mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
101 lines
3.2 KiB
101 lines
3.2 KiB
from functools import partial
|
|
|
|
import colossalai
|
|
import pytest
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
import torch.distributed as dist
|
|
from colossalai.testing import rerun_if_address_is_in_use
|
|
from colossalai.utils import free_port, get_current_device
|
|
from colossalai.tensor import ColoTensorSpec, ProcessGroup, ColoTensor, ShardSpec
|
|
from colossalai.tensor.distspec import DistPlacementPattern
|
|
from tests.test_tensor.common_utils import split_param_row_tp1d, split_param_col_tp1d, debug_print
|
|
|
|
|
|
def exam_view_core(pg):
|
|
# the case of replicated ColoTensors
|
|
x = torch.randn(4, 4).cuda()
|
|
x_colo = ColoTensor(x, ColoTensorSpec(pg))
|
|
|
|
y = x.view(2, -1, 2)
|
|
y_colo = x_colo.view(2, -1, 2)
|
|
|
|
assert torch.all(y == y_colo)
|
|
assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE
|
|
# the perfect case of col-sliced ColoTensors
|
|
split_param_col_tp1d(x_colo, pg)
|
|
|
|
z = x.view(torch.Size((2, 1, 2, -1)))
|
|
z_colo = x_colo.view(torch.Size((2, 1, 2, -1)))
|
|
if dist.get_rank() == 0:
|
|
z = z[:, :, :, 0:2]
|
|
else:
|
|
z = z[:, :, :, 2:]
|
|
assert torch.all(z == z_colo)
|
|
assert z_colo.dist_spec == x_colo.dist_spec
|
|
# the perfect case of row-sliced ColoTensors
|
|
split_param_row_tp1d(x_colo, pg)
|
|
|
|
z = x.view(torch.Size((-1, 2, 2)))
|
|
z_colo = x_colo.view(torch.Size((-1, 2, 2)))
|
|
if dist.get_rank() == 0:
|
|
z = z[0:2, :, :]
|
|
else:
|
|
z = z[2:, :, :]
|
|
assert torch.all(z == z_colo)
|
|
assert z_colo.dist_spec == x_colo.dist_spec
|
|
# the normal case of row-sliced ColoTensors
|
|
z = x.view(-1, 2, 2, 2)
|
|
z_colo = x_colo.view(-1, 2, 2, 2)
|
|
assert torch.all(z == z_colo)
|
|
assert y_colo.dist_spec.placement == DistPlacementPattern.REPLICATE
|
|
|
|
|
|
def exam_view_autograd(pg):
|
|
x = torch.randn(8, 2, device=get_current_device(), requires_grad=True)
|
|
y = torch.randn(8, 2, device=get_current_device(), requires_grad=True)
|
|
with torch.no_grad():
|
|
y.copy_(x)
|
|
y = ColoTensor(y, ColoTensorSpec(pg))
|
|
y_slice = y.redistribute(ShardSpec([-1], [pg.tp_world_size()]))
|
|
|
|
xx = x.view(2, 2, -1)
|
|
yy_slice = y_slice.view(2, 2, -1)
|
|
yy = yy_slice.to_replicate()
|
|
grad = torch.randn(2, 2, 4, device=get_current_device())
|
|
|
|
xx.backward(grad)
|
|
yy.backward(grad)
|
|
assert torch.all(x.grad == y.grad)
|
|
|
|
|
|
def exam_view_errors(pg):
|
|
x = torch.randn(8, 2, device=get_current_device())
|
|
x = ColoTensor(x, ColoTensorSpec(pg))
|
|
split_param_row_tp1d(x, pg)
|
|
|
|
x.view('a', 'b', 'c')
|
|
x.view(8, -1)
|
|
x.view([-2, -2, -2])
|
|
x.view((-1, -1, -1))
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(config=dict(), rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
pg = ProcessGroup(tp_degree=torch.distributed.get_world_size())
|
|
exam_view_core(pg)
|
|
exam_view_autograd(pg)
|
|
# exam_view_errors(pg)
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [2])
|
|
@rerun_if_address_is_in_use()
|
|
def test_view(world_size):
|
|
run_func = partial(run_dist, world_size=world_size, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_view(2)
|