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268 lines
8.9 KiB
268 lines
8.9 KiB
# Copyright (c) 2021 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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import argparse
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import logging
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import os
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import shutil
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from pathlib import Path
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import jsonlines
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import numpy as np
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import paddle
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import yaml
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from paddle import DataParallel
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from paddle import distributed as dist
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from paddle import nn
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from paddle.io import DataLoader
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from paddle.io import DistributedBatchSampler
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from paddle.optimizer import Adam # No RAdaom
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from paddle.optimizer.lr import StepDecay
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from yacs.config import CfgNode
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from paddlespeech.t2s.datasets.data_table import DataTable
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from paddlespeech.t2s.datasets.vocoder_batch_fn import Clip
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from paddlespeech.t2s.models.parallel_wavegan import PWGDiscriminator
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from paddlespeech.t2s.models.parallel_wavegan import PWGEvaluator
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from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
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from paddlespeech.t2s.models.parallel_wavegan import PWGUpdater
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from paddlespeech.t2s.modules.stft_loss import MultiResolutionSTFTLoss
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from paddlespeech.t2s.training.extensions.snapshot import Snapshot
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from paddlespeech.t2s.training.extensions.visualizer import VisualDL
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from paddlespeech.t2s.training.seeding import seed_everything
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from paddlespeech.t2s.training.trainer import Trainer
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def train_sp(args, config):
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# decides device type and whether to run in parallel
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# setup running environment correctly
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world_size = paddle.distributed.get_world_size()
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if (not paddle.is_compiled_with_cuda()) or args.ngpu == 0:
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paddle.set_device("cpu")
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else:
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paddle.set_device("gpu")
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if world_size > 1:
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paddle.distributed.init_parallel_env()
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# set the random seed, it is a must for multiprocess training
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seed_everything(config.seed)
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print(
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f"rank: {dist.get_rank()}, pid: {os.getpid()}, parent_pid: {os.getppid()}",
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)
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# dataloader has been too verbose
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logging.getLogger("DataLoader").disabled = True
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# construct dataset for training and validation
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with jsonlines.open(args.train_metadata, 'r') as reader:
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train_metadata = list(reader)
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train_dataset = DataTable(
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data=train_metadata,
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fields=["wave", "feats"],
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converters={
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"wave": np.load,
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"feats": np.load,
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}, )
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with jsonlines.open(args.dev_metadata, 'r') as reader:
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dev_metadata = list(reader)
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dev_dataset = DataTable(
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data=dev_metadata,
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fields=["wave", "feats"],
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converters={
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"wave": np.load,
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"feats": np.load,
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}, )
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# collate function and dataloader
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train_sampler = DistributedBatchSampler(
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train_dataset,
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batch_size=config.batch_size,
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shuffle=True,
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drop_last=True)
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dev_sampler = DistributedBatchSampler(
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dev_dataset,
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batch_size=config.batch_size,
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shuffle=False,
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drop_last=False)
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print("samplers done!")
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train_batch_fn = Clip(
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batch_max_steps=config.batch_max_steps,
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hop_size=config.n_shift,
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aux_context_window=config.generator_params.aux_context_window)
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train_dataloader = DataLoader(
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train_dataset,
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batch_sampler=train_sampler,
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collate_fn=train_batch_fn,
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num_workers=config.num_workers)
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dev_dataloader = DataLoader(
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dev_dataset,
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batch_sampler=dev_sampler,
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collate_fn=train_batch_fn,
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num_workers=config.num_workers)
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print("dataloaders done!")
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generator = PWGGenerator(**config["generator_params"])
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discriminator = PWGDiscriminator(**config["discriminator_params"])
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if world_size > 1:
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generator = DataParallel(generator)
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discriminator = DataParallel(discriminator)
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print("models done!")
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criterion_stft = MultiResolutionSTFTLoss(**config["stft_loss_params"])
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criterion_mse = nn.MSELoss()
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print("criterions done!")
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lr_schedule_g = StepDecay(**config["generator_scheduler_params"])
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gradient_clip_g = nn.ClipGradByGlobalNorm(config["generator_grad_norm"])
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optimizer_g = Adam(
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learning_rate=lr_schedule_g,
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grad_clip=gradient_clip_g,
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parameters=generator.parameters(),
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**config["generator_optimizer_params"])
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lr_schedule_d = StepDecay(**config["discriminator_scheduler_params"])
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gradient_clip_d = nn.ClipGradByGlobalNorm(config["discriminator_grad_norm"])
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optimizer_d = Adam(
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learning_rate=lr_schedule_d,
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grad_clip=gradient_clip_d,
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parameters=discriminator.parameters(),
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**config["discriminator_optimizer_params"])
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print("optimizers done!")
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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if dist.get_rank() == 0:
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config_name = args.config.split("/")[-1]
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# copy conf to output_dir
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shutil.copyfile(args.config, output_dir / config_name)
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updater = PWGUpdater(
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models={
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"generator": generator,
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"discriminator": discriminator,
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},
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optimizers={
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"generator": optimizer_g,
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"discriminator": optimizer_d,
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},
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criterions={
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"stft": criterion_stft,
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"mse": criterion_mse,
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},
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schedulers={
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"generator": lr_schedule_g,
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"discriminator": lr_schedule_d,
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},
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dataloader=train_dataloader,
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discriminator_train_start_steps=config.discriminator_train_start_steps,
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lambda_adv=config.lambda_adv,
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output_dir=output_dir)
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evaluator = PWGEvaluator(
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models={
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"generator": generator,
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"discriminator": discriminator,
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},
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criterions={
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"stft": criterion_stft,
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"mse": criterion_mse,
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},
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dataloader=dev_dataloader,
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lambda_adv=config.lambda_adv,
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output_dir=output_dir)
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trainer = Trainer(
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updater,
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stop_trigger=(config.train_max_steps, "iteration"),
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out=output_dir,
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profiler_options=args.profiler_options)
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if dist.get_rank() == 0:
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trainer.extend(
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evaluator, trigger=(config.eval_interval_steps, 'iteration'))
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trainer.extend(VisualDL(output_dir), trigger=(1, 'iteration'))
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trainer.extend(
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Snapshot(max_size=config.num_snapshots),
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trigger=(config.save_interval_steps, 'iteration'))
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# print(trainer.extensions.keys())
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print("Trainer Done!")
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trainer.run()
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def main():
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# parse args and config and redirect to train_sp
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def str2bool(str):
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return True if str.lower() == 'true' else False
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parser = argparse.ArgumentParser(
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description="Train a ParallelWaveGAN model.")
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parser.add_argument(
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"--config", type=str, help="config file to overwrite default config.")
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parser.add_argument("--train-metadata", type=str, help="training data.")
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parser.add_argument("--dev-metadata", type=str, help="dev data.")
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parser.add_argument("--output-dir", type=str, help="output dir.")
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parser.add_argument(
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"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
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parser.add_argument("--verbose", type=int, default=1, help="verbose.")
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benchmark_group = parser.add_argument_group(
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'benchmark', 'arguments related to benchmark.')
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benchmark_group.add_argument(
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"--batch-size", type=int, default=8, help="batch size.")
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benchmark_group.add_argument(
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"--max-iter", type=int, default=400000, help="train max steps.")
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benchmark_group.add_argument(
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"--run-benchmark",
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type=str2bool,
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default=False,
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help="runing benchmark or not, if True, use the --batch-size and --max-iter."
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)
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benchmark_group.add_argument(
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"--profiler_options",
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type=str,
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default=None,
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help="The option of profiler, which should be in format \"key1=value1;key2=value2;key3=value3\"."
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)
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args = parser.parse_args()
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with open(args.config, 'rt') as f:
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config = CfgNode(yaml.safe_load(f))
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# 增加 --batch_size --max_iter 用于 benchmark 调用
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if args.run_benchmark:
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config.batch_size = args.batch_size
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config.train_max_steps = args.max_iter
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print("========Args========")
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print(yaml.safe_dump(vars(args)))
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print("========Config========")
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print(config)
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print(
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f"master see the word size: {dist.get_world_size()}, from pid: {os.getpid()}"
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)
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# dispatch
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if args.ngpu > 1:
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dist.spawn(train_sp, (args, config), nprocs=args.ngpu)
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else:
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train_sp(args, config)
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if __name__ == "__main__":
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main()
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