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# Owner(s): ["oncall: quantization"]
import copy
import unittest
import torch
import torch._dynamo as torchdynamo
from torch.ao.quantization.pt2e.graph_utils import (
find_sequential_partitions,
get_equivalent_types,
update_equivalent_types_dict,
)
from torch.testing._internal.common_utils import (
IS_WINDOWS,
raise_on_run_directly,
TestCase,
)
class TestGraphUtils(TestCase):
@unittest.skipIf(IS_WINDOWS, "torch.compile is not supported on Windows")
def test_conv_bn_conv_relu(self):
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = torch.nn.Conv2d(3, 3, 3)
self.bn1 = torch.nn.BatchNorm2d(3)
self.conv2 = torch.nn.Conv2d(3, 3, 3)
self.relu2 = torch.nn.ReLU()
def forward(self, x):
bn_out = self.bn1(self.conv1(x))
relu_out = torch.nn.functional.relu(bn_out)
return self.relu2(self.conv2(relu_out))
m = M().eval()
example_inputs = (torch.randn(1, 3, 5, 5),)
# program capture
m, guards = torchdynamo.export( # noqa: F841
m,
*copy.deepcopy(example_inputs),
aten_graph=True,
)
fused_partitions = find_sequential_partitions(
m, [torch.nn.Conv2d, torch.nn.BatchNorm2d]
)
self.assertEqual(len(fused_partitions), 1)
fused_partitions = find_sequential_partitions(
m, [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.ReLU]
)
self.assertEqual(len(fused_partitions), 1)
def x():
find_sequential_partitions(
m,
[
torch.nn.Conv2d,
torch.nn.BatchNorm2d,
torch.nn.ReLU,
torch.nn.functional.conv2d,
],
)
self.assertRaises(ValueError, x)
@unittest.skipIf(IS_WINDOWS, "torch.compile is not supported on Windows")
def test_conv_bn_relu(self):
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.bn1 = torch.nn.BatchNorm2d(3)
self.conv2 = torch.nn.Conv2d(3, 3, 3)
self.relu2 = torch.nn.ReLU()
def forward(self, x):
bn_out = self.bn1(x)
return self.relu2(self.conv2(bn_out))
m = M().eval()
example_inputs = (torch.randn(1, 3, 5, 5),)
# program capture
m, guards = torchdynamo.export( # noqa: F841
m,
*copy.deepcopy(example_inputs),
aten_graph=True,
)
fused_partitions = find_sequential_partitions(
m, [torch.nn.Conv2d, torch.nn.BatchNorm2d]
)
self.assertEqual(len(fused_partitions), 0)
fused_partitions = find_sequential_partitions(
m, [torch.nn.BatchNorm2d, torch.nn.Conv2d]
)
self.assertEqual(len(fused_partitions), 1)
fused_partitions = find_sequential_partitions(
m, [torch.nn.BatchNorm2d, torch.nn.ReLU]
)
self.assertEqual(len(fused_partitions), 0)
@unittest.skipIf(IS_WINDOWS, "torch.compile is not supported on Windows")
def test_customized_equivalet_types_dict(self):
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv = torch.nn.Conv2d(3, 3, 3)
def forward(self, x):
return torch.nn.functional.relu6(self.conv(x))
m = M().eval()
example_inputs = (torch.randn(1, 3, 5, 5),)
# program capture
m, guards = torchdynamo.export( # noqa: F841
m,
*copy.deepcopy(example_inputs),
aten_graph=True,
)
customized_equivalent_types = get_equivalent_types()
customized_equivalent_types.append({torch.nn.ReLU6, torch.nn.functional.relu6})
update_equivalent_types_dict(customized_equivalent_types)
fused_partitions = find_sequential_partitions(
m,
[torch.nn.Conv2d, torch.nn.ReLU6],
)
self.assertEqual(len(fused_partitions), 1)
if __name__ == "__main__":
raise_on_run_directly("test/test_quantization.py")
|
