ColossalAI/tests/test_ops/test_linear_tp.py

49 lines
1.7 KiB
Python

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