mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-01-03 03:08:41 +08:00
464 lines
18 KiB
Python
464 lines
18 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 The vLLM team.
|
|
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from typing import Dict, Iterable, List, Literal, Optional, Tuple, TypedDict
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
from PIL import Image
|
|
from transformers import CLIPVisionConfig, PretrainedConfig
|
|
|
|
from vllm.attention import AttentionMetadata
|
|
from vllm.config import CacheConfig, VisionLanguageConfig
|
|
from vllm.inputs import INPUT_REGISTRY, InputContext
|
|
from vllm.logger import init_logger
|
|
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
|
from vllm.model_executor.layers.quantization.base_config import (
|
|
QuantizationConfig)
|
|
from vllm.model_executor.layers.sampler import Sampler
|
|
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
|
|
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
|
from vllm.model_executor.models.clip import CLIPVisionModel
|
|
from vllm.model_executor.models.llama import LlamaModel
|
|
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
|
from vllm.multimodal import MULTIMODAL_REGISTRY
|
|
from vllm.sequence import IntermediateTensors, SamplerOutput
|
|
|
|
from .clip import dummy_image_for_clip, dummy_seq_data_for_clip
|
|
from .interfaces import SupportsVision
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
_KEYS_TO_MODIFY_MAPPING = {
|
|
"model.vision_embed_tokens": "vision_embed_tokens",
|
|
}
|
|
|
|
CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(dropout=0.0,
|
|
hidden_act="quick_gelu",
|
|
hidden_size=1024,
|
|
image_size=336,
|
|
intermediate_size=4096,
|
|
num_attention_heads=16,
|
|
num_channels=3,
|
|
num_hidden_layers=24,
|
|
patch_size=14,
|
|
projection_dim=768)
|
|
|
|
|
|
class Phi3ImageEmbeddingBase(nn.Module):
|
|
|
|
def __init__(self, wte=None) -> None:
|
|
super().__init__()
|
|
self.wte = wte
|
|
self.layer_idx: int
|
|
self.type_feature: str
|
|
self.img_processor: CLIPVisionModel
|
|
|
|
def get_img_features(self,
|
|
img_embeds: torch.FloatTensor) -> torch.FloatTensor:
|
|
LAYER_IDX = self.layer_idx
|
|
TYPE_FEATURE = self.type_feature
|
|
|
|
# NOTE: we skip the step to select the vision feature layer since
|
|
# this is already done inside the img_processor
|
|
img_feature = self.img_processor(img_embeds,
|
|
vision_feature_layer=LAYER_IDX)
|
|
|
|
if TYPE_FEATURE == "patch":
|
|
patch_feature = img_feature[:, 1:]
|
|
return patch_feature
|
|
|
|
if TYPE_FEATURE == "cls_patch":
|
|
return img_feature
|
|
|
|
raise NotImplementedError
|
|
|
|
|
|
# adapted from https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_embedding_phi3_v.py
|
|
class Phi3HDImageEmbedding(Phi3ImageEmbeddingBase):
|
|
"""Phi3 Image embedding with HD transform."""
|
|
|
|
def __init__(self,
|
|
vision_language_config: VisionLanguageConfig,
|
|
config: PretrainedConfig,
|
|
wte=None) -> None:
|
|
super().__init__(wte)
|
|
|
|
self.image_token_id = vision_language_config.image_token_id
|
|
# n_embed or hidden_size
|
|
hidden_size = config.n_embd if hasattr(
|
|
config, 'n_embd') else config.hidden_size
|
|
|
|
clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG
|
|
self.img_processor = CLIPVisionModel(clip_config)
|
|
image_dim_out = config.img_processor['image_dim_out']
|
|
self.num_img_tokens = config.img_processor['num_img_tokens']
|
|
|
|
self.image_dim_out = image_dim_out
|
|
|
|
# global_gn and sub_gn for hd transform, serves as line separator
|
|
self.use_hd_transform = config.embd_layer.get('use_hd_transform',
|
|
False)
|
|
self.with_learnable_separator = config.embd_layer.get(
|
|
'with_learnable_separator', False)
|
|
self.hd_transform_order = config.embd_layer.get(
|
|
'hd_transform_order', 'glb_sub')
|
|
# with_hd_transform and with_learnable_separator should have same value
|
|
assert self.use_hd_transform and self.with_learnable_separator
|
|
|
|
# 1024 * 4, merge spatial to channel dimension
|
|
self.glb_GN = nn.Parameter(torch.empty([1, 1, self.image_dim_out * 4]))
|
|
self.sub_GN = nn.Parameter(
|
|
torch.empty([1, 1, 1, self.image_dim_out * 4]))
|
|
|
|
dim_projection = hidden_size
|
|
depth = 2
|
|
layers = [nn.Linear(image_dim_out * 4, dim_projection)]
|
|
for _ in range(1, depth):
|
|
layers.extend(
|
|
[nn.GELU(),
|
|
nn.Linear(dim_projection, dim_projection)])
|
|
self.img_projection = nn.Sequential(*layers)
|
|
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.layer_idx = config.img_processor.get('layer_idx', -2)
|
|
self.type_feature = config.img_processor.get('type_feature', 'patch')
|
|
|
|
def forward(self, input_ids: torch.LongTensor,
|
|
pixel_values: torch.FloatTensor,
|
|
image_sizes: torch.Tensor) -> torch.FloatTensor:
|
|
"""process and merge text embeddings with image embeddings."""
|
|
|
|
# (batch_size, max_num_crops, 3, height, width)
|
|
img_embeds = pixel_values
|
|
|
|
# (batch_size, 2)
|
|
img_sizes = image_sizes
|
|
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
|
|
positions = torch.nonzero(input_ids == self.image_token_id)
|
|
|
|
select = False
|
|
|
|
target_dtype = self.img_projection[0].bias.dtype
|
|
|
|
if len(positions.tolist()) > 0:
|
|
# if self.use_hd_transform and img_sizes:
|
|
# img_embeds: (num_images, max_num_crops, 3, H, W)
|
|
# img_sizes: (num_images, 2).view(1, -1)
|
|
|
|
bs = img_embeds.shape[0]
|
|
# Nx(HW)xC
|
|
img_features = self.get_img_features(img_embeds.flatten(0, 1))
|
|
base_feat_height = base_feat_width = int(
|
|
img_features.shape[1]**0.5)
|
|
|
|
# bs x max_num_crops x (24x24) x C
|
|
img_features = img_features.view(
|
|
bs, -1, base_feat_height * base_feat_width, self.image_dim_out)
|
|
C = self.image_dim_out
|
|
H = base_feat_height
|
|
|
|
output_imgs = []
|
|
output_len = []
|
|
|
|
for _bs in range(bs):
|
|
h, w = img_sizes[_bs]
|
|
h = h // 336
|
|
w = w // 336
|
|
B_ = h * w
|
|
|
|
# 1 x (24x24) x 1024
|
|
global_img_feature = img_features[_bs, :1]
|
|
|
|
# 1 x 12 x 12 x 4096
|
|
glb_img = global_img_feature \
|
|
.reshape(1, H // 2, 2, H // 2, 2,C) \
|
|
.permute(0, 1, 3, 2, 4, 5) \
|
|
.reshape(1, H // 2, H // 2, 4 * C)
|
|
temp_glb_GN = self.sub_GN.repeat(1, H // 2, 1, 1)
|
|
|
|
# 1 x 156 x 4096
|
|
glb_img = torch.cat([glb_img, temp_glb_GN],
|
|
dim=2).reshape(1, -1, 4 * C)
|
|
|
|
# (max_num_crops-1) x (12x12) x C
|
|
sub_img = img_features[_bs, 1:]
|
|
# 16x574x1024
|
|
# get rid of padding sub_img
|
|
sub_img = sub_img[:B_]
|
|
|
|
sub_img = sub_img.reshape(B_, H // 2, 2, H // 2, 2, C) \
|
|
.permute(0, 1, 3, 2, 4, 5).reshape(B_, -1, 4 * C)
|
|
sub_img = sub_img.reshape(1, h, w, 12, 12, -1) \
|
|
.permute(0, 1, 3, 2, 4, 5) \
|
|
.reshape(1, h * 12, w * 12, 4 * C)
|
|
temp_sub_GN = self.sub_GN.repeat(1, h * 12, 1, 1)
|
|
sub_img = torch.cat([sub_img, temp_sub_GN],
|
|
dim=2).reshape(1, -1, 4 * C)
|
|
# (1, num_img_tokens, 1024*4)
|
|
|
|
# glb + sub
|
|
if self.hd_transform_order == 'glb_sub':
|
|
output_imgs.append(
|
|
torch.cat([glb_img, self.glb_GN, sub_img], dim=1))
|
|
elif self.hd_transform_order == 'sub_glb':
|
|
output_imgs.append(
|
|
torch.cat([sub_img, self.glb_GN, glb_img], dim=1))
|
|
|
|
temp_len = int((h * w + 1) * 144 + 1 + (h + 1) * 12)
|
|
output_len.append(temp_len)
|
|
|
|
num_img_tokens = output_len
|
|
img_set_tensor = []
|
|
for _output_img in output_imgs:
|
|
img_feature_proj = self.img_projection(
|
|
_output_img.to(target_dtype))
|
|
img_set_tensor.append(img_feature_proj)
|
|
select = True
|
|
|
|
input_ids.clamp_min_(0).clamp_max_(self.vocab_size)
|
|
|
|
hidden_states = self.wte(input_ids)
|
|
|
|
if select:
|
|
idx = 0
|
|
for i, cnt in enumerate(num_img_tokens):
|
|
hidden_states[positions[idx, 0],
|
|
positions[idx, 1]:positions[idx, 1] +
|
|
cnt] = (img_set_tensor[i].to(
|
|
hidden_states.dtype))
|
|
idx += cnt
|
|
|
|
return hidden_states.squeeze(0)
|
|
|
|
|
|
class Phi3VImagePixelInputs(TypedDict):
|
|
type: Literal["pixel_values"]
|
|
data: torch.Tensor
|
|
"""Shape: (batch_size, 1 + num_patches, num_channels, height, width)"""
|
|
|
|
image_sizes: torch.Tensor
|
|
"""Shape: (batch_size, 2)"""
|
|
|
|
|
|
def _get_phi3v_image_feature_size(
|
|
*,
|
|
input_height: int,
|
|
input_width: int,
|
|
) -> int:
|
|
h, w = input_height, input_width
|
|
|
|
# https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L178
|
|
return (h // 336 * w // 336 + 1) * 144 + 1 + (h // 336 + 1) * 12
|
|
|
|
|
|
def dummy_data_for_phi3v(ctx: InputContext, seq_len: int):
|
|
multimodal_config = ctx.get_multimodal_config()
|
|
|
|
#TODO: change the logic for dummy data to support dynamic shape
|
|
_, _, dummy_height, dummy_width = multimodal_config.image_input_shape
|
|
image_feature_size = _get_phi3v_image_feature_size(
|
|
input_height=dummy_height,
|
|
input_width=dummy_width,
|
|
)
|
|
|
|
seq_data = dummy_seq_data_for_clip(
|
|
CLIP_VIT_LARGE_PATCH14_336_CONFIG,
|
|
seq_len,
|
|
image_token_id=32044,
|
|
image_feature_size_override=image_feature_size,
|
|
)
|
|
mm_data = dummy_image_for_clip(
|
|
CLIP_VIT_LARGE_PATCH14_336_CONFIG,
|
|
image_width_override=dummy_width,
|
|
image_height_override=dummy_height,
|
|
)
|
|
|
|
return seq_data, mm_data
|
|
|
|
|
|
# Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py
|
|
def _calc_padded_size(*, width: int, height: int, padding_unit: int = 336):
|
|
target_height = int(np.ceil(height / padding_unit) * padding_unit)
|
|
top_padding = int((target_height - height) / 2)
|
|
bottom_padding = target_height - height - top_padding
|
|
padded_width = width
|
|
padded_height = height + top_padding + bottom_padding
|
|
return padded_width, padded_height
|
|
|
|
|
|
# Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py
|
|
def _calc_hd_transform_size(*, width: int, height: int, hd_num: int = 16):
|
|
transposed = False
|
|
if width < height:
|
|
width, height = height, width
|
|
transposed = True
|
|
|
|
ratio = width / height
|
|
scale = 1
|
|
while scale * np.ceil(scale / ratio) <= hd_num:
|
|
scale += 1
|
|
scale -= 1
|
|
|
|
new_width = int(scale * 336)
|
|
new_height = int(new_width / ratio)
|
|
|
|
padded_width, padded_height = _calc_padded_size(width=new_width,
|
|
height=new_height)
|
|
|
|
if transposed:
|
|
padded_width, padded_height = padded_height, padded_width
|
|
|
|
return padded_width, padded_height
|
|
|
|
|
|
def _image_processor(ctx: InputContext,
|
|
image: object) -> Dict[str, torch.Tensor]:
|
|
|
|
if isinstance(image, Image.Image):
|
|
# Temporary patch before dynamic number of image tokens is supported
|
|
_, _, h, w = ctx.get_multimodal_config().image_input_shape
|
|
if (w, h) != _calc_hd_transform_size(width=image.width,
|
|
height=image.height):
|
|
logger.warning(
|
|
"Dynamic image shape is currently not supported. "
|
|
"Resizing input image to (%d, %d).", w, h)
|
|
|
|
image = image.resize((w, h))
|
|
|
|
return MULTIMODAL_REGISTRY._get_plugin("image") \
|
|
._default_input_mapper(ctx, image)
|
|
raise TypeError(f"Invalid type for 'image': {type(image)}")
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_image_input_mapper(_image_processor)
|
|
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_phi3v)
|
|
class Phi3VForCausalLM(nn.Module, SupportsVision):
|
|
|
|
def __init__(self,
|
|
config: PretrainedConfig,
|
|
vlm_config: VisionLanguageConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None) -> None:
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
self.vlm_config = vlm_config
|
|
|
|
self.model = LlamaModel(config, cache_config, quant_config)
|
|
self.vision_embed_tokens = Phi3HDImageEmbedding(
|
|
vlm_config, config, self.model.embed_tokens)
|
|
self.lm_head = ParallelLMHead(config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config)
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.sampler = Sampler()
|
|
|
|
def _parse_and_validate_image_input(
|
|
self, **kwargs: object) -> Optional[Phi3VImagePixelInputs]:
|
|
pixel_values = kwargs.pop("pixel_values", None)
|
|
image_sizes = kwargs.pop("image_sizes", None)
|
|
|
|
if pixel_values is not None and image_sizes is not None:
|
|
return Phi3VImagePixelInputs(type="pixel_values",
|
|
data=pixel_values,
|
|
image_sizes=image_sizes)
|
|
|
|
return None
|
|
|
|
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):
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
|
|
if image_input is not None:
|
|
inputs_embeds = self.vision_embed_tokens(
|
|
input_ids, image_input["data"], image_input["image_sizes"])
|
|
|
|
input_ids = None
|
|
else:
|
|
inputs_embeds = None
|
|
|
|
hidden_states = self.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) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
(".qkv_proj", ".q_proj", "q"),
|
|
(".qkv_proj", ".k_proj", "k"),
|
|
(".qkv_proj", ".v_proj", "v"),
|
|
(".gate_up_proj", ".gate_proj", 0),
|
|
(".gate_up_proj", ".up_proj", 1),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
# post_layernorm is not needed in CLIPVisionModel
|
|
if "vision_model.post_layernorm" in name:
|
|
continue
|
|
for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
|
|
if key_to_modify in name:
|
|
name = name.replace(key_to_modify, new_key)
|
|
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
|
# We only do sharding for language model
|
|
# and not vision model for now.
|
|
if "vision_embed_tokens" in name and self.vision_embed_tokens:
|
|
continue
|
|
if weight_name not in name:
|
|
continue
|
|
param = params_dict[name.replace(weight_name, param_name)]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|