mirror of https://github.com/hpcaitech/ColossalAI
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
import torch
|
|
import colossalai
|
|
import pytest
|
|
import torch.multiprocessing as mp
|
|
from typing import List
|
|
from functools import partial
|
|
from colossalai.tensor import ChunkManager
|
|
from colossalai.testing import rerun_if_address_is_in_use, parameterize
|
|
from colossalai.utils import free_port
|
|
from colossalai.core import global_context as gpc
|
|
from colossalai.context import ParallelMode
|
|
|
|
|
|
def check_has_params(params: List[torch.Tensor], has_tensors: List[bool]):
|
|
for p, has_tensor in zip(params, has_tensors):
|
|
if has_tensor:
|
|
assert p.storage().size() > 0
|
|
assert p.device.type == 'cuda'
|
|
else:
|
|
assert p.storage().size() == 0
|
|
|
|
|
|
# HAS_TENSORS[use_chunk][use_zero]
|
|
HAS_TENSORS = {
|
|
True: {
|
|
True: [[True, True, False], [False, False, True]],
|
|
False: [[True, True, True], [True, True, True]]
|
|
},
|
|
False: {
|
|
True: [[True, False, True], [False, True, False]],
|
|
False: [[True, True, True], [True, True, True]]
|
|
}
|
|
}
|
|
|
|
|
|
@parameterize('use_chunk', [False, True])
|
|
@parameterize('use_zero', [False, True])
|
|
def run_chunk_zero(use_chunk, use_zero):
|
|
rank = gpc.get_local_rank(ParallelMode.DATA)
|
|
if rank == 0:
|
|
print(f'use_chunk={use_chunk}, use_zero={use_zero}')
|
|
params = [torch.rand(32, 32) for _ in range(3)]
|
|
chunk_size = 2048 if use_chunk else None
|
|
chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero)
|
|
for p in params:
|
|
chunk_manager.append_tensor(p, 'param')
|
|
check_has_params(params, HAS_TENSORS[use_chunk][use_zero][rank])
|
|
for p in params:
|
|
chunk_manager.access_chunk(p)
|
|
check_has_params(params, [True, True, True])
|
|
for p in params:
|
|
chunk_manager.release_chunk(p)
|
|
check_has_params(params, HAS_TENSORS[use_chunk][use_zero][rank])
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
run_chunk_zero()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize('world_size', [2])
|
|
@rerun_if_address_is_in_use()
|
|
def test_chunk_mapping(world_size):
|
|
run_func = partial(run_dist, world_size=world_size, port=free_port())
|
|
mp.spawn(run_func, nprocs=world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_chunk_mapping(2)
|