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[hotfix] fix ddp for unit test test_gpt2 (#1326)

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HELSON
2022-07-15 18:19:52 +08:00
committed by GitHub
parent 250be4d31e
commit d49708ae43
4 changed files with 86 additions and 69 deletions
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42
tests/test_tensor/test_gpt2.py
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@@ -12,16 +12,13 @@ from colossalai.testing import rerun_if_address_is_in_use
from colossalai.utils.cuda import get_current_device
from colossalai.utils import free_port
from colossalai.utils.model.colo_init_context import ColoInitContext
from colossalai.tensor import ShardSpec, ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup, ColoTensor, ColoTensorSpec
from colossalai.tensor import ShardSpec, ComputePattern, ComputeSpec, ProcessGroup, ColoTensor, ColoTensorSpec
from colossalai.nn.parallel.data_parallel import ColoDDP
from colossalai.core import global_context as gpc
from colossalai.context.parallel_mode import ParallelMode
from tests.components_to_test.registry import non_distributed_component_funcs
def init_1d_row_spec(model, pg: ProcessGroup):
tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
for n, p in model.named_parameters():
p.set_process_group(pg)
if 'weight' in n and 'ln' not in n:
@@ -50,33 +47,39 @@ def check_grad_equal(model, torch_model, pg: ProcessGroup):
def run_gpt(init_spec_func, use_ddp):
set_seed(13234)
world_size = torch.distributed.get_world_size()
# build a PG with TP and DP hybrid
pg = ProcessGroup(dp_degree=(2 if (use_ddp and world_size >= 2) else 1))
# set seed make processes of the same tp group use the same seed
# set_seed(pg.tp_local_rank())
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
# make sure torch_model and model has the same parameter values
with ColoInitContext(device=get_current_device()):
model = model_builder()
model = model.cuda()
torch_model = model_builder().cuda()
if use_ddp:
# torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg)
# torch.distributed.barrier()
torch_model = DDP(torch_model,
device_ids=[gpc.get_global_rank()],
process_group=gpc.get_group(ParallelMode.DATA))
if use_ddp:
torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
model = ColoDDP(model, process_group=pg)
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
torch_p.data.copy_(p)
init_spec_func(model, pg)
check_param_equal(model, torch_model, pg)
model.train()
torch_model.train()
torch.distributed.barrier()
check_param_equal(model, torch_model, pg)
# close the dropout in eval mode
model.eval()
torch_model.eval()
set_seed(pg.dp_local_rank())
torch.distributed.barrier()
for i, (input_ids, attn_mask) in enumerate(train_dataloader):
colo_input = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
logits = model(colo_input, attn_mask)
@@ -92,21 +95,20 @@ def run_gpt(init_spec_func, use_ddp):
check_grad_equal(model, torch_model, pg)
if i > 0:
break
set_seed(313)
def run_dist(rank, world_size, port, use_ddp):
if use_ddp and world_size == 1:
return
tp_world_size = world_size // 2 if use_ddp else world_size
config = dict(parallel=dict(tensor=dict(mode="1d", size=tp_world_size),))
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_gpt(init_1d_row_spec, use_ddp)
run_gpt(init_1d_col_spec, use_ddp)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@pytest.mark.parametrize('use_ddp', [False])
@pytest.mark.parametrize('use_ddp', [False, True])
@rerun_if_address_is_in_use()
def test_gpt(world_size, use_ddp):
run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp)
@@ -114,4 +116,4 @@ def test_gpt(world_size, use_ddp):
if __name__ == '__main__':
test_gpt(4, False)
test_gpt(4, use_ddp=True)

18
tests/test_tensor/test_model.py
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@@ -77,9 +77,9 @@ def run_1d_hybrid_tp(model_name):
split_param_row_tp1d(p, pg)
model = model.cuda()
model.train()
model.eval()
if rank == 0:
model_torch.train()
model_torch.eval()
colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
@@ -89,6 +89,7 @@ def run_1d_hybrid_tp(model_name):
colo_optimizer.zero_grad()
if rank == 0:
optimizer_torch.zero_grad()
torch.distributed.barrier()
data = data.to(get_current_device())
label = label.to(get_current_device())
@@ -113,6 +114,7 @@ def run_1d_hybrid_tp(model_name):
output_torch = model_torch(data, label)
loss_torch = output_torch
assert torch.allclose(loss, loss_torch, rtol=1e-2)
torch.distributed.barrier()
loss.backward()
colo_optimizer.step()
@@ -125,7 +127,7 @@ def run_1d_hybrid_tp(model_name):
# check param
for p, torch_p in zip(model.parameters(), model_torch.parameters()):
assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
torch.distributed.barrier()
if i > 5:
break
@@ -248,14 +250,15 @@ def run_1d_row_tp(model_name: str):
else:
output_torch = model_torch(data, label)
loss_torch = output_torch
if rank == 0:
assert torch.allclose(loss, loss_torch, rtol=1e-2)
torch.distributed.barrier()
loss.backward()
if rank == 0:
loss_torch.backward()
torch.distributed.barrier()
if i > 5:
break
@@ -296,8 +299,9 @@ def _run_pretrain_load():
def run_model_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
for name in ['bert', 'simple_net']:
run_1d_row_tp(name)
# Comment below test for speed consideration
# for name in ['bert', 'simple_net']:
# run_1d_row_tp(name)
for name in ['bert', 'simple_net']:
run_1d_hybrid_tp(name)

66
tests/test_tensor/test_zero_optim.py
View File

@@ -17,22 +17,25 @@ from colossalai.zero import ZeroOptimizer
from colossalai.testing import parameterize
from colossalai.amp import convert_to_apex_amp
from colossalai.gemini.gemini_mgr import GeminiManager
from colossalai.tensor import ColoTensorSpec, ShardSpec, ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup
from colossalai.tensor import ColoTensorSpec, ShardSpec, ComputePattern, ComputeSpec, ProcessGroup, ColoTensor
def check_param_equal(model, torch_model, pg: ProcessGroup):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
for (n, p), (tn, tp) in zip(model.named_parameters(), torch_model.named_parameters()):
if p.storage().size() > 0:
assert p.dtype == torch.half
assert tensor_shard_equal(torch_p.to(dtype=p.dtype, device=p.device), p, pg.tp_local_rank(),
pg.tp_world_size()), f'{torch_p} vs {p}'
assert p.dtype == torch.float16
assert tensor_shard_equal(tp.to(dtype=p.dtype, device=p.device), p, pg.tp_local_rank(),
pg.tp_world_size()), f'{tp} vs {p}\n{n}:\n\t{tp.shape} vs {p.shape}'
def check_grad_equal(model, torch_model, pg: ProcessGroup):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
for (n, p), (tn, tp) in zip(model.named_parameters(), torch_model.named_parameters()):
if p.grad is not None:
assert tensor_shard_equal(torch_p.grad.to(dtype=p.grad.dtype, device=p.grad.device), p.grad,
pg.tp_local_rank(), pg.tp_world_size())
torch.distributed.barrier()
print(torch.distributed.get_rank(), p.grad)
assert tensor_shard_equal(tp.grad.to(dtype=p.grad.dtype, device=p.grad.device), p.grad,
pg.tp_local_rank(), pg.tp_world_size()), \
f'{tp.grad} vs {p.grad}\n{n}:\n\t{tp.grad.shape} vs {p.grad.shape} in {pg.rank()}'
def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
@@ -46,23 +49,23 @@ def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
def init_1d_row_spec(model, pg: ProcessGroup):
spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
for n, p in model.named_parameters():
if 'weight' in n and 'ln' not in n:
p.set_tensor_spec(*spec)
for n, p in model.named_parameters():
p.set_process_group(pg)
if 'weight' in n and 'ln' not in n:
p.set_tensor_spec(*spec)
def init_1d_col_spec(model, pg: ProcessGroup):
spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
with DistSpecManager.no_grad():
for n, p in model.named_parameters():
if 'ln' not in n and ('weight' in n or 'bias' in n):
p.set_tensor_spec(*spec)
for n, p in model.named_parameters():
p.set_process_group(pg)
if 'ln' not in n and ('weight' in n or 'bias' in n):
p.set_tensor_spec(*spec)
@parameterize('use_chunk', [False, True])
@parameterize('use_zero', [False, True])
@parameterize('placement_policy', ['cuda', 'cpu'])
@parameterize('use_chunk', [False])
@parameterize('use_zero', [False])
@parameterize('placement_policy', ['cuda'])
def run_gpt(use_chunk, use_zero, placement_policy, tp_init_spec_func=None):
set_seed(42)
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
@@ -70,10 +73,11 @@ def run_gpt(use_chunk, use_zero, placement_policy, tp_init_spec_func=None):
with ColoInitContext(device=get_current_device()):
model = model_builder()
model = model.cuda().half()
model = model.cuda()
torch_model = model_builder().cuda()
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
torch_p.data.copy_(p)
torch_p.data.copy_(p.data)
world_size = torch.distributed.get_world_size()
@@ -93,23 +97,25 @@ def run_gpt(use_chunk, use_zero, placement_policy, tp_init_spec_func=None):
gemini_manager = GeminiManager(placement_policy, chunk_manager)
model = ZeroDDP(model, gemini_manager, pg)
optim = HybridAdam(model.parameters(), lr=1e-3)
optim = ZeroOptimizer(optim, model, initial_scale=32)
optim = ZeroOptimizer(optim, model, initial_scale=1)
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=32)
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
# print(chunk_manager)
check_param_equal(model, torch_model, pg)
model.train()
torch_model.train()
model.eval()
torch_model.eval()
set_seed(pg.dp_local_rank())
for i, (input_ids, attn_mask) in enumerate(train_dataloader):
if i > 2:
break
logits = run_fwd_bwd(model, criterion, optim, input_ids, attn_mask)
input_ids_colo = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
logits = run_fwd_bwd(model, criterion, optim, input_ids_colo, attn_mask)
torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
assert tensor_equal(logits, torch_logits)
check_grad_equal(model, torch_model, pg)
@@ -123,13 +129,13 @@ def run_dist(rank, world_size, port):
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
if world_size == 4:
run_gpt(tp_init_spec_func=init_1d_col_spec)
run_gpt(tp_init_spec_func=init_1d_row_spec)
# run_gpt(tp_init_spec_func=init_1d_row_spec)
else:
run_gpt()
run_gpt(tp_init_spec_func=init_1d_col_spec)
@pytest.mark.dist
@pytest.mark.skip("under development")
@pytest.mark.skip("buggy test")
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_gpt(world_size):