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.
127 lines
4.3 KiB
127 lines
4.3 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.accelerator import get_accelerator
|
|
from colossalai.legacy.amp import convert_to_apex_amp
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.testing import 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}, # zero2
|
|
{"placement_policy": "static", "shard_param_frac": 1.0}, # zero3
|
|
{"placement_policy": "static", "shard_param_frac": 0.5}, # zero3-half
|
|
{"placement_policy": "auto"},
|
|
]
|
|
|
|
|
|
def check_grad(model: GeminiDDP, 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)
|
|
if not model.chunk_manager.reuse_fp16_chunk:
|
|
chunk_list = [chunk.grad_chunk for chunk in chunk_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("placement_config", PLACEMENT_CONFIGS)
|
|
@parameterize("keep_gather", [False, True])
|
|
@parameterize("model_name", ["transformers_gpt_lm"])
|
|
@parameterize("use_grad_checkpoint", [False, True])
|
|
@parameterize("master_weights", [False, True])
|
|
@parameterize("max_prefetch", [0, 4])
|
|
@parameterize("enable_async_reduce", [False, True])
|
|
def exam_gpt_fwd_bwd(
|
|
placement_config,
|
|
keep_gather,
|
|
model_name: str,
|
|
use_grad_checkpoint: bool = False,
|
|
master_weights: bool = True,
|
|
max_prefetch: int = 0,
|
|
enable_async_reduce=True,
|
|
):
|
|
init_device = get_accelerator().get_current_device()
|
|
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
|
|
iter(model_zoo.get_sub_registry(model_name).values())
|
|
)
|
|
|
|
set_seed(42)
|
|
model = model_builder()
|
|
|
|
set_seed(42)
|
|
torch_model = model_builder().cuda()
|
|
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
|
torch_p.data.copy_(p.data)
|
|
|
|
if use_grad_checkpoint:
|
|
model.gradient_checkpointing_enable()
|
|
torch_model.gradient_checkpointing_enable()
|
|
|
|
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"] = keep_gather
|
|
model = GeminiDDP(
|
|
model,
|
|
config_dict,
|
|
init_device,
|
|
pin_memory=True,
|
|
**placement_config,
|
|
master_weights=master_weights,
|
|
max_prefetch=max_prefetch,
|
|
enable_async_reduce=enable_async_reduce,
|
|
)
|
|
optimizer = HybridAdam(model.parameters(), lr=1e-3)
|
|
zero_optim = GeminiOptimizer(optimizer, model, initial_scale=1)
|
|
|
|
rank = dist.get_rank()
|
|
amp_config = dict(opt_level="O2", keep_batchnorm_fp32=False, loss_scale=1, master_weights=master_weights)
|
|
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=[rank])
|
|
|
|
set_seed(rank)
|
|
|
|
data = data_gen_fn()
|
|
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
|
|
|
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, data, output_transform_fn, loss_fn, optimizer=torch_optim)
|
|
set_seed(42)
|
|
loss = run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
|
|
|
|
assert_close(torch_loss.float(), loss.float())
|
|
|
|
check_grad(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_gpt_fwd_bwd()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [1, 4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_gpt(world_size):
|
|
spawn(run_dist, world_size)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_gpt(1)
|