53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
"""Utilities for selecting and loading models."""
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from typing import Type
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.config import ModelConfig
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from vllm.model_executor.models import (GPT2LMHeadModel, GPTBigCodeForCausalLM,
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GPTNeoXForCausalLM, LlamaForCausalLM,
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OPTForCausalLM)
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from vllm.model_executor.weight_utils import initialize_dummy_weights
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# TODO(woosuk): Lazy-load the model classes.
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_MODEL_REGISTRY = {
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"GPT2LMHeadModel": GPT2LMHeadModel,
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"GPTBigCodeForCausalLM": GPTBigCodeForCausalLM,
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"GPTNeoXForCausalLM": GPTNeoXForCausalLM,
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"LlamaForCausalLM": LlamaForCausalLM,
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"LLaMAForCausalLM": LlamaForCausalLM,
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"OPTForCausalLM": OPTForCausalLM,
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}
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def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
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architectures = getattr(config, "architectures", [])
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for arch in architectures:
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if arch in _MODEL_REGISTRY:
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return _MODEL_REGISTRY[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: {list(_MODEL_REGISTRY.keys())}")
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def get_model(model_config: ModelConfig) -> nn.Module:
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model_class = _get_model_architecture(model_config.hf_config)
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torch.set_default_dtype(model_config.dtype)
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# Create a model instance.
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# The weights will be initialized as empty tensors.
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model = model_class(model_config.hf_config)
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if model_config.use_dummy_weights:
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model = model.cuda()
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# NOTE(woosuk): For accurate performance evaluation, we assign
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# random values to the weights.
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initialize_dummy_weights(model)
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else:
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# Load the weights from the cached or downloaded files.
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model.load_weights(model_config.model, model_config.download_dir,
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model_config.use_np_weights)
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model = model.cuda()
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return model.eval()
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