mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
130 lines
4.8 KiB
130 lines
4.8 KiB
from contextlib import nullcontext
|
|
from typing import Optional
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
|
|
import colossalai
|
|
from colossalai.booster import Booster
|
|
from colossalai.booster.plugin import GeminiPlugin
|
|
from colossalai.fx import is_compatible_with_meta
|
|
from colossalai.lazy.lazy_init import LazyInitContext
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.tensor.colo_parameter import ColoParameter
|
|
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
|
from tests.kit.model_zoo import model_zoo
|
|
|
|
|
|
def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn) -> Optional[str]:
|
|
try:
|
|
if init_method == 'lazy':
|
|
ctx = LazyInitContext()
|
|
else:
|
|
ctx = nullcontext()
|
|
plugin = GeminiPlugin(max_norm=1.0, initial_scale=2**5)
|
|
booster = Booster(plugin=plugin)
|
|
with ctx:
|
|
model = model_fn()
|
|
optimizer = HybridAdam(model.parameters(), lr=1e-3)
|
|
criterion = lambda x: x.mean()
|
|
data = data_gen_fn()
|
|
|
|
data = {
|
|
k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
|
|
}
|
|
|
|
model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
|
|
|
|
for n, p in model.named_parameters():
|
|
assert isinstance(p, ColoParameter), f'{n} is not a ColoParameter'
|
|
|
|
output = model(**data)
|
|
output = output_transform_fn(output)
|
|
output_key = list(output.keys())[0]
|
|
loss = criterion(output[output_key])
|
|
|
|
booster.backward(loss, optimizer)
|
|
optimizer.step()
|
|
|
|
except Exception as e:
|
|
# raise e
|
|
return repr(e)
|
|
|
|
|
|
# TODO(ver217): CI does not support lazy now
|
|
# @parameterize('init_method', ['lazy', 'none', 'colo'])
|
|
|
|
|
|
@parameterize('subset', ['torchvision', 'transformers', 'diffusers'])
|
|
@parameterize('init_method', ['none'])
|
|
def check_gemini_plugin(subset: str, init_method: str = 'none', early_stop: bool = True):
|
|
"""check gemini plugin over model zoo
|
|
|
|
Args:
|
|
early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True.
|
|
"""
|
|
is_support_meta = is_compatible_with_meta()
|
|
if not is_support_meta and init_method == 'lazy':
|
|
return
|
|
|
|
passed_models = []
|
|
failed_info = {} # (model_name, error) pair
|
|
|
|
for name, (model_fn, data_gen_fn, output_transform_fn, _, _) in model_zoo.get_sub_registry(subset).items():
|
|
# These models lead to CUDA error
|
|
if name in ('diffusers_auto_encoder_kl', 'diffusers_vq_model', 'diffusers_unet2d_model', 'timm_resmlp',
|
|
'timm_gmixer_12_224', 'timm_gmlp_b16_224', 'timm_mixer_b16_224', 'timm_convnext',
|
|
'torchvision_convnext_base'):
|
|
continue
|
|
# These models are not compatible with gemini
|
|
if name in [
|
|
'timm_convit',
|
|
'timm_dm_nfnet',
|
|
'torchvision_vit_b_16',
|
|
'transformers_t5',
|
|
'transformers_t5_for_conditional_generation',
|
|
'transformers_t5_encoder_model', # does not support apex rmsnorm
|
|
'transformers_chatglm',
|
|
'transformers_sam',
|
|
'transformers_vit',
|
|
'transformers_gpt_double_heads', # TODO check why does the model fail to run using Gemini
|
|
]:
|
|
continue
|
|
|
|
if init_method == 'lazy' and name in [
|
|
'timm_convmixer', 'timm_vision_transformer', 'timm_deit', 'timm_deit3', 'timm_inception_v3',
|
|
'timm_tnt_b_patch16_224', 'timm_rexnet', 'torchvision_densenet121', 'torchvision_efficientnet_b0',
|
|
'torchvision_mobilenet_v2', 'torchvision_mnasnet0_5', 'torchvision_regnet_x_16gf',
|
|
'torchvision_shufflenet_v2_x0_5', 'torchvision_efficientnet_v2_s'
|
|
]:
|
|
continue
|
|
err = run_fn(init_method, model_fn, data_gen_fn, output_transform_fn)
|
|
torch.cuda.empty_cache()
|
|
if err is None:
|
|
passed_models.append(name)
|
|
else:
|
|
failed_info[name] = err
|
|
if early_stop:
|
|
break
|
|
|
|
if dist.get_rank() == 0:
|
|
print(f'Init method: {init_method}')
|
|
print(f'Passed models({len(passed_models)}): {passed_models}\n\n')
|
|
print(f'Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n')
|
|
assert len(failed_info) == 0, '\n'.join([f'{k}: {v}' for k, v in failed_info.items()])
|
|
|
|
|
|
def run_dist(rank, world_size, port, early_stop: bool = True):
|
|
# init dist env
|
|
colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
|
|
check_gemini_plugin(early_stop=early_stop)
|
|
|
|
|
|
@rerun_if_address_is_in_use()
|
|
def test_gemini_plugin(early_stop: bool = True):
|
|
spawn(run_dist, 4, early_stop=early_stop)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_gemini_plugin(early_stop=False)
|