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#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import sys
from argparse import Namespace
from itertools import chain
import torch
from fairseq import checkpoint_utils, distributed_utils, options, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import metrics, progress_bar
from fairseq.utils import reset_logging
from omegaconf import DictConfig
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
logger = logging.getLogger("fairseq_cli.validate")
def main(cfg: DictConfig, override_args=None):
if isinstance(cfg, Namespace):
cfg = convert_namespace_to_omegaconf(cfg)
utils.import_user_module(cfg.common)
reset_logging()
assert (
cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
), "Must specify batch size either with --max-tokens or --batch-size"
use_fp16 = cfg.common.fp16
use_cuda = torch.cuda.is_available() and not cfg.common.cpu
if use_cuda:
torch.cuda.set_device(cfg.distributed_training.device_id)
if cfg.distributed_training.distributed_world_size > 1:
data_parallel_world_size = distributed_utils.get_data_parallel_world_size()
data_parallel_rank = distributed_utils.get_data_parallel_rank()
else:
data_parallel_world_size = 1
data_parallel_rank = 0
if override_args is not None:
overrides = vars(override_args)
overrides.update(eval(getattr(override_args, "model_overrides", "{}")))
else:
overrides = None
# Load ensemble
logger.info("loading model(s) from {}".format(cfg.common_eval.path))
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[cfg.common_eval.path],
arg_overrides=overrides,
suffix=cfg.checkpoint.checkpoint_suffix,
)
model = models[0]
# Move models to GPU
for model in models:
model.eval()
if use_fp16:
model.half()
if use_cuda:
model.cuda()
# Print args
logger.info(saved_cfg)
# Build criterion
criterion = task.build_criterion(saved_cfg.criterion)
criterion.eval()
for subset in cfg.dataset.valid_subset.split(","):
try:
task.load_dataset(subset, combine=False, epoch=1, task_cfg=saved_cfg.task)
dataset = task.dataset(subset)
except KeyError:
raise Exception("Cannot find dataset: " + subset)
# Initialize data iterator
itr = task.get_batch_iterator(
dataset=dataset,
max_tokens=cfg.dataset.max_tokens,
max_sentences=cfg.dataset.batch_size,
max_positions=utils.resolve_max_positions(
task.max_positions(),
*[m.max_positions() for m in models],
),
ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
seed=cfg.common.seed,
num_shards=data_parallel_world_size,
shard_id=data_parallel_rank,
num_workers=cfg.dataset.num_workers,
data_buffer_size=cfg.dataset.data_buffer_size,
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=cfg.common.log_format,
log_interval=cfg.common.log_interval,
prefix=f"valid on '{subset}' subset",
default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
)
log_outputs = []
for i, sample in enumerate(progress):
sample = utils.move_to_cuda(sample) if use_cuda else sample
_loss, _sample_size, log_output = task.valid_step(sample, model, criterion)
progress.log(log_output, step=i)
log_outputs.append(log_output)
if data_parallel_world_size > 1:
log_outputs = distributed_utils.all_gather_list(
log_outputs,
max_size=cfg.common.all_gather_list_size,
group=distributed_utils.get_data_parallel_group(),
)
log_outputs = list(chain.from_iterable(log_outputs))
with metrics.aggregate() as agg:
task.reduce_metrics(log_outputs, criterion)
log_output = agg.get_smoothed_values()
progress.print(log_output, tag=subset, step=i)
def cli_main():
parser = options.get_validation_parser()
args = options.parse_args_and_arch(parser)
# only override args that are explicitly given on the command line
override_parser = options.get_validation_parser()
override_args = options.parse_args_and_arch(
override_parser, suppress_defaults=True
)
distributed_utils.call_main(
convert_namespace_to_omegaconf(args), main, override_args=override_args
)
if __name__ == "__main__":
cli_main()
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