…nels-packed int8 tensors

Pull Request resolved: #19397

A RefineNet segmentation model spends ~860 us (~17% of inference) on the textbook decomposed PyTorch PixelShuffle chain (q8ta_dequantize -> view -> permute -> view -> q8ta_quantize) repeated four times in the segmentation head. This is wasteful: it materializes three buffers and round-trips through fp32 just to perform what is fundamentally a byte permutation on an int8 tensor.

This diff introduces et_vk.q8ta_pixel_shuffle.default, a single fused kernel that operates directly on int8x4 packed buffers. Each thread writes one output int32 word (= 4 consecutive output channels at one (n, oh, ow) spatial position). Dispatch is 1D over total output int words, sized as N * div_up_4(C_out) * H_out * W_out with a 64-thread local workgroup. The four channel lanes inside an output int come from four different input int words (input channels are spaced by r*r), so each thread issues four input loads. The (oh % r, ow % r) -> input lane mapping is constant for a given thread because all four output lanes share (oh, ow). The first byte index is computed via the layout-aware helper tensor4d_idx_to_buf_idx; subsequent lanes derive their byte index by adding stride[packed_dim] * block_numel, a layout-only constant, so only one helper call is needed per thread. When input/output share scale and zero-point (the typical residual-path case), the requantize math is skipped and the kernel becomes a pure byte shuffle (selected via the passthrough push constant).

The op accepts the channels-packed PACKED_INT8 family (PACKED_INT8_4W4C, PACKED_INT8_4C1W, PACKED_INT8_CONV2D) on both input and output. The partitioner routes the op into whichever channels-packed layout the surrounding q8ta_conv2d_pw / q8ta_add ops produce/consume (PACKED_INT8_4W4C on RefineNet). Restricting to the channels-packed family means the inner block axis is always C and the lane within an int word is constant per thread, which removes the need for layout-block-config spec consts in the shader.

Rather than matching the decomposed view -> permute -> view chain after to_edge lowering, this diff preserves aten.pixel_shuffle.default through to_edge by adding it to the partitioner's ops_to_not_decompose list. The matcher then operates on the much simpler dq -> [clone] -> aten.pixel_shuffle.default -> [clone] -> q form. This keeps the matcher robust against edge-dialect / clone-insertion variations.

Pieces in this diff:

- Partitioner / fuser:
  - partitioner/vulkan_partitioner.py — adds aten.pixel_shuffle.default to ops_to_not_decompose so the framework preserves the op through to_edge lowering.
  - patterns/quantized_pixel_shuffle.py — detects dq -> [clone] -> aten.pixel_shuffle.default -> [clone] -> q and rewrites it to et_vk.q8ta_pixel_shuffle.default. Transparently skips clone / _clone_dim_order nodes between any pair of nodes.
- Runtime kernel:
  - runtime/graph/ops/glsl/q8ta_pixel_shuffle.glsl + .yaml
  - runtime/graph/ops/impl/Q8taPixelShuffle.cpp + .h
- Op definitions:
  - custom_ops_lib.py: register et_vk.q8ta_pixel_shuffle (Python op definition).
  - op_registry.py: inputs_storage = utils.PACKED_INT8_CHANNELS_PACKED_BUFFER.
- Tests:
  - test/custom_ops/impl/TestQ8taPixelShuffle.cpp: test op that runs q -> [fused | unfused chain] -> dq, with selectable input/output int8 layouts via str args. The op accepts the channels-packed family; the layout_from_string helper currently exercises 4W4C.
  - test/custom_ops/test_q8ta_pixel_shuffle.cpp: 16 ACCU + 8 PERF cases (4 shapes x 2 qparam settings x 2 impl_selectors x 1 layout combination, 4W4C -> 4W4C).
  - test/test_vulkan_passes.py: positive and negative pattern-matcher unit tests against the un-decomposed form.
ghstack-source-id: 379519848
@exported-using-ghexport

Differential Revision: [D104099055](https://our.internmc.facebook.com/intern/diff/D104099055/)