mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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.
43 lines
1.5 KiB
43 lines
1.5 KiB
import pytest |
|
import torch |
|
from torch.nn import functional as F |
|
|
|
import colossalai |
|
from colossalai.tensor import ColoParameter, ColoTensorSpec, ProcessGroup |
|
from colossalai.testing import rerun_if_address_is_in_use, spawn |
|
from tests.test_tensor.common_utils import split_param_col_tp1d, tensor_equal, tensor_shard_equal |
|
|
|
|
|
def run_with_spec(spec_init_func): |
|
pg = ProcessGroup(tp_degree=torch.distributed.get_world_size()) |
|
model = torch.nn.EmbeddingBag(10, 4).cuda() |
|
weight = ColoParameter(model.weight.clone(), True, ColoTensorSpec(pg)) |
|
|
|
spec_init_func(weight, pg) |
|
|
|
inputs = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9]).cuda() |
|
offsets = torch.tensor([0, 4]).cuda() |
|
out = model(inputs, offsets=offsets) |
|
colo_out = F.embedding_bag(inputs, weight, offsets=offsets) |
|
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()) |
|
|
|
|
|
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(split_param_col_tp1d) |
|
|
|
|
|
@pytest.mark.dist |
|
@pytest.mark.parametrize('world_size', [1, 4]) |
|
@rerun_if_address_is_in_use() |
|
def test_embedding_bag_1d(world_size): |
|
spawn(run_dist, world_size) |
|
|
|
|
|
if __name__ == '__main__': |
|
test_embedding_bag_1d(4)
|
|
|