ColossalAI/tests/test_zero/test_legacy/test_state_dict.py

56 lines
2.1 KiB
Python

#!/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)