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.
75 lines
2.7 KiB
75 lines
2.7 KiB
from functools import partial
|
|
import pytest
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.multiprocessing as mp
|
|
import torch.distributed as dist
|
|
import colossalai
|
|
from colossalai.utils import free_port, get_current_device
|
|
from colossalai.nn.layer.moe import Top1Router, UniformNoiseGenerator, MoeLayer, Experts
|
|
from colossalai.context.moe_context import MOE_CONTEXT
|
|
from colossalai.utils.moe import sync_moe_model_param
|
|
from colossalai.engine.gradient_handler import MoeGradientHandler
|
|
from colossalai.testing import assert_equal_in_group
|
|
from colossalai.testing import rerun_on_exception
|
|
|
|
BATCH_SIZE = 4
|
|
DIM = 16
|
|
CONFIG = dict()
|
|
|
|
|
|
def run_test(rank, world_size, port):
|
|
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
expert_module = nn.Linear
|
|
expert_factor = dict(in_features=DIM, out_features=DIM, device=get_current_device())
|
|
|
|
MOE_CONTEXT.setup(42) # MOE initialization
|
|
noisy_func = UniformNoiseGenerator()
|
|
router = Top1Router(noisy_func=noisy_func)
|
|
num_experts_list = [1, 2, 4]
|
|
layer_list = []
|
|
for num_experts in num_experts_list:
|
|
exp = Experts(expert_module, num_experts, **expert_factor)
|
|
moe_layer = MoeLayer(DIM, num_experts, router, exp)
|
|
layer_list.append(moe_layer)
|
|
|
|
model = nn.Sequential(*layer_list)
|
|
model = model.to(get_current_device())
|
|
sync_moe_model_param(model)
|
|
|
|
dist_dict = MOE_CONTEXT.parallel_info_dict
|
|
assert_equal_in_group(layer_list[0].experts.experts[0].weight.data, dist_dict[1].dp_group)
|
|
assert_equal_in_group(layer_list[1].experts.experts[0].weight.data, dist_dict[2].dp_group)
|
|
# MoE model synchronization passed
|
|
|
|
grad_handler = MoeGradientHandler(model, 0)
|
|
|
|
rank = dist.get_rank()
|
|
torch.cuda.manual_seed(78 + rank)
|
|
data = torch.randn(BATCH_SIZE, DIM, device=get_current_device())
|
|
grad = torch.randn_like(data)
|
|
|
|
MOE_CONTEXT.reset_loss()
|
|
outputs = model(data)
|
|
outputs.backward(grad)
|
|
grad_handler.handle_gradient()
|
|
|
|
assert_equal_in_group(layer_list[0].experts.experts[0].weight.grad, dist_dict[1].dp_group)
|
|
assert_equal_in_group(layer_list[0].experts.experts[0].bias.grad, dist_dict[1].dp_group)
|
|
|
|
assert_equal_in_group(layer_list[1].experts.experts[0].weight.grad, dist_dict[2].dp_group)
|
|
assert_equal_in_group(layer_list[1].experts.experts[0].bias.grad, dist_dict[2].dp_group)
|
|
# MoE grad handler test passed
|
|
|
|
|
|
@pytest.mark.dist
|
|
@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
|
|
def test_grad_handler():
|
|
world_size = 4
|
|
run_func = partial(run_test, world_size=world_size, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_grad_handler()
|