[ET-VK][quantization] Implement layout-flexible quantize/dequantize operators

[SS-JIA]

Stack from ghstack (oldest at bottom):

• [ET-VK][qconv] Add flexible layout impl for quantized pointwise conv #17221
• [ET-VK][testing] Create dedicated test binary for pointwise convolutions #17220
• [ET-VK][qconv] Add layout-agnostic general shader for quantized conv #17219
• [ET-VK][quantization] Add layout-flexible clone for int8x4 tensors #17171
• [ET-VK][qconv] Add layout-flexible impl of quantized depthwise conv2d #17108
• [ET-VK][ez] Implement helper functions to get fastest moving dim #17107
• -> [ET-VK][quantization] Implement layout-flexible quantize/dequantize operators #17106
• [ET-VK] Add alignment fields to PackedDimInfo for padded size calculation #17170
• [ET-VK][testing] Add per-shader timing breakdown to benchmark output #17105

Implemented quantize_per_tensor and dequantize_per_tensor GLSL shaders
and C++ dispatch logic to support the new single-dimension packed INT8 layouts
(kPackedInt8_4W, kPackedInt8_4C, kPackedInt8_4H). These operators enable
conversion between floating-point tensors and packed int8 representations with
per-tensor scale and zero-point parameters.

The implementation includes:

• GLSL shaders: quantize_per_tensor and dequantize_per_tensor with support for
  both texture->buffer and buffer->buffer data flows, including GL_EXT_debug_printf
  statements for debugging
• QuantizeDequantize.cpp: Added dispatch functions for the new layouts and
  registered etvk.q_dq_8bit_per_tensor.default operator
• Test infrastructure: Created q_dq_8bit_per_tensor test binary with DEBUG_MODE
  support and reference CPU implementation for validation

The shaders implement the quantization formula Q = clamp(round(x/scale) + zp, -128, 127)
and dequantization formula x' = (Q - zp) * scale, with proper int8 packing/unpacking
using little-endian byte ordering and sign extension.

Differential Revision: D92061370

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/17106

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

❌ 3 New Failures, 2 Pending

As of commit 67f063b with merge base 477867a ():

NEW FAILURES - The following jobs have failed:

• pull / test-vulkan-operators-linux / linux-job (gh)
  RuntimeError: Command docker exec -t f507d2ce9904df37c1b7bae3c5cb948794f33aafed6a6f9249e6fae6f3cfc5a2 /exec failed with exit code 134
• pull / unittest / macos / macos-job (gh)
  export/tests/test_target_recipes.py::TestTargetRecipes::test_linear_model
• Test CUDA Builds / test-models-cuda (conv1d) / linux-job (gh)
  RuntimeError: Command docker exec -t a6dcae6b83d74401c4df1128a2c0b85786ac62f0f44502a02691fef2f16bacb2 /exec failed with exit code 1

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[github-actions]

This PR needs a release notes: label

If your change should be included in the release notes (i.e. would users of this library care about this change?), please use a label starting with release notes:. This helps us keep track and include your important work in the next release notes.

To add a label, you can comment to pytorchbot, for example
@pytorchbot label "release notes: none"

For more information, see
https://github.com/pytorch/pytorch/wiki/PyTorch-AutoLabel-Bot#why-categorize-for-release-notes-and-how-does-it-work.