mirror of https://github.com/hpcaitech/ColossalAI
107 lines
4.2 KiB
Python
107 lines
4.2 KiB
Python
from functools import partial
|
|
|
|
import colossalai
|
|
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.multiprocessing as mp
|
|
from colossalai.nn.optimizer import CPUAdam
|
|
from colossalai.testing import parameterize
|
|
from colossalai.utils import free_port
|
|
from colossalai.zero.init_ctx import ZeroInitContext
|
|
from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
|
|
from colossalai.zero.sharded_model import ShardedModelV2
|
|
from colossalai.zero.sharded_model.utils import col_model_deepcopy
|
|
from colossalai.zero.sharded_optim import ShardedOptimizerV2
|
|
from colossalai.zero.sharded_optim._utils import has_inf_or_nan
|
|
from tests.components_to_test.registry import non_distributed_component_funcs
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
|
|
from common import CONFIG, check_sharded_params_padding
|
|
from colossalai.amp import convert_to_apex_amp
|
|
|
|
|
|
def _run_step(model, optimizer, data, label, criterion, enable_autocast=False):
|
|
model.train()
|
|
optimizer.zero_grad()
|
|
with torch.cuda.amp.autocast(enabled=enable_autocast):
|
|
if criterion:
|
|
y = model(data)
|
|
loss = criterion(y, label)
|
|
else:
|
|
loss = model(data, label)
|
|
|
|
loss = loss.float()
|
|
if isinstance(model, ShardedModelV2):
|
|
optimizer.backward(loss)
|
|
else:
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
|
|
@parameterize("cpu_offload", [True, False])
|
|
@parameterize("use_cpuadam", [True, False])
|
|
@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
|
|
def _run_test_sharded_optim_v2(cpu_offload, shard_strategy_class, use_cpuadam):
|
|
test_models = ['repeated_computed_layers', 'resnet18', 'bert']
|
|
shard_strategy = shard_strategy_class()
|
|
|
|
if use_cpuadam and cpu_offload is False:
|
|
return
|
|
|
|
for model_name in test_models:
|
|
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
|
model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func()
|
|
|
|
with ZeroInitContext(convert_fp16=True,
|
|
target_device=torch.device(f'cpu:0'),
|
|
shard_strategy=shard_strategy,
|
|
shard_param=True,
|
|
rm_torch_payload_on_the_fly=False):
|
|
zero_model = model_builder(checkpoint=True)
|
|
zero_model = ShardedModelV2(zero_model,
|
|
shard_strategy,
|
|
offload_config=dict(device='cpu') if cpu_offload else None)
|
|
|
|
model = model_builder(checkpoint=True).half()
|
|
col_model_deepcopy(zero_model, model)
|
|
model = model.cuda().float()
|
|
|
|
if use_cpuadam:
|
|
optimizer_class = CPUAdam
|
|
optim = optimizer_class(model.parameters(), lr=1e-3)
|
|
sharded_optim = optimizer_class(zero_model.parameters(), lr=1e-3)
|
|
sharded_optim = ShardedOptimizerV2(zero_model, sharded_optim, cpu_offload=cpu_offload, initial_scale=2**5)
|
|
|
|
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False)
|
|
apex_model, apex_optimizer = convert_to_apex_amp(model, optim, amp_config)
|
|
if dist.get_world_size() > 1:
|
|
apex_model = DDP(apex_model)
|
|
|
|
for i, (data, label) in enumerate(train_dataloader):
|
|
if i > 5:
|
|
break
|
|
data, label = data.cuda(), label.cuda()
|
|
_run_step(apex_model, apex_optimizer, data, label, criterion, False)
|
|
_run_step(zero_model, sharded_optim, data, label, criterion, False)
|
|
check_sharded_params_padding(model, zero_model, loose=True)
|
|
for param in model.parameters():
|
|
assert not has_inf_or_nan(param)
|
|
|
|
|
|
def _run_dist(rank, world_size, port):
|
|
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
_run_test_sharded_optim_v2()
|
|
|
|
|
|
# use_cpuadam = True can be used with cpu_offload = False
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [1, 2])
|
|
def test_sharded_optim_v2(world_size):
|
|
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(world_size=2)
|