mirror of https://github.com/hpcaitech/ColossalAI
114 lines
4.4 KiB
Python
114 lines
4.4 KiB
Python
from functools import partial
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from torch.testing import assert_close
|
|
|
|
import colossalai
|
|
from colossalai.amp import convert_to_apex_amp
|
|
from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
|
|
from colossalai.gemini.gemini_mgr import GeminiManager
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer
|
|
from colossalai.nn.parallel import ZeroDDP
|
|
from colossalai.tensor import ProcessGroup
|
|
from colossalai.testing import parameterize, rerun_if_address_is_in_use
|
|
from colossalai.utils import free_port
|
|
from colossalai.utils.cuda import get_current_device
|
|
from colossalai.utils.model.colo_init_context import ColoInitContext
|
|
from tests.components_to_test import run_fwd_bwd
|
|
from tests.components_to_test.registry import non_distributed_component_funcs
|
|
from tests.test_tensor.common_utils import set_seed
|
|
|
|
|
|
def check_grad(model: ZeroDDP, torch_model: torch.nn.Module):
|
|
chunk_manager = model.chunk_manager
|
|
param_list = [p for p in model.parameters()]
|
|
chunk_list = chunk_manager.get_chunks(param_list)
|
|
for chunk in chunk_list:
|
|
chunk_manager.access_chunk(chunk)
|
|
|
|
for (p0, p1) in zip(model.parameters(), torch_model.parameters()):
|
|
assert_close(p0, p1.grad, rtol=1e-3, atol=5e-5)
|
|
|
|
|
|
@parameterize('init_device', [get_current_device()])
|
|
@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
|
|
@parameterize('keep_gather', [False, True])
|
|
@parameterize('model_name', ['gpt2', 'bert', 'albert'])
|
|
@parameterize('use_grad_checkpoint', [False, True])
|
|
def exam_gpt_fwd_bwd(placement_policy,
|
|
keep_gather,
|
|
model_name: str,
|
|
use_grad_checkpoint: bool = False,
|
|
init_device=get_current_device()):
|
|
|
|
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
|
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
|
|
|
set_seed(42)
|
|
with ColoInitContext(device=init_device):
|
|
model = model_builder(use_grad_checkpoint)
|
|
|
|
set_seed(42)
|
|
torch_model = model_builder(use_grad_checkpoint).cuda()
|
|
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
|
torch_p.data.copy_(p.data)
|
|
|
|
world_size = torch.distributed.get_world_size()
|
|
config_dict, _ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
|
|
config_dict[world_size]['chunk_size'] = 5000
|
|
config_dict[world_size]['keep_gathered'] = keep_gather
|
|
chunk_manager = ChunkManager(config_dict)
|
|
gemini_manager = GeminiManager(placement_policy, chunk_manager)
|
|
model = ZeroDDP(model, gemini_manager, pin_memory=True)
|
|
optimizer = HybridAdam(model.parameters(), lr=1e-3)
|
|
zero_optim = ZeroOptimizer(optimizer, model, initial_scale=1)
|
|
|
|
pg = ProcessGroup()
|
|
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())
|
|
|
|
set_seed(pg.dp_local_rank())
|
|
for i, (input_ids, label) in enumerate(train_dataloader):
|
|
# you can only test a single fwd + bwd.
|
|
# after bwd param is grad for Gemini, due to the chunk reuse optimization.
|
|
if i > 0:
|
|
break
|
|
input_ids, label = input_ids.cuda(), label.cuda()
|
|
|
|
torch_optim.zero_grad()
|
|
zero_optim.zero_grad()
|
|
|
|
# set random seed is same as torch_model.eval()
|
|
set_seed(42)
|
|
torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
|
|
set_seed(42)
|
|
loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
|
|
|
|
assert torch.equal(torch_loss, loss)
|
|
|
|
check_grad(model, torch_model)
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
config = {}
|
|
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
exam_gpt_fwd_bwd()
|
|
|
|
|
|
@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)
|