mirror of https://github.com/hpcaitech/ColossalAI
94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
import pytest
|
|
import colossalai
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
from colossalai.context.parallel_mode import ParallelMode
|
|
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 import ColoInitContext
|
|
from colossalai.tensor import ChunkManager
|
|
from colossalai.core import global_context as gpc
|
|
from functools import partial
|
|
from _utils import tensor_equal, tensor_shard_equal, set_seed
|
|
from tests.components_to_test.registry import non_distributed_component_funcs
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from colossalai.nn.parallel import ColoDDPV2
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.zero import ZeroOptimizer
|
|
from colossalai.testing import parameterize
|
|
from colossalai.amp import convert_to_apex_amp
|
|
|
|
|
|
def check_param_equal(model, torch_model):
|
|
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
|
|
if p.storage().size() > 0:
|
|
assert p.dtype == torch.half
|
|
assert tensor_equal(torch_p, p), f'{torch_p} vs {p}'
|
|
|
|
|
|
def run_step(model, criterion, optimizer, input_ids, attn_mask):
|
|
optimizer.zero_grad()
|
|
logits = model(input_ids, attn_mask)
|
|
logits = logits.float()
|
|
loss = criterion(logits, input_ids)
|
|
optimizer.backward(loss)
|
|
optimizer.step()
|
|
return logits
|
|
|
|
|
|
@parameterize('use_chunk', [False, True])
|
|
@parameterize('use_zero', [False, True])
|
|
def run_gpt(use_chunk, use_zero):
|
|
set_seed(42)
|
|
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
|
|
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
|
|
|
with ColoInitContext(device=get_current_device()):
|
|
model = model_builder()
|
|
model = model.cuda().half()
|
|
torch_model = model_builder().cuda()
|
|
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
|
torch_p.data.copy_(p)
|
|
|
|
chunk_size = 38 * 1024**2 if use_chunk else None
|
|
chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero)
|
|
model = ColoDDPV2(model, chunk_manager)
|
|
optim = HybridAdam(model.parameters(), lr=1e-3)
|
|
optim = ZeroOptimizer(optim, model, initial_scale=32)
|
|
|
|
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=32)
|
|
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=[gpc.get_global_rank()], process_group=gpc.get_group(ParallelMode.DATA))
|
|
|
|
# print(chunk_manager)
|
|
check_param_equal(model, torch_model)
|
|
model.train()
|
|
torch_model.train()
|
|
set_seed(gpc.get_local_rank(ParallelMode.DATA))
|
|
for i, (input_ids, attn_mask) in enumerate(train_dataloader):
|
|
if i > 2:
|
|
break
|
|
logits = run_step(model, criterion, optim, input_ids, attn_mask)
|
|
torch_logits = run_step(torch_model, criterion, torch_optim, input_ids, attn_mask)
|
|
assert tensor_equal(logits, torch_logits)
|
|
check_param_equal(model, torch_model)
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
run_gpt()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [1, 4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_gpt(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_gpt(4)
|