mirror of https://github.com/hpcaitech/ColossalAI
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
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.nn.functional as F
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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 ColoTensorSpec, ProcessGroup, ColoTensor
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from tests.test_tensor.common_utils import tensor_equal, tensor_shard_equal, split_param_col_tp1d, split_param_row_tp1d
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def run_with_spec(spec_init_func, split_bias):
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pg = ProcessGroup(tp_degree=torch.distributed.get_world_size())
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model = torch.nn.Linear(4, 8).cuda()
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weight = ColoTensor(torch.nn.Parameter(model.weight.detach()), ColoTensorSpec(pg))
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bias = ColoTensor(torch.nn.Parameter(model.bias.detach()), ColoTensorSpec(pg))
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spec_init_func(weight, pg)
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if split_bias:
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spec_init_func(bias, pg)
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x = torch.rand(2, 4).cuda()
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out = model(x)
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colo_out = F.linear(x, weight, bias)
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colo_out = colo_out.to_replicate()
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assert tensor_equal(out, colo_out)
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grad = torch.rand_like(out)
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out.backward(grad)
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colo_out.backward(grad)
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assert tensor_shard_equal(model.weight.grad, weight.grad, pg.tp_local_rank(), pg.tp_world_size())
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assert tensor_shard_equal(model.bias.grad, bias.grad, pg.tp_local_rank(), pg.tp_world_size())
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def run_dist(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_with_spec(spec_init_func=split_param_col_tp1d, split_bias=False)
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run_with_spec(spec_init_func=split_param_row_tp1d, split_bias=True)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_linear_1d(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_linear_1d(4)
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