fix(llm-serving): V1 prefill scheduling + admission-control observability (#51)

The reasoning engine starves every other request while one long prompt
prefills. Measured: 262 tok/s contended prefill, ~250s for a 65k prompt,
with Avg generation throughput 0.0-0.1 tok/s at Running: 2-4 reqs.

Root cause is NOT what the issue first diagnosed. The issue proposed
--max-num-partial-prefills / --max-long-partial-prefills, which are V0-only
knobs. Verified in the live container: vLLM 0.11.0 ships V1 only (vllm/core/
is absent, so there is no V0 to fall back to), and EngineArgs.
_is_v1_supported_oracle returns False whenever either flag differs from its
default, calling _raise_or_fallback("Concurrent Partial Prefill") ->
"Falling back to V0" -> AssertionError. With strategy Recreate that is an
outage, not a slow rollout. Both flags are therefore NOT set, and a comment
records why so they are not reintroduced.

V1 needs no such flag: it has no prefill/decode phase at all. Every step it
walks self.running and hands out tokens from one shared budget
(vllm/v1/core/sched/scheduler.py). The real defect is that the per-request
cap is disabled by default (long_prefill_token_threshold=0), so one long
prompt takes the WHOLE budget, token_budget hits 0, the `while ... and
token_budget > 0` loop exits, and every other request is skipped.

The cap is applied before the budget clamp, so threshold >= budget never
binds. Replaying the loop: 8192/8192 serves 1 request per step (still
starved); 8192/2048 serves 4, and two long prompts advance together, which
is the issue's actual acceptance criterion.

  --max-num-batched-tokens=8192      (was unset -> default 2048)
  --long-prefill-token-threshold=2048 (was unset -> 0, cap disabled)
  --max-num-seqs=5, --gpu-memory-utilization=0.95

The ~166k KV figure is marked as a projection; the live reading is
num_gpu_blocks=9527 x block_size=16 = 152,432 tokens and is authoritative
post-boot. A preemption alert covers the accept-and-watch risk.

Observability for the companion admission gate (homelab-frontend#64, now
deployed at 64f2bfc1):
  - four dashboard panels: parked, rejected by reason, engine state stale,
    and gateway-view vs engine-truth. Each carries a description, because
    an unexplained blank panel reads as a broken dashboard.
  - alerts on observer_degraded and engine_state_stale_total, both with an
    absent() guard: the gate fails OPEN, so a missing or renamed metric must
    alert rather than look healthy. Verified live that the guards do not
    false-fire (both series present, 3 each).
  - LLMPreemptions and LLMPrefillStarvation: the acceptance metric and the
    regression direction this change introduces.
  - max(vllm_requests_running), not sum(): one engine-global gauge is read
    by all three gateway replicas, so sum() read 3x and could never agree.

Also corrects comments that were factually wrong. An earlier revision of
this branch claimed llm_ttft_seconds/llm_itl_seconds/llm_tokens_total were
dead code; homelab-frontend#63 wired them, verified live as 3 series each.
request_duration_seconds is the genuinely dead one (0 samples, zero
non-test call sites) and is now marked as such instead of being listed as
working.

Closes #51

k8s/apps/llm-serving/reasoning.yaml

@@ -28,9 +28,14 @@ spec:
       # KV cache math: 256KB/token total (64 layers, 8 KV heads, 128
       # head_dim, fp16), PP=2 splits both weights and KV load ~evenly, so
       # each GPU carries ~9.67GB weights + ~128KB/token KV. At
-      # gpu-memory-utilization=0.90 (28.8GB/GPU usable), that leaves
-      # ~19.1GB/GPU for KV cache -> ~156k tokens/GPU capacity, comfortably
-      # above the 131072 target with room to spare -- the old
+      # gpu-memory-utilization=0.95 (30.4GB/GPU usable), that leaves
+      # ~20.7GB/GPU for KV cache. NOTE this is a projection, not a
+      # measurement: the live reading at 0.90 is num_gpu_blocks=9527 x
+      # block_size=16 = 152,432 tokens (vllm:cache_config_info). Re-read that
+      # metric after the rollout -- max_num_batched_tokens=8192 also raises
+      # activation memory out of the same budget, so the post-boot
+      # num_gpu_blocks is the authoritative number, and --max-num-seqs=5 is
+      # only valid if it still covers 2x65k + 3x8k. The old
       # OffloadingConnector CPU-DRAM spillover (tuned for the previous
       # model's much smaller 16384 context) is no longer needed and is
       # dropped. Staying on PP=2 and vLLM 0.11.0 (no version bump needed,
@@ -54,10 +59,37 @@ spec:
       - --tensor-parallel-size=1
       - --pipeline-parallel-size=2
       - --max-model-len=131072
-      - --gpu-memory-utilization=0.90
-      - --max-num-seqs=4
+      - --gpu-memory-utilization=0.95
+      - --max-num-seqs=5
+      # Prefill scheduling -- see the measured starvation this fixes.
+      # V1's scheduler has no prefill/decode phase: it walks self.running and
+      # hands out tokens from one shared budget (vllm/v1/core/sched/scheduler.py:
+      # "There's no decoding phase nor prefill phase in the scheduler"). The cap
+      # per request is applied BEFORE the budget clamp:
+      #     if 0 < long_prefill_token_threshold < num_new_tokens: take = threshold
+      #     take = min(take, token_budget)
+      # The loop is `while ... and token_budget > 0`. With the threshold at its
+      # default of 0 the cap is disabled, so one long prompt takes the ENTIRE
+      # budget, token_budget hits 0, the loop exits and every other request is
+      # skipped for that step -- measured as 262 tok/s contended prefill and
+      # 0.0-0.1 tok/s generation for everything else, Running: 2-4 reqs.
+      # Hence threshold must be strictly BELOW the budget or it never binds:
+      # at 8192/8192 a single request is served per step (still starved), while
+      # 8192/2048 serves four. Verified by replaying the loop.
+      - --max-num-batched-tokens=8192
+      - --long-prefill-token-threshold=2048
       - --enable-chunked-prefill
       - --enable-prefix-caching
+      #
+      # NOT set, deliberately: --max-num-partial-prefills and
+      # --max-long-partial-prefills. Those are V0-only knobs. vLLM 0.11.0 ships
+      # V1 only (vllm/core/ absent), and EngineArgs._is_v1_supported_oracle
+      # rejects any value other than the default for either one, calling
+      # _raise_or_fallback("Concurrent Partial Prefill") -> "Falling back to V0"
+      # -> AssertionError, because there is no V0 engine to fall back to.
+      # The Deployment uses strategy Recreate, so setting them is an outage.
+      # V1 does concurrent chunked prefill natively; it needs no flag, only the
+      # budget/threshold ratio above.
       # qwen3 is vLLM's dedicated reasoning parser for this family's <think>
       # blocks.
       - --reasoning-parser=qwen3