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53 lines
1.9 KiB
53 lines
1.9 KiB
import torch
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import pytest
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import colossalai
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import torch.nn.functional as F
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import torch.multiprocessing as mp
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from functools import partial
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from colossalai.tensor import ColoTensor, ProcessGroup, ColoTensorSpec
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from colossalai.utils import get_current_device
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.tensor import ShardSpec, ComputeSpec, ComputePattern
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def check_cross_entropy():
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input_t = torch.randn(4, 4, device=get_current_device(), requires_grad=True)
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input_ct = torch.randn(4, 4, device=get_current_device(), requires_grad=True)
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with torch.no_grad():
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input_ct.copy_(input_t)
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target = torch.randint(4, (4,), dtype=torch.int64, device=get_current_device())
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world_size = torch.distributed.get_world_size()
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pg = ProcessGroup(tp_degree=world_size)
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input_t_colo = ColoTensor.from_torch_tensor(tensor=input_ct, spec=ColoTensorSpec(pg))
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input_shard = input_t_colo.redistribute(ShardSpec([-1], [pg.tp_world_size()]))
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input_shard.set_tensor_spec(dist_spec=None, compute_spec=ComputeSpec(ComputePattern.TP1D))
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output = F.cross_entropy(input_t, target)
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output_colo = F.cross_entropy(input_shard, target)
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assert torch.allclose(output_colo, output)
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output.backward()
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output_colo.backward()
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assert torch.allclose(input_t.grad, input_ct.grad)
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def run_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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check_cross_entropy()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 2])
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@rerun_if_address_is_in_use()
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def test_loss_func(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_loss_func(1)
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