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/pipeline/schedule/_utils.py`
Content:
```
1 from typing import Any, List, Optional
2 
3 import torch
4 import torch.cuda
5 from torch.nn import Module
6 from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten
7 
8 
9 def to_device(x: Any, device: Optional[torch.device] = None) -> Any:
10     """Move object to device if it is a tensor.
11 
12     Args:
13         x (Any): Object to be moved.
14         device (Optional[torch.device], optional): Target device. Defaults to None.
15 
16     Returns:
17         Any: Moved object.
18     """
19     if isinstance(x, torch.Tensor):
20         return x.to(device)
21     return x
22 
23 
24 def get_batch_size(batch: Any) -> int:
25     """Get the batch size (size of dimension-0) of the first tensor in the batch.
26 
27     Args:
28         batch (Any): Batch to be inspected.
29 
30     Raises:
31         RuntimeError: If no tensor is found in the batch.
32 
33     Returns:
34         int: Batch size.
35     """
36     data_list, _ = tree_flatten(batch)
37     for data in data_list:
38         if isinstance(data, torch.Tensor):
39             return data.size(0)
40     raise RuntimeError('No tensor found in the batch')
41 
42 
43 def get_micro_batch(batch: Any, start: int, micro_batch_size: int) -> Any:
44     """Get a micro batch of the original batch.
45 
46     Args:
47         batch (Any): Batch to be sliced.
48         start (int): Start index of the micro batch.
49         micro_batch_size (int): Size of the micro batch.
50 
51     Returns:
52         Any: Target micro batch.
53     """
54 
55     def _get_tensor_slice(x: Any):
56         if isinstance(x, torch.Tensor):
57             return x[start:start + micro_batch_size]
58         return x
59 
60     return tree_map(_get_tensor_slice, batch)
61 
62 
63 def model_forward(model: Module, data: Any, internal_inputs: Optional[dict]) -> Any:
64     """Call model forward function with data and internal inputs.
65 
66     Args:
67         model (Module): Model to be called.
68         data (Any): Data loaded from data iterator.
69         internal_inputs (Optional[dict]): Data from previous stage. It must be a dict or None if it's the first stage.
70 
71     Returns:
72         Any: Outputs of the model.
73     """
74     if internal_inputs is None:
75         internal_inputs = {}
76     if isinstance(data, (list, tuple)):
77         return model(*data, **internal_inputs)
78     elif isinstance(data, dict):
79         return model(**data, **internal_inputs)
80     return model(data, **internal_inputs)
81 
82 
83 def retain_grad(x: Any) -> None:
84     """Call retain_grad() on a tensor.
85 
86     Args:
87         x (Any): Object to be called.
88     """
89     if isinstance(x, torch.Tensor) and x.requires_grad:
90         x.retain_grad()
91 
92 
93 def detach(x: Any) -> Any:
94     """Call detach() on a tensor.
95 
96     Args:
97         x (Any): Object to be called.
98 
99     Returns:
100         Any: The detached object.
101     """
102     if isinstance(x, torch.Tensor):
103         return x.detach()
104     return x
105 
106 
107 def merge_batch(data: List[Any]) -> Any:
108     """Merge micro batches into a batch.
109 
110     Args:
111         data (List[Any]): A list of micro batches.
112 
113     Returns:
114         Any: Merge batch.
115     """
116     if len(data) == 0:
117         return
118     flattened_data = []
119     tree_spec = None
120     for d in data:
121         elems, tree_spec = tree_flatten(d)
122         flattened_data.append(elems)
123     merged_data = []
124     for elem_batch in zip(*flattened_data):
125         if isinstance(elem_batch[0], torch.Tensor):
126             merged_data.append(torch.cat(elem_batch, dim=0))
127         else:
128             merged_data.append(list(elem_batch))
129     return tree_unflatten(merged_data, tree_spec)
130
```
--- 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/colossalai/pipeline/schedule/_utils.py b/colossalai/pipeline/schedule/_utils.py
--- a/colossalai/pipeline/schedule/_utils.py
+++ b/colossalai/pipeline/schedule/_utils.py
@@ -123,7 +123,10 @@
     merged_data = []
     for elem_batch in zip(*flattened_data):
         if isinstance(elem_batch[0], torch.Tensor):
-            merged_data.append(torch.cat(elem_batch, dim=0))
+            if len(elem_batch[0].shape) == 0:    # set loss to None in pipeline outputs
+                merged_data.append(None)
+            else:
+                merged_data.append(torch.cat(elem_batch, dim=0))
         else:
             merged_data.append(list(elem_batch))
     return tree_unflatten(merged_data, tree_spec)

{"golden_diff": "diff --git a/colossalai/pipeline/schedule/_utils.py b/colossalai/pipeline/schedule/_utils.py\n--- a/colossalai/pipeline/schedule/_utils.py\n+++ b/colossalai/pipeline/schedule/_utils.py\n@@ -123,7 +123,10 @@\n     merged_data = []\n     for elem_batch in zip(*flattened_data):\n         if isinstance(elem_batch[0], torch.Tensor):\n-            merged_data.append(torch.cat(elem_batch, dim=0))\n+            if len(elem_batch[0].shape) == 0:    # set loss to None in pipeline outputs\n+                merged_data.append(None)\n+            else:\n+                merged_data.append(torch.cat(elem_batch, dim=0))\n         else:\n             merged_data.append(list(elem_batch))\n     return tree_unflatten(merged_data, tree_spec)\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "from typing import Any, List, Optional\n\nimport torch\nimport torch.cuda\nfrom torch.nn import Module\nfrom torch.utils._pytree import tree_flatten, tree_map, tree_unflatten\n\n\ndef to_device(x: Any, device: Optional[torch.device] = None) -> Any:\n    \"\"\"Move object to device if it is a tensor.\n\n    Args:\n        x (Any): Object to be moved.\n        device (Optional[torch.device], optional): Target device. Defaults to None.\n\n    Returns:\n        Any: Moved object.\n    \"\"\"\n    if isinstance(x, torch.Tensor):\n        return x.to(device)\n    return x\n\n\ndef get_batch_size(batch: Any) -> int:\n    \"\"\"Get the batch size (size of dimension-0) of the first tensor in the batch.\n\n    Args:\n        batch (Any): Batch to be inspected.\n\n    Raises:\n        RuntimeError: If no tensor is found in the batch.\n\n    Returns:\n        int: Batch size.\n    \"\"\"\n    data_list, _ = tree_flatten(batch)\n    for data in data_list:\n        if isinstance(data, torch.Tensor):\n            return data.size(0)\n    raise RuntimeError('No tensor found in the batch')\n\n\ndef get_micro_batch(batch: Any, start: int, micro_batch_size: int) -> Any:\n    \"\"\"Get a micro batch of the original batch.\n\n    Args:\n        batch (Any): Batch to be sliced.\n        start (int): Start index of the micro batch.\n        micro_batch_size (int): Size of the micro batch.\n\n    Returns:\n        Any: Target micro batch.\n    \"\"\"\n\n    def _get_tensor_slice(x: Any):\n        if isinstance(x, torch.Tensor):\n            return x[start:start + micro_batch_size]\n        return x\n\n    return tree_map(_get_tensor_slice, batch)\n\n\ndef model_forward(model: Module, data: Any, internal_inputs: Optional[dict]) -> Any:\n    \"\"\"Call model forward function with data and internal inputs.\n\n    Args:\n        model (Module): Model to be called.\n        data (Any): Data loaded from data iterator.\n        internal_inputs (Optional[dict]): Data from previous stage. It must be a dict or None if it's the first stage.\n\n    Returns:\n        Any: Outputs of the model.\n    \"\"\"\n    if internal_inputs is None:\n        internal_inputs = {}\n    if isinstance(data, (list, tuple)):\n        return model(*data, **internal_inputs)\n    elif isinstance(data, dict):\n        return model(**data, **internal_inputs)\n    return model(data, **internal_inputs)\n\n\ndef retain_grad(x: Any) -> None:\n    \"\"\"Call retain_grad() on a tensor.\n\n    Args:\n        x (Any): Object to be called.\n    \"\"\"\n    if isinstance(x, torch.Tensor) and x.requires_grad:\n        x.retain_grad()\n\n\ndef detach(x: Any) -> Any:\n    \"\"\"Call detach() on a tensor.\n\n    Args:\n        x (Any): Object to be called.\n\n    Returns:\n        Any: The detached object.\n    \"\"\"\n    if isinstance(x, torch.Tensor):\n        return x.detach()\n    return x\n\n\ndef merge_batch(data: List[Any]) -> Any:\n    \"\"\"Merge micro batches into a batch.\n\n    Args:\n        data (List[Any]): A list of micro batches.\n\n    Returns:\n        Any: Merge batch.\n    \"\"\"\n    if len(data) == 0:\n        return\n    flattened_data = []\n    tree_spec = None\n    for d in data:\n        elems, tree_spec = tree_flatten(d)\n        flattened_data.append(elems)\n    merged_data = []\n    for elem_batch in zip(*flattened_data):\n        if isinstance(elem_batch[0], torch.Tensor):\n            merged_data.append(torch.cat(elem_batch, dim=0))\n        else:\n            merged_data.append(list(elem_batch))\n    return tree_unflatten(merged_data, tree_spec)\n", "path": "colossalai/pipeline/schedule/_utils.py"}], "after_files": [{"content": "from typing import Any, List, Optional\n\nimport torch\nimport torch.cuda\nfrom torch.nn import Module\nfrom torch.utils._pytree import tree_flatten, tree_map, tree_unflatten\n\n\ndef to_device(x: Any, device: Optional[torch.device] = None) -> Any:\n    \"\"\"Move object to device if it is a tensor.\n\n    Args:\n        x (Any): Object to be moved.\n        device (Optional[torch.device], optional): Target device. Defaults to None.\n\n    Returns:\n        Any: Moved object.\n    \"\"\"\n    if isinstance(x, torch.Tensor):\n        return x.to(device)\n    return x\n\n\ndef get_batch_size(batch: Any) -> int:\n    \"\"\"Get the batch size (size of dimension-0) of the first tensor in the batch.\n\n    Args:\n        batch (Any): Batch to be inspected.\n\n    Raises:\n        RuntimeError: If no tensor is found in the batch.\n\n    Returns:\n        int: Batch size.\n    \"\"\"\n    data_list, _ = tree_flatten(batch)\n    for data in data_list:\n        if isinstance(data, torch.Tensor):\n            return data.size(0)\n    raise RuntimeError('No tensor found in the batch')\n\n\ndef get_micro_batch(batch: Any, start: int, micro_batch_size: int) -> Any:\n    \"\"\"Get a micro batch of the original batch.\n\n    Args:\n        batch (Any): Batch to be sliced.\n        start (int): Start index of the micro batch.\n        micro_batch_size (int): Size of the micro batch.\n\n    Returns:\n        Any: Target micro batch.\n    \"\"\"\n\n    def _get_tensor_slice(x: Any):\n        if isinstance(x, torch.Tensor):\n            return x[start:start + micro_batch_size]\n        return x\n\n    return tree_map(_get_tensor_slice, batch)\n\n\ndef model_forward(model: Module, data: Any, internal_inputs: Optional[dict]) -> Any:\n    \"\"\"Call model forward function with data and internal inputs.\n\n    Args:\n        model (Module): Model to be called.\n        data (Any): Data loaded from data iterator.\n        internal_inputs (Optional[dict]): Data from previous stage. It must be a dict or None if it's the first stage.\n\n    Returns:\n        Any: Outputs of the model.\n    \"\"\"\n    if internal_inputs is None:\n        internal_inputs = {}\n    if isinstance(data, (list, tuple)):\n        return model(*data, **internal_inputs)\n    elif isinstance(data, dict):\n        return model(**data, **internal_inputs)\n    return model(data, **internal_inputs)\n\n\ndef retain_grad(x: Any) -> None:\n    \"\"\"Call retain_grad() on a tensor.\n\n    Args:\n        x (Any): Object to be called.\n    \"\"\"\n    if isinstance(x, torch.Tensor) and x.requires_grad:\n        x.retain_grad()\n\n\ndef detach(x: Any) -> Any:\n    \"\"\"Call detach() on a tensor.\n\n    Args:\n        x (Any): Object to be called.\n\n    Returns:\n        Any: The detached object.\n    \"\"\"\n    if isinstance(x, torch.Tensor):\n        return x.detach()\n    return x\n\n\ndef merge_batch(data: List[Any]) -> Any:\n    \"\"\"Merge micro batches into a batch.\n\n    Args:\n        data (List[Any]): A list of micro batches.\n\n    Returns:\n        Any: Merge batch.\n    \"\"\"\n    if len(data) == 0:\n        return\n    flattened_data = []\n    tree_spec = None\n    for d in data:\n        elems, tree_spec = tree_flatten(d)\n        flattened_data.append(elems)\n    merged_data = []\n    for elem_batch in zip(*flattened_data):\n        if isinstance(elem_batch[0], torch.Tensor):\n            if len(elem_batch[0].shape) == 0:    # set loss to None in pipeline outputs\n                merged_data.append(None)\n            else:\n                merged_data.append(torch.cat(elem_batch, dim=0))\n        else:\n            merged_data.append(list(elem_batch))\n    return tree_unflatten(merged_data, tree_spec)\n", "path": "colossalai/pipeline/schedule/_utils.py"}]}