Two independent fixes validated end-to-end on fresh fixtures:
1. KV cache disk I/O (issue_diskio.md opportunities #1 + #4):
- kv_disk_append: fopen/fclose every turn -> persistent FILE* kept open
for the engine lifetime, lazy open on first append, closed in
serve_ctx_free. Eliminates per-turn handle creation overhead.
- kv_disk_append: ~157 small fwrites per position -> one contiguous
record memcpy'd into a staging buffer then a single fwrite per
position. The staging buffer grows on demand via realloc.
- kv_disk_truncate: closes the persistent handle before truncating
so the file actually shrinks on disc, then reopens lazily.
- KVState gains disk_fp, disk_buf, disk_buf_cap fields.
- Verified: serve-mode round-trip, write 11 tokens then reload and
resume with no re-prefill, then append 8 more and reload to 19.
2. Expert weight unfusing in test-model generators:
- The real GLM-5.2-FP8 checkpoint stores routed experts UNFUSED as
per-expert 2-D tensors, each with its own _scale_inv. HF fuses
gate+up into a single 3-D gate_up_proj for compute efficiency.
- The converter and C engine both expect the unfused layout. The
fused 3-D tensors were silently skipped by the converter, and the
engine crashed with missing-tensor errors.
- New unfuse_experts in glm_fp8_emit.py splits gate_up_proj and
down_proj into per-expert 2-D tensors. Called after reference
generation but before saving, in both generators, both FP8 and bf16.
- Also fixed: make_glm_oracle.py FP8 round-trip guard used p.dim()<2
which let 3-D fused experts through and crashed fp8_block_quantize.
Changed to p.dim()!=2 to match the converter ndim!=2 guard.
Validated full chain on fresh fixtures:
generator --fp8 -> 570 e4m3 tensors + 629 scale_inv, was 90 when fused
converter --group-size 0 -> per-row int4 fmt=2, engine loads clean
converter --group-size 128 -> grouped int4 fmt=4, 8-16x more scales,
engine loads clean, fmt=4 auto-detected in both mmap and slab paths
dequant error: grouped 1.14-1.22x lower than per-row vs FP8 source
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