PyTorch-NLP

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Expected Behavior

I want to convert torch.nn.Linear modules to weight drop linear modules in my model (possibly big), and I want to train my model with multi-GPUs. However, I have RuntimeError in my sample code. First, I have _weight_drop() which drops some part of weights in torch.nn.Linear (see the code below).

Actual Behavior

RuntimeError: arguments are located on different GPUs at /opt/conda/conda-bld/pytorch_1556653114079/work/aten/src/THC/generic/THCTensorMathBlas.cu:255

Steps to Reproduce the Problem

1. Run this code in python 3.7 and pytorch 1.1.0 with 2-GPUs

    import torch
    from torchnlp.nn import _weight_drop
    from torch.utils.data import Dataset, DataLoader
   
    input_size = 5
    hidden_size = 5
    output_size =2
    batch_size = 30
    data_size = 100
   
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
   
    class RandomDataset(Dataset):
   
        def __init__(self, size, length):
            self.len = length
            self.data = torch.randn(length, size)
   
        def __getitem__(self, index):
            return self.data[index]
   
        def __len__(self):
            return self.len
   
    rand_loader = DataLoader(dataset=RandomDataset(input_size, data_size),
                             batch_size=batch_size, shuffle=True)
   
    class Model(torch.nn.Module):
        # Our model
        def __init__(self, D_in, H, D_out):
            super(Model, self).__init__()
            self.linear1 = torch.nn.Linear(D_in, H)
            self.linear2 = torch.nn.Linear(H, D_out)
   
        def forward(self, input):
            h_relu = self.linear1(input).clamp(min=0)
            output = self.linear2(h_relu)
            print("\tIn Model: input size", input.size(),
                  "output size", output.size(), torch.cuda.current_device())
            return output
   
    model = Model(input_size, hidden_size, output_size)
    linear_module_list = [v for v in model.named_modules() if isinstance(v[1], torch.nn.Linear)]
    for name, module in linear_module_list:
        _weight_drop(module, ['weight'], dropout=0.5)
    model.to(device)
    model = torch.nn.DataParallel(model)
   
    data = list(rand_loader)[0]
    input = data.to(device)
    output = model(input)
   

2. However, “output=model(input)” is not computed in this code with this error message.

    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2]) 0
    ---------------------------------------------------------------------------
    RuntimeError                              Traceback (most recent call last)
    <ipython-input-3-4a83ee11bad2> in <module>
          2 while(True):
          3     input = data.to(device)
    ----> 4     output = model(input)
          5     print("Outside: input size", input.size(),
          6           "output_size", output.size())
   
    ~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
        491             result = self._slow_forward(*input, **kwargs)
        492         else:
    --> 493             result = self.forward(*input, **kwargs)
        494         for hook in self._forward_hooks.values():
        495             hook_result = hook(self, input, result)
   
    ~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py in forward(self, *inputs, **kwargs)
        150             return self.module(*inputs[0], **kwargs[0])
        151         replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
    --> 152         outputs = self.parallel_apply(replicas, inputs, kwargs)
        153         return self.gather(outputs, self.output_device)
        154 
   
    ~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py in parallel_apply(self, replicas, inputs, kwargs)
        160 
        161     def parallel_apply(self, replicas, inputs, kwargs):
    --> 162         return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
        163 
        164     def gather(self, outputs, output_device):
   
    ~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/parallel/parallel_apply.py in parallel_apply(modules, inputs, kwargs_tup, devices)
         81         output = results[i]
         82         if isinstance(output, Exception):
    ---> 83             raise output
         84         outputs.append(output)
         85     return outputs
   
    ~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/parallel/parallel_apply.py in _worker(i, module, input, kwargs, device)
         57                 if not isinstance(input, (list, tuple)):
         58                     input = (input,)
    ---> 59                 output = module(*input, **kwargs)
         60             with lock:
         61                 results[i] = output
   
    ~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
        491             result = self._slow_forward(*input, **kwargs)
        492         else:
    --> 493             result = self.forward(*input, **kwargs)
        494         for hook in self._forward_hooks.values():
        495             hook_result = hook(self, input, result)
   
    <ipython-input-2-ac863085502c> in forward(self, input)
         33 
         34     def forward(self, input):
    ---> 35         h_relu = self.linear1(input).clamp(min=0)
         36         output = self.linear2(h_relu)
         37         print("\tIn Model: input size", input.size(),
   
    ~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
        491             result = self._slow_forward(*input, **kwargs)
        492         else:
    --> 493             result = self.forward(*input, **kwargs)
        494         for hook in self._forward_hooks.values():
        495             hook_result = hook(self, input, result)
   
    <ipython-input-1-66fbe470597c> in forward(*args, **kwargs)
         18             #w.to(device)
         19             setattr(module, name_w, w)
    ---> 20         return original_module_forward(*args)
         21 
         22     def extra_repr(*args):
   
    ~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input)
         90     @weak_script_method
         91     def forward(self, input):
    ---> 92         return F.linear(input, self.weight, self.bias)
         93 
         94     def extra_repr(self):
   
    ~/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/functional.py in linear(input, weight, bias)
       1404     if input.dim() == 2 and bias is not None:
       1405         # fused op is marginally faster
    -> 1406         ret = torch.addmm(bias, input, weight.t())
       1407     else:
       1408         output = input.matmul(weight.t())
   
    RuntimeError: arguments are located on different GPUs at /opt/conda/conda-bld/pytorch_1556653114079/work/aten/src/THC/generic/THCTensorMathBlas.cu:255
   

The main reason for this error is that I try to compute linear multiplication between two tensors belonging to different GPUs. I try to modify my _weight_drop() function to manually assign the current device in the DataParallel process, but it does not work. Is there any idea to figure out this problem? This code works fine in single GPU or CPU mode

First of all, there is no guaranteed support for the usage of private methods _weight_drop.

That said, were you able to figure out a solution to this issue? Any changes that should be contributed to torchnlp?

Same problem when I use WeightDropLinear. It shows "arguments are located on different GPUs ".

Similar problem here. When I try to use WeightDropGRU and move it to GPU, I get an error: AttributeError: 'WeightDropGRU' object has no attribute '_flat_weights'

Any idea how to solve this?