prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [BUG]: Wrong import in ColossalAuto's meta_registry/binary_elementwise_ops.py
  ### 🐛 Describe the bug
  
  # Problem description
  
  The file `colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py` contains the following line: 
  
  ```python
  from ..constants import BCAST_FUNC_OP
  ```
  
  However, the file `colossalai/auto_parallel/meta_profiler/constants.py` which this import refers to does not contain any `BCAST_FUNC_OP`. This leads to an `ImportError` when running ColossalAuto in release 0.3.3 and newer. 
  
  This constant can be found in the file `colossalai/auto_parallel/tensor_shard/constants.py`. The last commit to `colossalai/auto_parallel/meta_profiler/constants.py` (commit ID `079bf3cb`) removes the import of tensor_shard's `constants.py` from meta_profiler's `constants.py` (seemingly due to an automated refactoring).
  
  # Solution
  
  Since no other file in the `meta_registry` module uses constants from the `tensor_shard/constants.py` and to avoid automated removal of "unused" imports in the future, the import statement in question in above-mentioned `binary_elementwise_ops.py` could be changed to: 
  
  ```python
  from colossalai.auto_parallel.tensor_shard.constants import BCAST_FUNC_OP
  ```
  
  ### Environment
  
  - Python 3.8
  - Torch 1.12.0
  - no CUDA
  
  </issue>
  <code>
  [start of colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py]
  1 from typing import List, Tuple
  2 
  3 import torch
  4 
  5 from colossalai._analyzer._subclasses.flop_tensor import flop_mapping
  6 from colossalai._analyzer.fx.node_util import compute_size_in_bytes as activation_size
  7 from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem
  8 
  9 from ..constants import BCAST_FUNC_OP
  10 from ..registry import meta_register
  11 
  12 __all__ = ["binary_elementwise_meta_info"]
  13 
  14 
  15 @meta_register.register(BCAST_FUNC_OP)
  16 def binary_elementwise_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
  17     """Meta information generator for binary elementwise operations
  18     NOTE: Some of the binary elementwise operations will discard the input activation after computation, as they
  19     don't need those tensors for back propagation, for example, if there are two tensors being sent for `torch.add`,
  20     they will be discarded right after add operation is done. We create a simple API in `ShardMetaInfo` class to identify
  21     this behavior, it is critical for better memory estimation.
  22 
  23     Returns:
  24         Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]: compute cost, memory cost and forward inputs
  25     """
  26 
  27     input_op_data = [arg for arg in args if arg.type != OperationDataType.OUTPUT]
  28     output_op_data = next(filter(lambda arg: arg.type == OperationDataType.OUTPUT, args))
  29 
  30     # construct forward args for flop mapping
  31     fwd_in_args = [opdata.data for opdata in input_op_data]
  32     fwd_out_args = [output_op_data.data]
  33 
  34     # calculate cost
  35 
  36     # calculate compute cost
  37     # NOTE: we set bwd_compute_cost two times of fwd_compute_cost in this case
  38     fwd_compute_cost = flop_mapping[torch.ops.aten.add.Tensor](fwd_in_args, fwd_out_args)
  39     bwd_compute_cost = fwd_compute_cost * 2
  40     compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost)
  41 
  42     # calculate memory cost
  43     param_mem_cost = activation_size([arg.data for arg in input_op_data if arg.type == OperationDataType.PARAM])
  44     fwd_mem_cost = MemoryCost(
  45         activation=activation_size(output_op_data.data),
  46         parameter=param_mem_cost,
  47     )
  48     bwd_mem_cost = MemoryCost(
  49         activation=activation_size(fwd_in_args),
  50         parameter=param_mem_cost,
  51     )
  52 
  53     # total cost
  54     total_mem_cost = MemoryCost(
  55         activation=fwd_mem_cost.activation + bwd_mem_cost.activation,
  56         parameter=fwd_mem_cost.parameter + bwd_mem_cost.parameter,
  57     )
  58 
  59     memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
  60 
  61     # store fwd_in, fwd_buffer, fwd_out
  62     fwd_in = []
  63     fwd_buffer = []
  64     fwd_out = [torch.zeros_like(output_op_data.data, device="meta")]
  65 
  66     return compute_cost, memory_cost, fwd_in, fwd_buffer, fwd_out
  67
  [end of colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.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/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py b/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py
  --- a/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py
  +++ b/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py
  @@ -4,9 +4,9 @@
   
   from colossalai._analyzer._subclasses.flop_tensor import flop_mapping
   from colossalai._analyzer.fx.node_util import compute_size_in_bytes as activation_size
  +from colossalai.auto_parallel.tensor_shard.constants import BCAST_FUNC_OP
   from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem
   
  -from ..constants import BCAST_FUNC_OP
   from ..registry import meta_register
   
   __all__ = ["binary_elementwise_meta_info"]
  
verification_info: {"golden_diff": "diff --git a/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py b/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py\n--- a/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py\n+++ b/colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py\n@@ -4,9 +4,9 @@\n \n from colossalai._analyzer._subclasses.flop_tensor import flop_mapping\n from colossalai._analyzer.fx.node_util import compute_size_in_bytes as activation_size\n+from colossalai.auto_parallel.tensor_shard.constants import BCAST_FUNC_OP\n from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem\n \n-from ..constants import BCAST_FUNC_OP\n from ..registry import meta_register\n \n __all__ = [\"binary_elementwise_meta_info\"]\n", "issue": "[BUG]: Wrong import in ColossalAuto's meta_registry/binary_elementwise_ops.py\n### \ud83d\udc1b Describe the bug\n\n# Problem description\r\n\r\nThe file `colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py` contains the following line: \r\n\r\n```python\r\nfrom ..constants import BCAST_FUNC_OP\r\n```\r\n\r\nHowever, the file `colossalai/auto_parallel/meta_profiler/constants.py` which this import refers to does not contain any `BCAST_FUNC_OP`. This leads to an `ImportError` when running ColossalAuto in release 0.3.3 and newer. \r\n\r\nThis constant can be found in the file `colossalai/auto_parallel/tensor_shard/constants.py`. The last commit to `colossalai/auto_parallel/meta_profiler/constants.py` (commit ID `079bf3cb`) removes the import of tensor_shard's `constants.py` from meta_profiler's `constants.py` (seemingly due to an automated refactoring).\r\n\r\n# Solution\r\n\r\nSince no other file in the `meta_registry` module uses constants from the `tensor_shard/constants.py` and to avoid automated removal of \"unused\" imports in the future, the import statement in question in above-mentioned `binary_elementwise_ops.py` could be changed to: \r\n\r\n```python\r\nfrom colossalai.auto_parallel.tensor_shard.constants import BCAST_FUNC_OP\r\n```\n\n### Environment\n\n- Python 3.8\r\n- Torch 1.12.0\r\n- no CUDA\n", "before_files": [{"content": "from typing import List, Tuple\n\nimport torch\n\nfrom colossalai._analyzer._subclasses.flop_tensor import flop_mapping\nfrom colossalai._analyzer.fx.node_util import compute_size_in_bytes as activation_size\nfrom colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem\n\nfrom ..constants import BCAST_FUNC_OP\nfrom ..registry import meta_register\n\n__all__ = [\"binary_elementwise_meta_info\"]\n\n\n@meta_register.register(BCAST_FUNC_OP)\ndef binary_elementwise_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:\n    \"\"\"Meta information generator for binary elementwise operations\n    NOTE: Some of the binary elementwise operations will discard the input activation after computation, as they\n    don't need those tensors for back propagation, for example, if there are two tensors being sent for `torch.add`,\n    they will be discarded right after add operation is done. We create a simple API in `ShardMetaInfo` class to identify\n    this behavior, it is critical for better memory estimation.\n\n    Returns:\n        Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]: compute cost, memory cost and forward inputs\n    \"\"\"\n\n    input_op_data = [arg for arg in args if arg.type != OperationDataType.OUTPUT]\n    output_op_data = next(filter(lambda arg: arg.type == OperationDataType.OUTPUT, args))\n\n    # construct forward args for flop mapping\n    fwd_in_args = [opdata.data for opdata in input_op_data]\n    fwd_out_args = [output_op_data.data]\n\n    # calculate cost\n\n    # calculate compute cost\n    # NOTE: we set bwd_compute_cost two times of fwd_compute_cost in this case\n    fwd_compute_cost = flop_mapping[torch.ops.aten.add.Tensor](fwd_in_args, fwd_out_args)\n    bwd_compute_cost = fwd_compute_cost * 2\n    compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost)\n\n    # calculate memory cost\n    param_mem_cost = activation_size([arg.data for arg in input_op_data if arg.type == OperationDataType.PARAM])\n    fwd_mem_cost = MemoryCost(\n        activation=activation_size(output_op_data.data),\n        parameter=param_mem_cost,\n    )\n    bwd_mem_cost = MemoryCost(\n        activation=activation_size(fwd_in_args),\n        parameter=param_mem_cost,\n    )\n\n    # total cost\n    total_mem_cost = MemoryCost(\n        activation=fwd_mem_cost.activation + bwd_mem_cost.activation,\n        parameter=fwd_mem_cost.parameter + bwd_mem_cost.parameter,\n    )\n\n    memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)\n\n    # store fwd_in, fwd_buffer, fwd_out\n    fwd_in = []\n    fwd_buffer = []\n    fwd_out = [torch.zeros_like(output_op_data.data, device=\"meta\")]\n\n    return compute_cost, memory_cost, fwd_in, fwd_buffer, fwd_out\n", "path": "colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py"}]}