70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
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#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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# This file is a part of the vllm-ascend project.
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#
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import json
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import os
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from pathlib import Path
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import torch
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from vllm.config.load import LoadConfig
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from vllm.model_executor.model_loader import ShardedStateLoader
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class ShardedStateLoader310(ShardedStateLoader):
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def __init__(self, load_config: LoadConfig):
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super().__init__(load_config)
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@staticmethod
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def save_model(
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model: torch.nn.Module,
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path: str,
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pattern: str | None = None,
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max_size: int | None = None,
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) -> None:
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from safetensors.torch import save_file
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from vllm.distributed import get_tensor_model_parallel_rank
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rank = get_tensor_model_parallel_rank()
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part_idx = 0
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state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())
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filename = ShardedStateLoader.DEFAULT_PATTERN.format(rank=rank, part=part_idx)
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save_file(
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state_dict,
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os.path.join(path, filename),
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)
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@staticmethod
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def generate_quant_description(model: torch.nn.Module, path: str):
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"""Generate a mapping of parameter names to their corresponding quantization types."""
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quant_description = {}
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quantize_type = model.quant_config.quant_description.get("model_quant_type", "FLOAT")
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quant_description["model_quant_type"] = quantize_type
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quant_description["version"] = "1.0.0"
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state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())
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for name, tensor in state_dict.items():
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if name.endswith(".weight") or name.endswith(".bias"):
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if tensor.dtype in [torch.int8, torch.int32, torch.int64]:
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quant_description[name] = quantize_type
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else:
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quant_description[name] = "FLOAT"
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else:
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quant_description[name] = "FLOAT"
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json_path = Path(path) / "parameters_type_map.json"
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with json_path.open("w", encoding="utf-8") as f:
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json.dump(quant_description, f, indent=2)
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