prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Bug in `GatherLayer.backward`
  Hi,
  
  We've been implementing a model at [cellarium-ml](https://github.com/cellarium-ai/cellarium-ml) using your `NTXentLoss`. Comparing the model training with a single GPU and two GPUs we noticed that they do not match. By investigating it we found an apparent bug in the `GatherLayer.backward` where gradients are not sum-reduced over GPUs. Here is our fixed version (https://github.com/cellarium-ai/cellarium-ml/blob/main/cellarium/ml/distributed/gather.py#L17-L21):
  
  ```py
      @staticmethod
      def backward(ctx, *grads) -> torch.Tensor:
          grad_out = grads[dist.get_rank()].contiguous()
          dist.all_reduce(grad_out, op=dist.ReduceOp.SUM)
          return grad_out
  ```
  
  and the [test](https://github.com/cellarium-ai/cellarium-ml/blob/main/tests/distributed/test_gather.py) we wrote. Would you agree that this is indeed a bug? I would be happy to contribute a PR with the fix.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `lightly/utils/dist.py`
  Content:
  ```
  1 from typing import Optional, Tuple
  2 
  3 import torch
  4 import torch.distributed as dist
  5 
  6 
  7 class GatherLayer(torch.autograd.Function):
  8     """Gather tensors from all processes, supporting backward propagation.
  9 
  10     This code was taken and adapted from here:
  11     https://github.com/Spijkervet/SimCLR
  12 
  13     """
  14 
  15     @staticmethod
  16     def forward(ctx, input: torch.Tensor) -> Tuple[torch.Tensor, ...]:
  17         ctx.save_for_backward(input)
  18         output = [torch.empty_like(input) for _ in range(dist.get_world_size())]
  19         dist.all_gather(output, input)
  20         return tuple(output)
  21 
  22     @staticmethod
  23     def backward(ctx, *grads: torch.Tensor) -> torch.Tensor:
  24         (input,) = ctx.saved_tensors
  25         grad_out = torch.empty_like(input)
  26         grad_out[:] = grads[dist.get_rank()]
  27         return grad_out
  28 
  29 
  30 def rank() -> int:
  31     """Returns the rank of the current process."""
  32     return dist.get_rank() if dist.is_initialized() else 0
  33 
  34 
  35 def world_size() -> int:
  36     """Returns the current world size (number of distributed processes)."""
  37     return dist.get_world_size() if dist.is_initialized() else 1
  38 
  39 
  40 def gather(input: torch.Tensor) -> Tuple[torch.Tensor]:
  41     """Gathers this tensor from all processes. Supports backprop."""
  42     return GatherLayer.apply(input)
  43 
  44 
  45 def eye_rank(n: int, device: Optional[torch.device] = None) -> torch.Tensor:
  46     """Returns an (n, n * world_size) zero matrix with the diagonal for the rank
  47     of this process set to 1.
  48 
  49     Example output where n=3, the current process has rank 1, and there are
  50     4 processes in total:
  51 
  52         rank0   rank1   rank2   rank3
  53         0 0 0 | 1 0 0 | 0 0 0 | 0 0 0
  54         0 0 0 | 0 1 0 | 0 0 0 | 0 0 0
  55         0 0 0 | 0 0 1 | 0 0 0 | 0 0 0
  56 
  57     Equivalent to torch.eye for undistributed settings or if world size == 1.
  58 
  59     Args:
  60         n:
  61             Size of the square matrix on a single process.
  62         device:
  63             Device on which the matrix should be created.
  64 
  65     """
  66     rows = torch.arange(n, device=device, dtype=torch.long)
  67     cols = rows + rank() * n
  68     diag_mask = torch.zeros((n, n * world_size()), dtype=torch.bool)
  69     diag_mask[(rows, cols)] = True
  70     return diag_mask
  71 
  72 
  73 def rank_zero_only(fn):
  74     """Decorator that only runs the function on the process with rank 0.
  75 
  76     Example:
  77         >>> @rank_zero_only
  78         >>> def print_rank_zero(message: str):
  79         >>>     print(message)
  80         >>>
  81         >>> print_rank_zero("Hello from rank 0!")
  82 
  83     """
  84 
  85     def wrapped(*args, **kwargs):
  86         if rank() == 0:
  87             return fn(*args, **kwargs)
  88 
  89     return wrapped
  90 
  91 
  92 @rank_zero_only
  93 def print_rank_zero(*args, **kwargs) -> None:
  94     """Equivalent to print, but only runs on the process with rank 0."""
  95     print(*args, **kwargs)
  96
  ```
  --- 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()
  
  ```
  
golden_diff: diff --git a/lightly/utils/dist.py b/lightly/utils/dist.py
  --- a/lightly/utils/dist.py
  +++ b/lightly/utils/dist.py
  @@ -1,29 +1,29 @@
  -from typing import Optional, Tuple
  +from typing import Any, Callable, Optional, Tuple, TypeVar
   
   import torch
   import torch.distributed as dist
  +from torch.autograd.function import FunctionCtx
   
   
   class GatherLayer(torch.autograd.Function):
       """Gather tensors from all processes, supporting backward propagation.
   
       This code was taken and adapted from here:
  -    https://github.com/Spijkervet/SimCLR
  +    https://github.com/vturrisi/solo-learn/blob/b69b4bd27472593919956d9ac58902a301537a4d/solo/utils/misc.py#L187
   
       """
   
       @staticmethod
  -    def forward(ctx, input: torch.Tensor) -> Tuple[torch.Tensor, ...]:
  -        ctx.save_for_backward(input)
  +    def forward(ctx, input: torch.Tensor) -> Tuple[torch.Tensor, ...]:  # type: ignore
           output = [torch.empty_like(input) for _ in range(dist.get_world_size())]
           dist.all_gather(output, input)
           return tuple(output)
   
       @staticmethod
  -    def backward(ctx, *grads: torch.Tensor) -> torch.Tensor:
  -        (input,) = ctx.saved_tensors
  -        grad_out = torch.empty_like(input)
  -        grad_out[:] = grads[dist.get_rank()]
  +    def backward(ctx, *grads) -> torch.Tensor:  # type: ignore
  +        all_gradients = torch.stack(grads)
  +        dist.all_reduce(all_gradients)
  +        grad_out = all_gradients[dist.get_rank()]
           return grad_out
   
   
  @@ -39,7 +39,7 @@
   
   def gather(input: torch.Tensor) -> Tuple[torch.Tensor]:
       """Gathers this tensor from all processes. Supports backprop."""
  -    return GatherLayer.apply(input)
  +    return GatherLayer.apply(input)  # type: ignore[no-any-return]
   
   
   def eye_rank(n: int, device: Optional[torch.device] = None) -> torch.Tensor:
  @@ -70,7 +70,10 @@
       return diag_mask
   
   
  -def rank_zero_only(fn):
  +R = TypeVar("R")
  +
  +
  +def rank_zero_only(fn: Callable[..., R]) -> Callable[..., Optional[R]]:
       """Decorator that only runs the function on the process with rank 0.
   
       Example:
  @@ -79,17 +82,17 @@
           >>>     print(message)
           >>>
           >>> print_rank_zero("Hello from rank 0!")
  -
       """
   
  -    def wrapped(*args, **kwargs):
  +    def wrapped(*args: Any, **kwargs: Any) -> Optional[R]:
           if rank() == 0:
               return fn(*args, **kwargs)
  +        return None
   
       return wrapped
   
   
   @rank_zero_only
  -def print_rank_zero(*args, **kwargs) -> None:
  +def print_rank_zero(*args: Any, **kwargs: Any) -> None:  # type: ignore[misc]
       """Equivalent to print, but only runs on the process with rank 0."""
       print(*args, **kwargs)
  
verification_info: {"golden_diff": "diff --git a/lightly/utils/dist.py b/lightly/utils/dist.py\n--- a/lightly/utils/dist.py\n+++ b/lightly/utils/dist.py\n@@ -1,29 +1,29 @@\n-from typing import Optional, Tuple\n+from typing import Any, Callable, Optional, Tuple, TypeVar\n \n import torch\n import torch.distributed as dist\n+from torch.autograd.function import FunctionCtx\n \n \n class GatherLayer(torch.autograd.Function):\n     \"\"\"Gather tensors from all processes, supporting backward propagation.\n \n     This code was taken and adapted from here:\n-    https://github.com/Spijkervet/SimCLR\n+    https://github.com/vturrisi/solo-learn/blob/b69b4bd27472593919956d9ac58902a301537a4d/solo/utils/misc.py#L187\n \n     \"\"\"\n \n     @staticmethod\n-    def forward(ctx, input: torch.Tensor) -> Tuple[torch.Tensor, ...]:\n-        ctx.save_for_backward(input)\n+    def forward(ctx, input: torch.Tensor) -> Tuple[torch.Tensor, ...]:  # type: ignore\n         output = [torch.empty_like(input) for _ in range(dist.get_world_size())]\n         dist.all_gather(output, input)\n         return tuple(output)\n \n     @staticmethod\n-    def backward(ctx, *grads: torch.Tensor) -> torch.Tensor:\n-        (input,) = ctx.saved_tensors\n-        grad_out = torch.empty_like(input)\n-        grad_out[:] = grads[dist.get_rank()]\n+    def backward(ctx, *grads) -> torch.Tensor:  # type: ignore\n+        all_gradients = torch.stack(grads)\n+        dist.all_reduce(all_gradients)\n+        grad_out = all_gradients[dist.get_rank()]\n         return grad_out\n \n \n@@ -39,7 +39,7 @@\n \n def gather(input: torch.Tensor) -> Tuple[torch.Tensor]:\n     \"\"\"Gathers this tensor from all processes. Supports backprop.\"\"\"\n-    return GatherLayer.apply(input)\n+    return GatherLayer.apply(input)  # type: ignore[no-any-return]\n \n \n def eye_rank(n: int, device: Optional[torch.device] = None) -> torch.Tensor:\n@@ -70,7 +70,10 @@\n     return diag_mask\n \n \n-def rank_zero_only(fn):\n+R = TypeVar(\"R\")\n+\n+\n+def rank_zero_only(fn: Callable[..., R]) -> Callable[..., Optional[R]]:\n     \"\"\"Decorator that only runs the function on the process with rank 0.\n \n     Example:\n@@ -79,17 +82,17 @@\n         >>>     print(message)\n         >>>\n         >>> print_rank_zero(\"Hello from rank 0!\")\n-\n     \"\"\"\n \n-    def wrapped(*args, **kwargs):\n+    def wrapped(*args: Any, **kwargs: Any) -> Optional[R]:\n         if rank() == 0:\n             return fn(*args, **kwargs)\n+        return None\n \n     return wrapped\n \n \n @rank_zero_only\n-def print_rank_zero(*args, **kwargs) -> None:\n+def print_rank_zero(*args: Any, **kwargs: Any) -> None:  # type: ignore[misc]\n     \"\"\"Equivalent to print, but only runs on the process with rank 0.\"\"\"\n     print(*args, **kwargs)\n", "issue": "Bug in `GatherLayer.backward`\nHi,\r\n\r\nWe've been implementing a model at [cellarium-ml](https://github.com/cellarium-ai/cellarium-ml) using your `NTXentLoss`. Comparing the model training with a single GPU and two GPUs we noticed that they do not match. By investigating it we found an apparent bug in the `GatherLayer.backward` where gradients are not sum-reduced over GPUs. Here is our fixed version (https://github.com/cellarium-ai/cellarium-ml/blob/main/cellarium/ml/distributed/gather.py#L17-L21):\r\n\r\n```py\r\n    @staticmethod\r\n    def backward(ctx, *grads) -> torch.Tensor:\r\n        grad_out = grads[dist.get_rank()].contiguous()\r\n        dist.all_reduce(grad_out, op=dist.ReduceOp.SUM)\r\n        return grad_out\r\n```\r\n\r\nand the [test](https://github.com/cellarium-ai/cellarium-ml/blob/main/tests/distributed/test_gather.py) we wrote. Would you agree that this is indeed a bug? I would be happy to contribute a PR with the fix.\n", "before_files": [{"content": "from typing import Optional, Tuple\n\nimport torch\nimport torch.distributed as dist\n\n\nclass GatherLayer(torch.autograd.Function):\n    \"\"\"Gather tensors from all processes, supporting backward propagation.\n\n    This code was taken and adapted from here:\n    https://github.com/Spijkervet/SimCLR\n\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, input: torch.Tensor) -> Tuple[torch.Tensor, ...]:\n        ctx.save_for_backward(input)\n        output = [torch.empty_like(input) for _ in range(dist.get_world_size())]\n        dist.all_gather(output, input)\n        return tuple(output)\n\n    @staticmethod\n    def backward(ctx, *grads: torch.Tensor) -> torch.Tensor:\n        (input,) = ctx.saved_tensors\n        grad_out = torch.empty_like(input)\n        grad_out[:] = grads[dist.get_rank()]\n        return grad_out\n\n\ndef rank() -> int:\n    \"\"\"Returns the rank of the current process.\"\"\"\n    return dist.get_rank() if dist.is_initialized() else 0\n\n\ndef world_size() -> int:\n    \"\"\"Returns the current world size (number of distributed processes).\"\"\"\n    return dist.get_world_size() if dist.is_initialized() else 1\n\n\ndef gather(input: torch.Tensor) -> Tuple[torch.Tensor]:\n    \"\"\"Gathers this tensor from all processes. Supports backprop.\"\"\"\n    return GatherLayer.apply(input)\n\n\ndef eye_rank(n: int, device: Optional[torch.device] = None) -> torch.Tensor:\n    \"\"\"Returns an (n, n * world_size) zero matrix with the diagonal for the rank\n    of this process set to 1.\n\n    Example output where n=3, the current process has rank 1, and there are\n    4 processes in total:\n\n        rank0   rank1   rank2   rank3\n        0 0 0 | 1 0 0 | 0 0 0 | 0 0 0\n        0 0 0 | 0 1 0 | 0 0 0 | 0 0 0\n        0 0 0 | 0 0 1 | 0 0 0 | 0 0 0\n\n    Equivalent to torch.eye for undistributed settings or if world size == 1.\n\n    Args:\n        n:\n            Size of the square matrix on a single process.\n        device:\n            Device on which the matrix should be created.\n\n    \"\"\"\n    rows = torch.arange(n, device=device, dtype=torch.long)\n    cols = rows + rank() * n\n    diag_mask = torch.zeros((n, n * world_size()), dtype=torch.bool)\n    diag_mask[(rows, cols)] = True\n    return diag_mask\n\n\ndef rank_zero_only(fn):\n    \"\"\"Decorator that only runs the function on the process with rank 0.\n\n    Example:\n        >>> @rank_zero_only\n        >>> def print_rank_zero(message: str):\n        >>>     print(message)\n        >>>\n        >>> print_rank_zero(\"Hello from rank 0!\")\n\n    \"\"\"\n\n    def wrapped(*args, **kwargs):\n        if rank() == 0:\n            return fn(*args, **kwargs)\n\n    return wrapped\n\n\n@rank_zero_only\ndef print_rank_zero(*args, **kwargs) -> None:\n    \"\"\"Equivalent to print, but only runs on the process with rank 0.\"\"\"\n    print(*args, **kwargs)\n", "path": "lightly/utils/dist.py"}], "after_files": [{"content": "from typing import Any, Callable, Optional, Tuple, TypeVar\n\nimport torch\nimport torch.distributed as dist\nfrom torch.autograd.function import FunctionCtx\n\n\nclass GatherLayer(torch.autograd.Function):\n    \"\"\"Gather tensors from all processes, supporting backward propagation.\n\n    This code was taken and adapted from here:\n    https://github.com/vturrisi/solo-learn/blob/b69b4bd27472593919956d9ac58902a301537a4d/solo/utils/misc.py#L187\n\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, input: torch.Tensor) -> Tuple[torch.Tensor, ...]:  # type: ignore\n        output = [torch.empty_like(input) for _ in range(dist.get_world_size())]\n        dist.all_gather(output, input)\n        return tuple(output)\n\n    @staticmethod\n    def backward(ctx, *grads) -> torch.Tensor:  # type: ignore\n        all_gradients = torch.stack(grads)\n        dist.all_reduce(all_gradients)\n        grad_out = all_gradients[dist.get_rank()]\n        return grad_out\n\n\ndef rank() -> int:\n    \"\"\"Returns the rank of the current process.\"\"\"\n    return dist.get_rank() if dist.is_initialized() else 0\n\n\ndef world_size() -> int:\n    \"\"\"Returns the current world size (number of distributed processes).\"\"\"\n    return dist.get_world_size() if dist.is_initialized() else 1\n\n\ndef gather(input: torch.Tensor) -> Tuple[torch.Tensor]:\n    \"\"\"Gathers this tensor from all processes. Supports backprop.\"\"\"\n    return GatherLayer.apply(input)  # type: ignore[no-any-return]\n\n\ndef eye_rank(n: int, device: Optional[torch.device] = None) -> torch.Tensor:\n    \"\"\"Returns an (n, n * world_size) zero matrix with the diagonal for the rank\n    of this process set to 1.\n\n    Example output where n=3, the current process has rank 1, and there are\n    4 processes in total:\n\n        rank0   rank1   rank2   rank3\n        0 0 0 | 1 0 0 | 0 0 0 | 0 0 0\n        0 0 0 | 0 1 0 | 0 0 0 | 0 0 0\n        0 0 0 | 0 0 1 | 0 0 0 | 0 0 0\n\n    Equivalent to torch.eye for undistributed settings or if world size == 1.\n\n    Args:\n        n:\n            Size of the square matrix on a single process.\n        device:\n            Device on which the matrix should be created.\n\n    \"\"\"\n    rows = torch.arange(n, device=device, dtype=torch.long)\n    cols = rows + rank() * n\n    diag_mask = torch.zeros((n, n * world_size()), dtype=torch.bool)\n    diag_mask[(rows, cols)] = True\n    return diag_mask\n\n\nR = TypeVar(\"R\")\n\n\ndef rank_zero_only(fn: Callable[..., R]) -> Callable[..., Optional[R]]:\n    \"\"\"Decorator that only runs the function on the process with rank 0.\n\n    Example:\n        >>> @rank_zero_only\n        >>> def print_rank_zero(message: str):\n        >>>     print(message)\n        >>>\n        >>> print_rank_zero(\"Hello from rank 0!\")\n    \"\"\"\n\n    def wrapped(*args: Any, **kwargs: Any) -> Optional[R]:\n        if rank() == 0:\n            return fn(*args, **kwargs)\n        return None\n\n    return wrapped\n\n\n@rank_zero_only\ndef print_rank_zero(*args: Any, **kwargs: Any) -> None:  # type: ignore[misc]\n    \"\"\"Equivalent to print, but only runs on the process with rank 0.\"\"\"\n    print(*args, **kwargs)\n", "path": "lightly/utils/dist.py"}]}