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[Misc] Move registry to its own file (vllm-project#9064)
Signed-off-by: Alvant <alvasian@yandex.ru>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,325 +1,16 @@ | ||
| import importlib | ||
| import string | ||
| import subprocess | ||
| import sys | ||
| import uuid | ||
| from functools import lru_cache, partial | ||
| from typing import Callable, Dict, List, Optional, Tuple, Type, Union | ||
|
|
||
| import torch.nn as nn | ||
|
|
||
| from vllm.logger import init_logger | ||
| from vllm.utils import is_hip | ||
|
|
||
| from .interfaces import supports_multimodal, supports_pp | ||
|
|
||
| logger = init_logger(__name__) | ||
|
|
||
| _GENERATION_MODELS = { | ||
| "AquilaModel": ("llama", "LlamaForCausalLM"), | ||
| "AquilaForCausalLM": ("llama", "LlamaForCausalLM"), # AquilaChat2 | ||
| "ArcticForCausalLM": ("arctic", "ArcticForCausalLM"), | ||
| "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"), # baichuan-7b | ||
| "BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"), # baichuan-13b | ||
| "BloomForCausalLM": ("bloom", "BloomForCausalLM"), | ||
| "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"), | ||
| "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"), | ||
| "CohereForCausalLM": ("commandr", "CohereForCausalLM"), | ||
| "DbrxForCausalLM": ("dbrx", "DbrxForCausalLM"), | ||
| "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"), | ||
| "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"), | ||
| "DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"), | ||
| "ExaoneForCausalLM": ("exaone", "ExaoneForCausalLM"), | ||
| "FalconForCausalLM": ("falcon", "FalconForCausalLM"), | ||
| "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"), | ||
| "Gemma2ForCausalLM": ("gemma2", "Gemma2ForCausalLM"), | ||
| "GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"), | ||
| "GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"), | ||
| "GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"), | ||
| "GPTNeoXForCausalLM": ("gpt_neox", "GPTNeoXForCausalLM"), | ||
| "GraniteForCausalLM": ("granite", "GraniteForCausalLM"), | ||
| "GraniteMoeForCausalLM": ("granitemoe", "GraniteMoeForCausalLM"), | ||
| "InternLMForCausalLM": ("llama", "LlamaForCausalLM"), | ||
| "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"), | ||
| "JAISLMHeadModel": ("jais", "JAISLMHeadModel"), | ||
| "JambaForCausalLM": ("jamba", "JambaForCausalLM"), | ||
| "LlamaForCausalLM": ("llama", "LlamaForCausalLM"), | ||
| # For decapoda-research/llama-* | ||
| "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"), | ||
| "MistralForCausalLM": ("llama", "LlamaForCausalLM"), | ||
| "MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"), | ||
| "QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"), | ||
| # transformers's mpt class has lower case | ||
| "MptForCausalLM": ("mpt", "MPTForCausalLM"), | ||
| "MPTForCausalLM": ("mpt", "MPTForCausalLM"), | ||
| "MiniCPMForCausalLM": ("minicpm", "MiniCPMForCausalLM"), | ||
| "MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"), | ||
| "NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"), | ||
| "OlmoForCausalLM": ("olmo", "OlmoForCausalLM"), | ||
| "OlmoeForCausalLM": ("olmoe", "OlmoeForCausalLM"), | ||
| "OPTForCausalLM": ("opt", "OPTForCausalLM"), | ||
| "OrionForCausalLM": ("orion", "OrionForCausalLM"), | ||
| "PersimmonForCausalLM": ("persimmon", "PersimmonForCausalLM"), | ||
| "PhiForCausalLM": ("phi", "PhiForCausalLM"), | ||
| "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"), | ||
| "Phi3SmallForCausalLM": ("phi3_small", "Phi3SmallForCausalLM"), | ||
| "PhiMoEForCausalLM": ("phimoe", "PhiMoEForCausalLM"), | ||
| "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"), | ||
| "Qwen2MoeForCausalLM": ("qwen2_moe", "Qwen2MoeForCausalLM"), | ||
| "Qwen2VLForConditionalGeneration": | ||
| ("qwen2_vl", "Qwen2VLForConditionalGeneration"), | ||
| "RWForCausalLM": ("falcon", "FalconForCausalLM"), | ||
| "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"), | ||
| "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"), | ||
| "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"), | ||
| "SolarForCausalLM": ("solar", "SolarForCausalLM"), | ||
| "XverseForCausalLM": ("xverse", "XverseForCausalLM"), | ||
| # NOTE: The below models are for speculative decoding only | ||
| "MedusaModel": ("medusa", "Medusa"), | ||
| "EAGLEModel": ("eagle", "EAGLE"), | ||
| "MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"), | ||
| } | ||
|
|
||
| _EMBEDDING_MODELS = { | ||
| "MistralModel": ("llama_embedding", "LlamaEmbeddingModel"), | ||
| "Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"), | ||
| } | ||
|
|
||
| _MULTIMODAL_MODELS = { | ||
| "Blip2ForConditionalGeneration": | ||
| ("blip2", "Blip2ForConditionalGeneration"), | ||
| "ChameleonForConditionalGeneration": | ||
| ("chameleon", "ChameleonForConditionalGeneration"), | ||
| "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"), | ||
| "InternVLChatModel": ("internvl", "InternVLChatModel"), | ||
| "LlavaForConditionalGeneration": ("llava", | ||
| "LlavaForConditionalGeneration"), | ||
| "LlavaNextForConditionalGeneration": ("llava_next", | ||
| "LlavaNextForConditionalGeneration"), | ||
| "LlavaNextVideoForConditionalGeneration": | ||
| ("llava_next_video", "LlavaNextVideoForConditionalGeneration"), | ||
| "LlavaOnevisionForConditionalGeneration": | ||
| ("llava_onevision", "LlavaOnevisionForConditionalGeneration"), | ||
| "MiniCPMV": ("minicpmv", "MiniCPMV"), | ||
| "PaliGemmaForConditionalGeneration": ("paligemma", | ||
| "PaliGemmaForConditionalGeneration"), | ||
| "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"), | ||
| "PixtralForConditionalGeneration": ("pixtral", | ||
| "PixtralForConditionalGeneration"), | ||
| "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"), | ||
| "Qwen2VLForConditionalGeneration": ("qwen2_vl", | ||
| "Qwen2VLForConditionalGeneration"), | ||
| "UltravoxModel": ("ultravox", "UltravoxModel"), | ||
| "MllamaForConditionalGeneration": ("mllama", | ||
| "MllamaForConditionalGeneration"), | ||
| } | ||
| _CONDITIONAL_GENERATION_MODELS = { | ||
| "BartModel": ("bart", "BartForConditionalGeneration"), | ||
| "BartForConditionalGeneration": ("bart", "BartForConditionalGeneration"), | ||
| } | ||
|
|
||
| _MODELS = { | ||
| **_GENERATION_MODELS, | ||
| **_EMBEDDING_MODELS, | ||
| **_MULTIMODAL_MODELS, | ||
| **_CONDITIONAL_GENERATION_MODELS, | ||
| } | ||
|
|
||
| # Architecture -> type. | ||
| # out of tree models | ||
| _OOT_MODELS: Dict[str, Type[nn.Module]] = {} | ||
|
|
||
| # Models not supported by ROCm. | ||
| _ROCM_UNSUPPORTED_MODELS: List[str] = [] | ||
|
|
||
| # Models partially supported by ROCm. | ||
| # Architecture -> Reason. | ||
| _ROCM_SWA_REASON = ("Sliding window attention (SWA) is not yet supported in " | ||
| "Triton flash attention. For half-precision SWA support, " | ||
| "please use CK flash attention by setting " | ||
| "`VLLM_USE_TRITON_FLASH_ATTN=0`") | ||
| _ROCM_PARTIALLY_SUPPORTED_MODELS: Dict[str, str] = { | ||
| "Qwen2ForCausalLM": | ||
| _ROCM_SWA_REASON, | ||
| "MistralForCausalLM": | ||
| _ROCM_SWA_REASON, | ||
| "MixtralForCausalLM": | ||
| _ROCM_SWA_REASON, | ||
| "PaliGemmaForConditionalGeneration": | ||
| ("ROCm flash attention does not yet " | ||
| "fully support 32-bit precision on PaliGemma"), | ||
| "Phi3VForCausalLM": | ||
| ("ROCm Triton flash attention may run into compilation errors due to " | ||
| "excessive use of shared memory. If this happens, disable Triton FA " | ||
| "by setting `VLLM_USE_TRITON_FLASH_ATTN=0`") | ||
| } | ||
|
|
||
|
|
||
| class ModelRegistry: | ||
|
|
||
| @staticmethod | ||
| def _get_module_cls_name(model_arch: str) -> Tuple[str, str]: | ||
| module_relname, cls_name = _MODELS[model_arch] | ||
| return f"vllm.model_executor.models.{module_relname}", cls_name | ||
|
|
||
| @staticmethod | ||
| @lru_cache(maxsize=128) | ||
| def _try_get_model_stateful(model_arch: str) -> Optional[Type[nn.Module]]: | ||
| if model_arch not in _MODELS: | ||
| return None | ||
|
|
||
| module_name, cls_name = ModelRegistry._get_module_cls_name(model_arch) | ||
| module = importlib.import_module(module_name) | ||
| return getattr(module, cls_name, None) | ||
|
|
||
| @staticmethod | ||
| def _try_get_model_stateless(model_arch: str) -> Optional[Type[nn.Module]]: | ||
| if model_arch in _OOT_MODELS: | ||
| return _OOT_MODELS[model_arch] | ||
|
|
||
| if is_hip(): | ||
| if model_arch in _ROCM_UNSUPPORTED_MODELS: | ||
| raise ValueError( | ||
| f"Model architecture {model_arch} is not supported by " | ||
| "ROCm for now.") | ||
| if model_arch in _ROCM_PARTIALLY_SUPPORTED_MODELS: | ||
| logger.warning( | ||
| "Model architecture %s is partially supported by ROCm: %s", | ||
| model_arch, _ROCM_PARTIALLY_SUPPORTED_MODELS[model_arch]) | ||
|
|
||
| return None | ||
|
|
||
| @staticmethod | ||
| def _try_load_model_cls(model_arch: str) -> Optional[Type[nn.Module]]: | ||
| model = ModelRegistry._try_get_model_stateless(model_arch) | ||
| if model is not None: | ||
| return model | ||
|
|
||
| return ModelRegistry._try_get_model_stateful(model_arch) | ||
|
|
||
| @staticmethod | ||
| def resolve_model_cls( | ||
| architectures: Union[str, List[str]], ) -> Tuple[Type[nn.Module], str]: | ||
| if isinstance(architectures, str): | ||
| architectures = [architectures] | ||
| if not architectures: | ||
| logger.warning("No model architectures are specified") | ||
|
|
||
| for arch in architectures: | ||
| model_cls = ModelRegistry._try_load_model_cls(arch) | ||
| if model_cls is not None: | ||
| return (model_cls, arch) | ||
|
|
||
| raise ValueError( | ||
| f"Model architectures {architectures} are not supported for now. " | ||
| f"Supported architectures: {ModelRegistry.get_supported_archs()}") | ||
|
|
||
| @staticmethod | ||
| def get_supported_archs() -> List[str]: | ||
| return list(_MODELS.keys()) + list(_OOT_MODELS.keys()) | ||
|
|
||
| @staticmethod | ||
| def register_model(model_arch: str, model_cls: Type[nn.Module]): | ||
| if model_arch in _MODELS: | ||
| logger.warning( | ||
| "Model architecture %s is already registered, and will be " | ||
| "overwritten by the new model class %s.", model_arch, | ||
| model_cls.__name__) | ||
|
|
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| _OOT_MODELS[model_arch] = model_cls | ||
|
|
||
| @staticmethod | ||
| @lru_cache(maxsize=128) | ||
| def _check_stateless( | ||
| func: Callable[[Type[nn.Module]], bool], | ||
| model_arch: str, | ||
| *, | ||
| default: Optional[bool] = None, | ||
| ) -> bool: | ||
| """ | ||
| Run a boolean function against a model and return the result. | ||
| If the model is not found, returns the provided default value. | ||
| If the model is not already imported, the function is run inside a | ||
| subprocess to avoid initializing CUDA for the main program. | ||
| """ | ||
| model = ModelRegistry._try_get_model_stateless(model_arch) | ||
| if model is not None: | ||
| return func(model) | ||
|
|
||
| if model_arch not in _MODELS and default is not None: | ||
| return default | ||
|
|
||
| module_name, cls_name = ModelRegistry._get_module_cls_name(model_arch) | ||
|
|
||
| valid_name_characters = string.ascii_letters + string.digits + "._" | ||
| if any(s not in valid_name_characters for s in module_name): | ||
| raise ValueError(f"Unsafe module name detected for {model_arch}") | ||
| if any(s not in valid_name_characters for s in cls_name): | ||
| raise ValueError(f"Unsafe class name detected for {model_arch}") | ||
| if any(s not in valid_name_characters for s in func.__module__): | ||
| raise ValueError(f"Unsafe module name detected for {func}") | ||
| if any(s not in valid_name_characters for s in func.__name__): | ||
| raise ValueError(f"Unsafe class name detected for {func}") | ||
|
|
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| err_id = uuid.uuid4() | ||
|
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| stmts = ";".join([ | ||
| f"from {module_name} import {cls_name}", | ||
| f"from {func.__module__} import {func.__name__}", | ||
| f"assert {func.__name__}({cls_name}), '{err_id}'", | ||
| ]) | ||
|
|
||
| result = subprocess.run([sys.executable, "-c", stmts], | ||
| capture_output=True) | ||
|
|
||
| if result.returncode != 0: | ||
| err_lines = [line.decode() for line in result.stderr.splitlines()] | ||
| if err_lines and err_lines[-1] != f"AssertionError: {err_id}": | ||
| err_str = "\n".join(err_lines) | ||
| raise RuntimeError( | ||
| "An unexpected error occurred while importing the model in " | ||
| f"another process. Error log:\n{err_str}") | ||
|
|
||
| return result.returncode == 0 | ||
|
|
||
| @staticmethod | ||
| def is_embedding_model(architectures: Union[str, List[str]]) -> bool: | ||
| if isinstance(architectures, str): | ||
| architectures = [architectures] | ||
| if not architectures: | ||
| logger.warning("No model architectures are specified") | ||
|
|
||
| return any(arch in _EMBEDDING_MODELS for arch in architectures) | ||
|
|
||
| @staticmethod | ||
| def is_multimodal_model(architectures: Union[str, List[str]]) -> bool: | ||
| if isinstance(architectures, str): | ||
| architectures = [architectures] | ||
| if not architectures: | ||
| logger.warning("No model architectures are specified") | ||
|
|
||
| is_mm = partial(ModelRegistry._check_stateless, | ||
| supports_multimodal, | ||
| default=False) | ||
|
|
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| return any(is_mm(arch) for arch in architectures) | ||
|
|
||
| @staticmethod | ||
| def is_pp_supported_model(architectures: Union[str, List[str]]) -> bool: | ||
| if isinstance(architectures, str): | ||
| architectures = [architectures] | ||
| if not architectures: | ||
| logger.warning("No model architectures are specified") | ||
|
|
||
| is_pp = partial(ModelRegistry._check_stateless, | ||
| supports_pp, | ||
| default=False) | ||
|
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| return any(is_pp(arch) for arch in architectures) | ||
|
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| from .interfaces import (HasInnerState, SupportsLoRA, SupportsMultiModal, | ||
| SupportsPP, has_inner_state, supports_lora, | ||
| supports_multimodal, supports_pp) | ||
| from .registry import ModelRegistry | ||
|
|
||
| __all__ = [ | ||
| "ModelRegistry", | ||
| "HasInnerState", | ||
| "has_inner_state", | ||
| "SupportsLoRA", | ||
| "supports_lora", | ||
| "SupportsMultiModal", | ||
| "supports_multimodal", | ||
| "SupportsPP", | ||
| "supports_pp", | ||
| ] |
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