ColossalAI/tests/test_analyzer/test_fx/test_mod_dir.py

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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))