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427 lines
17 KiB
Python
427 lines
17 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable, Mapping
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from typing import Annotated, Literal, Optional, Union
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import torch
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import torch.nn as nn
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from transformers import BatchFeature, PretrainedConfig
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from transformers.models.llava_next.modeling_llava_next import (
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get_anyres_image_grid_shape, unpad_image)
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import MultiModalFieldConfig
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from vllm.sequence import IntermediateTensors
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from vllm.utils.jsontree import json_map_leaves
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .clip import CLIPVisionModel
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from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
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from .llava import (BaseLlavaMultiModalProcessor, LlavaDummyInputsBuilder,
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init_vision_tower_for_llava)
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from .llava_next import LlavaNextProcessingInfo
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from .pixtral import PixtralHFVisionModel
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from .siglip import SiglipVisionModel
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from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
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maybe_prefix, merge_multimodal_embeddings)
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class MiniMaxVL01ImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- np: Number of patches + 1
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- c: Number of channels (3)
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- h: Height
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- w: Width
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Note that `num_patches` may be different per batch and image,
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in which case the data is passed as a list instead of a batched tensor.
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"""
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type: Literal["pixel_values"] = "pixel_values"
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pixel_values: Annotated[
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Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("bn", "np", 3, "h", "w", dynamic_dims={"np", "h", "w"})]
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image_sizes: Annotated[Optional[torch.Tensor], TensorShape("bn", 2)]
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# This should be in `(height, width)` format.
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class MiniMaxVL01ImageEmbeddingInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- ifs: Image feature size
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- hs: Hidden size (must match language model backbone)
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"""
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type: Literal["image_embeds"] = "image_embeds"
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data: Annotated[torch.Tensor, TensorShape("bn", "ifs", "hs")]
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MiniMaxVL01ImageInputs = Union[MiniMaxVL01ImagePixelInputs,
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MiniMaxVL01ImageEmbeddingInputs]
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class MiniMaxVL01MultiModalProjector(nn.Module):
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def __init__(self,
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vision_hidden_size: int,
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text_hidden_size: int,
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projector_hidden_act: str,
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multimodal_projector_bias: bool,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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super().__init__()
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self.linear_1 = ColumnParallelLinear(vision_hidden_size,
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text_hidden_size,
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bias=multimodal_projector_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.linear_1")
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self.act = get_act_fn(projector_hidden_act)
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self.linear_2 = RowParallelLinear(text_hidden_size,
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text_hidden_size,
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bias=multimodal_projector_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.linear_2")
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def forward(self, image_features: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.linear_1(image_features)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.linear_2(hidden_states)
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return hidden_states
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class MiniMaxVL01DummyInputsBuilder(LlavaDummyInputsBuilder):
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pass
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class MiniMaxVL01ProcessingInfo(LlavaNextProcessingInfo):
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def get_hf_config(self): # Need to override the config type
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return self.ctx.get_hf_config(PretrainedConfig)
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def get_hf_processor(self, **kwargs: object):
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hf_processor = self.ctx.get_hf_processor(**kwargs)
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image_processor = hf_processor.image_processor
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image_processor.anyres_preprocess = (
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image_processor.anyres_for_vllm_preprocess)
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return hf_processor
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"image": None}
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class MiniMaxVL01MultiModalProcessor(
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BaseLlavaMultiModalProcessor[MiniMaxVL01ProcessingInfo]):
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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tok_kwargs: Mapping[str, object],
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) -> BatchFeature:
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processed_outputs = super()._call_hf_processor(
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prompt=prompt,
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mm_data=mm_data,
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mm_kwargs=mm_kwargs,
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tok_kwargs=tok_kwargs,
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)
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pixel_values = processed_outputs.get("pixel_values")
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if pixel_values is not None:
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# Avoid padding since we need the output for each image to be
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# independent of other images for the cache to work correctly
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image_sizes = processed_outputs["image_sizes"]
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assert len(pixel_values) == len(image_sizes)
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processed_outputs["pixel_values"] = [
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p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
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]
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return processed_outputs
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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return {
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"pixel_values": MultiModalFieldConfig.batched("image"),
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"image_sizes": MultiModalFieldConfig.batched("image"),
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"image_embeds": MultiModalFieldConfig.batched("image"),
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}
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@MULTIMODAL_REGISTRY.register_processor(
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MiniMaxVL01MultiModalProcessor,
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info=MiniMaxVL01ProcessingInfo,
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dummy_inputs=MiniMaxVL01DummyInputsBuilder)
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class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
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SupportsPP):
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packed_modules_mapping = {
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
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"gate_up_proj": ["gate_proj", "up_proj"]
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}
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@classmethod
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def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
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if modality.startswith("image"):
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return "<image>"
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raise ValueError("Only image modality is supported")
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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multimodal_config = vllm_config.model_config.multimodal_config
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self.config = config
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self.multimodal_config = multimodal_config
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# TODO: Optionally initializes this for supporting embeddings.
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self.vision_tower = init_vision_tower_for_llava(
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config,
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quant_config,
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require_post_norm=False,
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prefix=maybe_prefix(prefix, "vision_tower"))
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self.multi_modal_projector = MiniMaxVL01MultiModalProjector(
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vision_hidden_size=config.vision_config.hidden_size,
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text_hidden_size=config.text_config.hidden_size,
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projector_hidden_act=config.projector_hidden_act,
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multimodal_projector_bias=True,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "multi_modal_projector"))
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self.image_newline = nn.Parameter(
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torch.empty(config.text_config.hidden_size))
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self.language_model = init_vllm_registered_model(
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vllm_config=vllm_config,
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hf_config=config.text_config,
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prefix=maybe_prefix(prefix, "language_model"),
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)
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self.vision_feature_layer = config.vision_feature_layer
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self.vocab_size = config.text_config.vocab_size
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self.pad_token_id = -1
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if self.config.pad_token_id is not None:
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self.pad_token_id = self.config.pad_token_id
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self.make_empty_intermediate_tensors = (
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self.language_model.make_empty_intermediate_tensors)
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def get_input_embeddings(
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self,
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input_ids: torch.Tensor,
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multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
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) -> torch.Tensor:
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inputs_embeds = self.language_model.get_input_embeddings(input_ids)
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if multimodal_embeddings is not None \
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and len(multimodal_embeddings) != 0:
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inputs_embeds = merge_multimodal_embeddings(
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input_ids,
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inputs_embeds,
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multimodal_embeddings,
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self.config.image_token_index,
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)
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return inputs_embeds
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def get_language_model(self) -> torch.nn.Module:
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return self.language_model
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def _select_image_features(self, image_features: torch.Tensor, *,
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strategy: str) -> torch.Tensor:
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if strategy == "default":
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return image_features[:, 1:]
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elif strategy == "full":
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return image_features
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raise ValueError(f"Unexpected select feature strategy: {strategy}")
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def _image_pixels_to_features(
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self,
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vision_tower: Union[CLIPVisionModel, SiglipVisionModel,
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PixtralHFVisionModel],
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pixel_values: Union[torch.Tensor, list[torch.Tensor]],
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) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
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# NOTE: we skip the step to select the vision feature layer since
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# this is already done inside the vision tower
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image_features: tuple[torch.Tensor, ...] = \
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tuple(vision_tower(p) for p in pixel_values)
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def select_features(leaf: torch.Tensor):
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return self._select_image_features(
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leaf,
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strategy=self.config.vision_feature_select_strategy,
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)
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return json_map_leaves(select_features, image_features)
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# adapted from https://huggingface.co/MiniMaxAI/MiniMax-VL-01/blob/main/modeling_minimax_vl_01.py#L616-L631
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def pack_image_features(self, image_features: list[torch.Tensor],
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image_sizes: torch.Tensor):
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new_image_features = []
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for image_idx, image_feature in enumerate(image_features):
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if image_feature.shape[0] > 1:
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base_image_feature = image_feature[0]
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image_feature = image_feature[1:]
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height = width = (self.config.vision_config.image_size //
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self.config.vision_config.patch_size)
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if height * width != base_image_feature.shape[0]:
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raise ValueError(
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"The number of patches is not consistent with "
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"the image size.")
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num_patch_height, num_patch_width = get_anyres_image_grid_shape(
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image_sizes[image_idx],
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self.config.image_grid_pinpoints,
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self.config.vision_config.image_size,
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)
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image_feature = image_feature.view(num_patch_height,
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num_patch_width, height,
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width, -1)
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image_feature = image_feature.permute(4, 0, 2, 1,
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3).contiguous()
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image_feature = image_feature.flatten(1, 2).flatten(2, 3)
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image_feature = unpad_image(image_feature,
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image_sizes[image_idx])
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image_feature = torch.cat(
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(
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image_feature,
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self.image_newline[:, None, None].expand(
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*image_feature.shape[:-1], 1).to(
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image_feature.dtype),
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),
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dim=-1,
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)
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image_feature = image_feature.flatten(1, 2).transpose(0, 1)
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image_feature = torch.cat((base_image_feature, image_feature),
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dim=0)
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else:
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image_feature = image_feature[0]
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image_feature = torch.cat(
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(image_feature,
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self.image_newline[None].to(image_feature)),
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dim=0)
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new_image_features.append(image_feature)
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return new_image_features
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def _process_image_pixels(
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self,
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inputs: MiniMaxVL01ImagePixelInputs,
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) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
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assert self.vision_tower is not None
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pixel_values = inputs["pixel_values"]
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return self._image_pixels_to_features(self.vision_tower, pixel_values)
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def _process_image_input(
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self,
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image_input: MiniMaxVL01ImageInputs,
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) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
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if image_input["type"] == "image_embeds":
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return image_input["data"]
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assert self.vision_tower is not None
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image_features = self._process_image_pixels(image_input)
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if isinstance(image_features, torch.Tensor):
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return self.multi_modal_projector(image_features)
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feature_sizes = [
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image_feature.shape[0] for image_feature in image_features
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]
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image_embeds = self.multi_modal_projector(torch.cat(image_features))
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image_embeds = torch.split(image_embeds, feature_sizes)
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image_sizes = image_input.get("image_sizes")
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return self.pack_image_features(image_embeds, image_sizes)
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def _parse_and_validate_image_input(
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self, **kwargs: object) -> Optional[MiniMaxVL01ImageInputs]:
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pixel_values = kwargs.pop("pixel_values", None)
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image_sizes = kwargs.pop("image_sizes", None)
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image_embeds = kwargs.pop("image_embeds", None)
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if pixel_values is None and image_embeds is None:
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return None
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if pixel_values is not None and image_sizes is not None:
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if not isinstance(pixel_values, (torch.Tensor, list)):
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raise ValueError("Incorrect type of pixel values. "
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f"Got type: {type(pixel_values)}")
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if not isinstance(image_sizes, (torch.Tensor, list)):
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raise ValueError("Incorrect type of image sizes. "
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f"Got type: {type(image_sizes)}")
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return MiniMaxVL01ImagePixelInputs(
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type="pixel_values",
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pixel_values=flatten_bn(pixel_values),
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image_sizes=flatten_bn(image_sizes, concat=True),
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)
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if image_embeds is not None:
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if not isinstance(image_embeds, (torch.Tensor, list)):
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raise ValueError("Incorrect type of image embeddings. "
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f"Got type: {type(image_embeds)}")
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return MiniMaxVL01ImageEmbeddingInputs(
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type="image_embeds",
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data=flatten_bn(image_embeds, concat=True),
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)
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raise AssertionError("This line should be unreachable.")
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def get_multimodal_embeddings(self,
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**kwargs: object) -> MultiModalEmbeddings:
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image_input = self._parse_and_validate_image_input(**kwargs)
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if image_input is None:
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return []
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return self._process_image_input(image_input)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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**kwargs: object,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if intermediate_tensors is not None:
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inputs_embeds = None
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elif inputs_embeds is None:
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vision_embeddings = self.get_multimodal_embeddings(**kwargs)
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inputs_embeds = self.get_input_embeddings(input_ids,
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vision_embeddings)
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input_ids = None
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hidden_states = self.language_model.model(input_ids,
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positions,
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intermediate_tensors,
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inputs_embeds=inputs_embeds)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> Optional[torch.Tensor]:
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return self.language_model.compute_logits(hidden_states)
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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