Files
colibri/c/tools
woolcoxm 2c6946c478 test-models: add --fp8 emission for the FP8->int4 converter test path
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).
2026-07-15 02:32:12 -04:00
..

Tools

These scripts support model preparation and offline engineering work. They are not runtime dependencies of the C engine.

  • convert_fp8_to_int4.py, download_glm52.py: model preparation
  • make_glm_oracle.py, make_glm_bench_model.py: deterministic fixtures
  • benchmark_cuda_fixture.py, eval_glm.py, fetch_benchmarks.py: benchmarks
  • gen_unicode.py: tokenizer table generation

Run them from c/, for example:

python3 tools/convert_fp8_to_int4.py --selftest
python3 tools/make_glm_bench_model.py --output /tmp/colibri-bench