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zhwesky2010
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May 21, 2025
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文档需要修改，注意与文档思路保持对齐一致
zhwesky2010
reviewed
May 26, 2025
paconvert/api_matcher.py
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| CODE_TEMPLATE = textwrap.dedent( | ||
| """ | ||
| def reduce_scatter_tensor(output, input, op, group, async_op): | ||
| input_list = [input[i] for i in range(input.shape[0])] |
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这个的切分方式需要根据world_size来，看下文档的comment
| print(f"Rank {rank} output tensor: {output_tensor}") | ||
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| if rank == 0: |
| rank = dist.get_rank() | ||
| torch.cuda.set_device(rank) | ||
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| input = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32).cuda() |
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这个其实是按world_size切分，不是按shape[0]切分，但这里shape[0] == world_size，导致绕过了这个问题
所以设置input时，设置shape[0] != world_size吧，这样能测试的更充分。另外torch的shape可能有两种情况，都测试一下
zhwesky2010
reviewed
May 27, 2025
zhwesky2010
reviewed
May 28, 2025
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| print(f"Rank {rank} output tensor: {output_tensor}") | ||
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| if rank == 0: |
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这个如果要保存两次，两个结果都能测到吗，测不到的话 可能需要写两个单测
| torch.cuda.set_device(rank) | ||
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| input = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32).cuda() | ||
| output = torch.empty(1, 2, dtype=torch.float32).cuda() |
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同上，两个结果都能测到吗，测不到可能需要写两个文件
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