ColossalAI/tests/test_zero/test_gemini/test_runtime_mem_tracer.py

55 lines
1.9 KiB
Python

from copy import deepcopy
import numpy as np
import pytest
import torch
from colossalai.testing import clear_cache_before_run
from colossalai.zero.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer
from tests.components_to_test import run_fwd_bwd
from tests.components_to_test.registry import non_distributed_component_funcs
@pytest.mark.skip("this is not used")
@clear_cache_before_run()
def test_runtime_mem_tracer():
test_models = ['gpt2', 'bert', 'simple_net', 'repeated_computed_layers', 'nested_model', 'albert']
for model_name in test_models:
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, _, _, criterion = get_components_func()
model = model_builder(checkpoint=False).cuda()
model_bk = deepcopy(model)
runtime_mem_tracer = RuntimeMemTracer(model)
for i, (data, label) in enumerate(train_dataloader):
if i > 1:
break
data = data.cuda()
label = label.cuda()
run_fwd_bwd(runtime_mem_tracer, data, label, criterion, optimizer=runtime_mem_tracer)
for p1, p2 in zip(model_bk.parameters(), model.parameters()):
torch.allclose(p1.to(torch.half), p2)
non_model_data_list = runtime_mem_tracer._memstats.non_model_data_list('cuda')
cuda_non_model_data_list = np.array(non_model_data_list) / 1024**2
print("cuda_non_model_data_list", len(cuda_non_model_data_list))
print(non_model_data_list)
cnt1 = 0
for p in runtime_mem_tracer.parameters_in_runtime_order():
cnt1 += 1
cnt2 = 0
for p in model.parameters():
cnt2 += 1
assert cnt2 == cnt1, f'visited param number {cnt1} vs real param number {cnt2}'
del model
if __name__ == '__main__':
test_runtime_mem_tracer()