Sync from v0.13
This commit is contained in:
408
vllm/model_executor/models/paligemma.py
Normal file
408
vllm/model_executor/models/paligemma.py
Normal file
@@ -0,0 +1,408 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
from collections.abc import Iterable, Mapping, Sequence
|
||||
from typing import Annotated, Literal, TypeAlias
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers import BatchFeature, PaliGemmaConfig
|
||||
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.config.multimodal import BaseDummyOptions
|
||||
from vllm.logger import init_logger
|
||||
from vllm.multimodal import MULTIMODAL_REGISTRY
|
||||
from vllm.multimodal.inputs import (
|
||||
MultiModalDataDict,
|
||||
MultiModalFieldConfig,
|
||||
MultiModalInputs,
|
||||
MultiModalKwargsItems,
|
||||
MultiModalUUIDDict,
|
||||
)
|
||||
from vllm.multimodal.parse import (
|
||||
ImageEmbeddingItems,
|
||||
ImageProcessorItems,
|
||||
MultiModalDataItems,
|
||||
)
|
||||
from vllm.multimodal.processing import (
|
||||
BaseMultiModalProcessor,
|
||||
BaseProcessingInfo,
|
||||
PromptIndexTargets,
|
||||
PromptInsertion,
|
||||
PromptUpdate,
|
||||
PromptUpdateDetails,
|
||||
)
|
||||
from vllm.multimodal.profiling import BaseDummyInputsBuilder
|
||||
from vllm.sequence import IntermediateTensors
|
||||
from vllm.utils.tensor_schema import TensorSchema, TensorShape
|
||||
|
||||
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
|
||||
from .siglip import SiglipVisionModel
|
||||
from .utils import (
|
||||
AutoWeightsLoader,
|
||||
WeightsMapper,
|
||||
init_vllm_registered_model,
|
||||
maybe_prefix,
|
||||
)
|
||||
from .vision import get_vision_encoder_info
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class PaliGemmaImagePixelInputs(TensorSchema):
|
||||
"""
|
||||
Dimensions:
|
||||
- bn: Batch size * number of images
|
||||
- c: Number of channels (3)
|
||||
- h: Height
|
||||
- w: Width
|
||||
"""
|
||||
|
||||
type: Literal["pixel_values"] = "pixel_values"
|
||||
data: Annotated[torch.Tensor, TensorShape("bn", 3, "h", "w")]
|
||||
|
||||
|
||||
class PaliGemmaImageEmbeddingInputs(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")]
|
||||
|
||||
|
||||
PaliGemmaImageInputs: TypeAlias = (
|
||||
PaliGemmaImagePixelInputs | PaliGemmaImageEmbeddingInputs
|
||||
)
|
||||
|
||||
|
||||
class PaliGemmaMultiModalProjector(nn.Module):
|
||||
def __init__(self, vision_hidden_size: int, projection_dim: int):
|
||||
super().__init__()
|
||||
|
||||
self.linear = nn.Linear(vision_hidden_size, projection_dim, bias=True)
|
||||
|
||||
def forward(self, image_features: torch.Tensor) -> torch.Tensor:
|
||||
hidden_states = self.linear(image_features)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class PaliGemmaProcessingInfo(BaseProcessingInfo):
|
||||
def get_hf_config(self):
|
||||
return self.ctx.get_hf_config(PaliGemmaConfig)
|
||||
|
||||
def get_vision_encoder_info(self):
|
||||
return get_vision_encoder_info(self.get_hf_config())
|
||||
|
||||
def get_supported_mm_limits(self) -> Mapping[str, int | None]:
|
||||
return {"image": 1}
|
||||
|
||||
def get_num_image_tokens(
|
||||
self,
|
||||
*,
|
||||
image_width: int,
|
||||
image_height: int,
|
||||
) -> int:
|
||||
vision_encoder_info = self.get_vision_encoder_info()
|
||||
|
||||
return vision_encoder_info.get_num_image_tokens(
|
||||
image_width=image_width,
|
||||
image_height=image_height,
|
||||
)
|
||||
|
||||
|
||||
class PaliGemmaDummyInputsBuilder(BaseDummyInputsBuilder[PaliGemmaProcessingInfo]):
|
||||
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
|
||||
return ""
|
||||
|
||||
def get_dummy_mm_data(
|
||||
self,
|
||||
seq_len: int,
|
||||
mm_counts: Mapping[str, int],
|
||||
mm_options: Mapping[str, BaseDummyOptions] | None = None,
|
||||
) -> MultiModalDataDict:
|
||||
hf_config = self.info.get_hf_config()
|
||||
vision_config = hf_config.vision_config
|
||||
max_image_size = vision_config.image_size
|
||||
|
||||
num_images = mm_counts.get("image", 0)
|
||||
|
||||
image_overrides = mm_options.get("image") if mm_options else None
|
||||
|
||||
return {
|
||||
"image": self._get_dummy_images(
|
||||
width=max_image_size,
|
||||
height=max_image_size,
|
||||
num_images=num_images,
|
||||
overrides=image_overrides,
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
class PaliGemmaMultiModalProcessor(BaseMultiModalProcessor[PaliGemmaProcessingInfo]):
|
||||
def _call_hf_processor(
|
||||
self,
|
||||
prompt: str,
|
||||
mm_data: Mapping[str, object],
|
||||
mm_kwargs: Mapping[str, object],
|
||||
tok_kwargs: Mapping[str, object],
|
||||
) -> BatchFeature:
|
||||
tokenizer = self.info.get_tokenizer()
|
||||
if not mm_data:
|
||||
prompt_ids = tokenizer.encode(prompt, add_special_tokens=False)
|
||||
return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
|
||||
|
||||
return super()._call_hf_processor(
|
||||
prompt=prompt,
|
||||
mm_data=mm_data,
|
||||
mm_kwargs=mm_kwargs,
|
||||
tok_kwargs=tok_kwargs,
|
||||
)
|
||||
|
||||
def _get_mm_fields_config(
|
||||
self,
|
||||
hf_inputs: BatchFeature,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
) -> Mapping[str, MultiModalFieldConfig]:
|
||||
return dict(pixel_values=MultiModalFieldConfig.batched("image"))
|
||||
|
||||
def _get_prompt_updates(
|
||||
self,
|
||||
mm_items: MultiModalDataItems,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
out_mm_kwargs: MultiModalKwargsItems,
|
||||
) -> Sequence[PromptUpdate]:
|
||||
hf_config = self.info.get_hf_config()
|
||||
image_token_id = hf_config.image_token_index
|
||||
|
||||
tokenizer = self.info.get_tokenizer()
|
||||
|
||||
bos_token_id = tokenizer.bos_token_id
|
||||
assert isinstance(bos_token_id, int)
|
||||
|
||||
def get_insertion(item_idx: int):
|
||||
images = mm_items.get_items(
|
||||
"image", (ImageEmbeddingItems, ImageProcessorItems)
|
||||
)
|
||||
|
||||
if isinstance(images, ImageEmbeddingItems):
|
||||
num_image_tokens = images.get_feature_size(item_idx)
|
||||
else:
|
||||
image_size = images.get_image_size(item_idx)
|
||||
num_image_tokens = self.info.get_num_image_tokens(
|
||||
image_width=image_size.width,
|
||||
image_height=image_size.height,
|
||||
)
|
||||
|
||||
image_tokens = [image_token_id] * num_image_tokens
|
||||
|
||||
return PromptUpdateDetails.select_token_id(
|
||||
image_tokens + [bos_token_id],
|
||||
embed_token_id=image_token_id,
|
||||
)
|
||||
|
||||
# Paligemma 1 and 2 have different tokenizer.add_bos_token
|
||||
# Insert <image>*n + <bos> after <bos> for Paligemma 1
|
||||
# Insert <image>*n + <bos> for Paligemma 2
|
||||
return [
|
||||
PromptInsertion(
|
||||
modality="image",
|
||||
target=PromptIndexTargets.prefix(
|
||||
[bos_token_id] if tokenizer.add_bos_token else []
|
||||
),
|
||||
insertion=get_insertion,
|
||||
)
|
||||
]
|
||||
|
||||
def apply(
|
||||
self,
|
||||
prompt: str | list[int],
|
||||
mm_data: MultiModalDataDict,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
tokenization_kwargs: Mapping[str, object] | None = None,
|
||||
mm_uuids: MultiModalUUIDDict | None = None,
|
||||
) -> MultiModalInputs:
|
||||
mm_inputs = super().apply(
|
||||
prompt,
|
||||
mm_data,
|
||||
hf_processor_mm_kwargs,
|
||||
tokenization_kwargs,
|
||||
mm_uuids=mm_uuids,
|
||||
)
|
||||
prompt_token_ids = mm_inputs["prompt_token_ids"]
|
||||
|
||||
tokenizer = self.info.get_tokenizer()
|
||||
newline_prompt = "\n"
|
||||
newline_token_id = tokenizer.encode(newline_prompt)[-1] # 108
|
||||
# Force to add newline at the end of prompt for paligemma's format
|
||||
# This step can NOT be replacemented by current PromptUpdate methods
|
||||
if len(prompt_token_ids) and prompt_token_ids[-1] != newline_token_id:
|
||||
prompt_token_ids.append(newline_token_id)
|
||||
mm_inputs["prompt_token_ids"] = prompt_token_ids
|
||||
|
||||
return mm_inputs
|
||||
|
||||
|
||||
@MULTIMODAL_REGISTRY.register_processor(
|
||||
PaliGemmaMultiModalProcessor,
|
||||
info=PaliGemmaProcessingInfo,
|
||||
dummy_inputs=PaliGemmaDummyInputsBuilder,
|
||||
)
|
||||
class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
],
|
||||
"gate_up_proj": [
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
],
|
||||
}
|
||||
|
||||
hf_to_vllm_mapper = WeightsMapper(
|
||||
orig_to_new_prefix={
|
||||
# mapping for new names in checkpoint saved after transformers v4.52
|
||||
"model.language_model.": "language_model.model.",
|
||||
"model.vision_tower.": "vision_tower.",
|
||||
"model.multi_modal_projector.": "multi_modal_projector.",
|
||||
"lm_head.": "language_model.lm_head.",
|
||||
}
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
|
||||
if modality.startswith("image"):
|
||||
return None
|
||||
|
||||
raise ValueError("Only image modality is supported")
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
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
|
||||
|
||||
self.vision_tower = SiglipVisionModel(
|
||||
config.vision_config,
|
||||
quant_config,
|
||||
prefix=maybe_prefix(prefix, "vision_tower"),
|
||||
)
|
||||
self.multi_modal_projector = PaliGemmaMultiModalProjector(
|
||||
vision_hidden_size=config.vision_config.hidden_size,
|
||||
projection_dim=config.vision_config.projection_dim,
|
||||
)
|
||||
|
||||
self.quant_config = quant_config
|
||||
|
||||
if config.text_config.model_type == "gemma":
|
||||
config.text_config.architectures = ["GemmaForCausalLM"]
|
||||
else:
|
||||
config.text_config.architectures = ["Gemma2ForCausalLM"]
|
||||
self.language_model = init_vllm_registered_model(
|
||||
vllm_config=vllm_config,
|
||||
hf_config=config.text_config,
|
||||
prefix=maybe_prefix(prefix, "language_model"),
|
||||
)
|
||||
logit_scale = getattr(config, "logit_scale", 1.0)
|
||||
self.language_model.logits_processor.scale *= logit_scale
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.language_model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object
|
||||
) -> PaliGemmaImageInputs | None:
|
||||
pixel_values = kwargs.pop("pixel_values", 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:
|
||||
h = w = self.config.vision_config.image_size
|
||||
|
||||
return PaliGemmaImagePixelInputs(
|
||||
type="pixel_values",
|
||||
data=pixel_values,
|
||||
resolve_bindings={"h": h, "w": w},
|
||||
)
|
||||
|
||||
if image_embeds is not None:
|
||||
return PaliGemmaImageEmbeddingInputs(
|
||||
type="image_embeds",
|
||||
data=image_embeds,
|
||||
)
|
||||
|
||||
raise AssertionError("This line should be unreachable.")
|
||||
|
||||
def _image_pixels_to_features(
|
||||
self,
|
||||
vision_tower: SiglipVisionModel,
|
||||
pixel_values: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
target_dtype = vision_tower.get_input_embeddings().weight.dtype
|
||||
image_features = vision_tower(pixel_values.to(dtype=target_dtype))
|
||||
|
||||
return image_features
|
||||
|
||||
def _process_image_input(
|
||||
self,
|
||||
image_input: PaliGemmaImageInputs,
|
||||
) -> torch.Tensor:
|
||||
if image_input["type"] == "image_embeds":
|
||||
return image_input["data"]
|
||||
|
||||
assert self.vision_tower is not None
|
||||
pixel_values = image_input["data"]
|
||||
image_features = self._image_pixels_to_features(
|
||||
self.vision_tower,
|
||||
pixel_values,
|
||||
)
|
||||
|
||||
return self.multi_modal_projector(image_features)
|
||||
|
||||
def get_language_model(self) -> torch.nn.Module:
|
||||
return self.language_model
|
||||
|
||||
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
|
||||
image_input = self._parse_and_validate_image_input(**kwargs)
|
||||
if image_input is None:
|
||||
return []
|
||||
vision_embeddings = self._process_image_input(image_input)
|
||||
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/paligemma/modeling_paligemma.py#L294 # noqa
|
||||
vision_embeddings = vision_embeddings * (self.config.hidden_size**-0.5)
|
||||
return vision_embeddings
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs: object,
|
||||
) -> IntermediateTensors:
|
||||
if intermediate_tensors is not None:
|
||||
inputs_embeds = 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, mapper=self.hf_to_vllm_mapper)
|
||||
Reference in New Issue
Block a user