add qwen3
This commit is contained in:
Chranos
912
vllm-v0.6.2/vllm/model_executor/models/llava_onevision.py
Normal file
912
vllm-v0.6.2/vllm/model_executor/models/llava_onevision.py
Normal file
@@ -0,0 +1,912 @@
|
||||
import math
|
||||
from functools import cached_property
|
||||
from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
|
||||
TypedDict, Union)
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from PIL import Image
|
||||
from transformers import (CLIPVisionConfig, LlavaOnevisionConfig,
|
||||
SiglipVisionConfig)
|
||||
|
||||
# Conditional import for transformers compatibility
|
||||
try:
|
||||
from transformers.models.llava_onevision.modeling_llava_onevision import (
|
||||
get_anyres_image_grid_shape, unpad_image)
|
||||
except ImportError:
|
||||
def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
|
||||
"""Fallback implementation"""
|
||||
height, width = image_size
|
||||
best_resolution = None
|
||||
for pinpoint in grid_pinpoints:
|
||||
if pinpoint[0] >= height and pinpoint[1] >= width:
|
||||
if best_resolution is None or (pinpoint[0] * pinpoint[1] < best_resolution[0] * best_resolution[1]):
|
||||
best_resolution = pinpoint
|
||||
if best_resolution is None:
|
||||
best_resolution = grid_pinpoints[-1]
|
||||
return (best_resolution[0] // patch_size, best_resolution[1] // patch_size)
|
||||
|
||||
def unpad_image(tensor, original_size):
|
||||
"""Fallback implementation"""
|
||||
return tensor
|
||||
|
||||
from typing_extensions import NotRequired
|
||||
|
||||
from vllm.attention import AttentionMetadata
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
|
||||
InputContext, token_inputs)
|
||||
from vllm.model_executor.layers.activation import get_act_fn
|
||||
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.utils import (cached_get_tokenizer,
|
||||
repeat_and_pad_placeholder_tokens)
|
||||
from vllm.sequence import IntermediateTensors
|
||||
from vllm.utils import is_list_of
|
||||
|
||||
from .clip import (CLIPVisionModel, dummy_seq_data_for_clip,
|
||||
dummy_video_for_clip, get_clip_image_feature_size,
|
||||
get_clip_patch_grid_length, input_processor_for_clip)
|
||||
from .interfaces import SupportsMultiModal, SupportsPP
|
||||
from .llava import init_vision_tower_for_llava
|
||||
from .siglip import (SiglipVisionModel, dummy_seq_data_for_siglip,
|
||||
dummy_video_for_siglip, get_siglip_image_feature_size,
|
||||
get_siglip_patch_grid_length, input_processor_for_siglip)
|
||||
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
|
||||
maybe_prefix, merge_multimodal_embeddings)
|
||||
|
||||
# Result in the max possible feature size (2x2 grid of 336x336px tiles)
|
||||
MAX_IMAGE_FEATURE_SIZE_HEIGHT = MAX_IMAGE_FEATURE_SIZE_WIDTH = 448
|
||||
|
||||
# For profile run
|
||||
_MAX_FRAMES_PER_VIDEO = 16
|
||||
|
||||
|
||||
class LlavaOnevisionVideoPixelInputs(TypedDict):
|
||||
type: Literal["pixel_values_videos"]
|
||||
data: Union[torch.Tensor, List[torch.Tensor]]
|
||||
"""
|
||||
Shape: `(batch_size, num_videos, num_frames, num_channels, height, width)`
|
||||
|
||||
Note that `num_videos` may be different for each batch, and 'num_frames'
|
||||
may be different for each video, in which case the data is passed as a
|
||||
list instead of a batched tensor.
|
||||
"""
|
||||
|
||||
|
||||
class LlavaOnevisionImagePixelInputs(TypedDict):
|
||||
type: Literal["pixel_values"]
|
||||
data: Union[torch.Tensor, List[torch.Tensor]]
|
||||
"""
|
||||
Shape:
|
||||
`(batch_size * num_images, 1 + num_patches, num_channels, height, 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.
|
||||
"""
|
||||
|
||||
image_sizes: NotRequired[torch.Tensor]
|
||||
"""
|
||||
Shape: `(batch_size * num_images, 2)`
|
||||
|
||||
This should be in `(height, width)` format.
|
||||
"""
|
||||
|
||||
|
||||
class LlavaOnevisionImageEmbeddingInputs(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.
|
||||
"""
|
||||
|
||||
|
||||
LlavaOnevisionImageInputs = Union[LlavaOnevisionImagePixelInputs,
|
||||
LlavaOnevisionImageEmbeddingInputs]
|
||||
|
||||
LlavaOnevisionMultiInputs = Union[LlavaOnevisionImageInputs,
|
||||
LlavaOnevisionVideoPixelInputs]
|
||||
|
||||
|
||||
def _get_llava_onevision_image_unppaded_feature_size(height, width, patches,
|
||||
scale_height,
|
||||
scale_width):
|
||||
current_height = patches * scale_height
|
||||
current_width = patches * scale_width
|
||||
|
||||
original_aspect_ratio = width / height
|
||||
current_aspect_ratio = current_width / current_height
|
||||
if original_aspect_ratio > current_aspect_ratio:
|
||||
new_height = int(height * (current_width / width))
|
||||
padding = (current_height - new_height) // 2
|
||||
current_height -= padding * 2
|
||||
else:
|
||||
new_width = int(width * (current_height / height))
|
||||
padding = (current_width - new_width) // 2
|
||||
current_width -= padding * 2
|
||||
|
||||
unpadded_features = current_height * current_width
|
||||
newline_features = current_height
|
||||
|
||||
ratio = math.sqrt(current_height * current_width / (9 * patches**2))
|
||||
if ratio > 1.1:
|
||||
unpadded_features = int(current_height // ratio) * int(
|
||||
current_width // ratio)
|
||||
newline_features = int(current_height // ratio)
|
||||
|
||||
return (unpadded_features, newline_features)
|
||||
|
||||
|
||||
def get_llava_onevision_image_feature_size(
|
||||
hf_config: LlavaOnevisionConfig,
|
||||
*,
|
||||
input_height: int,
|
||||
input_width: int,
|
||||
) -> int:
|
||||
vision_config = hf_config.vision_config
|
||||
|
||||
if isinstance(vision_config, CLIPVisionConfig):
|
||||
num_patches = get_clip_patch_grid_length(
|
||||
image_size=vision_config.image_size,
|
||||
patch_size=vision_config.patch_size,
|
||||
)
|
||||
base_feature_size = get_clip_image_feature_size(vision_config)
|
||||
elif isinstance(vision_config, SiglipVisionConfig):
|
||||
num_patches = get_siglip_patch_grid_length(
|
||||
image_size=vision_config.image_size,
|
||||
patch_size=vision_config.patch_size,
|
||||
)
|
||||
base_feature_size = get_siglip_image_feature_size(vision_config)
|
||||
else:
|
||||
msg = f"Unsupported vision config: {type(vision_config)}"
|
||||
raise NotImplementedError(msg)
|
||||
|
||||
strategy = hf_config.vision_feature_select_strategy
|
||||
if strategy == "default":
|
||||
base_feature_size -= 1
|
||||
elif strategy == "full":
|
||||
pass
|
||||
else:
|
||||
raise ValueError(f"Unexpected select feature strategy: {strategy}")
|
||||
|
||||
num_patch_height, num_patch_width = get_anyres_image_grid_shape(
|
||||
image_size=(input_height, input_width),
|
||||
grid_pinpoints=hf_config.image_grid_pinpoints,
|
||||
patch_size=vision_config.image_size,
|
||||
)
|
||||
|
||||
(
|
||||
unpadded_feature_size,
|
||||
newline_feature_size,
|
||||
) = _get_llava_onevision_image_unppaded_feature_size(
|
||||
input_height, input_width, num_patches, num_patch_height,
|
||||
num_patch_width)
|
||||
|
||||
return unpadded_feature_size + newline_feature_size + base_feature_size
|
||||
|
||||
|
||||
def get_max_llava_onevision_image_tokens(ctx: InputContext):
|
||||
return get_llava_onevision_image_feature_size(
|
||||
ctx.get_hf_config(LlavaOnevisionConfig),
|
||||
input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
|
||||
input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH,
|
||||
)
|
||||
|
||||
|
||||
def get_llava_onevision_video_frame_feature_size(
|
||||
hf_config: LlavaOnevisionConfig) -> int:
|
||||
# Support both CLIPVisionConfig and SiglipVisionConfig
|
||||
image_size = hf_config.vision_config.image_size
|
||||
patch_size = hf_config.vision_config.patch_size
|
||||
spatial_pool_stride = hf_config.spatial_pool_stride if hasattr(
|
||||
hf_config, "spatial_pool_stride") else 2
|
||||
|
||||
height = width = image_size // patch_size
|
||||
return math.ceil(height / spatial_pool_stride) * math.ceil(
|
||||
width / spatial_pool_stride)
|
||||
|
||||
|
||||
def get_llava_onevision_video_tokens(ctx: InputContext,
|
||||
num_frames: int) -> int:
|
||||
hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
|
||||
|
||||
# TODO: support configuring (not supported by HF right now)
|
||||
num_token_image_newline = 1
|
||||
tokens_per_frame = get_llava_onevision_video_frame_feature_size(hf_config)
|
||||
video_feature_size = num_frames * tokens_per_frame + num_token_image_newline
|
||||
|
||||
return video_feature_size
|
||||
|
||||
|
||||
def get_max_llava_onevision_video_tokens(ctx: InputContext) -> int:
|
||||
return get_llava_onevision_video_tokens(ctx, _MAX_FRAMES_PER_VIDEO)
|
||||
|
||||
|
||||
def dummy_data_for_llava_onevision(ctx: InputContext, seq_len: int,
|
||||
mm_counts: Mapping[str, int]):
|
||||
hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
|
||||
vision_config = hf_config.vision_config
|
||||
|
||||
num_videos = mm_counts["video"]
|
||||
|
||||
# TODO: support configuring the number of frames
|
||||
num_frames = _MAX_FRAMES_PER_VIDEO
|
||||
video_feature_size = get_llava_onevision_video_tokens(ctx, num_frames)
|
||||
|
||||
if isinstance(vision_config, CLIPVisionConfig):
|
||||
seq_data, ranges = dummy_seq_data_for_clip(
|
||||
vision_config,
|
||||
seq_len,
|
||||
num_videos,
|
||||
image_token_id=hf_config.video_token_index,
|
||||
image_feature_size_override=video_feature_size,
|
||||
mm_key="video")
|
||||
|
||||
mm_data = dummy_video_for_clip(vision_config,
|
||||
num_frames=num_frames,
|
||||
num_videos=num_videos)
|
||||
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_videos,
|
||||
image_token_id=hf_config.video_token_index,
|
||||
image_feature_size_override=video_feature_size,
|
||||
mm_key="video")
|
||||
|
||||
mm_data = dummy_video_for_siglip(vision_config,
|
||||
num_frames=num_frames,
|
||||
num_videos=num_videos)
|
||||
return DummyData(seq_data, mm_data, ranges)
|
||||
|
||||
msg = f"Unsupported vision config: {type(vision_config)}"
|
||||
raise NotImplementedError(msg)
|
||||
|
||||
|
||||
def input_processor_when_multimodal_input_image(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(LlavaOnevisionConfig)
|
||||
vision_config = hf_config.vision_config
|
||||
|
||||
image_data = multi_modal_data["image"]
|
||||
if isinstance(image_data, Image.Image):
|
||||
width, height = image_data.size
|
||||
|
||||
image_feature_size = get_llava_onevision_image_feature_size(
|
||||
hf_config,
|
||||
input_height=height,
|
||||
input_width=width,
|
||||
)
|
||||
elif is_list_of(image_data, Image.Image):
|
||||
image_feature_size = [
|
||||
get_llava_onevision_image_feature_size(hf_config,
|
||||
input_height=img.height,
|
||||
input_width=img.width)
|
||||
for img in 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)}")
|
||||
|
||||
vision_config = hf_config.vision_config
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
msg = f"Unsupported vision config: {type(vision_config)}"
|
||||
raise NotImplementedError(msg)
|
||||
|
||||
|
||||
def input_processor_when_multimodal_input_video(ctx: InputContext,
|
||||
inputs: DecoderOnlyInputs):
|
||||
multi_modal_data = inputs.get("multi_modal_data")
|
||||
if multi_modal_data is None or "video" not in multi_modal_data:
|
||||
return inputs
|
||||
video_data = multi_modal_data["video"]
|
||||
|
||||
model_config = ctx.model_config
|
||||
hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
|
||||
|
||||
if isinstance(video_data, np.ndarray):
|
||||
# Supports both CLIP and Siglip
|
||||
num_frames = video_data.shape[0]
|
||||
video_feature_size = get_llava_onevision_video_tokens(ctx, num_frames)
|
||||
tokenizer = cached_get_tokenizer(model_config.tokenizer)
|
||||
|
||||
new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
|
||||
tokenizer,
|
||||
inputs.get("prompt"),
|
||||
inputs["prompt_token_ids"],
|
||||
placeholder_token_id=hf_config.video_token_index,
|
||||
repeat_count=video_feature_size,
|
||||
)
|
||||
|
||||
return token_inputs(prompt_token_ids=new_token_ids,
|
||||
prompt=new_prompt,
|
||||
multi_modal_data=multi_modal_data,
|
||||
multi_modal_placeholders={"video": ranges})
|
||||
|
||||
elif is_list_of(video_data, np.ndarray):
|
||||
video_feature_size = []
|
||||
for video in video_data:
|
||||
num_frames = video.shape[0]
|
||||
video_feature_size.append(
|
||||
get_llava_onevision_video_tokens(ctx, num_frames))
|
||||
|
||||
tokenizer = cached_get_tokenizer(model_config.tokenizer)
|
||||
new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
|
||||
tokenizer,
|
||||
inputs.get("prompt"),
|
||||
inputs["prompt_token_ids"],
|
||||
placeholder_token_id=hf_config.video_token_index,
|
||||
repeat_count=video_feature_size,
|
||||
)
|
||||
return token_inputs(prompt_token_ids=new_token_ids,
|
||||
prompt=new_prompt,
|
||||
multi_modal_data=multi_modal_data,
|
||||
multi_modal_placeholders={"video": ranges})
|
||||
else:
|
||||
raise TypeError(f"Invalid video type: {type(video_data)}")
|
||||
|
||||
msg = f"Unsupported video type: {type(video_data)}"
|
||||
raise NotImplementedError(msg)
|
||||
|
||||
|
||||
def input_processor_for_llava_onevision(ctx: InputContext,
|
||||
inputs: DecoderOnlyInputs):
|
||||
multi_modal_data = inputs.get("multi_modal_data")
|
||||
if multi_modal_data is None or ("video" not in multi_modal_data
|
||||
and "image" not in multi_modal_data):
|
||||
return inputs
|
||||
if "image" in multi_modal_data:
|
||||
return input_processor_when_multimodal_input_image(ctx, inputs)
|
||||
if "video" in multi_modal_data:
|
||||
return input_processor_when_multimodal_input_video(ctx, inputs)
|
||||
|
||||
msg = "Unsupported multi data type"
|
||||
raise NotImplementedError(msg)
|
||||
|
||||
|
||||
class LlavaOnevisionMultiModalProjector(nn.Module):
|
||||
|
||||
def __init__(self, config: LlavaOnevisionConfig):
|
||||
super().__init__()
|
||||
|
||||
self.linear_1 = nn.Linear(config.vision_config.hidden_size,
|
||||
config.text_config.hidden_size,
|
||||
bias=True)
|
||||
self.act = get_act_fn(config.projector_hidden_act)
|
||||
self.linear_2 = nn.Linear(config.text_config.hidden_size,
|
||||
config.text_config.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
|
||||
|
||||
|
||||
@MULTIMODAL_REGISTRY.register_image_input_mapper()
|
||||
@MULTIMODAL_REGISTRY.register_input_mapper("video")
|
||||
@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
|
||||
"image", get_max_llava_onevision_image_tokens)
|
||||
@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
|
||||
"video", get_max_llava_onevision_video_tokens)
|
||||
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_onevision)
|
||||
@INPUT_REGISTRY.register_input_processor(input_processor_for_llava_onevision)
|
||||
class LlavaOnevisionForConditionalGeneration(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
|
||||
|
||||
# Initialize the vision tower only up to the required feature layer
|
||||
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 = LlavaOnevisionMultiModalProjector(config)
|
||||
self.language_model = init_vllm_registered_model(
|
||||
config.text_config,
|
||||
vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "language_model"))
|
||||
self.image_newline = nn.Parameter(
|
||||
torch.empty(config.text_config.hidden_size))
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.language_model.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_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
|
||||
expected_dims = (2, )
|
||||
|
||||
def _validate_shape(d: torch.Tensor):
|
||||
actual_dims = tuple(d.shape)
|
||||
|
||||
if actual_dims != expected_dims:
|
||||
expected_expr = str(expected_dims)
|
||||
raise ValueError(
|
||||
f"The expected shape of image sizes per image per batch "
|
||||
f"is {expected_expr}. You supplied {tuple(d.shape)}.")
|
||||
|
||||
for d in data:
|
||||
_validate_shape(d)
|
||||
|
||||
return data
|
||||
|
||||
def _validate_image_pixel_values(
|
||||
self, data: Union[torch.Tensor, List[torch.Tensor]]
|
||||
) -> Union[torch.Tensor, List[torch.Tensor]]:
|
||||
|
||||
h = w = self.config.vision_config.image_size
|
||||
expected_dims = (3, h, w)
|
||||
|
||||
def _validate_shape(d: torch.Tensor):
|
||||
actual_dims = tuple(d.shape[1:])
|
||||
|
||||
if actual_dims != expected_dims:
|
||||
expected_expr = ("num_patches", *map(str, expected_dims))
|
||||
raise ValueError(
|
||||
"The expected shape of pixel values per image per batch "
|
||||
f"is {expected_expr}. You supplied {tuple(d.shape)}.")
|
||||
|
||||
for d in data:
|
||||
_validate_shape(d)
|
||||
|
||||
return data
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object) -> Optional[LlavaOnevisionImageInputs]:
|
||||
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)}")
|
||||
|
||||
if not isinstance(image_sizes, (torch.Tensor, list)):
|
||||
raise ValueError("Incorrect type of image sizes. "
|
||||
f"Got type: {type(image_sizes)}")
|
||||
|
||||
return LlavaOnevisionImagePixelInputs(
|
||||
type="pixel_values",
|
||||
data=self._validate_image_pixel_values(
|
||||
flatten_bn(pixel_values)),
|
||||
image_sizes=self._validate_image_sizes(
|
||||
flatten_bn(image_sizes, concat=True)),
|
||||
)
|
||||
|
||||
if image_embeds is not None:
|
||||
if not isinstance(image_embeds, torch.Tensor):
|
||||
raise ValueError("Incorrect type of image embeds. "
|
||||
f"Got type: {type(image_embeds)}")
|
||||
|
||||
return LlavaOnevisionImageEmbeddingInputs(
|
||||
type="image_embeds",
|
||||
data=flatten_bn(image_embeds),
|
||||
)
|
||||
|
||||
raise AssertionError("This line should be unreachable.")
|
||||
|
||||
def _validate_video_pixel_values(
|
||||
self, data: Union[torch.Tensor, List[torch.Tensor]]
|
||||
) -> Union[torch.Tensor, List[torch.Tensor]]:
|
||||
|
||||
h = w = self.config.vision_config.image_size
|
||||
expected_dims = (3, h, w)
|
||||
|
||||
def _validate_shape(d: torch.Tensor):
|
||||
actual_dims = tuple(d.shape[2:])
|
||||
|
||||
if actual_dims != expected_dims:
|
||||
expected_expr = ("num_frames", *map(str, expected_dims))
|
||||
raise ValueError(
|
||||
"The expected shape of pixel values in each video frame "
|
||||
f"is {expected_expr}. You supplied {tuple(d.shape)}.")
|
||||
|
||||
for d in data:
|
||||
_validate_shape(d)
|
||||
|
||||
return data
|
||||
|
||||
def _parse_and_validate_video_input(
|
||||
self,
|
||||
**kwargs: object) -> Optional[LlavaOnevisionVideoPixelInputs]:
|
||||
"""
|
||||
A legal video input should have the following dimensions:
|
||||
{
|
||||
"pixel_values_videos" :
|
||||
List[b, Tensor(nb_frames, nb_channels, height, width)]
|
||||
}
|
||||
"""
|
||||
pixel_values = kwargs.pop("pixel_values_videos", None)
|
||||
|
||||
if pixel_values is None:
|
||||
return None
|
||||
|
||||
if not (is_list_of(pixel_values,
|
||||
(torch.Tensor)) # different shape videos
|
||||
or isinstance(pixel_values,
|
||||
torch.Tensor)): # same shape videos
|
||||
raise ValueError("Incorrect type of pixel values. "
|
||||
f"Got type: {type(pixel_values)}")
|
||||
|
||||
return LlavaOnevisionVideoPixelInputs(
|
||||
type="pixel_values_videos",
|
||||
data=pixel_values,
|
||||
)
|
||||
|
||||
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
|
||||
modalities = {}
|
||||
|
||||
if "pixel_values" in kwargs:
|
||||
modalities["images"] = self._parse_and_validate_image_input(
|
||||
**kwargs)
|
||||
|
||||
if "pixel_values_videos" in kwargs:
|
||||
modalities["videos"] = self._parse_and_validate_video_input(
|
||||
**kwargs)
|
||||
|
||||
return modalities
|
||||
|
||||
def _select_image_features(self, image_features: torch.Tensor, *,
|
||||
strategy: str) -> torch.Tensor:
|
||||
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],
|
||||
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,
|
||||
)
|
||||
|
||||
# Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py
|
||||
def _merge_image_patch_embeddings(self,
|
||||
image_size: torch.Tensor,
|
||||
patch_embeddings: torch.Tensor,
|
||||
*,
|
||||
image_newline=None,
|
||||
vision_aspect_ratio="anyres_max_9",
|
||||
strategy: str) -> torch.Tensor:
|
||||
if strategy == "flat":
|
||||
return patch_embeddings.flatten(0, 1)
|
||||
|
||||
if strategy.startswith("spatial"):
|
||||
height = width = self.config.vision_config.image_size \
|
||||
// self.config.vision_config.patch_size
|
||||
|
||||
base_patch_embeds = patch_embeddings[0]
|
||||
if height * width != base_patch_embeds.shape[0]:
|
||||
raise ValueError(
|
||||
"The number of patches is not consistent with the "
|
||||
"image size.")
|
||||
|
||||
if patch_embeddings.shape[0] > 1:
|
||||
other_patch_embeds = patch_embeddings[1:]
|
||||
|
||||
# Move to CPU to avoid floating-point errors
|
||||
orig_height, orig_width = image_size.tolist()
|
||||
|
||||
# image_aspect_ratio == "anyres"
|
||||
num_patch_height, num_patch_width = get_anyres_image_grid_shape(
|
||||
(orig_height, orig_width),
|
||||
self.config.image_grid_pinpoints,
|
||||
self.config.vision_config.image_size,
|
||||
)
|
||||
num_patches = num_patch_height * num_patch_width
|
||||
|
||||
# Image patches might be padded for batch processing
|
||||
other_patch_embeds = other_patch_embeds[:num_patches] \
|
||||
.view(num_patch_height, num_patch_width, height, width, -1)
|
||||
|
||||
if "unpad" in strategy:
|
||||
other_patch_embeds = other_patch_embeds \
|
||||
.permute(4, 0, 2, 1, 3).contiguous() \
|
||||
.flatten(1, 2).flatten(2, 3)
|
||||
other_patch_embeds = unpad_image(other_patch_embeds,
|
||||
(orig_height, orig_width))
|
||||
max_num_patches = int(
|
||||
vision_aspect_ratio.removeprefix("anyres_max_"))
|
||||
channels, curr_height, curr_width = other_patch_embeds.shape
|
||||
ratio = math.sqrt(curr_height * curr_width /
|
||||
(max_num_patches * height**2))
|
||||
if ratio > 1.1:
|
||||
other_patch_embeds = other_patch_embeds[None]
|
||||
other_patch_embeds = nn.functional.interpolate(
|
||||
other_patch_embeds, [
|
||||
int(curr_height // ratio),
|
||||
int(curr_width // ratio)
|
||||
],
|
||||
mode="bilinear")[0]
|
||||
if image_newline is not None:
|
||||
other_patch_embeds = torch.cat(
|
||||
(
|
||||
other_patch_embeds,
|
||||
image_newline[:, None, None] \
|
||||
.expand(*other_patch_embeds.shape[:-1], 1) \
|
||||
.to(other_patch_embeds.device),
|
||||
),
|
||||
dim=-1)
|
||||
other_patch_embeds = other_patch_embeds \
|
||||
.flatten(1, 2).transpose(0, 1)
|
||||
else:
|
||||
other_patch_embeds = other_patch_embeds \
|
||||
.permute(0, 2, 1, 3, 4).contiguous() \
|
||||
.flatten(0, 3)
|
||||
|
||||
merged_patch_embeddings = torch.cat(
|
||||
(base_patch_embeds, other_patch_embeds), dim=0)
|
||||
else:
|
||||
if "unpad" in strategy:
|
||||
merged_patch_embeddings = torch.cat(
|
||||
(base_patch_embeds,
|
||||
self.image_newline[None] \
|
||||
.to(base_patch_embeds.device)
|
||||
), dim=0)
|
||||
else:
|
||||
merged_patch_embeddings = base_patch_embeds
|
||||
|
||||
return merged_patch_embeddings
|
||||
|
||||
raise ValueError(f"Unexpected patch merge strategy: {strategy}")
|
||||
|
||||
def _process_image_pixels(
|
||||
self,
|
||||
inputs: LlavaOnevisionImagePixelInputs,
|
||||
) -> Union[torch.Tensor, List[torch.Tensor]]:
|
||||
assert self.vision_tower is not None
|
||||
|
||||
pixel_values = inputs["data"]
|
||||
|
||||
if isinstance(pixel_values, torch.Tensor):
|
||||
b, num_patches, c, h, w = pixel_values.shape
|
||||
stacked_pixel_values = pixel_values.view(b * num_patches, c, h, w)
|
||||
stacked_image_features = self._image_pixels_to_features(
|
||||
self.vision_tower, stacked_pixel_values)
|
||||
stacked_patch_embeddings = self.multi_modal_projector(
|
||||
stacked_image_features)
|
||||
|
||||
return stacked_patch_embeddings.view(
|
||||
b, num_patches, *stacked_patch_embeddings.shape[1:])
|
||||
|
||||
num_patches_per_batch = [v.shape[0] for v in pixel_values]
|
||||
stacked_pixel_values = torch.cat(pixel_values)
|
||||
stacked_image_features = self._image_pixels_to_features(
|
||||
self.vision_tower, stacked_pixel_values)
|
||||
|
||||
return [
|
||||
self.multi_modal_projector(image_features) for image_features in
|
||||
torch.split(stacked_image_features, num_patches_per_batch)
|
||||
]
|
||||
|
||||
def _process_image_input(
|
||||
self,
|
||||
image_input: LlavaOnevisionImageInputs,
|
||||
) -> Union[torch.Tensor, List[torch.Tensor]]:
|
||||
if image_input["type"] == "image_embeds":
|
||||
return [image_input["data"]]
|
||||
|
||||
patch_embeddings = self._process_image_pixels(image_input)
|
||||
|
||||
image_sizes = image_input.get("image_sizes")
|
||||
if image_sizes is None:
|
||||
batch_size = len(image_input["data"])
|
||||
vision_config = self.config.vision_config
|
||||
default_height = default_width = vision_config.image_size
|
||||
image_sizes = torch.as_tensor([[default_height, default_width]
|
||||
for _ in range(batch_size)])
|
||||
|
||||
return [
|
||||
self._merge_image_patch_embeddings(
|
||||
image_sizes[i],
|
||||
patch_features_batch,
|
||||
image_newline=self.image_newline,
|
||||
strategy="spatial_unpad")
|
||||
for i, patch_features_batch in enumerate(patch_embeddings)
|
||||
]
|
||||
|
||||
def _add_image_newline(
|
||||
self,
|
||||
video_features: torch.Tensor,
|
||||
videos: int = 1,
|
||||
frames: int = 1,
|
||||
strategy: str = "one_token",
|
||||
) -> torch.Tensor:
|
||||
if strategy == "one_token":
|
||||
video_features = video_features.reshape(
|
||||
videos, frames * video_features.shape[1], -1)
|
||||
image_newline = self.image_newline[None, None, :].repeat(
|
||||
videos, 1, 1).to(video_features.device)
|
||||
video_features = torch.cat((video_features, image_newline), dim=1)
|
||||
return video_features
|
||||
raise ValueError(f"Unexpected video newline strategy: {strategy}")
|
||||
|
||||
def _video_pixels_to_features(
|
||||
self,
|
||||
vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
|
||||
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
|
||||
video_features = vision_tower(pixel_values)
|
||||
video_features = self._select_image_features(
|
||||
video_features,
|
||||
strategy=self.config.vision_feature_select_strategy,
|
||||
)
|
||||
video_features = self.multi_modal_projector(video_features)
|
||||
video_features = self.apply_pooling(video_features)
|
||||
return video_features
|
||||
|
||||
def _process_video_pixels(self, inputs: LlavaOnevisionVideoPixelInputs):
|
||||
assert self.vision_tower is not None
|
||||
|
||||
video_pixels = inputs["data"]
|
||||
|
||||
if isinstance(video_pixels, torch.Tensor):
|
||||
b, num_videos, frames, c, h, w = video_pixels.shape
|
||||
pixel_values = video_pixels.view(b * num_videos * frames, c, h, w)
|
||||
stacked_embeddings = self._video_pixels_to_features(
|
||||
self.vision_tower, pixel_values)
|
||||
stacked_embeddings = self._add_image_newline(stacked_embeddings,
|
||||
videos=b * num_videos,
|
||||
frames=frames,
|
||||
strategy="one_token")
|
||||
return stacked_embeddings
|
||||
elif is_list_of(video_pixels, torch.Tensor):
|
||||
stacked_embeddings = []
|
||||
for video_pixel in video_pixels:
|
||||
num_videos, frames, c, h, w = video_pixel.shape
|
||||
pixel_values = video_pixel.view(num_videos * frames, c, h, w)
|
||||
embeddings = self._video_pixels_to_features(
|
||||
self.vision_tower, pixel_values)
|
||||
embeddings = self._add_image_newline(embeddings,
|
||||
videos=num_videos,
|
||||
frames=frames,
|
||||
strategy="one_token")
|
||||
stacked_embeddings.append(embeddings)
|
||||
return stacked_embeddings
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported type of video input {type(video_pixels)}")
|
||||
|
||||
def apply_pooling(self, image_features, stride=2):
|
||||
vision_config = self.config.vision_config
|
||||
height = width = vision_config.image_size // vision_config.patch_size
|
||||
batch_frames, _, dim = image_features.shape
|
||||
image_features = image_features.view(batch_frames, height, width, -1)
|
||||
image_features = image_features.permute(0, 3, 1, 2)
|
||||
|
||||
# TODO support other pooling types config
|
||||
height, width = image_features.shape[2:]
|
||||
scaled_shape = [math.ceil(height / stride), math.ceil(width / stride)]
|
||||
image_feature = nn.functional.interpolate(image_features,
|
||||
size=scaled_shape,
|
||||
mode='bilinear')
|
||||
image_feature = image_feature.permute(0, 2, 3, 1)
|
||||
image_feature = image_feature.view(batch_frames, -1, dim)
|
||||
return image_feature
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
kv_caches: List[torch.Tensor],
|
||||
attn_metadata: AttentionMetadata,
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
**kwargs: object,
|
||||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||||
"""Run forward pass for LlaVA-Onevision.
|
||||
Args:
|
||||
input_ids: Flattened (concatenated) input_ids corresponding to a
|
||||
batch.
|
||||
pixel_values_videos: Pixels in each frames for each input videos.
|
||||
"""
|
||||
if intermediate_tensors is not None:
|
||||
input_ids = None
|
||||
inputs_embeds = None
|
||||
else:
|
||||
modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
|
||||
if modalities:
|
||||
inputs_embeds = self.language_model.model.get_input_embeddings(
|
||||
input_ids)
|
||||
if "images" in modalities:
|
||||
image_input = modalities["images"]
|
||||
vision_embeddings = self._process_image_input(image_input)
|
||||
inputs_embeds = merge_multimodal_embeddings(
|
||||
input_ids, inputs_embeds, vision_embeddings,
|
||||
self.config.image_token_index)
|
||||
if "videos" in modalities:
|
||||
video_input = modalities["videos"]
|
||||
video_embeddings = self._process_video_pixels(video_input)
|
||||
inputs_embeds = merge_multimodal_embeddings(
|
||||
input_ids, inputs_embeds, video_embeddings,
|
||||
self.config.video_token_index)
|
||||
input_ids = None
|
||||
else:
|
||||
inputs_embeds = 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]]):
|
||||
loader = AutoWeightsLoader(self)
|
||||
loader.load_weights(weights)
|
||||
Reference in New Issue
Block a user