ColossalAI/tests/test_moe/test_moe_zero_optim.py

121 lines
4.8 KiB
Python

import pytest
import torch
import colossalai
from colossalai.amp import convert_to_apex_amp
from colossalai.context import MOE_CONTEXT
from colossalai.engine.gradient_handler import MoeGradientHandler
from colossalai.nn import MoeLoss
from colossalai.nn.optimizer import CPUAdam
from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use, spawn
from colossalai.utils import get_current_device
from colossalai.zero.legacy.init_ctx import ZeroInitContext
from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy
from colossalai.zero.legacy.sharded_model import ShardedModelV2
from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy
from colossalai.zero.legacy.sharded_optim import ShardedOptimizerV2
from colossalai.zero.low_level._utils import has_inf_or_nan
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_moe.test_moe_zero_init import MoeModel
from tests.test_zero.test_legacy.common import CONFIG, check_sharded_model_params
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])
@parameterize("use_cpuadam", [True]) # We do not use Hybrid Adam right now, since it has a little bug
@parameterize("reuse_fp16_shard", [True, False])
@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
def _run_test_sharded_optim_v2(cpu_offload,
shard_strategy_class,
use_cpuadam,
reuse_fp16_shard,
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('hanging_param_model')
_, train_dataloader, _, optimizer_class, _ = get_components_func()
criterion = MoeLoss(aux_weight=0.01, loss_fn=torch.nn.CrossEntropyLoss)
with ZeroInitContext(target_device=torch.device('cpu') if cpu_offload else get_current_device(),
shard_strategy=shard_strategy,
shard_param=True):
zero_model = MoeModel(checkpoint=True)
zero_model = ShardedModelV2(zero_model,
shard_strategy,
tensor_placement_policy='cpu' if cpu_offload else 'cuda',
reuse_fp16_shard=reuse_fp16_shard)
# 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.colo_attr.is_replicated:
assert_equal_in_group(p.colo_attr.data_payload.to(get_current_device()))
model = MoeModel(checkpoint=True).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,
initial_scale=2**5,
gpu_margin_mem_ratio=gpu_margin_mem_ratio)
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)
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_if_address_is_in_use()
def test_moe_zero_optim(world_size):
spawn(_run_dist, world_size)
if __name__ == '__main__':
test_moe_zero_optim(world_size=4)