My program stops at loss.backward() without any prompt in cmd

My aim is to make a five-category text classification

I am running transformers fine tuning bert with cnnbase model but my program stops at loss.backward() without any prompt in cmd.

I debug find that the program stop at the loss.backward line without any error prompt

My program executed successfully in rnn base such as lstm and rcnn.

But when I am running some cnnbase model the strange bug appears.

My cnn model code:

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.modeling_bert import BertPreTrainedModel, BertModel
n_filters = 200
filter_sizes = [2,3,4]
class BertCNN(BertPreTrainedModel):
    def __init__(self, config):
        super(BertPreTrainedModel, self).__init__(config)
        self.num_filters = n_filters
        self.filter_sizes = filter_sizes
        self.bert = BertModel(config)
        for param in self.bert.parameters():
            param.requires_grad = True
        self.convs = nn.ModuleList(
            [nn.Conv2d(1, self.num_filters, (k, config.hidden_size))
                for k in self.filter_sizes])
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.fc_cnn = nn.Linear(self.num_filters *
                                len(self.filter_sizes), config.num_labels)

    def conv_and_pool(self, x, conv):
        x = F.relu(conv(x)).squeeze(3)
        x = F.max_pool1d(x, x.size(2)).squeeze(2)
        return x

    def forward(self, input_ids,
                attention_mask=None, token_type_ids=None, head_mask=None):
        outputs = self.bert(input_ids,
        encoder_out, text_cls = outputs
        out = encoder_out.unsqueeze(1)
        out =[self.conv_and_pool(out, conv)
                         for conv in self.convs], 1)
        out = self.dropout(out)
        out = self.fc_cnn(out)
        return out

My train code:

        for step, batch in enumerate(data):
            batch = tuple( for t in batch)
            input_ids, input_mask, segment_ids, label_ids = batch
            print("input_ids, input_mask, segment_ids, label_ids SIZE: \n")   
            print(input_ids.size(), input_mask.size(),segment_ids.size(), label_ids.size()) 
            # torch.Size([2, 80]) torch.Size([2, 80]) torch.Size([2, 80]) torch.Size([2])
            logits = self.model(input_ids, segment_ids, input_mask)
            print("logits and label ids size: ",logits.size(), label_ids.size())
            # torch.Size([2, 5]) torch.Size([2])
            loss = self.criterion(output=logits, target=label_ids) #loss function:CrossEntropyLoss()
            if len(self.n_gpu) >= 2:
                loss = loss.mean()
            if self.gradient_accumulation_steps > 1:
                loss = loss / self.gradient_accumulation_steps
            if self.fp16:
                with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                clip_grad_norm_(amp.master_params(self.optimizer), self.grad_clip)
                loss.backward() # I debug find that the program stop at this line without any error prompt

HELP~!~ 、
I posted my questions on various community platforms,stackoverflow、other github repositories.
No one replied to me.

I changed the batch size to 1 the bug still occured. sad…

the step1 logits :
logits tensor([[ 0.8831, -0.0368, -0.2206, -2.3484, -1.3595]], device=‘cuda:1’,
the step1 loss:
tensor(1.5489, device=‘cuda:1’, grad_fn=NllLossBackward>)
but why can’t loss.backward()?

Could you try to create a (small) executable code snippet, so that we could try to reproduce this issue?

I I tried to run my program on linux platform, and it ran successfully.

Therefore, it is very likely that it is caused by different os

Previous os:win 10