[digantdesai]

Stack from ghstack (oldest at bottom):

• [executorch][native] Record dynamic-shape linears as chained bundled samples #24405
• [executorch][native] Run the ggml CPU act_quant sample test in CI #24373
• [executorch][vulkan] Round f16 image stores to nearest even #24372
• [executorch][native-vk] Note the int4 weight-only N % 4 and rank-4 linear gaps #24371
• [executorch][vulkan] Fix dq8ca q4gsw weight sums stride when N % 8 is 1 to 4 #24370
• [executorch][vulkan] Accept 1-D activations in 8da4w linear #24369
• [executorch][native] Cover negative, rounded and subnormal GGUF super-block scales in the op samples #24368
• [executorch][native] Decode quantized weights in float32 for precise samples #24367
• -> [executorch][native] Add GGUF q4_k and q6_k packages to the op samples #24366
• [executorch][native] Add FACTO-drawn linear cases to the op samples #24365
• [executorch][native] Suggest rounding to nearest when engines narrow values #24364
• [executorch][native-ggml] Compute cuBLAS GEMMs in f32 in the CUDA sample tests #24363
• [executorch][native] Judge activation-quantized engines against an int8-activation golden #24362
• [executorch][native-ggml] Reject weight-only quantized linears unless activation quant is allowed #24361
• [executorch][native] Run engines on bundled op samples with gtest #24360
• [executorch][native] Judge engine outputs against golden and precise samples #24359
• [executorch][native] Pin eager kernels when generating op samples #24358
• [executorch][native] Record op packages with bundled golden and precise samples #24357
• [executorch][native] Split engine host factories out of the PTN runners #24356
• [executorch][test-suite] Feed the Linear tests inputs with the model's 67 input features #24355

PTN carries GGUF k-quant constants, but no op package exercised them. A Gguf scheme, as gguf_q4_k and gguf_q6_k, writes random valid blocks to a .gguf with gguf-py and loads them back as a checkpoint would. golden-int8_act now comes from per-token int8 pre-hooks on the weight-only model, which also work for GGUF, so Scheme.weight_only replaces Scheme.act_quant. FACTO also keeps the tuples whose input features fit 256-element blocks.

Authored with Claude Code.

Differential Revision: D128254890

NOTE FOR REVIEWERS: This PR has internal Meta-specific changes or comments, please review them on Phabricator!

Differential Revision: D128254890

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As of commit 820b01d with merge base 0f8e096 ():

NEW FAILURE - The following job has failed:

• pull / nxp-mcuxpresso-test / linux-job (gh)
  undefined reference to SYS_INFO_PrintAll'`

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