Stack from ghstack (oldest at bottom):

• [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_q4gsw_dw #20940
• [ExecuTorch][Vulkan] Add et_vk.linear_q4gsw_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

Adds the Vulkan et_vk.q4gsw_requant kernel — straight-through re-quant of fp32 latent weights into the frozen-scale 4-bit codes, for on-device weight training. Writes the codes directly in the forward's W_4X8 layout so no per-step re-pack is needed.

Problem: et_vk.q4gsw_requant is registered in the shared Vulkan partitioner but Vulkan had no runtime kernel.

Solution: a GLSL kernel that quantizes round(latent / scale) clamped to [-8, 7] and packs the codes in the W_4X8 block layout the forward reads, reusing the exact byte-pair convention of glsl/pack_q4_linear_weight__w_4x8.glsl (even-N low nibble, odd-N high; one ivec4 per 4K x 8N block at (k4, n8); OOB upper tile = the bias-zero 0x88888888). A zero scale yields code 8 (no divide-by-zero), matching the eager reference.

Key changes:

• glsl/q4gsw_requant.{glsl,yaml} — buffer x float; 2D dispatch over (k4, n8).
• impl/QuantizedLinearRequant.cpp — prepacks the constant scales; output is the W_4X8 int buffer [K4 * N4_padded * 2]; group_size spec constant; dispatch-grid guard.

Design note (for review): per the layout decision, requant writes W_4X8 to match the Vulkan forward. Today the forward and backward each prepack their own flat [N, K/2] weight internally, so nothing yet consumes an externally-produced W_4X8 buffer — closing the training loop needs a forward path that reads a mutable pre-packed weight (the weight-lifecycle follow-up). The op's AOT meta in custom_ops_lib.py still describes the flat [N, K/2] output and should be reconciled with this W_4X8 output.

Constraints: buffer storage, fp32 latent/scales; N % 4 == 0, K % 4 == 0, group_size % 4 == 0.
@exported-using-ghexport

Differential Revision: D111797527

Differential Revision: D111797527