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| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| import atexit | ||
| import copy | ||
| import multiprocessing | ||
| import os | ||
| import time | ||
|
|
||
| import paddle | ||
|
|
||
| from paddlenlp.utils.log import logger | ||
|
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|
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| def _save_optimizer(obj, name_mapping, path, saved_signal_path, protocol): | ||
| start_time = time.time() | ||
| for k, v in obj.items(): | ||
| if k == "master_weights" and isinstance(v, dict): | ||
| for kk, vv in v.items(): | ||
| if isinstance(vv, paddle.Tensor): | ||
| vv.name = name_mapping["master_weights"][kk] | ||
| else: | ||
| if k in name_mapping and isinstance(v, paddle.Tensor): | ||
| v.name = name_mapping[k] | ||
| paddle.save(obj, path, protocol) | ||
| # dump saved_signal | ||
| with open(saved_signal_path, mode="w+") as f: | ||
| f.write("1") | ||
| f.flush() | ||
| os.fsync(f.fileno()) | ||
| end_time = time.time() | ||
| elapsed_time = end_time - start_time | ||
| logger.info(f"Async save optimizer took {elapsed_time:.6f} seconds to execute.") | ||
|
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|
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| class AsyncSaver: | ||
| def __init__(self): | ||
| self.context = multiprocessing.get_context("spawn") | ||
| self.cpu_optimizer_state_dict = {} | ||
| self.pool = self.context.Pool(1) | ||
| self.result = None | ||
| self.name_mapping = None | ||
|
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| atexit.register(self.shutdown) | ||
|
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| def run(self, optimizer_state_dict, path, saved_signal_path, protocol=4): | ||
| logger.info(f"Started saving optimizer_state_dict to {os.path.abspath(path)}.") | ||
| self._wait_for_previous_result() | ||
|
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| self._reset_state(path, saved_signal_path, protocol) | ||
| self._process_optimizer_state_dict(optimizer_state_dict) | ||
|
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| self.result = self.pool.apply_async( | ||
| _save_optimizer, | ||
| args=(self.cpu_optimizer_state_dict, self.name_mapping, self.path, self.saved_signal_path, self.protocol), | ||
| ) | ||
|
|
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| logger.info("Finished launching saving optimizer_state_dict process") | ||
|
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| def _wait_for_previous_result(self): | ||
| if self.result is not None: | ||
| max_retries = 5 | ||
| for retries in range(max_retries): | ||
| try: | ||
| self.result.get() | ||
| break | ||
| except Exception as e: | ||
| if retries == max_retries - 1: | ||
| raise RuntimeError(f"Failed after {max_retries} retries during async save.") | ||
|
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||
| time.sleep(1 + retries * 2) | ||
| logger.warning(f"An error occurred during async save: {e}. Retrying...") | ||
| self.result = self.pool.apply_async( | ||
| _save_optimizer, | ||
| args=( | ||
| self.cpu_optimizer_state_dict, | ||
| self.name_mapping, | ||
| self.path, | ||
| self.saved_signal_path, | ||
| self.protocol, | ||
| ), | ||
| ) | ||
|
|
||
| if self.result.ready() and not self.result.successful(): | ||
| raise RuntimeError("The previous async save task failed.") | ||
| else: | ||
| pass | ||
|
|
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| def _reset_state(self, path, saved_signal_path, protocol): | ||
| self.cpu_optimizer_state_dict.clear() | ||
| self.name_mapping = {"master_weights": {}} | ||
| self.path = path | ||
| self.saved_signal_path = saved_signal_path | ||
| self.protocol = protocol | ||
|
|
||
| def _process_optimizer_state_dict(self, optimizer_state_dict): | ||
| for k, v in optimizer_state_dict.items(): | ||
| if k == "master_weights": | ||
| self.cpu_optimizer_state_dict[k] = {} | ||
| for kk, vv in v.items(): | ||
| self.cpu_optimizer_state_dict[k][kk] = vv.pin_memory() | ||
| self.name_mapping[k][kk] = vv.name | ||
| elif k == "LR_Scheduler": | ||
| self.cpu_optimizer_state_dict[k] = copy.deepcopy(v) | ||
| else: | ||
| self.cpu_optimizer_state_dict[k] = v.pin_memory() | ||
| self.name_mapping[k] = v.name | ||
| paddle.device.synchronize() | ||
|
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| def shutdown(self): | ||
| self.pool.close() | ||
| self.pool.join() | ||
|
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| def __del__(self): | ||
| self.shutdown() | ||