288 lines
9.2 KiB
Python
288 lines
9.2 KiB
Python
# Copyright (c) ByteDance, Inc. and its affiliates.
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# Copyright (c) Chutong Meng
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#
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# This source code is licensed under the CC BY-NC license found in the
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# LICENSE file in the root directory of this source tree.
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# Based on AudioDec (https://github.com/facebookresearch/AudioDec)
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import logging
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import os
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from collections import defaultdict
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import torch
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from tensorboardX import SummaryWriter
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from tqdm import tqdm
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logger = logging.getLogger("repcodec_train")
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class Trainer:
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def __init__(
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self,
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steps: int,
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epochs: int,
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data_loader: dict,
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model: dict,
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criterion: dict,
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optimizer: dict,
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scheduler: dict,
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config: dict,
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device=torch.device("cpu"),
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):
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self.steps = steps
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self.epochs = epochs
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self.data_loader = data_loader
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self.model = model
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self.criterion = criterion
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self.optimizer = optimizer
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self.scheduler = scheduler
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self.config = config
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self.device = device
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self.writer = SummaryWriter(config["outdir"])
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self.total_train_loss = defaultdict(float)
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self.total_eval_loss = defaultdict(float)
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self.train_max_steps = config.get("train_max_steps", 0)
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def _train_step(self, batch):
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"""Single step of training."""
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mode = "train"
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x = batch
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x = x.to(self.device)
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codec_loss = 0.0
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y_, zq, z, vqloss, perplexity = self.model["repcodec"](x)
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self._perplexity(perplexity, mode=mode)
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codec_loss += self._vq_loss(vqloss, mode=mode)
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codec_loss += self._metric_loss(y_, x, mode=mode)
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self._record_loss("codec_loss", codec_loss, mode=mode)
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self._update_repcodec(codec_loss)
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self.steps += 1
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self.tqdm.update(1)
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self._check_train_finish()
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@torch.no_grad()
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def _eval_step(self, batch):
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"""Single step of evaluation."""
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mode = "eval"
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x = batch
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x = x.to(self.device)
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codec_loss = 0.0
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y_, zq, z, vqloss, perplexity = self.model["repcodec"](x)
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self._perplexity(perplexity, mode=mode)
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codec_loss += self._vq_loss(vqloss, mode=mode)
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codec_loss += self._metric_loss(y_, x, mode=mode)
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self._record_loss("codec_loss", codec_loss, mode=mode)
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def run(self):
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"""Run training."""
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self.finish_train = False
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self.tqdm = tqdm(
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initial=self.steps, total=self.train_max_steps, desc="[train]"
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)
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while True:
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self._train_epoch()
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# check whether training is finished
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if self.finish_train:
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break
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self.tqdm.close()
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logger.info("Finished training.")
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def save_checkpoint(self, checkpoint_path: str):
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state_dict = {
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"model": {
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"repcodec": self.model["repcodec"].state_dict()
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},
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"optimizer": {
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"repcodec": self.optimizer["repcodec"].state_dict(),
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},
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"scheduler": {
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"repcodec": self.scheduler["repcodec"].state_dict(),
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},
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"steps": self.steps,
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"epochs": self.epochs,
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}
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if not os.path.exists(os.path.dirname(checkpoint_path)):
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os.makedirs(os.path.dirname(checkpoint_path))
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torch.save(state_dict, checkpoint_path)
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def load_checkpoint(
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self,
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checkpoint_path: str,
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strict: bool = True,
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load_only_params: bool = False
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):
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state_dict = torch.load(checkpoint_path, map_location="cpu")
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self.model["repcodec"].load_state_dict(
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state_dict["model"]["repcodec"], strict=strict
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)
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if not load_only_params:
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self.steps = state_dict["steps"]
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self.epochs = state_dict["epochs"]
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self.optimizer["repcodec"].load_state_dict(
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state_dict["optimizer"]["repcodec"]
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)
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self.scheduler["repcodec"].load_state_dict(
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state_dict["scheduler"]["repcodec"]
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)
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def _train_epoch(self):
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"""One epoch of training."""
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for train_steps_per_epoch, batch in enumerate(self.data_loader["train"], 1):
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# train one step
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self._train_step(batch)
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# check interval
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self._check_log_interval()
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self._check_eval_interval()
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self._check_save_interval()
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# check whether training is finished
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if self.finish_train:
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return
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# update
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self.epochs += 1
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self.train_steps_per_epoch = train_steps_per_epoch
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if train_steps_per_epoch > 200:
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logger.info(
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f"(Steps: {self.steps}) Finished {self.epochs} epoch training "
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f"({self.train_steps_per_epoch} steps per epoch)."
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)
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def _eval_epoch(self):
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"""One epoch of evaluation."""
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logger.info(f"(Steps: {self.steps}) Start evaluation.")
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# change mode
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for key in self.model.keys():
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self.model[key].eval()
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# calculate loss for each batch
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for eval_steps_per_epoch, batch in enumerate(
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tqdm(self.data_loader["dev"], desc="[eval]"), 1
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):
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# eval one step
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self._eval_step(batch)
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logger.info(
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f"(Steps: {self.steps}) Finished evaluation "
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f"({eval_steps_per_epoch} steps per epoch)."
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)
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# average loss
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for key in self.total_eval_loss.keys():
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self.total_eval_loss[key] /= eval_steps_per_epoch
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logger.info(
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f"(Steps: {self.steps}) {key} = {self.total_eval_loss[key]:.4f}."
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)
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# record
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self._write_to_tensorboard(self.total_eval_loss)
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# reset
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self.total_eval_loss = defaultdict(float)
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# restore mode
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for key in self.model.keys():
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self.model[key].train()
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def _metric_loss(self, predict_y, natural_y, mode='train'):
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"""Metric losses."""
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metric_loss = 0.0
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repr_reconstruct_loss = self.criterion["repr_reconstruct_loss"](predict_y, natural_y)
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repr_reconstruct_loss *= self.config["lambda_repr_reconstruct_loss"]
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self._record_loss("reconstruct_loss", repr_reconstruct_loss, mode=mode)
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metric_loss += repr_reconstruct_loss
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return metric_loss
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def _update_repcodec(self, repr_loss):
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"""Update generator."""
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self.optimizer["repcodec"].zero_grad()
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repr_loss.backward()
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if self.config["grad_norm"] > 0:
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torch.nn.utils.clip_grad_norm_(
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self.model["repcodec"].parameters(),
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self.config["grad_norm"],
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)
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self.optimizer["repcodec"].step()
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self.scheduler["repcodec"].step()
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def _record_loss(self, name: str, loss, mode='train'):
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"""Record loss."""
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if torch.is_tensor(loss):
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loss = loss.item()
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if mode == 'train':
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self.total_train_loss[f"train/{name}"] += loss
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elif mode == 'eval':
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self.total_eval_loss[f"eval/{name}"] += loss
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else:
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raise NotImplementedError(f"Mode ({mode}) is not supported!")
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def _write_to_tensorboard(self, loss):
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"""Write to tensorboard."""
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for key, value in loss.items():
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self.writer.add_scalar(key, value, self.steps)
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def _check_save_interval(self):
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if self.steps and (self.steps % self.config["save_interval_steps"] == 0):
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self.save_checkpoint(
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os.path.join(self.config["outdir"], f"checkpoint-{self.steps}steps.pkl")
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)
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logger.info(f"Successfully saved checkpoint @ {self.steps} steps.")
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def _check_eval_interval(self):
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if self.steps % self.config["eval_interval_steps"] == 0:
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self._eval_epoch()
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def _check_log_interval(self):
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if self.steps % self.config["log_interval_steps"] == 0:
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for key in self.total_train_loss.keys():
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self.total_train_loss[key] /= self.config["log_interval_steps"]
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logger.info(
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f"(Steps: {self.steps}) {key} = {self.total_train_loss[key]:.4f}."
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)
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self._write_to_tensorboard(self.total_train_loss)
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# reset
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self.total_train_loss = defaultdict(float)
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def _check_train_finish(self):
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if self.steps >= self.train_max_steps:
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self.finish_train = True
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else:
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self.finish_train = False
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return self.finish_train
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def _perplexity(self, perplexity, label=None, mode='train'):
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if label:
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name = f"{mode}/ppl_{label}"
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else:
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name = f"{mode}/ppl"
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if torch.numel(perplexity) > 1:
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perplexity = perplexity.tolist()
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for idx, ppl in enumerate(perplexity):
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self._record_loss(f"{name}_{idx}", ppl, mode=mode)
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else:
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self._record_loss(name, perplexity, mode=mode)
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def _vq_loss(self, vqloss, label=None, mode='train'):
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if label:
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name = f"{mode}/vqloss_{label}"
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else:
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name = f"{mode}/vqloss"
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vqloss = torch.sum(vqloss)
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vqloss *= self.config["lambda_vq_loss"]
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self._record_loss(name, vqloss, mode=mode)
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return vqloss
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