mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
73 lines
2.5 KiB
73 lines
2.5 KiB
2 years ago
|
from functools import partial
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
import torch
|
||
|
|
import torch.multiprocessing as mp
|
||
|
|
|
||
|
|
import colossalai
|
||
|
|
from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup
|
||
|
|
from colossalai.testing import parameterize, rerun_if_address_is_in_use
|
||
|
|
from colossalai.utils import free_port
|
||
|
2 years ago
|
from colossalai.zero.gemini.chunk import ChunkManager
|
||
|
2 years ago
|
from tests.test_tensor.common_utils import debug_print
|
||
|
|
|
||
|
|
CUDA_MEM_0 = {False: 512, True: 1024}
|
||
|
|
CUDA_MEM_1 = {False: 0, True: 1024}
|
||
|
|
CPU_MEM = {True: {True: 0, False: 0}, False: {True: 512, False: 0}}
|
||
|
|
|
||
|
|
|
||
|
|
@parameterize('keep_gathered', [True, False])
|
||
|
|
@parameterize('pin_memory', [True, False])
|
||
|
|
def exam_chunk_memory(keep_gathered, pin_memory):
|
||
|
|
pg = ProcessGroup()
|
||
|
|
|
||
|
|
debug_print([0], "keep_gathered: {}, pin_memory: {}".format(keep_gathered, pin_memory))
|
||
|
|
|
||
|
|
params = [ColoTensor(torch.rand(8, 8), spec=ColoTensorSpec(pg)) for _ in range(3)]
|
||
|
|
config = {2: dict(chunk_size=128, keep_gathered=keep_gathered)}
|
||
|
|
|
||
|
|
chunk_manager = ChunkManager(config)
|
||
|
|
assert chunk_manager.total_mem['cpu'] == 0
|
||
|
|
assert chunk_manager.total_mem['cuda'] == 0
|
||
|
|
|
||
|
|
for p in params:
|
||
|
|
chunk_manager.register_tensor(p, 'param', 2, pin_memory=pin_memory)
|
||
|
|
chunk_manager.close_all_groups()
|
||
|
|
assert chunk_manager.total_mem['cpu'] == CPU_MEM[keep_gathered][pin_memory]
|
||
|
|
assert chunk_manager.total_mem['cuda'] == CUDA_MEM_0[keep_gathered]
|
||
|
|
|
||
|
|
chunks = chunk_manager.get_chunks(params)
|
||
|
|
|
||
|
|
for chunk in chunks:
|
||
|
|
chunk_manager.access_chunk(chunk)
|
||
|
|
assert chunk_manager.total_mem['cpu'] == CPU_MEM[keep_gathered][pin_memory]
|
||
|
|
assert chunk_manager.total_mem['cuda'] == CUDA_MEM_0[True]
|
||
|
|
|
||
|
|
for chunk in chunks:
|
||
|
|
chunk_manager.release_chunk(chunk)
|
||
|
|
|
||
|
|
assert chunk_manager.total_mem['cpu'] == CPU_MEM[keep_gathered][pin_memory]
|
||
|
|
assert chunk_manager.total_mem['cuda'] == CUDA_MEM_0[keep_gathered]
|
||
|
|
|
||
|
|
for chunk in chunks:
|
||
|
|
chunk_manager.move_chunk(chunk, torch.device('cpu'))
|
||
|
|
assert chunk_manager.total_mem['cpu'] == CPU_MEM[keep_gathered][True]
|
||
|
|
assert chunk_manager.total_mem['cuda'] == CUDA_MEM_1[keep_gathered]
|
||
|
|
|
||
|
|
|
||
|
|
def run_dist(rank, world_size, port):
|
||
|
|
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||
|
|
exam_chunk_memory()
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.dist
|
||
|
|
@pytest.mark.parametrize('world_size', [2])
|
||
|
|
@rerun_if_address_is_in_use()
|
||
|
|
def test_chunk_manager(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_manager(2)
|