diskio: KV write batching + persistent handle; generators: unfuse experts

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

c/tools/make_glm_bench_model.py

@@ -22,7 +22,7 @@ import torch
 from transformers import GlmMoeDsaConfig, GlmMoeDsaForCausalLM
 
 sys.path.insert(0, str(Path(__file__).resolve().parent))   # importa glm_fp8_emit se lanciato da c/
-from glm_fp8_emit import save_fp8_safetensors
+from glm_fp8_emit import save_fp8_safetensors, unfuse_experts
 
 
 def build_config() -> GlmMoeDsaConfig:
@@ -85,16 +85,6 @@ def main() -> None:
     output = Path(args.output)
     output.mkdir(parents=True, exist_ok=True)
     params = sum(p.numel() for p in model.parameters())
-    if args.fp8:
-        n_fp8, n_tot = save_fp8_safetensors(model.state_dict(), output / "model.safetensors")
-        # save_pretrained scrive config.json; nel path FP8 lo bypassiamo, quindi lo scriviamo
-        # a mano (serve al converter e al motore C). EN: save_pretrained writes config.json;
-        # the FP8 path bypasses it, so write it manually (converter + C engine need it).
-        (output / "config.json").write_text(json.dumps(cfg.to_dict()))
-        print(f"saved FP8: {n_fp8} e4m3 tensors (+{n_tot - n_fp8} scale_inv sidecars / f32) "
-              f"-> {output / 'model.safetensors'}")
-    else:
-        model.save_pretrained(output, safe_serialization=True, max_shard_size="4GB")
 
     model.to(args.device)
     prompt = [3, 14, 159, 26, 53, 58, 200, 11, 77, 240, 5, 99]
@@ -103,6 +93,25 @@ def main() -> None:
         full = model.generate(ids, max_new_tokens=8, do_sample=False, use_cache=True)[0]
         logits = model(full.unsqueeze(0), use_cache=False).logits[0]
 
+    # Unfuse experts AFTER reference generation (model needs fused weights for
+    # forward/generate) but BEFORE saving — the real checkpoint and the converter
+    # + C engine all expect per-expert 2-D gate_proj/up_proj/down_proj tensors.
+    sd = model.state_dict()
+    unfuse_experts(sd)
+
+    if args.fp8:
+        n_fp8, n_tot = save_fp8_safetensors(sd, output / "model.safetensors")
+        # save_pretrained scrive config.json; nel path FP8 lo bypassiamo, quindi lo scriviamo
+        # a mano (serve al converter e al motore C). EN: save_pretrained writes config.json;
+        # the FP8 path bypasses it, so write it manually (converter + C engine need it).
+        (output / "config.json").write_text(json.dumps(cfg.to_dict()))
+        print(f"saved FP8: {n_fp8} e4m3 tensors (+{n_tot - n_fp8} scale_inv sidecars / f32) "
+              f"-> {output / 'model.safetensors'}")
+    else:
+        from safetensors.torch import save_file
+        save_file({k: v.contiguous() for k, v in sd.items()}, str(output / "model.safetensors"))
+        (output / "config.json").write_text(json.dumps(cfg.to_dict()))
+
     ref = {
         "prompt_ids": prompt,
         "full_ids": full.cpu().tolist(),