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43 changes: 43 additions & 0 deletions backends/webgpu/test/op_tests/cases.py
Original file line number Diff line number Diff line change
@@ -1,3 +1,3 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
Expand Down Expand Up @@ -586,3 +586,46 @@
atol=1e-4,
rtol=1e-3,
)
from executorch.backends.webgpu.test.ops.test_upsample_nearest2d import (
_det_input as _upsample_det_input,
UpsampleNearest2dModule,
)


@register_op_test("upsample_nearest2d")
def _upsample_nearest2d_suite() -> WebGPUTestSuite:
# SAM2 FPN 2x upsample chain (36->72->144) + a cheap tiny eyeball case.
return WebGPUTestSuite(
module_factory=lambda scale_factor: UpsampleNearest2dModule(scale_factor),
cases=[
Case(
name="fpn_36_72",
construct={"scale_factor": 2.0},
inputs=(InputSpec(shape=(1, 8, 36, 36), gen=_upsample_det_input),),
),
Case(
name="fpn_72_144",
construct={"scale_factor": 2.0},
inputs=(InputSpec(shape=(1, 8, 72, 72), gen=_upsample_det_input),),
),
Case(
name="tiny",
construct={"scale_factor": 2.0},
inputs=(InputSpec(shape=(1, 4, 5, 7), gen=_upsample_det_input),),
),
Case(
# Non-integer, non-2x ratio (5->8): floor(oh*5/8) and the
# half-pixel-center formula round((oh+0.5)*5/8-0.5) diverge at
# oh=3,6 (1 vs 2, 3 vs 4) — locks in the legacy "nearest"
# formula (see upsample_nearest2d.wgsl) against a genuinely
# discriminating ratio, not just the 2x cases above where both
# formulas happen to agree.
name="non_2x_ratio",
construct={"scale_factor": 1.6},
inputs=(InputSpec(shape=(1, 2, 5, 5), gen=_upsample_det_input),),
),
],
golden_dtype="float32",
atol=1e-4,
rtol=1e-3,
)
33 changes: 33 additions & 0 deletions backends/webgpu/test/ops/test_upsample_nearest2d.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,33 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

"""`aten.upsample_nearest2d.vec` module for the WebGPU op-test framework.

Upsample is on the SAM2/SAM3 pixel-decoder / FPN path
(`F.interpolate(..., mode="nearest")`).
"""

import torch


class UpsampleNearest2dModule(torch.nn.Module):
def __init__(self, scale_factor: float) -> None:
super().__init__()
self.scale_factor = scale_factor

def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.interpolate(
x, scale_factor=self.scale_factor, mode="nearest"
)


def _det_input(shape) -> torch.Tensor:
# ((i % 23) - 11) / 16: exact in fp32, spans negatives through positives.
n = 1
for s in shape:
n *= s
idx = torch.arange(n, dtype=torch.int64)
return (((idx % 23) - 11).to(torch.float32) / 16.0).reshape(shape)
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