Both test-model generators (make_glm_oracle.py, make_glm_bench_model.py) can now
emit weights as FP8 e4m3 + 128x128 block scale_inv, in the same layout as the real
GLM-5.2-FP8 checkpoint. This lets convert_fp8_to_int4.py exercise its FP8->int4
dequant path on a local fixture without the 379 GB download.
- New shared helper glm_fp8_emit.py: FP8 block quantize/dequantize (FBGEMM/TE
scale=amax/448 convention) + state_dict emitter. Only exactly-2-D tensors are
quantized; 1-D/3-D and norms/router/e_score_correction_bias are kept as f32,
mirroring the converter's classify() + ndim!=2 guard.
- make_glm_bench_model.py: opt-in --fp8 writes model.safetensors in FP8 layout
(config.json written explicitly since the FP8 path bypasses save_pretrained);
manifest gains a 'format' field. Default bf16 behavior unchanged.
- make_glm_oracle.py: opt-in --fp8 round-trips quantizable weights through FP8
before computing ref_glm.json, so the reference reflects exactly the FP8 model
the converter ingests. Default bf16 oracle contract unchanged.
Verified end-to-end: FP8 model -> converter --indir -> int4 U8 + .qs F32 output,
bit-identical dequant between helper and converter (maxdiff 0.0).
woolcoxm
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