prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `colossalai/engine/gradient_handler/utils.py`
  Content:
  ```
  1 import torch.distributed as dist
  2 import torch.nn as nn
  3 from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  4 from typing import Iterable
  5 
  6 
  7 def bucket_allreduce(param_list: Iterable[nn.Parameter], group=None):
  8     # get communication world size
  9     comm_size = dist.get_world_size(group)
  10     # bucketize and all-reduce
  11     buckets = {}
  12     # Pack the buckets.
  13     for param in param_list:
  14         if param.requires_grad and param.grad is not None:
  15             tp = param.data.type()
  16             if tp not in buckets:
  17                 buckets[tp] = []
  18             buckets[tp].append(param)
  19 
  20     # For each bucket, all-reduce and copy all-reduced grads.
  21     for tp in buckets:
  22         bucket = buckets[tp]
  23         grads = [param.grad.data for param in bucket]
  24         coalesced = _flatten_dense_tensors(grads)
  25         coalesced /= comm_size
  26 
  27         dist.all_reduce(coalesced, group=group)
  28         for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
  29             buf.copy_(synced)
  30
  ```
  --- 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/colossalai/engine/gradient_handler/utils.py b/colossalai/engine/gradient_handler/utils.py
  --- a/colossalai/engine/gradient_handler/utils.py
  +++ b/colossalai/engine/gradient_handler/utils.py
  @@ -1,29 +1,30 @@
  -import torch.distributed as dist
  -import torch.nn as nn
  -from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  -from typing import Iterable
  -
  -
  -def bucket_allreduce(param_list: Iterable[nn.Parameter], group=None):
  -    # get communication world size
  -    comm_size = dist.get_world_size(group)
  -    # bucketize and all-reduce
  -    buckets = {}
  -    # Pack the buckets.
  -    for param in param_list:
  -        if param.requires_grad and param.grad is not None:
  -            tp = param.data.type()
  -            if tp not in buckets:
  -                buckets[tp] = []
  -            buckets[tp].append(param)
  -
  -    # For each bucket, all-reduce and copy all-reduced grads.
  -    for tp in buckets:
  -        bucket = buckets[tp]
  -        grads = [param.grad.data for param in bucket]
  -        coalesced = _flatten_dense_tensors(grads)
  -        coalesced /= comm_size
  -
  -        dist.all_reduce(coalesced, group=group)
  -        for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
  -            buf.copy_(synced)
  +from typing import Iterable
  +
  +import torch.distributed as dist
  +import torch.nn as nn
  +from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  +
  +
  +def bucket_allreduce(param_list: Iterable[nn.Parameter], group=None):
  +    # get communication world size
  +    comm_size = dist.get_world_size(group)
  +    # bucketize and all-reduce
  +    buckets = {}
  +    # Pack the buckets.
  +    for param in param_list:
  +        if param.requires_grad and param.grad is not None:
  +            tp = param.data.type()
  +            if tp not in buckets:
  +                buckets[tp] = []
  +            buckets[tp].append(param)
  +
  +    # For each bucket, all-reduce and copy all-reduced grads.
  +    for tp in buckets:
  +        bucket = buckets[tp]
  +        grads = [param.grad.data for param in bucket]
  +        coalesced = _flatten_dense_tensors(grads)
  +        coalesced /= comm_size
  +
  +        dist.all_reduce(coalesced, group=group)
  +        for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
  +            buf.copy_(synced)
  
verification_info: {"golden_diff": "diff --git a/colossalai/engine/gradient_handler/utils.py b/colossalai/engine/gradient_handler/utils.py\n--- a/colossalai/engine/gradient_handler/utils.py\n+++ b/colossalai/engine/gradient_handler/utils.py\n@@ -1,29 +1,30 @@\n-import torch.distributed as dist\r\n-import torch.nn as nn\r\n-from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors\r\n-from typing import Iterable\r\n-\r\n-\r\n-def bucket_allreduce(param_list: Iterable[nn.Parameter], group=None):\r\n-    # get communication world size\r\n-    comm_size = dist.get_world_size(group)\r\n-    # bucketize and all-reduce\r\n-    buckets = {}\r\n-    # Pack the buckets.\r\n-    for param in param_list:\r\n-        if param.requires_grad and param.grad is not None:\r\n-            tp = param.data.type()\r\n-            if tp not in buckets:\r\n-                buckets[tp] = []\r\n-            buckets[tp].append(param)\r\n-\r\n-    # For each bucket, all-reduce and copy all-reduced grads.\r\n-    for tp in buckets:\r\n-        bucket = buckets[tp]\r\n-        grads = [param.grad.data for param in bucket]\r\n-        coalesced = _flatten_dense_tensors(grads)\r\n-        coalesced /= comm_size\r\n-\r\n-        dist.all_reduce(coalesced, group=group)\r\n-        for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):\r\n-            buf.copy_(synced)\r\n+from typing import Iterable\n+\n+import torch.distributed as dist\n+import torch.nn as nn\n+from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors\n+\n+\n+def bucket_allreduce(param_list: Iterable[nn.Parameter], group=None):\n+    # get communication world size\n+    comm_size = dist.get_world_size(group)\n+    # bucketize and all-reduce\n+    buckets = {}\n+    # Pack the buckets.\n+    for param in param_list:\n+        if param.requires_grad and param.grad is not None:\n+            tp = param.data.type()\n+            if tp not in buckets:\n+                buckets[tp] = []\n+            buckets[tp].append(param)\n+\n+    # For each bucket, all-reduce and copy all-reduced grads.\n+    for tp in buckets:\n+        bucket = buckets[tp]\n+        grads = [param.grad.data for param in bucket]\n+        coalesced = _flatten_dense_tensors(grads)\n+        coalesced /= comm_size\n+\n+        dist.all_reduce(coalesced, group=group)\n+        for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):\n+            buf.copy_(synced)\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "import torch.distributed as dist\r\nimport torch.nn as nn\r\nfrom torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors\r\nfrom typing import Iterable\r\n\r\n\r\ndef bucket_allreduce(param_list: Iterable[nn.Parameter], group=None):\r\n    # get communication world size\r\n    comm_size = dist.get_world_size(group)\r\n    # bucketize and all-reduce\r\n    buckets = {}\r\n    # Pack the buckets.\r\n    for param in param_list:\r\n        if param.requires_grad and param.grad is not None:\r\n            tp = param.data.type()\r\n            if tp not in buckets:\r\n                buckets[tp] = []\r\n            buckets[tp].append(param)\r\n\r\n    # For each bucket, all-reduce and copy all-reduced grads.\r\n    for tp in buckets:\r\n        bucket = buckets[tp]\r\n        grads = [param.grad.data for param in bucket]\r\n        coalesced = _flatten_dense_tensors(grads)\r\n        coalesced /= comm_size\r\n\r\n        dist.all_reduce(coalesced, group=group)\r\n        for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):\r\n            buf.copy_(synced)\r\n", "path": "colossalai/engine/gradient_handler/utils.py"}], "after_files": [{"content": "from typing import Iterable\n\nimport torch.distributed as dist\nimport torch.nn as nn\nfrom torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors\n\n\ndef bucket_allreduce(param_list: Iterable[nn.Parameter], group=None):\n    # get communication world size\n    comm_size = dist.get_world_size(group)\n    # bucketize and all-reduce\n    buckets = {}\n    # Pack the buckets.\n    for param in param_list:\n        if param.requires_grad and param.grad is not None:\n            tp = param.data.type()\n            if tp not in buckets:\n                buckets[tp] = []\n            buckets[tp].append(param)\n\n    # For each bucket, all-reduce and copy all-reduced grads.\n    for tp in buckets:\n        bucket = buckets[tp]\n        grads = [param.grad.data for param in bucket]\n        coalesced = _flatten_dense_tensors(grads)\n        coalesced /= comm_size\n\n        dist.all_reduce(coalesced, group=group)\n        for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):\n            buf.copy_(synced)\n", "path": "colossalai/engine/gradient_handler/utils.py"}]}