+                mapping[config_cls.__name__] = archs.__name__
+        return mapping
+
+    def __init__(
+        self,
+        config: LlavaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        assert hasattr(config, "text_config")
+        assert hasattr(config, "vision_config")
+        self.config = config
+        self.text_config = config.text_config
+        self.vision_config = config.vision_config
+
+        if not hasattr(self.config, "vocab_size"):
+            self.config.vocab_size = self.config.text_config.vocab_size
+        if not hasattr(self.config, "image_aspect_ratio"):
+            self.config.image_aspect_ratio = "anyres"
+        if not hasattr(self.config, "image_grid_pinpoints"):
+            # from transformers.models.llava_onevision.configuration_llava_onevision import LlavaOnevisionConfig
+            # self.config.image_grid_pinpoints = LlavaOnevisionConfig().image_grid_pinpoints
+            self.config.image_grid_pinpoints = [
+                [96, 96],
+                [224, 224],
+                [384, 384],
+                [512, 512],
+                [768, 768],
+                [1024, 1024],
+            ]
+        if not hasattr(self.config, "mm_patch_merge_type"):
+            self.config.mm_patch_merge_type = "flat"
+        if not hasattr(self.config, "image_token_index"):
+            self.config.image_token_index = 10
+        if not hasattr(self.config, "projector_hidden_act"):
+            self.config.projector_hidden_act = "gelu"
+
+        self.vision_feature_layer = getattr(config, "vision_feature_layer", -1)
+        self.vision_feature_select_strategy = getattr(
+            config, "vision_feature_select_strategy", "full"
+        )
+        self.image_size = self.config.vision_config.image_size
+        self.patch_size = self.config.vision_config.patch_size
+
+        self.mm_patch_merge_type = config.mm_patch_merge_type
+        self.image_aspect_ratio = config.image_aspect_ratio
+        self.image_grid_pinpoints = config.image_grid_pinpoints
+
+        self.image_feature_len = int((self.image_size // self.patch_size) ** 2)
+
+        self.multi_modal_projector = self.MULTIMODAL_PROJECTOR_TYPE(config)
+
+        language_model_cls = self._get_sgl_model_cls(
+            config.text_config, AutoModelForCausalLM
+        )
+        vision_model_cls = self._get_sgl_model_cls(config.vision_config, AutoModel)
+        self.language_model = language_model_cls(
+            config.text_config,
+            quant_config=quant_config,
+            prefix=add_prefix("language_model", prefix),
+        )
+        self.vision_tower = vision_model_cls(
+            config.vision_config,
+            quant_config=quant_config,
+            prefix=add_prefix("vision_tower", prefix),
+        )
+
+        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
+            self.language_model.model.image_newline = nn.Parameter(
+                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
+            )
+
+    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        """Extract features from image inputs.
+
+        Args:
+            items: List of MultimodalDataItem objects containing image data
+                Note that an item can be either "image" or "multi-images"
+
+        Returns:
+            torch.Tensor: features from image inputs, concatenated
+        """
+        features = []
+        for item in items:
+            # in each item, we assume pixel_values is always batched
+            pixel_values, image_sizes = item.pixel_values, item.image_sizes
+            image_outputs = self.vision_tower(
+                pixel_values, image_sizes, output_hidden_states=True
+            )
+            selected_image_feature = image_outputs.hidden_states[
+                self.vision_feature_layer
+            ]
+
+            if self.vision_feature_select_strategy in ["default", "patch"]:
+                selected_image_feature = selected_image_feature[:, 1:]
+            elif self.vision_feature_select_strategy == "full":
+                selected_image_feature = selected_image_feature
+            else:
+                raise ValueError(
+                    f"Unexpected select feature: {self.vision_feature_select_strategy}"
+                )
+            features.append(
+                self.multi_modal_projector(selected_image_feature.squeeze(0))
+            )
+        ret = torch.cat(features, dim=0)
+        return ret
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        get_embedding: bool = False,
+    ):
+        hidden_states = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            get_embedding=get_embedding,
+            language_model=self.language_model,
+            image_data_embedding_func=self.get_image_feature,
+            placeholder_tokens=None,  # using mm_item.pad_value
+            positions=positions,
+        )
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        """Load weights for LlavaForConditionalGeneration.
+
+        Unlike the base class implementation, this one doesn't need to handle
+        weight name remapping as the weights are already properly structured with
+        'language_model' and 'vision_tower' prefixes in the safetensors files.
+        """
+        if (
+            self.vision_feature_select_strategy == "patch"
+            or self.vision_feature_select_strategy == "full"
+        ):
+            pass
+        elif self.vision_feature_select_strategy == "cls_patch":
+            self.image_feature_len += 1
+        else:
+            raise ValueError(
+                f"Unexpected select feature: {self.vision_feature_select_strategy}"
+            )
+
+        # Create dictionaries for direct parameter loading
+        params_dict = dict(self.named_parameters())
+
+        # Load weights directly without remapping
+        for name, loaded_weight in weights:
+            for part in ("language_model", "vision_tower"):
+                if name.startswith(part):
+                    name = name[len(part + ".") :]
+                    getattr(self, part).load_weights([(name, loaded_weight)])
+                    break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = [
+    LlavaLlamaForCausalLM,
+    LlavaQwenForCausalLM,
+    LlavaMistralForCausalLM,
+    LlavaForConditionalGeneration,
+]