mirror of https://github.com/hpcaitech/ColossalAI
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
import pytest
|
|
import torch
|
|
import torch.nn as nn
|
|
from torch.utils.checkpoint import checkpoint
|
|
|
|
from colossalai.testing import clear_cache_before_run
|
|
|
|
try:
|
|
from colossalai._analyzer.fx import symbolic_trace
|
|
except:
|
|
pass
|
|
|
|
|
|
class MyModule(nn.Module):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.a = nn.Linear(10, 10)
|
|
self.b = nn.Linear(10, 10)
|
|
self.c = nn.Linear(10, 10)
|
|
self.d = nn.Linear(10, 10)
|
|
self.e = nn.Linear(10, 10)
|
|
|
|
def checkpoint_0(self, x):
|
|
return checkpoint(self.checkpoint_0_0, x) + checkpoint(self.checkpoint_0_1, x) + self.e(x)
|
|
|
|
def checkpoint_0_0(self, x):
|
|
return checkpoint(self.checkpoint_0_0_0, x) + checkpoint(self.checkpoint_0_0_1, x)
|
|
|
|
def checkpoint_0_0_0(self, x):
|
|
return self.a(x) + checkpoint(self.checkpoint_0_0_0_0, x, use_reentrant=False)
|
|
|
|
def checkpoint_0_0_0_0(self, x):
|
|
return self.b(x)
|
|
|
|
def checkpoint_0_0_1(self, x):
|
|
return self.b(x) + self.c(x)
|
|
|
|
def checkpoint_0_1(self, x):
|
|
return self.d(x)
|
|
|
|
def forward(self, x):
|
|
return checkpoint(self.checkpoint_0, x)
|
|
|
|
|
|
@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
|
|
@clear_cache_before_run()
|
|
def test_nested_ckpt():
|
|
model = MyModule()
|
|
x = torch.rand(10, 10)
|
|
gm = symbolic_trace(model, meta_args={'x': x}, trace_act_ckpt=True)
|
|
assert torch.allclose(gm(x), model(x)), "The traced model should generate the same output as the original model."
|
|
for ckpt_def in filter(lambda s: s.startswith('checkpoint'), dir(model)):
|
|
assert ckpt_def in gm.code, f"Checkpoint {ckpt_def} should be in the traced code.\n Traced code = {gm.code}"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_nested_ckpt()
|