Sync from v0.13
This commit is contained in:
394
vllm/model_executor/models/minimax_vl_01.py
Normal file
394
vllm/model_executor/models/minimax_vl_01.py
Normal file
@@ -0,0 +1,394 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
from collections.abc import Iterable, Mapping
|
||||
from typing import Annotated, Literal, TypeAlias
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from transformers import BatchFeature, PretrainedConfig
|
||||
from transformers.models.llava_next.modeling_llava_next import (
|
||||
get_anyres_image_grid_shape,
|
||||
unpad_image,
|
||||
)
|
||||
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.model_executor.layers.activation import get_act_fn
|
||||
from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
|
||||
from vllm.model_executor.layers.quantization import QuantizationConfig
|
||||
from vllm.multimodal import MULTIMODAL_REGISTRY
|
||||
from vllm.multimodal.inputs import MultiModalFieldConfig
|
||||
from vllm.sequence import IntermediateTensors
|
||||
from vllm.utils.tensor_schema import TensorSchema, TensorShape
|
||||
|
||||
from .clip import CLIPVisionModel
|
||||
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
|
||||
from .llava import (
|
||||
BaseLlavaMultiModalProcessor,
|
||||
LlavaDummyInputsBuilder,
|
||||
init_vision_tower_for_llava,
|
||||
)
|
||||
from .llava_next import LlavaNextProcessingInfo
|
||||
from .pixtral import PixtralHFVisionModel
|
||||
from .siglip import SiglipVisionModel
|
||||
from .utils import (
|
||||
AutoWeightsLoader,
|
||||
init_vllm_registered_model,
|
||||
maybe_prefix,
|
||||
)
|
||||
|
||||
|
||||
class MiniMaxVL01ImagePixelInputs(TensorSchema):
|
||||
"""
|
||||
Dimensions:
|
||||
- bn: Batch size * number of images
|
||||
- np: Number of patches + 1
|
||||
- c: Number of channels (3)
|
||||
- h: Height
|
||||
- w: Width
|
||||
|
||||
Note that `num_patches` may be different per batch and image,
|
||||
in which case the data is passed as a list instead of a batched tensor.
|
||||
"""
|
||||
|
||||
type: Literal["pixel_values"] = "pixel_values"
|
||||
pixel_values: Annotated[
|
||||
torch.Tensor | list[torch.Tensor],
|
||||
TensorShape("bn", "np", 3, "h", "w", dynamic_dims={"np", "h", "w"}),
|
||||
]
|
||||
|
||||
image_sizes: Annotated[torch.Tensor | None, TensorShape("bn", 2)]
|
||||
# This should be in `(height, width)` format.
|
||||
|
||||
|
||||
class MiniMaxVL01ImageEmbeddingInputs(TensorSchema):
|
||||
"""
|
||||
Dimensions:
|
||||
- bn: Batch size * number of images
|
||||
- ifs: Image feature size
|
||||
- hs: Hidden size (must match language model backbone)
|
||||
"""
|
||||
|
||||
type: Literal["image_embeds"] = "image_embeds"
|
||||
data: Annotated[torch.Tensor, TensorShape("bn", "ifs", "hs")]
|
||||
|
||||
|
||||
MiniMaxVL01ImageInputs: TypeAlias = (
|
||||
MiniMaxVL01ImagePixelInputs | MiniMaxVL01ImageEmbeddingInputs
|
||||
)
|
||||
|
||||
|
||||
class MiniMaxVL01MultiModalProjector(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
vision_hidden_size: int,
|
||||
text_hidden_size: int,
|
||||
projector_hidden_act: str,
|
||||
multimodal_projector_bias: bool,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
prefix: str = "",
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.linear_1 = ColumnParallelLinear(
|
||||
vision_hidden_size,
|
||||
text_hidden_size,
|
||||
bias=multimodal_projector_bias,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.linear_1",
|
||||
)
|
||||
self.act = get_act_fn(projector_hidden_act)
|
||||
self.linear_2 = RowParallelLinear(
|
||||
text_hidden_size,
|
||||
text_hidden_size,
|
||||
bias=multimodal_projector_bias,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.linear_2",
|
||||
)
|
||||
|
||||
def forward(self, image_features: torch.Tensor) -> torch.Tensor:
|
||||
hidden_states, _ = self.linear_1(image_features)
|
||||
hidden_states = self.act(hidden_states)
|
||||
hidden_states, _ = self.linear_2(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class MiniMaxVL01DummyInputsBuilder(LlavaDummyInputsBuilder):
|
||||
pass
|
||||
|
||||
|
||||
class MiniMaxVL01ProcessingInfo(LlavaNextProcessingInfo):
|
||||
def get_hf_config(self): # Need to override the config type
|
||||
return self.ctx.get_hf_config(PretrainedConfig)
|
||||
|
||||
def get_hf_processor(self, **kwargs: object):
|
||||
hf_processor = self.ctx.get_hf_processor(**kwargs)
|
||||
image_processor = hf_processor.image_processor
|
||||
image_processor.anyres_preprocess = image_processor.anyres_for_vllm_preprocess
|
||||
|
||||
return hf_processor
|
||||
|
||||
def get_supported_mm_limits(self) -> Mapping[str, int | None]:
|
||||
return {"image": None}
|
||||
|
||||
|
||||
class MiniMaxVL01MultiModalProcessor(
|
||||
BaseLlavaMultiModalProcessor[MiniMaxVL01ProcessingInfo]
|
||||
):
|
||||
def _call_hf_processor(
|
||||
self,
|
||||
prompt: str,
|
||||
mm_data: Mapping[str, object],
|
||||
mm_kwargs: Mapping[str, object],
|
||||
tok_kwargs: Mapping[str, object],
|
||||
) -> BatchFeature:
|
||||
processed_outputs = super()._call_hf_processor(
|
||||
prompt=prompt,
|
||||
mm_data=mm_data,
|
||||
mm_kwargs=mm_kwargs,
|
||||
tok_kwargs=tok_kwargs,
|
||||
)
|
||||
|
||||
pixel_values = processed_outputs.get("pixel_values")
|
||||
if pixel_values is not None:
|
||||
# Avoid padding since we need the output for each image to be
|
||||
# independent of other images for the cache to work correctly
|
||||
image_sizes = processed_outputs["image_sizes"]
|
||||
assert len(pixel_values) == len(image_sizes)
|
||||
|
||||
processed_outputs["pixel_values"] = [
|
||||
p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
|
||||
]
|
||||
|
||||
return processed_outputs
|
||||
|
||||
def _get_mm_fields_config(
|
||||
self,
|
||||
hf_inputs: BatchFeature,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
) -> Mapping[str, MultiModalFieldConfig]:
|
||||
return {
|
||||
"pixel_values": MultiModalFieldConfig.batched("image"),
|
||||
"image_sizes": MultiModalFieldConfig.batched("image"),
|
||||
"image_embeds": MultiModalFieldConfig.batched("image"),
|
||||
}
|
||||
|
||||
|
||||
@MULTIMODAL_REGISTRY.register_processor(
|
||||
MiniMaxVL01MultiModalProcessor,
|
||||
info=MiniMaxVL01ProcessingInfo,
|
||||
dummy_inputs=MiniMaxVL01DummyInputsBuilder,
|
||||
)
|
||||
class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
|
||||
"gate_up_proj": ["gate_proj", "up_proj"],
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
|
||||
if modality.startswith("image"):
|
||||
return "<image>"
|
||||
|
||||
raise ValueError("Only image modality is supported")
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
|
||||
super().__init__()
|
||||
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
multimodal_config = vllm_config.model_config.multimodal_config
|
||||
|
||||
self.config = config
|
||||
self.multimodal_config = multimodal_config
|
||||
|
||||
# TODO: Optionally initializes this for supporting embeddings.
|
||||
self.vision_tower = init_vision_tower_for_llava(
|
||||
config,
|
||||
quant_config,
|
||||
require_post_norm=False,
|
||||
prefix=maybe_prefix(prefix, "vision_tower"),
|
||||
)
|
||||
self.multi_modal_projector = MiniMaxVL01MultiModalProjector(
|
||||
vision_hidden_size=config.vision_config.hidden_size,
|
||||
text_hidden_size=config.text_config.hidden_size,
|
||||
projector_hidden_act=config.projector_hidden_act,
|
||||
multimodal_projector_bias=True,
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "multi_modal_projector"),
|
||||
)
|
||||
self.image_newline = nn.Parameter(torch.empty(config.text_config.hidden_size))
|
||||
self.language_model = init_vllm_registered_model(
|
||||
vllm_config=vllm_config,
|
||||
hf_config=config.text_config,
|
||||
prefix=maybe_prefix(prefix, "language_model"),
|
||||
)
|
||||
self.vision_feature_layer = config.vision_feature_layer
|
||||
self.vocab_size = config.text_config.vocab_size
|
||||
self.pad_token_id = -1
|
||||
if self.config.pad_token_id is not None:
|
||||
self.pad_token_id = self.config.pad_token_id
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.language_model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
def get_language_model(self) -> torch.nn.Module:
|
||||
return self.language_model
|
||||
|
||||
def _image_pixels_to_features(
|
||||
self,
|
||||
vision_tower: CLIPVisionModel | SiglipVisionModel | PixtralHFVisionModel,
|
||||
pixel_values: torch.Tensor | list[torch.Tensor],
|
||||
) -> torch.Tensor | tuple[torch.Tensor, ...]:
|
||||
# NOTE: we skip the step to select the vision feature layer since
|
||||
# this is already done inside the vision tower
|
||||
feature_select_strategy = self.config.vision_feature_select_strategy
|
||||
return tuple(
|
||||
vision_tower(p, feature_select_strategy=feature_select_strategy)
|
||||
for p in pixel_values
|
||||
)
|
||||
|
||||
# adapted from https://huggingface.co/MiniMaxAI/MiniMax-VL-01/blob/main/modeling_minimax_vl_01.py#L616-L631
|
||||
def pack_image_features(
|
||||
self, image_features: list[torch.Tensor], image_sizes: torch.Tensor
|
||||
):
|
||||
new_image_features = []
|
||||
for image_idx, image_feature in enumerate(image_features):
|
||||
if image_feature.shape[0] > 1:
|
||||
base_image_feature = image_feature[0]
|
||||
image_feature = image_feature[1:]
|
||||
height = width = (
|
||||
self.config.vision_config.image_size
|
||||
// self.config.vision_config.patch_size
|
||||
)
|
||||
if height * width != base_image_feature.shape[0]:
|
||||
raise ValueError(
|
||||
"The number of patches is not consistent with the image size."
|
||||
)
|
||||
num_patch_height, num_patch_width = get_anyres_image_grid_shape(
|
||||
image_sizes[image_idx],
|
||||
self.config.image_grid_pinpoints,
|
||||
self.config.vision_config.image_size,
|
||||
)
|
||||
|
||||
image_feature = image_feature.view(
|
||||
num_patch_height, num_patch_width, height, width, -1
|
||||
)
|
||||
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
|
||||
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
|
||||
image_feature = unpad_image(image_feature, image_sizes[image_idx])
|
||||
|
||||
image_feature = torch.cat(
|
||||
(
|
||||
image_feature,
|
||||
self.image_newline[:, None, None]
|
||||
.expand(*image_feature.shape[:-1], 1)
|
||||
.to(image_feature.dtype),
|
||||
),
|
||||
dim=-1,
|
||||
)
|
||||
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
|
||||
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
|
||||
else:
|
||||
image_feature = image_feature[0]
|
||||
image_feature = torch.cat(
|
||||
(image_feature, self.image_newline[None].to(image_feature)), dim=0
|
||||
)
|
||||
new_image_features.append(image_feature)
|
||||
return new_image_features
|
||||
|
||||
def _process_image_pixels(
|
||||
self,
|
||||
inputs: MiniMaxVL01ImagePixelInputs,
|
||||
) -> torch.Tensor | tuple[torch.Tensor, ...]:
|
||||
assert self.vision_tower is not None
|
||||
|
||||
pixel_values = inputs["pixel_values"]
|
||||
return self._image_pixels_to_features(self.vision_tower, pixel_values)
|
||||
|
||||
def _process_image_input(
|
||||
self,
|
||||
image_input: MiniMaxVL01ImageInputs,
|
||||
) -> torch.Tensor | tuple[torch.Tensor, ...]:
|
||||
if image_input["type"] == "image_embeds":
|
||||
return image_input["data"]
|
||||
|
||||
assert self.vision_tower is not None
|
||||
image_features = self._process_image_pixels(image_input)
|
||||
|
||||
if isinstance(image_features, torch.Tensor):
|
||||
return self.multi_modal_projector(image_features)
|
||||
|
||||
feature_sizes = [image_feature.shape[0] for image_feature in image_features]
|
||||
|
||||
image_embeds = self.multi_modal_projector(torch.cat(image_features))
|
||||
image_embeds = torch.split(image_embeds, feature_sizes)
|
||||
image_sizes = image_input.get("image_sizes")
|
||||
return self.pack_image_features(image_embeds, image_sizes)
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object
|
||||
) -> MiniMaxVL01ImageInputs | None:
|
||||
pixel_values = kwargs.pop("pixel_values", None)
|
||||
image_sizes = kwargs.pop("image_sizes", None)
|
||||
image_embeds = kwargs.pop("image_embeds", None)
|
||||
|
||||
if pixel_values is None and image_embeds is None:
|
||||
return None
|
||||
|
||||
if pixel_values is not None and image_sizes is not None:
|
||||
return MiniMaxVL01ImagePixelInputs(
|
||||
type="pixel_values",
|
||||
pixel_values=pixel_values,
|
||||
image_sizes=image_sizes,
|
||||
)
|
||||
|
||||
if image_embeds is not None:
|
||||
return MiniMaxVL01ImageEmbeddingInputs(
|
||||
type="image_embeds",
|
||||
data=image_embeds,
|
||||
)
|
||||
|
||||
raise AssertionError("This line should be unreachable.")
|
||||
|
||||
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
|
||||
image_input = self._parse_and_validate_image_input(**kwargs)
|
||||
if image_input is None:
|
||||
return []
|
||||
|
||||
return self._process_image_input(image_input)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs: object,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
if intermediate_tensors is not None:
|
||||
inputs_embeds = None
|
||||
elif inputs_embeds is None:
|
||||
vision_embeddings = self.embed_multimodal(**kwargs)
|
||||
inputs_embeds = self.embed_input_ids(
|
||||
input_ids,
|
||||
vision_embeddings,
|
||||
is_multimodal=input_ids == self.config.image_token_index,
|
||||
)
|
||||
input_ids = None
|
||||
|
||||
hidden_states = self.language_model.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
|
||||
)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
return self.language_model.compute_logits(hidden_states)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(self)
|
||||
return loader.load_weights(weights)
|
||||
Reference in New Issue
Block a user