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aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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30 lines
1.1 KiB
30 lines
1.1 KiB
import torch |
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import torch.distributed as dist |
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from torch import Tensor |
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from torch.distributed import ProcessGroup |
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from torch.testing import assert_close |
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def assert_equal(a: Tensor, b: Tensor): |
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assert torch.all(a == b), f'expected a and b to be equal but they are not, {a} vs {b}' |
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def assert_not_equal(a: Tensor, b: Tensor): |
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assert not torch.all(a == b), f'expected a and b to be not equal but they are, {a} vs {b}' |
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def assert_close_loose(a: Tensor, b: Tensor, rtol: float = 1e-3, atol: float = 1e-3): |
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assert_close(a, b, rtol=rtol, atol=atol) |
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def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None): |
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# all gather tensors from different ranks |
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world_size = dist.get_world_size(process_group) |
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tensor_list = [torch.empty_like(tensor) for _ in range(world_size)] |
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dist.all_gather(tensor_list, tensor, group=process_group) |
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# check if they are equal one by one |
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for i in range(world_size - 1): |
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a = tensor_list[i] |
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b = tensor_list[i + 1] |
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assert torch.all(a == b), f'expected tensors on rank {i} and {i + 1} to be equal but they are not, {a} vs {b}'
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