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102 lines
4.1 KiB
102 lines
4.1 KiB
import torch
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from colossalai.utils.checkpoint_io.meta import ParamDistMeta
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from colossalai.utils.checkpoint_io.distributed import unflatten_zero_param, gather_tp_param, merge_param
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def test_unflatten_zero_param_even() -> None:
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dist_metas = [ParamDistMeta(i, 4, 0, 1, zero_numel=16, zero_orig_shape=[4, 4]) for i in range(4)]
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orig_tensor = torch.rand(4, 4)
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tensors = list(orig_tensor.reshape(-1).chunk(4))
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unflattened_tensor = unflatten_zero_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, unflattened_tensor)
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merged_tensor = merge_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, merged_tensor)
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def test_unflatten_zero_param_uneven() -> None:
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dist_metas = [ParamDistMeta(i, 4, 0, 1, zero_numel=16, zero_orig_shape=[4, 4]) for i in range(1, 3)]
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orig_tensor = torch.rand(4, 4)
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tensors = list(orig_tensor.reshape(-1).split([13, 3]))
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unflattened_tensor = unflatten_zero_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, unflattened_tensor)
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merged_tensor = merge_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, merged_tensor)
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def test_gather_tp_param_1d_row() -> None:
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dist_metas = [ParamDistMeta(0, 1, i, 4, tp_shard_dims=[0], tp_num_parts=[4]) for i in range(4)]
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orig_tensor = torch.rand(4, 4)
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tensors = [t.contiguous() for t in orig_tensor.chunk(4, 0)]
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gathered_tensor = gather_tp_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, gathered_tensor)
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merged_tensor = merge_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, merged_tensor)
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def test_gather_tp_param_1d_col() -> None:
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dist_metas = [ParamDistMeta(0, 1, i, 4, tp_shard_dims=[1], tp_num_parts=[4]) for i in range(4)]
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orig_tensor = torch.rand(4, 4)
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tensors = [t.contiguous() for t in orig_tensor.chunk(4, 1)]
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gathered_tensor = gather_tp_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, gathered_tensor)
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merged_tensor = merge_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, merged_tensor)
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def test_gather_tp_param_2d() -> None:
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dist_metas = [ParamDistMeta(0, 1, i, 6, tp_shard_dims=[0, 1], tp_num_parts=[2, 3]) for i in range(6)]
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orig_tensor = torch.rand(4, 6)
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tensors = [t.contiguous() for tl in orig_tensor.chunk(2, 0) for t in tl.chunk(3, 1)]
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gathered_tensor = gather_tp_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, gathered_tensor)
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merged_tensor = merge_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, merged_tensor)
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def test_gather_tp_param_2d_reverse() -> None:
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dist_metas = [ParamDistMeta(0, 1, i, 6, tp_shard_dims=[1, 0], tp_num_parts=[3, 2]) for i in range(6)]
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orig_tensor = torch.rand(4, 6)
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tensors = [t.contiguous() for tl in orig_tensor.chunk(2, 0) for t in tl.chunk(3, 1)]
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gathered_tensor = gather_tp_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, gathered_tensor)
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merged_tensor = merge_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, merged_tensor)
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def test_merge_param_hybrid() -> None:
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dist_metas = [
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ParamDistMeta(i % 2,
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2,
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i // 2,
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6,
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tp_shard_dims=[1, 0],
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tp_num_parts=[3, 2],
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zero_numel=4,
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zero_orig_shape=[2, 2]) for i in range(12)
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]
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orig_tensor = torch.rand(4, 6)
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tensors = [
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chunk for tl in orig_tensor.chunk(2, 0) for t in tl.chunk(3, 1)
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for chunk in t.contiguous().reshape(-1).split([1, 3])
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]
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merged_tensor = merge_param(tensors, dist_metas)
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assert torch.equal(orig_tensor, merged_tensor)
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def test_merge_param_dummy() -> None:
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dist_metas = [ParamDistMeta(0, 1, 0, 1)]
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orig_tensor = torch.rand(4, 6)
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merged_tensor = merge_param([orig_tensor], dist_metas)
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assert torch.equal(orig_tensor, merged_tensor)
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if __name__ == '__main__':
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test_unflatten_zero_param_even()
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test_unflatten_zero_param_uneven()
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test_gather_tp_param_1d_row()
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test_gather_tp_param_1d_col()
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test_gather_tp_param_2d()
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test_gather_tp_param_2d_reverse()
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test_merge_param_hybrid()
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test_merge_param_dummy()
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