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