mirror of https://github.com/hpcaitech/ColossalAI
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from copy import deepcopy
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from functools import partial
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import colossalai
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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 colossalai.utils import free_port
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from colossalai.zero.shard_utils.tensor_shard_strategy import \
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TensorShardStrategy
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from colossalai.zero.sharded_model import ShardedModelV2
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from tests.components_to_test.registry import non_distributed_component_funcs
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from common import CONFIG
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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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test_models = ['repeated_computed_layers', 'resnet18']
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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, train_dataloader, test_dataloader, optimizer, criterion = get_components_func()
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model = model()
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shard_strategy = TensorShardStrategy()
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model = model.half().cuda()
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zero_model = ShardedModelV2(deepcopy(model), shard_strategy)
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zero_state_dict = zero_model.state_dict()
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for key, val in model.state_dict().items():
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assert torch.equal(val, zero_state_dict[key])
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@pytest.mark.dist
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def test_zero_state_dict():
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world_size = 2
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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_zero_state_dict()
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