Skip to content

[EE/BE] Adding legacy partitioner tests #6920

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 4 commits into
base: main
Choose a base branch
from
Open

[EE/BE] Adding legacy partitioner tests #6920

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
54bf735
[EE/BE] Adding legacy partitioner tests
cbilgin Nov 17, 2024
51f2682
linter fixes
cbilgin Nov 17, 2024
d82b28f
linter fixes
cbilgin Nov 17, 2024
85f597e
more linter fixes
cbilgin Nov 17, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
78 changes: 46 additions & 32 deletions backends/xnnpack/test/ops/avgpool2d.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,17 +29,22 @@ def forward(self, x):
return self.avgPool(x)

def _test_argpool2d(self, inputs):
(
Tester(self.AvgPool2d(), inputs)
.export()
.check_count({"torch.ops.aten.avg_pool2d.default": 1})
.to_edge_transform_and_lower()
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.check_not(["executorch_exir_dialects_edge__ops_aten_avg_pool2d_default"])
.to_executorch()
.serialize()
.run_method_and_compare_outputs()
)
for legacy in (True, False):
tester = Tester(self.AvgPool2d(), inputs)
tester.export()
tester.check_count({"torch.ops.aten.avg_pool2d.default": 1})
if legacy:
tester.to_edge()
tester.partition()
else:
tester.to_edge_transform_and_lower()
tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
tester.check_not(
["executorch_exir_dialects_edge__ops_aten_avg_pool2d_default"]
)
tester.to_executorch()
tester.serialize()
tester.run_method_and_compare_outputs()

def test_fp16_avgpool2d(self):
inputs = (torch.randn(1, 1, 10, 10).to(torch.float16),)
Expand All @@ -54,36 +59,45 @@ def test_fp32_avgpool2d_ceil_mode_unsupported(self):
The XNNPACK backend does not support ceil mode.
"""
inputs = (torch.randn(1, 1, 10, 10),)
(
Tester(self.AvgPool2d(ceil_mode=True), inputs)
.export()
.check_count({"torch.ops.aten.avg_pool2d.default": 1})
.to_edge_transform_and_lower()
.check_not(["torch.ops.higher_order.executorch_call_delegate"])
)
for legacy in (True, False):
tester = Tester(self.AvgPool2d(ceil_mode=True), inputs)
tester.export()
tester.check_count({"torch.ops.aten.avg_pool2d.default": 1})
if legacy:
tester.to_edge()
tester.partition()
else:
tester.to_edge_transform_and_lower()
tester.check_not(["torch.ops.higher_order.executorch_call_delegate"])

def test_fp32_avgpool2d_count_include_pad_unsupported(self):
"""
The XNNPACK backend does not support count_include_pad=True.
"""
inputs = (torch.randn(1, 1, 10, 10),)
(
Tester(self.AvgPool2d(count_include_pad=True), inputs)
.export()
.check_count({"torch.ops.aten.avg_pool2d.default": 1})
.to_edge_transform_and_lower()
.check_not(["torch.ops.higher_order.executorch_call_delegate"])
)
for legacy in (True, False):
tester = Tester(self.AvgPool2d(count_include_pad=True), inputs)
tester.export()
tester.check_count({"torch.ops.aten.avg_pool2d.default": 1})
if legacy:
tester.to_edge()
tester.partition()
else:
tester.to_edge_transform_and_lower()
tester.check_not(["torch.ops.higher_order.executorch_call_delegate"])

def test_fp32_avgpool2d_divisor_override(self):
"""
The XNNPACK backend does not support divisor overrides not equal to the pooling region.
"""
inputs = (torch.randn(1, 1, 10, 10),)
(
Tester(self.AvgPool2d(divisor_override=5), inputs)
.export()
.check_count({"torch.ops.aten.avg_pool2d.default": 1})
.to_edge_transform_and_lower()
.check_not(["torch.ops.higher_order.executorch_call_delegate"])
)
for legacy in (True, False):
tester = Tester(self.AvgPool2d(divisor_override=5), inputs)
tester.export()
tester.check_count({"torch.ops.aten.avg_pool2d.default": 1})
if legacy:
tester.to_edge()
tester.partition()
else:
tester.to_edge_transform_and_lower()
tester.check_not(["torch.ops.higher_order.executorch_call_delegate"])
79 changes: 54 additions & 25 deletions backends/xnnpack/test/ops/bilinear2d.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -78,43 +78,72 @@ def forward(self, x):
"executorch_exir_dialects_edge__ops_aten_clamp_default",
}

@unittest.skip('Expected to not find "aten_index_Tensor"')
def test_fp32_static_resize_bilinear2d_legacy(self):
example_inputs = (torch.randn(2, 3, 4, 5),)
tester = Tester(self.StaticResizeBilinear2dModule(), example_inputs)
tester.export()
tester.to_edge()
tester.partition()
tester.check_not(self.ops)
tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
tester.to_executorch()
tester.serialize()
tester.run_method_and_compare_outputs()

def test_fp32_static_resize_bilinear2d(self):
example_inputs = (torch.randn(2, 3, 4, 5),)
(
Tester(self.StaticResizeBilinear2dModule(), example_inputs)
.export()
.to_edge_transform_and_lower()
.check_not(self.ops)
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.to_executorch()
.serialize()
.run_method_and_compare_outputs()
tester = Tester(self.StaticResizeBilinear2dModule(), example_inputs)
tester.export()
tester.to_edge_transform_and_lower()
tester.check_not(self.ops)
tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
tester.to_executorch()
tester.serialize()
tester.run_method_and_compare_outputs()

@unittest.skip('Expected to not find "aten_index_Tensor"')
def test_fp32_static_resize_bilinear2d_with_align_corners_legacy(self):
example_inputs = (torch.randn(2, 3, 4, 5),)
tester = Tester(
self.StaticResizeBilinear2dModuleWithAlignCorners(), example_inputs
)
tester.export()
tester.to_edge()
tester.partition()
tester.check_not(self.ops)
tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
tester.to_executorch()
tester.serialize()
tester.run_method_and_compare_outputs()

def test_fp32_static_resize_bilinear2d_with_align_corners(self):
example_inputs = (torch.randn(2, 3, 4, 5),)
(
Tester(self.StaticResizeBilinear2dModuleWithAlignCorners(), example_inputs)
.export()
.to_edge_transform_and_lower()
.check_not(self.ops)
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.to_executorch()
.serialize()
.run_method_and_compare_outputs()
tester = Tester(
self.StaticResizeBilinear2dModuleWithAlignCorners(), example_inputs
)
tester.export()
tester.to_edge_transform_and_lower()
tester.check_not(self.ops)
tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
tester.to_executorch()
tester.serialize()
tester.run_method_and_compare_outputs()

def test_fp32_static_resize_bilinear2d_antialiased(self):
# Check bilinear2d_aa is not partitioned
example_inputs = (torch.randn(2, 3, 4, 5),)
(
Tester(self.Bilinear2dAntiAlias(), example_inputs)
.export()
.to_edge_transform_and_lower()
.check_count(
for legacy in (True, False):
tester = Tester(self.Bilinear2dAntiAlias(), example_inputs)
tester.export()
if legacy:
tester.to_edge()
tester.partition()
else:
tester.to_edge_transform_and_lower()
tester.check_count(
{
"executorch_exir_dialects_edge__ops_aten__upsample_bilinear2d_aa_default": 2
}
)
.check_not(["torch.ops.higher_order.executorch_call_delegate"])
)
tester.check_not(["torch.ops.higher_order.executorch_call_delegate"])
25 changes: 14 additions & 11 deletions backends/xnnpack/test/ops/bmm.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,17 +19,20 @@ def forward(self, x, y):
return torch.bmm(x, y)

def _test_bmm(self, inputs):
(
Tester(self.BMM(), inputs)
.export()
.check_count({"torch.ops.aten.bmm.default": 1})
.to_edge_transform_and_lower()
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.check_not(["executorch_exir_dialects_edge__ops_aten_bmm_default"])
.to_executorch()
.serialize()
.run_method_and_compare_outputs()
)
for legacy in (True, False):
tester = Tester(self.BMM(), inputs)
tester.export()
tester.check_count({"torch.ops.aten.bmm.default": 1})
if legacy:
tester.to_edge()
tester.partition()
else:
tester.to_edge_transform_and_lower()
tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
tester.check_not(["executorch_exir_dialects_edge__ops_aten_bmm_default"])
tester.to_executorch()
tester.serialize()
tester.run_method_and_compare_outputs()

def test_fp16_bmm(self):
inputs = (
Expand Down
25 changes: 14 additions & 11 deletions backends/xnnpack/test/ops/ceil.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,17 +20,20 @@ def forward(self, x):
return z

def _test_ceil(self, inputs):
(
Tester(self.Ceil(), inputs)
.export()
.check_count({"torch.ops.aten.ceil.default": 1})
.to_edge_transform_and_lower()
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.check_not(["executorch_exir_dialects_edge__ops_aten_ceil_default"])
.to_executorch()
.serialize()
.run_method_and_compare_outputs()
)
for legacy in (True, False):
tester = Tester(self.Ceil(), inputs)
tester.export()
tester.check_count({"torch.ops.aten.ceil.default": 1})
if legacy:
tester.to_edge()
tester.partition()
else:
tester.to_edge_transform_and_lower()
tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
tester.check_not(["executorch_exir_dialects_edge__ops_aten_ceil_default"])
tester.to_executorch()
tester.serialize()
tester.run_method_and_compare_outputs()

def test_fp16_ceil(self):
inputs = (
Expand Down
54 changes: 30 additions & 24 deletions backends/xnnpack/test/ops/clamp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -22,17 +22,20 @@ def forward(self, x):
return z + z

def _test_clamp(self, module, inputs):
(
Tester(module, inputs)
.export()
.check_count({"torch.ops.aten.clamp.default": 1})
.to_edge_transform_and_lower()
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.check_not(["executorch_exir_dialects_edge__ops_aten_clamp_default"])
.to_executorch()
.serialize()
.run_method_and_compare_outputs()
)
for legacy in (True, False):
tester = Tester(module, inputs)
tester.export()
tester.check_count({"torch.ops.aten.clamp.default": 1})
if legacy:
tester.to_edge()
tester.partition()
else:
tester.to_edge_transform_and_lower()
tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
tester.check_not(["executorch_exir_dialects_edge__ops_aten_clamp_default"])
tester.to_executorch()
tester.serialize()
tester.run_method_and_compare_outputs()

def test_fp16_clamp(self):
inputs = (torch.randn(1, 4, 122, 122).to(torch.float16) * 2,)
Expand All @@ -56,21 +59,24 @@ def test_fp32_clamp_upper(self):

def test_qs8_clamp(self):
inputs = (torch.randn(1, 4, 122, 122),)
(
Tester(self.Clamp(min_val=-1, max_val=1), inputs)
.quantize()
.export()
.check_count({"torch.ops.aten.clamp.default": 1})
.check(["torch.ops.quantized_decomposed"])
.to_edge_transform_and_lower()
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.check_not(
for legacy in (True, False):
tester = Tester(self.Clamp(min_val=-1, max_val=1), inputs)
tester.quantize()
tester.export()
tester.check_count({"torch.ops.aten.clamp.default": 1})
tester.check(["torch.ops.quantized_decomposed"])
if legacy:
tester.to_edge()
tester.partition()
else:
tester.to_edge_transform_and_lower()
tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
tester.check_not(
[
"executorch_exir_dialects_edge__ops_aten_clamp_default",
"torch.ops.quantized_decomposed",
]
)
.to_executorch()
.serialize()
.run_method_and_compare_outputs()
)
tester.to_executorch()
tester.serialize()
tester.run_method_and_compare_outputs()
Loading
Loading