Fix HQQ MatMulNBits weight layout

[DelwinKim]

Describe your changes

Fix HQQ weight-only quantization for ONNX MatMulNBits.

MatMul stores its weight as [K, N], while MatMulNBits expects packed weights, scales, and zero points in output-channel-major order with independent K-axis blocks. The previous HQQ path grouped the row-major [K, N] initializer directly and flattened [K_blocks, N] quantization parameters into storage interpreted as [N, K_blocks]. The resulting model was structurally valid but applied quantization parameters to the wrong weight blocks, resulting in heavy degradation in model's performance.

This change:

• Transposes the weight to [N, K] before HQQ quantization.
• Groups along the transposed K axis so packed weights, scales, and zero points share the runtime's [N, K_blocks] ordering.
• Retains the original [K, N] dimensions for the MatMulNBits K and N attributes.
• Rejects unsupported nonzero source axes explicitly.
• Adds numerical regression coverage for both block-aligned and padded K dimensions.

Checklist before requesting a review

• Add unit tests for this change.
• Make sure all tests can pass.
• Update documents if necessary. (Not necessary; this corrects existing behavior.)
• Lint and apply fixes to your code by running lintrunner -a
• Is this a user-facing change? If yes, give a description of this change to be included in the release notes.

Release note: Fix HQQ ONNX quantization producing incorrect MatMulNBits weight, scale, and zero-point layouts.

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[Copilot]

Pull request overview

Fixes HQQ weight-only quantization for ONNX MatMulNBits by aligning packed weight/scales/zero-point layout with ORT’s expected output-channel-major (N-major) storage contract, preventing quantization parameters from being applied to the wrong blocks.

Changes:

• Transpose MatMul weights from (K, N) to (N, K) before HQQ quantization and quantize along the transposed K axis to match MatMulNBits block ordering.
• Explicitly reject unsupported quantization axes (non-zero).
• Add an ONNX Runtime numeric regression test covering both block-aligned and padded-K cases.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File	Description
olive/passes/onnx/hqq_quantization.py	Transposes weights and adjusts grouping/packing to produce MatMulNBits initializers in ORT’s expected (N, K_blocks, blob) layout; rejects unsupported axes.
test/passes/onnx/test_hqq_quantization.py	Adds ORT-based numerical regression coverage for the corrected MatMulNBits layout (aligned and padded K).

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[Copilot]

Pull request overview

Copilot reviewed 2 out of 2 changed files in this pull request and generated no new comments.

Comments suppressed due to low confidence (1)

olive/passes/onnx/hqq_quantization.py:234

• _quantize_internal_numpy can move tensors to CUDA (b_array_torch = b_array_torch.cuda()), but it returns packed_torch/scales/zero_points without forcing them back to CPU. ONNX initializers must be CPU-backed; returning CUDA tensors here is fragile (and can break if ir.tensor(...) doesn’t explicitly handle .cpu() conversion). Convert the outputs to CPU (ideally numpy) before returning so model creation/serialization is device-independent.

        blob_size = block_size // 2
        k_blocks = (rows + block_size - 1) // block_size
        packed_torch = packed_torch.reshape(cols, k_blocks, blob_size)

        return packed_torch, scales, zero_points