We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Difference in training results between single GPU and multi-GPU
For single GPU and multi-GPU training, the single card is 4% higher than the multi-card training. What is the cause?
Here is my configuration：
<img width="150" alt="image" src="https://user-images.githubusercontent.com/109410944/226503405-f70fa165-2fe6-4c23-b1bd-e4d4e7610c32.png">
<img width="207" alt="image" src="https://user-images.githubusercontent.com/109410944/226503453-a0e1ba34-47ff-482e-86ad-13602c338ef1.png">


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `onmt/utils/distributed.py`
Content:
```
1 """ Pytorch Distributed utils
2     This piece of code was heavily inspired by the equivalent of Fairseq-py
3     https://github.com/pytorch/fairseq
4 """
5 import os
6 import signal
7 import math
8 import pickle
9 import torch.distributed
10 from onmt.utils.logging import logger
11 
12 
13 def is_master(opt, device_id):
14     return opt.gpu_ranks[device_id] == 0
15 
16 
17 def multi_init(opt, device_id):
18     dist_init_method = 'tcp://{master_ip}:{master_port}'.format(
19         master_ip=opt.master_ip,
20         master_port=opt.master_port)
21     dist_world_size = opt.world_size
22     torch.distributed.init_process_group(
23         backend=opt.gpu_backend, init_method=dist_init_method,
24         world_size=dist_world_size, rank=opt.gpu_ranks[device_id])
25     gpu_rank = torch.distributed.get_rank()
26     if not is_master(opt, device_id):
27         logger.disabled = True
28 
29     return gpu_rank
30 
31 
32 def all_reduce_and_rescale_tensors(tensors, rescale_denom,
33                                    buffer_size=104857600):
34     """All-reduce and rescale tensors in chunks of the specified size.
35 
36     Args:
37         tensors: list of Tensors to all-reduce
38         rescale_denom: denominator for rescaling summed Tensors
39         buffer_size: all-reduce chunk size in bytes
40     """
41     # buffer size in bytes, determine equiv. # of elements based on data type
42     buffer_t = tensors[0].new(
43         math.ceil(buffer_size / tensors[0].element_size())).zero_()
44     buffer = []
45 
46     def all_reduce_buffer():
47         # copy tensors into buffer_t
48         offset = 0
49         for t in buffer:
50             numel = t.numel()
51             buffer_t[offset:offset+numel].copy_(t.view(-1))
52             offset += numel
53 
54         # all-reduce and rescale
55         torch.distributed.all_reduce(buffer_t[:offset], async_op=True)
56         buffer_t.div_(rescale_denom)
57 
58         # copy all-reduced buffer back into tensors
59         offset = 0
60         for t in buffer:
61             numel = t.numel()
62             t.view(-1).copy_(buffer_t[offset:offset+numel])
63             offset += numel
64 
65     filled = 0
66     for t in tensors:
67         sz = t.numel() * t.element_size()
68         # print(filled, sz)
69         if sz > buffer_size:
70             # tensor is bigger than buffer, all-reduce and rescale directly
71             torch.distributed.all_reduce(t, async_op=True)
72             t.div_(rescale_denom)
73         elif filled + sz > buffer_size:
74             # buffer is full, all-reduce and replace buffer with grad
75             all_reduce_buffer()
76             buffer = [t]
77             filled = sz
78         else:
79             # add tensor to buffer
80             buffer.append(t)
81             filled += sz
82 
83     if len(buffer) > 0:
84         all_reduce_buffer()
85 
86 
87 def all_gather_list(data, max_size=4096):
88     """Gathers arbitrary data from all nodes into a list."""
89     world_size = torch.distributed.get_world_size()
90     if not hasattr(all_gather_list, '_in_buffer') or \
91             max_size != all_gather_list._in_buffer.size():
92         all_gather_list._in_buffer = torch.cuda.ByteTensor(max_size)
93         all_gather_list._out_buffers = [
94             torch.cuda.ByteTensor(max_size)
95             for i in range(world_size)
96         ]
97     in_buffer = all_gather_list._in_buffer
98     out_buffers = all_gather_list._out_buffers
99 
100     enc = pickle.dumps(data)
101     enc_size = len(enc)
102     if enc_size + 2 > max_size:
103         raise ValueError(
104             'encoded data exceeds max_size: {}'.format(enc_size + 2))
105     assert max_size < 255*256
106     in_buffer[0] = enc_size // 255  # this encoding works for max_size < 65k
107     in_buffer[1] = enc_size % 255
108     in_buffer[2:enc_size+2] = torch.ByteTensor(list(enc))
109 
110     torch.distributed.all_gather(out_buffers, in_buffer.cuda())
111 
112     results = []
113     for i in range(world_size):
114         out_buffer = out_buffers[i]
115         size = (255 * out_buffer[0].item()) + out_buffer[1].item()
116 
117         bytes_list = bytes(out_buffer[2:size+2].tolist())
118         result = pickle.loads(bytes_list)
119         results.append(result)
120     return results
121 
122 
123 class ErrorHandler(object):
124     """A class that listens for exceptions in children processes and propagates
125     the tracebacks to the parent process."""
126 
127     def __init__(self, error_queue):
128         """ init error handler """
129         import signal
130         import threading
131         self.error_queue = error_queue
132         self.children_pids = []
133         self.error_thread = threading.Thread(
134             target=self.error_listener, daemon=True)
135         self.error_thread.start()
136         signal.signal(signal.SIGUSR1, self.signal_handler)
137 
138     def add_child(self, pid):
139         """ error handler """
140         self.children_pids.append(pid)
141 
142     def error_listener(self):
143         """ error listener """
144         (rank, original_trace) = self.error_queue.get()
145         self.error_queue.put((rank, original_trace))
146         os.kill(os.getpid(), signal.SIGUSR1)
147 
148     def signal_handler(self, signalnum, stackframe):
149         """ signal handler """
150         for pid in self.children_pids:
151             os.kill(pid, signal.SIGINT)  # kill children processes
152         (rank, original_trace) = self.error_queue.get()
153         msg = """\n\n-- Tracebacks above this line can probably
154                  be ignored --\n\n"""
155         msg += original_trace
156         raise Exception(msg)
157 
158 
159 def consumer(process_fn, opt, device_id, error_queue):  # noqa: E501
160     """Run `process_fn` on `device_id` with data from `batch_queue`."""
161     try:
162         gpu_rank = multi_init(opt, device_id)
163         if gpu_rank != opt.gpu_ranks[device_id]:
164             raise AssertionError("An error occurred in \
165                   Distributed initialization")
166         process_fn(opt, device_id=device_id)
167     except KeyboardInterrupt:
168         pass  # killed by parent, do nothing
169     except Exception:
170         # propagate exception to parent process, keeping original traceback
171         import traceback
172         error_queue.put((opt.gpu_ranks[device_id], traceback.format_exc()))
173
```
--- 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/onmt/utils/distributed.py b/onmt/utils/distributed.py
--- a/onmt/utils/distributed.py
+++ b/onmt/utils/distributed.py
@@ -52,7 +52,7 @@
             offset += numel
 
         # all-reduce and rescale
-        torch.distributed.all_reduce(buffer_t[:offset], async_op=True)
+        torch.distributed.all_reduce(buffer_t[:offset], async_op=False)
         buffer_t.div_(rescale_denom)
 
         # copy all-reduced buffer back into tensors
@@ -68,7 +68,7 @@
         # print(filled, sz)
         if sz > buffer_size:
             # tensor is bigger than buffer, all-reduce and rescale directly
-            torch.distributed.all_reduce(t, async_op=True)
+            torch.distributed.all_reduce(t, async_op=False)
             t.div_(rescale_denom)
         elif filled + sz > buffer_size:
             # buffer is full, all-reduce and replace buffer with grad

{"golden_diff": "diff --git a/onmt/utils/distributed.py b/onmt/utils/distributed.py\n--- a/onmt/utils/distributed.py\n+++ b/onmt/utils/distributed.py\n@@ -52,7 +52,7 @@\n             offset += numel\n \n         # all-reduce and rescale\n-        torch.distributed.all_reduce(buffer_t[:offset], async_op=True)\n+        torch.distributed.all_reduce(buffer_t[:offset], async_op=False)\n         buffer_t.div_(rescale_denom)\n \n         # copy all-reduced buffer back into tensors\n@@ -68,7 +68,7 @@\n         # print(filled, sz)\n         if sz > buffer_size:\n             # tensor is bigger than buffer, all-reduce and rescale directly\n-            torch.distributed.all_reduce(t, async_op=True)\n+            torch.distributed.all_reduce(t, async_op=False)\n             t.div_(rescale_denom)\n         elif filled + sz > buffer_size:\n             # buffer is full, all-reduce and replace buffer with grad\n", "issue": "Difference in training results between single GPU and multi-GPU\nFor single GPU and multi-GPU training, the single card is 4% higher than the multi-card training. What is the cause?\r\nHere is my configuration\uff1a\r\n<img width=\"150\" alt=\"image\" src=\"https://user-images.githubusercontent.com/109410944/226503405-f70fa165-2fe6-4c23-b1bd-e4d4e7610c32.png\">\r\n<img width=\"207\" alt=\"image\" src=\"https://user-images.githubusercontent.com/109410944/226503453-a0e1ba34-47ff-482e-86ad-13602c338ef1.png\">\r\n\n", "before_files": [{"content": "\"\"\" Pytorch Distributed utils\n    This piece of code was heavily inspired by the equivalent of Fairseq-py\n    https://github.com/pytorch/fairseq\n\"\"\"\nimport os\nimport signal\nimport math\nimport pickle\nimport torch.distributed\nfrom onmt.utils.logging import logger\n\n\ndef is_master(opt, device_id):\n    return opt.gpu_ranks[device_id] == 0\n\n\ndef multi_init(opt, device_id):\n    dist_init_method = 'tcp://{master_ip}:{master_port}'.format(\n        master_ip=opt.master_ip,\n        master_port=opt.master_port)\n    dist_world_size = opt.world_size\n    torch.distributed.init_process_group(\n        backend=opt.gpu_backend, init_method=dist_init_method,\n        world_size=dist_world_size, rank=opt.gpu_ranks[device_id])\n    gpu_rank = torch.distributed.get_rank()\n    if not is_master(opt, device_id):\n        logger.disabled = True\n\n    return gpu_rank\n\n\ndef all_reduce_and_rescale_tensors(tensors, rescale_denom,\n                                   buffer_size=104857600):\n    \"\"\"All-reduce and rescale tensors in chunks of the specified size.\n\n    Args:\n        tensors: list of Tensors to all-reduce\n        rescale_denom: denominator for rescaling summed Tensors\n        buffer_size: all-reduce chunk size in bytes\n    \"\"\"\n    # buffer size in bytes, determine equiv. # of elements based on data type\n    buffer_t = tensors[0].new(\n        math.ceil(buffer_size / tensors[0].element_size())).zero_()\n    buffer = []\n\n    def all_reduce_buffer():\n        # copy tensors into buffer_t\n        offset = 0\n        for t in buffer:\n            numel = t.numel()\n            buffer_t[offset:offset+numel].copy_(t.view(-1))\n            offset += numel\n\n        # all-reduce and rescale\n        torch.distributed.all_reduce(buffer_t[:offset], async_op=True)\n        buffer_t.div_(rescale_denom)\n\n        # copy all-reduced buffer back into tensors\n        offset = 0\n        for t in buffer:\n            numel = t.numel()\n            t.view(-1).copy_(buffer_t[offset:offset+numel])\n            offset += numel\n\n    filled = 0\n    for t in tensors:\n        sz = t.numel() * t.element_size()\n        # print(filled, sz)\n        if sz > buffer_size:\n            # tensor is bigger than buffer, all-reduce and rescale directly\n            torch.distributed.all_reduce(t, async_op=True)\n            t.div_(rescale_denom)\n        elif filled + sz > buffer_size:\n            # buffer is full, all-reduce and replace buffer with grad\n            all_reduce_buffer()\n            buffer = [t]\n            filled = sz\n        else:\n            # add tensor to buffer\n            buffer.append(t)\n            filled += sz\n\n    if len(buffer) > 0:\n        all_reduce_buffer()\n\n\ndef all_gather_list(data, max_size=4096):\n    \"\"\"Gathers arbitrary data from all nodes into a list.\"\"\"\n    world_size = torch.distributed.get_world_size()\n    if not hasattr(all_gather_list, '_in_buffer') or \\\n            max_size != all_gather_list._in_buffer.size():\n        all_gather_list._in_buffer = torch.cuda.ByteTensor(max_size)\n        all_gather_list._out_buffers = [\n            torch.cuda.ByteTensor(max_size)\n            for i in range(world_size)\n        ]\n    in_buffer = all_gather_list._in_buffer\n    out_buffers = all_gather_list._out_buffers\n\n    enc = pickle.dumps(data)\n    enc_size = len(enc)\n    if enc_size + 2 > max_size:\n        raise ValueError(\n            'encoded data exceeds max_size: {}'.format(enc_size + 2))\n    assert max_size < 255*256\n    in_buffer[0] = enc_size // 255  # this encoding works for max_size < 65k\n    in_buffer[1] = enc_size % 255\n    in_buffer[2:enc_size+2] = torch.ByteTensor(list(enc))\n\n    torch.distributed.all_gather(out_buffers, in_buffer.cuda())\n\n    results = []\n    for i in range(world_size):\n        out_buffer = out_buffers[i]\n        size = (255 * out_buffer[0].item()) + out_buffer[1].item()\n\n        bytes_list = bytes(out_buffer[2:size+2].tolist())\n        result = pickle.loads(bytes_list)\n        results.append(result)\n    return results\n\n\nclass ErrorHandler(object):\n    \"\"\"A class that listens for exceptions in children processes and propagates\n    the tracebacks to the parent process.\"\"\"\n\n    def __init__(self, error_queue):\n        \"\"\" init error handler \"\"\"\n        import signal\n        import threading\n        self.error_queue = error_queue\n        self.children_pids = []\n        self.error_thread = threading.Thread(\n            target=self.error_listener, daemon=True)\n        self.error_thread.start()\n        signal.signal(signal.SIGUSR1, self.signal_handler)\n\n    def add_child(self, pid):\n        \"\"\" error handler \"\"\"\n        self.children_pids.append(pid)\n\n    def error_listener(self):\n        \"\"\" error listener \"\"\"\n        (rank, original_trace) = self.error_queue.get()\n        self.error_queue.put((rank, original_trace))\n        os.kill(os.getpid(), signal.SIGUSR1)\n\n    def signal_handler(self, signalnum, stackframe):\n        \"\"\" signal handler \"\"\"\n        for pid in self.children_pids:\n            os.kill(pid, signal.SIGINT)  # kill children processes\n        (rank, original_trace) = self.error_queue.get()\n        msg = \"\"\"\\n\\n-- Tracebacks above this line can probably\n                 be ignored --\\n\\n\"\"\"\n        msg += original_trace\n        raise Exception(msg)\n\n\ndef consumer(process_fn, opt, device_id, error_queue):  # noqa: E501\n    \"\"\"Run `process_fn` on `device_id` with data from `batch_queue`.\"\"\"\n    try:\n        gpu_rank = multi_init(opt, device_id)\n        if gpu_rank != opt.gpu_ranks[device_id]:\n            raise AssertionError(\"An error occurred in \\\n                  Distributed initialization\")\n        process_fn(opt, device_id=device_id)\n    except KeyboardInterrupt:\n        pass  # killed by parent, do nothing\n    except Exception:\n        # propagate exception to parent process, keeping original traceback\n        import traceback\n        error_queue.put((opt.gpu_ranks[device_id], traceback.format_exc()))\n", "path": "onmt/utils/distributed.py"}], "after_files": [{"content": "\"\"\" Pytorch Distributed utils\n    This piece of code was heavily inspired by the equivalent of Fairseq-py\n    https://github.com/pytorch/fairseq\n\"\"\"\nimport os\nimport signal\nimport math\nimport pickle\nimport torch.distributed\nfrom onmt.utils.logging import logger\n\n\ndef is_master(opt, device_id):\n    return opt.gpu_ranks[device_id] == 0\n\n\ndef multi_init(opt, device_id):\n    dist_init_method = 'tcp://{master_ip}:{master_port}'.format(\n        master_ip=opt.master_ip,\n        master_port=opt.master_port)\n    dist_world_size = opt.world_size\n    torch.distributed.init_process_group(\n        backend=opt.gpu_backend, init_method=dist_init_method,\n        world_size=dist_world_size, rank=opt.gpu_ranks[device_id])\n    gpu_rank = torch.distributed.get_rank()\n    if not is_master(opt, device_id):\n        logger.disabled = True\n\n    return gpu_rank\n\n\ndef all_reduce_and_rescale_tensors(tensors, rescale_denom,\n                                   buffer_size=104857600):\n    \"\"\"All-reduce and rescale tensors in chunks of the specified size.\n\n    Args:\n        tensors: list of Tensors to all-reduce\n        rescale_denom: denominator for rescaling summed Tensors\n        buffer_size: all-reduce chunk size in bytes\n    \"\"\"\n    # buffer size in bytes, determine equiv. # of elements based on data type\n    buffer_t = tensors[0].new(\n        math.ceil(buffer_size / tensors[0].element_size())).zero_()\n    buffer = []\n\n    def all_reduce_buffer():\n        # copy tensors into buffer_t\n        offset = 0\n        for t in buffer:\n            numel = t.numel()\n            buffer_t[offset:offset+numel].copy_(t.view(-1))\n            offset += numel\n\n        # all-reduce and rescale\n        torch.distributed.all_reduce(buffer_t[:offset], async_op=False)\n        buffer_t.div_(rescale_denom)\n\n        # copy all-reduced buffer back into tensors\n        offset = 0\n        for t in buffer:\n            numel = t.numel()\n            t.view(-1).copy_(buffer_t[offset:offset+numel])\n            offset += numel\n\n    filled = 0\n    for t in tensors:\n        sz = t.numel() * t.element_size()\n        # print(filled, sz)\n        if sz > buffer_size:\n            # tensor is bigger than buffer, all-reduce and rescale directly\n            torch.distributed.all_reduce(t, async_op=False)\n            t.div_(rescale_denom)\n        elif filled + sz > buffer_size:\n            # buffer is full, all-reduce and replace buffer with grad\n            all_reduce_buffer()\n            buffer = [t]\n            filled = sz\n        else:\n            # add tensor to buffer\n            buffer.append(t)\n            filled += sz\n\n    if len(buffer) > 0:\n        all_reduce_buffer()\n\n\ndef all_gather_list(data, max_size=4096):\n    \"\"\"Gathers arbitrary data from all nodes into a list.\"\"\"\n    world_size = torch.distributed.get_world_size()\n    if not hasattr(all_gather_list, '_in_buffer') or \\\n            max_size != all_gather_list._in_buffer.size():\n        all_gather_list._in_buffer = torch.cuda.ByteTensor(max_size)\n        all_gather_list._out_buffers = [\n            torch.cuda.ByteTensor(max_size)\n            for i in range(world_size)\n        ]\n    in_buffer = all_gather_list._in_buffer\n    out_buffers = all_gather_list._out_buffers\n\n    enc = pickle.dumps(data)\n    enc_size = len(enc)\n    if enc_size + 2 > max_size:\n        raise ValueError(\n            'encoded data exceeds max_size: {}'.format(enc_size + 2))\n    assert max_size < 255*256\n    in_buffer[0] = enc_size // 255  # this encoding works for max_size < 65k\n    in_buffer[1] = enc_size % 255\n    in_buffer[2:enc_size+2] = torch.ByteTensor(list(enc))\n\n    torch.distributed.all_gather(out_buffers, in_buffer.cuda())\n\n    results = []\n    for i in range(world_size):\n        out_buffer = out_buffers[i]\n        size = (255 * out_buffer[0].item()) + out_buffer[1].item()\n\n        bytes_list = bytes(out_buffer[2:size+2].tolist())\n        result = pickle.loads(bytes_list)\n        results.append(result)\n    return results\n\n\nclass ErrorHandler(object):\n    \"\"\"A class that listens for exceptions in children processes and propagates\n    the tracebacks to the parent process.\"\"\"\n\n    def __init__(self, error_queue):\n        \"\"\" init error handler \"\"\"\n        import signal\n        import threading\n        self.error_queue = error_queue\n        self.children_pids = []\n        self.error_thread = threading.Thread(\n            target=self.error_listener, daemon=True)\n        self.error_thread.start()\n        signal.signal(signal.SIGUSR1, self.signal_handler)\n\n    def add_child(self, pid):\n        \"\"\" error handler \"\"\"\n        self.children_pids.append(pid)\n\n    def error_listener(self):\n        \"\"\" error listener \"\"\"\n        (rank, original_trace) = self.error_queue.get()\n        self.error_queue.put((rank, original_trace))\n        os.kill(os.getpid(), signal.SIGUSR1)\n\n    def signal_handler(self, signalnum, stackframe):\n        \"\"\" signal handler \"\"\"\n        for pid in self.children_pids:\n            os.kill(pid, signal.SIGINT)  # kill children processes\n        (rank, original_trace) = self.error_queue.get()\n        msg = \"\"\"\\n\\n-- Tracebacks above this line can probably\n                 be ignored --\\n\\n\"\"\"\n        msg += original_trace\n        raise Exception(msg)\n\n\ndef consumer(process_fn, opt, device_id, error_queue):  # noqa: E501\n    \"\"\"Run `process_fn` on `device_id` with data from `batch_queue`.\"\"\"\n    try:\n        gpu_rank = multi_init(opt, device_id)\n        if gpu_rank != opt.gpu_ranks[device_id]:\n            raise AssertionError(\"An error occurred in \\\n                  Distributed initialization\")\n        process_fn(opt, device_id=device_id)\n    except KeyboardInterrupt:\n        pass  # killed by parent, do nothing\n    except Exception:\n        # propagate exception to parent process, keeping original traceback\n        import traceback\n        error_queue.put((opt.gpu_ranks[device_id], traceback.format_exc()))\n", "path": "onmt/utils/distributed.py"}]}