[ExecuTorch][Vulkan] Tests for et_vk.q4gsw_requant

[JCNTH]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][WebGPU] Glob runtime/ops sources in CMakeLists #21005
• -> [ExecuTorch][Vulkan] Tests for et_vk.q4gsw_requant #20946
• [ExecuTorch][Vulkan] Add et_vk.q4gsw_requant kernel (STE re-quant to W_4X8) #20945
• [ExecuTorch][Vulkan] Tests for et_vk.linear_q4gsw_backward #20944
• [ExecuTorch][Vulkan] Add et_vk.linear_q4gsw_backward kernel (4-bit input-grad) #20943
• [ExecuTorch][Vulkan] Tests for et_vk.fused_ce #20942
• [ExecuTorch][Vulkan] Add et_vk.fused_ce kernels (fused cross-entropy loss + dlogits) #20941
• [ExecuTorch][Vulkan] Tests for et_vk.linear_dW #20940
• [ExecuTorch][Vulkan] Add et_vk.linear_dW kernel (dense fp32 weight-grad GEMM) #20939
• [ExecuTorch][Vulkan] Tests for et_vk.adamw_step #20938
• [ExecuTorch][Vulkan] Add et_vk.adamw_step kernel (on-GPU AdamW optimizer step) #20937
• [ExecuTorch][WebGPU] Tests for the adamw WebGPU ops #20936
• [ExecuTorch][WebGPU] Add et_vk.adamw_step (on-GPU AdamW optimizer step) to the WebGPU backend #20935
• [ExecuTorch][WebGPU] Tests for the fused_ce WebGPU ops #20934
• [ExecuTorch][WebGPU] Add et_vk.fused_ce (fused cross-entropy) to the WebGPU backend #20933
• [ExecuTorch][WebGPU] Tests for the q4gsw_train WebGPU ops #20932
• [ExecuTorch][WebGPU] Add q4gsw training kernels (backward, weight-grad, requant) to the WebGPU backend #20931
• [ExecuTorch][WebGPU] Tests for the shape_copy WebGPU ops #20930
• [ExecuTorch][WebGPU] Add shape/copy ops (dim_order, expand_copy, fill) to the WebGPU backend #20929
• [ExecuTorch][WebGPU] Tests for the gather WebGPU ops #20928
• [ExecuTorch][WebGPU] Add gather and embedding ops to the WebGPU backend #20927
• [ExecuTorch][WebGPU] Tests for the select_bool WebGPU ops #20926
• [ExecuTorch][WebGPU] Add select/boolean ops (where, scalar compares, logical_not) to the WebGPU backend #20925
• [ExecuTorch][WebGPU] Tests for the reduce WebGPU ops #20924
• [ExecuTorch][WebGPU] Add reduce op (sum/mean) to the WebGPU backend #20923
• [ExecuTorch][WebGPU] Tests for the binary WebGPU ops #20922
• [ExecuTorch][WebGPU] Add elementwise binary ops (div, sub) to the WebGPU backend #20921
• [ExecuTorch][WebGPU] Tests for the softmax WebGPU ops #20920
• [ExecuTorch][WebGPU] Add softmax ops (_softmax, _log_softmax) to the WebGPU backend #20919
• [ExecuTorch][WebGPU] Tests for the matmul WebGPU ops #20918
• [ExecuTorch][WebGPU] Add fp32 matmul ops (mm, bmm, linear) to the WebGPU backend #20917

Correctness tests for the Vulkan et_vk.q4gsw_requant kernel (stacked above the op diff).

Coverage: the golden codes are computed with ATen (round/clamp, zero-scale -> code 8), mirroring quant_nibble, then packed into the expected W_4X8 int buffer with a small bit-packing reference (data-reshaping only, no hand-rolled math). The kernel output is compared int-for-int against that buffer, which locks the exact byte layout the forward reads. The latent is built as code * scale so round() is unambiguous (no .5 tie-break divergence).

Cases:

• test_tile_aligned — single group, tile-aligned N/K.
• test_grouped — multiple quantization groups along K.
• test_odd_n4 — N % 8 != 0 (odd N4 -> padded stride + bias-zero OOB tile).
• test_zero_scale — a zero scale must yield code 8, not a divide-by-zero.

Also wires q4gsw_requant_test into targets.bzl + CMakeLists.txt.
@exported-using-ghexport

Differential Revision: D111797526

Differential Revision: D111797526

[pytorch-bot]

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