38 lines
1.6 KiB
Python
38 lines
1.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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import torch.nn as nn
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from vllm.config import ModelConfig
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from vllm.config.load import LoadConfig
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from vllm.model_executor.model_loader.base_loader import BaseModelLoader
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from vllm.model_executor.model_loader.reload.meta import materialize_meta_tensor
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from vllm.model_executor.model_loader.reload.utils import get_layer_tensors
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from vllm.model_executor.model_loader.weight_utils import initialize_dummy_weights
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class DummyModelLoader(BaseModelLoader):
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"""Model loader that will set model weights to random values."""
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def __init__(self, load_config: LoadConfig):
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super().__init__(load_config)
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if load_config.model_loader_extra_config:
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raise ValueError(
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f"Model loader extra config is not supported for "
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f"load format {load_config.load_format}"
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)
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def download_model(self, model_config: ModelConfig) -> None:
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pass # Nothing to download
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def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
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# materialize meta tensors as part of online quantization lifecycle
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for layer in model.modules():
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for name, param in get_layer_tensors(layer).items():
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if param.device == torch.device("meta"):
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setattr(layer, name, materialize_meta_tensor(param))
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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, model_config)
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