2023-06-15 10:03:38 +00:00
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from torch.testing import assert_close
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import colossalai
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from colossalai.shardformer.layer.layers import Linear1D_Col, Linear1D_Row
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2023-06-16 07:00:26 +00:00
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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2023-06-15 10:03:38 +00:00
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def check_linear_1d_col():
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linear = nn.Linear(32, 128).cuda()
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linear_col = Linear1D_Col.from_native_module(linear, process_group=None, gather_output=True)
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assert linear_col.weight.shape == torch.Size([64, 32])
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assert linear_col.bias.shape == torch.Size([64])
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# check computation correctness
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x = torch.rand(4, 32).cuda()
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out = linear(x)
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gather_out = linear_col(x)
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assert_close(out, gather_out)
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# check backward correctness
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out.sum().backward()
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gather_out.sum().backward()
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rank = dist.get_rank()
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target_grad = torch.chunk(linear.weight.grad, 2, dim=0)[rank]
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assert_close(target_grad, linear_col.weight.grad)
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def check_linear_1d_row():
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linear = nn.Linear(32, 128).cuda()
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linear_row = Linear1D_Row.from_native_module(linear, process_group=None, parallel_input=False)
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assert linear_row.weight.shape == torch.Size([128, 16])
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assert linear_row.bias.shape == torch.Size([128])
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# check computation correctness
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x = torch.rand(4, 32).cuda()
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out = linear(x)
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gather_out = linear_row(x)
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assert_close(out, gather_out)
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# check backward correctness
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out.sum().backward()
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gather_out.sum().backward()
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rank = dist.get_rank()
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target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank]
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assert_close(target_grad, linear_row.weight.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_linear_1d_col()
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check_linear_1d_row()
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@rerun_if_address_is_in_use()
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def test_linear():
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spawn(run_dist, nprocs=2)
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if __name__ == '__main__':
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test_linear()
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