mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-13 12:34:57 +08:00
370 lines
15 KiB
Python
370 lines
15 KiB
Python
import importlib
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import pickle
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import subprocess
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import sys
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import tempfile
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from functools import lru_cache, partial
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from typing import Callable, Dict, List, Optional, Tuple, Type, Union
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import cloudpickle
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import torch.nn as nn
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from vllm.logger import init_logger
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from vllm.utils import is_hip
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from .interfaces import supports_multimodal, supports_pp
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from .interfaces_base import is_embedding_model, is_text_generation_model
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logger = init_logger(__name__)
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# yapf: disable
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_TEXT_GENERATION_MODELS = {
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# [Decoder-only]
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"AquilaModel": ("llama", "LlamaForCausalLM"),
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"AquilaForCausalLM": ("llama", "LlamaForCausalLM"), # AquilaChat2
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"ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
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"BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"), # baichuan-7b
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"BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"), # baichuan-13b
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"BloomForCausalLM": ("bloom", "BloomForCausalLM"),
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"ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),
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"ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
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"CohereForCausalLM": ("commandr", "CohereForCausalLM"),
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"DbrxForCausalLM": ("dbrx", "DbrxForCausalLM"),
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"DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"),
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"DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"),
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"DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"),
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"ExaoneForCausalLM": ("exaone", "ExaoneForCausalLM"),
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"FalconForCausalLM": ("falcon", "FalconForCausalLM"),
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"GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
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"Gemma2ForCausalLM": ("gemma2", "Gemma2ForCausalLM"),
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"GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),
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"GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"),
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"GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"),
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"GPTNeoXForCausalLM": ("gpt_neox", "GPTNeoXForCausalLM"),
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"GraniteForCausalLM": ("granite", "GraniteForCausalLM"),
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"GraniteMoeForCausalLM": ("granitemoe", "GraniteMoeForCausalLM"),
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"InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
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"InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),
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"JAISLMHeadModel": ("jais", "JAISLMHeadModel"),
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"JambaForCausalLM": ("jamba", "JambaForCausalLM"),
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"LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
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# For decapoda-research/llama-*
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"LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
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"MistralForCausalLM": ("llama", "LlamaForCausalLM"),
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"MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"),
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"QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"),
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# transformers's mpt class has lower case
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"MptForCausalLM": ("mpt", "MPTForCausalLM"),
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"MPTForCausalLM": ("mpt", "MPTForCausalLM"),
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"MiniCPMForCausalLM": ("minicpm", "MiniCPMForCausalLM"),
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"MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"),
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"NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"),
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"OlmoForCausalLM": ("olmo", "OlmoForCausalLM"),
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"OlmoeForCausalLM": ("olmoe", "OlmoeForCausalLM"),
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"OPTForCausalLM": ("opt", "OPTForCausalLM"),
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"OrionForCausalLM": ("orion", "OrionForCausalLM"),
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"PersimmonForCausalLM": ("persimmon", "PersimmonForCausalLM"),
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"PhiForCausalLM": ("phi", "PhiForCausalLM"),
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"Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
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"Phi3SmallForCausalLM": ("phi3_small", "Phi3SmallForCausalLM"),
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"PhiMoEForCausalLM": ("phimoe", "PhiMoEForCausalLM"),
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"Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
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"Qwen2MoeForCausalLM": ("qwen2_moe", "Qwen2MoeForCausalLM"),
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"RWForCausalLM": ("falcon", "FalconForCausalLM"),
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"StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
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"StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
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"Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
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"SolarForCausalLM": ("solar", "SolarForCausalLM"),
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"XverseForCausalLM": ("xverse", "XverseForCausalLM"),
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# [Encoder-decoder]
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"BartModel": ("bart", "BartForConditionalGeneration"),
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"BartForConditionalGeneration": ("bart", "BartForConditionalGeneration"),
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}
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_EMBEDDING_MODELS = {
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"MistralModel": ("llama_embedding", "LlamaEmbeddingModel"),
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"Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"),
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"Gemma2Model": ("gemma2_embedding", "Gemma2EmbeddingModel"),
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}
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_MULTIMODAL_MODELS = {
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# [Decoder-only]
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"Blip2ForConditionalGeneration": ("blip2", "Blip2ForConditionalGeneration"),
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"ChameleonForConditionalGeneration": ("chameleon", "ChameleonForConditionalGeneration"), # noqa: E501
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"FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
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"InternVLChatModel": ("internvl", "InternVLChatModel"),
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"LlavaForConditionalGeneration": ("llava", "LlavaForConditionalGeneration"),
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"LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"), # noqa: E501
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"LlavaNextVideoForConditionalGeneration": ("llava_next_video", "LlavaNextVideoForConditionalGeneration"), # noqa: E501
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"LlavaOnevisionForConditionalGeneration": ("llava_onevision", "LlavaOnevisionForConditionalGeneration"), # noqa: E501
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"MiniCPMV": ("minicpmv", "MiniCPMV"),
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"NVLM_D": ("nvlm_d", "NVLM_D_Model"),
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"PaliGemmaForConditionalGeneration": ("paligemma", "PaliGemmaForConditionalGeneration"), # noqa: E501
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"Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
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"PixtralForConditionalGeneration": ("pixtral", "PixtralForConditionalGeneration"), # noqa: E501
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"QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
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"Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"), # noqa: E501
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"UltravoxModel": ("ultravox", "UltravoxModel"),
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# [Encoder-decoder]
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"MllamaForConditionalGeneration": ("mllama", "MllamaForConditionalGeneration"), # noqa: E501
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}
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_SPECULATIVE_DECODING_MODELS = {
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"EAGLEModel": ("eagle", "EAGLE"),
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"MedusaModel": ("medusa", "Medusa"),
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"MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),
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}
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# yapf: enable
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_MODELS = {
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**_TEXT_GENERATION_MODELS,
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**_EMBEDDING_MODELS,
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**_MULTIMODAL_MODELS,
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**_SPECULATIVE_DECODING_MODELS,
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}
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# Architecture -> type or (module, class).
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# out of tree models
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_OOT_MODELS: Dict[str, Type[nn.Module]] = {}
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_OOT_MODELS_LAZY: Dict[str, Tuple[str, str]] = {}
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# Models not supported by ROCm.
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_ROCM_UNSUPPORTED_MODELS: List[str] = []
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# Models partially supported by ROCm.
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# Architecture -> Reason.
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_ROCM_SWA_REASON = ("Sliding window attention (SWA) is not yet supported in "
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"Triton flash attention. For half-precision SWA support, "
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"please use CK flash attention by setting "
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"`VLLM_USE_TRITON_FLASH_ATTN=0`")
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_ROCM_PARTIALLY_SUPPORTED_MODELS: Dict[str, str] = {
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"Qwen2ForCausalLM":
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_ROCM_SWA_REASON,
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"MistralForCausalLM":
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_ROCM_SWA_REASON,
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"MixtralForCausalLM":
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_ROCM_SWA_REASON,
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"PaliGemmaForConditionalGeneration":
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("ROCm flash attention does not yet "
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"fully support 32-bit precision on PaliGemma"),
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"Phi3VForCausalLM":
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("ROCm Triton flash attention may run into compilation errors due to "
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"excessive use of shared memory. If this happens, disable Triton FA "
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"by setting `VLLM_USE_TRITON_FLASH_ATTN=0`")
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}
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class ModelRegistry:
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@staticmethod
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def _get_module_cls_name(model_arch: str) -> Tuple[str, str]:
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if model_arch in _MODELS:
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module_relname, cls_name = _MODELS[model_arch]
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return f"vllm.model_executor.models.{module_relname}", cls_name
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if model_arch in _OOT_MODELS_LAZY:
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return _OOT_MODELS_LAZY[model_arch]
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raise KeyError(model_arch)
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@staticmethod
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@lru_cache(maxsize=128)
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def _try_get_model_stateful(model_arch: str) -> Optional[Type[nn.Module]]:
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try:
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mod_name, cls_name = ModelRegistry._get_module_cls_name(model_arch)
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except KeyError:
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return None
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module = importlib.import_module(mod_name)
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return getattr(module, cls_name, None)
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@staticmethod
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def _try_get_model_stateless(model_arch: str) -> Optional[Type[nn.Module]]:
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if model_arch in _OOT_MODELS:
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return _OOT_MODELS[model_arch]
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if is_hip():
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if model_arch in _ROCM_UNSUPPORTED_MODELS:
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raise ValueError(
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f"Model architecture {model_arch} is not supported by "
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"ROCm for now.")
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if model_arch in _ROCM_PARTIALLY_SUPPORTED_MODELS:
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logger.warning(
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"Model architecture %s is partially supported by ROCm: %s",
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model_arch, _ROCM_PARTIALLY_SUPPORTED_MODELS[model_arch])
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return None
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@staticmethod
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def _try_load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]:
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model = ModelRegistry._try_get_model_stateless(model_arch)
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if model is not None:
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return model
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return ModelRegistry._try_get_model_stateful(model_arch)
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@staticmethod
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def resolve_model_cls(
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architectures: Union[str, List[str]], ) -> Tuple[Type[nn.Module], str]:
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if isinstance(architectures, str):
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architectures = [architectures]
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if not architectures:
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logger.warning("No model architectures are specified")
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for arch in architectures:
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model_cls = ModelRegistry._try_load_model_cls(arch)
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if model_cls is not None:
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return (model_cls, arch)
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raise ValueError(
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f"Model architectures {architectures} are not supported for now. "
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f"Supported architectures: {ModelRegistry.get_supported_archs()}")
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@staticmethod
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def get_supported_archs() -> List[str]:
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return list(_MODELS.keys()) + list(_OOT_MODELS.keys())
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@staticmethod
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def register_model(model_arch: str, model_cls: Union[Type[nn.Module],
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str]):
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"""
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Register an external model to be used in vLLM.
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:code:`model_cls` can be either:
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- A :class:`torch.nn.Module` class directly referencing the model.
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- A string in the format :code:`<module>:<class>` which can be used to
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lazily import the model. This is useful to avoid initializing CUDA
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when importing the model and thus the related error
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:code:`RuntimeError: Cannot re-initialize CUDA in forked subprocess`.
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"""
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if model_arch in _MODELS:
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logger.warning(
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"Model architecture %s is already registered, and will be "
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"overwritten by the new model class %s.", model_arch,
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model_cls)
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if isinstance(model_cls, str):
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split_str = model_cls.split(":")
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if len(split_str) != 2:
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msg = "Expected a string in the format `<module>:<class>`"
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raise ValueError(msg)
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module_name, cls_name = split_str
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_OOT_MODELS_LAZY[model_arch] = module_name, cls_name
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else:
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_OOT_MODELS[model_arch] = model_cls
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@staticmethod
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@lru_cache(maxsize=128)
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def _check_stateless(
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func: Callable[[Type[nn.Module]], bool],
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model_arch: str,
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*,
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default: Optional[bool] = None,
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) -> bool:
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"""
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Run a boolean function against a model and return the result.
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If the model is not found, returns the provided default value.
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If the model is not already imported, the function is run inside a
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subprocess to avoid initializing CUDA for the main program.
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"""
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model = ModelRegistry._try_get_model_stateless(model_arch)
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if model is not None:
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return func(model)
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try:
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mod_name, cls_name = ModelRegistry._get_module_cls_name(model_arch)
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except KeyError:
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if default is not None:
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return default
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raise
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with tempfile.NamedTemporaryFile() as output_file:
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# `cloudpickle` allows pickling lambda functions directly
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input_bytes = cloudpickle.dumps(
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(mod_name, cls_name, func, output_file.name))
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# cannot use `sys.executable __file__` here because the script
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# contains relative imports
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returned = subprocess.run(
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[sys.executable, "-m", "vllm.model_executor.models.registry"],
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input=input_bytes,
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capture_output=True)
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# check if the subprocess is successful
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try:
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returned.check_returncode()
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except Exception as e:
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# wrap raised exception to provide more information
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raise RuntimeError(f"Error happened when testing "
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f"model support for{mod_name}.{cls_name}:\n"
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f"{returned.stderr.decode()}") from e
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with open(output_file.name, "rb") as f:
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result = pickle.load(f)
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return result
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@staticmethod
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def is_text_generation_model(architectures: Union[str, List[str]]) -> bool:
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if isinstance(architectures, str):
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architectures = [architectures]
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if not architectures:
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logger.warning("No model architectures are specified")
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is_txt_gen = partial(ModelRegistry._check_stateless,
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is_text_generation_model,
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default=False)
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return any(is_txt_gen(arch) for arch in architectures)
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@staticmethod
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def is_embedding_model(architectures: Union[str, List[str]]) -> bool:
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if isinstance(architectures, str):
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architectures = [architectures]
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if not architectures:
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logger.warning("No model architectures are specified")
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is_emb = partial(ModelRegistry._check_stateless,
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is_embedding_model,
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default=False)
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return any(is_emb(arch) for arch in architectures)
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@staticmethod
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def is_multimodal_model(architectures: Union[str, List[str]]) -> bool:
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if isinstance(architectures, str):
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architectures = [architectures]
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if not architectures:
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logger.warning("No model architectures are specified")
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is_mm = partial(ModelRegistry._check_stateless,
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supports_multimodal,
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default=False)
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return any(is_mm(arch) for arch in architectures)
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@staticmethod
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def is_pp_supported_model(architectures: Union[str, List[str]]) -> bool:
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if isinstance(architectures, str):
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architectures = [architectures]
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if not architectures:
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logger.warning("No model architectures are specified")
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is_pp = partial(ModelRegistry._check_stateless,
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supports_pp,
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default=False)
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return any(is_pp(arch) for arch in architectures)
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if __name__ == "__main__":
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(mod_name, cls_name, func,
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output_file) = pickle.loads(sys.stdin.buffer.read())
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mod = importlib.import_module(mod_name)
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klass = getattr(mod, cls_name)
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result = func(klass)
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with open(output_file, "wb") as f:
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f.write(pickle.dumps(result))
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