[Model] Initialize Phi-3-vision support (#4986)
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Isotr0py
379
vllm/model_executor/models/phi3v.py
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379
vllm/model_executor/models/phi3v.py
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# coding=utf-8
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# Copyright 2024 The vLLM team.
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# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Iterable, List, Literal, Optional, Tuple, TypedDict
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import torch
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import torch.nn as nn
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from transformers import CLIPVisionConfig, CLIPVisionModel, PretrainedConfig
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from transformers.utils import logging
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, VisionLanguageConfig
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.llama import LlamaModel
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from vllm.model_executor.models.vlm_base import VisionLanguageModelBase
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.image import get_dummy_image_data
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from vllm.sequence import SamplerOutput
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logger = logging.get_logger(__name__)
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_KEYS_TO_MODIFY_MAPPING = {
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"model.vision_embed_tokens": "vision_embed_tokens",
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}
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CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(dropout=0.0,
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hidden_act="quick_gelu",
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hidden_size=1024,
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image_size=336,
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intermediate_size=4096,
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num_attention_heads=16,
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num_channels=3,
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num_hidden_layers=24,
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patch_size=14,
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projection_dim=768)
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class Phi3ImageEmbeddingBase(nn.Module):
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def __init__(self, wte=None) -> None:
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super().__init__()
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self.wte = wte
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self.layer_idx: int
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self.type_feature: str
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self.img_processor: CLIPVisionModel
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def set_img_features(self, img_features: torch.FloatTensor) -> None:
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self.img_features = img_features
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def set_img_sizes(self, img_sizes: torch.LongTensor) -> None:
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self.img_sizes = img_sizes
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def get_img_features(self,
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img_embeds: torch.FloatTensor) -> torch.FloatTensor:
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LAYER_IDX = self.layer_idx
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TYPE_FEATURE = self.type_feature
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img_processor_output = self.img_processor(img_embeds,
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output_hidden_states=True)
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img_feature = img_processor_output.hidden_states[LAYER_IDX]
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if TYPE_FEATURE == "patch":
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patch_feature = img_feature[:, 1:]
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return patch_feature
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if TYPE_FEATURE == "cls_patch":
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return img_feature
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raise NotImplementedError
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# adapted from https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_embedding_phi3_v.py
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class Phi3HDImageEmbedding(Phi3ImageEmbeddingBase):
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"""Phi3 Image embedding with HD transform."""
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def __init__(self,
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vision_language_config: VisionLanguageConfig,
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config: PretrainedConfig,
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wte=None) -> None:
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super().__init__(wte)
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self.image_token_id = vision_language_config.image_token_id
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# n_embed or hidden_size
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hidden_size = config.n_embd if hasattr(
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config, 'n_embd') else config.hidden_size
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clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG
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self.img_processor = CLIPVisionModel(clip_config)
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image_dim_out = config.img_processor['image_dim_out']
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self.num_img_tokens = config.img_processor['num_img_tokens']
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self.image_dim_out = image_dim_out
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self.img_sizes = None
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# global_gn and sub_gn for hd transform, serves as line separator
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self.use_hd_transform = config.embd_layer.get('use_hd_transform',
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False)
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self.with_learnable_separator = config.embd_layer.get(
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'with_learnable_separator', False)
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self.hd_transform_order = config.embd_layer.get(
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'hd_transform_order', 'glb_sub')
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# with_hd_transform and with_learnable_separator should have same value
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assert self.use_hd_transform and self.with_learnable_separator
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# 1024 * 4, merge spatial to channel dimension
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self.glb_GN = nn.Parameter(torch.empty([1, 1, self.image_dim_out * 4]))
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self.sub_GN = nn.Parameter(
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torch.empty([1, 1, 1, self.image_dim_out * 4]))
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dim_projection = hidden_size
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depth = 2
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layers = [nn.Linear(image_dim_out * 4, dim_projection)]
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for _ in range(1, depth):
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layers.extend(
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[nn.GELU(),
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nn.Linear(dim_projection, dim_projection)])
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self.img_projection = nn.Sequential(*layers)
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self.vocab_size = config.vocab_size
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self.img_features = None
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self.layer_idx = config.img_processor.get('layer_idx', -2)
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self.type_feature = config.img_processor.get('type_feature', 'patch')
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def forward(self,
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input_ids: torch.LongTensor,
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pixel_values: torch.FloatTensor,
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image_sizes=None) -> torch.FloatTensor:
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"""process and merge text embeddings with image embeddings."""
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img_embeds = pixel_values
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img_sizes = image_sizes
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if self.img_features is not None:
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img_embeds = self.img_features.clone()
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self.img_features = None
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if self.img_sizes is not None:
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img_sizes = self.img_sizes
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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positions = torch.nonzero(input_ids == self.image_token_id)
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select = False
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target_device = self.img_projection[0].bias.device
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target_dtype = self.img_projection[0].bias.dtype
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if len(positions.tolist()) > 0:
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# if self.use_hd_transform and img_sizes:
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# img_embeds: (num_images, max_num_crops, 3, H, W)
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# img_sizes: (num_images, 2).view(1, -1)
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bs = img_embeds.shape[0]
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# Nx(HW)xC
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img_features = self.get_img_features(img_embeds.flatten(0, 1))
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base_feat_height = base_feat_width = int(
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img_features.shape[1]**0.5)
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# bs x max_num_crops x (24x24) x C
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img_features = img_features.view(
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bs, -1, base_feat_height * base_feat_width, self.image_dim_out)
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C = self.image_dim_out
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H = base_feat_height
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output_imgs = []
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output_len = []
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if isinstance(img_sizes, torch.Tensor):
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img_sizes.squeeze_(0)
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for _bs in range(bs):
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h, w = img_sizes
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h = h // 336
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w = w // 336
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B_ = h * w
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# 1 x (24x24) x 1024
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global_img_feature = img_features[_bs, :1]
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# 1 x 12 x 12 x 4096
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glb_img = global_img_feature \
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.reshape(1, H // 2, 2, H // 2, 2,C) \
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.permute(0, 1, 3, 2, 4, 5) \
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.reshape(1, H // 2, H // 2, 4 * C)
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temp_glb_GN = self.sub_GN.repeat(1, H // 2, 1, 1)
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# 1 x 156 x 4096
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glb_img = torch.cat([glb_img, temp_glb_GN],
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dim=2).reshape(1, -1, 4 * C)
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# (max_num_crops-1) x (12x12) x C
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sub_img = img_features[_bs, 1:]
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# 16x574x1024
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# get rid of padding sub_img
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sub_img = sub_img[:B_]
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sub_img = sub_img.reshape(B_, H // 2, 2, H // 2, 2, C) \
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.permute(0, 1, 3, 2, 4, 5).reshape(B_, -1, 4 * C)
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sub_img = sub_img.reshape(1, h, w, 12, 12, -1) \
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.permute(0, 1, 3, 2, 4, 5) \
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.reshape(1, h * 12, w * 12, 4 * C)
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temp_sub_GN = self.sub_GN.repeat(1, h * 12, 1, 1)
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sub_img = torch.cat([sub_img, temp_sub_GN],
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dim=2).reshape(1, -1, 4 * C)
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# (1, num_img_tokens, 1024*4)
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# glb + sub
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if self.hd_transform_order == 'glb_sub':
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output_imgs.append(
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torch.cat([glb_img, self.glb_GN, sub_img], dim=1))
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elif self.hd_transform_order == 'sub_glb':
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output_imgs.append(
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torch.cat([sub_img, self.glb_GN, glb_img], dim=1))
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temp_len = int((h * w + 1) * 144 + 1 + (h + 1) * 12)
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output_len.append(temp_len)
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num_img_tokens = output_len
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img_set_tensor = []
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for _output_img in output_imgs:
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img_feature_proj = self.img_projection(
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_output_img.to(target_device, target_dtype))
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img_set_tensor.append(img_feature_proj)
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select = True
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input_ids.clamp_min_(0).clamp_max_(self.vocab_size)
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hidden_states = self.wte(input_ids)
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if select:
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idx = 0
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for i, cnt in enumerate(num_img_tokens):
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hidden_states[positions[idx, 0],
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positions[idx, 1]:positions[idx, 1] +
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cnt] = (img_set_tensor[i].to(
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hidden_states.device, hidden_states.dtype))
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idx += cnt
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return hidden_states.squeeze(0)
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class Phi3VImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: torch.Tensor
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"""Shape: (batch_size, 1 + num_patches, num_channels, height, width)"""
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image_sizes: torch.Tensor
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"""Shape: (batch_size, 2)"""
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@MULTIMODAL_REGISTRY.register_image_pixel_input()
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@MULTIMODAL_REGISTRY.register_dummy_data(get_dummy_image_data)
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class Phi3VForCausalLM(VisionLanguageModelBase):
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def __init__(self,
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config: PretrainedConfig,
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vision_language_config: VisionLanguageConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None) -> None:
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super().__init__(vision_language_config)
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self.config = config
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self.model = LlamaModel(config, cache_config, quant_config)
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self.vision_embed_tokens = Phi3HDImageEmbedding(
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vision_language_config, config, self.model.embed_tokens)
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self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.sampler = Sampler()
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def _parse_and_validate_image_input(
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self, **kwargs: object) -> Optional[Phi3VImagePixelInputs]:
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pixel_values = kwargs.pop("pixel_values", None)
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image_sizes = kwargs.pop("image_sizes", None)
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expected_input_type = self.vision_language_config.image_input_type
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ImageInputType = VisionLanguageConfig.ImageInputType
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if expected_input_type != ImageInputType.PIXEL_VALUES:
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raise ValueError(
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f"Unexpected image input type: {expected_input_type}."
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"Phi3v only support pixel_values input currently.")
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if pixel_values is not None and image_sizes is not None:
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return Phi3VImagePixelInputs(type="pixel_values",
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data=pixel_values,
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image_sizes=image_sizes)
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return None
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def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata, **kwargs: object):
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image_input = self._parse_and_validate_image_input(**kwargs)
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if image_input is not None:
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inputs_embeds = self.vision_embed_tokens(
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input_ids, image_input["data"], image_input["image_sizes"])
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input_ids = None
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else:
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inputs_embeds = None
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hidden_states = self.model(input_ids,
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positions,
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kv_caches,
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attn_metadata,
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inputs_embeds=inputs_embeds)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head.weight, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
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if key_to_modify in name:
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name = name.replace(key_to_modify, new_key)
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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# We only do sharding for language model
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# and not vision model for now.
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if "vision_embed_tokens" in name and self.vision_embed_tokens:
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continue
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if weight_name not in name:
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continue
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param = params_dict[name.replace(weight_name, param_name)]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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