mirror of
https://github.com/hpcaitech/ColossalAI.git
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26b7aac0be
* [zero] refactor low-level zero folder structure * [zero] fix legacy zero import path * [zero] fix legacy zero import path * [zero] remove useless import * [zero] refactor gemini folder structure * [zero] refactor gemini folder structure * [zero] refactor legacy zero import path * [zero] refactor gemini folder structure * [zero] refactor gemini folder structure * [zero] refactor gemini folder structure * [zero] refactor legacy zero import path * [zero] fix test import path * [zero] fix test * [zero] fix circular import * [zero] update import
73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
from functools import partial
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import pytest
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import torch
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import torch.multiprocessing as mp
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from common import CONFIG
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import colossalai
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.utils.cuda import get_current_device
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from colossalai.zero.legacy.init_ctx import ZeroInitContext
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from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy
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from colossalai.zero.legacy.sharded_model import ShardedModelV2
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from colossalai.zero.legacy.sharded_optim import ShardedOptimizerV2
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from colossalai.zero.low_level._utils import has_inf_or_nan
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from tests.components_to_test.registry import non_distributed_component_funcs
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from tests.test_zero.test_sharded_optim_v2 import _run_step
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@parameterize("cpu_offload", [True, False])
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@parameterize("shard_strategy_class", [BucketTensorShardStrategy])
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@parameterize("gpu_margin_mem_ratio", [0.0, 0.7])
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def _run_test_found_inf(cpu_offload, shard_strategy_class, gpu_margin_mem_ratio):
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test_models = ['repeated_computed_layers']
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shard_strategy = shard_strategy_class()
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for model_name in test_models:
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func()
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with ZeroInitContext(target_device=torch.device(f'cpu:0') if cpu_offload else get_current_device(),
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shard_strategy=shard_strategy,
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shard_param=True):
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zero_model = model_builder(checkpoint=True)
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zero_model = ShardedModelV2(
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zero_model,
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shard_strategy,
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tensor_placement_policy='cpu' if cpu_offload else 'cuda',
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reuse_fp16_shard=True,
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)
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sharded_optim = HybridAdam(zero_model.parameters(), lr=1e-3)
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sharded_optim = ShardedOptimizerV2(zero_model, sharded_optim, gpu_margin_mem_ratio=gpu_margin_mem_ratio)
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for i, (data, label) in enumerate(train_dataloader):
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if i > 1:
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break
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assert zero_model.overflow_counter == 0
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data, label = data.cuda(), label.cuda()
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_run_step(zero_model, sharded_optim, data, label, criterion, False)
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for param in zero_model.parameters():
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assert not has_inf_or_nan(param.colo_attr.data_payload)
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def _run_dist(rank, world_size, port):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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_run_test_found_inf()
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# use_cpuadam = True can be used with cpu_offload = False
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [1, 2])
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@rerun_if_address_is_in_use()
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def test_found_inf(world_size):
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run_func = partial(_run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_found_inf(world_size=2)
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