wehub-resource-sync
65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
import torch.nn as nn
|
|
|
|
from vllm.config import ModelConfig
|
|
from vllm.config.load import LoadConfig
|
|
from vllm.model_executor.layers.quantization.base_config import QuantizeMethodBase
|
|
from vllm.model_executor.model_loader.base_loader import BaseModelLoader
|
|
from vllm.model_executor.model_loader.reload.layerwise import (
|
|
_get_original_loader,
|
|
get_layerwise_info,
|
|
)
|
|
from vllm.model_executor.model_loader.reload.meta import materialize_layer
|
|
from vllm.model_executor.model_loader.reload.types import LayerReloadingInfo
|
|
from vllm.model_executor.model_loader.reload.utils import get_layer_tensors
|
|
from vllm.model_executor.model_loader.weight_utils import (
|
|
initialize_dummy_weights,
|
|
initialize_single_dummy_weight,
|
|
)
|
|
|
|
|
|
class DummyModelLoader(BaseModelLoader):
|
|
"""Model loader that will set model weights to random values."""
|
|
|
|
def __init__(self, load_config: LoadConfig):
|
|
super().__init__(load_config)
|
|
if load_config.model_loader_extra_config:
|
|
raise ValueError(
|
|
f"Model loader extra config is not supported for "
|
|
f"load format {load_config.load_format}"
|
|
)
|
|
|
|
def download_model(self, model_config: ModelConfig) -> None:
|
|
pass # Nothing to download
|
|
|
|
def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
|
|
for layer in model.modules():
|
|
info = get_layerwise_info(layer)
|
|
if info.can_load():
|
|
self._process_online_quant_layer(layer, info)
|
|
else:
|
|
# NOTE(woosuk): For accurate performance evaluation, we assign
|
|
# random values to the weights.
|
|
initialize_dummy_weights(layer, model_config)
|
|
|
|
def _process_online_quant_layer(
|
|
self,
|
|
layer: nn.Module,
|
|
info: LayerReloadingInfo,
|
|
) -> None:
|
|
"""Materialize, apply dummy weights, and run quantization processing."""
|
|
materialize_layer(layer, info)
|
|
|
|
for tensor in get_layer_tensors(layer).values():
|
|
initialize_single_dummy_weight(tensor)
|
|
|
|
for param in get_layer_tensors(layer).values():
|
|
param.weight_loader = _get_original_loader(param)
|
|
|
|
quant_method = getattr(layer, "quant_method", None)
|
|
if isinstance(quant_method, QuantizeMethodBase):
|
|
quant_method.process_weights_after_loading(layer)
|
|
|
|
info.reset()
|