Making large AI models cheaper, faster and more accessible
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import torch
import torch.distributed as dist
from torch import Tensor
from torch.distributed import ProcessGroup
from torch.testing import assert_close
def assert_equal(a: Tensor, b: Tensor):
assert torch.all(a == b), f'expected a and b to be equal but they are not, {a} vs {b}'
def assert_not_equal(a: Tensor, b: Tensor):
assert not torch.all(a == b), f'expected a and b to be not equal but they are, {a} vs {b}'
def assert_close_loose(a: Tensor, b: Tensor, rtol: float = 1e-3, atol: float = 1e-3):
assert_close(a, b, rtol=rtol, atol=atol)
def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None):
# all gather tensors from different ranks
world_size = dist.get_world_size(process_group)
tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
dist.all_gather(tensor_list, tensor, group=process_group)
# check if they are equal one by one
for i in range(world_size - 1):
a = tensor_list[i]
b = tensor_list[i + 1]
assert torch.all(a == b), f'expected tensors on rank {i} and {i + 1} to be equal but they are not, {a} vs {b}'