Support multi-GPU validation in fairseq-validate (#2162)

Summary: Pull Request resolved: #2162

Reviewed By: ngoyal2707

Differential Revision: D21663181

Pulled By: myleott

fbshipit-source-id: d01e64f97482f76bd601cd8b20232c0ef637bb8a

fairseq/criterions/adaptive_loss.py

@@ -23,7 +23,7 @@ def __init__(self, task, sentence_avg):
 
     @classmethod
     def build_criterion(cls, args, task):
-        if args.ddp_backend == 'c10d':
+        if getattr(args, 'ddp_backend', None) == 'c10d':
             raise Exception(
                 'AdaptiveLoss is not compatible with the c10d '
                 'version of DistributedDataParallel. Please use '

fairseq/options.py

@@ -53,6 +53,7 @@ def get_eval_lm_parser(default_task="language_modeling"):
 def get_validation_parser(default_task=None):
     parser = get_parser("Validation", default_task)
     add_dataset_args(parser, train=True)
+    add_distributed_training_args(parser)
     group = parser.add_argument_group("Evaluation")
     add_common_eval_args(group)
     return parser

fairseq_cli/validate.py

@@ -5,14 +5,15 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
+from itertools import chain
 import logging
 import sys
 
 import torch
 
 from fairseq import checkpoint_utils, distributed_utils, options, utils
 from fairseq.logging import metrics, progress_bar
-from fairseq.options import add_distributed_training_args
+
 
 logging.basicConfig(
     format='%(asctime)s | %(levelname)s | %(name)s | %(message)s',
@@ -32,6 +33,9 @@ def main(args, override_args=None):
     use_fp16 = args.fp16
     use_cuda = torch.cuda.is_available() and not args.cpu
 
+    if use_cuda:
+        torch.cuda.set_device(args.device_id)
+
     if override_args is not None:
         overrides = vars(override_args)
         overrides.update(eval(getattr(override_args, 'model_overrides', '{}')))
@@ -80,6 +84,8 @@ def main(args, override_args=None):
             ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
             required_batch_size_multiple=args.required_batch_size_multiple,
             seed=args.seed,
+            num_shards=args.distributed_world_size,
+            shard_id=args.distributed_rank,
             num_workers=args.num_workers,
         ).next_epoch_itr(shuffle=False)
         progress = progress_bar.progress_bar(
@@ -97,6 +103,13 @@ def main(args, override_args=None):
             progress.log(log_output, step=i)
             log_outputs.append(log_output)
 
+        if args.distributed_world_size > 1:
+            log_outputs = distributed_utils.all_gather_list(
+                log_outputs,
+                max_size=getattr(args, 'all_gather_list_size', 16384),
+            )
+            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()
@@ -106,12 +119,10 @@ def main(args, override_args=None):
 
 def cli_main():
     parser = options.get_validation_parser()
-    add_distributed_training_args(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()
-    add_distributed_training_args(override_parser)
     override_args = options.parse_args_and_arch(override_parser, suppress_defaults=True)
 
     distributed_utils.call_main(args, main, override_args=override_args)