prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Buck run device error
  buck run reported the following error:
  ```
  [trainer1]:RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and cuda:0! (when checking argument for argument weight in method wrapper__native_layer_norm)
  ```
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `pippy/utils.py`
  Content:
  ```
  1 # Copyright (c) Meta Platforms, Inc. and affiliates
  2 import os
  3 import socket
  4 import logging
  5 
  6 # Pinning process to a separate GPU if not yet done by launch script
  7 # Notes:
  8 # 1. Needed to work around the issue of RPC not automatically pinning spawned worker threads to CUDA device of the main
  9 # thread
  10 # 2. Must be done before `import torch` at which point CUDA context may be created
  11 cuda_devices_str = os.getenv('CUDA_VISIBLE_DEVICES')
  12 if (cuda_devices_str is None                        # not set
  13         or len(cuda_devices_str.split(',')) > 1):   # or set to all devices
  14     # If launchers like Torchrun sets `LOCAL_RANK`, we would use this information
  15     local_rank_str = os.getenv('LOCAL_RANK')
  16     if local_rank_str is not None:
  17         os.environ['CUDA_VISIBLE_DEVICES'] = local_rank_str
  18         print(f"Pinning local process {local_rank_str} to gpu {os.getenv('CUDA_VISIBLE_DEVICES')}")
  19 
  20 import torch
  21 import torch.multiprocessing as mp
  22 import torch.distributed.rpc as rpc
  23 
  24 
  25 VERBOSE = bool(int(os.environ.get('VERBOSE', False)))
  26 
  27 if VERBOSE:
  28     logging.getLogger().setLevel(logging.DEBUG)
  29 
  30 
  31 def has_efa() -> bool:
  32     try:
  33         import subprocess
  34         return subprocess.run(["fi_info", "-p", "efa", "-t", "FI_EP_RDM"],
  35                               stdout=subprocess.DEVNULL,
  36                               stderr=subprocess.DEVNULL).returncode == 0
  37     except FileNotFoundError:
  38         return False
  39     except PermissionError:
  40         return False
  41 
  42 
  43 def tp_transports():
  44     return ["shm", "uv"] if has_efa() else None
  45 
  46 
  47 def run_pippy(run_master, args, *extra_args):
  48     if not hasattr(args, 'world_size'):
  49         assert hasattr(args, 'pp_group_size')
  50         args.dp_group_size = args.dp_group_size if hasattr(args, 'dp_group_size') else 1
  51     else:
  52         if not hasattr(args, 'dp_group_size'):
  53             args.pp_group_size = args.pp_group_size if hasattr(args, 'pp_group_size') else args.world_size
  54             assert args.world_size % args.pp_group_size == 0
  55             args.dp_group_size = args.world_size // args.pp_group_size
  56         elif not hasattr(args, 'pp_group_size'):
  57             args.dp_group_size = args.dp_group_size if hasattr(args, 'dp_group_size') else 1
  58             assert args.world_size % args.dp_group_size == 0
  59             args.pp_group_size = args.world_size // args.dp_group_size
  60         else:
  61             pass
  62             # TODO: doesn't work for PiPPyTrainingArguments
  63             # assert args.world_size == args.dp_group_size * args.pp_group_size
  64 
  65     actual_world_size = args.dp_group_size * args.pp_group_size
  66     print(f'[PiPPy] World size: {actual_world_size}, '
  67           f'DP group size: {args.dp_group_size}, '
  68           f'PP group size: {args.pp_group_size}')
  69 
  70     if args.rank == -1:
  71         mp.spawn(run_worker, args=(run_master, args, *extra_args), nprocs=actual_world_size, join=True)
  72     elif args.rank < actual_world_size:
  73         run_worker(args.rank, run_master, args, *extra_args)
  74     else:
  75         print("I'm unused, exiting")
  76 
  77 
  78 def run_worker(rank, run_master, args, *extra_args):
  79     args.rank = rank
  80 
  81     os.environ['MASTER_ADDR'] = args.master_addr
  82     os.environ['MASTER_PORT'] = args.master_port
  83 
  84     actual_world_size = args.dp_group_size * args.pp_group_size
  85 
  86     # TODO: Move to training args, blocked by: cannot pickle 'TensorPipeRpcBackendOptions' object
  87     # Exclude IB for metadata transport due to lack of EFA support on AWS
  88     options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=512,
  89                                               rpc_timeout=1800,
  90                                               _transports=tp_transports())
  91     if args.cuda:
  92         n_devs = torch.cuda.device_count()
  93         if n_devs > 0:
  94             dev_id = rank % n_devs
  95             for i in range(actual_world_size):
  96                 options.set_device_map(f"worker{i}", {dev_id: i % n_devs})
  97             # Does not seem effective for RPC device pinning. TODO
  98             # options.set_devices([f'cuda:{dev_id}'])
  99         else:
  100             args.cuda = 0
  101             print('Warning: no CUDA device found. Running on CPU instead.')
  102 
  103     args.device = f'cuda:{dev_id}' if args.cuda else 'cpu'
  104     print(f"rank = {rank} host/pid/device = "
  105           f"{socket.gethostname()}/{os.getpid()}/{args.device}")
  106 
  107     # Init DDP process group
  108     backend = "nccl" if args.cuda else "gloo"
  109     torch.distributed.init_process_group(backend=backend, rank=rank, world_size=actual_world_size)
  110 
  111     rpc.init_rpc(
  112         f"worker{rank}",
  113         rank=rank,
  114         world_size=actual_world_size,
  115         rpc_backend_options=options
  116     )
  117 
  118     global dp_pg_per_pp_rank
  119     dp_ranks_per_pp_rank = torch.arange(actual_world_size).reshape(args.pp_group_size,
  120                                                                    args.dp_group_size).tolist()
  121     dp_pg_per_pp_rank = [torch.distributed.new_group(ranks) for ranks in dp_ranks_per_pp_rank]
  122 
  123     pp_ranks_per_dp_group = [[i * args.dp_group_size + rank for i in range(args.pp_group_size)]
  124                              for rank in range(args.dp_group_size)]
  125 
  126     args.driver_group = torch.distributed.new_group(list(range(args.dp_group_size)))
  127 
  128     global exclude_master
  129     exclude_master = args.exclude_master if hasattr(args, 'exclude_master') else 0
  130 
  131     if rank >= 0 and rank // args.dp_group_size == 0:
  132         args.driver_index = rank
  133         args.local_driver_index = os.getenv('LOCAL_RANK', rank)
  134         run_master(pp_ranks_per_dp_group[rank], args, *extra_args)
  135     rpc.shutdown()
  136
  ```
  --- 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/pippy/utils.py b/pippy/utils.py
  --- a/pippy/utils.py
  +++ b/pippy/utils.py
  @@ -8,14 +8,17 @@
   # 1. Needed to work around the issue of RPC not automatically pinning spawned worker threads to CUDA device of the main
   # thread
   # 2. Must be done before `import torch` at which point CUDA context may be created
  -cuda_devices_str = os.getenv('CUDA_VISIBLE_DEVICES')
  -if (cuda_devices_str is None                        # not set
  -        or len(cuda_devices_str.split(',')) > 1):   # or set to all devices
  -    # If launchers like Torchrun sets `LOCAL_RANK`, we would use this information
  -    local_rank_str = os.getenv('LOCAL_RANK')
  -    if local_rank_str is not None:
  -        os.environ['CUDA_VISIBLE_DEVICES'] = local_rank_str
  -        print(f"Pinning local process {local_rank_str} to gpu {os.getenv('CUDA_VISIBLE_DEVICES')}")
  +# 3. Currently this is enabled by default (as long as #1 is not implemented in RPC). Users may set `PIPPY_PIN_DEVICE` to
  +# 0 to disable the pinning
  +if os.getenv('PIPPY_PIN_DEVICE', '1') == '1':
  +    cuda_devices_str = os.getenv('CUDA_VISIBLE_DEVICES')
  +    if (cuda_devices_str is None                        # not set
  +            or len(cuda_devices_str.split(',')) > 1):   # or set to all devices
  +        # If launchers like Torchrun sets `LOCAL_RANK`, we would use this information
  +        local_rank_str = os.getenv('LOCAL_RANK')
  +        if local_rank_str is not None:
  +            os.environ['CUDA_VISIBLE_DEVICES'] = local_rank_str
  +            print(f"Pinning local process {local_rank_str} to gpu {os.getenv('CUDA_VISIBLE_DEVICES')}")
   
   import torch
   import torch.multiprocessing as mp
  
verification_info: {"golden_diff": "diff --git a/pippy/utils.py b/pippy/utils.py\n--- a/pippy/utils.py\n+++ b/pippy/utils.py\n@@ -8,14 +8,17 @@\n # 1. Needed to work around the issue of RPC not automatically pinning spawned worker threads to CUDA device of the main\n # thread\n # 2. Must be done before `import torch` at which point CUDA context may be created\n-cuda_devices_str = os.getenv('CUDA_VISIBLE_DEVICES')\n-if (cuda_devices_str is None                        # not set\n-        or len(cuda_devices_str.split(',')) > 1):   # or set to all devices\n-    # If launchers like Torchrun sets `LOCAL_RANK`, we would use this information\n-    local_rank_str = os.getenv('LOCAL_RANK')\n-    if local_rank_str is not None:\n-        os.environ['CUDA_VISIBLE_DEVICES'] = local_rank_str\n-        print(f\"Pinning local process {local_rank_str} to gpu {os.getenv('CUDA_VISIBLE_DEVICES')}\")\n+# 3. Currently this is enabled by default (as long as #1 is not implemented in RPC). Users may set `PIPPY_PIN_DEVICE` to\n+# 0 to disable the pinning\n+if os.getenv('PIPPY_PIN_DEVICE', '1') == '1':\n+    cuda_devices_str = os.getenv('CUDA_VISIBLE_DEVICES')\n+    if (cuda_devices_str is None                        # not set\n+            or len(cuda_devices_str.split(',')) > 1):   # or set to all devices\n+        # If launchers like Torchrun sets `LOCAL_RANK`, we would use this information\n+        local_rank_str = os.getenv('LOCAL_RANK')\n+        if local_rank_str is not None:\n+            os.environ['CUDA_VISIBLE_DEVICES'] = local_rank_str\n+            print(f\"Pinning local process {local_rank_str} to gpu {os.getenv('CUDA_VISIBLE_DEVICES')}\")\n \n import torch\n import torch.multiprocessing as mp\n", "issue": "Buck run device error\nbuck run reported the following error:\r\n```\r\n[trainer1]:RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and cuda:0! (when checking argument for argument weight in method wrapper__native_layer_norm)\r\n```\n", "before_files": [{"content": "# Copyright (c) Meta Platforms, Inc. and affiliates\nimport os\nimport socket\nimport logging\n\n# Pinning process to a separate GPU if not yet done by launch script\n# Notes:\n# 1. Needed to work around the issue of RPC not automatically pinning spawned worker threads to CUDA device of the main\n# thread\n# 2. Must be done before `import torch` at which point CUDA context may be created\ncuda_devices_str = os.getenv('CUDA_VISIBLE_DEVICES')\nif (cuda_devices_str is None                        # not set\n        or len(cuda_devices_str.split(',')) > 1):   # or set to all devices\n    # If launchers like Torchrun sets `LOCAL_RANK`, we would use this information\n    local_rank_str = os.getenv('LOCAL_RANK')\n    if local_rank_str is not None:\n        os.environ['CUDA_VISIBLE_DEVICES'] = local_rank_str\n        print(f\"Pinning local process {local_rank_str} to gpu {os.getenv('CUDA_VISIBLE_DEVICES')}\")\n\nimport torch\nimport torch.multiprocessing as mp\nimport torch.distributed.rpc as rpc\n\n\nVERBOSE = bool(int(os.environ.get('VERBOSE', False)))\n\nif VERBOSE:\n    logging.getLogger().setLevel(logging.DEBUG)\n\n\ndef has_efa() -> bool:\n    try:\n        import subprocess\n        return subprocess.run([\"fi_info\", \"-p\", \"efa\", \"-t\", \"FI_EP_RDM\"],\n                              stdout=subprocess.DEVNULL,\n                              stderr=subprocess.DEVNULL).returncode == 0\n    except FileNotFoundError:\n        return False\n    except PermissionError:\n        return False\n\n\ndef tp_transports():\n    return [\"shm\", \"uv\"] if has_efa() else None\n\n\ndef run_pippy(run_master, args, *extra_args):\n    if not hasattr(args, 'world_size'):\n        assert hasattr(args, 'pp_group_size')\n        args.dp_group_size = args.dp_group_size if hasattr(args, 'dp_group_size') else 1\n    else:\n        if not hasattr(args, 'dp_group_size'):\n            args.pp_group_size = args.pp_group_size if hasattr(args, 'pp_group_size') else args.world_size\n            assert args.world_size % args.pp_group_size == 0\n            args.dp_group_size = args.world_size // args.pp_group_size\n        elif not hasattr(args, 'pp_group_size'):\n            args.dp_group_size = args.dp_group_size if hasattr(args, 'dp_group_size') else 1\n            assert args.world_size % args.dp_group_size == 0\n            args.pp_group_size = args.world_size // args.dp_group_size\n        else:\n            pass\n            # TODO: doesn't work for PiPPyTrainingArguments\n            # assert args.world_size == args.dp_group_size * args.pp_group_size\n\n    actual_world_size = args.dp_group_size * args.pp_group_size\n    print(f'[PiPPy] World size: {actual_world_size}, '\n          f'DP group size: {args.dp_group_size}, '\n          f'PP group size: {args.pp_group_size}')\n\n    if args.rank == -1:\n        mp.spawn(run_worker, args=(run_master, args, *extra_args), nprocs=actual_world_size, join=True)\n    elif args.rank < actual_world_size:\n        run_worker(args.rank, run_master, args, *extra_args)\n    else:\n        print(\"I'm unused, exiting\")\n\n\ndef run_worker(rank, run_master, args, *extra_args):\n    args.rank = rank\n\n    os.environ['MASTER_ADDR'] = args.master_addr\n    os.environ['MASTER_PORT'] = args.master_port\n\n    actual_world_size = args.dp_group_size * args.pp_group_size\n\n    # TODO: Move to training args, blocked by: cannot pickle 'TensorPipeRpcBackendOptions' object\n    # Exclude IB for metadata transport due to lack of EFA support on AWS\n    options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=512,\n                                              rpc_timeout=1800,\n                                              _transports=tp_transports())\n    if args.cuda:\n        n_devs = torch.cuda.device_count()\n        if n_devs > 0:\n            dev_id = rank % n_devs\n            for i in range(actual_world_size):\n                options.set_device_map(f\"worker{i}\", {dev_id: i % n_devs})\n            # Does not seem effective for RPC device pinning. TODO\n            # options.set_devices([f'cuda:{dev_id}'])\n        else:\n            args.cuda = 0\n            print('Warning: no CUDA device found. Running on CPU instead.')\n\n    args.device = f'cuda:{dev_id}' if args.cuda else 'cpu'\n    print(f\"rank = {rank} host/pid/device = \"\n          f\"{socket.gethostname()}/{os.getpid()}/{args.device}\")\n\n    # Init DDP process group\n    backend = \"nccl\" if args.cuda else \"gloo\"\n    torch.distributed.init_process_group(backend=backend, rank=rank, world_size=actual_world_size)\n\n    rpc.init_rpc(\n        f\"worker{rank}\",\n        rank=rank,\n        world_size=actual_world_size,\n        rpc_backend_options=options\n    )\n\n    global dp_pg_per_pp_rank\n    dp_ranks_per_pp_rank = torch.arange(actual_world_size).reshape(args.pp_group_size,\n                                                                   args.dp_group_size).tolist()\n    dp_pg_per_pp_rank = [torch.distributed.new_group(ranks) for ranks in dp_ranks_per_pp_rank]\n\n    pp_ranks_per_dp_group = [[i * args.dp_group_size + rank for i in range(args.pp_group_size)]\n                             for rank in range(args.dp_group_size)]\n\n    args.driver_group = torch.distributed.new_group(list(range(args.dp_group_size)))\n\n    global exclude_master\n    exclude_master = args.exclude_master if hasattr(args, 'exclude_master') else 0\n\n    if rank >= 0 and rank // args.dp_group_size == 0:\n        args.driver_index = rank\n        args.local_driver_index = os.getenv('LOCAL_RANK', rank)\n        run_master(pp_ranks_per_dp_group[rank], args, *extra_args)\n    rpc.shutdown()\n", "path": "pippy/utils.py"}], "after_files": [{"content": "# Copyright (c) Meta Platforms, Inc. and affiliates\nimport os\nimport socket\nimport logging\n\n# Pinning process to a separate GPU if not yet done by launch script\n# Notes:\n# 1. Needed to work around the issue of RPC not automatically pinning spawned worker threads to CUDA device of the main\n# thread\n# 2. Must be done before `import torch` at which point CUDA context may be created\n# 3. Currently this is enabled by default (as long as #1 is not implemented in RPC). Users may set `PIPPY_PIN_DEVICE` to\n# 0 to disable the pinning\nif os.getenv('PIPPY_PIN_DEVICE', '1') == '1':\n    cuda_devices_str = os.getenv('CUDA_VISIBLE_DEVICES')\n    if (cuda_devices_str is None                        # not set\n            or len(cuda_devices_str.split(',')) > 1):   # or set to all devices\n        # If launchers like Torchrun sets `LOCAL_RANK`, we would use this information\n        local_rank_str = os.getenv('LOCAL_RANK')\n        if local_rank_str is not None:\n            os.environ['CUDA_VISIBLE_DEVICES'] = local_rank_str\n            print(f\"Pinning local process {local_rank_str} to gpu {os.getenv('CUDA_VISIBLE_DEVICES')}\")\n\nimport torch\nimport torch.multiprocessing as mp\nimport torch.distributed.rpc as rpc\n\n\nVERBOSE = bool(int(os.environ.get('VERBOSE', False)))\n\nif VERBOSE:\n    logging.getLogger().setLevel(logging.DEBUG)\n\n\ndef has_efa() -> bool:\n    try:\n        import subprocess\n        return subprocess.run([\"fi_info\", \"-p\", \"efa\", \"-t\", \"FI_EP_RDM\"],\n                              stdout=subprocess.DEVNULL,\n                              stderr=subprocess.DEVNULL).returncode == 0\n    except FileNotFoundError:\n        return False\n    except PermissionError:\n        return False\n\n\ndef tp_transports():\n    return [\"shm\", \"uv\"] if has_efa() else None\n\n\ndef run_pippy(run_master, args, *extra_args):\n    if not hasattr(args, 'world_size'):\n        assert hasattr(args, 'pp_group_size')\n        args.dp_group_size = args.dp_group_size if hasattr(args, 'dp_group_size') else 1\n    else:\n        if not hasattr(args, 'dp_group_size'):\n            args.pp_group_size = args.pp_group_size if hasattr(args, 'pp_group_size') else args.world_size\n            assert args.world_size % args.pp_group_size == 0\n            args.dp_group_size = args.world_size // args.pp_group_size\n        elif not hasattr(args, 'pp_group_size'):\n            args.dp_group_size = args.dp_group_size if hasattr(args, 'dp_group_size') else 1\n            assert args.world_size % args.dp_group_size == 0\n            args.pp_group_size = args.world_size // args.dp_group_size\n        else:\n            pass\n            # TODO: doesn't work for PiPPyTrainingArguments\n            # assert args.world_size == args.dp_group_size * args.pp_group_size\n\n    actual_world_size = args.dp_group_size * args.pp_group_size\n    print(f'[PiPPy] World size: {actual_world_size}, '\n          f'DP group size: {args.dp_group_size}, '\n          f'PP group size: {args.pp_group_size}')\n\n    if args.rank == -1:\n        mp.spawn(run_worker, args=(run_master, args, *extra_args), nprocs=actual_world_size, join=True)\n    elif args.rank < actual_world_size:\n        run_worker(args.rank, run_master, args, *extra_args)\n    else:\n        print(\"I'm unused, exiting\")\n\n\ndef run_worker(rank, run_master, args, *extra_args):\n    args.rank = rank\n\n    os.environ['MASTER_ADDR'] = args.master_addr\n    os.environ['MASTER_PORT'] = args.master_port\n\n    actual_world_size = args.dp_group_size * args.pp_group_size\n\n    # TODO: Move to training args, blocked by: cannot pickle 'TensorPipeRpcBackendOptions' object\n    # Exclude IB for metadata transport due to lack of EFA support on AWS\n    options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=512,\n                                              rpc_timeout=1800,\n                                              _transports=tp_transports())\n    if args.cuda:\n        n_devs = torch.cuda.device_count()\n        if n_devs > 0:\n            dev_id = rank % n_devs\n            for i in range(actual_world_size):\n                options.set_device_map(f\"worker{i}\", {dev_id: i % n_devs})\n            # Does not seem effective for RPC device pinning. TODO\n            # options.set_devices([f'cuda:{dev_id}'])\n        else:\n            args.cuda = 0\n            print('Warning: no CUDA device found. Running on CPU instead.')\n\n    args.device = f'cuda:{dev_id}' if args.cuda else 'cpu'\n    print(f\"rank = {rank} host/pid/device = \"\n          f\"{socket.gethostname()}/{os.getpid()}/{args.device}\")\n\n    # Init DDP process group\n    backend = \"nccl\" if args.cuda else \"gloo\"\n    torch.distributed.init_process_group(backend=backend, rank=rank, world_size=actual_world_size)\n\n    rpc.init_rpc(\n        f\"worker{rank}\",\n        rank=rank,\n        world_size=actual_world_size,\n        rpc_backend_options=options\n    )\n\n    global dp_pg_per_pp_rank\n    dp_ranks_per_pp_rank = torch.arange(actual_world_size).reshape(args.pp_group_size,\n                                                                   args.dp_group_size).tolist()\n    dp_pg_per_pp_rank = [torch.distributed.new_group(ranks) for ranks in dp_ranks_per_pp_rank]\n\n    pp_ranks_per_dp_group = [[i * args.dp_group_size + rank for i in range(args.pp_group_size)]\n                             for rank in range(args.dp_group_size)]\n\n    args.driver_group = torch.distributed.new_group(list(range(args.dp_group_size)))\n\n    global exclude_master\n    exclude_master = args.exclude_master if hasattr(args, 'exclude_master') else 0\n\n    if rank >= 0 and rank // args.dp_group_size == 0:\n        args.driver_index = rank\n        args.local_driver_index = os.getenv('LOCAL_RANK', rank)\n        run_master(pp_ranks_per_dp_group[rank], args, *extra_args)\n    rpc.shutdown()\n", "path": "pippy/utils.py"}]}