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[test] merge zero optim tests (#428)

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pull/433/head
Jiarui Fang 3 years ago committed by GitHub
parent f0d6e2208b
commit f9c762df85
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2 changed files with 48 additions and 102 deletions
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65
tests/test_zero_data_parallel/test_sharded_optim_v2.py
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@ -12,11 +12,12 @@ from colossalai.zero.sharded_model import ShardedModelV2
from colossalai.zero.sharded_optim import ShardedOptimizerV2
from tests.components_to_test.registry import non_distributed_component_funcs
from torch.nn.parallel import DistributedDataParallel as DDP
from colossalai.nn.optimizer import CPUAdam
from common import CONFIG, check_sharded_params_padding
def run_step(model, optimizer, data, label, criterion, enable_autocast=False):
def _run_step(model, optimizer, data, label, criterion, enable_autocast=False):
model.train()
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=enable_autocast):
@ -34,13 +35,17 @@ def run_step(model, optimizer, data, label, criterion, enable_autocast=False):
optimizer.step()
def run_dist(rank, world_size, port, cpu_offload, shard_strategy):
def _run_dist(rank, world_size, port, cpu_offload, shard_strategy, use_cpuadam):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
test_models = ['repeated_computed_layers', 'resnet18', 'bert']
shard_strategy = shard_strategy()
if use_cpuadam and cpu_offload is False:
return
for model_name in test_models:
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
model, train_dataloader, _, optimizer_class, criterion = get_components_func()
model = model(checkpoint=True).cuda()
zero_model = ShardedModelV2(copy.deepcopy(model),
shard_strategy,
@ -48,33 +53,59 @@ def run_dist(rank, world_size, port, cpu_offload, shard_strategy):
if dist.get_world_size() > 1:
model = DDP(model)
lr = 1e-3
optim = optimizer_class(model.parameters(), lr=lr)
sharded_optim = ShardedOptimizerV2(zero_model,
optimizer_class,
cpu_offload=cpu_offload,
initial_scale=2**5,
lr=lr)
if use_cpuadam:
optim = torch.optim.Adam(model.parameters(), lr=lr)
sharded_optim = ShardedOptimizerV2(zero_model, CPUAdam, cpu_offload=cpu_offload, initial_scale=2**5, lr=lr)
else:
optim = optimizer_class(model.parameters(), lr=lr)
sharded_optim = ShardedOptimizerV2(zero_model,
optimizer_class,
cpu_offload=cpu_offload,
initial_scale=2**5,
lr=lr)
for i, (data, label) in enumerate(train_dataloader):
if i > 2:
#FIXME() if i > 5, the unittest will fail
if i > 3:
break
data, label = data.cuda(), label.cuda()
run_step(model, optim, data, label, criterion, False)
run_step(zero_model, sharded_optim, data, label, criterion, False)
_run_step(model, optim, data, label, criterion, False)
_run_step(zero_model, sharded_optim, data, label, criterion, False)
check_sharded_params_padding(model, zero_model, loose=True)
# use_cpuadam = True can be used with cpu_offload = False
@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2])
@pytest.mark.parametrize("cpu_offload", [False])
@pytest.mark.parametrize("use_cpuadam", [False])
@pytest.mark.parametrize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
def test_sharded_optim_v2(world_size, cpu_offload, shard_strategy, use_cpuadam):
run_func = partial(_run_dist,
world_size=world_size,
port=free_port(),
cpu_offload=cpu_offload,
shard_strategy=shard_strategy,
use_cpuadam=use_cpuadam)
mp.spawn(run_func, nprocs=world_size)
@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2])
@pytest.mark.parametrize("cpu_offload", [True, False])
@pytest.mark.parametrize("cpu_offload", [True])
@pytest.mark.parametrize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
def test_sharded_optim_v2(world_size, cpu_offload, shard_strategy):
run_func = partial(run_dist,
@pytest.mark.parametrize("use_cpuadam", [True, False])
def test_sharded_optim_v2_cpu_adam(world_size, cpu_offload, shard_strategy, use_cpuadam):
run_func = partial(_run_dist,
world_size=world_size,
port=free_port(),
cpu_offload=cpu_offload,
shard_strategy=shard_strategy)
shard_strategy=shard_strategy,
use_cpuadam=use_cpuadam)
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_sharded_optim_v2(world_size=2, cpu_offload=True, shard_strategy=TensorShardStrategy)
test_sharded_optim_v2_cpu_adam(world_size=2,
cpu_offload=False,
shard_strategy=TensorShardStrategy,
use_cpuadam=True)

85
tests/test_zero_data_parallel/test_sharded_optim_v2_with_cpu_adam.py
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@ -1,85 +0,0 @@
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import copy
from functools import partial
import colossalai
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from colossalai.nn.optimizer import CPUAdam
from colossalai.utils import free_port
from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
from colossalai.zero.sharded_model import ShardedModelV2
from colossalai.zero.sharded_optim import ShardedOptimizerV2
from tests.components_to_test.registry import non_distributed_component_funcs
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import Adam
from common import CONFIG, check_sharded_params_padding
def run_step(model, optimizer, data, label, criterion, enable_autocast=False):
model.train()
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=enable_autocast):
y = model(data)
loss = criterion(y, label)
loss = loss.float()
if isinstance(model, ShardedModelV2):
optimizer.backward(loss)
else:
loss.backward()
optimizer.step()
def run_step_no_criterion(model, optimizer, data, label, enable_autocast=False):
model.train()
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=enable_autocast):
loss = model(data, label)
if isinstance(model, ShardedModelV2):
optimizer.backward(loss)
else:
loss.backward()
optimizer.step()
def run_dist(rank, world_size, port, shard_strategy):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
test_models = ['repeated_computed_layers', 'resnet18', 'bert']
shard_strategy = shard_strategy()
for model_name in test_models:
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model, train_dataloader, test_dataloader, optimizer, criterion = get_components_func()
model = model(checkpoint=True).cuda()
zero_model = ShardedModelV2(copy.deepcopy(model), shard_strategy, offload_config={'device': 'cpu'})
if dist.get_world_size() > 1:
model = DDP(model)
optim = Adam(model.parameters(), lr=1e-3)
sharded_optim = ShardedOptimizerV2(zero_model, CPUAdam, initial_scale=2**5, cpu_offload=True, lr=1e-3)
for i, (data, label) in enumerate(train_dataloader):
if i > 2:
break
data, label = data.cuda(), label.cuda()
if criterion is None:
run_step_no_criterion(model, optim, data, label, False)
run_step_no_criterion(zero_model, sharded_optim, data, label, False)
else:
run_step(model, optim, data, label, criterion, False)
run_step(zero_model, sharded_optim, data, label, criterion, False)
check_sharded_params_padding(model, zero_model, loose=True)
@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2])
@pytest.mark.parametrize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
def test_sharded_optim_v2(world_size, shard_strategy):
run_func = partial(run_dist, world_size=world_size, port=free_port(), shard_strategy=shard_strategy)
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_sharded_optim_v2(world_size=2, shard_strategy=TensorShardStrategy)
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