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[zero] polish ShardedOptimV2 unittest (#385)

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* place params on cpu after zero init context

* polish code

* bucketzed cpu gpu tensor transter

* find a bug in sharded optim unittest

* add offload unittest for ShardedOptimV2.

* polish code and make it more robust
This commit is contained in:
Jiarui Fang
2022-03-11 14:40:01 +08:00
committed by Frank Lee
parent ce7b2c9ae3
commit 3af13a2c3e
3 changed files with 26 additions and 26 deletions
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44
tests/test_zero_data_parallel/test_sharded_optim_v2.py
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@@ -24,8 +24,12 @@ def run_step(model, optimizer, data, label, criterion, enable_autocast=False):
model.train()
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=enable_autocast):
y = model(data)
loss = criterion(y, label)
if criterion:
y = model(data)
loss = criterion(y, label)
else:
loss = model(data, label)
loss = loss.float()
if isinstance(model, ShardedModelV2):
optimizer.backward(loss)
@@ -34,19 +38,7 @@ def run_step(model, optimizer, data, label, criterion, enable_autocast=False):
optimizer.step()
def run_step_no_criterion(model, optimizer, data, label, enable_autocast=False):
model.train()
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=enable_autocast):
loss = model(data, label)
if isinstance(model, ShardedModelV2):
optimizer.backward(loss)
else:
loss.backward()
optimizer.step()
def run_dist(rank, world_size, port):
def run_dist(rank, world_size, port, cpu_offload):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
test_models = ['repeated_computed_layers', 'resnet18', 'bert']
for model_name in test_models:
@@ -54,33 +46,33 @@ def run_dist(rank, world_size, port):
shard_strategy = TensorShardStrategy()
model, train_dataloader, test_dataloader, optimizer, criterion = get_components_func()
model = model(checkpoint=True).cuda()
zero_model = ShardedModelV2(copy.deepcopy(model), shard_strategy)
zero_model = ShardedModelV2(copy.deepcopy(model),
shard_strategy,
offload_config=dict(device='cpu') if cpu_offload else None)
if dist.get_world_size() > 1:
model = DDP(model)
optim = Adam(model.parameters(), lr=1e-3)
sharded_optim = ShardedOptimizerV2(Adam(zero_model.parameters(), lr=1e-3),
zero_model,
shard_strategy,
cpu_offload=cpu_offload,
initial_scale=2**5)
for i, (data, label) in enumerate(train_dataloader):
if i > 2:
break
data, label = data.cuda(), label.cuda()
if criterion is None:
run_step_no_criterion(model, optim, data, label, False)
run_step_no_criterion(zero_model, sharded_optim, data, label, False)
else:
run_step(model, optim, data, label, criterion, False)
run_step(zero_model, sharded_optim, data, label, criterion, False)
run_step(model, optim, data, label, criterion, False)
run_step(zero_model, sharded_optim, data, label, criterion, False)
check_sharded_params_padding(model, zero_model, loose=True)
@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2, 4])
def test_sharded_optim_v2(world_size):
run_func = partial(run_dist, world_size=world_size, port=free_port())
@pytest.mark.parametrize("world_size", [1, 2])
@pytest.mark.parametrize("cpu_offload", [True, False])
def test_sharded_optim_v2(world_size, cpu_offload):
run_func = partial(run_dist, world_size=world_size, port=free_port(), cpu_offload=cpu_offload)
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_sharded_optim_v2(world_size=2)
test_sharded_optim_v2(world_size=2, cpu_offload=True)