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add bert for unitest and sharded model is not able to pass the bert case

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jiaruifang
2022-03-09 10:39:02 +08:00
committed by ver217
parent e35f66b159
commit d984dff88c
6 changed files with 104 additions and 14 deletions
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2
tests/test_zero_data_parallel/common.py
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@@ -75,7 +75,7 @@ def check_grads_padding(model, zero_model, loose=False):
if zero_grad.size(0) > grad.size(0):
zero_grad = zero_grad[:grad.size(0)]
assert grad.dtype == zero_grad.dtype
assert allclose(grad, zero_grad, loose=loose), f'{grad} vs {zero_grad}'
assert allclose(grad, zero_grad, loose=loose), f'diff: {grad - zero_grad}'
def check_params_padding(model, zero_model, loose=False):

30
tests/test_zero_data_parallel/test_shard_model_v2.py
View File

@@ -31,14 +31,25 @@ def run_fwd_bwd(model, data, label, criterion, enable_autocast=False):
loss.backward()
def run_bert_fwd_bwd(model, data, label, enable_autocast=False):
model.train()
with torch.cuda.amp.autocast(enabled=enable_autocast):
output = model(input_ids=data, labels=label)
loss = output[0]
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')
test_models = ['repeated_computed_layers', 'resnet18']
test_models = ['repeated_computed_layers', 'resnet18', 'bert']
shard_strategy = TensorShardStrategy()
for model_name in test_models:
get_components_func = non_distributed_component_funcs.get_callable(model_name)
shard_strategy = TensorShardStrategy()
model, train_dataloader, test_dataloader, optimizer, criterion = get_components_func()
model = model().half().cuda()
model = model(checkpoint=True).half().cuda()
zero_model = ShardedModelV2(copy.deepcopy(model), shard_strategy)
if dist.get_world_size() > 1:
model = DDP(model)
@@ -46,9 +57,16 @@ def run_dist(rank, world_size, port):
for i, (data, label) in enumerate(train_dataloader):
if i > 2:
break
data, label = data.half().cuda(), label.cuda()
run_fwd_bwd(model, data, label, criterion, False)
run_fwd_bwd(zero_model, data, label, criterion, False)
if model_name == 'bert':
data, label = data.cuda(), label.cuda()
run_bert_fwd_bwd(model, data, label, False)
run_bert_fwd_bwd(zero_model, data, label, False)
else:
data, label = data.half().cuda(), label.cuda()
run_fwd_bwd(model, data, label, criterion, False)
run_fwd_bwd(zero_model, data, label, criterion, False)
if dist.get_world_size() > 1:
check_grads_padding(model, zero_model, loose=True)
else: