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ColossalAI/tests/test_moe/test_moe_zero_optim.py

133 lines
5.2 KiB

from functools import partial
import colossalai
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):
shard_strategy = shard_strategy_class()
if use_cpuadam and cpu_offload is False:
return
MOE_CONTEXT.reset_loss()
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)