55 lines
2.2 KiB
Python
55 lines
2.2 KiB
Python
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#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from collections.abc import Iterable
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import torch
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.qwen3 import Qwen3ForCausalLM
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_orig_qwen3_causal_lm_load_weights = Qwen3ForCausalLM.load_weights
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def _patched_qwen3_causal_lm_load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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quant_config = self.quant_config
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if quant_config is None or not callable(getattr(quant_config, "get_cache_scale", None)):
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return _orig_qwen3_causal_lm_load_weights(self, weights)
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params_dict = dict(self.named_parameters())
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c8_loaded_params: set[str] = set()
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def _intercept_c8_scales(
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raw_weights: Iterable[tuple[str, torch.Tensor]],
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) -> Iterable[tuple[str, torch.Tensor]]:
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for name, loaded_weight in raw_weights:
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scale_name = quant_config.get_cache_scale(name)
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if scale_name is not None:
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if scale_name in params_dict:
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight.squeeze())
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c8_loaded_params.add(scale_name)
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else:
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yield name, loaded_weight
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loaded_params = _orig_qwen3_causal_lm_load_weights(self, _intercept_c8_scales(weights))
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loaded_params.update(c8_loaded_params)
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return loaded_params
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Qwen3ForCausalLM.load_weights = _patched_qwen3_causal_lm_load_weights
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