mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. 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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"""
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import paddle
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from paddle import nn
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from typing_extensions import assert_never
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from fastdeploy.config import FDConfig, LoadConfig, ModelConfig
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from fastdeploy.model_executor.load_weight_utils import (
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get_weight_iterator,
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is_weight_cache_enabled,
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load_weights_from_cache,
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measure_time,
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save_model,
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)
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from fastdeploy.model_executor.model_loader.base_loader import BaseModelLoader
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from fastdeploy.model_executor.models.adapters import as_embedding_model
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from fastdeploy.model_executor.models.model_base import ModelRegistry
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from fastdeploy.platforms import current_platform
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class DefaultModelLoaderV1(BaseModelLoader):
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"""ModelLoader that can load registered models"""
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def __init__(self, load_config: LoadConfig):
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super().__init__(load_config)
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def download_model(self, model_config: ModelConfig) -> None:
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pass
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def clean_memory_fragments(self) -> None:
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"""clean_memory_fragments"""
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if current_platform.is_cuda():
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paddle.device.cuda.empty_cache()
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paddle.device.synchronize()
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@save_model()
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@measure_time()
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def load_weights(self, model, fd_config: FDConfig, enable_cache: bool = False) -> None:
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weights_iterator = get_weight_iterator(fd_config.model_config.model)
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if enable_cache:
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load_weights_from_cache(model, weights_iterator)
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else:
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model.load_weights(weights_iterator)
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self.clean_memory_fragments()
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def load_model(self, fd_config: FDConfig) -> nn.Layer:
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architectures = fd_config.model_config.architectures[0]
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context = paddle.LazyGuard()
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if fd_config.load_config.dynamic_load_weight:
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# register rl model
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import fastdeploy.rl # noqa
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architectures = architectures + "RL"
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enable_cache, _, weight_cache_context = is_weight_cache_enabled(fd_config)
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with weight_cache_context:
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with context:
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model_cls = ModelRegistry.get_class(architectures)
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convert_type = fd_config.model_config.convert_type
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if convert_type == "none":
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pass
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elif convert_type == "embed":
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model_cls = as_embedding_model(model_cls)
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else:
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assert_never(convert_type)
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model = model_cls(fd_config)
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model.eval()
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# RL model not need set_state_dict
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if fd_config.load_config.dynamic_load_weight:
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return model
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self.load_weights(model, fd_config, enable_cache)
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return model
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