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()

```

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

{"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"}]}