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Use LossRecord to record and print loss for the training process
This commit is contained in:
Mingshuang Luo
parent
73f21a379b
commit
e74e75acc6
@ -1,6 +1,7 @@
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#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang,
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# Wei Kang)
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# Wei Kang
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# Mingshuang Luo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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@ -18,16 +19,21 @@
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import argparse
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import collections
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import copy
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import logging
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from pathlib import Path
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from shutil import copyfile
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from typing import Optional
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from typing import Optional, Tuple, List
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import k2
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import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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import torch.nn as nn
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from torch import Tensor
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from asr_datamodule import LibriSpeechAsrDataModule
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from conformer import Conformer
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from lhotse.utils import fix_random_seed
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@ -281,13 +287,80 @@ def save_checkpoint(
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copyfile(src=filename, dst=best_valid_filename)
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class LossRecord(collections.defaultdict):
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def __init__(self):
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# Passing the type 'int' to the base-class constructor
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# makes undefined items default to int() which is zero.
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super(LossRecord, self).__init__(int)
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def __add__(self, other: 'LossRecord') -> 'LossRecord':
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ans = LossRecord()
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for k, v in self.items():
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ans[k] = v
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for k, v in other.items():
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ans[k] = ans[k] + v
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return ans
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def __mul__(self, alpha: float) -> 'LossRecord':
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ans = LossRecord()
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for k, v in self.items():
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ans[k] = v * alpha
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return ans
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def __str__(self) -> str:
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ans = ''
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for k, v in self.norm_items():
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norm_value = '%.4g' % v
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ans += (str(k) + '=' + str(norm_value) + ', ')
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frames = str(self['frames'])
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ans += 'over ' + frames + ' frames.'
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return ans
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def norm_items(self) -> List[Tuple[str, float]]:
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"""
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Returns a list of pairs, like:
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[('ctc_loss', 0.1), ('att_loss', 0.07)]
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"""
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num_frames = self['frames'] if 'frames' in self else 1
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ans = []
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for k, v in self.items():
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if k != 'frames':
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norm_value = float(v) / num_frames
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ans.append((k, norm_value))
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return ans
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def reduce(self, device):
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"""
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Reduce using torch.distributed, which I believe ensures that
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all processes get the total.
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"""
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keys = sorted(self.keys())
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s = torch.tensor([ float(self[k]) for k in keys ],
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device=device)
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dist.all_reduce(s, op=dist.ReduceOp.SUM)
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for k, v in zip(keys, s.cpu().tolist()):
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self[k] = v
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def write_summary(self, tb_writer: SummaryWriter, prefix: str, batch_idx: int) -> None:
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"""
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Add logging information to a TensorBoard writer.
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tb_writer: a TensorBoard writer
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prefix: a prefix for the name of the loss, e.g. "train/valid_",
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or "train/current_"
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batch_idx: The current batch index, used as the x-axis of the plot.
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"""
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for k, v in self.norm_items():
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tb_writer.add_scalar(prefix + k, v, batch_idx)
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def compute_loss(
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params: AttributeDict,
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model: nn.Module,
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batch: dict,
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graph_compiler: BpeCtcTrainingGraphCompiler,
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is_training: bool,
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):
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) -> Tuple[Tensor, LossRecord]:
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"""
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Compute CTC loss given the model and its inputs.
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@ -367,15 +440,18 @@ def compute_loss(
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loss = ctc_loss
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att_loss = torch.tensor([0])
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# train_frames and valid_frames are used for printing.
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if is_training:
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params.train_frames = supervision_segments[:, 2].sum().item()
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else:
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params.valid_frames = supervision_segments[:, 2].sum().item()
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assert loss.requires_grad == is_training
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return loss, ctc_loss.detach(), att_loss.detach()
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info = LossRecord()
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# TODO: there are many GPU->CPU transfers here, maybe combine them into one.
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info['frames'] = supervision_segments[:, 2].sum().item()
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info['ctc_loss'] = ctc_loss.detach().cpu().item()
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if params.att_rate != 0.0:
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info['att_loss'] = att_loss.detach().cpu().item()
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info['loss'] = loss.detach().cpu().item()
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return loss, info
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def compute_validation_loss(
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@ -384,18 +460,14 @@ def compute_validation_loss(
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graph_compiler: BpeCtcTrainingGraphCompiler,
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valid_dl: torch.utils.data.DataLoader,
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world_size: int = 1,
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) -> None:
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"""Run the validation process. The validation loss
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is saved in `params.valid_loss`.
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"""
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) -> LossRecord:
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"""Run the validation process."""
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model.eval()
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tot_loss = 0.0
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tot_ctc_loss = 0.0
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tot_att_loss = 0.0
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tot_frames = 0.0
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tot_loss = LossRecord()
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for batch_idx, batch in enumerate(valid_dl):
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loss, ctc_loss, att_loss = compute_loss(
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loss, loss_info = compute_loss(
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params=params,
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model=model,
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batch=batch,
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@ -403,36 +475,17 @@ def compute_validation_loss(
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is_training=False,
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)
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assert loss.requires_grad is False
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assert ctc_loss.requires_grad is False
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assert att_loss.requires_grad is False
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loss_cpu = loss.detach().cpu().item()
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tot_loss += loss_cpu
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tot_ctc_loss += ctc_loss.detach().cpu().item()
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tot_att_loss += att_loss.detach().cpu().item()
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tot_frames += params.valid_frames
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tot_loss = tot_loss + loss_info
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if world_size > 1:
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s = torch.tensor(
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[tot_loss, tot_ctc_loss, tot_att_loss, tot_frames],
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device=loss.device,
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)
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dist.all_reduce(s, op=dist.ReduceOp.SUM)
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s = s.cpu().tolist()
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tot_loss = s[0]
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tot_ctc_loss = s[1]
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tot_att_loss = s[2]
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tot_frames = s[3]
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tot_loss.reduce(loss.device)
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params.valid_loss = tot_loss / tot_frames
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params.valid_ctc_loss = tot_ctc_loss / tot_frames
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params.valid_att_loss = tot_att_loss / tot_frames
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if params.valid_loss < params.best_valid_loss:
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loss_value = tot_loss['loss'] / tot_loss['frames']
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if loss_value < params.best_valid_loss:
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params.best_valid_epoch = params.cur_epoch
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params.best_valid_loss = params.valid_loss
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params.best_valid_loss = loss_value
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return tot_loss
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def train_one_epoch(
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@ -471,24 +524,21 @@ def train_one_epoch(
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"""
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model.train()
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tot_loss = 0.0 # sum of losses over all batches
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tot_ctc_loss = 0.0
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tot_att_loss = 0.0
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tot_loss = LossRecord()
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tot_frames = 0.0 # sum of frames over all batches
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params.tot_loss = 0.0
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params.tot_frames = 0.0
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for batch_idx, batch in enumerate(train_dl):
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params.batch_idx_train += 1
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batch_size = len(batch["supervisions"]["text"])
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loss, ctc_loss, att_loss = compute_loss(
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loss, loss_info = compute_loss(
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params=params,
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model=model,
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batch=batch,
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graph_compiler=graph_compiler,
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is_training=True,
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)
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# summary stats
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tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
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# NOTE: We use reduction==sum and loss is computed over utterances
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# in the batch and there is no normalization to it so far.
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@ -498,75 +548,21 @@ def train_one_epoch(
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clip_grad_norm_(model.parameters(), 5.0, 2.0)
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optimizer.step()
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loss_cpu = loss.detach().cpu().item()
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ctc_loss_cpu = ctc_loss.detach().cpu().item()
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att_loss_cpu = att_loss.detach().cpu().item()
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tot_frames += params.train_frames
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tot_loss += loss_cpu
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tot_ctc_loss += ctc_loss_cpu
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tot_att_loss += att_loss_cpu
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params.tot_frames += params.train_frames
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params.tot_loss += loss_cpu
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tot_avg_loss = tot_loss / tot_frames
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tot_avg_ctc_loss = tot_ctc_loss / tot_frames
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tot_avg_att_loss = tot_att_loss / tot_frames
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if batch_idx % params.log_interval == 0:
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logging.info(
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f"Epoch {params.cur_epoch}, batch {batch_idx}, "
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f"batch avg ctc loss {ctc_loss_cpu/params.train_frames:.4f}, "
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f"batch avg att loss {att_loss_cpu/params.train_frames:.4f}, "
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f"batch avg loss {loss_cpu/params.train_frames:.4f}, "
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f"total avg ctc loss: {tot_avg_ctc_loss:.4f}, "
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f"total avg att loss: {tot_avg_att_loss:.4f}, "
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f"total avg loss: {tot_avg_loss:.4f}, "
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f"batch size: {batch_size}"
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f"Epoch {params.cur_epoch}, batch {batch_idx}, loss[{loss_info}]"
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f"tot_loss[{tot_loss}], batch size: {batch_size}"
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)
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if batch_idx % 10 == 0:
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if tb_writer is not None:
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tb_writer.add_scalar(
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"train/current_ctc_loss",
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ctc_loss_cpu / params.train_frames,
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params.batch_idx_train,
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)
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tb_writer.add_scalar(
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"train/current_att_loss",
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att_loss_cpu / params.train_frames,
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params.batch_idx_train,
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)
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tb_writer.add_scalar(
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"train/current_loss",
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loss_cpu / params.train_frames,
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params.batch_idx_train,
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)
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tb_writer.add_scalar(
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"train/tot_avg_ctc_loss",
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tot_avg_ctc_loss,
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params.batch_idx_train,
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)
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tb_writer.add_scalar(
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"train/tot_avg_att_loss",
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tot_avg_att_loss,
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params.batch_idx_train,
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)
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tb_writer.add_scalar(
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"train/tot_avg_loss",
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tot_avg_loss,
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params.batch_idx_train,
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)
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if batch_idx > 0 and batch_idx % params.reset_interval == 0:
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tot_loss = 0.0 # sum of losses over all batches
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tot_ctc_loss = 0.0
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tot_att_loss = 0.0
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tot_frames = 0.0 # sum of frames over all batches
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loss_info.write_summary(tb_writer, "train/current_", params.batch_idx_train)
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tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
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if batch_idx > 0 and batch_idx % params.valid_interval == 0:
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compute_validation_loss(
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logging.info("Computing validation loss")
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valid_info = compute_validation_loss(
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params=params,
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model=model,
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graph_compiler=graph_compiler,
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@ -575,32 +571,13 @@ def train_one_epoch(
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)
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model.train()
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logging.info(
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f"Epoch {params.cur_epoch}, "
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f"valid ctc loss {params.valid_ctc_loss:.4f},"
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f"valid att loss {params.valid_att_loss:.4f},"
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f"valid loss {params.valid_loss:.4f},"
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f" best valid loss: {params.best_valid_loss:.4f} "
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f"best valid epoch: {params.best_valid_epoch}"
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)
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f"Epoch {params.cur_epoch}, validation: {valid_info}"
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)
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if tb_writer is not None:
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tb_writer.add_scalar(
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"train/valid_ctc_loss",
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params.valid_ctc_loss,
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params.batch_idx_train,
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)
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tb_writer.add_scalar(
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"train/valid_att_loss",
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params.valid_att_loss,
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params.batch_idx_train,
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)
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tb_writer.add_scalar(
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"train/valid_loss",
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params.valid_loss,
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params.batch_idx_train,
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)
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params.train_loss = params.tot_loss / params.tot_frames
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valid_info.write_summary(tb_writer, "train/valid_", params.batch_idx_train)
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loss_value = tot_loss['loss'] / tot_loss['frames']
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params.train_loss = loss_value
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if params.train_loss < params.best_train_loss:
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params.best_train_epoch = params.cur_epoch
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params.best_train_loss = params.train_loss
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@ -739,4 +716,4 @@ torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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if __name__ == "__main__":
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main()
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main()
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@ -1,5 +1,6 @@
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#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang
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# Mingshuang Luo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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@ -18,9 +19,10 @@
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import argparse
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import logging
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import collections
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from pathlib import Path
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from shutil import copyfile
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from typing import Optional
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from typing import Optional, Tuple, List
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import k2
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import torch
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@ -28,6 +30,8 @@ import torch.distributed as dist
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import torch.multiprocessing as mp
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import torch.nn as nn
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import torch.optim as optim
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from torch import Tensor
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from asr_datamodule import LibriSpeechAsrDataModule
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from lhotse.utils import fix_random_seed
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from model import TdnnLstm
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@ -260,6 +264,71 @@ def save_checkpoint(
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best_valid_filename = params.exp_dir / "best-valid-loss.pt"
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copyfile(src=filename, dst=best_valid_filename)
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class LossRecord(collections.defaultdict):
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def __init__(self):
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# Passing the type 'int' to the base-class constructor
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# makes undefined items default to int() which is zero.
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super(LossRecord, self).__init__(int)
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def __add__(self, other: 'LossRecord') -> 'LossRecord':
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ans = LossRecord()
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for k, v in self.items():
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ans[k] = v
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for k, v in other.items():
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ans[k] = ans[k] + v
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return ans
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def __mul__(self, alpha: float) -> 'LossRecord':
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ans = LossRecord()
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for k, v in self.items():
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ans[k] = v * alpha
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return ans
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def __str__(self) -> str:
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ans = ''
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for k, v in self.norm_items():
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norm_value = '%.4g' % v
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ans += (str(k) + '=' + str(norm_value) + ', ')
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frames = str(self['frames'])
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ans += 'over ' + frames + ' frames.'
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return ans
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def norm_items(self) -> List[Tuple[str, float]]:
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"""
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Returns a list of pairs, like:
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[('ctc_loss', 0.1), ('att_loss', 0.07)]
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"""
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num_frames = self['frames'] if 'frames' in self else 1
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ans = []
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for k, v in self.items():
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if k != 'frames':
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norm_value = float(v) / num_frames
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ans.append((k, norm_value))
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return ans
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def reduce(self, device):
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"""
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Reduce using torch.distributed, which I believe ensures that
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all processes get the total.
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"""
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keys = sorted(self.keys())
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s = torch.tensor([ float(self[k]) for k in keys ],
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device=device)
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dist.all_reduce(s, op=dist.ReduceOp.SUM)
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for k, v in zip(keys, s.cpu().tolist()):
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self[k] = v
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def write_summary(self, tb_writer: SummaryWriter, prefix: str, batch_idx: int) -> None:
|
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"""
|
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Add logging information to a TensorBoard writer.
|
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tb_writer: a TensorBoard writer
|
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prefix: a prefix for the name of the loss, e.g. "train/valid_",
|
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or "train/current_"
|
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batch_idx: The current batch index, used as the x-axis of the plot.
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"""
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for k, v in self.norm_items():
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tb_writer.add_scalar(prefix + k, v, batch_idx)
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def compute_loss(
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params: AttributeDict,
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@ -267,7 +336,7 @@ def compute_loss(
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batch: dict,
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graph_compiler: CtcTrainingGraphCompiler,
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is_training: bool,
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):
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) -> Tuple[Tensor, LossRecord]:
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"""
|
||||
Compute CTC loss given the model and its inputs.
|
||||
|
||||
@ -324,13 +393,12 @@ def compute_loss(
|
||||
|
||||
assert loss.requires_grad == is_training
|
||||
|
||||
# train_frames and valid_frames are used for printing.
|
||||
if is_training:
|
||||
params.train_frames = supervision_segments[:, 2].sum().item()
|
||||
else:
|
||||
params.valid_frames = supervision_segments[:, 2].sum().item()
|
||||
info = LossRecord()
|
||||
# TODO: there are many GPU->CPU transfers here, maybe combine them into one.
|
||||
info['frames'] = supervision_segments[:, 2].sum().item()
|
||||
info['loss'] = loss.detach().cpu().item()
|
||||
|
||||
return loss
|
||||
return loss, info
|
||||
|
||||
|
||||
def compute_validation_loss(
|
||||
@ -339,16 +407,16 @@ def compute_validation_loss(
|
||||
graph_compiler: CtcTrainingGraphCompiler,
|
||||
valid_dl: torch.utils.data.DataLoader,
|
||||
world_size: int = 1,
|
||||
) -> None:
|
||||
) -> LossRecord:
|
||||
"""Run the validation process. The validation loss
|
||||
is saved in `params.valid_loss`.
|
||||
"""
|
||||
model.eval()
|
||||
|
||||
tot_loss = 0.0
|
||||
tot_frames = 0.0
|
||||
tot_loss = LossRecord()
|
||||
|
||||
for batch_idx, batch in enumerate(valid_dl):
|
||||
loss = compute_loss(
|
||||
loss, loss_info = compute_loss(
|
||||
params=params,
|
||||
model=model,
|
||||
batch=batch,
|
||||
@ -357,22 +425,18 @@ def compute_validation_loss(
|
||||
)
|
||||
assert loss.requires_grad is False
|
||||
|
||||
loss_cpu = loss.detach().cpu().item()
|
||||
tot_loss += loss_cpu
|
||||
tot_frames += params.valid_frames
|
||||
|
||||
tot_loss = tot_loss + loss_info
|
||||
|
||||
if world_size > 1:
|
||||
s = torch.tensor([tot_loss, tot_frames], device=loss.device)
|
||||
dist.all_reduce(s, op=dist.ReduceOp.SUM)
|
||||
s = s.cpu().tolist()
|
||||
tot_loss = s[0]
|
||||
tot_frames = s[1]
|
||||
tot_loss.reduce(loss.device)
|
||||
|
||||
params.valid_loss = tot_loss / tot_frames
|
||||
loss_value = tot_loss['loss'] / tot_loss['frames']
|
||||
|
||||
if params.valid_loss < params.best_valid_loss:
|
||||
if loss_value < params.best_valid_loss:
|
||||
params.best_valid_epoch = params.cur_epoch
|
||||
params.best_valid_loss = params.valid_loss
|
||||
params.best_valid_loss = loss_value
|
||||
|
||||
return tot_loss
|
||||
|
||||
|
||||
def train_one_epoch(
|
||||
@ -411,23 +475,21 @@ def train_one_epoch(
|
||||
"""
|
||||
model.train()
|
||||
|
||||
tot_loss = 0.0 # reset after params.reset_interval of batches
|
||||
tot_frames = 0.0 # reset after params.reset_interval of batches
|
||||
|
||||
params.tot_loss = 0.0
|
||||
params.tot_frames = 0.0
|
||||
tot_loss = LossRecord()
|
||||
|
||||
for batch_idx, batch in enumerate(train_dl):
|
||||
params.batch_idx_train += 1
|
||||
batch_size = len(batch["supervisions"]["text"])
|
||||
|
||||
loss = compute_loss(
|
||||
loss, loss_info = compute_loss(
|
||||
params=params,
|
||||
model=model,
|
||||
batch=batch,
|
||||
graph_compiler=graph_compiler,
|
||||
is_training=True,
|
||||
)
|
||||
# summary stats.
|
||||
tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
|
||||
|
||||
# NOTE: We use reduction==sum and loss is computed over utterances
|
||||
# in the batch and there is no normalization to it so far.
|
||||
@ -437,41 +499,19 @@ def train_one_epoch(
|
||||
clip_grad_norm_(model.parameters(), 5.0, 2.0)
|
||||
optimizer.step()
|
||||
|
||||
loss_cpu = loss.detach().cpu().item()
|
||||
|
||||
tot_frames += params.train_frames
|
||||
tot_loss += loss_cpu
|
||||
tot_avg_loss = tot_loss / tot_frames
|
||||
|
||||
params.tot_frames += params.train_frames
|
||||
params.tot_loss += loss_cpu
|
||||
|
||||
if batch_idx % params.log_interval == 0:
|
||||
logging.info(
|
||||
f"Epoch {params.cur_epoch}, batch {batch_idx}, "
|
||||
f"batch avg loss {loss_cpu/params.train_frames:.4f}, "
|
||||
f"total avg loss: {tot_avg_loss:.4f}, "
|
||||
f"batch size: {batch_size}"
|
||||
f"Epoch {params.cur_epoch}, batch {batch_idx}, loss[{loss_info}]"
|
||||
f"tot_loss[{tot_loss}], batch size: {batch_size}"
|
||||
)
|
||||
if batch_idx % 10 == 0:
|
||||
|
||||
if tb_writer is not None:
|
||||
tb_writer.add_scalar(
|
||||
"train/current_loss",
|
||||
loss_cpu / params.train_frames,
|
||||
params.batch_idx_train,
|
||||
)
|
||||
|
||||
tb_writer.add_scalar(
|
||||
"train/tot_avg_loss",
|
||||
tot_avg_loss,
|
||||
params.batch_idx_train,
|
||||
)
|
||||
|
||||
if batch_idx > 0 and batch_idx % params.reset_interval == 0:
|
||||
tot_loss = 0
|
||||
tot_frames = 0
|
||||
loss_info.write_summary(tb_writer, "train/current_", params.batch_idx_train)
|
||||
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
|
||||
|
||||
if batch_idx > 0 and batch_idx % params.valid_interval == 0:
|
||||
compute_validation_loss(
|
||||
valid_info = compute_validation_loss(
|
||||
params=params,
|
||||
model=model,
|
||||
graph_compiler=graph_compiler,
|
||||
@ -480,12 +520,17 @@ def train_one_epoch(
|
||||
)
|
||||
model.train()
|
||||
logging.info(
|
||||
f"Epoch {params.cur_epoch}, valid loss {params.valid_loss:.4f},"
|
||||
f" best valid loss: {params.best_valid_loss:.4f} "
|
||||
f"best valid epoch: {params.best_valid_epoch}"
|
||||
)
|
||||
f"Epoch {params.cur_epoch}, validation {valid_info}"
|
||||
)
|
||||
if tb_writer is not None:
|
||||
valid_info.write_summary(
|
||||
tb_writer,
|
||||
"train/valid_",
|
||||
params.batch_idx_train,
|
||||
)
|
||||
|
||||
params.train_loss = params.tot_loss / params.tot_frames
|
||||
loss_value = tot_loss['loss'] / tot_loss['frames']
|
||||
params.train_loss = loss_value
|
||||
|
||||
if params.train_loss < params.best_train_loss:
|
||||
params.best_train_epoch = params.cur_epoch
|
||||
@ -613,4 +658,4 @@ def main():
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
main()
|
||||
@ -2,9 +2,10 @@
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import collections
|
||||
from pathlib import Path
|
||||
from shutil import copyfile
|
||||
from typing import Optional
|
||||
from typing import Optional, Tuple, List
|
||||
|
||||
import k2
|
||||
import torch
|
||||
@ -12,6 +13,8 @@ import torch.distributed as dist
|
||||
import torch.multiprocessing as mp
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from torch import Tensor
|
||||
|
||||
from asr_datamodule import YesNoAsrDataModule
|
||||
from lhotse.utils import fix_random_seed
|
||||
from model import Tdnn
|
||||
@ -122,6 +125,8 @@ def get_params() -> AttributeDict:
|
||||
|
||||
- valid_interval: Run validation if batch_idx % valid_interval` is 0
|
||||
|
||||
- reset_interval: Reset statistics if batch_idx % reset_interval is 0
|
||||
|
||||
- beam_size: It is used in k2.ctc_loss
|
||||
|
||||
- reduction: It is used in k2.ctc_loss
|
||||
@ -142,6 +147,7 @@ def get_params() -> AttributeDict:
|
||||
"best_valid_epoch": -1,
|
||||
"batch_idx_train": 0,
|
||||
"log_interval": 10,
|
||||
"reset_interval": 20,
|
||||
"valid_interval": 10,
|
||||
"beam_size": 10,
|
||||
"reduction": "sum",
|
||||
@ -239,13 +245,79 @@ def save_checkpoint(
|
||||
copyfile(src=filename, dst=best_valid_filename)
|
||||
|
||||
|
||||
class LossRecord(collections.defaultdict):
|
||||
def __init__(self):
|
||||
# Passing the type 'int' to the base-class constructor
|
||||
# makes undefined items default to int() which is zero.
|
||||
super(LossRecord, self).__init__(int)
|
||||
|
||||
def __add__(self, other: 'LossRecord') -> 'LossRecord':
|
||||
ans = LossRecord()
|
||||
for k, v in self.items():
|
||||
ans[k] = v
|
||||
for k, v in other.items():
|
||||
ans[k] = ans[k] + v
|
||||
return ans
|
||||
|
||||
def __mul__(self, alpha: float) -> 'LossRecord':
|
||||
ans = LossRecord()
|
||||
for k, v in self.items():
|
||||
ans[k] = v * alpha
|
||||
return ans
|
||||
|
||||
def __str__(self) -> str:
|
||||
ans = ''
|
||||
for k, v in self.norm_items():
|
||||
norm_value = '%.4g' % v
|
||||
ans += (str(k) + '=' + str(norm_value) + ', ')
|
||||
frames = str(self['frames'])
|
||||
ans += 'over ' + frames + ' frames.'
|
||||
return ans
|
||||
|
||||
def norm_items(self) -> List[Tuple[str, float]]:
|
||||
"""
|
||||
Returns a list of pairs, like:
|
||||
[('ctc_loss', 0.1), ('att_loss', 0.07)]
|
||||
"""
|
||||
num_frames = self['frames'] if 'frames' in self else 1
|
||||
ans = []
|
||||
for k, v in self.items():
|
||||
if k != 'frames':
|
||||
norm_value = float(v) / num_frames
|
||||
ans.append((k, norm_value))
|
||||
return ans
|
||||
|
||||
def reduce(self, device):
|
||||
"""
|
||||
Reduce using torch.distributed, which I believe ensures that
|
||||
all processes get the total.
|
||||
"""
|
||||
keys = sorted(self.keys())
|
||||
s = torch.tensor([ float(self[k]) for k in keys ],
|
||||
device=device)
|
||||
dist.all_reduce(s, op=dist.ReduceOp.SUM)
|
||||
for k, v in zip(keys, s.cpu().tolist()):
|
||||
self[k] = v
|
||||
|
||||
def write_summary(self, tb_writer: SummaryWriter, prefix: str, batch_idx: int) -> None:
|
||||
"""
|
||||
Add logging information to a TensorBoard writer.
|
||||
tb_writer: a TensorBoard writer
|
||||
prefix: a prefix for the name of the loss, e.g. "train/valid_",
|
||||
or "train/current_"
|
||||
batch_idx: The current batch index, used as the x-axis of the plot.
|
||||
"""
|
||||
for k, v in self.norm_items():
|
||||
tb_writer.add_scalar(prefix + k, v, batch_idx)
|
||||
|
||||
|
||||
def compute_loss(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
batch: dict,
|
||||
graph_compiler: CtcTrainingGraphCompiler,
|
||||
is_training: bool,
|
||||
):
|
||||
) -> Tuple[Tensor, LossRecord]:
|
||||
"""
|
||||
Compute CTC loss given the model and its inputs.
|
||||
|
||||
@ -304,14 +376,13 @@ def compute_loss(
|
||||
)
|
||||
|
||||
assert loss.requires_grad == is_training
|
||||
|
||||
info = LossRecord()
|
||||
# TODO: there are many GPU->CPU transfers here, maybe combine them into one.
|
||||
info['frames'] = supervision_segments[:, 2].sum().item()
|
||||
info['loss'] = loss.detach().cpu().item()
|
||||
|
||||
# train_frames and valid_frames are used for printing.
|
||||
if is_training:
|
||||
params.train_frames = supervision_segments[:, 2].sum().item()
|
||||
else:
|
||||
params.valid_frames = supervision_segments[:, 2].sum().item()
|
||||
|
||||
return loss
|
||||
return loss, info
|
||||
|
||||
|
||||
def compute_validation_loss(
|
||||
@ -320,16 +391,16 @@ def compute_validation_loss(
|
||||
graph_compiler: CtcTrainingGraphCompiler,
|
||||
valid_dl: torch.utils.data.DataLoader,
|
||||
world_size: int = 1,
|
||||
) -> None:
|
||||
) -> LossRecord:
|
||||
"""Run the validation process. The validation loss
|
||||
is saved in `params.valid_loss`.
|
||||
"""
|
||||
model.eval()
|
||||
|
||||
tot_loss = 0.0
|
||||
tot_frames = 0.0
|
||||
tot_loss = LossRecord()
|
||||
|
||||
for batch_idx, batch in enumerate(valid_dl):
|
||||
loss = compute_loss(
|
||||
loss, loss_info = compute_loss(
|
||||
params=params,
|
||||
model=model,
|
||||
batch=batch,
|
||||
@ -338,22 +409,18 @@ def compute_validation_loss(
|
||||
)
|
||||
assert loss.requires_grad is False
|
||||
|
||||
loss_cpu = loss.detach().cpu().item()
|
||||
tot_loss += loss_cpu
|
||||
tot_frames += params.valid_frames
|
||||
|
||||
tot_loss = tot_loss + loss_info
|
||||
|
||||
if world_size > 1:
|
||||
s = torch.tensor([tot_loss, tot_frames], device=loss.device)
|
||||
dist.all_reduce(s, op=dist.ReduceOp.SUM)
|
||||
s = s.cpu().tolist()
|
||||
tot_loss = s[0]
|
||||
tot_frames = s[1]
|
||||
tot_loss.reduce(loss.device)
|
||||
|
||||
params.valid_loss = tot_loss / tot_frames
|
||||
loss_value = tot_loss['loss'] / tot_loss['frames']
|
||||
|
||||
if params.valid_loss < params.best_valid_loss:
|
||||
if loss_value < params.best_valid_loss:
|
||||
params.best_valid_epoch = params.cur_epoch
|
||||
params.best_valid_loss = params.valid_loss
|
||||
params.best_valid_loss = loss_value
|
||||
|
||||
return tot_loss
|
||||
|
||||
|
||||
def train_one_epoch(
|
||||
@ -391,20 +458,22 @@ def train_one_epoch(
|
||||
Number of nodes in DDP training. If it is 1, DDP is disabled.
|
||||
"""
|
||||
model.train()
|
||||
|
||||
tot_loss = LossRecord()
|
||||
|
||||
tot_loss = 0.0 # sum of losses over all batches
|
||||
tot_frames = 0.0 # sum of frames over all batches
|
||||
for batch_idx, batch in enumerate(train_dl):
|
||||
params.batch_idx_train += 1
|
||||
batch_size = len(batch["supervisions"]["text"])
|
||||
|
||||
loss = compute_loss(
|
||||
loss, loss_info = compute_loss(
|
||||
params=params,
|
||||
model=model,
|
||||
batch=batch,
|
||||
graph_compiler=graph_compiler,
|
||||
is_training=True,
|
||||
)
|
||||
# summary stats.
|
||||
tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
|
||||
|
||||
# NOTE: We use reduction==sum and loss is computed over utterances
|
||||
# in the batch and there is no normalization to it so far.
|
||||
@ -414,35 +483,19 @@ def train_one_epoch(
|
||||
clip_grad_norm_(model.parameters(), 5.0, 2.0)
|
||||
optimizer.step()
|
||||
|
||||
loss_cpu = loss.detach().cpu().item()
|
||||
|
||||
tot_frames += params.train_frames
|
||||
tot_loss += loss_cpu
|
||||
tot_avg_loss = tot_loss / tot_frames
|
||||
|
||||
if batch_idx % params.log_interval == 0:
|
||||
logging.info(
|
||||
f"Epoch {params.cur_epoch}, batch {batch_idx}, "
|
||||
f"batch avg loss {loss_cpu/params.train_frames:.4f}, "
|
||||
f"total avg loss: {tot_avg_loss:.4f}, "
|
||||
f"batch size: {batch_size}"
|
||||
f"Epoch {params.cur_epoch}, batch {batch_idx}, loss[{loss_info}]"
|
||||
f"tot_loss[{tot_loss}], batch size: {batch_size}"
|
||||
)
|
||||
if batch_idx % 10 == 0:
|
||||
|
||||
if tb_writer is not None:
|
||||
tb_writer.add_scalar(
|
||||
"train/current_loss",
|
||||
loss_cpu / params.train_frames,
|
||||
params.batch_idx_train,
|
||||
)
|
||||
|
||||
tb_writer.add_scalar(
|
||||
"train/tot_avg_loss",
|
||||
tot_avg_loss,
|
||||
params.batch_idx_train,
|
||||
)
|
||||
loss_info.write_summary(tb_writer, "train/current_", params.batch_idx_train)
|
||||
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
|
||||
|
||||
if batch_idx > 0 and batch_idx % params.valid_interval == 0:
|
||||
compute_validation_loss(
|
||||
valid_info = compute_validation_loss(
|
||||
params=params,
|
||||
model=model,
|
||||
graph_compiler=graph_compiler,
|
||||
@ -451,18 +504,17 @@ def train_one_epoch(
|
||||
)
|
||||
model.train()
|
||||
logging.info(
|
||||
f"Epoch {params.cur_epoch}, valid loss {params.valid_loss:.4f},"
|
||||
f" best valid loss: {params.best_valid_loss:.4f} "
|
||||
f"best valid epoch: {params.best_valid_epoch}"
|
||||
)
|
||||
f"Epoch {params.cur_epoch}, validation {valid_info}"
|
||||
)
|
||||
if tb_writer is not None:
|
||||
tb_writer.add_scalar(
|
||||
"train/valid_loss",
|
||||
params.valid_loss,
|
||||
valid_info.write_summary(
|
||||
tb_writer,
|
||||
"train/valid_",
|
||||
params.batch_idx_train,
|
||||
)
|
||||
|
||||
params.train_loss = tot_loss / tot_frames
|
||||
loss_value = tot_loss['loss'] / tot_loss['frames']
|
||||
params.train_loss = loss_value
|
||||
|
||||
if params.train_loss < params.best_train_loss:
|
||||
params.best_train_epoch = params.cur_epoch
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user