mirror of https://github.com/hpcaitech/ColossalAI
101 lines
3.2 KiB
Python
101 lines
3.2 KiB
Python
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from functools import partial
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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import torch.distributed as dist
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils import free_port, get_current_device
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from colossalai.tensor import ColoTensorSpec, ProcessGroup, ColoTensor, ShardSpec
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from colossalai.tensor.distspec import DistPlacementPattern
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from tests.test_tensor.common_utils import split_param_row_tp1d, split_param_col_tp1d, debug_print
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def exam_view_core(pg):
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# the case of replicated ColoTensors
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x = torch.randn(4, 4).cuda()
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x_colo = ColoTensor(x, ColoTensorSpec(pg))
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y = x.view(2, -1, 2)
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y_colo = x_colo.view(2, -1, 2)
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assert torch.all(y == y_colo)
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assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE
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# the perfect case of col-sliced ColoTensors
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split_param_col_tp1d(x_colo, pg)
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z = x.view(torch.Size((2, 1, 2, -1)))
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z_colo = x_colo.view(torch.Size((2, 1, 2, -1)))
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if dist.get_rank() == 0:
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z = z[:, :, :, 0:2]
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else:
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z = z[:, :, :, 2:]
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assert torch.all(z == z_colo)
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assert z_colo.dist_spec == x_colo.dist_spec
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# the perfect case of row-sliced ColoTensors
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split_param_row_tp1d(x_colo, pg)
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z = x.view(torch.Size((-1, 2, 2)))
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z_colo = x_colo.view(torch.Size((-1, 2, 2)))
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if dist.get_rank() == 0:
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z = z[0:2, :, :]
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else:
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z = z[2:, :, :]
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assert torch.all(z == z_colo)
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assert z_colo.dist_spec == x_colo.dist_spec
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# the normal case of row-sliced ColoTensors
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z = x.view(-1, 2, 2, 2)
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z_colo = x_colo.view(-1, 2, 2, 2)
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assert torch.all(z == z_colo)
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assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE
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def exam_view_autograd(pg):
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x = torch.randn(8, 2, device=get_current_device(), requires_grad=True)
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y = torch.randn(8, 2, device=get_current_device(), requires_grad=True)
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with torch.no_grad():
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y.copy_(x)
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y = ColoTensor(y, ColoTensorSpec(pg))
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y_slice = y.redistribute(ShardSpec([-1], [pg.tp_world_size()]))
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xx = x.view(2, 2, -1)
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yy_slice = y_slice.view(2, 2, -1)
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yy = yy_slice.to_replicate()
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grad = torch.randn(2, 2, 4, device=get_current_device())
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xx.backward(grad)
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yy.backward(grad)
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assert torch.all(x.grad == y.grad)
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def exam_view_errors(pg):
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x = torch.randn(8, 2, device=get_current_device())
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x = ColoTensor(x, ColoTensorSpec(pg))
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split_param_row_tp1d(x, pg)
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x.view('a', 'b', 'c')
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x.view(8, -1)
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x.view([-2, -2, -2])
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x.view((-1, -1, -1))
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def run_dist(rank, world_size, port):
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colossalai.launch(config=dict(), rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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pg = ProcessGroup(tp_degree=torch.distributed.get_world_size())
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exam_view_core(pg)
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exam_view_autograd(pg)
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# exam_view_errors(pg)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [2])
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@rerun_if_address_is_in_use()
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def test_view(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_view(2)
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