79 lines
3.2 KiB
Python
79 lines
3.2 KiB
Python
#
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
# This file is a part of the vllm-ascend project.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
import logging
|
|
from collections.abc import Callable, Iterable
|
|
|
|
import torch
|
|
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
|
from vllm.model_executor.models.glm4_moe import Glm4MoeForCausalLM
|
|
from vllm.model_executor.models.minimax_m2 import MiniMaxM2ForCausalLM
|
|
from vllm.model_executor.models.qwen3 import Qwen3ForCausalLM
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
_orig_qwen3_causal_lm_load_weights = Qwen3ForCausalLM.load_weights
|
|
_orig_Glm4_causal_lm_load_weights = Glm4MoeForCausalLM.load_weights
|
|
_orig_Minimax_m2_causal_lm_load_weights = MiniMaxM2ForCausalLM.load_weights
|
|
|
|
|
|
def _patched_causal_lm_load_weights(
|
|
self, weights: Iterable[tuple[str, torch.Tensor]], original_load_weights: Callable
|
|
) -> set[str]:
|
|
quant_config = self.quant_config
|
|
if quant_config is None or not callable(getattr(quant_config, "get_cache_scale", None)):
|
|
return original_load_weights(self, weights)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
c8_loaded_params: set[str] = set()
|
|
|
|
def _intercept_c8_scales(
|
|
raw_weights: Iterable[tuple[str, torch.Tensor]],
|
|
) -> Iterable[tuple[str, torch.Tensor]]:
|
|
for name, loaded_weight in raw_weights:
|
|
scale_name = quant_config.get_cache_scale(name)
|
|
if scale_name is not None:
|
|
if scale_name in params_dict:
|
|
param = params_dict[scale_name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight.squeeze())
|
|
c8_loaded_params.add(scale_name)
|
|
else:
|
|
logger.warning(
|
|
"Cache scale %s found in quant_config for weight %s "
|
|
"but not found in model parameters; weight will be skipped.",
|
|
scale_name,
|
|
name,
|
|
)
|
|
else:
|
|
yield name, loaded_weight
|
|
|
|
loaded_params = original_load_weights(self, _intercept_c8_scales(weights))
|
|
loaded_params.update(c8_loaded_params)
|
|
return loaded_params
|
|
|
|
|
|
Qwen3ForCausalLM.load_weights = lambda self, weights: _patched_causal_lm_load_weights(
|
|
self, weights, _orig_qwen3_causal_lm_load_weights
|
|
)
|
|
Glm4MoeForCausalLM.load_weights = lambda self, weights: _patched_causal_lm_load_weights(
|
|
self, weights, _orig_Glm4_causal_lm_load_weights
|
|
)
|
|
MiniMaxM2ForCausalLM.load_weights = lambda self, weights: _patched_causal_lm_load_weights(
|
|
self, weights, _orig_Minimax_m2_causal_lm_load_weights
|
|
)
|