Model: Support Qwen 2.5 vl (#3258)
This commit is contained in:
Mick
@@ -427,6 +427,8 @@ def match_chat_ml(model_path: str):
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if "tinyllama" in model_path:
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return get_chat_template("chatml")
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# Now the suffix for qwen2 chat model is "instruct"
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if "qwen" in model_path and "vl" in model_path:
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return get_chat_template("qwen2-vl")
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if "qwen" in model_path:
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if "vl" in model_path:
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return get_chat_template("qwen2-vl")
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@@ -443,6 +445,12 @@ def match_chat_ml(model_path: str):
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return get_chat_template("chatml-llava")
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@register_chat_template_matching_function
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def match_chat_minicpm(model_path: str):
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if "minicpm" in model_path:
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return get_chat_template("minicpmv")
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@register_chat_template_matching_function
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def match_chat_yi(model_path: str):
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model_path = model_path.lower()
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@@ -1,12 +1,15 @@
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from sglang.srt.configs.chatglm import ChatGLMConfig
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from sglang.srt.configs.dbrx import DbrxConfig
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from sglang.srt.configs.exaone import ExaoneConfig
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from sglang.srt.configs.qwen2vl import Qwen2VLConfig, Qwen2VLVisionConfig
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from sglang.srt.configs.qwen2_5_vl_config import (
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Qwen2_5_VLConfig,
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Qwen2_5_VLVisionConfig,
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)
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__all__ = [
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"ExaoneConfig",
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"Qwen2VLConfig",
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"Qwen2VLVisionConfig",
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"ChatGLMConfig",
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"DbrxConfig",
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"Qwen2_5_VLConfig",
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"Qwen2_5_VLVisionConfig",
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]
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@@ -403,6 +403,7 @@ def is_multimodal_model(model_architectures: List[str]):
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or "LlavaVidForCausalLM" in model_architectures
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or "MllamaForConditionalGeneration" in model_architectures
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or "Qwen2VLForConditionalGeneration" in model_architectures
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or "Qwen2_5_VLForConditionalGeneration" in model_architectures
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or "MiniCPMV" in model_architectures
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):
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return True
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1003
python/sglang/srt/configs/qwen2_5_vl_config.py
Normal file
1003
python/sglang/srt/configs/qwen2_5_vl_config.py
Normal file
File diff suppressed because it is too large
Load Diff
@@ -1,130 +0,0 @@
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# coding=utf-8
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# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team.
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# 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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"""Qwen2VL model configuration"""
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import os
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from typing import Union
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from transformers import PretrainedConfig
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class Qwen2VLVisionConfig(PretrainedConfig):
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model_type = "qwen2_vl"
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def __init__(
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self,
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depth=32,
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embed_dim=1280,
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hidden_size=3584,
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hidden_act="quick_gelu",
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mlp_ratio=4,
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num_heads=16,
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in_channels=3,
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patch_size=14,
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spatial_merge_size=2,
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temporal_patch_size=2,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.depth = depth
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self.embed_dim = embed_dim
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self.hidden_size = hidden_size
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self.hidden_act = hidden_act
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self.mlp_ratio = mlp_ratio
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self.num_heads = num_heads
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self.in_channels = in_channels
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self.patch_size = patch_size
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self.spatial_merge_size = spatial_merge_size
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self.temporal_patch_size = temporal_patch_size
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@classmethod
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def from_pretrained(
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cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
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) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(
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pretrained_model_name_or_path, **kwargs
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)
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if config_dict.get("model_type") == "qwen2_vl":
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config_dict = config_dict["vision_config"]
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return cls.from_dict(config_dict, **kwargs)
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class Qwen2VLConfig(PretrainedConfig):
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model_type = "qwen2_vl"
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def __init__(
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self,
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vocab_size=152064,
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hidden_size=8192,
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intermediate_size=29568,
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num_hidden_layers=80,
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num_attention_heads=64,
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num_key_value_heads=8,
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hidden_act="silu",
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max_position_embeddings=32768,
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initializer_range=0.02,
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rms_norm_eps=1e-05,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=1000000.0,
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use_sliding_window=False,
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sliding_window=4096,
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max_window_layers=80,
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attention_dropout=0.0,
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vision_config=None,
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rope_scaling=None,
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**kwargs,
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):
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if isinstance(vision_config, dict):
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self.vision_config = Qwen2VLVisionConfig(**vision_config)
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elif vision_config is None:
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self.vision_config = Qwen2VLVisionConfig()
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.use_sliding_window = use_sliding_window
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self.sliding_window = sliding_window
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self.max_window_layers = max_window_layers
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.attention_dropout = attention_dropout
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self.rope_scaling = rope_scaling
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# NOTE(HandH1998): This is necessary for configuring the `rope_type`` of qwen2vl models after removing dependencies on vllm.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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if self.rope_scaling["type"] == "mrope":
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self.rope_scaling["type"] = "default"
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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@@ -30,16 +30,15 @@ from transformers import (
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)
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from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
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from sglang.srt.configs import ChatGLMConfig, DbrxConfig, ExaoneConfig, Qwen2VLConfig
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from sglang.srt.configs import ChatGLMConfig, DbrxConfig, ExaoneConfig, Qwen2_5_VLConfig
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_CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
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ChatGLMConfig.model_type: ChatGLMConfig,
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DbrxConfig.model_type: DbrxConfig,
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ExaoneConfig.model_type: ExaoneConfig,
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Qwen2VLConfig.model_type: Qwen2VLConfig,
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Qwen2_5_VLConfig.model_type: Qwen2_5_VLConfig,
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}
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for name, cls in _CONFIG_REGISTRY.items():
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with contextlib.suppress(ValueError):
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AutoConfig.register(name, cls)
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@@ -1,6 +1,7 @@
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# TODO: also move pad_input_ids into this module
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import asyncio
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import concurrent.futures
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import dataclasses
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import logging
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import multiprocessing as mp
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import os
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@@ -8,6 +9,7 @@ from abc import ABC, abstractmethod
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from typing import List, Optional, Union
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import numpy as np
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import PIL
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import transformers
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from decord import VideoReader, cpu
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from PIL import Image
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@@ -34,11 +36,22 @@ def init_global_processor(server_args: ServerArgs):
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)
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@dataclasses.dataclass
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class BaseImageProcessorOutput:
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image_hashes: list[int]
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image_sizes: list[int]
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all_frames: [PIL.Image]
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# input_text, with each frame of video/image represented with a image_token
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input_text: str
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class BaseImageProcessor(ABC):
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def __init__(self, hf_config, server_args, _processor):
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self.hf_config = hf_config
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self._processor = _processor
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self.server_args = server_args
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# FIXME: not accurate, model and image specific
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self.NUM_TOKEN_PER_FRAME = 330
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self.executor = concurrent.futures.ProcessPoolExecutor(
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initializer=init_global_processor,
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@@ -48,9 +61,128 @@ class BaseImageProcessor(ABC):
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)
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@abstractmethod
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async def process_images_async(self, image_data, input_text, **kwargs):
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async def process_images_async(
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self, image_data, input_text, max_req_input_len, **kwargs
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):
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pass
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def get_estimated_frames_list(self, image_data):
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"""
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estimate the total frame count from all visual input
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"""
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# Before processing inputs
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estimated_frames_list = []
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for image in image_data:
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if isinstance(image, str) and image.startswith("video:"):
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path = image[len("video:") :]
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# Estimate frames for the video
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vr = VideoReader(path, ctx=cpu(0))
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num_frames = len(vr)
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else:
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# For images, each contributes one frame
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num_frames = 1
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estimated_frames_list.append(num_frames)
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return estimated_frames_list
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def encode_video(self, video_path, frame_count_limit=None):
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if not os.path.exists(video_path):
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logger.error(f"Video {video_path} does not exist")
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return []
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if frame_count_limit == 0:
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return []
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def uniform_sample(l, n):
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gap = len(l) / n
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idxs = [int(i * gap + gap / 2) for i in range(n)]
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return [l[i] for i in idxs]
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vr = VideoReader(video_path, ctx=cpu(0))
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sample_fps = round(vr.get_avg_fps() / 1) # FPS
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frame_idx = [i for i in range(0, len(vr), sample_fps)]
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if frame_count_limit is not None and len(frame_idx) > frame_count_limit:
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frame_idx = uniform_sample(frame_idx, frame_count_limit)
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frames = vr.get_batch(frame_idx).asnumpy()
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frames = [Image.fromarray(v.astype("uint8")) for v in frames]
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return frames
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def load_images(
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self,
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max_req_input_len: int,
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input_ids: list,
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image_data,
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image_token: str,
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) -> BaseImageProcessorOutput:
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"""
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Each frame of video/image will be replaced by a single image token
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"""
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image_hashes, image_sizes = [], []
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all_frames = []
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new_text_parts = []
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if isinstance(input_ids, list):
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assert len(input_ids) and isinstance(input_ids[0], int)
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input_text = self._processor.tokenizer.decode(input_ids)
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else:
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input_text = input_ids
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text_parts = input_text.split(image_token)
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# roughly calculate the max number of frames under the max_req_input_len limit
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def calculate_max_num_frames() -> int:
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ret = (max_req_input_len - len(input_ids)) // self.NUM_TOKEN_PER_FRAME
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return min(ret, 100)
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MAX_NUM_FRAMES = calculate_max_num_frames()
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estimated_frames_list = self.get_estimated_frames_list(image_data=image_data)
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total_frame_count = sum(estimated_frames_list)
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# a heuristic value, suggesting the maximum fraction of frames to embed from all visual inputs.
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# e.g., 0.1 suggests that 1 frame out of 10 input frames should be used
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scaling_factor = min(1.0, MAX_NUM_FRAMES / total_frame_count)
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# Process each input with allocated frames
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for image_index, (image, estimated_frames) in enumerate(
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zip(image_data, estimated_frames_list)
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):
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if len(all_frames) >= MAX_NUM_FRAMES:
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frames_to_process = 0
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else:
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frames_to_process = max(1, int(estimated_frames * scaling_factor))
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if frames_to_process == 0:
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frames = []
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else:
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try:
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if isinstance(image, str) and image.startswith("video:"):
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path = image[len("video:") :]
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frames = self.encode_video(
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path, frame_count_limit=frames_to_process
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)
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else:
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raw_image, _size = load_image(image)
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frames = [raw_image]
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if len(frames) == 0:
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continue
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except FileNotFoundError as e:
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print(e)
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return None
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image_sizes += frames[0].size * len(frames)
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image_hashes += [hash(image)] * len(frames)
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all_frames += frames
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new_text_parts.append(text_parts[image_index])
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if frames_to_process != 0:
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new_text_parts.append(image_token * len(frames))
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assert frames_to_process == len(frames)
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new_text_parts.append(text_parts[-1])
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input_text = "".join(new_text_parts)
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return BaseImageProcessorOutput(
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image_hashes, image_sizes, all_frames, input_text
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)
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class DummyImageProcessor(BaseImageProcessor):
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def __init__(self):
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@@ -248,9 +380,9 @@ class MiniCPMVImageProcessor(BaseImageProcessor):
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text=input_text, images=images, return_tensors="pt"
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)
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return {
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"input_ids": result["input_ids"],
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"pixel_values": result["pixel_values"],
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"tgt_sizes": result["tgt_sizes"],
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"input_ids": result.input_ids,
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"pixel_values": result.pixel_values,
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"tgt_sizes": result.tgt_sizes,
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}
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async def _process_images(self, images, input_text):
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@@ -278,124 +410,20 @@ class MiniCPMVImageProcessor(BaseImageProcessor):
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):
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if not image_data:
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return None
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if not isinstance(image_data, list):
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image_data = [image_data]
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image_hashes, image_sizes = [], []
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all_frames = []
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# roughly calculate the max number of frames under the max_req_input_len limit
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def calculate_max_num_frames() -> int:
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# Model-specific
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NUM_TOKEN_PER_FRAME = 330
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ret = (max_req_input_len - len(input_ids)) // NUM_TOKEN_PER_FRAME
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return min(ret, 100)
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MAX_NUM_FRAMES = calculate_max_num_frames()
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# print(f"MAX_NUM_FRAMES: {MAX_NUM_FRAMES}")
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def get_estimated_frames_list():
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"""
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estimate the total frame count from all visual input
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"""
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# Before processing inputs
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estimated_frames_list = []
|
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for image in image_data:
|
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if isinstance(image, str) and image.startswith("video:"):
|
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path = image[len("video:") :]
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# Estimate frames for the video
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vr = VideoReader(path, ctx=cpu(0))
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num_frames = len(vr)
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else:
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# For images, each contributes one frame
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num_frames = 1
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estimated_frames_list.append(num_frames)
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return estimated_frames_list
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estimated_frames_list = get_estimated_frames_list()
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total_frame_count = sum(estimated_frames_list)
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scaling_factor = min(1.0, MAX_NUM_FRAMES / total_frame_count)
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def encode_video(video_path, frame_count_limit=None):
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if not os.path.exists(video_path):
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logger.error(f"Video {video_path} does not exist")
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return []
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|
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if frame_count_limit == 0:
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return []
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def uniform_sample(l, n):
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gap = len(l) / n
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idxs = [int(i * gap + gap / 2) for i in range(n)]
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return [l[i] for i in idxs]
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vr = VideoReader(video_path, ctx=cpu(0))
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sample_fps = round(vr.get_avg_fps() / 1) # FPS
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frame_idx = [i for i in range(0, len(vr), sample_fps)]
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if frame_count_limit is not None and len(frame_idx) > frame_count_limit:
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frame_idx = uniform_sample(frame_idx, frame_count_limit)
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frames = vr.get_batch(frame_idx).asnumpy()
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frames = [Image.fromarray(v.astype("uint8")) for v in frames]
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return frames
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|
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if isinstance(input_ids, list):
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assert len(input_ids) and isinstance(input_ids[0], int)
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input_text = self._processor.tokenizer.decode(input_ids)
|
||||
else:
|
||||
input_text = input_ids
|
||||
# MiniCPMV requires each frame of video as a single image token
|
||||
text_parts = input_text.split(self.IMAGE_TOKEN)
|
||||
new_text_parts = []
|
||||
|
||||
# Process each input with allocated frames
|
||||
for image_index, (image, estimated_frames) in enumerate(
|
||||
zip(image_data, estimated_frames_list)
|
||||
):
|
||||
if len(all_frames) >= MAX_NUM_FRAMES:
|
||||
frames_to_process = 0
|
||||
else:
|
||||
frames_to_process = max(1, int(estimated_frames * scaling_factor))
|
||||
|
||||
if frames_to_process == 0:
|
||||
frames = []
|
||||
else:
|
||||
try:
|
||||
if isinstance(image, str) and image.startswith("video:"):
|
||||
path = image[len("video:") :]
|
||||
frames = encode_video(path, frame_count_limit=frames_to_process)
|
||||
else:
|
||||
raw_image, _size = load_image(image)
|
||||
frames = [raw_image]
|
||||
if len(frames) == 0:
|
||||
continue
|
||||
except FileNotFoundError as e:
|
||||
print(e)
|
||||
return None
|
||||
image_sizes += frames[0].size * len(frames)
|
||||
image_hashes += [hash(image)] * len(frames)
|
||||
all_frames += frames
|
||||
|
||||
assert frames_to_process == len(frames)
|
||||
|
||||
new_text_parts.append(text_parts[image_index])
|
||||
|
||||
if frames_to_process != 0:
|
||||
new_text_parts.append(self.IMAGE_TOKEN * len(frames))
|
||||
|
||||
new_text_parts.append(text_parts[-1])
|
||||
|
||||
input_text = "".join(new_text_parts)
|
||||
|
||||
if len(all_frames) == 0:
|
||||
base_output = self.load_images(
|
||||
max_req_input_len, input_ids, image_data, self.IMAGE_TOKEN
|
||||
)
|
||||
if base_output is None:
|
||||
return None
|
||||
res = await self._process_images(images=all_frames, input_text=input_text)
|
||||
pixel_values = res["pixel_values"]
|
||||
tgt_sizes = res["tgt_sizes"]
|
||||
input_ids = res["input_ids"]
|
||||
|
||||
if len(base_output.all_frames) == 0:
|
||||
return None
|
||||
res = await self._process_images(
|
||||
images=base_output.all_frames, input_text=base_output.input_text
|
||||
)
|
||||
|
||||
# Collect special token ids
|
||||
tokenizer = self._processor.tokenizer
|
||||
@@ -405,10 +433,10 @@ class MiniCPMVImageProcessor(BaseImageProcessor):
|
||||
slice_start_id = [tokenizer.slice_start_id]
|
||||
slice_end_id = [tokenizer.slice_end_id]
|
||||
return {
|
||||
"input_ids": input_ids.flatten().tolist(),
|
||||
"pixel_values": pixel_values,
|
||||
"tgt_sizes": tgt_sizes,
|
||||
"image_hashes": image_hashes,
|
||||
"input_ids": res["input_ids"].flatten().tolist(),
|
||||
"pixel_values": res["pixel_values"],
|
||||
"tgt_sizes": res["tgt_sizes"],
|
||||
"image_hashes": base_output.image_hashes,
|
||||
"modalities": request_obj.modalities or ["image"],
|
||||
"im_start_id": im_start_id,
|
||||
"im_end_id": im_end_id,
|
||||
@@ -536,13 +564,80 @@ class Qwen2VLImageProcessor(BaseImageProcessor):
|
||||
}
|
||||
|
||||
|
||||
class Qwen2_5VLImageProcessor(BaseImageProcessor):
|
||||
def __init__(self, hf_config, server_args, _processor):
|
||||
super().__init__(hf_config, server_args, _processor)
|
||||
self.IMAGE_TOKEN = "<|vision_start|><|image_pad|><|vision_end|>"
|
||||
self.IM_START_TOKEN_ID = hf_config.vision_start_token_id
|
||||
self.IM_END_TOKEN_ID = hf_config.vision_end_token_id
|
||||
self.NUM_TOKEN_PER_FRAME = 770
|
||||
|
||||
@staticmethod
|
||||
def _process_images_task(images, input_text):
|
||||
result = global_processor.__call__(
|
||||
text=input_text, images=images, return_tensors="pt"
|
||||
)
|
||||
return {
|
||||
"input_ids": result.input_ids,
|
||||
"pixel_values": result.pixel_values,
|
||||
"image_grid_thws": result.image_grid_thw,
|
||||
}
|
||||
|
||||
async def _process_images(self, images, input_text) -> dict:
|
||||
if self.executor is not None:
|
||||
loop = asyncio.get_event_loop()
|
||||
return await loop.run_in_executor(
|
||||
self.executor,
|
||||
Qwen2_5VLImageProcessor._process_images_task,
|
||||
images,
|
||||
input_text,
|
||||
)
|
||||
else:
|
||||
return self._process_images_task(images, input_text)
|
||||
|
||||
async def process_images_async(
|
||||
self,
|
||||
image_data: List[Union[str, bytes]],
|
||||
input_ids,
|
||||
request_obj,
|
||||
max_req_input_len,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
if not image_data:
|
||||
return None
|
||||
if isinstance(image_data, str):
|
||||
image_data = [image_data]
|
||||
|
||||
image_token = self.IMAGE_TOKEN
|
||||
base_output = self.load_images(
|
||||
max_req_input_len, input_ids, image_data, image_token
|
||||
)
|
||||
|
||||
ret = await self._process_images(base_output.all_frames, base_output.input_text)
|
||||
|
||||
return {
|
||||
"input_ids": ret["input_ids"].flatten().tolist(),
|
||||
"pixel_values": ret["pixel_values"],
|
||||
"image_hashes": base_output.image_hashes,
|
||||
"modalities": request_obj.modalities or ["image"],
|
||||
"image_grid_thws": ret["image_grid_thws"],
|
||||
"im_start_id": self.IM_START_TOKEN_ID,
|
||||
"im_end_id": self.IM_END_TOKEN_ID,
|
||||
}
|
||||
|
||||
|
||||
def get_image_processor(
|
||||
hf_config, server_args: ServerArgs, processor
|
||||
) -> BaseImageProcessor:
|
||||
if "MllamaForConditionalGeneration" in hf_config.architectures:
|
||||
return MllamaImageProcessor(hf_config, server_args, processor)
|
||||
elif "Qwen2VLForConditionalGeneration" in hf_config.architectures:
|
||||
return Qwen2VLImageProcessor(hf_config, server_args, processor.image_processor)
|
||||
|
||||
return Qwen2VLImageProcessor(hf_config, server_args, processor)
|
||||
elif "Qwen2_5_VLForConditionalGeneration" in hf_config.architectures:
|
||||
return Qwen2_5VLImageProcessor(hf_config, server_args, processor)
|
||||
|
||||
elif "MiniCPMV" in hf_config.architectures:
|
||||
return MiniCPMVImageProcessor(hf_config, server_args, processor)
|
||||
else:
|
||||
|
||||
722
python/sglang/srt/models/qwen2_5_vl.py
Normal file
722
python/sglang/srt/models/qwen2_5_vl.py
Normal file
@@ -0,0 +1,722 @@
|
||||
# coding=utf-8
|
||||
# Adapted from
|
||||
# https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
|
||||
# Copyright 2024 The Qwen team.
|
||||
# Copyright 2023 The vLLM team.
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# 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.
|
||||
"""Inference-only Qwen2-VL model compatible with HuggingFace weights."""
|
||||
import logging
|
||||
from functools import lru_cache, partial
|
||||
from typing import Iterable, List, Optional, Tuple, Type
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange
|
||||
from transformers import AutoModel, Qwen2VLConfig
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm
|
||||
|
||||
from sglang.srt.configs import Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig
|
||||
from sglang.srt.distributed import (
|
||||
get_tensor_model_parallel_rank,
|
||||
get_tensor_model_parallel_world_size,
|
||||
)
|
||||
from sglang.srt.hf_transformers_utils import get_processor
|
||||
from sglang.srt.layers.attention.vision import VisionAttention
|
||||
from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
|
||||
from sglang.srt.layers.logits_processor import LogitsProcessor
|
||||
from sglang.srt.layers.pooler import Pooler, PoolingType
|
||||
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
||||
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
|
||||
from sglang.srt.managers.schedule_batch import ImageInputs
|
||||
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
|
||||
from sglang.srt.model_loader.weight_utils import default_weight_loader
|
||||
from sglang.srt.models.qwen2 import Qwen2Model
|
||||
from sglang.srt.models.qwen2_vl import Qwen2VLImageInputs, Qwen2VLVideoInputs
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Qwen2_5_VLMLP(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
hidden_features: int = None,
|
||||
bias: bool = True,
|
||||
hidden_act="silu",
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.gate_proj = ColumnParallelLinear(
|
||||
in_features, hidden_features, bias=bias, quant_config=quant_config
|
||||
)
|
||||
self.up_proj = ColumnParallelLinear(
|
||||
in_features, hidden_features, bias=bias, quant_config=quant_config
|
||||
)
|
||||
self.down_proj = RowParallelLinear(
|
||||
hidden_features, in_features, bias=bias, quant_config=quant_config
|
||||
)
|
||||
self.act = ACT2FN[hidden_act]
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x_parallel_gate, _ = self.gate_proj(x)
|
||||
x_parallel_gate = self.act(x_parallel_gate)
|
||||
x_parallel_up, _ = self.up_proj(x)
|
||||
x_parallel = x_parallel_gate * x_parallel_up
|
||||
x, _ = self.down_proj(x_parallel)
|
||||
return x
|
||||
|
||||
|
||||
class Qwen2_5_VisionBlock(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
intermediate_dim: int,
|
||||
num_heads: int,
|
||||
hidden_act="silu",
|
||||
norm_layer: Type[nn.Module] = None,
|
||||
attn_implementation: Optional[str] = "sdpa",
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
if norm_layer is None:
|
||||
norm_layer = partial(nn.LayerNorm, eps=1e-6)
|
||||
self.norm1 = Qwen2RMSNorm(dim, eps=1e-6)
|
||||
self.norm2 = Qwen2RMSNorm(dim, eps=1e-6)
|
||||
if attn_implementation == "sdpa":
|
||||
use_context_forward = False
|
||||
use_full_precision_softmax = False
|
||||
elif attn_implementation == "flash_attention_2":
|
||||
use_full_precision_softmax = False
|
||||
use_context_forward = True
|
||||
elif attn_implementation == "eager":
|
||||
use_full_precision_softmax = True
|
||||
use_context_forward = False
|
||||
|
||||
self.attn = VisionAttention(
|
||||
embed_dim=dim,
|
||||
num_heads=num_heads,
|
||||
projection_size=dim,
|
||||
use_qkv_parallel=False,
|
||||
use_context_forward=use_context_forward,
|
||||
use_full_precision_softmax=use_full_precision_softmax,
|
||||
flatten_batch=True,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
self.mlp = Qwen2_5_VLMLP(
|
||||
dim, intermediate_dim, hidden_act=hidden_act, quant_config=quant_config
|
||||
)
|
||||
|
||||
def forward(
|
||||
self, x: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.norm1(x)
|
||||
hidden_states = rearrange(hidden_states, "s b ... -> b s ...")
|
||||
attn = self.attn(
|
||||
hidden_states, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
|
||||
)
|
||||
attn = rearrange(attn, "b s ... -> s b ...")
|
||||
x = x + attn
|
||||
norm2 = self.norm2(x)
|
||||
mlp = self.mlp(norm2)
|
||||
x = x + mlp
|
||||
return x
|
||||
|
||||
|
||||
class Qwen2_5_VisionPatchEmbed(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
patch_size: int = 14,
|
||||
temporal_patch_size: int = 2,
|
||||
in_chans: int = 3,
|
||||
embed_dim: int = 1152,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.patch_size = patch_size
|
||||
self.temporal_patch_size = temporal_patch_size
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
kernel_size = [temporal_patch_size, patch_size, patch_size]
|
||||
self.proj = nn.Conv3d(
|
||||
in_chans, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
L, C = x.shape
|
||||
x = x.view(L, -1, self.temporal_patch_size, self.patch_size, self.patch_size)
|
||||
x = self.proj(x).view(L, self.embed_dim)
|
||||
return x
|
||||
|
||||
|
||||
class Qwen2_5_VisionPatchMerger(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
context_dim: int,
|
||||
spatial_merge_size: int = 2,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = context_dim * (spatial_merge_size**2)
|
||||
self.ln_q = Qwen2RMSNorm(context_dim, eps=1e-6)
|
||||
self.mlp = nn.ModuleList(
|
||||
[
|
||||
ColumnParallelLinear(
|
||||
self.hidden_size,
|
||||
self.hidden_size,
|
||||
bias=True,
|
||||
quant_config=quant_config,
|
||||
),
|
||||
nn.GELU(),
|
||||
RowParallelLinear(
|
||||
self.hidden_size, dim, bias=True, quant_config=quant_config
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.ln_q(x)
|
||||
x = x.view(-1, self.hidden_size)
|
||||
|
||||
mlp_fc1, mlp_act, mlp_fc2 = self.mlp
|
||||
x_parallel, _ = mlp_fc1(x)
|
||||
x_parallel = mlp_act(x_parallel)
|
||||
out, _ = mlp_fc2(x_parallel)
|
||||
return out
|
||||
|
||||
|
||||
class Qwen2_5_VisionRotaryEmbedding(nn.Module):
|
||||
|
||||
def __init__(self, dim: int, theta: float = 10000.0) -> None:
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.theta = theta
|
||||
inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
self._seq_len_cached = 0
|
||||
self._freqs_cached = None
|
||||
|
||||
def update_freqs_cache(self, seqlen: int) -> None:
|
||||
if seqlen > self._seq_len_cached:
|
||||
seqlen *= 2
|
||||
self._seq_len_cached = seqlen
|
||||
self.inv_freq = 1.0 / (
|
||||
self.theta
|
||||
** (
|
||||
torch.arange(
|
||||
0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device
|
||||
)
|
||||
/ self.dim
|
||||
)
|
||||
)
|
||||
seq = torch.arange(
|
||||
seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
|
||||
)
|
||||
freqs = torch.outer(seq, self.inv_freq)
|
||||
self._freqs_cached = freqs
|
||||
|
||||
def forward(self, seqlen: int) -> torch.Tensor:
|
||||
self.update_freqs_cache(seqlen)
|
||||
return self._freqs_cached[:seqlen]
|
||||
|
||||
|
||||
class Qwen2_5_VisionTransformer(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vision_config: Qwen2_5_VLVisionConfig,
|
||||
norm_eps: float = 1e-6,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
patch_size: int = vision_config.patch_size
|
||||
temporal_patch_size: int = vision_config.temporal_patch_size
|
||||
spatial_merge_size: int = vision_config.spatial_merge_size
|
||||
self.spatial_merge_size = spatial_merge_size
|
||||
self.spatial_merge_unit: int = spatial_merge_size * spatial_merge_size
|
||||
in_chans: int = vision_config.in_chans
|
||||
hidden_size: int = vision_config.hidden_size
|
||||
depth: int = vision_config.depth
|
||||
num_heads: int = vision_config.num_heads
|
||||
self.fullatt_block_indexes = vision_config.fullatt_block_indexes
|
||||
self.window_size = vision_config.window_size
|
||||
self.patch_size = vision_config.patch_size
|
||||
mlp_hidden_size: int = vision_config.intermediate_size
|
||||
self.patch_embed = Qwen2_5_VisionPatchEmbed(
|
||||
patch_size=patch_size,
|
||||
temporal_patch_size=temporal_patch_size,
|
||||
in_chans=in_chans,
|
||||
embed_dim=hidden_size,
|
||||
)
|
||||
|
||||
norm_layer = partial(nn.LayerNorm, eps=norm_eps)
|
||||
head_dim = hidden_size // num_heads
|
||||
self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
Qwen2_5_VisionBlock(
|
||||
dim=hidden_size,
|
||||
intermediate_dim=mlp_hidden_size,
|
||||
num_heads=num_heads,
|
||||
hidden_act=vision_config.hidden_act,
|
||||
norm_layer=norm_layer,
|
||||
attn_implementation="sdpa",
|
||||
quant_config=quant_config,
|
||||
)
|
||||
for _ in range(depth)
|
||||
]
|
||||
)
|
||||
self.merger = Qwen2_5_VisionPatchMerger(
|
||||
dim=vision_config.out_hidden_size,
|
||||
context_dim=hidden_size,
|
||||
spatial_merge_size=spatial_merge_size,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
|
||||
def get_window_index(self, grid_thw):
|
||||
window_index: list = []
|
||||
cu_window_seqlens: list = [0]
|
||||
window_index_id = 0
|
||||
vit_merger_window_size = (
|
||||
self.window_size // self.spatial_merge_size // self.patch_size
|
||||
)
|
||||
|
||||
for grid_t, grid_h, grid_w in grid_thw:
|
||||
llm_grid_h, llm_grid_w = (
|
||||
grid_h // self.spatial_merge_size,
|
||||
grid_w // self.spatial_merge_size,
|
||||
)
|
||||
index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(
|
||||
grid_t, llm_grid_h, llm_grid_w
|
||||
)
|
||||
pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
|
||||
pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
|
||||
num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
|
||||
num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
|
||||
index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
|
||||
index_padded = index_padded.reshape(
|
||||
grid_t,
|
||||
num_windows_h,
|
||||
vit_merger_window_size,
|
||||
num_windows_w,
|
||||
vit_merger_window_size,
|
||||
)
|
||||
index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
|
||||
grid_t,
|
||||
num_windows_h * num_windows_w,
|
||||
vit_merger_window_size,
|
||||
vit_merger_window_size,
|
||||
)
|
||||
seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
|
||||
index_padded = index_padded.reshape(-1)
|
||||
index_new = index_padded[index_padded != -100]
|
||||
window_index.append(index_new + window_index_id)
|
||||
cu_seqlens_tmp = (
|
||||
seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
|
||||
)
|
||||
cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
|
||||
window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
|
||||
window_index = torch.cat(window_index, dim=0)
|
||||
|
||||
return window_index, cu_window_seqlens
|
||||
|
||||
@property
|
||||
def dtype(self) -> torch.dtype:
|
||||
return self.blocks[0].mlp.gate_proj.weight.dtype
|
||||
|
||||
@property
|
||||
def device(self) -> torch.device:
|
||||
return self.blocks[0].mlp.gate_proj.weight.device
|
||||
|
||||
def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
|
||||
pos_ids = []
|
||||
for t, h, w in grid_thw:
|
||||
hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
|
||||
wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
|
||||
hpos_ids = (
|
||||
hpos_ids.reshape(
|
||||
h // self.spatial_merge_size,
|
||||
self.spatial_merge_size,
|
||||
w // self.spatial_merge_size,
|
||||
self.spatial_merge_size,
|
||||
)
|
||||
.permute(0, 2, 1, 3)
|
||||
.flatten()
|
||||
)
|
||||
wpos_ids = (
|
||||
wpos_ids.reshape(
|
||||
h // self.spatial_merge_size,
|
||||
self.spatial_merge_size,
|
||||
w // self.spatial_merge_size,
|
||||
self.spatial_merge_size,
|
||||
)
|
||||
.permute(0, 2, 1, 3)
|
||||
.flatten()
|
||||
)
|
||||
pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
|
||||
pos_ids = torch.cat(pos_ids, dim=0)
|
||||
max_grid_size = grid_thw[:, 1:].max()
|
||||
rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
|
||||
rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
|
||||
return rotary_pos_emb
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
grid_thw: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
# patchify
|
||||
x = x.to(device=self.device, dtype=self.dtype)
|
||||
x = self.patch_embed(x)
|
||||
|
||||
# compute position embedding
|
||||
rotary_pos_emb = self.rot_pos_emb(grid_thw)
|
||||
|
||||
window_index, cu_window_seqlens = self.get_window_index(grid_thw)
|
||||
cu_window_seqlens = torch.tensor(
|
||||
cu_window_seqlens,
|
||||
device=x.device,
|
||||
dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
|
||||
)
|
||||
cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
|
||||
|
||||
seq_len, _ = x.size()
|
||||
|
||||
x = x.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
|
||||
x = x[window_index, :, :]
|
||||
x = x.reshape(seq_len, -1)
|
||||
rotary_pos_emb = rotary_pos_emb.reshape(
|
||||
seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1
|
||||
)
|
||||
rotary_pos_emb = rotary_pos_emb[window_index, :, :]
|
||||
rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
|
||||
|
||||
# compute cu_seqlens
|
||||
cu_seqlens = torch.repeat_interleave(
|
||||
grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
|
||||
).cumsum(dim=0, dtype=torch.int32)
|
||||
cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
|
||||
|
||||
# transformers
|
||||
x = x.unsqueeze(1)
|
||||
for layer_num, blk in enumerate(self.blocks):
|
||||
if layer_num in self.fullatt_block_indexes:
|
||||
cu_seqlens_now = cu_seqlens
|
||||
else:
|
||||
cu_seqlens_now = cu_window_seqlens
|
||||
x = blk(x, cu_seqlens=cu_seqlens_now, rotary_pos_emb=rotary_pos_emb)
|
||||
|
||||
# adapter
|
||||
x = self.merger(x)
|
||||
|
||||
reverse_indices = torch.argsort(window_index)
|
||||
x = x[reverse_indices, :]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
cached_get_processor = lru_cache(get_processor)
|
||||
|
||||
|
||||
class Qwen2_5_VLForConditionalGeneration(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: Qwen2VLConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
self.visual = Qwen2_5_VisionTransformer(
|
||||
config.vision_config,
|
||||
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
|
||||
# NOTE: Qwen2-VL vision encoder does not support any
|
||||
# quantization method now.
|
||||
quant_config=None,
|
||||
)
|
||||
|
||||
self.model = Qwen2Model(config, quant_config)
|
||||
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head = self.model.embed_tokens
|
||||
else:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size, config.hidden_size, quant_config=quant_config
|
||||
)
|
||||
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
|
||||
|
||||
def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]):
|
||||
processor = cached_get_processor(self.config._name_or_path)
|
||||
grid_t, grid_h, grid_w = image_grid_thw
|
||||
num_image_tokens = (
|
||||
grid_t
|
||||
* grid_h
|
||||
* grid_w
|
||||
// processor.image_processor.merge_size
|
||||
// processor.image_processor.merge_size
|
||||
)
|
||||
return num_image_tokens
|
||||
|
||||
def pad_input_ids(self, input_ids: List[int], image_inputs: ImageInputs):
|
||||
new_input_ids = []
|
||||
last_idx = 0
|
||||
image_idx = -1
|
||||
image_inputs.image_offsets = []
|
||||
|
||||
# Get all special token IDs
|
||||
im_start_id = image_inputs.im_start_id
|
||||
im_end_id = image_inputs.im_end_id
|
||||
|
||||
# Find all start and end positions for both types
|
||||
start_indices = [i for i, x in enumerate(input_ids) if x == im_start_id]
|
||||
end_indices = [i for i, x in enumerate(input_ids) if x == im_end_id]
|
||||
|
||||
if len(start_indices) != len(end_indices):
|
||||
return input_ids
|
||||
# Process each region (both image and slice)
|
||||
for start_idx, end_idx in zip(start_indices, end_indices):
|
||||
# Add non-image tokens before this region
|
||||
new_input_ids.extend(input_ids[last_idx : start_idx + 1])
|
||||
|
||||
is_image_start = input_ids[start_idx] == im_start_id
|
||||
|
||||
if is_image_start:
|
||||
image_inputs.image_offsets += [start_idx]
|
||||
image_idx += 1
|
||||
|
||||
num_tokens = end_idx - start_idx - 1 # exclude start and end tokens
|
||||
|
||||
# Generate pad_ids
|
||||
pad_values = [image_inputs.pad_values[image_idx]]
|
||||
|
||||
pad_ids = pad_values * ((num_tokens + len(pad_values)) // len(pad_values))
|
||||
pad_ids = pad_ids[:num_tokens]
|
||||
|
||||
# Add pad_ids
|
||||
new_input_ids.extend(pad_ids)
|
||||
|
||||
# Update last_idx to after end token
|
||||
last_idx = end_idx
|
||||
|
||||
# Add remaining tokens after last region
|
||||
new_input_ids.extend(input_ids[last_idx:])
|
||||
assert len(input_ids) == len(new_input_ids)
|
||||
return new_input_ids
|
||||
|
||||
def _process_image_input(self, image_input: Qwen2VLImageInputs) -> torch.Tensor:
|
||||
pixel_values = image_input["pixel_values"].type(self.visual.dtype)
|
||||
image_embeds = self.visual(pixel_values, grid_thw=image_input["image_grid_thw"])
|
||||
return image_embeds
|
||||
|
||||
def _process_video_input(self, video_input: Qwen2VLVideoInputs) -> torch.Tensor:
|
||||
pixel_values_videos = video_input["pixel_values_videos"].type(self.visual.dtype)
|
||||
video_embeds = self.visual(
|
||||
pixel_values_videos, grid_thw=video_input["video_grid_thw"]
|
||||
)
|
||||
return video_embeds
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
get_embedding: bool = False,
|
||||
):
|
||||
"""Run forward pass for Qwen2_5-VL.
|
||||
|
||||
Args:
|
||||
input_ids: Flattened (concatenated) input_ids corresponding to a
|
||||
batch.
|
||||
positions: Flattened (concatenated) position ids corresponding to a
|
||||
batch.
|
||||
**NOTE**: If mrope is enabled (default setting for Qwen2-VL
|
||||
opensource models), the shape will be `(3, seq_len)`,
|
||||
otherwise it will be `(seq_len,).
|
||||
(Use input_metadata.mrope_positions to replace it)
|
||||
"""
|
||||
if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
|
||||
positions = forward_batch.mrope_positions
|
||||
|
||||
image_inputs = None
|
||||
if forward_batch.image_inputs is not None:
|
||||
image_inputs = [
|
||||
img for img in forward_batch.image_inputs if img is not None
|
||||
]
|
||||
|
||||
if (
|
||||
forward_batch.forward_mode.is_decode()
|
||||
or image_inputs is None
|
||||
or len(image_inputs) == 0
|
||||
):
|
||||
inputs_embeds = self.model.embed_tokens(input_ids)
|
||||
else:
|
||||
if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
|
||||
assert positions.ndim == 2 and positions.size(0) == 3, (
|
||||
"multimodal section rotary embedding requires "
|
||||
f"(3, seq_len) positions, but got {positions.size()}"
|
||||
)
|
||||
|
||||
# Clamp input ids. This is because the input_ids for the image tokens are
|
||||
# filled with the hash values of the image for the prefix matching in the radix attention.
|
||||
# There values are useless because their embeddings will be replaced by vision embeddings anyway.
|
||||
input_ids.clamp_(min=0, max=self.config.vocab_size - 1)
|
||||
# [B, s, hidden_size]
|
||||
inputs_embeds = self.model.embed_tokens(input_ids)
|
||||
extend_start_loc_cpu = forward_batch.extend_start_loc.cpu().numpy()
|
||||
prefix_lens_cpu = forward_batch.extend_prefix_lens_cpu
|
||||
for i, image in enumerate(forward_batch.image_inputs):
|
||||
if image is None:
|
||||
continue
|
||||
start_idx = extend_start_loc_cpu[i]
|
||||
prefix_len = prefix_lens_cpu[i]
|
||||
|
||||
pixel_values = image.pixel_values.clone().detach().requires_grad_(False)
|
||||
image_grid_thws = torch.tensor(
|
||||
np.array(image.image_grid_thws), device="cuda"
|
||||
)
|
||||
image_offsets = image.image_offsets
|
||||
image_input = Qwen2VLImageInputs(
|
||||
pixel_values=pixel_values, image_grid_thw=image_grid_thws
|
||||
)
|
||||
image_embeds = self._process_image_input(image_input)
|
||||
|
||||
image_embeds_offset = 0
|
||||
for idx, image_offset in enumerate(image_offsets):
|
||||
if image_offset < prefix_len:
|
||||
continue
|
||||
num_image_tokens = self.calculate_num_image_tokens(
|
||||
image_grid_thws[idx]
|
||||
)
|
||||
|
||||
left_idx = start_idx + (image_offset - prefix_len)
|
||||
right_idx = left_idx + num_image_tokens
|
||||
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
|
||||
hidden_size = image_embeds.shape[-1]
|
||||
|
||||
if hidden_size % tp_size != 0:
|
||||
padding_size = tp_size - (hidden_size % tp_size)
|
||||
image_embeds = F.pad(image_embeds, (0, padding_size))
|
||||
inputs_embeds = F.pad(inputs_embeds, (0, padding_size))
|
||||
|
||||
hidden_chunk_size = image_embeds.shape[-1] // tp_size
|
||||
rank = get_tensor_model_parallel_rank()
|
||||
start_dim = rank * hidden_chunk_size
|
||||
end_dim = (rank + 1) * hidden_chunk_size
|
||||
inputs_embeds[left_idx:right_idx, ..., start_dim:end_dim] = (
|
||||
image_embeds[
|
||||
image_embeds_offset : image_embeds_offset
|
||||
+ num_image_tokens,
|
||||
...,
|
||||
start_dim:end_dim,
|
||||
]
|
||||
)
|
||||
image_embeds_offset += num_image_tokens
|
||||
|
||||
input_ids = None
|
||||
hidden_states = self.model(
|
||||
input_ids=input_ids,
|
||||
positions=positions,
|
||||
forward_batch=forward_batch,
|
||||
input_embeds=inputs_embeds,
|
||||
)
|
||||
|
||||
if not get_embedding:
|
||||
return self.logits_processor(
|
||||
input_ids, hidden_states, self.lm_head, forward_batch
|
||||
)
|
||||
else:
|
||||
return self.pooler(hidden_states, forward_batch)
|
||||
|
||||
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", "up_proj", 1),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
]
|
||||
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
if "visual" in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
if "visual" in name and "qkv.weight" in name:
|
||||
visual_num_heads = self.config.vision_config.num_heads
|
||||
visual_embed_dim = self.config.vision_config.hidden_size
|
||||
head_size = visual_embed_dim // visual_num_heads
|
||||
loaded_weight = loaded_weight.view(
|
||||
3, visual_num_heads, head_size, visual_embed_dim
|
||||
)
|
||||
loaded_weight = loaded_weight.transpose(0, 1)
|
||||
loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
|
||||
elif "visual" in name and "qkv.bias" in name:
|
||||
visual_num_heads = self.config.vision_config.num_heads
|
||||
visual_embed_dim = self.config.vision_config.hidden_size
|
||||
head_size = visual_embed_dim // visual_num_heads
|
||||
loaded_weight = loaded_weight.view(3, visual_num_heads, head_size)
|
||||
loaded_weight = loaded_weight.transpose(0, 1)
|
||||
loaded_weight = loaded_weight.reshape(-1)
|
||||
|
||||
if "visual" in name:
|
||||
# adapt to VisionAttention
|
||||
name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
|
||||
|
||||
try:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
except KeyError:
|
||||
print(params_dict.keys())
|
||||
raise
|
||||
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
EntryClass = [Qwen2_5_VLForConditionalGeneration]
|
||||
AutoModel.register(Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration)
|
||||
@@ -31,8 +31,9 @@ import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange
|
||||
from transformers import Qwen2VLConfig
|
||||
from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLVisionConfig
|
||||
|
||||
from sglang.srt.configs import Qwen2VLConfig, Qwen2VLVisionConfig
|
||||
from sglang.srt.hf_transformers_utils import get_processor
|
||||
from sglang.srt.layers.activation import QuickGELU
|
||||
from sglang.srt.layers.attention.vision import VisionAttention
|
||||
|
||||
Reference in New Issue
Block a user