mirror of https://github.com/hpcaitech/ColossalAI
82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
import pytest
|
|
import torch
|
|
|
|
from colossalai.testing import clear_cache_before_run, parameterize
|
|
|
|
try:
|
|
from colossalai._analyzer.fx import symbolic_trace
|
|
except:
|
|
pass
|
|
|
|
|
|
class LinearModel(torch.nn.Module):
|
|
|
|
def __init__(self, in_features, out_features, bias):
|
|
super().__init__()
|
|
self.linear = torch.nn.Linear(in_features, out_features, bias=bias)
|
|
|
|
def forward(self, x):
|
|
x = self.linear(x)
|
|
return x
|
|
|
|
|
|
class ConvModel(torch.nn.Module):
|
|
|
|
def __init__(self, in_channel, out_channels, kernel_size, bias) -> None:
|
|
super().__init__()
|
|
self.conv = torch.nn.Conv2d(in_channel,
|
|
out_channels,
|
|
kernel_size,
|
|
bias=bias,
|
|
padding=1,
|
|
stride=2,
|
|
dilation=2,
|
|
groups=3)
|
|
self.conv_transpose = torch.nn.ConvTranspose2d(out_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
bias=bias,
|
|
padding=1,
|
|
stride=2,
|
|
dilation=2,
|
|
groups=3)
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
x = self.conv_transpose(x)
|
|
return x
|
|
|
|
|
|
class AModel(torch.nn.Module):
|
|
|
|
def __init__(self, bias) -> None:
|
|
super().__init__()
|
|
self.linear_1 = LinearModel(3, 3, bias)
|
|
self.linear_2 = LinearModel(3, 3, bias)
|
|
self.conv = ConvModel(3, 6, 3, bias)
|
|
|
|
def forward(self, x):
|
|
for i in range(x.shape[0]):
|
|
x = self.linear_1(x)
|
|
x = self.linear_2(x)
|
|
x = self.conv(x)
|
|
return x
|
|
|
|
|
|
@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
|
|
@clear_cache_before_run()
|
|
@parameterize("bias", [True, False])
|
|
@parameterize("bias_addition_split", [True, False])
|
|
@parameterize("shape", [(3, 3, 3), (3, 3, 3, 3)])
|
|
def test_mod_dir(bias, bias_addition_split, shape):
|
|
model = AModel(bias=bias)
|
|
x = torch.rand(shape)
|
|
gm = symbolic_trace(model, meta_args={'x': x}, bias_addition_split=bias_addition_split)
|
|
for node in gm.graph.nodes:
|
|
assert len(node.meta['info'].mod_dir), f"{node} should have non-trivial ``mod_dir``."
|
|
print(node, node.meta['info'].mod_dir)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_mod_dir(bias=True, bias_addition_split=True, shape=(3, 3, 3))
|