Making large AI models cheaper, faster and more accessible
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import pytest
import torch
import torch.distributed as dist
from torch.distributed.distributed_c10d import _get_default_group
import colossalai
from colossalai.accelerator import get_accelerator
from colossalai.tensor import ColoParameter
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
from colossalai.zero.gemini import TensorState
from colossalai.zero.gemini.chunk import Chunk
def dist_sum(x):
temp = torch.tensor([x], device=get_accelerator().get_current_device())
dist.all_reduce(temp)
return temp.item()
def add_param(param_list, param_cp_list, *args, **kwargs):
param = ColoParameter(torch.randn(*args, **kwargs))
param_list.append(param)
param_cp_list.append(param.clone())
def check_equal(param, param_cp):
if param.device != param_cp.device:
temp = param.data.to(param_cp.device)
else:
temp = param.data
return torch.equal(temp, param_cp.data)
@parameterize("init_device", [None, torch.device("cpu")])
@parameterize("keep_gathered", [True, False])
@parameterize("pin_memory", [True, False])
def exam_chunk_basic(init_device, keep_gathered, pin_memory):
world_size = torch.distributed.get_world_size()
pg = _get_default_group()
my_chunk = Chunk(
chunk_size=1024,
zero_group=pg,
dtype=torch.float32,
init_device=init_device,
cpu_shard_init=True,
keep_gathered=keep_gathered,
pin_memory=pin_memory,
)
param_list = []
param_cp_list = []
add_param(param_list, param_cp_list, 8, 8, 8, device="cuda")
add_param(param_list, param_cp_list, 4, 4)
add_param(param_list, param_cp_list, 4, 8, 2, device="cuda")
add_param(param_list, param_cp_list, 1, 1, 5)
for param in param_list:
my_chunk.append_tensor(param)
assert my_chunk.utilized_size == 597
for param, param_cp in zip(param_list, param_cp_list):
check_equal(param, param_cp)
my_chunk.close_chunk()
if keep_gathered is False:
assert my_chunk.cpu_shard.size(0) == 1024 // world_size
assert my_chunk.device_type == "cpu"
assert my_chunk.can_move
my_chunk.shard_move(get_accelerator().get_current_device())
else:
assert my_chunk.cuda_global_chunk.size(0) == 1024
assert my_chunk.device_type == "cuda"
assert not my_chunk.can_move
assert dist_sum(my_chunk.valid_end) == my_chunk.utilized_size
flag = my_chunk.has_inf_or_nan
assert not flag, "has_inf_or_nan is {}".format(flag)
my_chunk.access_chunk()
assert my_chunk.device_type == "cuda"
for param, param_cp in zip(param_list, param_cp_list):
check_equal(param, param_cp)
assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 4
my_chunk.tensor_trans_state(param_list[0], TensorState.COMPUTE)
assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 3
assert my_chunk.tensor_state_cnter[TensorState.COMPUTE] == 1
assert not my_chunk.can_release
for param in param_list:
my_chunk.tensor_trans_state(param, TensorState.COMPUTE)
my_chunk.tensor_trans_state(param, TensorState.HOLD_AFTER_BWD)
my_chunk.tensor_trans_state(param, TensorState.READY_FOR_REDUCE)
assert my_chunk.tensor_state_cnter[TensorState.READY_FOR_REDUCE] == 4
assert my_chunk.can_reduce
my_chunk.reduce()
assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 4
if keep_gathered is False:
assert my_chunk.cuda_shard.size(0) == 1024 // world_size
assert my_chunk.device_type == "cuda"
assert my_chunk.can_move
else:
assert my_chunk.cuda_global_chunk.size(0) == 1024
assert my_chunk.device_type == "cuda"
assert not my_chunk.can_move
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
exam_chunk_basic()
@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 2, 4])
@rerun_if_address_is_in_use()
def test_chunk_function(world_size):
spawn(run_dist, world_size)
if __name__ == "__main__":
test_chunk_function(4)