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https://github.com/PaddlePaddle/FastDeploy.git
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2e9e53ff7e
* remove max_num_batched_tokens in parallel config * remove max_num_seqs * update test case * fix test * fix --------- Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
82 lines
2.5 KiB
Python
82 lines
2.5 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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from paddle import nn
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from fastdeploy.config import FDConfig
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from fastdeploy.utils import get_logger
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from fastdeploy.worker.output import ModelRunnerOutput
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logger = get_logger("model_runner_base", "model_runner_base.log")
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class ModelRunnerBase(ABC):
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"""
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Engine -> (WIP)Executor -> Worker -> ModelRunner -> Model
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ModelRunner interface abstracts the model execution logic that
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contain input preparation, token generation, and tokenprocessing.
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"""
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def __init__(self, fd_config: FDConfig, device: str) -> None:
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"""
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Initialize FDConfig
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"""
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self.fd_config = fd_config
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self.model_config = fd_config.model_config
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self.load_config = fd_config.load_config
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self.device_config = fd_config.device_config
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self.speculative_config = fd_config.speculative_config
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self.parallel_config = fd_config.parallel_config
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self.graph_opt_config = fd_config.graph_opt_config
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self.quant_config = fd_config.quant_config
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self.cache_config = fd_config.cache_config
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self.scheduler_config = fd_config.scheduler_config
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# ... config
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self.device = device
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@abstractmethod
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def load_model(self) -> None:
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"""
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Load model from local path or remote(will download) path
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"""
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raise NotImplementedError
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@abstractmethod
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def get_model(self) -> nn.Layer:
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"""
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Get current model
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"""
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raise NotImplementedError
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@abstractmethod
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def execute_model(
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self,
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) -> ModelRunnerOutput:
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"""
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Execute model with and get output
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"""
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raise NotImplementedError
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@abstractmethod
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def profile_run(self) -> None:
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"""
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Execute a forward pass with dummy inputs to profile the memory usage of the model."
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"""
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raise NotImplementedError
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