#!/usr/bin/env python # -*- encoding: utf-8 -*- from functools import partial import pytest import torch from common import CONFIG import colossalai from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn from colossalai.zero.legacy.init_ctx import ZeroInitContext from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy from colossalai.zero.legacy.sharded_model import ShardedModelV2 from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy from tests.components_to_test.registry import non_distributed_component_funcs @parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy]) def run_zero_state_dict(shard_strategy_class): test_models = ['repeated_computed_layers', 'resnet18'] shard_strategy = shard_strategy_class() for model_name in test_models: get_components_func = non_distributed_component_funcs.get_callable(model_name) model_builder, train_dataloader, test_dataloader, optimizer, criterion = get_components_func() with ZeroInitContext(target_device=torch.device('cuda', torch.cuda.current_device()), shard_strategy=shard_strategy, shard_param=True): zero_model = model_builder(checkpoint=True) zero_model = ShardedModelV2(zero_model, shard_strategy) model = model_builder(checkpoint=True).half() col_model_deepcopy(zero_model, model) model = model.cuda() zero_state_dict = zero_model.state_dict() for key, val in model.state_dict().items(): assert torch.equal(val, zero_state_dict[key].to(val.device)) def run_dist(rank, world_size, port): colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') run_zero_state_dict() @pytest.mark.dist @pytest.mark.parametrize("world_size", [1, 2]) @rerun_if_address_is_in_use() def test_zero_state_dict(world_size): spawn(run_dist, world_size) if __name__ == '__main__': test_zero_state_dict(2)