mirror of https://github.com/hpcaitech/ColossalAI
63 lines
1.7 KiB
Python
63 lines
1.7 KiB
Python
#!/usr/bin/env python
|
|
# -*- encoding: utf-8 -*-
|
|
|
|
import copy
|
|
from functools import partial
|
|
|
|
import colossalai
|
|
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.multiprocessing as mp
|
|
from colossalai.context.parallel_mode import ParallelMode
|
|
from colossalai.core import global_context as gpc
|
|
from colossalai.utils import free_port
|
|
from colossalai.zero.sharded_model import ShardedModelV2
|
|
|
|
from common import CONFIG, Net, check_grads, check_grads_padding
|
|
|
|
|
|
def run_fwd_bwd(model, x, enable_autocast=False):
|
|
model.train()
|
|
with torch.cuda.amp.autocast(enabled=enable_autocast):
|
|
y = model(x)
|
|
loss = y.sum()
|
|
loss = loss.float()
|
|
if isinstance(model, ShardedModelV2):
|
|
model.backward(loss)
|
|
else:
|
|
loss.backward()
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(config=CONFIG,
|
|
rank=rank,
|
|
world_size=world_size,
|
|
host='localhost',
|
|
port=port,
|
|
backend='nccl')
|
|
|
|
model = Net(checkpoint=True).cuda()
|
|
zero_model = copy.deepcopy(model)
|
|
zero_model = ShardedModelV2(zero_model, process_group=gpc.get_group(ParallelMode.DATA))
|
|
|
|
for _ in range(2):
|
|
x = torch.rand(2, 5).cuda()
|
|
run_fwd_bwd(zero_model, x, False)
|
|
run_fwd_bwd(model, x, False)
|
|
if dist.get_world_size() > 1:
|
|
check_grads_padding(model, zero_model)
|
|
else:
|
|
check_grads(model, zero_model)
|
|
|
|
|
|
@pytest.mark.dist
|
|
def test_shard_model_v2():
|
|
world_size = 2
|
|
run_func = partial(run_dist, world_size=world_size, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_shard_model_v2()
|