mirror of https://github.com/hpcaitech/ColossalAI
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
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import pytest
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import torch
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import torch.nn.functional as F
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import colossalai
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from colossalai.logging import disable_existing_loggers
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from colossalai.shardformer.layer import cross_entropy_1d
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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CONFIG = dict(parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode='1d')),)
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def check_dist_crossentropy(rank, world_size, port, ignore_index):
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disable_existing_loggers()
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, port=port, host='localhost', backend='nccl')
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# prepare data
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pred = torch.randn(2, 4, 8, requires_grad=True)
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labels = torch.randint(8, (2, 4))
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# set some label to -100 to test the ignore index
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labels[0, -1] = ignore_index
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org_pred = pred.view(-1, 8)
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org_labels = labels.view(-1)
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org_loss = F.cross_entropy(org_pred, org_labels)
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dist_pred = pred.chunk(world_size, -1)[rank]
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dist_loss = cross_entropy_1d(dist_pred.to('cuda'), labels.to('cuda'), ignore_index=ignore_index)
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assert torch.allclose(org_loss, dist_loss,
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atol=1e-5), f"dist cross entropy loss is not equal to orgin loss\n{org_loss}\n{dist_loss}"
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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def test_dist_crossentropy():
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ignore_index = -100
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spawn(check_dist_crossentropy, 2, ignore_index=ignore_index)
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if __name__ == '__main__':
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test_dist_crossentropy()
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