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test_pool_aten.py
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import torch
from parameterized import param, parameterized
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input
from .harness import DispatchTestCase
class TestPoolConverter(DispatchTestCase):
@parameterized.expand(
[
(3, 1, 0),
((3,), (1,), (1,)),
((2,), [], (0,)),
((4,), (1,), (1,)),
((5,), (2,), (0,)),
((7,), (2,), (1,)),
((3,), (1,), (1,), 0, True),
((7,), (2,), (1,), 0, True),
]
)
def test_avg_pool1d(
self,
kernel_size,
stride=1,
padding=0,
ceil_mode=False,
count_include_pad=True,
):
class TestModule(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.avg_pool1d.default(
x, kernel_size, stride, padding, ceil_mode, count_include_pad
)
inputs = [torch.randn(1, 3, 32)]
self.run_test(
TestModule(),
inputs,
use_dynamo_tracer=True,
)
@parameterized.expand(
[
(3, 1, 0),
(3, 1, 1),
((2, 2), [], (1, 0)),
((4, 3), (1, 1), (1, 1)),
((4, 3), (1, 1), (1, 1), True),
((5, 4), (2, 1), (1, 0)),
((5, 4), (2, 1), (1, 0), True),
((7, 7), (1, 2), (0, 1)),
((7, 7), (1, 2), (0, 1), True),
]
)
def test_avg_pool2d(
self,
kernel_size,
stride,
padding,
ceil_mode=False,
count_include_pad=True,
divisor_override=None,
):
class TestModule(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.avg_pool2d.default(
x,
kernel_size,
stride,
padding,
ceil_mode,
count_include_pad,
divisor_override,
)
inputs = [torch.randn(1, 3, 32, 32)]
self.run_test(TestModule(), inputs, rtol=5e-03, atol=5e-03, use_dynamo_tracer=True)
@parameterized.expand(
[
(3, 1, 0),
(3, 1, 1),
((2, 2, 3), [], (1, 0, 1)),
((4, 3, 2), (1, 1, 1), (1, 1, 0)),
((5, 4, 3), (2, 1, 2), (1, 0, 1)),
((7, 7, 7), (1, 2, 1), (0, 1, 1)),
((7, 7, 7), (1, 2, 1), (0, 1, 1), True),
((5, 4, 3), (2, 1, 2), (1, 0, 1), True),
]
)
def test_avg_pool3d(
self,
kernel_size,
stride,
padding,
ceil_mode=False,
count_include_pad=True,
divisor_override=None,
):
class TestModule(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.avg_pool3d.default(
x,
kernel_size,
stride,
padding,
ceil_mode,
count_include_pad,
divisor_override,
)
inputs = [torch.randn(1, 3, 32, 32, 32)]
self.run_test(TestModule(), inputs, use_dynamo_tracer=True)
@parameterized.expand(
[
(
(1, 1, 1),
(2, 2, 2),
(3, 3, 3),
torch.float,
(3,),
(1,),
(1,),
),
]
)
def test_dynamic_shape_pool1d(
self,
min_shape,
opt_shape,
max_shape,
type,
kernel_size,
stride=1,
padding=0,
ceil_mode=False,
count_include_pad=True,
):
class pool1d(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.avg_pool1d.default(
x, kernel_size, stride, padding, ceil_mode, count_include_pad
)
input_specs = [
Input(
min_shape=min_shape,
opt_shape=opt_shape,
max_shape=max_shape,
dtype=type,
),
]
self.run_test_with_dynamic_shape(pool1d(), input_specs, use_dynamo_tracer=True)
@parameterized.expand(
[
(
(1, 1, 1, 1),
(2, 2, 2, 2),
(3, 3, 3, 3),
torch.float,
3,
1,
1,
),
(
(1, 1, 1, 1),
(2, 2, 2, 2),
(3, 3, 3, 3),
torch.float,
(3, 3),
(1, 1),
(1, 1),
),
(
(1, 1, 1, 1),
(2, 2, 2, 2),
(3, 3, 3, 3),
torch.float,
(3, 3),
(1, 1),
(1, 1),
True
),
]
)
def test_dynamic_shape_pool2d(
self,
min_shape,
opt_shape,
max_shape,
type,
kernel_size,
stride=1,
padding=0,
ceil_mode=False,
count_include_pad=True,
):
class pool2d(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.avg_pool2d.default(
x, kernel_size, stride, padding, ceil_mode, count_include_pad
)
input_specs = [
Input(
min_shape=min_shape,
opt_shape=opt_shape,
max_shape=max_shape,
dtype=type,
),
]
self.run_test_with_dynamic_shape(pool2d(), input_specs, use_dynamo_tracer=True)
@parameterized.expand(
[
(
(1, 1, 1, 1, 1),
(2, 2, 2, 2, 2),
(3, 3, 3, 3, 3),
torch.float,
2,
1,
1,
),
(
(1, 1, 1, 1, 1),
(2, 2, 2, 2, 2),
(3, 3, 3, 3, 3),
torch.float,
(2, 2, 2),
(1, 1, 1),
(1, 1, 1),
),
]
)
def test_dynamic_shape_pool3d(
self,
min_shape,
opt_shape,
max_shape,
type,
kernel_size,
stride=1,
padding=0,
ceil_mode=False,
count_include_pad=True,
):
class pool3d(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.avg_pool3d.default(
x, kernel_size, stride, padding, ceil_mode, count_include_pad
)
input_specs = [
Input(
min_shape=min_shape,
opt_shape=opt_shape,
max_shape=max_shape,
dtype=type,
),
]
self.run_test_with_dynamic_shape(pool3d(), input_specs, use_dynamo_tracer=True)
@parameterized.expand(
[
(3, 1, 0),
((3,), (1,), (1,)),
((2,), [], (0,)),
((4,), (1,), (1,)),
((5,), (2,), (0,)),
((7,), (2,), (1,)),
((7,), (2,), (1,), 1, True),
]
)
def test_max_pool1d(
self,
kernel_size,
stride,
padding,
dilation=1,
ceil_mode=False,
):
class TestModule(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.max_pool1d.default(
x, kernel_size, stride, padding, dilation, ceil_mode
)
inputs = [torch.randn(1, 3, 32)]
self.run_test(
TestModule(),
inputs,
use_dynamo_tracer=True,
enable_passes=True,
)
@parameterized.expand(
[
(3, 1, 0),
(3, 1, 1),
((2, 2), [], (1, 0)),
((4, 3), (1, 1), (1, 1)),
((5, 4), (2, 1), (1, 0)),
((7, 7), (1, 2), (0, 1)),
((4, 3), (1, 1), (1, 1), 1, True),
((5, 4), (2, 1), (1, 0), 1, True),
((7, 7), (1, 2), (0, 1), 1, True),
]
)
def test_max_pool2d(
self,
kernel_size,
stride,
padding,
dilation=1,
ceil_mode=False,
):
class TestModule(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.max_pool2d.default(
x, kernel_size, stride, padding, dilation, ceil_mode
)
inputs = [torch.randn(1, 3, 32, 32)]
self.run_test(
TestModule(),
inputs,
use_dynamo_tracer=True,
enable_passes=True,
)
@parameterized.expand(
[
(3, 1, 0),
(3, 1, 1),
((2, 2, 3), [], (1, 0, 1)),
((4, 3, 2), (1, 1, 1), (1, 1, 0)),
((5, 4, 3), (2, 1, 2), (1, 0, 1)),
((7, 7, 7), (1, 2, 1), (0, 1, 1)),
((7, 7, 7), (1, 2, 1), (0, 1, 1), 1, True),
]
)
def test_max_pool3d(
self,
kernel_size,
stride,
padding,
dilation=1,
ceil_mode=False,
):
class TestModule(torch.nn.Module):
def forward(self, x):
return torch.ops.aten.max_pool3d.default(
x, kernel_size, stride, padding, dilation, ceil_mode
)
inputs = [torch.randn(1, 3, 32, 32, 32)]
self.run_test(
TestModule(),
inputs,
use_dynamo_tracer=True,
enable_passes=True,
)
if __name__ == "__main__":
run_tests()