mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
139 lines
4.7 KiB
139 lines
4.7 KiB
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from torch.testing import assert_close
|
|
|
|
import colossalai
|
|
from colossalai.legacy.amp import convert_to_apex_amp
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
|
|
from colossalai.utils import set_seed
|
|
from colossalai.zero import GeminiDDP, GeminiOptimizer
|
|
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
|
from tests.kit.model_zoo import model_zoo, run_fwd_bwd
|
|
|
|
PLACEMENT_CONFIGS = [
|
|
{
|
|
"placement_policy": "static",
|
|
"shard_param_frac": 0.0,
|
|
"offload_optim_frac": 0.0,
|
|
"offload_param_frac": 0.0,
|
|
}, # zero2
|
|
{
|
|
"placement_policy": "static",
|
|
"shard_param_frac": 0.0,
|
|
"offload_optim_frac": 1.0,
|
|
"offload_param_frac": 0.0,
|
|
}, # zero2-offload
|
|
{
|
|
"placement_policy": "static",
|
|
"shard_param_frac": 0.0,
|
|
"offload_optim_frac": 0.5,
|
|
"offload_param_frac": 0.0,
|
|
}, # zero2-offload-half
|
|
{"placement_policy": "auto"},
|
|
]
|
|
|
|
|
|
def check_param(model: GeminiDDP, torch_model: torch.nn.Module):
|
|
zero_dict = model.state_dict(only_rank_0=False)
|
|
torch_dict = torch_model.state_dict()
|
|
|
|
for key, value in torch_dict.items():
|
|
# key is 'module.model.PARAMETER', so we truncate it
|
|
key = key[7:]
|
|
assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
|
|
temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
|
|
# debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
|
|
assert_close(value, temp_zero_value, rtol=1e-3, atol=4e-3)
|
|
|
|
|
|
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
|
@parameterize("model_name", ["transformers_gpt_lm"])
|
|
@parameterize("master_weights", [True, False])
|
|
@parameterize("max_prefetch", [0, 1, 4])
|
|
@parameterize("enable_async_reduce", [False, True])
|
|
def exam_grad_clipping(
|
|
placement_config, model_name: str, master_weights: bool, max_prefetch: int, enable_async_reduce: bool
|
|
):
|
|
set_seed(1912)
|
|
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
|
|
iter(model_zoo.get_sub_registry(model_name).values())
|
|
)
|
|
|
|
torch_model = model_builder().cuda()
|
|
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=[dist.get_rank()])
|
|
|
|
model = model_builder()
|
|
|
|
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
|
p.data.copy_(torch_p.data)
|
|
|
|
world_size = torch.distributed.get_world_size()
|
|
config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
|
|
config_dict[world_size]["chunk_size"] = 5000
|
|
config_dict[world_size]["keep_gathered"] = False
|
|
if placement_config["placement_policy"] != "cuda":
|
|
init_device = torch.device("cpu")
|
|
else:
|
|
init_device = None
|
|
|
|
model = GeminiDDP(
|
|
model,
|
|
chunk_config_dict=config_dict,
|
|
chunk_init_device=init_device,
|
|
pin_memory=True,
|
|
master_weights=master_weights,
|
|
max_prefetch=max_prefetch,
|
|
enable_async_reduce=enable_async_reduce,
|
|
**placement_config,
|
|
)
|
|
|
|
optimizer = HybridAdam(model.parameters(), lr=1e-3)
|
|
zero_optim = GeminiOptimizer(optimizer, model, initial_scale=32, max_norm=1.0)
|
|
|
|
model.train()
|
|
torch_model.train()
|
|
|
|
set_seed(dist.get_rank() * 3 + 128)
|
|
train_dataloader = DummyDataloader(data_gen_fn)
|
|
for i, data in enumerate(train_dataloader):
|
|
if i > 2:
|
|
break
|
|
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
|
|
|
zero_optim.zero_grad()
|
|
torch_optim.zero_grad()
|
|
|
|
run_fwd_bwd(torch_model, data, output_transform_fn, loss_fn, optimizer=torch_optim)
|
|
run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
|
|
|
|
import apex.amp as apex_amp
|
|
|
|
torch.nn.utils.clip_grad_norm_(apex_amp.master_params(torch_optim), 1.0)
|
|
torch_optim.step()
|
|
zero_optim.step()
|
|
|
|
if master_weights:
|
|
check_param(model, torch_model)
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
exam_grad_clipping()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [1, 2])
|
|
@rerun_if_address_is_in_use()
|
|
def test_grad_clip(world_size):
|
|
spawn(run_dist, world_size)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_grad_clip(2)
|