mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-01-19 12:44:33 +08:00
554 lines
22 KiB
Python
554 lines
22 KiB
Python
from functools import cached_property
|
|
from typing import (Iterable, List, Literal, Mapping, Optional, Protocol, Set,
|
|
Tuple, TypedDict, Union)
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from PIL import Image
|
|
from transformers import (CLIPVisionConfig, LlavaConfig, PixtralVisionConfig,
|
|
PretrainedConfig, SiglipVisionConfig)
|
|
|
|
from vllm.attention import AttentionMetadata
|
|
from vllm.config import VllmConfig
|
|
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
|
|
InputContext)
|
|
from vllm.model_executor.layers.activation import get_act_fn
|
|
from vllm.model_executor.layers.quantization import QuantizationConfig
|
|
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
|
|
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
|
from vllm.multimodal import MULTIMODAL_REGISTRY
|
|
from vllm.multimodal.inputs import NestedTensors
|
|
from vllm.sequence import IntermediateTensors
|
|
from vllm.utils import is_list_of
|
|
|
|
from .clip import (CLIPVisionModel, dummy_image_for_clip,
|
|
dummy_seq_data_for_clip, get_max_clip_image_tokens,
|
|
input_processor_for_clip)
|
|
from .interfaces import SupportsMultiModal, SupportsPP
|
|
from .pixtral import (PixtralHFVisionModel, dummy_image_for_pixtral_hf,
|
|
dummy_seq_data_for_pixtral_hf,
|
|
get_max_pixtral_hf_image_tokens,
|
|
input_processor_for_pixtral_hf)
|
|
from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
|
|
dummy_seq_data_for_siglip, get_max_siglip_image_tokens,
|
|
input_processor_for_siglip)
|
|
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
|
|
maybe_prefix, merge_multimodal_embeddings)
|
|
|
|
|
|
class LlavaImagePixelInputs(TypedDict):
|
|
type: Literal["pixel_values"]
|
|
data: Union[torch.Tensor, List[torch.Tensor]]
|
|
"""
|
|
Shape: `(batch_size * num_images, num_channels, height, width)`
|
|
|
|
Note that `height` or `width` may be different per batch and image,
|
|
in which case the data is passed as a list instead of a batched tensor.
|
|
"""
|
|
|
|
|
|
class LlavaImageEmbeddingInputs(TypedDict):
|
|
type: Literal["image_embeds"]
|
|
data: torch.Tensor
|
|
"""Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
|
|
|
|
`hidden_size` must match the hidden size of language model backbone.
|
|
"""
|
|
|
|
|
|
LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageEmbeddingInputs]
|
|
|
|
|
|
# TODO(xwjiang): Run benchmark and decide if TP.
|
|
class LlavaMultiModalProjector(nn.Module):
|
|
|
|
def __init__(self, vision_hidden_size: int, text_hidden_size: int,
|
|
projector_hidden_act: str):
|
|
super().__init__()
|
|
|
|
self.linear_1 = nn.Linear(vision_hidden_size,
|
|
text_hidden_size,
|
|
bias=True)
|
|
self.act = get_act_fn(projector_hidden_act)
|
|
self.linear_2 = nn.Linear(text_hidden_size,
|
|
text_hidden_size,
|
|
bias=True)
|
|
|
|
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
|
|
|
|
|
|
def get_max_llava_image_tokens(ctx: InputContext):
|
|
hf_config = ctx.get_hf_config(LlavaConfig)
|
|
vision_config = hf_config.vision_config
|
|
|
|
if isinstance(vision_config, CLIPVisionConfig):
|
|
num_image_tokens = get_max_clip_image_tokens(vision_config)
|
|
elif isinstance(vision_config, SiglipVisionConfig):
|
|
num_image_tokens = get_max_siglip_image_tokens(vision_config)
|
|
elif isinstance(vision_config, PixtralVisionConfig):
|
|
num_image_tokens = get_max_pixtral_hf_image_tokens(vision_config)
|
|
else:
|
|
msg = f"Unsupported vision config: {type(vision_config)}"
|
|
raise NotImplementedError(msg)
|
|
|
|
strategy = hf_config.vision_feature_select_strategy
|
|
if strategy == "default":
|
|
return num_image_tokens - 1
|
|
elif strategy == "full":
|
|
return num_image_tokens
|
|
else:
|
|
raise ValueError(f"Unexpected select feature strategy: {strategy}")
|
|
|
|
|
|
def dummy_data_for_llava(ctx: InputContext, seq_len: int,
|
|
mm_counts: Mapping[str, int]):
|
|
hf_config = ctx.get_hf_config(LlavaConfig)
|
|
vision_config = hf_config.vision_config
|
|
num_images = mm_counts["image"]
|
|
|
|
image_feature_size = get_max_llava_image_tokens(ctx)
|
|
|
|
if isinstance(vision_config, CLIPVisionConfig):
|
|
seq_data, ranges = dummy_seq_data_for_clip(
|
|
vision_config,
|
|
seq_len,
|
|
num_images,
|
|
image_token_id=hf_config.image_token_index,
|
|
image_feature_size_override=image_feature_size,
|
|
)
|
|
|
|
mm_data = dummy_image_for_clip(vision_config, num_images)
|
|
return DummyData(seq_data, mm_data, ranges)
|
|
elif isinstance(vision_config, SiglipVisionConfig):
|
|
seq_data, ranges = dummy_seq_data_for_siglip(
|
|
vision_config,
|
|
seq_len,
|
|
num_images,
|
|
image_token_id=hf_config.image_token_index,
|
|
image_feature_size_override=image_feature_size,
|
|
)
|
|
|
|
mm_data = dummy_image_for_siglip(vision_config, num_images)
|
|
return DummyData(seq_data, mm_data, ranges)
|
|
elif isinstance(vision_config, PixtralVisionConfig):
|
|
seq_data, ranges = dummy_seq_data_for_pixtral_hf(
|
|
vision_config,
|
|
seq_len,
|
|
num_images,
|
|
image_token_id=hf_config.image_token_index,
|
|
image_feature_size_override=image_feature_size,
|
|
)
|
|
|
|
mm_data = dummy_image_for_pixtral_hf(vision_config, num_images)
|
|
return DummyData(seq_data, mm_data, ranges)
|
|
|
|
msg = f"Unsupported vision config: {type(vision_config)}"
|
|
raise NotImplementedError(msg)
|
|
|
|
|
|
def input_processor_for_llava(ctx: InputContext, inputs: DecoderOnlyInputs):
|
|
multi_modal_data = inputs.get("multi_modal_data")
|
|
if multi_modal_data is None or "image" not in multi_modal_data:
|
|
return inputs
|
|
|
|
model_config = ctx.model_config
|
|
hf_config = ctx.get_hf_config(LlavaConfig)
|
|
vision_config = hf_config.vision_config
|
|
|
|
image_data = multi_modal_data["image"]
|
|
if isinstance(image_data, Image.Image):
|
|
image_feature_size = get_max_llava_image_tokens(ctx)
|
|
elif is_list_of(image_data, Image.Image):
|
|
image_feature_size = [get_max_llava_image_tokens(ctx)
|
|
] * len(image_data)
|
|
elif isinstance(image_data, torch.Tensor):
|
|
num_images, image_feature_size, hidden_size = image_data.shape
|
|
elif is_list_of(image_data, torch.Tensor):
|
|
image_feature_size = [item.shape[1] for item in image_data]
|
|
else:
|
|
raise TypeError(f"Invalid image type: {type(image_data)}")
|
|
|
|
if isinstance(vision_config, CLIPVisionConfig):
|
|
return input_processor_for_clip(
|
|
model_config,
|
|
vision_config,
|
|
inputs,
|
|
image_token_id=hf_config.image_token_index,
|
|
image_feature_size_override=image_feature_size,
|
|
)
|
|
elif isinstance(vision_config, SiglipVisionConfig):
|
|
return input_processor_for_siglip(
|
|
model_config,
|
|
vision_config,
|
|
inputs,
|
|
image_token_id=hf_config.image_token_index,
|
|
image_feature_size_override=image_feature_size,
|
|
)
|
|
elif isinstance(vision_config, PixtralVisionConfig):
|
|
# We ignore image_feature_size_override since we have non-uniform
|
|
# image sizes for Pixtral
|
|
return input_processor_for_pixtral_hf(
|
|
model_config,
|
|
vision_config,
|
|
inputs,
|
|
image_token_id=hf_config.image_token_index,
|
|
)
|
|
|
|
msg = f"Unsupported vision config: {type(vision_config)}"
|
|
raise NotImplementedError(msg)
|
|
|
|
|
|
class LlavaLikeConfig(Protocol):
|
|
vision_config: PretrainedConfig
|
|
vision_feature_layer: int
|
|
|
|
|
|
def init_vision_tower_for_llava(
|
|
hf_config: LlavaLikeConfig,
|
|
quant_config: Optional[QuantizationConfig],
|
|
*,
|
|
require_post_norm: Optional[bool] = None,
|
|
prefix: str = "",
|
|
):
|
|
vision_config = hf_config.vision_config
|
|
|
|
# Initialize the vision tower only up to the required feature layer
|
|
vision_feature_layer = hf_config.vision_feature_layer
|
|
if vision_feature_layer < 0:
|
|
num_hidden_layers = hf_config.vision_config.num_hidden_layers \
|
|
+ vision_feature_layer + 1
|
|
else:
|
|
num_hidden_layers = vision_feature_layer + 1
|
|
|
|
if isinstance(vision_config, CLIPVisionConfig):
|
|
return CLIPVisionModel(
|
|
vision_config,
|
|
quant_config=quant_config,
|
|
num_hidden_layers_override=num_hidden_layers,
|
|
require_post_norm=require_post_norm,
|
|
prefix=prefix,
|
|
)
|
|
elif isinstance(vision_config, SiglipVisionConfig):
|
|
return SiglipVisionModel(
|
|
vision_config,
|
|
quant_config=quant_config,
|
|
num_hidden_layers_override=num_hidden_layers,
|
|
require_post_norm=require_post_norm,
|
|
prefix=prefix,
|
|
)
|
|
elif isinstance(vision_config, PixtralVisionConfig):
|
|
return PixtralHFVisionModel(
|
|
vision_config,
|
|
quant_config=quant_config,
|
|
num_hidden_layers_override=num_hidden_layers,
|
|
require_post_norm=require_post_norm,
|
|
prefix=prefix,
|
|
)
|
|
|
|
msg = f"Unsupported vision config: {type(vision_config)}"
|
|
raise NotImplementedError(msg)
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_image_input_mapper()
|
|
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_image_tokens)
|
|
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava)
|
|
@INPUT_REGISTRY.register_input_processor(input_processor_for_llava)
|
|
class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
|
|
|
|
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
|
|
|
|
# NOTE: These are special cases for Pixtral-12B in the HF-format
|
|
# https://huggingface.co/mistral-community/pixtral-12b/blob/main/config.json # noqa
|
|
if (config.text_config.architectures is None
|
|
and config.text_config.model_type == "mistral"):
|
|
config.text_config.architectures = ["MistralForCausalLM"]
|
|
if (config.projector_hidden_act is None
|
|
and config.vision_config.hidden_act == "gelu"):
|
|
config.projector_hidden_act = "gelu"
|
|
|
|
# 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 = LlavaMultiModalProjector(
|
|
vision_hidden_size=config.vision_config.hidden_size,
|
|
text_hidden_size=config.text_config.hidden_size,
|
|
projector_hidden_act=config.projector_hidden_act)
|
|
|
|
self.language_model = init_vllm_registered_model(
|
|
config.text_config,
|
|
vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "language_model"))
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.language_model.make_empty_intermediate_tensors)
|
|
|
|
@cached_property
|
|
def sampler(self):
|
|
if hasattr(self.language_model, "sampler"):
|
|
return self.language_model.sampler
|
|
|
|
return get_sampler()
|
|
|
|
def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
|
|
h = w = self.config.vision_config.image_size
|
|
expected_dims = (3, h, w)
|
|
actual_dims = tuple(data.shape[1:])
|
|
|
|
if actual_dims != expected_dims:
|
|
expected_expr = ("batch_size", *map(str, expected_dims))
|
|
raise ValueError(
|
|
f"The expected shape of pixel values is {expected_expr}. "
|
|
f"You supplied {tuple(data.shape)}.")
|
|
|
|
return data
|
|
|
|
def _validate_image_sizes(self, images: List[torch.Tensor],
|
|
sizes: List[torch.Tensor]) -> List[torch.Tensor]:
|
|
if not isinstance(sizes, list):
|
|
sizes = [sizes]
|
|
|
|
total_images = sum(size.numel() // 2 for size in sizes)
|
|
if total_images != len(images):
|
|
raise ValueError("Mismatch in number of images. "
|
|
f"Expected {total_images}, got {len(images)}")
|
|
img_idx = 0
|
|
for size in sizes:
|
|
# Flatten the size tensor to a list of (height, width) pairs
|
|
size = size.view(-1, 2).tolist()
|
|
for expected_h, expected_w in size:
|
|
if img_idx >= len(images):
|
|
raise ValueError("Ran out of images before sizes. "
|
|
f"{img_idx} >= {len(images)}")
|
|
img = images[img_idx]
|
|
if img.shape[-2:] != (expected_h, expected_w):
|
|
raise ValueError(
|
|
"Image size mismatch. Expected "
|
|
f"{(expected_h, expected_w)}, got {img.shape[-2:]}")
|
|
if img.shape[-3] != 3:
|
|
raise ValueError("Image channel mismatch. Expected 3, "
|
|
f"got {img.shape[-3]}")
|
|
img_idx += 1
|
|
return images
|
|
|
|
def _parse_and_validate_image_input(
|
|
self, **kwargs: object) -> Optional[LlavaImageInputs]:
|
|
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:
|
|
if not isinstance(pixel_values, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of pixel values. "
|
|
f"Got type: {type(pixel_values)}")
|
|
|
|
# Case for models like PixtralHF that have dynamic image sizes
|
|
# so we need to produce a list of tensors
|
|
if image_sizes is not None:
|
|
images = pixel_values
|
|
|
|
def flatten_to_3d_tensors(item):
|
|
if isinstance(item, torch.Tensor):
|
|
if item.dim() >= 3:
|
|
return [t for t in item.view(-1, *item.shape[-3:])]
|
|
else:
|
|
raise ValueError(
|
|
f"Unexpected tensor dimension: {item.dim()}")
|
|
elif isinstance(item, list):
|
|
return [
|
|
t for subitem in item
|
|
for t in flatten_to_3d_tensors(subitem)
|
|
]
|
|
else:
|
|
raise ValueError(f"Unexpected type: {type(item)}")
|
|
|
|
# Restructure the batched images into a list of lists of images
|
|
images = flatten_to_3d_tensors(pixel_values)
|
|
|
|
return LlavaImagePixelInputs(
|
|
type="pixel_values",
|
|
data=self._validate_image_sizes(images, image_sizes),
|
|
)
|
|
|
|
return LlavaImagePixelInputs(
|
|
type="pixel_values",
|
|
data=self._validate_pixel_values(
|
|
flatten_bn(pixel_values, concat=True)),
|
|
)
|
|
|
|
if image_embeds is not None:
|
|
if not isinstance(image_embeds, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of image embeddings. "
|
|
f"Got type: {type(image_embeds)}")
|
|
|
|
return LlavaImageEmbeddingInputs(
|
|
type="image_embeds",
|
|
data=flatten_bn(image_embeds, concat=True),
|
|
)
|
|
|
|
raise AssertionError("This line should be unreachable.")
|
|
|
|
def _select_image_features(self, image_features: torch.Tensor, *,
|
|
strategy: str) -> torch.Tensor:
|
|
# Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421 # noqa
|
|
if strategy == "default":
|
|
return image_features[:, 1:]
|
|
elif strategy == "full":
|
|
return image_features
|
|
|
|
raise ValueError(f"Unexpected select feature strategy: {strategy}")
|
|
|
|
def _image_pixels_to_features(
|
|
self,
|
|
vision_tower: Union[CLIPVisionModel, SiglipVisionModel,
|
|
PixtralHFVisionModel],
|
|
pixel_values: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
|
|
# NOTE: we skip the step to select the vision feature layer since
|
|
# this is already done inside the vision tower
|
|
image_features = vision_tower(pixel_values)
|
|
|
|
return self._select_image_features(
|
|
image_features,
|
|
strategy=self.config.vision_feature_select_strategy,
|
|
)
|
|
|
|
def _process_image_pixels(self,
|
|
inputs: LlavaImagePixelInputs) -> torch.Tensor:
|
|
assert self.vision_tower is not None
|
|
|
|
pixel_values = inputs["data"]
|
|
|
|
return self._image_pixels_to_features(self.vision_tower, pixel_values)
|
|
|
|
def _process_image_input(self,
|
|
image_input: LlavaImageInputs) -> 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)
|
|
return self.multi_modal_projector(image_features)
|
|
|
|
def process_mm_inputs(self, **kwargs):
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
if image_input is None:
|
|
return None
|
|
vision_embeddings = self._process_image_input(image_input)
|
|
return vision_embeddings
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
vision_embeddings: Optional[NestedTensors] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
|
|
if vision_embeddings is not None:
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, vision_embeddings,
|
|
self.config.image_token_index)
|
|
return inputs_embeds
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
"""Run forward pass for LLaVA-1.5.
|
|
|
|
One key thing to understand is the `input_ids` already accounts for the
|
|
positions of the to-be-inserted image embeddings.
|
|
|
|
Concretely, consider a text prompt:
|
|
`"USER: <image>\\nWhat's the content of the image?\\nASSISTANT:"`.
|
|
|
|
Tokenizer outputs:
|
|
`[1, 3148, 1001, 29901, 29871, 32000, 29871, 13, 5618, 29915, 29879,
|
|
278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901]`.
|
|
|
|
To reserve space in KV cache, we have to insert placeholder tokens
|
|
before they are inputted to the model, so the input processor prepends
|
|
additional image tokens (denoted as `32000`), resulting in:
|
|
`[1, 3148, 1001, 29901, 29871, 32000, ..., 32000, 29871, 13, 5618,
|
|
29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566,
|
|
29901]`.
|
|
|
|
We insert 575 tokens so that including the original image token in the
|
|
input, there are a total of 576 (24 * 24) image tokens, which
|
|
corresponds to the number of image tokens inputted to the language
|
|
model, i.e. the number of image tokens outputted by the visual encoder.
|
|
|
|
This way, the `positions` and `attn_metadata` are consistent
|
|
with the `input_ids`.
|
|
|
|
Args:
|
|
input_ids: Flattened (concatenated) input_ids corresponding to a
|
|
batch.
|
|
pixel_values: The pixels in each input image.
|
|
|
|
See also:
|
|
:class:`LlavaImageInputs`
|
|
"""
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
elif inputs_embeds is None:
|
|
vision_embeddings = self.process_mm_inputs(**kwargs)
|
|
# always pass the input via `inputs_embeds`
|
|
# to make sure the computation graph is consistent
|
|
inputs_embeds = self.get_input_embeddings(input_ids,
|
|
vision_embeddings)
|
|
input_ids = None
|
|
|
|
hidden_states = self.language_model.model(input_ids,
|
|
positions,
|
|
kv_caches,
|
|
attn_metadata,
|
|
intermediate_tensors,
|
|
inputs_embeds=inputs_embeds)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
return self.language_model.compute_logits(hidden_states,
|
|
sampling_metadata)
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
return self.language_model.sample(logits, sampling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|