Making large AI models cheaper, faster and more accessible
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import pytest
import torch
import torch.nn.functional as F
from torch.nn import Parameter
import colossalai
from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup, ShardSpec
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils import get_current_device
def _run_layer_norm():
ln_op = torch.nn.LayerNorm(2, 3, device=get_current_device())
input_t = torch.randn(3, 2, device=get_current_device())
pg = ProcessGroup(tp_degree=torch.distributed.get_world_size())
input_t_colo = ColoTensor.from_torch_tensor(input_t.clone().detach(), ColoTensorSpec(pg))
# prepare colossalai LN
weight = ColoTensor(Parameter(ln_op.weight.detach()), ColoTensorSpec(pg))
bias = ColoTensor(Parameter(ln_op.bias.detach()), ColoTensorSpec(pg))
output = ln_op(input_t)
output_colo = F.layer_norm(input_t_colo, ln_op.normalized_shape, weight, bias, ln_op.eps)
assert torch.allclose(output_colo, output)
torch.mean(output).backward()
torch.mean(output_colo).backward()
assert torch.allclose(ln_op.weight.grad, weight.grad)
def check_spec_eq(tensor, other):
assert isinstance(tensor, ColoTensor) and isinstance(other, ColoTensor)
for k in dir(tensor.dist_spec):
if not k.startswith('__'):
assert hasattr(other.dist_spec, k), f"{k}"
assert getattr(tensor.dist_spec, k) == getattr(other.dist_spec, k)
def check_element_wise_ops():
world_size = torch.distributed.get_world_size()
pg = ProcessGroup(tp_degree=world_size)
t = torch.rand(2, 2)
x = ColoTensor(t, spec=ColoTensorSpec(pg, ShardSpec([0], [pg.tp_world_size()])))
check_spec_eq(x, x.cuda())
assert torch.equal(x.cuda(), t.cuda())
check_spec_eq(x, torch.abs(x))
assert torch.equal(torch.abs(x), torch.abs(t))
check_spec_eq(x, F.sigmoid(x))
assert torch.equal(F.sigmoid(x), F.sigmoid(t))
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
check_element_wise_ops()
_run_layer_norm()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [2])
@rerun_if_address_is_in_use()
def test_element_wise_ops(world_size):
spawn(run_dist, world_size)
def run_dist2(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
_run_layer_norm()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1])
@rerun_if_address_is_in_use()
def test_ln(world_size):
spawn(run_dist2, world_size)
def check_all():
test_element_wise_ops(2)
if __name__ == '__main__':
check_all()