RuntimeError: The size of tensor a (128) must match the size of tensor b (32) at non-singleton dimension 1
# 📚 Migration from pytorch-pretrained-bert to transfomers

## Information

<!-- Important information -->

Model I am using (Bert, XLNet ...): bert

Language I am using the model on (English, Chinese ...):english

The problem arises when using:
* [ ] the official example scripts: (give details below)

when I use this model my code work correctly

```py
from pytorch_pretrained_bert.modeling import BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)
```
but when I change to below I get the error, what Is the problem

```py
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained('bert-base-cased', num_labels=num_labels)
```

```
error :
Traceback (most recent call last):
  File "paragraph_selection/train.py", line 293, in <module>
    loss = model(input_ids, segment_ids, input_mask, label_ids)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py", line 1375, in forward
    return_dict=return_dict,
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py", line 862, in forward
    input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py", line 204, in forward
    embeddings += position_embeddings
RuntimeError: The size of tensor a (128) must match the size of tensor b (32) at non-singleton dimension 1
```
  
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)

## Details

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## Environment info
colab
 
- `transformers` version:
- Platform:
- Python version:
- PyTorch version (GPU?):
- Tensorflow version (GPU?):
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

<!-- IMPORTANT: which version of the former library do you use? -->
* `pytorch-transformers` or `pytorch-pretrained-bert` version (or branch):


## Checklist

- [ ] I have read the migration guide in the readme.
 ([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers);
  [pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))
- [ ] I checked if a related official extension example runs on my machine.

Hi, could you please provide either:
- a reproducible code example
- some more information related to your script. The error doesn't happen at the model load, but here: `File "paragraph_selection/train.py", line 293, in <module>`.
- What is your `num_labels`

It's complicated to identify the issue here, but could you try replacing the following line:
```py
loss = model(input_ids, segment_ids, input_mask, label_ids)
```
with:
```py
loss = model(input_ids, attention_mask=input_mask, token_type_ids=segment_ids, labels=label_ids)
```<|||||>yes, your code is correct
thank you<|||||>**RuntimeError: The size of tensor a (128) must match the size of tensor b (32) at non-singleton dimension 3**
Main.py 


import torch.nn as nn
import torch
from torchvision import models
from utils import save_net,load_net

class CSRNet(nn.Module):
    def __init__(self, load_weights=False):
        super(CSRNet, self).__init__()
        self.seen = 0
        self.frontend_feat = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512,512,512,'M']
        self.backend_feat  = [512, 512, 512,256,128,64]
        self.frontend = make_layers(self.frontend_feat)
        self.backend = make_layers(self.backend_feat,in_channels = 512,dilation = True)
        self.output_layer = nn.Conv2d(64, 1, kernel_size=1)
        if not load_weights:
            mod = models.vgg16(pretrained = True)
            self._initialize_weights()
            for i in range(len(self.frontend.state_dict().items())):
                list(self.frontend.state_dict().items())[i][1].data[:] = list(mod.state_dict().items())[i][1].data[:]
    def forward(self,x):
        x = self.frontend(x)
        x = self.backend(x)
        x = self.output_layer(x)
        return x
    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, std=0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            
                
def make_layers(cfg, in_channels = 3,batch_norm=False,dilation = False):
    if dilation:
        d_rate = 2
    else:
        d_rate = 1
    layers = []
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=d_rate,dilation = d_rate)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)  <|||||>Train.py



import sys
import os

import warnings

from model import CSRNet

from utils import save_checkpoint

import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms

import numpy as np
import argparse
import json
import cv2
import dataset
import time

parser = argparse.ArgumentParser(description='PyTorch CSRNet')

parser.add_argument('train_json', metavar='TRAIN',
                    help='path to train json')
parser.add_argument('test_json', metavar='TEST',
                    help='path to test json')

parser.add_argument('--pre', '-p', metavar='PRETRAINED', default=None,type=str,
                    help='path to the pretrained model')

parser.add_argument('gpu',metavar='GPU', type=str,
                    help='GPU id to use.')

parser.add_argument('task',metavar='TASK', type=str,
                    help='task id to use.')

def main():
    
    global args,best_prec1
    
    best_prec1 = 1e6
    
    args = parser.parse_args()
    args.original_lr = 1e-7
    args.lr = 1e-7
    args.batch_size    = 1
    args.momentum      = 0.95
    args.decay         = 5*1e-4
    args.start_epoch   = 0
    args.epochs = 400
    args.steps         = [-1,1,100,150]
    args.scales        = [1,1,1,1]
    args.workers = 4
    args.seed = time.time()
    args.print_freq = 30
    with open(args.train_json, 'r') as outfile:        
        train_list = json.load(outfile)
    with open(args.test_json, 'r') as outfile:       
        val_list = json.load(outfile)
    
    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
    torch.cuda.manual_seed(args.seed)
    
    model = CSRNet()
    
    model = model.cuda()
    
    criterion = nn.MSELoss(size_average=False).cuda()
    
    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.decay)

    if args.pre:
        if os.path.isfile(args.pre):
            print("=> loading checkpoint '{}'".format(args.pre))
            checkpoint = torch.load(args.pre)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.pre, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.pre))
            
    for epoch in range(args.start_epoch, args.epochs):
        
        adjust_learning_rate(optimizer, epoch)
        
        train(train_list, model, criterion, optimizer, epoch)
        prec1 = validate(val_list, model, criterion)
        
        is_best = prec1 < best_prec1
        best_prec1 = min(prec1, best_prec1)
        print(' * best MAE {mae:.3f} '
              .format(mae=best_prec1))
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.pre,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
            'optimizer' : optimizer.state_dict(),
        }, is_best,args.task)

def train(train_list, model, criterion, optimizer, epoch):
    
    losses = AverageMeter()
    batch_time = AverageMeter()
    data_time = AverageMeter()
    
    
    train_loader = torch.utils.data.DataLoader(
        dataset.listDataset(train_list,
                       shuffle=True,
                       transform=transforms.Compose([
                       transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225]),
                   ]), 
                       train=True, 
                       seen=model.seen,
                       batch_size=args.batch_size,
                       num_workers=args.workers),
        batch_size=args.batch_size)
    print('epoch %d, processed %d samples, lr %.10f' % (epoch, epoch * len(train_loader.dataset), args.lr))
    
    model.train()
    end = time.time()
    
    for i,(img, target)in enumerate(train_loader):
        data_time.update(time.time() - end)
        
        img = img.cuda()
        img = Variable(img)
        output = model(img)
        
        
        
        
        target = target.type(torch.FloatTensor).unsqueeze(0).cuda()
        target = Variable(target)
        
        
        loss = criterion(output, target)
        
        losses.update(loss.item(), img.size(0))
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()    
        
        batch_time.update(time.time() - end)
        end = time.time()
        
        if i % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  .format(
                   epoch, i, len(train_loader), batch_time=batch_time,
                   data_time=data_time, loss=losses))
    
def validate(val_list, model, criterion):
    print ('begin test')
    test_loader = torch.utils.data.DataLoader(
    dataset.listDataset(val_list,
                   shuffle=False,
                   transform=transforms.Compose([
                       transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225]),
                   ]),  train=False),
    batch_size=args.batch_size)    
    
    model.eval()
    
    mae = 0
    
    for i,(img, target) in enumerate(test_loader):
        img = img.cuda()
        img = Variable(img)
        output = model(img)
        
        mae += abs(output.data.sum()-target.sum().type(torch.FloatTensor).cuda())
        
    mae = mae/len(test_loader)    
    print(' * MAE {mae:.3f} '
              .format(mae=mae))

    return mae    
        
def adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    
    
    args.lr = args.original_lr
    
    for i in range(len(args.steps)):
        
        scale = args.scales[i] if i < len(args.scales) else 1
        
        
        if epoch >= args.steps[i]:
            args.lr = args.lr * scale
            if epoch == args.steps[i]:
                break
        else:
            break
    for param_group in optimizer.param_groups:
        param_group['lr'] = args.lr
        
class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count    
    
if __name__ == '__main__':
    main()     <|||||>> Hi, could you please provide either:
> 
> * a reproducible code example
> * some more information related to your script. The error doesn't happen at the model load, but here: `File "paragraph_selection/train.py", line 293, in <module>`.
> * What is your `num_labels`
> 
> It's complicated to identify the issue here, but could you try replacing the following line:
> 
> ```python
> loss = model(input_ids, segment_ids, input_mask, label_ids)
> ```
> 
> with:
> 
> ```python
> loss = model(input_ids, attention_mask=input_mask, token_type_ids=segment_ids, labels=label_ids)
> ```

Thank you very much for your help