Skip to content

[ExecuTorch][WebGPU] et_vk conv2d im2col-GEMM tests #20874

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
JCNTH wants to merge 3 commits into
base: gh/JCNTH/49/base
Choose a base branch
from
Open

[ExecuTorch][WebGPU] et_vk conv2d im2col-GEMM tests #20874

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
38a3f95
Update
JCNTH Jul 10, 2026
86e7999
Update
JCNTH Jul 16, 2026
cde6634
Update
JCNTH Jul 16, 2026
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
22 changes: 21 additions & 1 deletion backends/webgpu/test/op_tests/cases.py
View file
Open in desktop
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 @@ -418,7 +418,11 @@
@register_op_test("conv2d")
def _conv2d_suite() -> WebGPUTestSuite:
# DaViT patch-embed / downsample convs + conv_transpose2d (same registration,
# folded by the `transposed` arg). NCHW fp32.
# folded by the `transposed` arg). NCHW fp32. Routing coverage (all vs the
# same fp64 golden): patch_embed/conv3x3_pad1/strided/gemm_batched are
# groups==1 → im2col tiled GEMM (gemm_batched pins the B>1 output write);
# grouped_vec4 (groups=2, icpg=4) → direct vec4 kernel; depthwise (groups=8,
# icpg=1) → direct scalar; transpose2x → conv_transpose2d.
return WebGPUTestSuite(
module_factory=make_conv,
cases=[
Expand Down Expand Up @@ -454,6 +458,22 @@
},
inputs=(InputSpec(shape=(1, 8, 8, 8), gen=_chw_ramp),),
),
Case(
name="grouped_vec4",
construct={
"in_ch": 8,
"out_ch": 8,
"kernel": 3,
"padding": 1,
"groups": 2,
},
inputs=(InputSpec(shape=(1, 8, 8, 8), gen=_chw_ramp),),
),
Case(
name="gemm_batched",
construct={"in_ch": 8, "out_ch": 16, "kernel": 3, "padding": 1},
inputs=(InputSpec(shape=(2, 8, 16, 16), gen=_chw_ramp),),
),
Case(
name="transpose2x",
construct={
Expand Down
Loading