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cache feature (#3857)
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@@ -20,9 +20,11 @@ from paddleformers.utils.log import logger
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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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fast_weights_iterator,
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get_all_safetensors,
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get_weight_iterator,
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is_weight_cache_enabled,
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load_weights_form_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.model_base import ModelRegistry
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@@ -44,11 +46,14 @@ class DefaultModelLoaderV1(BaseModelLoader):
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paddle.device.cuda.empty_cache()
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paddle.device.synchronize()
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@measure_time
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def load_weights(self, model, fd_config: FDConfig) -> None:
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_, safetensor_files = get_all_safetensors(fd_config.model_config.model)
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weights_iterator = fast_weights_iterator(safetensor_files)
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model.load_weights(weights_iterator)
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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_form_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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@@ -61,14 +66,15 @@ class DefaultModelLoaderV1(BaseModelLoader):
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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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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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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)
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self.load_weights(model, fd_config, enable_cache)
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return model
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