# Buggy code
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
    self.args = args
    super().__init__(dictionary)
    self.register_buffer("version", torch.Tensor([3]))
    self._future_mask = torch.empty(0)

    self.dropout = args.dropout
    self.decoder_layerdrop = args.decoder_layerdrop
    self.share_input_output_embed = args.share_decoder_input_output_embed

    input_embed_dim = embed_tokens.embedding_dim
    embed_dim = args.decoder_embed_dim
    self.embed_dim = embed_dim
    self.output_embed_dim = args.decoder_output_dim

    self.padding_idx = embed_tokens.padding_idx
    self.max_target_positions = args.max_target_positions

    self.embed_tokens = embed_tokens

    self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)

    if not args.adaptive_input and args.quant_noise_pq > 0:
        self.quant_noise = apply_quant_noise_(
            nn.Linear(embed_dim, embed_dim, bias=False),
            args.quant_noise_pq,
            args.quant_noise_pq_block_size,
        )
    else:
        self.quant_noise = None

    self.project_in_dim = (
        Linear(input_embed_dim, embed_dim, bias=False)
        if embed_dim != input_embed_dim
        else None
    )

    self.embed_positions = (
        PositionalEmbedding(
            args.max_target_positions,
            embed_dim,
            self.padding_idx,
            learned=args.decoder_learned_pos,
        )
        if not args.no_token_positional_embeddings
        else None
    )

    self.cross_self_attention = getattr(args, "cross_self_attention", False)
    self.layer_wise_attention = getattr(args, "layer_wise_attention", False)

    self.layers = nn.ModuleList([])
    self.layers.extend(
        [
            self.build_decoder_layer(args, no_encoder_attn)
            for _ in range(args.decoder_layers)
        ]
    )
    self.num_layers = len(self.layers)

    self.adaptive_softmax = None

    self.project_out_dim = (
        Linear(embed_dim, self.output_embed_dim, bias=False)
        if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights
        else None
    )

    if args.adaptive_softmax_cutoff is not None:
        self.adaptive_softmax = AdaptiveSoftmax(
            len(dictionary),
            self.output_embed_dim,
            options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
            dropout=args.adaptive_softmax_dropout,
            adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
            factor=args.adaptive_softmax_factor,
            tie_proj=args.tie_adaptive_proj,
        )

    if args.decoder_normalize_before and not getattr(
        args, "no_decoder_final_norm", False
    ):
        self.layer_norm = LayerNorm(embed_dim)
    else:
        self.layer_norm = None
    if getattr(args, "layernorm_embedding", False):
        self.layernorm_embedding = LayerNorm(embed_dim)
    else:
        self.layernorm_embedding = None

    if self.share_input_output_embed:
        self.output_projection = nn.Linear(
            self.embed_tokens.weight.shape[1],
            self.embed_tokens.weight.shape[0],
            bias=False,
        )
        self.output_projection.weight = self.embed_tokens.weight
    else:
        self.output_projection = nn.Linear(
            self.output_embed_dim, len(dictionary), bias=False
        )
        nn.init.normal_(
            self.output_projection.weight, mean=0, std=self.output_embed_dim**-0.5
        )


# Traceback (IndexError)
bash train1.sh
2020-04-29 21:50:54 | INFO | fairseq.distributed_utils | distributed init (rank 1): tcp://localhost:14321
2020-04-29 21:50:54 | INFO | fairseq.distributed_utils | distributed init (rank 0): tcp://localhost:14321
2020-04-29 21:50:55 | INFO | fairseq.distributed_utils | initialized host zixi-MS-7B79 as rank 1
2020-04-29 21:50:55 | INFO | fairseq.distributed_utils | initialized host zixi-MS-7B79 as rank 0
2020-04-29 21:50:57 | INFO | fairseq_cli.train | Namespace(activation_dropout=0.1, activation_fn='relu', adam_betas='(0.9, 0.999)', adam_eps=1e-08, adaptive_input=False, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, all_gather_list_size=16384, arch='transformer', attention_dropout=0.1, best_checkpoint_metric='ppl', bpe='subword_nmt', bpe_codes=None, bpe_separator='@@', broadcast_buffers=False, bucket_cap_mb=25, checkpoint_suffix='', clip_norm=25, cpu=False, criterion='cross_entropy', cross_self_attention=False, curriculum=0, data='temp/bpe/bin', data_buffer_size=0, dataset_impl=None, ddp_backend='no_c10d', decoder_attention_heads=4, decoder_embed_dim=256, decoder_embed_path=None, decoder_ffn_embed_dim=256, decoder_input_dim=256, decoder_layerdrop=0.1, decoder_layers=4, decoder_layers_to_keep=None, decoder_learned_pos=False, decoder_normalize_before=True, decoder_output_dim=256, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method='tcp://localhost:14321', distributed_no_spawn=False, distributed_port=-1, distributed_rank=0, distributed_world_size=2, distributed_wrapper='DDP', dropout=0.1, empty_cache_freq=0, encoder_attention_heads=4, encoder_embed_dim=256, encoder_embed_path=None, encoder_ffn_embed_dim=256, encoder_layerdrop=0.1, encoder_layers=4, encoder_layers_to_keep=None, encoder_learned_pos=False, encoder_normalize_before=True, 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, fix_batches_to_gpus=False, fixed_validation_seed=None, force_anneal=None, fp16=False, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, keep_best_checkpoints=10, keep_interval_updates=-1, keep_last_epochs=-1, layer_wise_attention=False, layernorm_embedding=False, left_pad_source='True', left_pad_target='False', load_alignments=False, localsgd_frequency=3, log_format=None, log_interval=100, lr=[0.005], lr_scheduler='fixed', lr_shrink=0.5, max_epoch=0, max_sentences=None, max_sentences_valid=None, max_source_positions=1024, max_target_positions=1024, max_tokens=5000, max_tokens_valid=5000, max_update=0, maximize_best_checkpoint_metric=False, memory_efficient_fp16=False, min_loss_scale=0.0001, min_lr=-1, model_parallel_size=1, no_cross_attention=False, no_epoch_checkpoints=False, no_last_checkpoints=False, no_progress_bar=False, no_save=False, no_save_optimizer_state=True, no_scale_embedding=False, no_token_positional_embeddings=False, nprocs_per_node=2, num_workers=1, optimizer='adam', optimizer_overrides='{}', patience=8, quant_noise_pq=0, quant_noise_pq_block_size=8, quant_noise_scalar=0, quantization_config_path=None, required_batch_size_multiple=8, reset_dataloader=False, reset_lr_scheduler=False, reset_meters=False, reset_optimizer=False, restore_file='checkpoint_last.pt', save_dir='checkpoints/transformer', save_interval=1, save_interval_updates=0, seed=1, sentence_avg=False, share_all_embeddings=False, share_decoder_input_output_embed=False, skip_invalid_size_inputs_valid_test=False, slowmo_algorithm='LocalSGD', slowmo_momentum=None, source_lang=None, target_lang=None, task='translation', tensorboard_logdir='', threshold_loss_scale=None, tokenizer=None, train_subset='train', truncate_source=False, update_freq=[8], upsample_primary=1, use_bmuf=False, use_old_adam=False, user_dir=None, valid_subset='valid', validate_interval=1, warmup_updates=0, weight_decay=0.0)
2020-04-29 21:50:57 | INFO | fairseq.tasks.translation | [src] dictionary: 48947 types
2020-04-29 21:50:57 | INFO | fairseq.tasks.translation | [tgt] dictionary: 48613 types
2020-04-29 21:50:57 | INFO | fairseq.data.data_utils | loaded 8204 examples from: temp/bpe/bin/valid.src-tgt.src
2020-04-29 21:50:57 | INFO | fairseq.data.data_utils | loaded 8204 examples from: temp/bpe/bin/valid.src-tgt.tgt
2020-04-29 21:50:57 | INFO | fairseq.tasks.translation | temp/bpe/bin valid src-tgt 8204 examples
2020-04-29 21:50:58 | INFO | fairseq_cli.train | TransformerModel(
(encoder): TransformerEncoder(
(embed_tokens): Embedding(48947, 256, padding_idx=1)
(embed_positions): SinusoidalPositionalEmbedding()
(layers): ModuleList(
(0): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=256, bias=True)
(final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(1): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=256, bias=True)
(final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(2): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=256, bias=True)
(final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(3): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=256, bias=True)
(final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(decoder): TransformerDecoder(
(embed_tokens): Embedding(48613, 256, padding_idx=1)
(embed_positions): SinusoidalPositionalEmbedding()
(layers): ModuleList(
(0): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=256, bias=True)
(final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(1): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=256, bias=True)
(final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(2): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=256, bias=True)
(final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
(3): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(k_proj): Linear(in_features=256, out_features=256, bias=True)
(v_proj): Linear(in_features=256, out_features=256, bias=True)
(q_proj): Linear(in_features=256, out_features=256, bias=True)
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=256, bias=True)
(final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
)
(layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(output_projection): Linear(in_features=256, out_features=48613, bias=False)
)
)
2020-04-29 21:50:58 | INFO | fairseq_cli.train | model transformer, criterion CrossEntropyCriterion
2020-04-29 21:50:58 | INFO | fairseq_cli.train | num. model params: 41642240 (num. trained: 41642240)
2020-04-29 21:50:58 | INFO | fairseq_cli.train | training on 2 GPUs
2020-04-29 21:50:58 | INFO | fairseq_cli.train | max tokens per GPU = 5000 and max sentences per GPU = None
2020-04-29 21:50:58 | INFO | fairseq.trainer | no existing checkpoint found checkpoints/transformer/checkpoint_last.pt
2020-04-29 21:50:58 | INFO | fairseq.trainer | loading train data for epoch 1
2020-04-29 21:50:58 | INFO | fairseq.data.data_utils | loaded 1130841 examples from: temp/bpe/bin/train.src-tgt.src
2020-04-29 21:50:58 | INFO | fairseq.data.data_utils | loaded 1130841 examples from: temp/bpe/bin/train.src-tgt.tgt
2020-04-29 21:50:58 | INFO | fairseq.tasks.translation | temp/bpe/bin train src-tgt 1130841 examples
2020-04-29 21:51:05 | INFO | fairseq.trainer | NOTE: your device may support faster training with --fp16
epoch 001:   0%|                                                                                                                                                    | 0/210 [00:00<?, ?it/s]/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/python_arg_parser.cpp:756: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha)
/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/python_arg_parser.cpp:756: UserWarning: This overload of add_ is deprecated:
add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other, *, Number alpha)
epoch 001 | valid on 'valid' subset | loss 4.067 | ppl 16.77 | wps 119770 | wpb 6793.9 | bsz 341.8 | num_updates 210
epoch 001 | valid on 'valid' subset | loss 4.067 | ppl 16.77 | wps 119685 | wpb 6793.9 | bsz 341.8 | num_updates 210
epoch 001 | loss 6.743 | ppl 107.11 | wps 59759.2 | ups 0.79 | wpb 76004.5 | bsz 5385 | num_updates 210 | lr 0.005 | gnorm 0.564 | clip 0 | train_wall 236 | wall 276
epoch 002:   0%|                                                                                                                                                    | 0/210 [00:00<?, ?it/s]2020-04-29 21:55:35 | INFO | fairseq.checkpoint_utils | saved checkpoint checkpoints/transformer/checkpoint1.pt (epoch 1 @ 210 updates, score 16.77) (writing took 0.49035206900043704 seconds)
epoch 001 | loss 6.743 | ppl 107.11 | wps 59649.1 | ups 0.78 | wpb 76004.5 | bsz 5385 | num_updates 210 | lr 0.005 | gnorm 0.564 | clip 0 | train_wall 236 | wall 276
epoch 002 | valid on 'valid' subset | loss 1.464 | ppl 2.76 | wps 121245 | wpb 6793.9 | bsz 341.8 | num_updates 420 | best_ppl 2.76
epoch 002 | valid on 'valid' subset | loss 1.464 | ppl 2.76 | wps 118177 | wpb 6793.9 | bsz 341.8 | num_updates 420 | best_ppl 2.76
epoch 002 | loss 1.986 | ppl 3.96 | wps 57927.6 | ups 0.76 | wpb 76004.5 | bsz 5385 | num_updates 420 | lr 0.005 | gnorm 0.262 | clip 0 | train_wall 243 | wall 551
epoch 003:   0%|                                                                                                                                                    | 0/210 [00:00<?, ?it/s]2020-04-29 22:00:13 | INFO | fairseq.checkpoint_utils | saved checkpoint checkpoints/transformer/checkpoint2.pt (epoch 2 @ 420 updates, score 2.76) (writing took 3.3108816809999553 seconds)
epoch 002 | loss 1.986 | ppl 3.96 | wps 57341.3 | ups 0.75 | wpb 76004.5 | bsz 5385 | num_updates 420 | lr 0.005 | gnorm 0.262 | clip 0 | train_wall 243 | wall 555
epoch 003 | valid on 'valid' subset | loss 1.184 | ppl 2.27 | wps 120108 | wpb 6793.9 | bsz 341.8 | num_updates 630 | best_ppl 2.27
epoch 003 | valid on 'valid' subset | loss 1.184 | ppl 2.27 | wps 117797 | wpb 6793.9 | bsz 341.8 | num_updates 630 | best_ppl 2.27
epoch 003 | loss 1.224 | ppl 2.34 | wps 57350.6 | ups 0.75 | wpb 76004.5 | bsz 5385 | num_updates 630 | lr 0.005 | gnorm 0.137 | clip 0 | train_wall 246 | wall 830
epoch 004:   0%|                                                                                                                                                    | 0/210 [00:00<?, ?it/s]2020-04-29 22:04:51 | INFO | fairseq.checkpoint_utils | saved checkpoint checkpoints/transformer/checkpoint3.pt (epoch 3 @ 630 updates, score 2.27) (writing took 3.109906989000592 seconds)
epoch 003 | loss 1.224 | ppl 2.34 | wps 57392.5 | ups 0.76 | wpb 76004.5 | bsz 5385 | num_updates 630 | lr 0.005 | gnorm 0.137 | clip 0 | train_wall 243 | wall 833
Traceback (most recent call last):
File "train.py", line 11, in <module>
cli_main()
File "/home/zixi/EE-599/fairseq/fairseq_cli/train.py", line 355, in cli_main
nprocs=args.distributed_world_size,
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 200, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 158, in start_processes
while not context.join():
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 119, in join
raise Exception(msg)
Exception:

-- Process 1 terminated with the following error:
Traceback (most recent call last):
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
fn(i, *args)
File "/home/zixi/EE-599/fairseq/fairseq_cli/train.py", line 324, in distributed_main
main(args, init_distributed=True)
File "/home/zixi/EE-599/fairseq/fairseq_cli/train.py", line 117, in main
valid_losses = train(args, trainer, task, epoch_itr, max_update)
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/contextlib.py", line 74, in inner
return func(*args, **kwds)
File "/home/zixi/EE-599/fairseq/fairseq_cli/train.py", line 187, in train
log_output = trainer.train_step(samples)
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/contextlib.py", line 74, in inner
return func(*args, **kwds)
File "/home/zixi/EE-599/fairseq/fairseq/trainer.py", line 379, in train_step
ignore_grad=is_dummy_batch,
File "/home/zixi/EE-599/fairseq/fairseq/tasks/fairseq_task.py", line 341, in train_step
loss, sample_size, logging_output = criterion(model, sample)
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/zixi/EE-599/fairseq/fairseq/criterions/cross_entropy.py", line 29, in forward
net_output = model(**sample['net_input'])
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/zixi/EE-599/fairseq/fairseq/legacy_distributed_data_parallel.py", line 86, in forward
return self.module(*inputs, **kwargs)
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/zixi/EE-599/fairseq/fairseq/models/transformer.py", line 272, in forward
return_all_hiddens=return_all_hiddens,
File "/home/zixi/anaconda3/envs/ROC/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/zixi/EE-599/fairseq/fairseq/models/transformer.py", line 498, in forward
encoder_states[-1] = x
IndexError: list assignment index out of range

# Fixed code
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
    self.args = args
    super().__init__(dictionary)
    self.register_buffer("version", torch.Tensor([3]))
    self._future_mask = torch.empty(0)

    self.dropout = args.dropout
    self.decoder_layerdrop = args.decoder_layerdrop
    self.share_input_output_embed = args.share_decoder_input_output_embed

    input_embed_dim = embed_tokens.embedding_dim
    embed_dim = args.decoder_embed_dim
    self.embed_dim = embed_dim
    self.output_embed_dim = args.decoder_output_dim

    self.padding_idx = embed_tokens.padding_idx
    self.max_target_positions = args.max_target_positions

    self.embed_tokens = embed_tokens

    self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)

    if not args.adaptive_input and args.quant_noise_pq > 0:
        self.quant_noise = apply_quant_noise_(
            nn.Linear(embed_dim, embed_dim, bias=False),
            args.quant_noise_pq,
            args.quant_noise_pq_block_size,
        )
    else:
        self.quant_noise = None

    self.project_in_dim = (
        Linear(input_embed_dim, embed_dim, bias=False)
        if embed_dim != input_embed_dim
        else None
    )

    self.embed_positions = (
        PositionalEmbedding(
            args.max_target_positions,
            embed_dim,
            self.padding_idx,
            learned=args.decoder_learned_pos,
        )
        if not args.no_token_positional_embeddings
        else None
    )

    self.cross_self_attention = getattr(args, "cross_self_attention", False)

    if self.decoder_layerdrop > 0.0:
        self.layers = LayerDropModuleList(p=self.decoder_layerdrop)
    else:
        self.layers = nn.ModuleList([])
    self.layers.extend(
        [
            self.build_decoder_layer(args, no_encoder_attn)
            for _ in range(args.decoder_layers)
        ]
    )
    self.num_layers = len(self.layers)

    self.adaptive_softmax = None

    self.project_out_dim = (
        Linear(embed_dim, self.output_embed_dim, bias=False)
        if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights
        else None
    )

    if args.adaptive_softmax_cutoff is not None:
        self.adaptive_softmax = AdaptiveSoftmax(
            len(dictionary),
            self.output_embed_dim,
            options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
            dropout=args.adaptive_softmax_dropout,
            adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
            factor=args.adaptive_softmax_factor,
            tie_proj=args.tie_adaptive_proj,
        )

    if args.decoder_normalize_before and not getattr(
        args, "no_decoder_final_norm", False
    ):
        self.layer_norm = LayerNorm(embed_dim)
    else:
        self.layer_norm = None
    if getattr(args, "layernorm_embedding", False):
        self.layernorm_embedding = LayerNorm(embed_dim)
    else:
        self.layernorm_embedding = None

    if self.share_input_output_embed:
        self.output_projection = nn.Linear(
            self.embed_tokens.weight.shape[1],
            self.embed_tokens.weight.shape[0],
            bias=False,
        )
        self.output_projection.weight = self.embed_tokens.weight
    else:
        self.output_projection = nn.Linear(
            self.output_embed_dim, len(dictionary), bias=False
        )
        nn.init.normal_(
            self.output_projection.weight, mean=0, std=self.output_embed_dim**-0.5
        )