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OOM problem with the model nat_ctc_sd_ss #13

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YudiZh opened this issue Sep 7, 2022 · 1 comment
Open

OOM problem with the model nat_ctc_sd_ss #13

YudiZh opened this issue Sep 7, 2022 · 1 comment

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@YudiZh
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YudiZh commented Sep 7, 2022 •
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I trained the model "nat_ctc_sd_ss" with the command in the README.md on Tesla V100 GPU, but i got Out of memory problem. Is there anything to be changed?
My train command:

python3 train.py $DATA --source-lang en --target-lang de  --save-dir checkpoints/NAT_CTC_DSLP_MT  --eval-tokenized-bleu \
   --keep-interval-updates 5 --save-interval-updates 500 --validate-interval-updates 500 --maximize-best-checkpoint-metric \
   --eval-bleu-remove-bpe --eval-bleu-print-samples --best-checkpoint-metric bleu --log-format simple --log-interval 100 \
   --eval-bleu --eval-bleu-detok space --keep-last-epochs 5 --keep-best-checkpoints 5  --fixed-validation-seed 7 --ddp-backend=no_c10d \
   --share-all-embeddings --decoder-learned-pos --encoder-learned-pos  --optimizer adam --adam-betas "(0.9,0.98)" --lr 0.0005 \
   --lr-scheduler inverse_sqrt --stop-min-lr 1e-09 --warmup-updates 10000 --warmup-init-lr 1e-07 --apply-bert-init --weight-decay 0.01 \
   --fp16 --clip-norm 2.0 --max-update 300000  --task translation_lev --criterion nat_loss --arch nat_ctc_sd_ss --noise full_mask \
   --src-upsample-scale 2 --use-ctc-decoder --ctc-beam-size 1  --concat-yhat --concat-dropout 0.0  --label-smoothing 0.0 \
   --activation-fn gelu --dropout 0.1  --max-tokens 8192 --ss-ratio 0.3 --fixed-ss-ratio

My training log is as follows:

2022-09-07 20:31:12 | INFO | fairseq_cli.train | {'_name': None, 'common': {'_name': None, 'no_progress_bar': False, 'log_interval': 100, 'log_format': 'simple', 'tensorboard_logdir': None, 'wandb_project': None, 'azureml_logging': False, 'seed': 1, 'cpu': False, 'tpu': False, 'bf16': False, 'memory_efficient_bf16': False, 'fp16': True, 'memory_efficient_fp16': False, 'fp16_no_flatten_grads': False, 'fp16_init_scale': 128, 'fp16_scale_window': None, 'fp16_scale_tolerance': 0.0, 'min_loss_scale': 0.0001, 'threshold_loss_scale': None, 'user_dir': None, 'empty_cache_freq': 0, 'all_gather_list_size': 16384, 'model_parallel_size': 1, 'quantization_config_path': None, 'profile': False, 'reset_logging': True, 'suppress_crashes': False}, 'common_eval': {'_name': None, 'path': None, 'post_process': None, 'quiet': False, 'model_overrides': '{}', 'results_path': None}, 'distributed_training': {'_name': None, 'distributed_world_size': 8, 'distributed_rank': 0, 'distributed_backend': 'nccl', 'distributed_init_method': 'tcp://localhost:15846', 'distributed_port': -1, 'device_id': 0, 'distributed_no_spawn': False, 'ddp_backend': 'no_c10d', 'bucket_cap_mb': 25, 'fix_batches_to_gpus': False, 'find_unused_parameters': False, 'fast_stat_sync': False, 'heartbeat_timeout': -1, 'broadcast_buffers': False, 'distributed_wrapper': 'DDP', 'slowmo_momentum': None, 'slowmo_algorithm': 'LocalSGD', 'localsgd_frequency': 3, 'nprocs_per_node': 8, 'pipeline_model_parallel': False, 'pipeline_balance': None, 'pipeline_devices': None, 'pipeline_chunks': 0, 'pipeline_encoder_balance': None, 'pipeline_encoder_devices': None, 'pipeline_decoder_balance': None, 'pipeline_decoder_devices': None, 'pipeline_checkpoint': 'never', 'zero_sharding': 'none', 'tpu': False, 'distributed_num_procs': 8}, 'dataset': {'_name': None, 'num_workers': 1, 'skip_invalid_size_inputs_valid_test': False, 'max_tokens': 8192, 'batch_size': None, 'required_batch_size_multiple': 8, 'required_seq_len_multiple': 1, 'dataset_impl': None, 'data_buffer_size': 10, 'train_subset': 'train', 'valid_subset': 'valid', 'validate_interval': 1, 'validate_interval_updates': 500, 'validate_after_updates': 0, 'fixed_validation_seed': 7, 'disable_validation': False, 'max_tokens_valid': 8192, 'batch_size_valid': None, 'curriculum': 0, 'gen_subset': 'test', 'num_shards': 1, 'shard_id': 0}, 'optimization': {'_name': None, 'max_epoch': 0, 'max_update': 300000, 'stop_time_hours': 0.0, 'clip_norm': 2.0, 'sentence_avg': False, 'update_freq': [1], 'lr': [0.0005], 'stop_min_lr': 1e-09, 'use_bmuf': False}, 'checkpoint': {'_name': None, 'save_dir': 'checkpoints/NAT_CTC_DSLP_MT', 'restore_file': 'checkpoint_last.pt', 'finetune_from_model': None, 'reset_dataloader': False, 'reset_lr_scheduler': False, 'reset_meters': False, 'reset_optimizer': False, 'optimizer_overrides': '{}', 'save_interval': 1, 'save_interval_updates': 500, 'keep_interval_updates': 5, 'keep_last_epochs': 5, 'keep_best_checkpoints': 5, 'no_save': False, 'no_epoch_checkpoints': False, 'no_last_checkpoints': False, 'no_save_optimizer_state': False, 'best_checkpoint_metric': 'bleu', 'maximize_best_checkpoint_metric': True, 'patience': -1, 'checkpoint_suffix': '', 'checkpoint_shard_count': 1, 'load_checkpoint_on_all_dp_ranks': False, 'model_parallel_size': 1, 'distributed_rank': 0}, 'bmuf': {'_name': None, 'block_lr': 1.0, 'block_momentum': 0.875, 'global_sync_iter': 50, 'warmup_iterations': 500, 'use_nbm': False, 'average_sync': False, 'distributed_world_size': 8}, 'generation': {'_name': None, 'beam': 5, 'nbest': 1, 'max_len_a': 0.0, 'max_len_b': 200, 'min_len': 1, 'match_source_len': False, 'unnormalized': False, 'no_early_stop': False, 'no_beamable_mm': False, 'lenpen': 1.0, 'unkpen': 0.0, 'replace_unk': None, 'sacrebleu': False, 'score_reference': False, 'prefix_size': 0, 'no_repeat_ngram_size': 0, 'sampling': False, 'sampling_topk': -1, 'sampling_topp': -1.0, 'constraints': None, 'temperature': 1.0, 'diverse_beam_groups': -1, 'diverse_beam_strength': 0.5, 'diversity_rate': -1.0, 'print_alignment': None, 'print_step': False, 'lm_path': None, 'lm_weight': 0.0, 'iter_decode_eos_penalty': 0.0, 'iter_decode_max_iter': 10, 'iter_decode_force_max_iter': False, 'iter_decode_with_beam': 1, 'iter_decode_with_external_reranker': False, 'retain_iter_history': False, 'retain_dropout': False, 'retain_dropout_modules': None, 'decoding_format': None, 'no_seed_provided': False, 'force_no_target': False}, 'eval_lm': {'_name': None, 'output_word_probs': False, 'output_word_stats': False, 'context_window': 0, 'softmax_batch': 9223372036854775807}, 'interactive': {'_name': None, 'buffer_size': 0, 'input': '-'}, 'model': Namespace(_name='nat_ctc_sd_ss', activation_dropout=0.0, activation_fn='gelu', adam_betas='(0.9,0.98)', adam_eps=1e-08, adaptive_input=False, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, all_gather_list_size=16384, all_layer_drop=False, apply_bert_init=True, arch='nat_ctc_sd_ss', attention_dropout=0.0, azureml_logging=False, batch_size=None, batch_size_valid=None, best_checkpoint_metric='bleu', bf16=False, bpe=None, broadcast_buffers=False, bucket_cap_mb=25, checkpoint_shard_count=1, checkpoint_suffix='', clip_norm=2.0, concat_dropout=0.0, concat_yhat=True, copy_src_token=False, cpu=False, criterion='nat_loss', cross_self_attention=False, ctc_beam_size=1, ctc_beam_size_train=1, curriculum=0, data='../Enrich_Syn_NAT/data/wmt14_ende_distill/bin', data_buffer_size=10, dataset_impl=None, ddp_backend='no_c10d', decoder_attention_heads=8, decoder_embed_dim=512, decoder_embed_path=None, decoder_ffn_embed_dim=2048, decoder_input_dim=512, decoder_layerdrop=0, decoder_layers=6, decoder_layers_to_keep=None, decoder_learned_pos=True, decoder_normalize_before=False, decoder_output_dim=512, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method=None, distributed_no_spawn=False, distributed_port=-1, distributed_rank=0, distributed_world_size=8, distributed_wrapper='DDP', dropout=0.1, dropout_anneal=False, dropout_anneal_end_ratio=0, empty_cache_freq=0, encoder_attention_heads=8, encoder_embed_dim=512, encoder_embed_path=None, encoder_ffn_embed_dim=2048, encoder_layerdrop=0, encoder_layers=6, encoder_layers_to_keep=None, encoder_learned_pos=True, encoder_normalize_before=False, eos=2, eval_bleu=True, eval_bleu_args=None, eval_bleu_detok='space', eval_bleu_detok_args=None, eval_bleu_print_samples=True, eval_bleu_remove_bpe='@@ ', eval_tokenized_bleu=True, fast_stat_sync=False, find_unused_parameters=False, finetune_from_model=None, fix_batches_to_gpus=False, fixed_ss_ratio=True, fixed_validation_seed=7, force_detach=False, force_ls=False, fp16=True, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, gen_subset='test', heartbeat_timeout=-1, inference_decoder_layer=-1, keep_best_checkpoints=5, keep_interval_updates=5, keep_last_epochs=5, label_smoothing=0.0, layer_drop_ratio=0.0, left_pad_source='True', left_pad_target='False', length_loss_factor=0.1, load_alignments=False, load_checkpoint_on_all_dp_ranks=False, localsgd_frequency=3, log_format='simple', log_interval=100, lr=[0.0005], lr_scheduler='inverse_sqrt', masked_loss=False, max_epoch=0, max_source_positions=1024, max_target_positions=1024, max_tokens=8192, max_tokens_valid=8192, max_update=300000, maximize_best_checkpoint_metric=True, memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_cross_attention=False, no_empty=False, no_epoch_checkpoints=False, no_last_checkpoints=False, no_progress_bar=False, no_save=False, no_save_optimizer_state=False, no_seed_provided=False, no_token_positional_embeddings=False, noise='full_mask', nprocs_per_node=8, num_batch_buckets=0, num_cross_layer_sample=0, num_shards=1, num_topk=1, num_workers=1, optimizer='adam', optimizer_overrides='{}', pad=1, patience=-1, pipeline_balance=None, pipeline_checkpoint='never', pipeline_chunks=0, pipeline_decoder_balance=None, pipeline_decoder_devices=None, pipeline_devices=None, pipeline_encoder_balance=None, pipeline_encoder_devices=None, pipeline_model_parallel=False, plain_ctc=False, pred_length_offset=False, profile=False, quant_noise_pq=0, quant_noise_pq_block_size=8, quant_noise_scalar=0, quantization_config_path=None, repeat_layer=0, required_batch_size_multiple=8, required_seq_len_multiple=1, reset_dataloader=False, reset_logging=True, reset_lr_scheduler=False, reset_meters=False, reset_optimizer=False, restore_file='checkpoint_last.pt', sample_option='hard', save_dir='checkpoints/NAT_CTC_DSLP_MT', save_interval=1, save_interval_updates=500, scoring='bleu', seed=1, sentence_avg=False, sg_length_pred=False, shard_id=0, share_all_embeddings=True, share_attn=False, share_decoder_input_output_embed=False, share_ffn=False, skip_invalid_size_inputs_valid_test=False, slowmo_algorithm='LocalSGD', slowmo_momentum=None, softcopy=False, softcopy_temp=5, softmax_temp=1, source_lang='en', src_embedding_copy=False, src_upsample_scale=2, ss_ratio=0.3, stop_min_lr=1e-09, stop_time_hours=0, suppress_crashes=False, target_lang='de', task='translation_lev', temp_anneal=False, tensorboard_logdir=None, threshold_loss_scale=None, tokenizer=None, tpu=False, train_subset='train', truncate_source=False, unk=3, update_freq=[1], upsample_primary=1, use_bmuf=False, use_ctc_decoder=True, use_old_adam=False, user_dir=None, valid_subset='valid', validate_after_updates=0, validate_interval=1, validate_interval_updates=500, wandb_project=None, warmup_init_lr=1e-07, warmup_updates=10000, weight_decay=0.01, yhat_posemb=False, zero_sharding='none'), 'task': Namespace(_name='translation_lev', activation_dropout=0.0, activation_fn='gelu', adam_betas='(0.9,0.98)', adam_eps=1e-08, adaptive_input=False, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, all_gather_list_size=16384, all_layer_drop=False, apply_bert_init=True, arch='nat_ctc_sd_ss', attention_dropout=0.0, azureml_logging=False, batch_size=None, batch_size_valid=None, best_checkpoint_metric='bleu', bf16=False, bpe=None, broadcast_buffers=False, bucket_cap_mb=25, checkpoint_shard_count=1, checkpoint_suffix='', clip_norm=2.0, concat_dropout=0.0, concat_yhat=True, copy_src_token=False, cpu=False, criterion='nat_loss', cross_self_attention=False, ctc_beam_size=1, ctc_beam_size_train=1, curriculum=0, data='../Enrich_Syn_NAT/data/wmt14_ende_distill/bin', data_buffer_size=10, dataset_impl=None, ddp_backend='no_c10d', decoder_attention_heads=8, decoder_embed_dim=512, decoder_embed_path=None, decoder_ffn_embed_dim=2048, decoder_input_dim=512, decoder_layerdrop=0, decoder_layers=6, decoder_layers_to_keep=None, decoder_learned_pos=True, decoder_normalize_before=False, decoder_output_dim=512, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method=None, distributed_no_spawn=False, distributed_port=-1, distributed_rank=0, distributed_world_size=8, distributed_wrapper='DDP', dropout=0.1, dropout_anneal=False, dropout_anneal_end_ratio=0, empty_cache_freq=0, encoder_attention_heads=8, encoder_embed_dim=512, encoder_embed_path=None, encoder_ffn_embed_dim=2048, encoder_layerdrop=0, encoder_layers=6, encoder_layers_to_keep=None, encoder_learned_pos=True, encoder_normalize_before=False, eos=2, eval_bleu=True, eval_bleu_args=None, eval_bleu_detok='space', eval_bleu_detok_args=None, eval_bleu_print_samples=True, eval_bleu_remove_bpe='@@ ', eval_tokenized_bleu=True, fast_stat_sync=False, find_unused_parameters=False, finetune_from_model=None, fix_batches_to_gpus=False, fixed_ss_ratio=True, fixed_validation_seed=7, force_detach=False, force_ls=False, fp16=True, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, gen_subset='test', heartbeat_timeout=-1, inference_decoder_layer=-1, keep_best_checkpoints=5, keep_interval_updates=5, keep_last_epochs=5, label_smoothing=0.0, layer_drop_ratio=0.0, left_pad_source='True', left_pad_target='False', length_loss_factor=0.1, load_alignments=False, load_checkpoint_on_all_dp_ranks=False, localsgd_frequency=3, log_format='simple', log_interval=100, lr=[0.0005], lr_scheduler='inverse_sqrt', masked_loss=False, max_epoch=0, max_source_positions=1024, max_target_positions=1024, max_tokens=8192, max_tokens_valid=8192, max_update=300000, maximize_best_checkpoint_metric=True, memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_cross_attention=False, no_empty=False, no_epoch_checkpoints=False, no_last_checkpoints=False, no_progress_bar=False, no_save=False, no_save_optimizer_state=False, no_seed_provided=False, no_token_positional_embeddings=False, noise='full_mask', nprocs_per_node=8, num_batch_buckets=0, num_cross_layer_sample=0, num_shards=1, num_topk=1, num_workers=1, optimizer='adam', optimizer_overrides='{}', pad=1, patience=-1, pipeline_balance=None, pipeline_checkpoint='never', pipeline_chunks=0, pipeline_decoder_balance=None, pipeline_decoder_devices=None, pipeline_devices=None, pipeline_encoder_balance=None, pipeline_encoder_devices=None, pipeline_model_parallel=False, plain_ctc=False, pred_length_offset=False, profile=False, quant_noise_pq=0, quant_noise_pq_block_size=8, quant_noise_scalar=0, quantization_config_path=None, repeat_layer=0, required_batch_size_multiple=8, required_seq_len_multiple=1, reset_dataloader=False, reset_logging=True, reset_lr_scheduler=False, reset_meters=False, reset_optimizer=False, restore_file='checkpoint_last.pt', sample_option='hard', save_dir='checkpoints/NAT_CTC_DSLP_MT', save_interval=1, save_interval_updates=500, scoring='bleu', seed=1, sentence_avg=False, sg_length_pred=False, shard_id=0, share_all_embeddings=True, share_attn=False, share_decoder_input_output_embed=False, share_ffn=False, skip_invalid_size_inputs_valid_test=False, slowmo_algorithm='LocalSGD', slowmo_momentum=None, softcopy=False, softcopy_temp=5, softmax_temp=1, source_lang='en', src_embedding_copy=False, src_upsample_scale=2, ss_ratio=0.3, stop_min_lr=1e-09, stop_time_hours=0, suppress_crashes=False, target_lang='de', task='translation_lev', temp_anneal=False, tensorboard_logdir=None, threshold_loss_scale=None, tokenizer=None, tpu=False, train_subset='train', truncate_source=False, unk=3, update_freq=[1], upsample_primary=1, use_bmuf=False, use_ctc_decoder=True, use_old_adam=False, user_dir=None, valid_subset='valid', validate_after_updates=0, validate_interval=1, validate_interval_updates=500, wandb_project=None, warmup_init_lr=1e-07, warmup_updates=10000, weight_decay=0.01, yhat_posemb=False, zero_sharding='none'), 'criterion': Namespace(_name='nat_loss', activation_dropout=0.0, activation_fn='gelu', adam_betas='(0.9,0.98)', adam_eps=1e-08, adaptive_input=False, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, all_gather_list_size=16384, all_layer_drop=False, apply_bert_init=True, arch='nat_ctc_sd_ss', attention_dropout=0.0, azureml_logging=False, batch_size=None, batch_size_valid=None, best_checkpoint_metric='bleu', bf16=False, bpe=None, broadcast_buffers=False, bucket_cap_mb=25, checkpoint_shard_count=1, checkpoint_suffix='', clip_norm=2.0, concat_dropout=0.0, concat_yhat=True, copy_src_token=False, cpu=False, criterion='nat_loss', cross_self_attention=False, ctc_beam_size=1, ctc_beam_size_train=1, curriculum=0, data='../Enrich_Syn_NAT/data/wmt14_ende_distill/bin', data_buffer_size=10, dataset_impl=None, ddp_backend='no_c10d', decoder_attention_heads=8, decoder_embed_dim=512, decoder_embed_path=None, decoder_ffn_embed_dim=2048, decoder_input_dim=512, decoder_layerdrop=0, decoder_layers=6, decoder_layers_to_keep=None, decoder_learned_pos=True, decoder_normalize_before=False, decoder_output_dim=512, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method=None, distributed_no_spawn=False, distributed_port=-1, distributed_rank=0, distributed_world_size=8, distributed_wrapper='DDP', dropout=0.1, dropout_anneal=False, dropout_anneal_end_ratio=0, empty_cache_freq=0, encoder_attention_heads=8, encoder_embed_dim=512, encoder_embed_path=None, encoder_ffn_embed_dim=2048, encoder_layerdrop=0, encoder_layers=6, encoder_layers_to_keep=None, encoder_learned_pos=True, encoder_normalize_before=False, eos=2, eval_bleu=True, eval_bleu_args=None, eval_bleu_detok='space', eval_bleu_detok_args=None, eval_bleu_print_samples=True, eval_bleu_remove_bpe='@@ ', eval_tokenized_bleu=True, fast_stat_sync=False, find_unused_parameters=False, finetune_from_model=None, fix_batches_to_gpus=False, fixed_ss_ratio=True, fixed_validation_seed=7, force_detach=False, force_ls=False, fp16=True, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, gen_subset='test', heartbeat_timeout=-1, inference_decoder_layer=-1, keep_best_checkpoints=5, keep_interval_updates=5, keep_last_epochs=5, label_smoothing=0.0, layer_drop_ratio=0.0, left_pad_source='True', left_pad_target='False', length_loss_factor=0.1, load_alignments=False, load_checkpoint_on_all_dp_ranks=False, localsgd_frequency=3, log_format='simple', log_interval=100, lr=[0.0005], lr_scheduler='inverse_sqrt', masked_loss=False, max_epoch=0, max_source_positions=1024, max_target_positions=1024, max_tokens=8192, max_tokens_valid=8192, max_update=300000, maximize_best_checkpoint_metric=True, memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_cross_attention=False, no_empty=False, no_epoch_checkpoints=False, no_last_checkpoints=False, no_progress_bar=False, no_save=False, no_save_optimizer_state=False, no_seed_provided=False, no_token_positional_embeddings=False, noise='full_mask', nprocs_per_node=8, num_batch_buckets=0, num_cross_layer_sample=0, num_shards=1, num_topk=1, num_workers=1, optimizer='adam', optimizer_overrides='{}', pad=1, patience=-1, pipeline_balance=None, pipeline_checkpoint='never', pipeline_chunks=0, pipeline_decoder_balance=None, pipeline_decoder_devices=None, pipeline_devices=None, pipeline_encoder_balance=None, pipeline_encoder_devices=None, pipeline_model_parallel=False, plain_ctc=False, pred_length_offset=False, profile=False, quant_noise_pq=0, quant_noise_pq_block_size=8, quant_noise_scalar=0, quantization_config_path=None, repeat_layer=0, required_batch_size_multiple=8, required_seq_len_multiple=1, reset_dataloader=False, reset_logging=True, reset_lr_scheduler=False, reset_meters=False, reset_optimizer=False, restore_file='checkpoint_last.pt', sample_option='hard', save_dir='checkpoints/NAT_CTC_DSLP_MT', save_interval=1, save_interval_updates=500, scoring='bleu', seed=1, sentence_avg=False, sg_length_pred=False, shard_id=0, share_all_embeddings=True, share_attn=False, share_decoder_input_output_embed=False, share_ffn=False, skip_invalid_size_inputs_valid_test=False, slowmo_algorithm='LocalSGD', slowmo_momentum=None, softcopy=False, softcopy_temp=5, softmax_temp=1, source_lang='en', src_embedding_copy=False, src_upsample_scale=2, ss_ratio=0.3, stop_min_lr=1e-09, stop_time_hours=0, suppress_crashes=False, target_lang='de', task='translation_lev', temp_anneal=False, tensorboard_logdir=None, threshold_loss_scale=None, tokenizer=None, tpu=False, train_subset='train', truncate_source=False, unk=3, update_freq=[1], upsample_primary=1, use_bmuf=False, use_ctc_decoder=True, use_old_adam=False, user_dir=None, valid_subset='valid', validate_after_updates=0, validate_interval=1, validate_interval_updates=500, wandb_project=None, warmup_init_lr=1e-07, warmup_updates=10000, weight_decay=0.01, yhat_posemb=False, zero_sharding='none'), 'optimizer': {'_name': 'adam', 'adam_betas': '(0.9,0.98)', 'adam_eps': 1e-08, 'weight_decay': 0.01, 'use_old_adam': False, 'tpu': False, 'lr': [0.0005]}, 'lr_scheduler': {'_name': 'inverse_sqrt', 'warmup_updates': 10000, 'warmup_init_lr': 1e-07, 'lr': [0.0005]}, 'scoring': {'_name': 'bleu', 'pad': 1, 'eos': 2, 'unk': 3}, 'bpe': None, 'tokenizer': None}
2022-09-07 20:31:12 | INFO | fairseq.tasks.translation | [en] dictionary: 39842 types
2022-09-07 20:31:12 | INFO | fairseq.tasks.translation | [de] dictionary: 39842 types
2022-09-07 20:31:12 | INFO | fairseq.data.data_utils | loaded 3,000 examples from: ../data/wmt14_ende_distill/bin/valid.en-de.en
2022-09-07 20:31:12 | INFO | fairseq.data.data_utils | loaded 3,000 examples from: ../data/wmt14_ende_distill/bin/valid.en-de.de
2022-09-07 20:31:12 | INFO | fairseq.tasks.translation | ../data/wmt14_ende_distill/bin valid en-de 3000 examples
2022-09-07 20:31:14 | INFO | fairseq_cli.train | NATransformerModel(
  (encoder): FairseqNATEncoder(
    (dropout_module): FairseqDropout()
    (embed_tokens): Embedding(39842, 512, padding_idx=1)
    (embed_positions): LearnedPositionalEmbedding(1026, 512, padding_idx=1)
    (layers): ModuleList(
      (0): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (1): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (2): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (3): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (4): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (5): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
    )
  )
  (decoder): NATransformerDecoder(
    (dropout_module): FairseqDropout()
    (embed_tokens): Embedding(39842, 512, padding_idx=1)
    (embed_positions): LearnedPositionalEmbedding(1026, 512, padding_idx=1)
    (layers): ModuleList(
      (0): TransformerSharedDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (1): TransformerSharedDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (2): TransformerSharedDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (3): TransformerSharedDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (4): TransformerSharedDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
      (5): TransformerSharedDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=512, out_features=512, bias=True)
          (v_proj): Linear(in_features=512, out_features=512, bias=True)
          (q_proj): Linear(in_features=512, out_features=512, bias=True)
          (out_proj): Linear(in_features=512, out_features=512, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=512, out_features=2048, bias=True)
        (fc2): Linear(in_features=2048, out_features=512, bias=True)
        (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
      )
    )
    (output_projection): Linear(in_features=512, out_features=39842, bias=False)
    (embed_length): Embedding(256, 512)
    (reduce_concat): ModuleList(
      (0): Linear(in_features=1024, out_features=512, bias=False)
      (1): Linear(in_features=1024, out_features=512, bias=False)
      (2): Linear(in_features=1024, out_features=512, bias=False)
      (3): Linear(in_features=1024, out_features=512, bias=False)
      (4): Linear(in_features=1024, out_features=512, bias=False)
    )
  )
)
2022-09-07 20:31:14 | INFO | fairseq_cli.train | task: TranslationLevenshteinTask
2022-09-07 20:31:14 | INFO | fairseq_cli.train | model: NATransformerModel
2022-09-07 20:31:14 | INFO | fairseq_cli.train | criterion: LabelSmoothedDualImitationCriterion
2022-09-07 20:31:14 | INFO | fairseq_cli.train | num. model params: 68,340,736 (num. trained: 68,340,736)
2022-09-07 20:31:14 | INFO | fairseq.trainer | detected shared parameter: encoder.embed_tokens.weight <- decoder.embed_tokens.weight
2022-09-07 20:31:14 | INFO | fairseq.trainer | detected shared parameter: encoder.embed_tokens.weight <- decoder.output_projection.weight
2022-09-07 20:31:14 | INFO | fairseq.trainer | detected shared parameter: decoder.output_projection.bias <- decoder.reduce_concat.0.bias
2022-09-07 20:31:14 | INFO | fairseq.trainer | detected shared parameter: decoder.output_projection.bias <- decoder.reduce_concat.1.bias
2022-09-07 20:31:14 | INFO | fairseq.trainer | detected shared parameter: decoder.output_projection.bias <- decoder.reduce_concat.2.bias
2022-09-07 20:31:14 | INFO | fairseq.trainer | detected shared parameter: decoder.output_projection.bias <- decoder.reduce_concat.3.bias
2022-09-07 20:31:14 | INFO | fairseq.trainer | detected shared parameter: decoder.output_projection.bias <- decoder.reduce_concat.4.bias
2022-09-07 20:31:14 | INFO | torch.distributed.distributed_c10d | Added key: store_based_barrier_key:2 to store for rank: 0
2022-09-07 20:31:14 | INFO | torch.distributed.distributed_c10d | Rank 0: Completed store-based barrier for key:store_based_barrier_key:2 with 8 nodes.
2022-09-07 20:31:14 | INFO | fairseq.utils | ***********************CUDA enviroments for all 8 workers***********************
2022-09-07 20:31:14 | INFO | fairseq.utils | rank   0: capabilities =  7.0  ; total memory = 31.749 GB ; name = Tesla V100-SXM2-32GB                    
2022-09-07 20:31:14 | INFO | fairseq.utils | rank   1: capabilities =  7.0  ; total memory = 31.749 GB ; name = Tesla V100-SXM2-32GB                    
2022-09-07 20:31:14 | INFO | fairseq.utils | rank   2: capabilities =  7.0  ; total memory = 31.749 GB ; name = Tesla V100-SXM2-32GB                    
2022-09-07 20:31:14 | INFO | fairseq.utils | rank   3: capabilities =  7.0  ; total memory = 31.749 GB ; name = Tesla V100-SXM2-32GB                    
2022-09-07 20:31:14 | INFO | fairseq.utils | rank   4: capabilities =  7.0  ; total memory = 31.749 GB ; name = Tesla V100-SXM2-32GB                    
2022-09-07 20:31:14 | INFO | fairseq.utils | rank   5: capabilities =  7.0  ; total memory = 31.749 GB ; name = Tesla V100-SXM2-32GB                    
2022-09-07 20:31:14 | INFO | fairseq.utils | rank   6: capabilities =  7.0  ; total memory = 31.749 GB ; name = Tesla V100-SXM2-32GB                    
2022-09-07 20:31:14 | INFO | fairseq.utils | rank   7: capabilities =  7.0  ; total memory = 31.749 GB ; name = Tesla V100-SXM2-32GB                    
2022-09-07 20:31:14 | INFO | fairseq.utils | ***********************CUDA enviroments for all 8 workers***********************
2022-09-07 20:31:14 | INFO | fairseq_cli.train | training on 8 devices (GPUs/TPUs)
2022-09-07 20:31:14 | INFO | fairseq_cli.train | max tokens per GPU = 8192 and batch size per GPU = None
2022-09-07 20:31:14 | INFO | fairseq.trainer | Preparing to load checkpoint checkpoints/NAT_CTC_DSLP_MT/checkpoint_last.pt
2022-09-07 20:31:14 | INFO | fairseq.trainer | No existing checkpoint found checkpoints/NAT_CTC_DSLP_MT/checkpoint_last.pt
2022-09-07 20:31:14 | INFO | fairseq.trainer | loading train data for epoch 1
2022-09-07 20:31:15 | INFO | fairseq.data.data_utils | loaded 3,961,179 examples from: ../data/wmt14_ende_distill/bin/train.en-de.en
2022-09-07 20:31:15 | INFO | fairseq.data.data_utils | loaded 3,961,179 examples from: ../data/wmt14_ende_distill/bin/train.en-de.de
2022-09-07 20:31:15 | INFO | fairseq.tasks.translation | ../data/wmt14_ende_distill/bin train en-de 3961179 examples
2022-09-07 20:31:17 | INFO | fairseq.tasks.translation_lev | Dataset original size: 3961179, filtered size: 3961117
2022-09-07 20:31:18 | INFO | fairseq.trainer | begin training epoch 1
2022-09-07 20:31:23 | WARNING | fairseq.trainer | OOM: Ran out of memory with exception: CUDA out of memory. Tried to allocate 1.14 GiB (GPU 7; 31.75 GiB total capacity; 28.04 GiB already allocated; 1.11 GiB free; 28.23 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
2022-09-07 20:31:23 | WARNING | fairseq.trainer | |===========================================================================|
|                  PyTorch CUDA memory summary, device ID 0                 |
|---------------------------------------------------------------------------|
|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |
|===========================================================================|
|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |
|---------------------------------------------------------------------------|
| Allocated memory      |       0 B  |       0 B  |       0 B  |       0 B  |
|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |
|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |
|---------------------------------------------------------------------------|
| Active memory         |       0 B  |       0 B  |       0 B  |       0 B  |
|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |
|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |
|---------------------------------------------------------------------------|
| GPU reserved memory   |       0 B  |       0 B  |       0 B  |       0 B  |
|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |
|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |
|---------------------------------------------------------------------------|
| Non-releasable memory |       0 B  |       0 B  |       0 B  |       0 B  |
|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |
|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |
|---------------------------------------------------------------------------|
| Allocations           |       0    |       0    |       0    |       0    |
|       from large pool |       0    |       0    |       0    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Active allocs         |       0    |       0    |       0    |       0    |
|       from large pool |       0    |       0    |       0    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| GPU reserved segments |       0    |       0    |       0    |       0    |
|       from large pool |       0    |       0    |       0    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Non-releasable allocs |       0    |       0    |       0    |       0    |
|       from large pool |       0    |       0    |       0    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Oversize allocations  |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Oversize GPU segments |       0    |       0    |       0    |       0    |
|===========================================================================|

2022-09-07 20:31:23 | WARNING | fairseq.trainer | |===========================================================================|
|                  PyTorch CUDA memory summary, device ID 1                 |
|---------------------------------------------------------------------------|
|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |
|===========================================================================|
|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |
|---------------------------------------------------------------------------|
| Allocated memory      |       0 B  |       0 B  |       0 B  |       0 B  |
|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |
|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |
|---------------------------------------------------------------------------|
| Active memory         |       0 B  |       0 B  |       0 B  |       0 B  |
|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |
|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |
|---------------------------------------------------------------------------|
| GPU reserved memory   |       0 B  |       0 B  |       0 B  |       0 B  |
|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |
|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |
|---------------------------------------------------------------------------|
| Non-releasable memory |       0 B  |       0 B  |       0 B  |       0 B  |
|       from large pool |       0 B  |       0 B  |       0 B  |       0 B  |
|       from small pool |       0 B  |       0 B  |       0 B  |       0 B  |
|---------------------------------------------------------------------------|
| Allocations           |       0    |       0    |       0    |       0    |
|       from large pool |       0    |       0    |       0    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Active allocs         |       0    |       0    |       0    |       0    |
|       from large pool |       0    |       0    |       0    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| GPU reserved segments |       0    |       0    |       0    |       0    |
|       from large pool |       0    |       0    |       0    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Non-releasable allocs |       0    |       0    |       0    |       0    |
|       from large pool |       0    |       0    |       0    |       0    |
|       from small pool |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Oversize allocations  |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Oversize GPU segments |       0    |       0    |       0    |       0    |
|===========================================================================|
@chenyangh
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Yeah. If you got the OOM error, you can reduce the --max-tokens config and increase the --update-freqconfig. In the end, we want the wpb (word per batch) in the training log is approximately 128K.

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