github
/
ColossalAI
mirror of https://github.com/hpcaitech/ColossalAI
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

[Tensor] polish model test (#915)

Browse Source
pull/916/head
Jiarui Fang 3 years ago committed by GitHub
parent 0fab86b12a
commit ed6426c300
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 79 additions and 121 deletions
Show Stats Download Patch File Download Diff File

200
tests/test_tensor/test_model.py
Unescape View File

@ -21,7 +21,9 @@ import numpy as np
# Make it available to our ColoTensor
from transformers.file_utils import ModelOutput
from dataclasses import fields
def post_init_colo(self):
def _post_init_colo(self):
class_fields = fields(self)
# Safety and consistency checks
if not len(class_fields):
@ -38,7 +40,7 @@ def post_init_colo(self):
"""
if isinstance(x, torch.Tensor):
return True
return isinstance(x, ColoTensor)
if other_fields_are_none and not is_tensor_with_colo(first_field):
@ -56,11 +58,7 @@ def post_init_colo(self):
# set the associated fields
if first_field_iterator:
for element in iterator:
if (
not isinstance(element, (list, tuple))
or not len(element) == 2
or not isinstance(element[0], str)
):
if (not isinstance(element, (list, tuple)) or not len(element) == 2 or not isinstance(element[0], str)):
break
setattr(self, element[0], element[1])
if element[1] is not None:
@ -73,9 +71,11 @@ def post_init_colo(self):
if v is not None:
self[field.name] = v
ModelOutput.__post_init__ = post_init_colo
ModelOutput.__post_init__ = _post_init_colo
# complete the hack
def set_seed(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
@ -85,9 +85,9 @@ def set_seed(seed):
torch.backends.cudnn.deterministic = True
def run_1d_col_tp():
def run_1d_col_tp(model_name):
# A simple net with two stacked nn.Linear
get_components_func = non_distributed_component_funcs.get_callable('simple_net')
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
@ -95,43 +95,66 @@ def run_1d_col_tp():
with ColoInitContext(device=get_current_device()):
model = model_builder(checkpoint=True)
parallel_action_list_row = [
ParallelAction(priority=1,
compute_pattern=ComputePattern.TP1DRow_Linear,
parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_row = TensorSpec(parallel_action_list_row)
parallel_action_list_col = [
ParallelAction(priority=1,
compute_pattern=ComputePattern.TP1DCol_Linear,
parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_col = TensorSpec(parallel_action_list_col)
parallel_action_list_embedding_col = [
ParallelAction(priority=1,
compute_pattern=ComputePattern.TP1DCol_Embedding,
parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_embedding_col = TensorSpec(parallel_action_list_embedding_col)
if 'bert' == model_name:
parallel_action_list_col = [
ParallelAction(priority=1,
compute_pattern=ComputePattern.TP1DCol_Linear,
parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_col = TensorSpec(parallel_action_list_col)
parallel_action_list_embedding_col = [
ParallelAction(priority=1,
compute_pattern=ComputePattern.TP1DCol_Embedding,
parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_embedding_col = TensorSpec(parallel_action_list_embedding_col)
for name, p in model.colo_named_parameters():
if not isinstance(p, ColoTensor):
continue
#print(name)
if 'classifier' in name and ('weight' in name or 'bias' in name):
p.set_spec(spec_col)
if '_embeddings' in name and 'weight' in name:
p.set_spec(spec_embedding_col)
elif "simple_net" == model_name:
parallel_action_list_row = [
ParallelAction(priority=1,
compute_pattern=ComputePattern.TP1DRow_Linear,
parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_row = TensorSpec(parallel_action_list_row)
parallel_action_list_col = [
ParallelAction(priority=1,
compute_pattern=ComputePattern.TP1DCol_Linear,
parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_col = TensorSpec(parallel_action_list_col)
parallel_action_list_embedding_col = [
ParallelAction(priority=1,
compute_pattern=ComputePattern.TP1DCol_Embedding,
parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_embedding_col = TensorSpec(parallel_action_list_embedding_col)
# A naive way to set spec for all weights in Linear
for name, p in model.colo_named_parameters():
if not isinstance(p, ColoTensor):
continue
if 'proj1' in name and ('weight' in name or 'bias' in name):
p.set_spec(spec_col)
if 'proj2' in name and 'weight' in name:
p.set_spec(spec_row)
if 'embed' in name and 'weight' in name:
p.set_spec(spec_embedding_col)
set_seed(1)
if rank == 0:
model_torch = model_builder(checkpoint=True)
model_torch = model_torch.cuda()
# A naive way to set spec for all weights in Linear
for name, p in model.colo_named_parameters():
if not isinstance(p, ColoTensor):
continue
if 'proj1' in name and ('weight' in name or 'bias' in name):
p.set_spec(spec_col)
if 'proj2' in name and 'weight' in name:
p.set_spec(spec_row)
if 'embed' in name and 'weight' in name:
p.set_spec(spec_embedding_col)
model = model.cuda()
for i, (data, label) in enumerate(train_dataloader):
@ -231,9 +254,9 @@ def test_colo_optimizer():
break
def run_1d_row_tp():
def run_1d_row_tp(model_name: str):
# A simple net with two stacked nn.Linear
get_components_func = non_distributed_component_funcs.get_callable('simple_net')
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
@ -241,6 +264,11 @@ def run_1d_row_tp():
with ColoInitContext(device=get_current_device()):
model = model_builder(checkpoint=True)
set_seed(1)
if rank == 0:
model_torch = model_builder(checkpoint=True)
model_torch = model_torch.cuda()
parallel_action_list = [
ParallelAction(priority=1,
compute_pattern=ComputePattern.TP1DRow_Linear,
@ -255,11 +283,6 @@ def run_1d_row_tp():
]
spec_embedding_row = TensorSpec(parallel_action_list_embedding_row)
set_seed(1)
if rank == 0:
model_torch = model_builder(checkpoint=True)
model_torch = model_torch.cuda()
# A naive way to set spec for all weights in Linear
for name, p in model.colo_named_parameters():
if not isinstance(p, ColoTensor):
@ -307,91 +330,26 @@ def run_1d_row_tp():
if i > 5:
break
def run_bert_1d():
get_components_func = non_distributed_component_funcs.get_callable('bert')
model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func()
device = get_current_device()
set_seed(1)
with ColoInitContext(device=device):
model = model_builder(checkpoint=True)
# parallel_action_list_row = [
# ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
# ]
# spec_row = TensorSpec(parallel_action_list_row)
parallel_action_list_col = [
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_col = TensorSpec(parallel_action_list_col)
parallel_action_list_embedding_col = [
ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Embedding, parallel_mode=ParallelMode.PARALLEL_1D)
]
spec_embedding_col = TensorSpec(parallel_action_list_embedding_col)
for name, p in model.colo_named_parameters():
if not isinstance(p, ColoTensor):
continue
#print(name)
if 'classifier' in name and ('weight' in name or 'bias' in name):
p.set_spec(spec_col)
if '_embeddings' in name and 'weight' in name:
p.set_spec(spec_embedding_col)
# for name, p in model.colo_named_parameters():
# if not isinstance(p, ColoTensor):
# continue
# print(f"{name}: is_gathered {p.is_gathered()}")
model = model.cuda()
for i, (data, label) in enumerate(train_dataloader):
if i > 5:
break
data = data.to(device)
label = label.to(device)
model.train()
if criterion:
output = model(data)
loss = criterion(output, label)
else:
output = model(data, label)
loss = output
loss.backward()
def run_dist(rank, world_size, port):
config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_1d_row_tp()
run_1d_col_tp()
for name in ['bert', 'simple_net']:
run_1d_row_tp(name)
run_1d_col_tp(name)
def run_dist_bert(rank, world_size, port):
config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_bert_1d()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_simple_net(world_size):
run_func = partial(run_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
@pytest.mark.dist
#@pytest.mark.parametrize('world_size', [1, 4])
#Don't really add it to pytest now. After finishing Classifier and Loss, I(jzy) will remove this annotation.
# FIXME(jzy) world size = 4 will fialed
# @pytest.mark.parametrize('world_size', [4])
@parameterize('world_size', [1])
@rerun_if_address_is_in_use()
def test_bert(world_size):
run_func = partial(run_dist_bert, world_size=world_size, port=free_port())
def test_model(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_simple_net()
# test_model_parameters()
# test_colo_optimizer()
test_bert()
test_model()

Write Preview
Loading…
Cancel
Save