ColossalAI/tests/test_gemini/update/test_gemini_use_rmt.py

93 lines
3.6 KiB
Python

from functools import partial
import pytest
import torch
import torch.multiprocessing as mp
import colossalai
from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
from colossalai.gemini.gemini_mgr import GeminiManager
from colossalai.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer
from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
from colossalai.nn.parallel import GeminiDDP, ZeroDDP
from colossalai.tensor import ProcessGroup
from colossalai.testing import parameterize, rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.utils.model.colo_init_context import ColoInitContext
from tests.components_to_test import run_fwd_bwd
from tests.components_to_test.registry import non_distributed_component_funcs
from tests.test_tensor.common_utils import set_seed
# run gemini use the runtime memory tracer
@parameterize('placement_policy', ['auto'])
@parameterize('keep_gather', [False])
@parameterize('model_name', ['bert', 'albert', 'gpt2'])
@parameterize('use_grad_checkpoint', [False, True])
def run_gemini_use_rmt(placement_policy, keep_gather, model_name: str, use_grad_checkpoint: bool = False):
set_seed(42)
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
with ColoInitContext(device='cpu'):
model = model_builder(use_grad_checkpoint)
print(f'model_name {model_name}')
runtime_mem_tracer = RuntimeMemTracer(model)
for i, (input_ids, label) in enumerate(train_dataloader):
if i > 0:
break
input_ids, label = input_ids.cuda(), label.cuda()
# mem tracing
if i == 0:
run_fwd_bwd(runtime_mem_tracer, input_ids, label, criterion, runtime_mem_tracer)
memstats = runtime_mem_tracer.memstats()
runtime_tracer_non_model_data = runtime_mem_tracer._memstats._non_model_data_cuda_list
print('runtime tracer: ', runtime_tracer_non_model_data)
print([memstats.param_used_timestep(p) for p in model.parameters()])
model = GeminiDDP(model, device='cuda', placement_policy=placement_policy, search_range_mb=1, memstats=memstats)
zero_optim = GeminiAdamOptimizer(model, lr=1e-3, initial_scale=1)
pg = ProcessGroup()
set_seed(pg.dp_local_rank())
for i, (input_ids, label) in enumerate(train_dataloader):
# you can only test a single fwd + bwd.
# after bwd param is grad for Gemini, due to the chunk reuse optimization.
# print(f'iteration {i}')
if i > 4:
break
input_ids, label = input_ids.cuda(), label.cuda()
zero_optim.zero_grad()
set_seed(42)
loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
zero_optim.step()
gemini_non_model_data = model.gemini_manager._mem_stats_collector._memstats.non_model_data_list('cuda')
# print('gemini non model data:', gemini_non_model_data)
assert len(gemini_non_model_data) == len(runtime_tracer_non_model_data), \
f'model_name {model_name} {len(gemini_non_model_data)} vs {len(runtime_tracer_non_model_data)}'
def run_dist(rank, world_size, port):
config = {}
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
run_gemini_use_rmt()
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_gemini_use_rmt(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_gemini_use_rmt(1)