ColossalAI/tests/test_zero_data_parallel/test_sharded_optim_v2.py

68 lines
2.1 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 colossalai.zero.sharded_optim import ShardedOptimizerV2
from torch.optim import Adam
from common import (CONFIG, Net, check_grads, check_grads_padding, check_params, check_sharded_params_padding)
def run_step(model, optimizer, x, enable_autocast=False):
model.train()
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=enable_autocast):
y = model(x)
loss = y.sum()
loss = loss.float()
if isinstance(model, ShardedModelV2):
optimizer.backward(loss)
else:
loss.backward()
optimizer.step()
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 n, p in zero_model.named_parameters():
p._name = n
optim = Adam(model.parameters(), lr=1e-3)
sharded_optim = ShardedOptimizerV2(Adam(zero_model.parameters(), lr=1e-3), zero_model)
for _ in range(2):
x = torch.rand(2, 5).cuda()
run_step(zero_model, sharded_optim, x, False)
run_step(model, optim, x, False)
if dist.get_world_size() > 1:
check_grads_padding(model, zero_model)
check_sharded_params_padding(model, zero_model)
else:
check_grads(model, zero_model)
check_params(model, zero_model)
@pytest.mark.dist
def test_sharded_optim_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_sharded_optim_v2()