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facebookresearch / fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
MIT License
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TypeError: forward() missing 1 required positional argument: 'prev_output_tokens' #4080

Open ghost opened 2 years ago

ghost commented 2 years ago

🐛 Bug

To Reproduce

Steps to reproduce the behavior (always include the command you ran):

  1. Run cmd

fairseq-train $DATASET_DIR --arch $BART --restore-file $PRETRAINED_MODEL \ --save-dir $SAVE_MODEL_DIR --tensorboard-logdir $TENSORBOARD_DIR \ --task translation_from_pretrained_bart --source-lang src --target-lang tgt \ --criterion label_smoothed_cross_entropy --label-smoothing 0.2 --dataset-impl raw \ --optimizer adam --adam-eps 1e-06 --adam-betas '{0.9, 0.98}' --lr-scheduler polynomial_decay --lr 3e-05 --min-lr -1 \ --warmup-updates 2500 --total-num-update 40000 --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ --max-tokens 1024 --update-freq 2 --save-interval -1 --no-epoch-checkpoints --seed 222 --log-format simple --log-interval 2 \ --reset-optimizer --reset-meters --reset-dataloader --reset-lr-scheduler --save-interval-updates 5000 \ --ddp-backend no_c10d --max-update 80000 \ --encoder-normalize-before --decoder-normalize-before --prepend-bos \ --fp16

  1. See error
2021-12-17 02:33:45 | INFO | fairseq_cli.train | Namespace(activation_fn='gelu', adam_betas='(0.9, 0.98)', adam_eps=1e-06, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, all_gather_list_size=16384, arch='bart_base', attention_dropout=0.1, batch_size=None, best_checkpoint_metric='loss', bf16=False, bpe=None, broadcast_buffers=False, bucket_cap_mb=25, checkpoint_suffix='', clip_norm=25.0, cpu=False, criterion='label_smoothed_cross_entropy', cross_self_attention=False, curriculum=0, data='/content/fairseq/dataset', data_buffer_size=10, dataset_impl='raw', ddp_backend='no_c10d', decoder_attention_heads=12, decoder_embed_dim=768, decoder_embed_path=None, decoder_ffn_embed_dim=3072, decoder_input_dim=768, decoder_layerdrop=0, decoder_layers=6, decoder_layers_to_keep=None, decoder_learned_pos=True, decoder_normalize_before=True, decoder_output_dim=768, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method=None, distributed_no_spawn=False, distributed_num_procs=1, distributed_port=-1, distributed_rank=0, distributed_world_size=1, distributed_wrapper='DDP', dropout=0.3, empty_cache_freq=0, encoder_attention_heads=12, encoder_embed_dim=768, encoder_embed_path=None, encoder_ffn_embed_dim=3072, encoder_layerdrop=0, encoder_layers=6, encoder_layers_to_keep=None, encoder_learned_pos=True, encoder_normalize_before=True, end_learning_rate=0.0, eval_bleu=False, eval_bleu_args=None, eval_bleu_detok='space', eval_bleu_detok_args=None, eval_bleu_print_samples=False, eval_bleu_remove_bpe=None, eval_tokenized_bleu=False, fast_stat_sync=False, find_unused_parameters=False, finetune_from_model=None, fix_batches_to_gpus=False, fixed_validation_seed=None, force_anneal=None, fp16=True, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, gen_subset='test', keep_best_checkpoints=-1, keep_interval_updates=-1, keep_last_epochs=-1, label_smoothing=0.2, langs=None, layernorm_embedding=True, left_pad_source='True', left_pad_target='False', load_alignments=False, local_rank=0, localsgd_frequency=3, log_format='simple', log_interval=2, lr=[3e-05], lr_scheduler='polynomial_decay', max_epoch=0, max_sentences=None, max_sentences_valid=None, max_source_positions=1024, max_target_positions=1024, max_tokens=1024, max_tokens_valid=1024, max_update=80000, maximize_best_checkpoint_metric=False, memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, min_lr=-1.0, model_parallel_size=1, no_cross_attention=False, no_epoch_checkpoints=True, no_last_checkpoints=False, no_progress_bar=False, no_save=False, no_save_optimizer_state=False, no_scale_embedding=True, no_seed_provided=False, no_token_positional_embeddings=False, nprocs_per_node=1, num_batch_buckets=0, num_shards=1, num_workers=1, optimizer='adam', optimizer_overrides='{}', patience=-1, pipeline_balance=None, pipeline_checkpoint='never', pipeline_chunks=0, pipeline_devices=None, pipeline_model_parallel=False, pooler_activation_fn='tanh', pooler_dropout=0.0, power=1.0, prepend_bos=True, profile=False, quant_noise_pq=0, quant_noise_pq_block_size=8, quant_noise_scalar=0, quantization_config_path=None, relu_dropout=0.0, required_batch_size_multiple=8, required_seq_len_multiple=1, reset_dataloader=True, reset_lr_scheduler=True, reset_meters=True, reset_optimizer=True, restore_file='/content/fairseq/japanese_bart_large_2.0/bart_model.pt', save_dir='/content/fairseq/checkpoints', save_interval=-1, save_interval_updates=5000, scoring='bleu', seed=222, sentence_avg=False, shard_id=0, share_all_embeddings=True, share_decoder_input_output_embed=True, skip_invalid_size_inputs_valid_test=False, slowmo_algorithm='LocalSGD', slowmo_momentum=None, source_lang='src', src_suffix=None, stop_time_hours=0, target_lang='tgt', task='translation_from_pretrained_bart', tensorboard_logdir='/content/fairseq/dataset/tensorboard_log', tgt_suffix=None, threshold_loss_scale=None, tokenizer=None, total_num_update=40000, tpu=False, train_subset='train', truncate_source=False, update_freq=[2], upsample_primary=1, use_bmuf=False, use_old_adam=False, user_dir=None, valid_subset='valid', validate_after_updates=0, validate_interval=1, validate_interval_updates=0, warmup_updates=2500, weight_decay=0.0, zero_sharding='none')
2021-12-17 02:33:45 | INFO | fairseq.tasks.translation | [src] dictionary: 32001 types
2021-12-17 02:33:45 | INFO | fairseq.tasks.translation | [tgt] dictionary: 32001 types
2021-12-17 02:33:45 | INFO | fairseq.data.data_utils | loaded 251 examples from: /content/fairseq/dataset/valid.src-tgt.src
2021-12-17 02:33:45 | INFO | fairseq.tasks.translation_from_pretrained_bart | /content/fairseq/dataset valid src-tgt 251 examples
2021-12-17 02:33:48 | INFO | fairseq_cli.train | BARTModel(
  (encoder): TransformerEncoder(
    (dropout_module): FairseqDropout()
    (embed_tokens): Embedding(32002, 768, padding_idx=1)
    (embed_positions): LearnedPositionalEmbedding(1026, 768, padding_idx=1)
    (layernorm_embedding): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (layers): ModuleList(
      (0): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (1): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (2): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (3): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (4): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (5): TransformerEncoderLayer(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
    )
    (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
  (decoder): TransformerDecoder(
    (dropout_module): FairseqDropout()
    (embed_tokens): Embedding(32002, 768, padding_idx=1)
    (embed_positions): LearnedPositionalEmbedding(1026, 768, padding_idx=1)
    (layernorm_embedding): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (layers): ModuleList(
      (0): TransformerDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (1): TransformerDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (2): TransformerDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (3): TransformerDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (4): TransformerDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
      (5): TransformerDecoderLayer(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=768, out_features=768, bias=True)
          (v_proj): Linear(in_features=768, out_features=768, bias=True)
          (q_proj): Linear(in_features=768, out_features=768, bias=True)
          (out_proj): Linear(in_features=768, out_features=768, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      )
    )
    (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (output_projection): Linear(in_features=768, out_features=32002, bias=False)
  )
  (classification_heads): ModuleDict()
)
2021-12-17 02:33:48 | INFO | fairseq_cli.train | task: translation_from_pretrained_bart (TranslationFromPretrainedBARTTask)
2021-12-17 02:33:48 | INFO | fairseq_cli.train | model: bart_base (BARTModel)
2021-12-17 02:33:48 | INFO | fairseq_cli.train | criterion: label_smoothed_cross_entropy (LabelSmoothedCrossEntropyCriterion)
2021-12-17 02:33:48 | INFO | fairseq_cli.train | num. model params: 125397504 (num. trained: 125397504)
2021-12-17 02:33:59 | INFO | fairseq.trainer | detected shared parameter: encoder.embed_tokens.weight <- decoder.embed_tokens.weight
2021-12-17 02:33:59 | INFO | fairseq.trainer | detected shared parameter: encoder.embed_tokens.weight <- decoder.output_projection.weight
2021-12-17 02:33:59 | INFO | fairseq.utils | ***********************CUDA enviroments for all 1 workers***********************
2021-12-17 02:33:59 | INFO | fairseq.utils | rank   0: capabilities =  7.0  ; total memory = 15.782 GB ; name = Tesla V100-SXM2-16GB                    
2021-12-17 02:33:59 | INFO | fairseq.utils | ***********************CUDA enviroments for all 1 workers***********************
2021-12-17 02:33:59 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs)
2021-12-17 02:33:59 | INFO | fairseq_cli.train | max tokens per GPU = 1024 and max sentences per GPU = None
2021-12-17 02:33:59 | INFO | fairseq.trainer | no existing checkpoint found /content/fairseq/japanese_bart_large_2.0/bart_model.pt
2021-12-17 02:33:59 | INFO | fairseq.trainer | loading train data for epoch 1
2021-12-17 02:33:59 | INFO | fairseq.data.data_utils | loaded 250 examples from: /content/fairseq/dataset/train.src-tgt.src
2021-12-17 02:33:59 | INFO | fairseq.tasks.translation_from_pretrained_bart | /content/fairseq/dataset train src-tgt 250 examples
2021-12-17 02:33:59 | INFO | fairseq.trainer | begin training epoch 1
Traceback (most recent call last):
  File "/usr/local/bin/fairseq-train", line 33, in <module>
    sys.exit(load_entry_point('fairseq', 'console_scripts', 'fairseq-train')())
  File "/content/fairseq/fairseq_cli/train.py", line 352, in cli_main
    distributed_utils.call_main(args, main)
  File "/content/fairseq/fairseq/distributed_utils.py", line 258, in call_main
    main(args, **kwargs)
  File "/content/fairseq/fairseq_cli/train.py", line 125, in main
    valid_losses, should_stop = train(args, trainer, task, epoch_itr)
  File "/usr/lib/python3.7/contextlib.py", line 74, in inner
    return func(*args, **kwds)
  File "/content/fairseq/fairseq_cli/train.py", line 208, in train
    log_output = trainer.train_step(samples)
  File "/usr/lib/python3.7/contextlib.py", line 74, in inner
    return func(*args, **kwds)
  File "/content/fairseq/fairseq/trainer.py", line 478, in train_step
    ignore_grad=is_dummy_batch,
  File "/content/fairseq/fairseq/tasks/fairseq_task.py", line 411, in train_step
    loss, sample_size, logging_output = criterion(model, sample)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py", line 56, in forward
    net_output = model(**sample['net_input'])
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'prev_output_tokens'

Code sample

Expected behavior

Environment

Additional context

ghost commented 2 years ago

same error. please help me.

! git clone https://github.com/pytorch/fairseq
%cd /content/fairseq
! pip install --editable ./
! fairseq-train $DATASET_DIR --arch mbart_large --restore-file /content/fairseq/mbart.cc25.v2/model.pt \
--save-dir $SAVE_MODEL_DIR \
--task translation_from_pretrained_bart --source-lang src --target-lang tgt \
--criterion label_smoothed_cross_entropy --label-smoothing 0.2 --dataset-impl raw \
--optimizer adam --adam-eps 1e-06 --adam-betas '{0.9, 0.98}' --lr-scheduler polynomial_decay --lr 3e-05 \
--warmup-updates 2500 --total-num-update 40000 --dropout 0.3 --attention-dropout 0.1  --weight-decay 0.0 \
--max-tokens 1024 --update-freq 2 --save-interval -1 --no-epoch-checkpoints --seed 222 --log-format simple --log-interval 2 \
--reset-optimizer --reset-meters --reset-dataloader --reset-lr-scheduler  --save-interval-updates 5000 \
--ddp-backend no_c10d --max-update 80000 \
--langs $LANGS \
--encoder-normalize-before --decoder-normalize-before --prepend-bos
2021-12-20 04:00:18 | INFO | fairseq.tasks.text_to_speech | Please install tensorboardX: pip install tensorboardX
2021-12-20 04:00:20 | INFO | fairseq_cli.train | {'_name': None, 'common': {'_name': None, 'no_progress_bar': False, 'log_interval': 2, 'log_format': 'simple', 'log_file': None, 'tensorboard_logdir': None, 'wandb_project': None, 'azureml_logging': False, 'seed': 222, 'cpu': False, 'tpu': False, 'bf16': False, 'memory_efficient_bf16': False, 'fp16': False, 'memory_efficient_fp16': False, 'fp16_no_flatten_grads': False, 'fp16_init_scale': 128, 'fp16_scale_window': None, 'fp16_scale_tolerance': 0.0, 'on_cpu_convert_precision': False, 'min_loss_scale': 0.0001, 'threshold_loss_scale': None, 'amp': False, 'amp_batch_retries': 2, 'amp_init_scale': 128, 'amp_scale_window': 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': False, 'suppress_crashes': False, 'use_plasma_view': False, 'plasma_path': '/tmp/plasma'}, 'common_eval': {'_name': None, 'path': None, 'post_process': None, 'quiet': False, 'model_overrides': '{}', 'results_path': None}, 'distributed_training': {'_name': None, 'distributed_world_size': 1, 'distributed_num_procs': 1, 'distributed_rank': 0, 'distributed_backend': 'nccl', 'distributed_init_method': None, 'distributed_port': -1, 'device_id': 0, 'distributed_no_spawn': False, 'ddp_backend': 'no_c10d', 'ddp_comm_hook': 'none', 'bucket_cap_mb': 25, 'fix_batches_to_gpus': False, 'find_unused_parameters': False, 'gradient_as_bucket_view': False, 'fast_stat_sync': False, 'heartbeat_timeout': -1, 'broadcast_buffers': False, 'slowmo_momentum': None, 'slowmo_base_algorithm': 'localsgd', 'localsgd_frequency': 3, 'nprocs_per_node': 1, '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', 'fp16': False, 'memory_efficient_fp16': False, 'tpu': False, 'no_reshard_after_forward': False, 'fp32_reduce_scatter': False, 'cpu_offload': False, 'use_sharded_state': False, 'not_fsdp_flatten_parameters': False}, 'dataset': {'_name': None, 'num_workers': 1, 'skip_invalid_size_inputs_valid_test': False, 'max_tokens': 1024, 'batch_size': None, 'required_batch_size_multiple': 8, 'required_seq_len_multiple': 1, 'dataset_impl': 'raw', 'data_buffer_size': 10, 'train_subset': 'train', 'valid_subset': 'valid', 'combine_valid_subsets': None, 'ignore_unused_valid_subsets': False, 'validate_interval': 1, 'validate_interval_updates': 0, 'validate_after_updates': 0, 'fixed_validation_seed': None, 'disable_validation': False, 'max_tokens_valid': 1024, 'batch_size_valid': None, 'max_valid_steps': None, 'curriculum': 0, 'gen_subset': 'test', 'num_shards': 1, 'shard_id': 0, 'grouped_shuffling': False, 'update_epoch_batch_itr': False, 'update_ordered_indices_seed': False}, 'optimization': {'_name': None, 'max_epoch': 0, 'max_update': 80000, 'stop_time_hours': 0.0, 'clip_norm': 0.0, 'sentence_avg': False, 'update_freq': [2], 'lr': [3e-05], 'stop_min_lr': -1.0, 'use_bmuf': False, 'skip_remainder_batch': False}, 'checkpoint': {'_name': None, 'save_dir': '/content/fairseq/checkpoints', 'restore_file': '/content/fairseq/mbart.cc25.v2/model.pt', 'finetune_from_model': None, 'reset_dataloader': True, 'reset_lr_scheduler': True, 'reset_meters': True, 'reset_optimizer': True, 'optimizer_overrides': '{}', 'save_interval': -1, 'save_interval_updates': 5000, 'keep_interval_updates': -1, 'keep_interval_updates_pattern': -1, 'keep_last_epochs': -1, 'keep_best_checkpoints': -1, 'no_save': False, 'no_epoch_checkpoints': True, 'no_last_checkpoints': False, 'no_save_optimizer_state': False, 'best_checkpoint_metric': 'loss', 'maximize_best_checkpoint_metric': False, 'patience': -1, 'checkpoint_suffix': '', 'checkpoint_shard_count': 1, 'load_checkpoint_on_all_dp_ranks': False, 'write_checkpoints_asynchronously': False, 'model_parallel_size': 1}, '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': 1}, '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}, '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='mbart_large', activation_fn='gelu', adam_betas='(0.9, 0.98)', adam_eps=1e-06, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, all_gather_list_size=16384, amp=False, amp_batch_retries=2, amp_init_scale=128, amp_scale_window=None, arch='mbart_large', attention_dropout=0.1, azureml_logging=False, batch_size=None, batch_size_valid=None, best_checkpoint_metric='loss', bf16=False, bpe=None, broadcast_buffers=False, bucket_cap_mb=25, checkpoint_shard_count=1, checkpoint_suffix='', clip_norm=0.0, combine_valid_subsets=None, cpu=False, cpu_offload=False, criterion='label_smoothed_cross_entropy', curriculum=0, data='/content/fairseq/dataset', data_buffer_size=10, dataset_impl='raw', ddp_backend='no_c10d', ddp_comm_hook='none', decoder_attention_heads=16, decoder_embed_dim=1024, decoder_embed_path=None, decoder_ffn_embed_dim=4096, decoder_input_dim=1024, decoder_layers=12, decoder_learned_pos=True, decoder_normalize_before=True, decoder_output_dim=1024, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method=None, distributed_no_spawn=False, distributed_num_procs=1, distributed_port=-1, distributed_rank=0, distributed_world_size=1, dropout=0.3, ema_decay=0.9999, ema_fp32=False, ema_seed_model=None, ema_start_update=0, ema_update_freq=1, empty_cache_freq=0, encoder_attention_heads=16, encoder_embed_dim=1024, encoder_embed_path=None, encoder_ffn_embed_dim=4096, encoder_layers=12, encoder_learned_pos=True, encoder_normalize_before=True, end_learning_rate=0.0, eos=2, eval_bleu=False, eval_bleu_args='{}', eval_bleu_detok='space', eval_bleu_detok_args='{}', eval_bleu_print_samples=False, eval_bleu_remove_bpe=None, eval_tokenized_bleu=False, fast_stat_sync=False, find_unused_parameters=False, finetune_from_model=None, fix_batches_to_gpus=False, fixed_validation_seed=None, force_anneal=None, fp16=False, fp16_adam_stats=False, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, fp32_reduce_scatter=False, gen_subset='test', gradient_as_bucket_view=False, grouped_shuffling=False, heartbeat_timeout=-1, ignore_prefix_size=0, ignore_unused_valid_subsets=False, keep_best_checkpoints=-1, keep_interval_updates=-1, keep_interval_updates_pattern=-1, keep_last_epochs=-1, label_smoothing=0.2, langs='ar_AR,cs_CZ,de_DE,en_XX,es_XX,et_EE,fi_FI,fr_XX,gu_IN,hi_IN,it_IT,ja_XX,kk_KZ,ko_KR,lt_LT,lv_LV,my_MM,ne_NP,nl_XX,ro_RO,ru_RU,si_LK,tr_TR,vi_VN,zh_CN', layernorm_embedding=True, left_pad_source=True, left_pad_target=False, load_alignments=False, load_checkpoint_on_all_dp_ranks=False, localsgd_frequency=3, log_file=None, log_format='simple', log_interval=2, lr=[3e-05], lr_scheduler='polynomial_decay', max_epoch=0, max_source_positions=1024, max_target_positions=1024, max_tokens=1024, max_tokens_valid=1024, max_update=80000, max_valid_steps=None, maximize_best_checkpoint_metric=False, memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_epoch_checkpoints=True, no_last_checkpoints=False, no_progress_bar=False, no_reshard_after_forward=False, no_save=False, no_save_optimizer_state=False, no_scale_embedding=False, no_seed_provided=False, not_fsdp_flatten_parameters=False, nprocs_per_node=1, num_batch_buckets=0, num_shards=1, num_workers=1, on_cpu_convert_precision=False, 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, plasma_path='/tmp/plasma', pooler_activation_fn='tanh', pooler_dropout=0.0, power=1.0, prepend_bos=True, profile=False, quantization_config_path=None, relu_dropout=0.0, report_accuracy=False, required_batch_size_multiple=8, required_seq_len_multiple=1, reset_dataloader=True, reset_logging=False, reset_lr_scheduler=True, reset_meters=True, reset_optimizer=True, restore_file='/content/fairseq/mbart.cc25.v2/model.pt', save_dir='/content/fairseq/checkpoints', save_interval=-1, save_interval_updates=5000, scoring='bleu', seed=222, sentence_avg=False, shard_id=0, share_all_embeddings=True, share_decoder_input_output_embed=True, skip_invalid_size_inputs_valid_test=False, skip_remainder_batch=False, slowmo_base_algorithm='localsgd', slowmo_momentum=None, source_lang='src', stop_min_lr=-1.0, stop_time_hours=0, store_ema=False, suppress_crashes=False, target_lang='tgt', task='translation_from_pretrained_bart', tensorboard_logdir=None, threshold_loss_scale=None, tokenizer=None, total_num_update='40000', tpu=False, train_subset='train', truncate_source=False, unk=3, update_epoch_batch_itr=False, update_freq=[2], update_ordered_indices_seed=False, upsample_primary=-1, use_bmuf=False, use_old_adam=False, use_plasma_view=False, use_sharded_state=False, user_dir=None, valid_subset='valid', validate_after_updates=0, validate_interval=1, validate_interval_updates=0, wandb_project=None, warmup_updates=2500, weight_decay=0.0, write_checkpoints_asynchronously=False, zero_sharding='none'), 'task': Namespace(_name='translation_from_pretrained_bart', activation_fn='gelu', adam_betas='(0.9, 0.98)', adam_eps=1e-06, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, all_gather_list_size=16384, amp=False, amp_batch_retries=2, amp_init_scale=128, amp_scale_window=None, arch='mbart_large', attention_dropout=0.1, azureml_logging=False, batch_size=None, batch_size_valid=None, best_checkpoint_metric='loss', bf16=False, bpe=None, broadcast_buffers=False, bucket_cap_mb=25, checkpoint_shard_count=1, checkpoint_suffix='', clip_norm=0.0, combine_valid_subsets=None, cpu=False, cpu_offload=False, criterion='label_smoothed_cross_entropy', curriculum=0, data='/content/fairseq/dataset', data_buffer_size=10, dataset_impl='raw', ddp_backend='no_c10d', ddp_comm_hook='none', decoder_attention_heads=16, decoder_embed_dim=1024, decoder_embed_path=None, decoder_ffn_embed_dim=4096, decoder_input_dim=1024, decoder_layers=12, decoder_learned_pos=True, decoder_normalize_before=True, decoder_output_dim=1024, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method=None, distributed_no_spawn=False, distributed_num_procs=1, distributed_port=-1, distributed_rank=0, distributed_world_size=1, dropout=0.3, ema_decay=0.9999, ema_fp32=False, ema_seed_model=None, ema_start_update=0, ema_update_freq=1, empty_cache_freq=0, encoder_attention_heads=16, encoder_embed_dim=1024, encoder_embed_path=None, encoder_ffn_embed_dim=4096, encoder_layers=12, encoder_learned_pos=True, encoder_normalize_before=True, end_learning_rate=0.0, eos=2, eval_bleu=False, eval_bleu_args='{}', eval_bleu_detok='space', eval_bleu_detok_args='{}', eval_bleu_print_samples=False, eval_bleu_remove_bpe=None, eval_tokenized_bleu=False, fast_stat_sync=False, find_unused_parameters=False, finetune_from_model=None, fix_batches_to_gpus=False, fixed_validation_seed=None, force_anneal=None, fp16=False, fp16_adam_stats=False, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, fp32_reduce_scatter=False, gen_subset='test', gradient_as_bucket_view=False, grouped_shuffling=False, heartbeat_timeout=-1, ignore_prefix_size=0, ignore_unused_valid_subsets=False, keep_best_checkpoints=-1, keep_interval_updates=-1, keep_interval_updates_pattern=-1, keep_last_epochs=-1, label_smoothing=0.2, langs='ar_AR,cs_CZ,de_DE,en_XX,es_XX,et_EE,fi_FI,fr_XX,gu_IN,hi_IN,it_IT,ja_XX,kk_KZ,ko_KR,lt_LT,lv_LV,my_MM,ne_NP,nl_XX,ro_RO,ru_RU,si_LK,tr_TR,vi_VN,zh_CN', layernorm_embedding=True, left_pad_source=True, left_pad_target=False, load_alignments=False, load_checkpoint_on_all_dp_ranks=False, localsgd_frequency=3, log_file=None, log_format='simple', log_interval=2, lr=[3e-05], lr_scheduler='polynomial_decay', max_epoch=0, max_source_positions=1024, max_target_positions=1024, max_tokens=1024, max_tokens_valid=1024, max_update=80000, max_valid_steps=None, maximize_best_checkpoint_metric=False, memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_epoch_checkpoints=True, no_last_checkpoints=False, no_progress_bar=False, no_reshard_after_forward=False, no_save=False, no_save_optimizer_state=False, no_scale_embedding=False, no_seed_provided=False, not_fsdp_flatten_parameters=False, nprocs_per_node=1, num_batch_buckets=0, num_shards=1, num_workers=1, on_cpu_convert_precision=False, 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, plasma_path='/tmp/plasma', pooler_activation_fn='tanh', pooler_dropout=0.0, power=1.0, prepend_bos=True, profile=False, quantization_config_path=None, relu_dropout=0.0, report_accuracy=False, required_batch_size_multiple=8, required_seq_len_multiple=1, reset_dataloader=True, reset_logging=False, reset_lr_scheduler=True, reset_meters=True, reset_optimizer=True, restore_file='/content/fairseq/mbart.cc25.v2/model.pt', save_dir='/content/fairseq/checkpoints', save_interval=-1, save_interval_updates=5000, scoring='bleu', seed=222, sentence_avg=False, shard_id=0, share_all_embeddings=True, share_decoder_input_output_embed=True, skip_invalid_size_inputs_valid_test=False, skip_remainder_batch=False, slowmo_base_algorithm='localsgd', slowmo_momentum=None, source_lang='src', stop_min_lr=-1.0, stop_time_hours=0, store_ema=False, suppress_crashes=False, target_lang='tgt', task='translation_from_pretrained_bart', tensorboard_logdir=None, threshold_loss_scale=None, tokenizer=None, total_num_update='40000', tpu=False, train_subset='train', truncate_source=False, unk=3, update_epoch_batch_itr=False, update_freq=[2], update_ordered_indices_seed=False, upsample_primary=-1, use_bmuf=False, use_old_adam=False, use_plasma_view=False, use_sharded_state=False, user_dir=None, valid_subset='valid', validate_after_updates=0, validate_interval=1, validate_interval_updates=0, wandb_project=None, warmup_updates=2500, weight_decay=0.0, write_checkpoints_asynchronously=False, zero_sharding='none'), 'criterion': {'_name': 'label_smoothed_cross_entropy', 'label_smoothing': 0.2, 'report_accuracy': False, 'ignore_prefix_size': 0, 'sentence_avg': False}, 'optimizer': {'_name': 'adam', 'adam_betas': '(0.9, 0.98)', 'adam_eps': 1e-06, 'weight_decay': 0.0, 'use_old_adam': False, 'fp16_adam_stats': False, 'tpu': False, 'lr': [3e-05]}, 'lr_scheduler': {'_name': 'polynomial_decay', 'warmup_updates': 2500, 'force_anneal': None, 'end_learning_rate': 0.0, 'power': 1.0, 'total_num_update': 40000.0, 'lr': [3e-05]}, 'scoring': {'_name': 'bleu', 'pad': 1, 'eos': 2, 'unk': 3}, 'bpe': None, 'tokenizer': None, 'ema': {'_name': None, 'store_ema': False, 'ema_decay': 0.9999, 'ema_start_update': 0, 'ema_seed_model': None, 'ema_update_freq': 1, 'ema_fp32': False}}
2021-12-20 04:00:20 | INFO | fairseq.tasks.translation | [src] dictionary: 250001 types
2021-12-20 04:00:20 | INFO | fairseq.tasks.translation | [tgt] dictionary: 250001 types
2021-12-20 04:00:38 | INFO | fairseq_cli.train | BARTModel(
  (encoder): TransformerEncoderBase(
    (dropout_module): FairseqDropout()
    (embed_tokens): Embedding(250027, 1024, padding_idx=1)
    (embed_positions): LearnedPositionalEmbedding(1026, 1024, padding_idx=1)
    (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
    (layers): ModuleList(
      (0): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (1): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (2): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (3): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (4): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (5): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (6): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (7): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (8): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (9): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (10): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (11): TransformerEncoderLayerBase(
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (dropout_module): FairseqDropout()
        (activation_dropout_module): FairseqDropout()
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
    )
    (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
  )
  (decoder): TransformerDecoderBase(
    (dropout_module): FairseqDropout()
    (embed_tokens): Embedding(250027, 1024, padding_idx=1)
    (embed_positions): LearnedPositionalEmbedding(1026, 1024, padding_idx=1)
    (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
    (layers): ModuleList(
      (0): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (1): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (2): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (3): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (4): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (5): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (6): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (7): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (8): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (9): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (10): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
      (11): TransformerDecoderLayerBase(
        (dropout_module): FairseqDropout()
        (self_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (activation_dropout_module): FairseqDropout()
        (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): MultiheadAttention(
          (dropout_module): FairseqDropout()
          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
          (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      )
    )
    (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
    (output_projection): Linear(in_features=1024, out_features=250027, bias=False)
  )
  (classification_heads): ModuleDict()
)
2021-12-20 04:00:38 | INFO | fairseq_cli.train | task: TranslationFromPretrainedBARTTask
2021-12-20 04:00:38 | INFO | fairseq_cli.train | model: BARTModel
2021-12-20 04:00:38 | INFO | fairseq_cli.train | criterion: LabelSmoothedCrossEntropyCriterion
2021-12-20 04:00:38 | INFO | fairseq_cli.train | num. shared model params: 610,851,840 (num. trained: 610,851,840)
2021-12-20 04:00:38 | INFO | fairseq_cli.train | num. expert model params: 0 (num. trained: 0)
2021-12-20 04:00:38 | INFO | fairseq.data.data_utils | loaded 251 examples from: /content/fairseq/dataset/valid.src-tgt.src
2021-12-20 04:00:38 | INFO | fairseq.tasks.translation | /content/fairseq/dataset valid src-tgt 251 examples
2021-12-20 04:00:43 | INFO | fairseq.trainer | detected shared parameter: encoder.embed_tokens.weight <- decoder.embed_tokens.weight
2021-12-20 04:00:43 | INFO | fairseq.trainer | detected shared parameter: encoder.embed_tokens.weight <- decoder.output_projection.weight
2021-12-20 04:00:43 | INFO | fairseq.utils | ***********************CUDA enviroments for all 1 workers***********************
2021-12-20 04:00:43 | INFO | fairseq.utils | rank   0: capabilities =  7.0  ; total memory = 15.782 GB ; name = Tesla V100-SXM2-16GB                    
2021-12-20 04:00:43 | INFO | fairseq.utils | ***********************CUDA enviroments for all 1 workers***********************
2021-12-20 04:00:43 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs)
2021-12-20 04:00:43 | INFO | fairseq_cli.train | max tokens per device = 1024 and max sentences per device = None
2021-12-20 04:00:43 | INFO | fairseq.trainer | Preparing to load checkpoint /content/fairseq/mbart.cc25.v2/model.pt
tcmalloc: large alloc 2443411456 bytes == 0x55adf5588000 @  0x7fb5c4fd0b6b 0x7fb5c4ff0379 0x7fb5bdb67cde 0x7fb5bdb69452 0x7fb4d7cc8571 0x7fb57fed20b6 0x7fb57f643aa8 0x55acb2756098 0x55acb27c94d9 0x55acb27c3ced 0x55acb2756bda 0x55acb27c4915 0x55acb27c3ced 0x55acb27567f3 0x55acb27562f9 0x55acb289d35d 0x55acb280ca0b 0x55acb27553a1 0x55acb2846e1d 0x55acb27c8e99 0x55acb27c3ced 0x55acb2695e2b 0x55acb27c5fe4 0x55acb27c39ee 0x55acb2756bda 0x55acb27c5737 0x55acb27c39ee 0x55acb2756bda 0x55acb27c5737 0x55acb27c39ee 0x55acb2756bda
tcmalloc: large alloc 2443411456 bytes == 0x55ae86fc0000 @  0x7fb5c4fd0b6b 0x7fb5c4ff0379 0x7fb5bdb67cde 0x7fb5bdb69452 0x7fb4d7cc8571 0x7fb57fed20b6 0x7fb57f643aa8 0x55acb2756098 0x55acb27c94d9 0x55acb27c3ced 0x55acb2756bda 0x55acb27c4915 0x55acb27c3ced 0x55acb27567f3 0x55acb27562f9 0x55acb289d35d 0x55acb280ca0b 0x55acb27553a1 0x55acb2846e1d 0x55acb27c8e99 0x55acb27c3ced 0x55acb2695e2b 0x55acb27c5fe4 0x55acb27c39ee 0x55acb2756bda 0x55acb27c5737 0x55acb27c39ee 0x55acb2756bda 0x55acb27c5737 0x55acb27c39ee 0x55acb2756bda
2021-12-20 04:00:50 | INFO | fairseq.trainer | NOTE: your device may support faster training with --fp16 or --amp
2021-12-20 04:00:50 | INFO | fairseq.trainer | Loaded checkpoint /content/fairseq/mbart.cc25.v2/model.pt (epoch 142 @ 0 updates)
2021-12-20 04:00:50 | INFO | fairseq.trainer | loading train data for epoch 1
2021-12-20 04:00:50 | INFO | fairseq.data.data_utils | loaded 250 examples from: /content/fairseq/dataset/train.src-tgt.src
2021-12-20 04:00:50 | INFO | fairseq.tasks.translation | /content/fairseq/dataset train src-tgt 250 examples
2021-12-20 04:00:50 | INFO | fairseq.data.iterators | grouped total_num_itrs = 6
2021-12-20 04:00:50 | INFO | fairseq.trainer | begin training epoch 1
2021-12-20 04:00:50 | INFO | fairseq_cli.train | Start iterating over samples
Traceback (most recent call last):
  File "/usr/local/bin/fairseq-train", line 33, in <module>
    sys.exit(load_entry_point('fairseq', 'console_scripts', 'fairseq-train')())
  File "/content/fairseq/fairseq_cli/train.py", line 528, in cli_main
    distributed_utils.call_main(cfg, main)
  File "/content/fairseq/fairseq/distributed/utils.py", line 369, in call_main
    main(cfg, **kwargs)
  File "/content/fairseq/fairseq_cli/train.py", line 188, in main
    valid_losses, should_stop = train(cfg, trainer, task, epoch_itr)
  File "/usr/lib/python3.7/contextlib.py", line 74, in inner
    return func(*args, **kwds)
  File "/content/fairseq/fairseq_cli/train.py", line 303, in train
    log_output = trainer.train_step(samples)
  File "/usr/lib/python3.7/contextlib.py", line 74, in inner
    return func(*args, **kwds)
  File "/content/fairseq/fairseq/trainer.py", line 767, in train_step
    **extra_kwargs,
  File "/content/fairseq/fairseq/tasks/fairseq_task.py", line 512, in train_step
    loss, sample_size, logging_output = criterion(model, sample)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py", line 79, in forward
    net_output = model(**sample["net_input"])
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'prev_output_tokens'
FaizaQamar commented 2 years ago

Hi,

I am facing the same issue. Did you guys find any solution?

WilliamTambellini commented 2 years ago

perhap a bug in fairseq/fairseq/criterions/label_smoothed_cross_entropy.py Some models explicitly require prev_output_tokens:

FairseqDecoder(nn.Module)
  def forward(self, prev_output_tokens, encoder_out=None, **kwargs):
...
class FairseqEncoderDecoderModel(BaseFairseqModel):
  def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):

perhaps print the type of 'model' to understand the expected forward inputs.

Jun-jie-Huang commented 2 years ago

I also face this issue. Did you find any solution?

fenimi commented 2 years ago

Did you get a solution?