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[zero] polish low level optimizer (#2473)

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HELSON
2023-01-13 14:56:17 +08:00
committed by GitHub
parent 8b7495dd54
commit a5dc4253c6
7 changed files with 95 additions and 124 deletions
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12
tests/test_zero/low_level_zero/test_grad_acc.py
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@@ -35,18 +35,15 @@ def exam_zero_1_2_grad_acc():
# create model
zero1_model = TestModel().cuda()
zero2_model = copy.deepcopy(zero1_model)
pg = ProcessGroup()
# create optimizer
zero1_optimizer = torch.optim.Adam(zero1_model.parameters(), lr=1)
zero2_optimizer = torch.optim.Adam(zero2_model.parameters(), lr=1)
zero1_optimizer = LowLevelZeroOptimizer(zero1_optimizer,
pg=pg,
overlap_communication=True,
initial_scale=32,
clip_grad_norm=1.0,
verbose=True)
zero2_optimizer = LowLevelZeroOptimizer(zero2_optimizer,
pg=pg,
overlap_communication=True,
partition_grad=True,
initial_scale=32,
@@ -86,7 +83,7 @@ def exam_zero_1_2_grad_acc():
assert torch.equal(z1p.data, z2p.data)
def exam_zero_1_grad_acc(use_pg=True):
def exam_zero_1_grad_acc():
local_rank = torch.distributed.get_rank()
grad_scale = 32
seed_all(2008)
@@ -105,9 +102,7 @@ def exam_zero_1_grad_acc(use_pg=True):
# we only test stage 1 here
# in `check_sharded_param_consistency.py`, we will test whether
# level 1 and 2 will produce exactly the same results
pg = ProcessGroup() if use_pg else None #ProcessGroup()
zero_optimizer = LowLevelZeroOptimizer(zero_optimizer,
pg=pg,
overlap_communication=False,
initial_scale=grad_scale,
reduce_bucket_size=262144,
@@ -158,9 +153,8 @@ def exam_zero_1_grad_acc(use_pg=True):
def run_dist(rank, world_size, port):
colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
exam_zero_1_grad_acc(True)
exam_zero_1_grad_acc(False)
# exam_zero_1_2_grad_acc()
exam_zero_1_grad_acc()
exam_zero_1_2_grad_acc()
@pytest.mark.dist

8
tests/test_zero/low_level_zero/test_zero1_2.py
View File

@@ -9,7 +9,6 @@ from torch.nn.parallel import DistributedDataParallel as DDP
from torch.testing import assert_close
import colossalai
from colossalai.tensor import ProcessGroup
from colossalai.testing.random import seed_all
from colossalai.utils import free_port
from colossalai.zero import LowLevelZeroOptimizer
@@ -59,17 +58,14 @@ def exam_zero_1_2():
zero1_model = TestModel().cuda()
zero2_model = copy.deepcopy(zero1_model)
pg = ProcessGroup()
# create optimizer
zero1_optimizer = torch.optim.Adam(zero1_model.parameters(), lr=1)
zero2_optimizer = torch.optim.Adam(zero2_model.parameters(), lr=1)
zero1_optimizer = LowLevelZeroOptimizer(zero1_optimizer,
pg=pg,
overlap_communication=True,
initial_scale=128,
verbose=True)
zero2_optimizer = LowLevelZeroOptimizer(zero2_optimizer,
pg=pg,
overlap_communication=True,
partition_grad=True,
initial_scale=128)
@@ -119,7 +115,7 @@ def exam_zero_1_torch_ddp():
torch_model = copy.deepcopy(zero_model)
zero_model = zero_model.cuda().half()
# torch_model = DDP(torch_model.cuda(), bucket_cap_mb=0)
torch_model = DDP(torch_model.cuda(), bucket_cap_mb=0)
torch_model = torch_model.cuda()
# for (n, p), z1p in zip(torch_model.named_parameters(), zero_model.parameters()):
@@ -131,9 +127,7 @@ def exam_zero_1_torch_ddp():
# we only test stage 1 here
# in `check_sharded_param_consistency.py`, we will test whether
# level 1 and 2 will produce exactly the same results
pg = ProcessGroup()
zero_optimizer = LowLevelZeroOptimizer(zero_optimizer,
pg=pg,
overlap_communication=True,
initial_scale=1,
reduce_bucket_size=262144)