We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
DataParallel tests are currently broken 
https://github.com/pytorch/pytorch/pull/2121/commits/d69669efcfe4333c223f53249185c2e22f76ed73 has broken DataParallel tests. Now that device_ids are explicitly sent to parallel_apply, this assert https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/parallel_apply.py#L30 gets triggered if inputs are not big enough to be on all devices (e.g. batch size of 20 on 8 GPUs gets chunked into 6*3+2, so that 8-th GPU is idle, and assert gets triggered). 


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `torch/nn/parallel/data_parallel.py`
Content:
```
1 import torch
2 from ..modules import Module
3 from .scatter_gather import scatter_kwargs, gather
4 from .replicate import replicate
5 from .parallel_apply import parallel_apply
6 
7 
8 class DataParallel(Module):
9     """Implements data parallelism at the module level.
10 
11     This container parallelizes the application of the given module by
12     splitting the input across the specified devices by chunking in the batch
13     dimension. In the forward pass, the module is replicated on each device,
14     and each replica handles a portion of the input. During the backwards
15     pass, gradients from each replica are summed into the original module.
16 
17     The batch size should be larger than the number of GPUs used. It should
18     also be an integer multiple of the number of GPUs so that each chunk is the
19     same size (so that each GPU processes the same number of samples).
20 
21     See also: :ref:`cuda-nn-dataparallel-instead`
22 
23     Arbitrary positional and keyword inputs are allowed to be passed into
24     DataParallel EXCEPT Tensors. All variables will be scattered on dim
25     specified (default 0). Primitive types will be broadcasted, but all
26     other types will be a shallow copy and can be corrupted if written to in
27     the model's forward pass.
28 
29     Args:
30         module: module to be parallelized
31         device_ids: CUDA devices (default: all devices)
32         output_device: device location of output (default: device_ids[0])
33 
34     Example::
35 
36         >>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2])
37         >>> output = net(input_var)
38     """
39 
40     # TODO: update notes/cuda.rst when this class handles 8+ GPUs well
41 
42     def __init__(self, module, device_ids=None, output_device=None, dim=0):
43         super(DataParallel, self).__init__()
44         if device_ids is None:
45             device_ids = list(range(torch.cuda.device_count()))
46         if output_device is None:
47             output_device = device_ids[0]
48         self.dim = dim
49         self.module = module
50         self.device_ids = device_ids
51         self.output_device = output_device
52         if len(self.device_ids) == 1:
53             self.module.cuda(device_ids[0])
54 
55     def forward(self, *inputs, **kwargs):
56         inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
57         if len(self.device_ids) == 1:
58             return self.module(*inputs[0], **kwargs[0])
59         replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
60         outputs = self.parallel_apply(replicas, inputs, kwargs)
61         return self.gather(outputs, self.output_device)
62 
63     def replicate(self, module, device_ids):
64         return replicate(module, device_ids)
65 
66     def scatter(self, inputs, kwargs, device_ids):
67         return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)
68 
69     def parallel_apply(self, replicas, inputs, kwargs):
70         return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
71 
72     def gather(self, outputs, output_device):
73         return gather(outputs, output_device, dim=self.dim)
74 
75 
76 def data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None):
77     """Evaluates module(input) in parallel across the GPUs given in device_ids.
78 
79     This is the functional version of the DataParallel module.
80 
81     Args:
82         module: the module to evaluate in parallel
83         inputs: inputs to the module
84         device_ids: GPU ids on which to replicate module
85         output_device: GPU location of the output  Use -1 to indicate the CPU.
86             (default: device_ids[0])
87     Returns:
88         a Variable containing the result of module(input) located on
89         output_device
90     """
91     if not isinstance(inputs, tuple):
92         inputs = (inputs,)
93 
94     if device_ids is None:
95         device_ids = list(range(torch.cuda.device_count()))
96 
97     if output_device is None:
98         output_device = device_ids[0]
99 
100     inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim)
101     if len(device_ids) == 1:
102         return module(*inputs[0], **module_kwargs[0])
103     replicas = replicate(module, device_ids[:len(inputs)])
104     outputs = parallel_apply(replicas, inputs, module_kwargs, device_ids)
105     return gather(outputs, output_device, dim)
106
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/torch/nn/parallel/data_parallel.py b/torch/nn/parallel/data_parallel.py
--- a/torch/nn/parallel/data_parallel.py
+++ b/torch/nn/parallel/data_parallel.py
@@ -100,6 +100,7 @@
     inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim)
     if len(device_ids) == 1:
         return module(*inputs[0], **module_kwargs[0])
-    replicas = replicate(module, device_ids[:len(inputs)])
-    outputs = parallel_apply(replicas, inputs, module_kwargs, device_ids)
+    used_device_ids = device_ids[:len(inputs)]
+    replicas = replicate(module, used_device_ids)
+    outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids)
     return gather(outputs, output_device, dim)

{"golden_diff": "diff --git a/torch/nn/parallel/data_parallel.py b/torch/nn/parallel/data_parallel.py\n--- a/torch/nn/parallel/data_parallel.py\n+++ b/torch/nn/parallel/data_parallel.py\n@@ -100,6 +100,7 @@\n     inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim)\n     if len(device_ids) == 1:\n         return module(*inputs[0], **module_kwargs[0])\n-    replicas = replicate(module, device_ids[:len(inputs)])\n-    outputs = parallel_apply(replicas, inputs, module_kwargs, device_ids)\n+    used_device_ids = device_ids[:len(inputs)]\n+    replicas = replicate(module, used_device_ids)\n+    outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids)\n     return gather(outputs, output_device, dim)\n", "issue": "DataParallel tests are currently broken \nhttps://github.com/pytorch/pytorch/pull/2121/commits/d69669efcfe4333c223f53249185c2e22f76ed73 has broken DataParallel tests. Now that device_ids are explicitly sent to parallel_apply, this assert https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/parallel_apply.py#L30 gets triggered if inputs are not big enough to be on all devices (e.g. batch size of 20 on 8 GPUs gets chunked into 6*3+2, so that 8-th GPU is idle, and assert gets triggered). \r\n\n", "before_files": [{"content": "import torch\nfrom ..modules import Module\nfrom .scatter_gather import scatter_kwargs, gather\nfrom .replicate import replicate\nfrom .parallel_apply import parallel_apply\n\n\nclass DataParallel(Module):\n    \"\"\"Implements data parallelism at the module level.\n\n    This container parallelizes the application of the given module by\n    splitting the input across the specified devices by chunking in the batch\n    dimension. In the forward pass, the module is replicated on each device,\n    and each replica handles a portion of the input. During the backwards\n    pass, gradients from each replica are summed into the original module.\n\n    The batch size should be larger than the number of GPUs used. It should\n    also be an integer multiple of the number of GPUs so that each chunk is the\n    same size (so that each GPU processes the same number of samples).\n\n    See also: :ref:`cuda-nn-dataparallel-instead`\n\n    Arbitrary positional and keyword inputs are allowed to be passed into\n    DataParallel EXCEPT Tensors. All variables will be scattered on dim\n    specified (default 0). Primitive types will be broadcasted, but all\n    other types will be a shallow copy and can be corrupted if written to in\n    the model's forward pass.\n\n    Args:\n        module: module to be parallelized\n        device_ids: CUDA devices (default: all devices)\n        output_device: device location of output (default: device_ids[0])\n\n    Example::\n\n        >>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2])\n        >>> output = net(input_var)\n    \"\"\"\n\n    # TODO: update notes/cuda.rst when this class handles 8+ GPUs well\n\n    def __init__(self, module, device_ids=None, output_device=None, dim=0):\n        super(DataParallel, self).__init__()\n        if device_ids is None:\n            device_ids = list(range(torch.cuda.device_count()))\n        if output_device is None:\n            output_device = device_ids[0]\n        self.dim = dim\n        self.module = module\n        self.device_ids = device_ids\n        self.output_device = output_device\n        if len(self.device_ids) == 1:\n            self.module.cuda(device_ids[0])\n\n    def forward(self, *inputs, **kwargs):\n        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)\n        if len(self.device_ids) == 1:\n            return self.module(*inputs[0], **kwargs[0])\n        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])\n        outputs = self.parallel_apply(replicas, inputs, kwargs)\n        return self.gather(outputs, self.output_device)\n\n    def replicate(self, module, device_ids):\n        return replicate(module, device_ids)\n\n    def scatter(self, inputs, kwargs, device_ids):\n        return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)\n\n    def parallel_apply(self, replicas, inputs, kwargs):\n        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])\n\n    def gather(self, outputs, output_device):\n        return gather(outputs, output_device, dim=self.dim)\n\n\ndef data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None):\n    \"\"\"Evaluates module(input) in parallel across the GPUs given in device_ids.\n\n    This is the functional version of the DataParallel module.\n\n    Args:\n        module: the module to evaluate in parallel\n        inputs: inputs to the module\n        device_ids: GPU ids on which to replicate module\n        output_device: GPU location of the output  Use -1 to indicate the CPU.\n            (default: device_ids[0])\n    Returns:\n        a Variable containing the result of module(input) located on\n        output_device\n    \"\"\"\n    if not isinstance(inputs, tuple):\n        inputs = (inputs,)\n\n    if device_ids is None:\n        device_ids = list(range(torch.cuda.device_count()))\n\n    if output_device is None:\n        output_device = device_ids[0]\n\n    inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim)\n    if len(device_ids) == 1:\n        return module(*inputs[0], **module_kwargs[0])\n    replicas = replicate(module, device_ids[:len(inputs)])\n    outputs = parallel_apply(replicas, inputs, module_kwargs, device_ids)\n    return gather(outputs, output_device, dim)\n", "path": "torch/nn/parallel/data_parallel.py"}], "after_files": [{"content": "import torch\nfrom ..modules import Module\nfrom .scatter_gather import scatter_kwargs, gather\nfrom .replicate import replicate\nfrom .parallel_apply import parallel_apply\n\n\nclass DataParallel(Module):\n    \"\"\"Implements data parallelism at the module level.\n\n    This container parallelizes the application of the given module by\n    splitting the input across the specified devices by chunking in the batch\n    dimension. In the forward pass, the module is replicated on each device,\n    and each replica handles a portion of the input. During the backwards\n    pass, gradients from each replica are summed into the original module.\n\n    The batch size should be larger than the number of GPUs used. It should\n    also be an integer multiple of the number of GPUs so that each chunk is the\n    same size (so that each GPU processes the same number of samples).\n\n    See also: :ref:`cuda-nn-dataparallel-instead`\n\n    Arbitrary positional and keyword inputs are allowed to be passed into\n    DataParallel EXCEPT Tensors. All variables will be scattered on dim\n    specified (default 0). Primitive types will be broadcasted, but all\n    other types will be a shallow copy and can be corrupted if written to in\n    the model's forward pass.\n\n    Args:\n        module: module to be parallelized\n        device_ids: CUDA devices (default: all devices)\n        output_device: device location of output (default: device_ids[0])\n\n    Example::\n\n        >>> net = torch.nn.DataParallel(model, device_ids=[0, 1, 2])\n        >>> output = net(input_var)\n    \"\"\"\n\n    # TODO: update notes/cuda.rst when this class handles 8+ GPUs well\n\n    def __init__(self, module, device_ids=None, output_device=None, dim=0):\n        super(DataParallel, self).__init__()\n        if device_ids is None:\n            device_ids = list(range(torch.cuda.device_count()))\n        if output_device is None:\n            output_device = device_ids[0]\n        self.dim = dim\n        self.module = module\n        self.device_ids = device_ids\n        self.output_device = output_device\n        if len(self.device_ids) == 1:\n            self.module.cuda(device_ids[0])\n\n    def forward(self, *inputs, **kwargs):\n        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)\n        if len(self.device_ids) == 1:\n            return self.module(*inputs[0], **kwargs[0])\n        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])\n        outputs = self.parallel_apply(replicas, inputs, kwargs)\n        return self.gather(outputs, self.output_device)\n\n    def replicate(self, module, device_ids):\n        return replicate(module, device_ids)\n\n    def scatter(self, inputs, kwargs, device_ids):\n        return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)\n\n    def parallel_apply(self, replicas, inputs, kwargs):\n        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])\n\n    def gather(self, outputs, output_device):\n        return gather(outputs, output_device, dim=self.dim)\n\n\ndef data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None):\n    \"\"\"Evaluates module(input) in parallel across the GPUs given in device_ids.\n\n    This is the functional version of the DataParallel module.\n\n    Args:\n        module: the module to evaluate in parallel\n        inputs: inputs to the module\n        device_ids: GPU ids on which to replicate module\n        output_device: GPU location of the output  Use -1 to indicate the CPU.\n            (default: device_ids[0])\n    Returns:\n        a Variable containing the result of module(input) located on\n        output_device\n    \"\"\"\n    if not isinstance(inputs, tuple):\n        inputs = (inputs,)\n\n    if device_ids is None:\n        device_ids = list(range(torch.cuda.device_count()))\n\n    if output_device is None:\n        output_device = device_ids[0]\n\n    inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim)\n    if len(device_ids) == 1:\n        return module(*inputs[0], **module_kwargs[0])\n    used_device_ids = device_ids[:len(inputs)]\n    replicas = replicate(module, used_device_ids)\n    outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids)\n    return gather(outputs, output_device, dim)\n", "path": "torch/nn/parallel/data_parallel.py"}]}