mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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122 lines
4.3 KiB
Python
122 lines
4.3 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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from abc import ABC, abstractmethod
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import paddle
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from paddle import nn
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from fastdeploy.config import FDConfig, LoadConfig, ModelConfig
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from fastdeploy.model_executor.load_weight_utils import load_composite_checkpoint
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from fastdeploy.model_executor.models.deepseek_v3 import DeepSeekV3PretrainedModel
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from fastdeploy.model_executor.models.ernie4_5_moe import Ernie4_5_PretrainedModel
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from fastdeploy.model_executor.models.ernie4_5_mtp import Ernie4_5_MTPPretrainedModel
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from fastdeploy.model_executor.models.ernie4_5_vl.ernie4_5_vl_moe import (
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Ernie4_5_VLPretrainedModel,
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)
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from fastdeploy.model_executor.models.model_base import ModelRegistry
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from fastdeploy.model_executor.models.qwen2 import Qwen2PretrainedModel
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from fastdeploy.model_executor.models.qwen3 import Qwen3PretrainedModel
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from fastdeploy.model_executor.models.qwen3moe import Qwen3MoePretrainedModel
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from fastdeploy.platforms import current_platform
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MODEL_CLASSES = {
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"Ernie4_5_MoeForCausalLM": Ernie4_5_PretrainedModel,
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"Ernie4_5_MTPForCausalLM": Ernie4_5_MTPPretrainedModel,
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"Qwen2ForCausalLM": Qwen2PretrainedModel,
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"Qwen3ForCausalLM": Qwen3PretrainedModel,
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"Qwen3MoeForCausalLM": Qwen3MoePretrainedModel,
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"Ernie4_5_ForCausalLM": Ernie4_5_PretrainedModel,
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"DeepseekV3ForCausalLM": DeepSeekV3PretrainedModel,
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"Ernie4_5_VLMoeForConditionalGeneration": Ernie4_5_VLPretrainedModel,
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}
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def get_model_from_loader(fd_config: FDConfig) -> nn.Layer:
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"""load or download model"""
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model_loader = DefaultModelLoader(fd_config.load_config)
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model = model_loader.load_model(fd_config)
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return model
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class BaseModelLoader(ABC):
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"""Base class for model loaders."""
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def __init__(self, load_config: LoadConfig):
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self.load_config = load_config
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@abstractmethod
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def download_model(self, load_config: ModelConfig) -> None:
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"""Download a model so that it can be immediately loaded."""
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raise NotImplementedError
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@abstractmethod
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def load_model(self, fd_config: FDConfig) -> nn.Layer:
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"""Load a model with the given configurations."""
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raise NotImplementedError
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class DefaultModelLoader(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, state_dict: dict) -> None:
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"""clean_memory_fragments"""
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if current_platform.is_cuda():
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if state_dict:
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for k, v in state_dict.items():
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if isinstance(v, paddle.Tensor):
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v.value().get_tensor()._clear()
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paddle.device.cuda.empty_cache()
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paddle.device.synchronize()
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def load_model(self, fd_config: FDConfig) -> nn.Layer:
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context = paddle.LazyGuard()
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architectures = fd_config.model_config.architectures[0]
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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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with context:
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model_cls = ModelRegistry.get_class(architectures)
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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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# TODO(gongshaotian): Now, only support safetensor
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model_class = MODEL_CLASSES[architectures]
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state_dict = load_composite_checkpoint(
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fd_config.parallel_config.model_name_or_path,
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model_class,
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fd_config,
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return_numpy=True,
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)
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model.set_state_dict(state_dict)
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self.clean_memory_fragments(state_dict)
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return model
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