DeepLearningExamples/PyTorch/SpeechRecognition/QuartzNet/common/tb_dllogger.py

# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import atexit
import glob
import os
import re
import numpy as np

import torch
from torch.utils.tensorboard import SummaryWriter

import dllogger
from dllogger import StdOutBackend, JSONStreamBackend, Verbosity


tb_loggers = {}


class TBLogger:
    """
    xyz_dummies: stretch the screen with empty plots so the legend would
                 always fit for other plots
    """
    def __init__(self, enabled, log_dir, name, interval=1, dummies=True):
        self.enabled = enabled
        self.interval = interval
        self.cache = {}
        if self.enabled:
            self.summary_writer = SummaryWriter(
                log_dir=os.path.join(log_dir, name),
                flush_secs=120, max_queue=200)
            atexit.register(self.summary_writer.close)
            if dummies:
                for key in ('aaa', 'zzz'):
                    self.summary_writer.add_scalar(key, 0.0, 1)

    def log(self, step, data):
        for k, v in data.items():
            self.log_value(step, k, v.item() if type(v) is torch.Tensor else v)

    def log_value(self, step, key, val, stat='mean'):
        if self.enabled:
            if key not in self.cache:
                self.cache[key] = []
            self.cache[key].append(val)
            if len(self.cache[key]) == self.interval:
                agg_val = getattr(np, stat)(self.cache[key])
                self.summary_writer.add_scalar(key, agg_val, step)
                del self.cache[key]

    def log_grads(self, step, model):
        if self.enabled:
            norms = [p.grad.norm().item() for p in model.parameters()
                     if p.grad is not None]
            for stat in ('max', 'min', 'mean'):
                self.log_value(step, f'grad_{stat}', getattr(np, stat)(norms),
                               stat=stat)


def unique_log_fpath(log_fpath):

    if not os.path.isfile(log_fpath):
        return log_fpath

    # Avoid overwriting old logs
    saved = sorted([int(re.search('\.(\d+)', f).group(1))
                    for f in glob.glob(f'{log_fpath}.*')])

    log_num = (saved[-1] if saved else 0) + 1
    return f'{log_fpath}.{log_num}'


def stdout_step_format(step):
    if isinstance(step, str):
        return step
    fields = []
    if len(step) > 0:
        fields.append("epoch {:>4}".format(step[0]))
    if len(step) > 1:
        fields.append("iter {:>4}".format(step[1]))
    if len(step) > 2:
        fields[-1] += "/{}".format(step[2])
    return " | ".join(fields)


def stdout_metric_format(metric, metadata, value):
    name = metadata.get("name", metric + " : ")
    unit = metadata.get("unit", None)
    format = f'{{{metadata.get("format", "")}}}'
    fields = [name, format.format(value) if value is not None else value, unit]
    fields = [f for f in fields if f is not None]
    return "| " + " ".join(fields)


def init_log(args):
    enabled = (args.local_rank == 0)
    if enabled:
        fpath = args.log_file or os.path.join(args.output_dir, 'nvlog.json')
        backends = [JSONStreamBackend(Verbosity.DEFAULT,
                                      unique_log_fpath(fpath)),
                    StdOutBackend(Verbosity.VERBOSE,
                                  step_format=stdout_step_format,
                                  metric_format=stdout_metric_format)]
    else:
        backends = []

    dllogger.init(backends=backends)
    dllogger.metadata("train_lrate", {"name": "lrate", "format": ":>3.2e"})

    for id_, pref in [('train', ''), ('train_avg', 'avg train '),
                      ('dev', '  avg dev '), ('dev_ema', '  EMA dev ')]:

        dllogger.metadata(f"{id_}_loss",
                          {"name": f"{pref}loss", "format": ":>7.2f"})

        dllogger.metadata(f"{id_}_wer",
                          {"name": f"{pref}wer", "format": ":>6.2f"})

        dllogger.metadata(f"{id_}_throughput",
                          {"name": f"{pref}utts/s", "format": ":>5.0f"})

        dllogger.metadata(f"{id_}_took",
                          {"name": "took", "unit": "s", "format": ":>5.2f"})

    tb_subsets = ['train', 'dev', 'dev_ema'] if args.ema else ['train', 'dev']
    global tb_loggers
    tb_loggers = {s: TBLogger(enabled, args.output_dir, name=s)
                  for s in tb_subsets}

    log_parameters(vars(args), tb_subset='train')


def log(step, tb_total_steps=None, subset='train', data={}):

    if tb_total_steps is not None:
        tb_loggers[subset].log(tb_total_steps, data)

    if subset != '':
        data = {f'{subset}_{key}': val for key, val in data.items()}
    dllogger.log(step, data=data)


def log_grads_tb(tb_total_steps, grads, tb_subset='train'):
    tb_loggers[tb_subset].log_grads(tb_total_steps, grads)


def log_parameters(data, verbosity=0, tb_subset=None):
    for k, v in data.items():
        dllogger.log(step="PARAMETER", data={k: v}, verbosity=verbosity)

    if tb_subset is not None and tb_loggers[tb_subset].enabled:
        tb_data = {k: v for k, v in data.items()
                   if type(v) in (str, bool, int, float)}
        tb_loggers[tb_subset].summary_writer.add_hparams(tb_data, {})


def flush_log():
    dllogger.flush()
    for tbl in tb_loggers.values():
        if tbl.enabled:
            tbl.summary_writer.flush()