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Zero
| # Copyright (c) 2020 Mobvoi Inc (Binbin Zhang) | |
| # 2024 Alibaba Inc (authors: Xiang Lyu) | |
| # | |
| # 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 logging | |
| from contextlib import nullcontext | |
| import os | |
| import torch | |
| import torch.distributed as dist | |
| from cosyvoice.utils.train_utils import update_parameter_and_lr, log_per_step, log_per_save, batch_forward, batch_backward, save_model, cosyvoice_join | |
| class Executor: | |
| def __init__(self): | |
| self.step = 0 | |
| self.epoch = 0 | |
| self.rank = int(os.environ.get('RANK', 0)) | |
| self.device = torch.device('cuda:{}'.format(self.rank)) | |
| def train_one_epoc(self, model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, group_join): | |
| ''' Train one epoch | |
| ''' | |
| lr = optimizer.param_groups[0]['lr'] | |
| logging.info('Epoch {} TRAIN info lr {} rank {}'.format(self.epoch, lr, self.rank)) | |
| logging.info('using accumulate grad, new batch size is {} times' | |
| ' larger than before'.format(info_dict['accum_grad'])) | |
| # A context manager to be used in conjunction with an instance of | |
| # torch.nn.parallel.DistributedDataParallel to be able to train | |
| # with uneven inputs across participating processes. | |
| model.train() | |
| model_context = model.join if info_dict['train_engine'] == 'torch_ddp' else nullcontext | |
| with model_context(): | |
| for batch_idx, batch_dict in enumerate(train_data_loader): | |
| info_dict["tag"] = "TRAIN" | |
| info_dict["step"] = self.step | |
| info_dict["epoch"] = self.epoch | |
| info_dict["batch_idx"] = batch_idx | |
| if cosyvoice_join(group_join, info_dict): | |
| break | |
| if info_dict["use_spk_embedding"] is True: | |
| batch_dict["embedding"] = batch_dict["spk_embedding"] | |
| else: | |
| batch_dict["embedding"] = batch_dict["utt_embedding"] | |
| # Disable gradient synchronizations across DDP processes. | |
| # Within this context, gradients will be accumulated on module | |
| # variables, which will later be synchronized. | |
| if info_dict['train_engine'] == 'torch_ddp' and (batch_idx + 1) % info_dict["accum_grad"] != 0: | |
| context = model.no_sync | |
| # Used for single gpu training and DDP gradient synchronization | |
| # processes. | |
| else: | |
| context = nullcontext | |
| with context(): | |
| info_dict = batch_forward(model, batch_dict, info_dict) | |
| info_dict = batch_backward(model, info_dict) | |
| info_dict = update_parameter_and_lr(model, optimizer, scheduler, info_dict) | |
| log_per_step(writer, info_dict) | |
| # NOTE specify save_per_step in cosyvoice.yaml if you want to enable step save | |
| if info_dict['save_per_step'] > 0 and (self.step + 1) % info_dict['save_per_step'] == 0 and (batch_idx + 1) % info_dict["accum_grad"] == 0: | |
| dist.barrier() | |
| self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=False) | |
| model.train() | |
| if (batch_idx + 1) % info_dict["accum_grad"] == 0: | |
| self.step += 1 | |
| dist.barrier() | |
| self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True) | |
| def cv(self, model, cv_data_loader, writer, info_dict, on_batch_end=True): | |
| ''' Cross validation on | |
| ''' | |
| logging.info('Epoch {} Step {} on_batch_end {} CV rank {}'.format(self.epoch, self.step + 1, on_batch_end, self.rank)) | |
| model.eval() | |
| total_num_utts, total_loss_dict = 0, {} # avoid division by 0 | |
| for batch_idx, batch_dict in enumerate(cv_data_loader): | |
| info_dict["tag"] = "CV" | |
| info_dict["step"] = self.step | |
| info_dict["epoch"] = self.epoch | |
| info_dict["batch_idx"] = batch_idx | |
| num_utts = len(batch_dict["utts"]) | |
| total_num_utts += num_utts | |
| info_dict = batch_forward(model, batch_dict, info_dict) | |
| for k, v in info_dict['loss_dict'].items(): | |
| if k not in total_loss_dict: | |
| total_loss_dict[k] = [] | |
| total_loss_dict[k].append(v.item() * num_utts) | |
| log_per_step(None, info_dict) | |
| for k, v in total_loss_dict.items(): | |
| total_loss_dict[k] = sum(v) / total_num_utts | |
| info_dict['loss_dict'] = total_loss_dict | |
| log_per_save(writer, info_dict) | |
| model_name = 'epoch_{}_whole'.format(self.epoch) if on_batch_end else 'epoch_{}_step_{}'.format(self.epoch, self.step + 1) | |
| save_model(model, model_name, info_dict) | |