868 lines
38 KiB
Python
868 lines
38 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
"""
|
|
Whenever you add an architecture to this page, please also update
|
|
`tests/models/registry.py` with example HuggingFace models for it.
|
|
"""
|
|
import importlib
|
|
import os
|
|
import pickle
|
|
import subprocess
|
|
import sys
|
|
import tempfile
|
|
from abc import ABC, abstractmethod
|
|
from collections.abc import Set
|
|
from dataclasses import dataclass, field
|
|
from functools import lru_cache
|
|
from typing import Callable, Optional, TypeVar, Union
|
|
|
|
import torch.nn as nn
|
|
import transformers
|
|
|
|
from vllm.config import (ModelConfig, ModelImpl, iter_architecture_defaults,
|
|
try_match_architecture_defaults)
|
|
from vllm.logger import init_logger
|
|
from vllm.transformers_utils.dynamic_module import (
|
|
try_get_class_from_dynamic_module)
|
|
|
|
from .interfaces import (has_inner_state, has_noops, is_attention_free,
|
|
is_hybrid, supports_cross_encoding,
|
|
supports_multimodal, supports_multimodal_raw_input,
|
|
supports_pp, supports_transcription, supports_v0_only)
|
|
from .interfaces_base import (get_default_pooling_type, is_pooling_model,
|
|
is_text_generation_model)
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
# yapf: disable
|
|
_TEXT_GENERATION_MODELS = {
|
|
# [Decoder-only]
|
|
"AquilaModel": ("llama", "LlamaForCausalLM"),
|
|
"AquilaForCausalLM": ("llama", "LlamaForCausalLM"), # AquilaChat2
|
|
"ArceeForCausalLM": ("arcee", "ArceeForCausalLM"),
|
|
"ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
|
|
"MiniMaxForCausalLM": ("minimax_text_01", "MiniMaxText01ForCausalLM"),
|
|
"MiniMaxText01ForCausalLM": ("minimax_text_01", "MiniMaxText01ForCausalLM"),
|
|
"MiniMaxM1ForCausalLM": ("minimax_text_01", "MiniMaxText01ForCausalLM"),
|
|
# baichuan-7b, upper case 'C' in the class name
|
|
"BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),
|
|
# baichuan-13b, lower case 'c' in the class name
|
|
"BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"),
|
|
"BailingMoeForCausalLM": ("bailing_moe", "BailingMoeForCausalLM"),
|
|
"BambaForCausalLM": ("bamba", "BambaForCausalLM"),
|
|
"BloomForCausalLM": ("bloom", "BloomForCausalLM"),
|
|
"ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),
|
|
"ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
|
|
"CohereForCausalLM": ("commandr", "CohereForCausalLM"),
|
|
"Cohere2ForCausalLM": ("commandr", "CohereForCausalLM"),
|
|
"DbrxForCausalLM": ("dbrx", "DbrxForCausalLM"),
|
|
"DeciLMForCausalLM": ("nemotron_nas", "DeciLMForCausalLM"),
|
|
"DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"),
|
|
"DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"),
|
|
"DeepseekV3ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"),
|
|
"Dots1ForCausalLM": ("dots1", "Dots1ForCausalLM"),
|
|
"Ernie4_5ForCausalLM": ("ernie45", "Ernie4_5ForCausalLM"),
|
|
"Ernie4_5_MoeForCausalLM": ("ernie45_moe", "Ernie4_5_MoeForCausalLM"),
|
|
"ExaoneForCausalLM": ("exaone", "ExaoneForCausalLM"),
|
|
"Exaone4ForCausalLM": ("exaone4", "Exaone4ForCausalLM"),
|
|
"FalconForCausalLM": ("falcon", "FalconForCausalLM"),
|
|
"Fairseq2LlamaForCausalLM": ("fairseq2_llama", "Fairseq2LlamaForCausalLM"),
|
|
"GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
|
|
"Gemma2ForCausalLM": ("gemma2", "Gemma2ForCausalLM"),
|
|
"Gemma3ForCausalLM": ("gemma3", "Gemma3ForCausalLM"),
|
|
"Gemma3nForCausalLM": ("gemma3n", "Gemma3nForCausalLM"),
|
|
"GlmForCausalLM": ("glm", "GlmForCausalLM"),
|
|
"Glm4ForCausalLM": ("glm4", "Glm4ForCausalLM"),
|
|
"Glm4MoeForCausalLM": ("glm4_moe", "Glm4MoeForCausalLM"),
|
|
"GptOssForCausalLM": ("gpt_oss", "GptOssForCausalLM"),
|
|
"GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),
|
|
"GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"),
|
|
"GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"),
|
|
"GPTNeoXForCausalLM": ("gpt_neox", "GPTNeoXForCausalLM"),
|
|
"GraniteForCausalLM": ("granite", "GraniteForCausalLM"),
|
|
"GraniteMoeForCausalLM": ("granitemoe", "GraniteMoeForCausalLM"),
|
|
"GraniteMoeHybridForCausalLM": ("granitemoehybrid", "GraniteMoeHybridForCausalLM"), # noqa: E501
|
|
"GraniteMoeSharedForCausalLM": ("granitemoeshared", "GraniteMoeSharedForCausalLM"), # noqa: E501
|
|
"GritLM": ("gritlm", "GritLM"),
|
|
"Grok1ModelForCausalLM": ("grok1", "Grok1ForCausalLM"),
|
|
"HunYuanMoEV1ForCausalLM": ("hunyuan_v1", "HunYuanMoEV1ForCausalLM"),
|
|
"HunYuanDenseV1ForCausalLM": ("hunyuan_v1", "HunYuanDenseV1ForCausalLM"),
|
|
"HCXVisionForCausalLM": ("hyperclovax_vision", "HCXVisionForCausalLM"),
|
|
"InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
|
|
"InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),
|
|
"InternLM2VEForCausalLM": ("internlm2_ve", "InternLM2VEForCausalLM"),
|
|
"InternLM3ForCausalLM": ("llama", "LlamaForCausalLM"),
|
|
"JAISLMHeadModel": ("jais", "JAISLMHeadModel"),
|
|
"JambaForCausalLM": ("jamba", "JambaForCausalLM"),
|
|
"Lfm2ForCausalLM": ("lfm2", "Lfm2ForCausalLM"),
|
|
"LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
|
|
"Llama4ForCausalLM": ("llama4", "Llama4ForCausalLM"), # noqa: E501
|
|
# For decapoda-research/llama-*
|
|
"LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
|
|
"MambaForCausalLM": ("mamba", "MambaForCausalLM"),
|
|
"FalconMambaForCausalLM": ("mamba", "MambaForCausalLM"),
|
|
"FalconH1ForCausalLM":("falcon_h1", "FalconH1ForCausalLM"),
|
|
"Mamba2ForCausalLM": ("mamba2", "Mamba2ForCausalLM"),
|
|
"MiniCPMForCausalLM": ("minicpm", "MiniCPMForCausalLM"),
|
|
"MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"),
|
|
"MistralForCausalLM": ("llama", "LlamaForCausalLM"),
|
|
"MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"),
|
|
"QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"),
|
|
# transformers's mpt class has lower case
|
|
"MptForCausalLM": ("mpt", "MPTForCausalLM"),
|
|
"MPTForCausalLM": ("mpt", "MPTForCausalLM"),
|
|
"MiMoForCausalLM": ("mimo", "MiMoForCausalLM"),
|
|
"NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"),
|
|
"NemotronHForCausalLM": ("nemotron_h", "NemotronHForCausalLM"),
|
|
"OlmoForCausalLM": ("olmo", "OlmoForCausalLM"),
|
|
"Olmo2ForCausalLM": ("olmo2", "Olmo2ForCausalLM"),
|
|
"OlmoeForCausalLM": ("olmoe", "OlmoeForCausalLM"),
|
|
"OPTForCausalLM": ("opt", "OPTForCausalLM"),
|
|
"OrionForCausalLM": ("orion", "OrionForCausalLM"),
|
|
"PersimmonForCausalLM": ("persimmon", "PersimmonForCausalLM"),
|
|
"PhiForCausalLM": ("phi", "PhiForCausalLM"),
|
|
"Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
|
|
"PhiMoEForCausalLM": ("phimoe", "PhiMoEForCausalLM"),
|
|
"Phi4FlashForCausalLM": ("phi4flash", "Phi4FlashForCausalLM"),
|
|
"Plamo2ForCausalLM": ("plamo2", "Plamo2ForCausalLM"),
|
|
"QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
|
|
"Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
|
|
"Qwen2MoeForCausalLM": ("qwen2_moe", "Qwen2MoeForCausalLM"),
|
|
"Qwen3ForCausalLM": ("qwen3", "Qwen3ForCausalLM"),
|
|
"Qwen3MoeForCausalLM": ("qwen3_moe", "Qwen3MoeForCausalLM"),
|
|
"RWForCausalLM": ("falcon", "FalconForCausalLM"),
|
|
"SeedOssForCausalLM": ("seed_oss", "SeedOssForCausalLM"),
|
|
"Step3TextForCausalLM": ("step3_text", "Step3TextForCausalLM"),
|
|
"StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
|
|
"StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
|
|
"Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
|
|
"SolarForCausalLM": ("solar", "SolarForCausalLM"),
|
|
"TeleChat2ForCausalLM": ("telechat2", "TeleChat2ForCausalLM"),
|
|
"TeleFLMForCausalLM": ("teleflm", "TeleFLMForCausalLM"),
|
|
"XverseForCausalLM": ("llama", "LlamaForCausalLM"),
|
|
"Zamba2ForCausalLM": ("zamba2", "Zamba2ForCausalLM"),
|
|
# [Encoder-decoder]
|
|
"BartModel": ("bart", "BartForConditionalGeneration"),
|
|
"BartForConditionalGeneration": ("bart", "BartForConditionalGeneration"),
|
|
"MBartForConditionalGeneration": ("bart", "MBartForConditionalGeneration"),
|
|
}
|
|
|
|
_EMBEDDING_MODELS = {
|
|
# [Text-only]
|
|
"BertModel": ("bert", "BertEmbeddingModel"),
|
|
"DeciLMForCausalLM": ("nemotron_nas", "DeciLMForCausalLM"),
|
|
"Gemma2Model": ("gemma2", "Gemma2ForCausalLM"),
|
|
"GlmForCausalLM": ("glm", "GlmForCausalLM"),
|
|
"GPT2ForSequenceClassification": ("gpt2", "GPT2ForSequenceClassification"),
|
|
"GritLM": ("gritlm", "GritLM"),
|
|
"GteModel": ("bert_with_rope", "SnowflakeGteNewModel"),
|
|
"GteNewModel": ("bert_with_rope", "GteNewModel"),
|
|
"InternLM2ForRewardModel": ("internlm2", "InternLM2ForRewardModel"),
|
|
"JambaForSequenceClassification": ("jamba", "JambaForSequenceClassification"), # noqa: E501
|
|
"LlamaModel": ("llama", "LlamaForCausalLM"),
|
|
**{
|
|
# Multiple models share the same architecture, so we include them all
|
|
k: (mod, arch) for k, (mod, arch) in _TEXT_GENERATION_MODELS.items()
|
|
if arch == "LlamaForCausalLM"
|
|
},
|
|
"MistralModel": ("llama", "LlamaForCausalLM"),
|
|
"ModernBertModel": ("modernbert", "ModernBertModel"),
|
|
"NomicBertModel": ("bert_with_rope", "NomicBertModel"),
|
|
"Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
|
|
"Qwen2Model": ("qwen2", "Qwen2ForCausalLM"),
|
|
"Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
|
|
"Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"),
|
|
"Qwen2ForProcessRewardModel": ("qwen2_rm", "Qwen2ForProcessRewardModel"),
|
|
"RobertaForMaskedLM": ("roberta", "RobertaEmbeddingModel"),
|
|
"RobertaModel": ("roberta", "RobertaEmbeddingModel"),
|
|
"TeleChat2ForCausalLM": ("telechat2", "TeleChat2ForCausalLM"),
|
|
"XLMRobertaModel": ("roberta", "RobertaEmbeddingModel"),
|
|
# [Multimodal]
|
|
"LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"), # noqa: E501
|
|
"Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
|
|
"Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"), # noqa: E501
|
|
# Technically PrithviGeoSpatialMAE is a model that works on images, both in
|
|
# input and output. I am adding it here because it piggy-backs on embedding
|
|
# models for the time being.
|
|
"PrithviGeoSpatialMAE": ("prithvi_geospatial_mae", "PrithviGeoSpatialMAE"),
|
|
}
|
|
|
|
_CROSS_ENCODER_MODELS = {
|
|
"BertForSequenceClassification": ("bert", "BertForSequenceClassification"),
|
|
"RobertaForSequenceClassification": ("roberta",
|
|
"RobertaForSequenceClassification"),
|
|
"XLMRobertaForSequenceClassification": ("roberta",
|
|
"RobertaForSequenceClassification"),
|
|
"ModernBertForSequenceClassification": ("modernbert",
|
|
"ModernBertForSequenceClassification"),
|
|
# [Auto-converted (see adapters.py)]
|
|
"JinaVLForRanking": ("jina_vl", "JinaVLForSequenceClassification"), # noqa: E501,
|
|
}
|
|
|
|
_MULTIMODAL_MODELS = {
|
|
# [Decoder-only]
|
|
"AriaForConditionalGeneration": ("aria", "AriaForConditionalGeneration"),
|
|
"AyaVisionForConditionalGeneration": ("aya_vision", "AyaVisionForConditionalGeneration"), # noqa: E501
|
|
"Blip2ForConditionalGeneration": ("blip2", "Blip2ForConditionalGeneration"),
|
|
"ChameleonForConditionalGeneration": ("chameleon", "ChameleonForConditionalGeneration"), # noqa: E501
|
|
"Cohere2VisionForConditionalGeneration": ("cohere2_vision", "Cohere2VisionForConditionalGeneration"), # noqa: E501
|
|
"DeepseekVLV2ForCausalLM": ("deepseek_vl2", "DeepseekVLV2ForCausalLM"),
|
|
"Ernie4_5_VLMoeForConditionalGeneration": ("ernie45_vl", "Ernie4_5_VLMoeForConditionalGeneration"), # noqa: E501
|
|
"FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
|
|
"Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"), # noqa: E501
|
|
"Gemma3nForConditionalGeneration": ("gemma3n_mm", "Gemma3nForConditionalGeneration"), # noqa: E501
|
|
"GLM4VForCausalLM": ("glm4v", "GLM4VForCausalLM"),
|
|
"Glm4vForConditionalGeneration": ("glm4_1v", "Glm4vForConditionalGeneration"), # noqa: E501
|
|
"Glm4vMoeForConditionalGeneration": ("glm4_1v", "Glm4vMoeForConditionalGeneration"), # noqa: E501
|
|
"GraniteSpeechForConditionalGeneration": ("granite_speech", "GraniteSpeechForConditionalGeneration"), # noqa: E501
|
|
"H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
|
|
"InternVLChatModel": ("internvl", "InternVLChatModel"),
|
|
"InternS1ForConditionalGeneration": ("interns1", "InternS1ForConditionalGeneration"), # noqa: E501
|
|
"Idefics3ForConditionalGeneration":("idefics3","Idefics3ForConditionalGeneration"),
|
|
"SmolVLMForConditionalGeneration": ("smolvlm","SmolVLMForConditionalGeneration"), # noqa: E501
|
|
"KeyeForConditionalGeneration": ("keye", "KeyeForConditionalGeneration"),
|
|
"RForConditionalGeneration": ("rvl", "RForConditionalGeneration"),
|
|
"KimiVLForConditionalGeneration": ("kimi_vl", "KimiVLForConditionalGeneration"), # noqa: E501
|
|
"Llama_Nemotron_Nano_VL": ("nemotron_vl", "LlamaNemotronVLChatModel"),
|
|
"LlavaForConditionalGeneration": ("llava", "LlavaForConditionalGeneration"),
|
|
"LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"), # noqa: E501
|
|
"LlavaNextVideoForConditionalGeneration": ("llava_next_video", "LlavaNextVideoForConditionalGeneration"), # noqa: E501
|
|
"LlavaOnevisionForConditionalGeneration": ("llava_onevision", "LlavaOnevisionForConditionalGeneration"), # noqa: E501
|
|
"MantisForConditionalGeneration": ("llava", "MantisForConditionalGeneration"), # noqa: E501
|
|
"MiniMaxVL01ForConditionalGeneration": ("minimax_vl_01", "MiniMaxVL01ForConditionalGeneration"), # noqa: E501
|
|
"MiniCPMO": ("minicpmo", "MiniCPMO"),
|
|
"MiniCPMV": ("minicpmv", "MiniCPMV"),
|
|
"Mistral3ForConditionalGeneration": ("mistral3", "Mistral3ForConditionalGeneration"), # noqa: E501
|
|
"MolmoForCausalLM": ("molmo", "MolmoForCausalLM"),
|
|
"NVLM_D": ("nvlm_d", "NVLM_D_Model"),
|
|
"Ovis": ("ovis", "Ovis"),
|
|
"Ovis2_5": ("ovis2_5", "Ovis2_5"),
|
|
"PaliGemmaForConditionalGeneration": ("paligemma", "PaliGemmaForConditionalGeneration"), # noqa: E501
|
|
"Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
|
|
"Phi4MMForCausalLM": ("phi4mm", "Phi4MMForCausalLM"),
|
|
"Phi4MultimodalForCausalLM": ("phi4_multimodal", "Phi4MultimodalForCausalLM"), # noqa: E501
|
|
"PixtralForConditionalGeneration": ("pixtral", "PixtralForConditionalGeneration"), # noqa: E501
|
|
"QwenVLForConditionalGeneration": ("qwen_vl", "QwenVLForConditionalGeneration"), # noqa: E501
|
|
"Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"), # noqa: E501
|
|
"Qwen2_5_VLForConditionalGeneration": ("qwen2_5_vl", "Qwen2_5_VLForConditionalGeneration"), # noqa: E501
|
|
"Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"), # noqa: E501
|
|
"Qwen2_5OmniModel": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"), # noqa: E501
|
|
"Qwen2_5OmniForConditionalGeneration": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"), # noqa: E501
|
|
"UltravoxModel": ("ultravox", "UltravoxModel"),
|
|
"Step3VLForConditionalGeneration": ("step3_vl", "Step3VLForConditionalGeneration"), # noqa: E501
|
|
"TarsierForConditionalGeneration": ("tarsier", "TarsierForConditionalGeneration"), # noqa: E501
|
|
"Tarsier2ForConditionalGeneration": ("qwen2_vl", "Tarsier2ForConditionalGeneration"), # noqa: E501
|
|
"VoxtralForConditionalGeneration": ("voxtral", "VoxtralForConditionalGeneration"), # noqa: E501
|
|
# [Encoder-decoder]
|
|
"DonutForConditionalGeneration": ("donut", "DonutForConditionalGeneration"),
|
|
"Florence2ForConditionalGeneration": ("florence2", "Florence2ForConditionalGeneration"), # noqa: E501
|
|
"MllamaForConditionalGeneration": ("mllama", "MllamaForConditionalGeneration"), # noqa: E501
|
|
"Llama4ForConditionalGeneration": ("mllama4", "Llama4ForConditionalGeneration"), # noqa: E501
|
|
"SkyworkR1VChatModel": ("skyworkr1v", "SkyworkR1VChatModel"),
|
|
"WhisperForConditionalGeneration": ("whisper", "WhisperForConditionalGeneration"), # noqa: E501
|
|
}
|
|
|
|
_SPECULATIVE_DECODING_MODELS = {
|
|
"MiMoMTPModel": ("mimo_mtp", "MiMoMTP"),
|
|
"EagleLlamaForCausalLM": ("llama_eagle", "EagleLlamaForCausalLM"),
|
|
"EagleLlama4ForCausalLM": ("llama4_eagle", "EagleLlama4ForCausalLM"),
|
|
"EagleMiniCPMForCausalLM": ("minicpm_eagle", "EagleMiniCPMForCausalLM"),
|
|
"Eagle3LlamaForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
|
|
# TODO: Re-enable this once tests/models/test_initialization.py is fixed, see PR #22333 #22611 # noqa: E501
|
|
# "LlamaForCausalLMEagle3": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
|
|
"EagleDeepSeekMTPModel": ("deepseek_eagle", "EagleDeepseekV3ForCausalLM"),
|
|
"DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"),
|
|
"ErnieMTPModel": ("ernie_mtp", "ErnieMTP"),
|
|
"Glm4MoeMTPModel": ("glm4_moe_mtp", "Glm4MoeMTP"),
|
|
"MedusaModel": ("medusa", "Medusa"),
|
|
# Temporarily disabled.
|
|
# # TODO(woosuk): Re-enable this once the MLP Speculator is supported in V1.
|
|
# "MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),
|
|
}
|
|
|
|
_TRANSFORMERS_SUPPORTED_MODELS = {
|
|
# Text generation models
|
|
"SmolLM3ForCausalLM": ("transformers", "TransformersForCausalLM"),
|
|
# Multimodal models
|
|
"Emu3ForConditionalGeneration": ("transformers", "TransformersForMultimodalLM"), # noqa: E501
|
|
}
|
|
|
|
_TRANSFORMERS_BACKEND_MODELS = {
|
|
"TransformersModel": ("transformers", "TransformersModel"),
|
|
"TransformersForCausalLM": ("transformers", "TransformersForCausalLM"),
|
|
"TransformersForMultimodalLM": ("transformers", "TransformersForMultimodalLM"), # noqa: E501
|
|
}
|
|
# yapf: enable
|
|
|
|
_VLLM_MODELS = {
|
|
**_TEXT_GENERATION_MODELS,
|
|
**_EMBEDDING_MODELS,
|
|
**_CROSS_ENCODER_MODELS,
|
|
**_MULTIMODAL_MODELS,
|
|
**_SPECULATIVE_DECODING_MODELS,
|
|
**_TRANSFORMERS_SUPPORTED_MODELS,
|
|
**_TRANSFORMERS_BACKEND_MODELS,
|
|
}
|
|
|
|
# This variable is used as the args for subprocess.run(). We
|
|
# can modify this variable to alter the args if needed. e.g.
|
|
# when we use par format to pack things together, sys.executable
|
|
# might not be the target we want to run.
|
|
_SUBPROCESS_COMMAND = [
|
|
sys.executable, "-m", "vllm.model_executor.models.registry"
|
|
]
|
|
|
|
_PREVIOUSLY_SUPPORTED_MODELS = {"Phi3SmallForCausalLM": "0.9.2"}
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class _ModelInfo:
|
|
architecture: str
|
|
is_text_generation_model: bool
|
|
is_pooling_model: bool
|
|
default_pooling_type: str
|
|
supports_cross_encoding: bool
|
|
supports_multimodal: bool
|
|
supports_multimodal_raw_input: bool
|
|
supports_pp: bool
|
|
has_inner_state: bool
|
|
is_attention_free: bool
|
|
is_hybrid: bool
|
|
has_noops: bool
|
|
supports_transcription: bool
|
|
supports_transcription_only: bool
|
|
supports_v0_only: bool
|
|
|
|
@staticmethod
|
|
def from_model_cls(model: type[nn.Module]) -> "_ModelInfo":
|
|
return _ModelInfo(
|
|
architecture=model.__name__,
|
|
is_text_generation_model=is_text_generation_model(model),
|
|
is_pooling_model=is_pooling_model(model),
|
|
default_pooling_type=get_default_pooling_type(model),
|
|
supports_cross_encoding=supports_cross_encoding(model),
|
|
supports_multimodal=supports_multimodal(model),
|
|
supports_multimodal_raw_input=supports_multimodal_raw_input(model),
|
|
supports_pp=supports_pp(model),
|
|
has_inner_state=has_inner_state(model),
|
|
is_attention_free=is_attention_free(model),
|
|
is_hybrid=is_hybrid(model),
|
|
supports_transcription=supports_transcription(model),
|
|
supports_transcription_only=(supports_transcription(model) and
|
|
model.supports_transcription_only),
|
|
supports_v0_only=supports_v0_only(model),
|
|
has_noops=has_noops(model),
|
|
)
|
|
|
|
|
|
class _BaseRegisteredModel(ABC):
|
|
|
|
@abstractmethod
|
|
def inspect_model_cls(self) -> _ModelInfo:
|
|
raise NotImplementedError
|
|
|
|
@abstractmethod
|
|
def load_model_cls(self) -> type[nn.Module]:
|
|
raise NotImplementedError
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class _RegisteredModel(_BaseRegisteredModel):
|
|
"""
|
|
Represents a model that has already been imported in the main process.
|
|
"""
|
|
|
|
interfaces: _ModelInfo
|
|
model_cls: type[nn.Module]
|
|
|
|
@staticmethod
|
|
def from_model_cls(model_cls: type[nn.Module]):
|
|
return _RegisteredModel(
|
|
interfaces=_ModelInfo.from_model_cls(model_cls),
|
|
model_cls=model_cls,
|
|
)
|
|
|
|
def inspect_model_cls(self) -> _ModelInfo:
|
|
return self.interfaces
|
|
|
|
def load_model_cls(self) -> type[nn.Module]:
|
|
return self.model_cls
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class _LazyRegisteredModel(_BaseRegisteredModel):
|
|
"""
|
|
Represents a model that has not been imported in the main process.
|
|
"""
|
|
module_name: str
|
|
class_name: str
|
|
|
|
# Performed in another process to avoid initializing CUDA
|
|
def inspect_model_cls(self) -> _ModelInfo:
|
|
return _run_in_subprocess(
|
|
lambda: _ModelInfo.from_model_cls(self.load_model_cls()))
|
|
|
|
def load_model_cls(self) -> type[nn.Module]:
|
|
mod = importlib.import_module(self.module_name)
|
|
return getattr(mod, self.class_name)
|
|
|
|
|
|
@lru_cache(maxsize=128)
|
|
def _try_load_model_cls(
|
|
model_arch: str,
|
|
model: _BaseRegisteredModel,
|
|
) -> Optional[type[nn.Module]]:
|
|
from vllm.platforms import current_platform
|
|
current_platform.verify_model_arch(model_arch)
|
|
try:
|
|
return model.load_model_cls()
|
|
except Exception:
|
|
logger.exception("Error in loading model architecture '%s'",
|
|
model_arch)
|
|
return None
|
|
|
|
|
|
@lru_cache(maxsize=128)
|
|
def _try_inspect_model_cls(
|
|
model_arch: str,
|
|
model: _BaseRegisteredModel,
|
|
) -> Optional[_ModelInfo]:
|
|
try:
|
|
return model.inspect_model_cls()
|
|
except Exception:
|
|
logger.exception("Error in inspecting model architecture '%s'",
|
|
model_arch)
|
|
return None
|
|
|
|
|
|
@dataclass
|
|
class _ModelRegistry:
|
|
# Keyed by model_arch
|
|
models: dict[str, _BaseRegisteredModel] = field(default_factory=dict)
|
|
|
|
def get_supported_archs(self) -> Set[str]:
|
|
return self.models.keys()
|
|
|
|
def register_model(
|
|
self,
|
|
model_arch: str,
|
|
model_cls: Union[type[nn.Module], str],
|
|
) -> None:
|
|
"""
|
|
Register an external model to be used in vLLM.
|
|
|
|
`model_cls` can be either:
|
|
|
|
- A [`torch.nn.Module`][] class directly referencing the model.
|
|
- A string in the format `<module>:<class>` which can be used to
|
|
lazily import the model. This is useful to avoid initializing CUDA
|
|
when importing the model and thus the related error
|
|
`RuntimeError: Cannot re-initialize CUDA in forked subprocess`.
|
|
"""
|
|
if not isinstance(model_arch, str):
|
|
msg = f"`model_arch` should be a string, not a {type(model_arch)}"
|
|
raise TypeError(msg)
|
|
|
|
if model_arch in self.models:
|
|
logger.warning(
|
|
"Model architecture %s is already registered, and will be "
|
|
"overwritten by the new model class %s.", model_arch,
|
|
model_cls)
|
|
|
|
if isinstance(model_cls, str):
|
|
split_str = model_cls.split(":")
|
|
if len(split_str) != 2:
|
|
msg = "Expected a string in the format `<module>:<class>`"
|
|
raise ValueError(msg)
|
|
|
|
model = _LazyRegisteredModel(*split_str)
|
|
elif isinstance(model_cls, type) and issubclass(model_cls, nn.Module):
|
|
model = _RegisteredModel.from_model_cls(model_cls)
|
|
else:
|
|
msg = ("`model_cls` should be a string or PyTorch model class, "
|
|
f"not a {type(model_arch)}")
|
|
raise TypeError(msg)
|
|
|
|
self.models[model_arch] = model
|
|
|
|
def _raise_for_unsupported(self, architectures: list[str]):
|
|
all_supported_archs = self.get_supported_archs()
|
|
|
|
if any(arch in all_supported_archs for arch in architectures):
|
|
raise ValueError(
|
|
f"Model architectures {architectures} failed "
|
|
"to be inspected. Please check the logs for more details.")
|
|
|
|
for arch in architectures:
|
|
if arch in _PREVIOUSLY_SUPPORTED_MODELS:
|
|
previous_version = _PREVIOUSLY_SUPPORTED_MODELS[arch]
|
|
|
|
raise ValueError(
|
|
f"Model architecture {arch} was supported in vLLM until "
|
|
f"v{previous_version}, and is not supported anymore. "
|
|
"Please use an older version of vLLM if you want to "
|
|
"use this model architecture.")
|
|
|
|
raise ValueError(
|
|
f"Model architectures {architectures} are not supported for now. "
|
|
f"Supported architectures: {all_supported_archs}")
|
|
|
|
def _try_load_model_cls(self,
|
|
model_arch: str) -> Optional[type[nn.Module]]:
|
|
if model_arch not in self.models:
|
|
return None
|
|
|
|
return _try_load_model_cls(model_arch, self.models[model_arch])
|
|
|
|
def _try_inspect_model_cls(self, model_arch: str) -> Optional[_ModelInfo]:
|
|
if model_arch not in self.models:
|
|
return None
|
|
|
|
return _try_inspect_model_cls(model_arch, self.models[model_arch])
|
|
|
|
def _try_resolve_transformers(
|
|
self,
|
|
architecture: str,
|
|
model_config: ModelConfig,
|
|
) -> Optional[str]:
|
|
if architecture in _TRANSFORMERS_BACKEND_MODELS:
|
|
return architecture
|
|
|
|
auto_map: dict[str, str] = getattr(model_config.hf_config, "auto_map",
|
|
None) or dict()
|
|
|
|
# Make sure that config class is always initialized before model class,
|
|
# otherwise the model class won't be able to access the config class,
|
|
# the expected auto_map should have correct order like:
|
|
# "auto_map": {
|
|
# "AutoConfig": "<your-repo-name>--<config-name>",
|
|
# "AutoModel": "<your-repo-name>--<config-name>",
|
|
# "AutoModelFor<Task>": "<your-repo-name>--<config-name>",
|
|
# },
|
|
for prefix in ("AutoConfig", "AutoModel"):
|
|
for name, module in auto_map.items():
|
|
if name.startswith(prefix):
|
|
try_get_class_from_dynamic_module(
|
|
module,
|
|
model_config.model,
|
|
revision=model_config.revision,
|
|
warn_on_fail=False,
|
|
)
|
|
|
|
model_module = getattr(transformers, architecture, None)
|
|
|
|
if model_module is None:
|
|
for name, module in auto_map.items():
|
|
if name.startswith("AutoModel"):
|
|
model_module = try_get_class_from_dynamic_module(
|
|
module,
|
|
model_config.model,
|
|
revision=model_config.revision,
|
|
warn_on_fail=True,
|
|
)
|
|
if model_module is not None:
|
|
break
|
|
else:
|
|
if model_config.model_impl != ModelImpl.TRANSFORMERS:
|
|
return None
|
|
|
|
raise ValueError(
|
|
f"Cannot find model module. {architecture!r} is not a "
|
|
"registered model in the Transformers library (only "
|
|
"relevant if the model is meant to be in Transformers) "
|
|
"and 'AutoModel' is not present in the model config's "
|
|
"'auto_map' (relevant if the model is custom).")
|
|
|
|
if not model_module.is_backend_compatible():
|
|
if model_config.model_impl != ModelImpl.TRANSFORMERS:
|
|
return None
|
|
|
|
raise ValueError(
|
|
f"The Transformers implementation of {architecture!r} "
|
|
"is not compatible with vLLM.")
|
|
|
|
return model_config._get_transformers_backend_cls()
|
|
|
|
def _normalize_arch(
|
|
self,
|
|
architecture: str,
|
|
model_config: ModelConfig,
|
|
) -> str:
|
|
if architecture in self.models:
|
|
return architecture
|
|
|
|
# This may be called in order to resolve runner_type and convert_type
|
|
# in the first place, in which case we consider the default match
|
|
match = try_match_architecture_defaults(
|
|
architecture,
|
|
runner_type=getattr(model_config, "runner_type", None),
|
|
convert_type=getattr(model_config, "convert_type", None),
|
|
)
|
|
if match:
|
|
suffix, _ = match
|
|
|
|
# Get the name of the base model to convert
|
|
for repl_suffix, _ in iter_architecture_defaults():
|
|
base_arch = architecture.replace(suffix, repl_suffix)
|
|
if base_arch in self.models:
|
|
return base_arch
|
|
|
|
return architecture
|
|
|
|
def inspect_model_cls(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> tuple[_ModelInfo, str]:
|
|
if isinstance(architectures, str):
|
|
architectures = [architectures]
|
|
if not architectures:
|
|
raise ValueError("No model architectures are specified")
|
|
|
|
# Require transformers impl
|
|
if model_config.model_impl == ModelImpl.TRANSFORMERS:
|
|
arch = self._try_resolve_transformers(architectures[0],
|
|
model_config)
|
|
if arch is not None:
|
|
model_info = self._try_inspect_model_cls(arch)
|
|
if model_info is not None:
|
|
return (model_info, arch)
|
|
|
|
# Fallback to transformers impl (after resolving convert_type)
|
|
if (all(arch not in self.models for arch in architectures)
|
|
and model_config.model_impl == ModelImpl.AUTO
|
|
and getattr(model_config, "convert_type", "none") == "none"):
|
|
arch = self._try_resolve_transformers(architectures[0],
|
|
model_config)
|
|
if arch is not None:
|
|
model_info = self._try_inspect_model_cls(arch)
|
|
if model_info is not None:
|
|
return (model_info, arch)
|
|
|
|
for arch in architectures:
|
|
normalized_arch = self._normalize_arch(arch, model_config)
|
|
model_info = self._try_inspect_model_cls(normalized_arch)
|
|
if model_info is not None:
|
|
return (model_info, arch)
|
|
|
|
# Fallback to transformers impl (before resolving runner_type)
|
|
if (all(arch not in self.models for arch in architectures)
|
|
and model_config.model_impl == ModelImpl.AUTO):
|
|
arch = self._try_resolve_transformers(architectures[0],
|
|
model_config)
|
|
if arch is not None:
|
|
model_info = self._try_inspect_model_cls(arch)
|
|
if model_info is not None:
|
|
return (model_info, arch)
|
|
|
|
return self._raise_for_unsupported(architectures)
|
|
|
|
def resolve_model_cls(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> tuple[type[nn.Module], str]:
|
|
if isinstance(architectures, str):
|
|
architectures = [architectures]
|
|
if not architectures:
|
|
raise ValueError("No model architectures are specified")
|
|
|
|
# Require transformers impl
|
|
if model_config.model_impl == ModelImpl.TRANSFORMERS:
|
|
arch = self._try_resolve_transformers(architectures[0],
|
|
model_config)
|
|
if arch is not None:
|
|
model_cls = self._try_load_model_cls(arch)
|
|
if model_cls is not None:
|
|
return (model_cls, arch)
|
|
|
|
# Fallback to transformers impl (after resolving convert_type)
|
|
if (all(arch not in self.models for arch in architectures)
|
|
and model_config.model_impl == ModelImpl.AUTO
|
|
and getattr(model_config, "convert_type", "none") == "none"):
|
|
arch = self._try_resolve_transformers(architectures[0],
|
|
model_config)
|
|
if arch is not None:
|
|
model_cls = self._try_load_model_cls(arch)
|
|
if model_cls is not None:
|
|
return (model_cls, arch)
|
|
|
|
for arch in architectures:
|
|
normalized_arch = self._normalize_arch(arch, model_config)
|
|
model_cls = self._try_load_model_cls(normalized_arch)
|
|
if model_cls is not None:
|
|
return (model_cls, arch)
|
|
|
|
# Fallback to transformers impl (before resolving runner_type)
|
|
if (all(arch not in self.models for arch in architectures)
|
|
and model_config.model_impl == ModelImpl.AUTO):
|
|
arch = self._try_resolve_transformers(architectures[0],
|
|
model_config)
|
|
if arch is not None:
|
|
model_cls = self._try_load_model_cls(arch)
|
|
if model_cls is not None:
|
|
return (model_cls, arch)
|
|
|
|
return self._raise_for_unsupported(architectures)
|
|
|
|
def is_text_generation_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.is_text_generation_model
|
|
|
|
def is_pooling_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.is_pooling_model
|
|
|
|
def is_cross_encoder_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.supports_cross_encoding
|
|
|
|
def is_multimodal_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.supports_multimodal
|
|
|
|
def supports_multimodal_raw_input(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.supports_multimodal_raw_input
|
|
|
|
def is_pp_supported_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.supports_pp
|
|
|
|
def model_has_inner_state(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.has_inner_state
|
|
|
|
def is_attention_free_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.is_attention_free
|
|
|
|
def is_hybrid_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.is_hybrid
|
|
|
|
def is_noops_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.has_noops
|
|
|
|
def is_transcription_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.supports_transcription
|
|
|
|
def is_transcription_only_model(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return model_cls.supports_transcription_only
|
|
|
|
def is_v1_compatible(
|
|
self,
|
|
architectures: Union[str, list[str]],
|
|
model_config: ModelConfig,
|
|
) -> bool:
|
|
model_cls, _ = self.inspect_model_cls(architectures, model_config)
|
|
return not model_cls.supports_v0_only
|
|
|
|
|
|
ModelRegistry = _ModelRegistry({
|
|
model_arch:
|
|
_LazyRegisteredModel(
|
|
module_name=f"vllm.model_executor.models.{mod_relname}",
|
|
class_name=cls_name,
|
|
)
|
|
for model_arch, (mod_relname, cls_name) in _VLLM_MODELS.items()
|
|
})
|
|
|
|
_T = TypeVar("_T")
|
|
|
|
|
|
def _run_in_subprocess(fn: Callable[[], _T]) -> _T:
|
|
# NOTE: We use a temporary directory instead of a temporary file to avoid
|
|
# issues like https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
output_filepath = os.path.join(tempdir, "registry_output.tmp")
|
|
|
|
# `cloudpickle` allows pickling lambda functions directly
|
|
import cloudpickle
|
|
input_bytes = cloudpickle.dumps((fn, output_filepath))
|
|
|
|
# cannot use `sys.executable __file__` here because the script
|
|
# contains relative imports
|
|
returned = subprocess.run(_SUBPROCESS_COMMAND,
|
|
input=input_bytes,
|
|
capture_output=True)
|
|
|
|
# check if the subprocess is successful
|
|
try:
|
|
returned.check_returncode()
|
|
except Exception as e:
|
|
# wrap raised exception to provide more information
|
|
raise RuntimeError(f"Error raised in subprocess:\n"
|
|
f"{returned.stderr.decode()}") from e
|
|
|
|
with open(output_filepath, "rb") as f:
|
|
return pickle.load(f)
|
|
|
|
|
|
def _run() -> None:
|
|
# Setup plugins
|
|
from vllm.plugins import load_general_plugins
|
|
load_general_plugins()
|
|
|
|
fn, output_file = pickle.loads(sys.stdin.buffer.read())
|
|
|
|
result = fn()
|
|
|
|
with open(output_file, "wb") as f:
|
|
f.write(pickle.dumps(result))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_run()
|