Error while training the Hybrid Model: Function CatBackward returned an invalid gradient at index 1 - got [85, 1, 512] but expected shape compatible with [57, 1, 512] failed.

[sawan16]

### Run time Log: python a2c-train.py -data dataset/train/processed_all.train.pt -save_dir dataset//result/ -embedding_w2v dataset/train/ -start_reinforce 10 -end_epoch 30 -critic_pretrain_epochs 10 -data_type hybrid -has_attn 1 -gpus 0 Start...

• vocabulary size. source = 50004; target = 31415
• number of XENT training sentences. 54426
• number of PG training sentences. 54426
• maximum batch size. 32 Building model... use_critic: True /usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:50: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.3 and num_layers=1 "num_layers={}".format(dropout, num_layers)) model: Hybrid2SeqModel( (code_encoder): TreeEncoder( (word_lut): Embedding(50004, 512, padding_idx=0) (leaf_module): BinaryTreeLeafModule( (cx): Linear(in_features=512, out_features=512, bias=True) (ox): Linear(in_features=512, out_features=512, bias=True) ) (composer): BinaryTreeComposer( (ilh): Linear(in_features=512, out_features=512, bias=True) (irh): Linear(in_features=512, out_features=512, bias=True) (lflh): Linear(in_features=512, out_features=512, bias=True) (lfrh): Linear(in_features=512, out_features=512, bias=True) (rflh): Linear(in_features=512, out_features=512, bias=True) (rfrh): Linear(in_features=512, out_features=512, bias=True) (ulh): Linear(in_features=512, out_features=512, bias=True) (urh): Linear(in_features=512, out_features=512, bias=True) ) ) (text_encoder): Encoder( (word_lut): Embedding(50004, 512, padding_idx=0) (rnn): LSTM(512, 512, dropout=0.3) ) (decoder): HybridDecoder( (word_lut): Embedding(31415, 512, padding_idx=0) (rnn): StackedLSTM( (dropout): Dropout(p=0.3, inplace=False) (layers): ModuleList( (0): LSTMCell(1024, 512) ) ) (attn): HybridAttention( (linear_in): Linear(in_features=512, out_features=512, bias=False) (sm): Softmax(dim=None) (linear_out): Linear(in_features=2048, out_features=512, bias=False) (tanh): Tanh() ) (dropout): Dropout(p=0.3, inplace=False) ) (generator): BaseGenerator( (generator): Linear(in_features=512, out_features=31415, bias=True) ) ) optim: <lib.train.Optim.Optim object at 0x7f34d70f0c50> opt.start_reinforce: 10
  • number of parameters: 92592823 opt.eval: False opt.eval_sample: False supervised_data.src: 54426 supervised_data.tgt: 54426 supervised_data.trees: 54426 supervised_data.leafs: 54426 supervised training.. start_epoch: 1
  • XENT epoch * Model optim lr: 0.001 <class 'lib.data.Dataset.Dataset'> 54426 /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1351: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead. warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.") /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1340: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead. warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.") /content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/model/HybridAttention.py:34: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. attn_tree = self.sm(attn_tree) /content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/model/HybridAttention.py:36: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. attn_txt = self.sm(attn_txt) outputs: torch.Size([26, 32, 512]) /content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/metric/Loss.py:8: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument. log_dist = F.log_softmax(logits) loss value: 3042.23095703125 ---else--- torch.Size([26, 32, 512]) torch.Size([26, 32, 512]) Traceback (most recent call last): File "a2c-train.py", line 339, in main() File "a2c-train.py", line 321, in main xent_trainer.train(opt.start_epoch, opt.start_reinforce - 1, start_time) File "/content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/train/Trainer.py", line 30, in train train_loss = self.train_epoch(epoch) File "/content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/train/Trainer.py", line 85, in train_epoch loss = self.model.backward(outputs, targets, weights, num_words, self.loss_func) File "/content/drive/My Drive/notebooks/Python_method_name_prediction/code_summarization_public/lib/model/EncoderDecoder.py", line 547, in backward outputs.backward(grad_output) File "/usr/local/lib/python3.6/dist-packages/torch/tensor.py", line 195, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/usr/local/lib/python3.6/dist-packages/torch/autograd/init.py", line 99, in backward allow_unreachable=True) # allow_unreachable flag RuntimeError: Function CatBackward returned an invalid gradient at index 1 - got [85, 1, 512] but expected shape compatible with [57, 1, 512] failed.

[hadhe145]

I have met the same problem, is there any solution for this?