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Merged
merged 3 commits into from
Jun 6, 2025
Merged

Arm backend: Support for dynamic shapes and fix resize bugs #11310

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f89910f
Arm backend: Support for dynamic shapes and fix resize bugs
Burton2000 Jun 2, 2025
baa2691
Merge branch 'main' into dynamic
zingo Jun 3, 2025
fc97a9b
Update to fix linting error
Burton2000 Jun 5, 2025
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34 changes: 20 additions & 14 deletions backends/arm/operators/op_upsample_bilinear2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,15 +49,18 @@ def define_node(
input_dtype = inputs[0].dtype

# tosa_shape output is NHWC, take HW
input_size_yx = torch.tensor(
tosa_shape(inputs[0].shape, inputs[0].dim_order)[1:3]
)
# Ignore scale and size parameters, directly use the output size as
# we only support static shapes currently
output_size_yx = torch.tensor(tosa_shape(output.shape, output.dim_order)[1:3])
input_size_yx = tuple([inputs[0].shape[dim] for dim in inputs[0].dim_order])[
1:3
]
output_size_yx = tuple([output.shape[dim] for dim in output.dim_order])[1:3]

# Get align_corners value from the node arguments.
align_corners = bool(node.args[2])
scale_n_yx, scale_d_yx, offset_yx, border_yx = get_resize_parameters(
input_size_yx, output_size_yx, ResizeMode.NEAREST, align_corners=True
input_size_yx,
output_size_yx,
ResizeMode.NEAREST,
align_corners=align_corners,
)

def in_int16_range(x):
Expand Down Expand Up @@ -139,15 +142,18 @@ def define_node(
input_dtype = inputs[0].dtype

# tosa_shape output is NHWC, take HW
input_size_yx = torch.tensor(
tosa_shape(inputs[0].shape, inputs[0].dim_order)[1:3]
)
# Ignore scale and size parameters, directly use the output size as
# we only support static shapes currently
output_size_yx = torch.tensor(tosa_shape(output.shape, output.dim_order)[1:3])
input_size_yx = tuple([inputs[0].shape[dim] for dim in inputs[0].dim_order])[
1:3
]
output_size_yx = tuple([output.shape[dim] for dim in output.dim_order])[1:3]

# Get align_corners value from the node arguments.
align_corners = bool(node.args[2])
scale_n_yx, scale_d_yx, offset_yx, border_yx = get_resize_parameters(
input_size_yx, output_size_yx, ResizeMode.NEAREST, align_corners=True
input_size_yx,
output_size_yx,
ResizeMode.NEAREST,
align_corners=align_corners,
)

def in_int16_range(x):
Expand Down
36 changes: 15 additions & 21 deletions backends/arm/operators/op_upsample_nearest2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
validate_same_dtype,
)
from executorch.backends.arm.tosa_mapping import TosaArg
from executorch.backends.arm.tosa_utils import get_resize_parameters, tosa_shape
from executorch.backends.arm.tosa_utils import get_resize_parameters

from tosa_tools.v0_80.tosa.ResizeMode import ResizeMode # type: ignore

Expand All @@ -43,19 +43,16 @@ def define_node(
validate_num_inputs(self.target, inputs, 3)
validate_same_dtype(self.target, [inputs[0], output])

if inputs[0].shape is None or output.shape is None:
raise ValueError("Only static shapes are supported")

# tosa_shape output is NHWC, take HW
input_size_yx = torch.tensor(
tosa_shape(inputs[0].shape, inputs[0].dim_order)[1:3]
)
# Ignore scale and size parameters, directly use the output size as
# we only support static shapes currently
output_size_yx = torch.tensor(tosa_shape(output.shape, output.dim_order)[1:3])
input_size_yx = tuple([inputs[0].shape[dim] for dim in inputs[0].dim_order])[
1:3
]
output_size_yx = tuple([output.shape[dim] for dim in output.dim_order])[1:3]

# Align corners shouldn't make a difference for nearest upsampling. We set to False so
# half pixel centers are used for resize parameter logic.
scale_n_yx, scale_d_yx, offset_yx, border_yx = get_resize_parameters(
input_size_yx, output_size_yx, ResizeMode.NEAREST, align_corners=True
input_size_yx, output_size_yx, ResizeMode.NEAREST, align_corners=False
)

def in_int16_range(x):
Expand Down Expand Up @@ -102,19 +99,16 @@ def define_node(
validate_num_inputs(self.target, inputs, 3)
validate_same_dtype(self.target, [inputs[0], output])

if inputs[0].shape is None or output.shape is None:
raise ValueError("Only static shapes are supported")

# tosa_shape output is NHWC, take HW
input_size_yx = torch.tensor(
tosa_shape(inputs[0].shape, inputs[0].dim_order)[1:3]
)
# Ignore scale and size parameters, directly use the output size as
# we only support static shapes currently
output_size_yx = torch.tensor(tosa_shape(output.shape, output.dim_order)[1:3])
input_size_yx = tuple([inputs[0].shape[dim] for dim in inputs[0].dim_order])[
1:3
]
output_size_yx = tuple([output.shape[dim] for dim in output.dim_order])[1:3]

# Align corners shouldn't make a difference for nearest upsampling. We set to False so
# half pixel centers are used for resize parameter logic.
scale_n_yx, scale_d_yx, offset_yx, border_yx = get_resize_parameters(
input_size_yx, output_size_yx, ResizeMode.NEAREST, align_corners=True
input_size_yx, output_size_yx, ResizeMode.NEAREST, align_corners=False
)

def in_int16_range(x):
Expand Down
167 changes: 167 additions & 0 deletions backends/arm/test/ops/test_upsample_nearest2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,6 +40,17 @@
"rand_one_and_half_size": lambda: (torch.rand(2, 4, 8, 3), (12, 4), None, False),
}

test_data_suite_dynamic = {
# (test_name, test_data, size, scale_factor, compare_outputs)
"rand_double_scale": lambda: (torch.rand(2, 4, 8, 3), None, 2.0, False),
"rand_double_scale_one_dim": lambda: (
torch.rand(2, 4, 8, 3),
None,
(1.0, 2.0),
False,
),
}


class UpsamplingNearest2d(torch.nn.Module):
def __init__(
Expand Down Expand Up @@ -161,3 +172,159 @@ def test_upsample_nearest2d_vec_tosa_BI_nearest(test_data: torch.Tensor):
pipeline.pop_stage(-1)

pipeline.run()


@common.parametrize("test_data", test_data_suite_dynamic)
def test_upsample_nearest2d_dynamic_MI_nearest(test_data: torch.Tensor):
test_data, size, scale_factor, compare_outputs = test_data()

batch_size = torch.export.Dim("batch", min=0, max=1000)
input_height = torch.export.Dim("input_height", min=0, max=1000)
input_width = torch.export.Dim("input_width", min=0, max=1000)

dynamic_shapes = {"x": {0: batch_size, 2: input_height, 3: input_width}}

pipeline = TosaPipelineMI[input_t1](
UpsamplingNearest2d(size, scale_factor),
(test_data,),
aten_op,
exir_op=[],
dynamic_shapes=dynamic_shapes,
)
if not compare_outputs:
pipeline.pop_stage(-1)
pipeline.run()


@common.parametrize("test_data", test_data_suite_dynamic)
def test_upsample_nearest2d_dynamic_BI_nearest(test_data: torch.Tensor):
test_data, size, scale_factor, compare_outputs = test_data()

batch_size = torch.export.Dim("batch", min=0, max=2)
input_height = torch.export.Dim("input_height", min=0, max=8)
input_width = torch.export.Dim("input_width", min=0, max=8)

dynamic_shapes = {"x": {0: batch_size, 2: input_height, 3: input_width}}

pipeline = TosaPipelineBI[input_t1](
UpsamplingNearest2d(size, scale_factor),
(test_data,),
aten_op,
exir_op=[],
dynamic_shapes=dynamic_shapes,
)
if not compare_outputs:
pipeline.pop_stage(-1)
pipeline.run()


@common.parametrize("test_data", test_data_suite_dynamic)
def test_upsample_nearest2d_dynamic_MI_interpolate(test_data: torch.Tensor):
test_data, size, scale_factor, compare_outputs = test_data()

batch_size = torch.export.Dim("batch", min=0, max=2)
input_height = torch.export.Dim("input_height", min=4, max=8)
input_width = torch.export.Dim("input_width", min=3, max=8)

dynamic_shapes = {
"x": {
0: batch_size,
2: input_height,
3: input_width,
}
}

pipeline = TosaPipelineMI[input_t1](
Interpolate(size, scale_factor),
(test_data,),
aten_op,
exir_op=[],
dynamic_shapes=dynamic_shapes,
)
if not compare_outputs:
pipeline.pop_stage(-1)
pipeline.run()


@common.parametrize("test_data", test_data_suite_dynamic)
def test_upsample_nearest2d_dynamic_BI_interpolate(test_data: torch.Tensor):
test_data, size, scale_factor, compare_outputs = test_data()

batch_size = torch.export.Dim("batch", min=0, max=2)
input_height = torch.export.Dim("input_height", min=4, max=8)
input_width = torch.export.Dim("input_width", min=3, max=8)

dynamic_shapes = {
"x": {
0: batch_size,
2: input_height,
3: input_width,
}
}

pipeline = TosaPipelineBI[input_t1](
Interpolate(size, scale_factor),
(test_data,),
aten_op,
exir_op=[],
dynamic_shapes=dynamic_shapes,
)
if not compare_outputs:
pipeline.pop_stage(-1)
pipeline.run()


@common.parametrize("test_data", test_data_suite_dynamic)
def test_upsample_nearest2d_dynamic_MI_upsample(test_data: torch.Tensor):
test_data, size, scale_factor, compare_outputs = test_data()

batch_size = torch.export.Dim("batch", min=0, max=1000)
input_height = torch.export.Dim("input_height", min=0, max=1000)
input_width = torch.export.Dim("input_width", min=0, max=1000)

dynamic_shapes = {
"x": {
0: batch_size,
2: input_height,
3: input_width,
}
}

pipeline = TosaPipelineMI[input_t1](
Upsample(size, scale_factor),
(test_data,),
aten_op,
exir_op=[],
dynamic_shapes=dynamic_shapes,
)
if not compare_outputs:
pipeline.pop_stage(-1)
pipeline.run()


@common.parametrize("test_data", test_data_suite_dynamic)
def test_upsample_nearest2d_dynamic_BI_upsample(test_data: torch.Tensor):
test_data, size, scale_factor, compare_outputs = test_data()

batch_size = torch.export.Dim("batch", min=0, max=2)
input_height = torch.export.Dim("input_height", min=0, max=8)
input_width = torch.export.Dim("input_width", min=0, max=8)

dynamic_shapes = {
"x": {
0: batch_size,
2: input_height,
3: input_width,
}
}

pipeline = TosaPipelineBI[input_t1](
Upsample(size, scale_factor),
(test_data,),
aten_op,
exir_op=[],
dynamic_shapes=dynamic_shapes,
)
if not compare_outputs:
pipeline.pop_stage(-1)
pipeline.run()
12 changes: 10 additions & 2 deletions backends/arm/test/tester/test_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
# LICENSE file in the root directory of this source tree.

import logging
from typing import Callable, Dict, Generic, List, Optional, Type, TypeVar
from typing import Any, Callable, Dict, Generic, List, Optional, Tuple, Type, TypeVar

import torch

Expand Down Expand Up @@ -88,10 +88,14 @@ def __init__(
compile_spec: List[CompileSpec],
exir_ops: Optional[str | List[str]] = None,
use_to_edge_transform_and_lower: bool = True,
dynamic_shapes: Optional[Tuple[Any]] = None,
):

self.tester = ArmTester(
module, example_inputs=test_data, compile_spec=compile_spec
module,
example_inputs=test_data,
compile_spec=compile_spec,
dynamic_shapes=dynamic_shapes,
)

self.aten_ops = aten_ops if isinstance(aten_ops, list) else [aten_ops]
Expand Down Expand Up @@ -283,6 +287,7 @@ def __init__(
atol: float = 1e-03,
rtol: float = 1e-03,
qtol: int = 1,
dynamic_shapes: Optional[Tuple[Any]] = None,
):
tosa_profiles = {
"0.80": TosaSpecification.create_from_string("TOSA-0.80+BI"),
Expand Down Expand Up @@ -310,6 +315,7 @@ def __init__(
compile_spec,
exir_op,
use_to_edge_transform_and_lower,
dynamic_shapes,
)
self.add_stage(self.tester.quantize, quant_stage, pos=0)

Expand Down Expand Up @@ -381,6 +387,7 @@ def __init__(
atol: float = 1e-03,
rtol: float = 1e-03,
qtol: int = 0,
dynamic_shapes: Optional[Tuple[Any]] = None,
):
tosa_profiles = {
"0.80": TosaSpecification.create_from_string("TOSA-0.80+MI"),
Expand All @@ -398,6 +405,7 @@ def __init__(
compile_spec,
exir_op,
use_to_edge_transform_and_lower,
dynamic_shapes,
)
self.add_stage_after(
"export",
Expand Down
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