ColossalAI/tests/test_zero_data_parallel/test_init_context.py

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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from functools import partial
import colossalai
import pytest
import torch
import torch.multiprocessing as mp
from colossalai.utils import free_port
from colossalai.zero.init_ctx import ZeroInitContext
from colossalai.zero.shard_utils.tensor_shard_strategy import \
TensorShardStrategy
from tests.components_to_test.registry import non_distributed_component_funcs
from common import CONFIG
from colossalai.utils.memory_tracer.allocator import GLOBAL_MODEL_DATA_TRACER
def run_dist(rank, world_size, port):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
for get_components_func in non_distributed_component_funcs:
model_builder, _, _, _, _ = get_components_func()
with ZeroInitContext(convert_fp16=True,
convert_cuda=True,
shard_strategy=TensorShardStrategy(),
shard_param=True):
model = model_builder(checkpoint=True)
for param in model.parameters():
assert hasattr(param, 'col_attr')
assert param.col_attr.data.dtype == torch.half
assert param.col_attr.data.is_sharded
assert param.col_attr.data.payload.device.type == 'cuda'
print(f'cuda usgae {GLOBAL_MODEL_DATA_TRACER.cuda_usage}')
assert (GLOBAL_MODEL_DATA_TRACER.cuda_usage > 0)
@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 4])
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def test_zero_init_context(world_size):
run_func = partial(run_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
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test_zero_init_context(2)