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/context/process_group_initializer/initializer_sequence.py]
  1 #!/usr/bin/env python
  2 # -*- encoding: utf-8 -*-
  3 import torch.distributed as dist
  4 
  5 from colossalai.registry import DIST_GROUP_INITIALIZER
  6 from .initializer_tensor import Initializer_Tensor
  7 from .process_group_initializer import ProcessGroupInitializer
  8 from ..parallel_mode import ParallelMode
  9 
  10 
  11 @DIST_GROUP_INITIALIZER.register_module
  12 class Initializer_Sequence_DP(ProcessGroupInitializer):
  13     """A ProcessGroupInitializer for sequence parallelism all-reduce.
  14 
  15     In Sequence Parallelism, each GPU holds the full copy of model weights,
  16     thus, gradient all-reduce occurs across all processes in the same pipeline stage
  17 
  18     Args:
  19         rank (int): The rank of current process
  20         world_size (int): Size of whole communication world
  21         config (Config): Running configuration
  22         data_parallel_size (int): Size of data parallel
  23         pipeline_parallel_size (int): Size of pipeline parallel
  24         tensor_parallel_size (int): Size of tensor parallel
  25     """
  26 
  27     def __init__(self, *args, **kwargs):
  28         super().__init__(*args, **kwargs)
  29         self.dp_size = self.world_size // self.pipeline_parallel_size
  30         self.num_group = self.pipeline_parallel_size
  31 
  32     def init_dist_group(self):
  33         """Initialize Sequence Parallel process groups used for gradient all-reduce.
  34 
  35         Returns:
  36             Tuple: A tuple (local_rank, group_world_size, process_group, ranks_in_group, mode).
  37         """
  38         local_rank = None
  39         ranks_in_group = None
  40         process_group = None
  41         cpu_group = None
  42         group_world_size = None
  43         mode = ParallelMode.SEQUENCE_DP
  44 
  45         for i in range(self.num_group):
  46             ranks = [i * self.dp_size + j for j in range(self.dp_size)]
  47             group = dist.new_group(ranks)
  48             group_cpu = dist.new_group(ranks, backend='gloo') if dist.get_backend() != 'gloo' else group
  49 
  50             if self.rank in ranks:
  51                 local_rank = ranks.index(self.rank)
  52                 group_world_size = len(ranks)
  53                 process_group = group
  54                 cpu_group = group_cpu
  55                 ranks_in_group = ranks
  56 
  57         return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode
  58 
  59 
  60 @DIST_GROUP_INITIALIZER.register_module
  61 class Initializer_Sequence(ProcessGroupInitializer):
  62     """A ProcessGroupInitializer for sequence parallelism.
  63 
  64     Args:
  65         rank (int): The rank of current process.
  66         world_size (int): Size of whole communication world.
  67         config (Config): Running configuration.
  68         data_parallel_size (int): Size of data parallel.
  69         pipeline_parallel_size (int): Size of pipeline parallel.
  70         tensor_parallel_size (int): Size of tensor parallel.
  71     """
  72 
  73     def __init__(self, *args, **kwargs):
  74         super().__init__(*args, **kwargs)
  75         # reuse tensor parallel initializer code
  76         self._sequence_initializer = Initializer_Tensor(*args, **kwargs)
  77         self._sequence_dp_initializer = Initializer_Sequence_DP(*args, **kwargs)
  78 
  79     def init_dist_group(self):
  80         """Initialize Sequence parallel process groups and assign local_ranks and groups to each gpu.
  81 
  82         Sequence parallelism requires 2 process groups. The first is for model forward where several processes
  83         exchange partial query, key and value embedding to compute self attention values. The second is for
  84         all-reduce to synchronize the model parameters.
  85 
  86         Returns:
  87             List[Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode)]:
  88                 A Sequence parallelism's information in list of tuples.
  89         """
  90 
  91         parallel_setting = []
  92 
  93         local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode = \
  94             self._sequence_initializer.init_dist_group()
  95         # change mode to sequence
  96         mode = ParallelMode.SEQUENCE
  97 
  98         parallel_setting.append((local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode))
  99         parallel_setting.append(self._sequence_dp_initializer.init_dist_group())
  100         return parallel_setting
  101
  [end of colossalai/context/process_group_initializer/initializer_sequence.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/context/process_group_initializer/initializer_sequence.py b/colossalai/context/process_group_initializer/initializer_sequence.py
  --- a/colossalai/context/process_group_initializer/initializer_sequence.py
  +++ b/colossalai/context/process_group_initializer/initializer_sequence.py
  @@ -3,9 +3,10 @@
   import torch.distributed as dist
   
   from colossalai.registry import DIST_GROUP_INITIALIZER
  +
  +from ..parallel_mode import ParallelMode
   from .initializer_tensor import Initializer_Tensor
   from .process_group_initializer import ProcessGroupInitializer
  -from ..parallel_mode import ParallelMode
   
   
   @DIST_GROUP_INITIALIZER.register_module
  
verification_info: {"golden_diff": "diff --git a/colossalai/context/process_group_initializer/initializer_sequence.py b/colossalai/context/process_group_initializer/initializer_sequence.py\n--- a/colossalai/context/process_group_initializer/initializer_sequence.py\n+++ b/colossalai/context/process_group_initializer/initializer_sequence.py\n@@ -3,9 +3,10 @@\n import torch.distributed as dist\n \n from colossalai.registry import DIST_GROUP_INITIALIZER\n+\n+from ..parallel_mode import ParallelMode\n from .initializer_tensor import Initializer_Tensor\n from .process_group_initializer import ProcessGroupInitializer\n-from ..parallel_mode import ParallelMode\n \n \n @DIST_GROUP_INITIALIZER.register_module\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\nimport torch.distributed as dist\n\nfrom colossalai.registry import DIST_GROUP_INITIALIZER\nfrom .initializer_tensor import Initializer_Tensor\nfrom .process_group_initializer import ProcessGroupInitializer\nfrom ..parallel_mode import ParallelMode\n\n\n@DIST_GROUP_INITIALIZER.register_module\nclass Initializer_Sequence_DP(ProcessGroupInitializer):\n    \"\"\"A ProcessGroupInitializer for sequence parallelism all-reduce.\n\n    In Sequence Parallelism, each GPU holds the full copy of model weights,\n    thus, gradient all-reduce occurs across all processes in the same pipeline stage\n\n    Args:\n        rank (int): The rank of current process\n        world_size (int): Size of whole communication world\n        config (Config): Running configuration\n        data_parallel_size (int): Size of data parallel\n        pipeline_parallel_size (int): Size of pipeline parallel\n        tensor_parallel_size (int): Size of tensor parallel\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.dp_size = self.world_size // self.pipeline_parallel_size\n        self.num_group = self.pipeline_parallel_size\n\n    def init_dist_group(self):\n        \"\"\"Initialize Sequence Parallel process groups used for gradient all-reduce.\n\n        Returns:\n            Tuple: A tuple (local_rank, group_world_size, process_group, ranks_in_group, mode).\n        \"\"\"\n        local_rank = None\n        ranks_in_group = None\n        process_group = None\n        cpu_group = None\n        group_world_size = None\n        mode = ParallelMode.SEQUENCE_DP\n\n        for i in range(self.num_group):\n            ranks = [i * self.dp_size + j for j in range(self.dp_size)]\n            group = dist.new_group(ranks)\n            group_cpu = dist.new_group(ranks, backend='gloo') if dist.get_backend() != 'gloo' else group\n\n            if self.rank in ranks:\n                local_rank = ranks.index(self.rank)\n                group_world_size = len(ranks)\n                process_group = group\n                cpu_group = group_cpu\n                ranks_in_group = ranks\n\n        return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode\n\n\n@DIST_GROUP_INITIALIZER.register_module\nclass Initializer_Sequence(ProcessGroupInitializer):\n    \"\"\"A ProcessGroupInitializer for sequence parallelism.\n\n    Args:\n        rank (int): The rank of current process.\n        world_size (int): Size of whole communication world.\n        config (Config): Running configuration.\n        data_parallel_size (int): Size of data parallel.\n        pipeline_parallel_size (int): Size of pipeline parallel.\n        tensor_parallel_size (int): Size of tensor parallel.\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        # reuse tensor parallel initializer code\n        self._sequence_initializer = Initializer_Tensor(*args, **kwargs)\n        self._sequence_dp_initializer = Initializer_Sequence_DP(*args, **kwargs)\n\n    def init_dist_group(self):\n        \"\"\"Initialize Sequence parallel process groups and assign local_ranks and groups to each gpu.\n\n        Sequence parallelism requires 2 process groups. The first is for model forward where several processes\n        exchange partial query, key and value embedding to compute self attention values. The second is for\n        all-reduce to synchronize the model parameters.\n\n        Returns:\n            List[Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode)]:\n                A Sequence parallelism's information in list of tuples.\n        \"\"\"\n\n        parallel_setting = []\n\n        local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode = \\\n            self._sequence_initializer.init_dist_group()\n        # change mode to sequence\n        mode = ParallelMode.SEQUENCE\n\n        parallel_setting.append((local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode))\n        parallel_setting.append(self._sequence_dp_initializer.init_dist_group())\n        return parallel_setting\n", "path": "colossalai/context/process_group_initializer/initializer_sequence.py"}]}