Hi,
I’m working on a text classification problem using Huggingface’s transformers library. Its a author prediction problem and I am using Huggingface’s datasets library to get my data up and ready for processing. After creating my dataloader, I am able to get a single batch out and run through the model to get the loss. However, when I do the same thing with a Lightning module and call trainer.fit, I get an error.
Here is the code snippet to run without Lightning:
args = Namespace(
model_name='bert-base-uncased',
max_seq_len=128,
batch_size=2,
n_labels=10,
learning_rate=1e-5,
)
train_ds,test_ds = dss['train'], dss['test']
train_ds.set_format(type='pt', columns=['input_ids', 'token_type_ids', 'attention_mask', 'labels'])
train_dl = DataLoader(train_ds, batch_size=args.batch_size)
itr = iter(train_dl)
idx = np.random.randint(len(train_ds))
input_ids = train_ds[idx]['input_ids']
labels = train_ds[idx]['labels'].item()
print(decode_ex(tokenizer, label2auth, input_ids, labels))
Decoded Text:
friend. what if the lad needed him? or what if he didn ’ t? making up his mind, zayn was heading to the door. before he could make it, a certain curly - haired lad appeared. “ harry! ” zayn cried out in relief but it was short - lived because he noticed the slight sway in the way that the lanky lad stood. oh, zayn was going to murder that tomlinson kid after he was done with harry. this could be his take on a modern romeo and juliet. drunk harry was a giggly and naive harry. anyone could have taken advantage of him. za
Author: author5
model = AutoModelForSequenceClassification.from_pretrained(args.model_name, num_labels=args.n_labels)
batch = itr.next()
model(**batch)
(tensor(2.2697, grad_fn=<NllLossBackward>), tensor([[-0.2113, 0.1161, -0.4819, -0.2238, 0.1152, 0.1523, -0.1757, 0.7545,
-0.2766, -0.2114],
[-0.1257, -0.0635, -0.5627, -0.1476, 0.0796, 0.2059, 0.0010, 0.8079,
-0.5680, -0.3751]], grad_fn=<AddmmBackward>))
Here is my Lightning module:
class PANAuthorAttrib(pl.LightningModule):
def __init__(self, hparams):
super(PANAuthorAttrib, self).__init__()
self.hparams = hparams
self.model = AutoModelForSequenceClassification.from_pretrained(self.hparams.model_name, num_labels=self.hparams.n_labels)
def forward(self, input_ids, attention_mask, token_type_ids, labels):
return self.model(input_ids, attention_mask, token_type_ids, labels)
def training_step(self, batch, batch_idx):
outputs = self(**batch)
loss = outputs[0]
return loss
def configure_optimizers(self):
return optim.AdamW(params=self.parameters(), lr=self.hparams.learning_rate)
model = PANAuthorAttrib(args)
trainer = pl.Trainer(fast_dev_run=True)
trainer.fit(model, train_dl)
This gives the following error:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-15-e2c2f2d0bf62> in <module>
----> 1 trainer.fit(model, train_dl)
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
438 self.call_hook('on_fit_start')
439
--> 440 results = self.accelerator_backend.train()
441 self.accelerator_backend.teardown()
442
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/pytorch_lightning/accelerators/cpu_accelerator.py in train(self)
46
47 # train or test
---> 48 results = self.train_or_test()
49 return results
50
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in train_or_test(self)
64 results = self.trainer.run_test()
65 else:
---> 66 results = self.trainer.train()
67 return results
68
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in train(self)
481
482 # run train epoch
--> 483 self.train_loop.run_training_epoch()
484
485 if self.max_steps and self.max_steps <= self.global_step:
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_epoch(self)
532 # TRAINING_STEP + TRAINING_STEP_END
533 # ------------------------------------
--> 534 batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
535
536 # when returning -1 from train_step, we end epoch early
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_batch(self, batch, batch_idx, dataloader_idx)
669 opt_idx,
670 optimizer,
--> 671 self.trainer.hiddens
672 )
673
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py in training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
751 """
752 # lightning module hook
--> 753 result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)
754
755 if result is None:
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py in training_step(self, split_batch, batch_idx, opt_idx, hiddens)
302 with self.trainer.profiler.profile('model_forward'):
303 args = self.build_train_args(split_batch, batch_idx, opt_idx, hiddens)
--> 304 training_step_output = self.trainer.accelerator_backend.training_step(args)
305 training_step_output = self.trainer.call_hook('training_step_end', training_step_output)
306
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/pytorch_lightning/accelerators/cpu_accelerator.py in training_step(self, args)
54 output = self.trainer.model.training_step(*args)
55 else:
---> 56 output = self.trainer.model.training_step(*args)
57 return output
58
<ipython-input-13-c94823a8e160> in training_step(self, batch, batch_idx)
9
10 def training_step(self, batch, batch_idx):
---> 11 outputs = self(**batch)
12 loss = outputs[0]
13 return loss
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
<ipython-input-13-c94823a8e160> in forward(self, input_ids, attention_mask, token_type_ids, labels)
6
7 def forward(self, input_ids, attention_mask, token_type_ids, labels):
----> 8 return self.model(input_ids, attention_mask, token_type_ids, labels)
9
10 def training_step(self, batch, batch_idx):
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/transformers/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)
1342 output_attentions=output_attentions,
1343 output_hidden_states=output_hidden_states,
-> 1344 return_dict=return_dict,
1345 )
1346
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/transformers/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, output_attentions, output_hidden_states, return_dict)
829
830 embedding_output = self.embeddings(
--> 831 input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
832 )
833 encoder_outputs = self.encoder(
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/net/vaosl01/opt/NFS/su0/miniconda3/envs/auth/lib/python3.7/site-packages/transformers/modeling_bert.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds)
199 token_type_embeddings = self.token_type_embeddings(token_type_ids)
200
--> 201 embeddings = inputs_embeds + position_embeddings + token_type_embeddings
202 embeddings = self.LayerNorm(embeddings)
203 embeddings = self.dropout(embeddings)
RuntimeError: The size of tensor a (128) must match the size of tensor b (2) at non-singleton dimension 1
I’m not sure why this is happening. I’m running the same batch through both (dataloader is not shuffled). The program crashes when attempting to compute the input embeddings for the words:
ipdb> inputs_embeds.shape
torch.Size([2, 128, 768])
ipdb> position_embeddings.shape
torch.Size([2, 768])
ipdb> token_type_embeddings.shape
torch.Size([2, 128, 768])
position_embeddings shape is torch.Size([2, 768]) and is being added to tensors of size torch.Size([2, 128, 768]) which seems to be a broadcasting error. But I don’t why this is happening in only Lightning and not when I just run through the model outside of Lightning. Could use some help on this.
Thanks.