Move meters, metrics and progress_bar into fairseq.logging (#1046)

Summary: Pull Request resolved: fairinternal/fairseq-py#1046

Differential Revision: D20030412

Pulled By: myleott

fbshipit-source-id: bd87391aa9cdb73306ee90a30eeb2bdeff3690f9

examples/speech_recognition/infer.py

@@ -14,8 +14,8 @@
 
 import sentencepiece as spm
 import torch
-from fairseq import checkpoint_utils, options, progress_bar, utils, tasks
-from fairseq.meters import StopwatchMeter, TimeMeter
+from fairseq import checkpoint_utils, options, utils, tasks
+from fairseq.logging import meters, progress_bar
 from fairseq.utils import import_user_module
 
 
@@ -199,9 +199,15 @@ def main(args):
 
     # Load dataset (possibly sharded)
     itr = get_dataset_itr(args, task)
+    progress = progress_bar.progress_bar(
+        itr,
+        log_format=args.log_format,
+        log_interval=args.log_interval,
+        default_log_format=('tqdm' if not args.no_progress_bar else 'none'),
+    )
 
     # Initialize generator
-    gen_timer = StopwatchMeter()
+    gen_timer = meters.StopwatchMeter()
     generator = task.build_generator(args)
 
     num_sentences = 0
@@ -213,36 +219,35 @@ def main(args):
     sp.Load(os.path.join(args.data, "spm.model"))
 
     res_files = prepare_result_files(args)
-    with progress_bar.build_progress_bar(args, itr) as t:
-        wps_meter = TimeMeter()
-        for sample in t:
-            sample = utils.move_to_cuda(sample) if use_cuda else sample
-            if "net_input" not in sample:
-                continue
-
-            prefix_tokens = None
-            if args.prefix_size > 0:
-                prefix_tokens = sample["target"][:, : args.prefix_size]
-
-            gen_timer.start()
-            hypos = task.inference_step(generator, models, sample, prefix_tokens)
-            num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
-            gen_timer.stop(num_generated_tokens)
-
-            for i, sample_id in enumerate(sample["id"].tolist()):
-                speaker = task.dataset(args.gen_subset).speakers[int(sample_id)]
-                id = task.dataset(args.gen_subset).ids[int(sample_id)]
-                target_tokens = (
-                    utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu()
-                )
-                # Process top predictions
-                process_predictions(
-                    args, hypos[i], sp, tgt_dict, target_tokens, res_files, speaker, id
-                )
-
-            wps_meter.update(num_generated_tokens)
-            t.log({"wps": round(wps_meter.avg)})
-            num_sentences += sample["nsentences"]
+    wps_meter = meters.TimeMeter()
+    for sample in progress:
+        sample = utils.move_to_cuda(sample) if use_cuda else sample
+        if "net_input" not in sample:
+            continue
+
+        prefix_tokens = None
+        if args.prefix_size > 0:
+            prefix_tokens = sample["target"][:, : args.prefix_size]
+
+        gen_timer.start()
+        hypos = task.inference_step(generator, models, sample, prefix_tokens)
+        num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
+        gen_timer.stop(num_generated_tokens)
+
+        for i, sample_id in enumerate(sample["id"].tolist()):
+            speaker = task.dataset(args.gen_subset).speakers[int(sample_id)]
+            id = task.dataset(args.gen_subset).ids[int(sample_id)]
+            target_tokens = (
+                utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu()
+            )
+            # Process top predictions
+            process_predictions(
+                args, hypos[i], sp, tgt_dict, target_tokens, res_files, speaker, id
+            )
+
+        wps_meter.update(num_generated_tokens)
+        progress.log({"wps": round(wps_meter.avg)})
+        num_sentences += sample["nsentences"]
 
     logger.info(
         "| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}"

fairseq/__init__.py

@@ -6,6 +6,14 @@
 __all__ = ['pdb']
 __version__ = '0.9.0'
 
+import sys
+
+# backwards compatibility to support `from fairseq.meters import AverageMeter`
+from fairseq.logging import meters, metrics, progress_bar  # noqa
+sys.modules['fairseq.meters'] = meters
+sys.modules['fairseq.metrics'] = metrics
+sys.modules['fairseq.progress_bar'] = progress_bar
+
 import fairseq.criterions  # noqa
 import fairseq.models  # noqa
 import fairseq.modules  # noqa

fairseq/progress_bar.py → fairseq/logging/progress_bar.py

@@ -7,53 +7,86 @@
 Wrapper around various loggers and progress bars (e.g., tqdm).
 """
 
-from collections import OrderedDict
-from contextlib import contextmanager
 import json
 import logging
-from numbers import Number
 import os
 import sys
+from collections import OrderedDict
+from contextlib import contextmanager
+from numbers import Number
+from typing import Optional
 
 import torch
 
-from fairseq import distributed_utils
-from fairseq.meters import AverageMeter, StopwatchMeter, TimeMeter
+from .meters import AverageMeter, StopwatchMeter, TimeMeter
 
 
 logger = logging.getLogger(__name__)
 
 
-def build_progress_bar(args, iterator, epoch=None, prefix=None, default='tqdm', no_progress_bar='none'):
-    if args.log_format is None:
-        args.log_format = no_progress_bar if args.no_progress_bar else default
-
-    if args.log_format == 'tqdm' and not sys.stderr.isatty():
-        args.log_format = 'simple'
-
-    if args.log_format == 'json':
-        bar = json_progress_bar(iterator, epoch, prefix, args.log_interval)
-    elif args.log_format == 'none':
-        bar = noop_progress_bar(iterator, epoch, prefix)
-    elif args.log_format == 'simple':
-        bar = simple_progress_bar(iterator, epoch, prefix, args.log_interval)
-    elif args.log_format == 'tqdm':
-        bar = tqdm_progress_bar(iterator, epoch, prefix)
+def progress_bar(
+    iterator,
+    log_format: Optional[str] = None,
+    log_interval: int = 100,
+    epoch: Optional[int] = None,
+    prefix: Optional[str] = None,
+    tensorboard_logdir: Optional[str] = None,
+    default_log_format: str = 'tqdm',
+):
+    if log_format is None:
+        log_format = default_log_format
+    if log_format == 'tqdm' and not sys.stderr.isatty():
+        log_format = 'simple'
+
+    if log_format == 'json':
+        bar = JsonProgressBar(iterator, epoch, prefix, log_interval)
+    elif log_format == 'none':
+        bar = NoopProgressBar(iterator, epoch, prefix)
+    elif log_format == 'simple':
+        bar = SimpleProgressBar(iterator, epoch, prefix, log_interval)
+    elif log_format == 'tqdm':
+        bar = TqdmProgressBar(iterator, epoch, prefix)
     else:
-        raise ValueError('Unknown log format: {}'.format(args.log_format))
+        raise ValueError('Unknown log format: {}'.format(log_format))
 
-    if args.tensorboard_logdir and distributed_utils.is_master(args):
+    if tensorboard_logdir:
         try:
             # [FB only] custom wrapper for TensorBoard
             import palaas  # noqa
-            from fairseq.fb_tbmf_wrapper import fb_tbmf_wrapper
-            bar = fb_tbmf_wrapper(bar, args, args.log_interval)
+            from .fb_tbmf_wrapper import FbTbmfWrapper
+            bar = FbTbmfWrapper(bar, log_interval)
         except ImportError:
-            bar = tensorboard_log_wrapper(bar, args.tensorboard_logdir, args)
+            bar = TensorboardProgressBarWrapper(bar, tensorboard_logdir)
 
     return bar
 
 
+def build_progress_bar(
+    args,
+    iterator,
+    epoch: Optional[int] = None,
+    prefix: Optional[str] = None,
+    default: str = 'tqdm',
+    no_progress_bar: str = 'none',
+):
+    """Legacy wrapper that takes an argparse.Namespace."""
+    if getattr(args, 'no_progress_bar', False):
+        default = no_progress_bar
+    if getattr(args, 'distributed_rank', 0) == 0:
+        tensorboard_logdir = getattr(args, 'tensorboard_logdir', None)
+    else:
+        tensorboard_logdir = None
+    return progress_bar(
+        iterator,
+        log_format=args.log_format,
+        log_interval=args.log_interval,
+        epoch=epoch,
+        prefix=prefix,
+        tensorboard_logdir=tensorboard_logdir,
+        default_log_format=default,
+    )
+
+
 def format_stat(stat):
     if isinstance(stat, Number):
         stat = '{:g}'.format(stat)
@@ -68,7 +101,7 @@ def format_stat(stat):
     return stat
 
 
-class progress_bar(object):
+class BaseProgressBar(object):
     """Abstract class for progress bars."""
     def __init__(self, iterable, epoch=None, prefix=None):
         self.iterable = iterable
@@ -125,7 +158,7 @@ def rename_logger(logger, new_name):
     logger.name = old_name
 
 
-class json_progress_bar(progress_bar):
+class JsonProgressBar(BaseProgressBar):
     """Log output in JSON format."""
 
     def __init__(self, iterable, epoch=None, prefix=None, log_interval=1000):
@@ -179,7 +212,7 @@ def _format_stats(self, stats, epoch=None, update=None):
         return postfix
 
 
-class noop_progress_bar(progress_bar):
+class NoopProgressBar(BaseProgressBar):
     """No logging."""
 
     def __init__(self, iterable, epoch=None, prefix=None):
@@ -198,7 +231,7 @@ def print(self, stats, tag=None, step=None):
         pass
 
 
-class simple_progress_bar(progress_bar):
+class SimpleProgressBar(BaseProgressBar):
     """A minimal logger for non-TTY environments."""
 
     def __init__(self, iterable, epoch=None, prefix=None, log_interval=1000):
@@ -233,7 +266,7 @@ def print(self, stats, tag=None, step=None):
             logger.info('{} | {}'.format(self.prefix, postfix))
 
 
-class tqdm_progress_bar(progress_bar):
+class TqdmProgressBar(BaseProgressBar):
     """Log to tqdm."""
 
     def __init__(self, iterable, epoch=None, prefix=None):
@@ -261,13 +294,12 @@ def print(self, stats, tag=None, step=None):
     SummaryWriter = None
 
 
-class tensorboard_log_wrapper(progress_bar):
+class TensorboardProgressBarWrapper(BaseProgressBar):
     """Log to tensorboard."""
 
-    def __init__(self, wrapped_bar, tensorboard_logdir, args):
+    def __init__(self, wrapped_bar, tensorboard_logdir):
         self.wrapped_bar = wrapped_bar
         self.tensorboard_logdir = tensorboard_logdir
-        self.args = args
 
         if SummaryWriter is None:
             logger.warning(
@@ -281,7 +313,6 @@ def _writer(self, key):
         _writers = _tensorboard_writers
         if key not in _writers:
             _writers[key] = SummaryWriter(os.path.join(self.tensorboard_logdir, key))
-            _writers[key].add_text('args', str(vars(self.args)))
             _writers[key].add_text('sys.argv', " ".join(sys.argv))
         return _writers[key]
 

fairseq/options.py

@@ -198,7 +198,7 @@ def get_parser(desc, default_task="translation"):
     parser = argparse.ArgumentParser(allow_abbrev=False)
     # fmt: off
     parser.add_argument('--no-progress-bar', action='store_true', help='disable progress bar')
-    parser.add_argument('--log-interval', type=int, default=1000, metavar='N',
+    parser.add_argument('--log-interval', type=int, default=100, metavar='N',
                         help='log progress every N batches (when progress bar is disabled)')
     parser.add_argument('--log-format', default=None, help='log format to use',
                         choices=['json', 'none', 'simple', 'tqdm'])

fairseq/trainer.py

@@ -17,9 +17,9 @@
 
 import torch
 
-from fairseq import checkpoint_utils, distributed_utils, metrics, models, optim, utils
+from fairseq import checkpoint_utils, distributed_utils, models, optim, utils
 from fairseq.file_io import PathManager
-from fairseq.meters import AverageMeter, StopwatchMeter, TimeMeter
+from fairseq.logging import meters, metrics
 from fairseq.optim import lr_scheduler
 
 
@@ -226,7 +226,7 @@ def load_checkpoint(
 
                 # reset TimeMeters, since their start times don't make sense anymore
                 for meter in metrics.get_meters("default"):
-                    if isinstance(meter, TimeMeter):
+                    if isinstance(meter, meters.TimeMeter):
                         meter.reset()
         else:
             logger.info("no existing checkpoint found {}".format(filename))