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.
135 lines
5.3 KiB
135 lines
5.3 KiB
from functools import partial
|
|
|
|
import colossalai
|
|
from colossalai.utils.cuda import get_current_device
|
|
import pytest
|
|
import torch
|
|
import torch.multiprocessing as mp
|
|
from colossalai.amp import convert_to_apex_amp
|
|
from colossalai.nn.optimizer import CPUAdam
|
|
from colossalai.testing import parameterize, rerun_on_exception
|
|
from colossalai.utils import free_port
|
|
from colossalai.zero.init_ctx import ZeroInitContext
|
|
from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
|
|
from colossalai.zero.sharded_model import ShardedModelV2
|
|
from colossalai.zero.sharded_model.utils import col_model_deepcopy
|
|
from colossalai.zero.sharded_optim import ShardedOptimizerV2
|
|
from colossalai.zero.sharded_optim._utils import has_inf_or_nan
|
|
from colossalai.utils import get_current_device
|
|
from tests.components_to_test.registry import non_distributed_component_funcs
|
|
from colossalai.engine.gradient_handler import MoeGradientHandler
|
|
from colossalai.context import MOE_CONTEXT
|
|
from colossalai.testing import assert_equal_in_group
|
|
|
|
from tests.test_zero_data_parallel.common import CONFIG, check_sharded_model_params
|
|
from tests.test_moe.test_moe_zero_init import MoeModel
|
|
|
|
|
|
def _run_step(model, optimizer, data, label, criterion, grad_handler):
|
|
model.train()
|
|
optimizer.zero_grad()
|
|
|
|
if criterion:
|
|
y = model(data)
|
|
loss = criterion(y, label)
|
|
else:
|
|
loss = model(data, label)
|
|
|
|
loss = loss.float()
|
|
if isinstance(model, ShardedModelV2):
|
|
optimizer.backward(loss)
|
|
else:
|
|
loss.backward()
|
|
|
|
if grad_handler is not None:
|
|
grad_handler.handle_gradient()
|
|
|
|
optimizer.step()
|
|
|
|
|
|
@parameterize("cpu_offload", [True, False])
|
|
@parameterize("use_cpuadam", [True, False])
|
|
@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
|
|
def _run_test_sharded_optim_v2(cpu_offload, shard_strategy_class, use_cpuadam, gpu_margin_mem_ratio=0.0):
|
|
MOE_CONTEXT.reset_loss()
|
|
shard_strategy = shard_strategy_class()
|
|
if use_cpuadam and cpu_offload is False:
|
|
return
|
|
|
|
get_components_func = non_distributed_component_funcs.get_callable('no_leaf_module')
|
|
_, train_dataloader, _, optimizer_class, criterion = get_components_func()
|
|
|
|
with ZeroInitContext(
|
|
target_device=torch.device('cpu') if cpu_offload else torch.device(f'cuda:{get_current_device()}'),
|
|
shard_strategy=shard_strategy,
|
|
shard_param=True,
|
|
rm_torch_payload_on_the_fly=False):
|
|
zero_model = MoeModel()
|
|
|
|
zero_model = ShardedModelV2(
|
|
zero_model,
|
|
shard_strategy,
|
|
offload_config=dict(device='cpu') if cpu_offload else None,
|
|
use_memory_tracer=gpu_margin_mem_ratio > 0.0,
|
|
reuse_fp16_shard=use_cpuadam,
|
|
)
|
|
|
|
# check whether parameters are identical in ddp
|
|
for name, p in zero_model.named_parameters():
|
|
if not p.colo_attr.param_is_sharded and p.is_replicated:
|
|
assert_equal_in_group(p.data.to(get_current_device()))
|
|
|
|
model = MoeModel().half()
|
|
col_model_deepcopy(zero_model, model)
|
|
model = model.cuda().float()
|
|
|
|
if use_cpuadam:
|
|
optimizer_class = CPUAdam
|
|
optim = optimizer_class(model.parameters(), lr=1e-3)
|
|
sharded_optim = optimizer_class(zero_model.parameters(), lr=1e-3)
|
|
sharded_optim = ShardedOptimizerV2(zero_model,
|
|
sharded_optim,
|
|
cpu_offload=cpu_offload,
|
|
initial_scale=2**5,
|
|
gpu_margin_mem_ratio=gpu_margin_mem_ratio,
|
|
keep_unsharded=True)
|
|
|
|
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False)
|
|
apex_model, apex_optimizer = convert_to_apex_amp(model, optim, amp_config)
|
|
apex_grad_handler = MoeGradientHandler(model)
|
|
|
|
# Since MOE is not compatible with apex_amp now, we need to convert gate weight to fp32
|
|
for (n, p), zp in zip(apex_model.named_parameters(), zero_model.parameters()):
|
|
if 'gate' in n:
|
|
p.data = p.float()
|
|
p.data.copy_(zp.data)
|
|
|
|
for i, (data, label) in enumerate(train_dataloader):
|
|
if i > 5:
|
|
break
|
|
data, label = data.cuda(), label.cuda()
|
|
_run_step(apex_model, apex_optimizer, data, label, criterion, apex_grad_handler)
|
|
_run_step(zero_model, sharded_optim, data, label, criterion, None)
|
|
check_sharded_model_params(model, zero_model, loose=True, reuse_fp16_shard=use_cpuadam)
|
|
for param in model.parameters():
|
|
assert not has_inf_or_nan(param)
|
|
|
|
|
|
def _run_dist(rank, world_size, port):
|
|
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
MOE_CONTEXT.setup(seed=42)
|
|
_run_test_sharded_optim_v2()
|
|
|
|
|
|
# use_cpuadam = True can be used with cpu_offload = False
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [2])
|
|
@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
|
|
def test_moe_zero_optim(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_moe_zero_optim(world_size=2)
|