Fix quantized Conv1d same padding with even kernels in XNNPACK

[SakshamKapoor2911]

Fixes #20558.
Related to #20553.

Summary

Quantized nn.Conv1d(..., padding="same") with an even kernel exports with
asymmetric padding (unequal left/right amounts), which cannot be folded into
the convolution's symmetric padding field. The original pad-folding approach
in this PR was replaced with the explicit-PAD approach from #20553:

• InsertPadQDQPass — inserts implicit quantize/dequantize pairs after
  constant_pad_nd nodes in quantized contexts so they serialize as quantized
  static pads. Refactored with additional guard checks (pad_value, pad_amounts,
  negative amounts) and correct idempotency.
• ConvolutionConfig._get_act_deps — pulls zero-valued constant_pad_nd nodes
  (and their QDQ chain if InsertPadQDQPass already ran) into the convolution's
  partition for both 1D and 2D convs. The merged method replaces separate 1D
  and 2D implementations that existed on this branch.
• Regression test — quantized Conv1d even-kernel same-padding covering both
  symmetric and asymmetric pad cases, validated by numerical comparison.
• Removed unrelated init.py re-ordering and no-op conv1d_unsqueeze_pass
  changes.

With these changes, an even-kernel padding="same" conv1d graph:

dequant -> constant_pad_nd -> convolution

becomes (after XNNPACK preprocessing and partitioning):

[dequant -> pad -> q -> dq -> conv]  (single XNNPACK delegate)

Test plan

python -m pytest backends/xnnpack/test/ops/test_conv1d.py -q
# 7 passed (1 new regression test with 4 subTests)

python -m pytest backends/xnnpack/test/ops/test_conv2d.py -q
# 32 passed

python -m pytest backends/xnnpack/test/passes/test_insert_pad_qdq.py -q
# 3 passed

python -m pytest backends/xnnpack/test/ops/test_static_constant_pad.py -q
# 8 passed

lintrunner -a
# No lint issues

cc @GregoryComer @digantdesai @cbilgin @JakeStevens @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell @rascani

[pytorch-bot]

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• ✅ login: SakshamKapoor2911 / name: SakshamKapoor2911 (e7636f5)

[Copilot]

Pull request overview

This PR fixes an XNNPACK delegation/runtime correctness issue for statically-quantized nn.Conv1d(padding="same") with even kernel sizes by folding the explicit temporal constant_pad_nd into asymmetric Conv2d padding during Conv1d→Conv2d lowering, and ensuring the serialized graph’s metadata matches the folded asymmetric padding.

Changes:

• Extend XNNPACK partitioning and Conv1d unsqueeze lowering to absorb temporal-only zero constant_pad_nd into the quantized Conv1d partition and fold it into asymmetric xnnpack_input_padding.
• Teach Conv2d serialization to use folded asymmetric padding metadata when present, and add a late pass to repair Conv1d folded-pad tensor metadata after retracing passes.
• Add a regression test covering quantized Conv1d even-kernel padding="same".

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
backends/xnnpack/test/ops/test_conv1d.py	Adds a regression test for quantized Conv1d even-kernel padding="same" and factors out calibration sample generation.
backends/xnnpack/partition/config/gemm_configs.py	Extends convolution activation-dependency discovery to include temporal-only zero constant_pad_nd for quantized Conv1d so it can be partitioned/delegated.
backends/xnnpack/operators/op_conv2d.py	Uses folded asymmetric xnnpack_input_padding metadata (top/right/bottom/left) when present instead of symmetric ATen padding args.
backends/xnnpack/_passes/conv1d_unsqueeze_pass.py	Folds eligible temporal-only constant_pad_nd into asymmetric input padding during Conv1d→Conv2d lowering and adds a pass to restore correct tensor metadata for folded-pad Conv1d.
backends/xnnpack/_passes/init.py	Registers the new folded-pad metadata repair pass in the default XNNPACK pass pipeline.

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[SakshamKapoor2911]

@pytorchbot label "release notes: xnnpack"

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[JakeStevens]

The PR for 2D has landed:

#20553

You can take the same approach here; have an explicit PAD op, which gets executed in XNNPACK. We can then later fold that XNNPACK pad in the delegated graph, this is a follow up to both of these PRs.

As is, this code will also fail on:

class M(torch.nn.Module):
    def __init__(self):
          super().__init__()
          self.conv = torch.nn.Conv1d(1, 8, kernel_size=4, padding="same")
      def forward(self, x):
          out = torch.reshape(x, (1, 1, 256))
          out = self.conv(out)
          return torch.flatten(out, 1)   # even kernel + this line => crash

[SakshamKapoor2911]

Addressed review feedback: adopted the same approach as #20553 — keep the constant_pad_nd as an explicit PAD op executed in XNNPACK instead of folding it into asymmetric Conv2d padding.

Changes:

1. Removed pad-folding from Conv1dUnsqueezePass — the pad is no longer absorbed into asymmetric conv padding
2. Removed Conv1dFoldedPadMetaPass — no longer needed since tensor metadata propagates correctly with the standalone pad
3. Reverted xnnpack_input_padding handling in op_conv2d.py — symmetric padding from ATen args is used again
4. Added InsertPadQDQPass — inserts implicit QDQ after quantized-context pads so they serialize as quantized static pads (same mechanism as Delegate even-kernel 'same'-padding convs via a quantized static pad (#20553) #20553)
5. The crash case with conv1d(padding='same', even kernel) → flatten is fixed because the pad is now a standalone op with correct shape propagation

The pad can be folded in the delegated graph later as a follow-up optimization (per the review suggestion).

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[JakeStevens]

I believe you need to rebase and make sure the commit I shared is landed in your branch, there are merge conflicts from duplicated code

[SakshamKapoor2911]

Rebased onto current main and resolved the duplicated-code conflict. The conflict was an add/add in insert_pad_qdq.py — #20553 and this PR each introduced an InsertPadQDQPass. Kept #20553's canonical version (already landed on main) and dropped the duplicate import from __init__.py. No other files conflicted. PR is ready for another look.

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[JakeStevens]

Please setup and run lintrunner to resolve the failures. See

https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#lintrunner

Also it seems like you've duplicated the get act dep in Gemm config instead of only updating as necessary.

I have not fully reviewed but I did notice that your comment said it kept the existing pad pass but the commit has substantial changes to it.

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