ColossalAI/tests/test_shardformer/test_layer/test_linear_1d.py

68 lines
1.9 KiB
Python
Raw Normal View History

import torch
import torch.distributed as dist
import torch.nn as nn
from torch.testing import assert_close
import colossalai
from colossalai.shardformer.layer.layers import Linear1D_Col, Linear1D_Row
from colossalai.testing import rerun_if_address_is_in_use, spawn
def check_linear_1d_col():
linear = nn.Linear(32, 128).cuda()
linear_col = Linear1D_Col.from_native_module(linear, process_group=None, gather_output=True)
assert linear_col.weight.shape == torch.Size([64, 32])
assert linear_col.bias.shape == torch.Size([64])
# check computation correctness
x = torch.rand(4, 32).cuda()
out = linear(x)
gather_out = linear_col(x)
assert_close(out, gather_out)
# check backward correctness
out.sum().backward()
gather_out.sum().backward()
rank = dist.get_rank()
target_grad = torch.chunk(linear.weight.grad, 2, dim=0)[rank]
assert_close(target_grad, linear_col.weight.grad)
def check_linear_1d_row():
linear = nn.Linear(32, 128).cuda()
linear_row = Linear1D_Row.from_native_module(linear, process_group=None, parallel_input=False)
assert linear_row.weight.shape == torch.Size([128, 16])
assert linear_row.bias.shape == torch.Size([128])
# check computation correctness
x = torch.rand(4, 32).cuda()
out = linear(x)
gather_out = linear_row(x)
assert_close(out, gather_out)
# check backward correctness
out.sum().backward()
gather_out.sum().backward()
rank = dist.get_rank()
target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank]
assert_close(target_grad, linear_row.weight.grad)
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
check_linear_1d_col()
check_linear_1d_row()
@rerun_if_address_is_in_use()
def test_linear():
spawn(run_dist, nprocs=2)
if __name__ == '__main__':
test_linear()