mirror of https://github.com/hpcaitech/ColossalAI
163 lines
5.8 KiB
Python
163 lines
5.8 KiB
Python
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
from apex import amp
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from torch.testing import assert_close
|
|
|
|
import colossalai
|
|
from colossalai.accelerator import get_accelerator
|
|
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
|
|
|
|
PLACEMENT_CONFIGS = [
|
|
{"placement_policy": "static", "shard_param_frac": 0.75},
|
|
{"placement_policy": "auto"},
|
|
]
|
|
|
|
|
|
def check_grad(model: GeminiDDP, torch_model: torch.nn.Module):
|
|
chunk_manager = model.chunk_manager
|
|
grad_chunk_list = []
|
|
device_list = []
|
|
|
|
# Access gradient chunks.
|
|
for p in model.parameters():
|
|
grad_chunk = chunk_manager.get_chunk(p).grad_chunk
|
|
if grad_chunk not in grad_chunk_list:
|
|
chunk_manager.access_chunk(grad_chunk)
|
|
grad_chunk_list.append(grad_chunk)
|
|
device_list.append(model.grads_device[p])
|
|
|
|
# Compare gradients.
|
|
for p0, p1 in zip(model.parameters(), torch_model.parameters()):
|
|
assert_close(p0, p1.grad, rtol=2e-3, atol=2e-2)
|
|
|
|
# Release gradient chunks and move them to gradient device.
|
|
for grad_chunk, device in zip(grad_chunk_list, device_list):
|
|
chunk_manager.release_chunk(grad_chunk)
|
|
chunk_manager.move_chunk(grad_chunk, device, force_copy=True)
|
|
|
|
|
|
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
|
@parameterize("keep_gathered", [False, True])
|
|
@parameterize("model_name", ["transformers_gpt_lm"])
|
|
@parameterize("master_weights", [False, True])
|
|
@parameterize("use_grad_checkpoint", [False, True])
|
|
@parameterize("max_prefetch", [0, 4])
|
|
@parameterize("enable_async_reduce", [False, True])
|
|
def exam_gemini_grad_acc(
|
|
placement_config,
|
|
keep_gathered: bool,
|
|
model_name: str,
|
|
master_weights: bool,
|
|
use_grad_checkpoint: bool,
|
|
max_prefetch: int,
|
|
enable_async_reduce: bool,
|
|
):
|
|
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)
|
|
gemini_model = model_builder()
|
|
|
|
set_seed(42)
|
|
torch_model = model_builder().cuda()
|
|
for torch_p, p in zip(torch_model.parameters(), gemini_model.parameters()):
|
|
torch_p.data.copy_(p.data)
|
|
|
|
if use_grad_checkpoint:
|
|
gemini_model.gradient_checkpointing_enable()
|
|
torch_model.gradient_checkpointing_enable()
|
|
|
|
world_size = torch.distributed.get_world_size()
|
|
config_dict, *_ = search_chunk_configuration(gemini_model, search_range_m=1, search_interval=100)
|
|
config_dict[world_size]["chunk_size"] = 5000
|
|
config_dict[world_size]["keep_gathered"] = keep_gathered
|
|
gemini_model = GeminiDDP(
|
|
gemini_model,
|
|
config_dict,
|
|
init_device,
|
|
pin_memory=True,
|
|
enable_gradient_accumulation=True,
|
|
master_weights=master_weights,
|
|
max_prefetch=max_prefetch,
|
|
enable_async_reduce=enable_async_reduce,
|
|
**placement_config,
|
|
)
|
|
optimizer = HybridAdam(gemini_model.parameters(), lr=1e-3)
|
|
gemini_optim = GeminiOptimizer(
|
|
optimizer, gemini_model, initial_scale=1, max_norm=1.0, enable_async_reduce=enable_async_reduce
|
|
)
|
|
|
|
rank = dist.get_rank()
|
|
|
|
# setting master_weights to False will cause overflow after optimizer.step()
|
|
amp_config = dict(
|
|
opt_level="O2", keep_batchnorm_fp32=False, loss_scale=1, min_loss_scale=1, max_loss_scale=1, master_weights=True
|
|
)
|
|
torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
|
|
torch_model, torch_optim = amp.initialize(torch_model, torch_optim, **amp_config)
|
|
torch_model = DDP(torch_model, device_ids=[rank])
|
|
|
|
set_seed(rank)
|
|
accum_iter = 2
|
|
train_dataloader = DummyDataloader(data_gen_fn)
|
|
for i, data in enumerate(train_dataloader):
|
|
delay_unscale = False if (i + 1) % accum_iter == 0 else True
|
|
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
|
|
|
set_seed(42 + rank)
|
|
torch_loss = run_fwd(torch_model, data, output_transform_fn, loss_fn)
|
|
torch_loss = torch_loss / accum_iter
|
|
with amp.scale_loss(torch_loss, torch_optim, delay_unscale=delay_unscale) as scaled_loss:
|
|
scaled_loss.backward()
|
|
|
|
set_seed(42 + rank)
|
|
gemini_loss = run_fwd(gemini_model, data, output_transform_fn, loss_fn)
|
|
gemini_loss = gemini_loss / accum_iter
|
|
gemini_optim.backward(gemini_loss)
|
|
|
|
assert torch.allclose(torch_loss.float(), gemini_loss.float(), rtol=1e-3, atol=1e-5)
|
|
|
|
check_grad(gemini_model, torch_model)
|
|
|
|
if (i + 1) % accum_iter == 0:
|
|
torch.nn.utils.clip_grad_norm_(amp.master_params(torch_optim), 1.0)
|
|
torch_optim.step()
|
|
gemini_optim.step()
|
|
torch_optim.zero_grad()
|
|
|
|
# check updated param
|
|
torch_dict = torch_model.state_dict()
|
|
gemini_dict = gemini_model.state_dict(only_rank_0=False)
|
|
|
|
for key, value in gemini_dict.items():
|
|
torch_key = "module." + key
|
|
torch_value = torch_dict[torch_key].to(value.device).to(value.dtype)
|
|
assert_close(value, torch_value, rtol=1e-3, atol=2e-3)
|
|
|
|
if i == accum_iter:
|
|
break
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
exam_gemini_grad_acc()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@rerun_if_address_is_in_use()
|
|
def test_grad_accumulation():
|
|
spawn(run_dist, 2)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_grad_accumulation()
|