prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  
  </issue>
  <code>
  [start of colossalai/pipeline/schedule/_utils.py]
  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 of colossalai/pipeline/schedule/_utils.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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)
  
verification_info: {"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"}]}