New analysis/characterization/joined_analysis.py joins replayer
metrics.jsonl + proxy breakdown.json + worker_state.jsonl by
request_id, plus engine_*.jsonl by worker_id, and emits:
- joined.jsonl per-request merged record
- reuse_decomposition.json real intra/cross/shared classification
using session_id + hash_ids + cached_tokens
- interference_index.json TPOT_p90(same-worker prefill overlap)
/ TPOT_p90(clean), per Batch 2
- hotspot_index.json max/median worker TTFT-p90, per Batch 3
- failure_label.jsonl per-slow-request cause label, per Batch 5
- failure_breakdown.json label histogram
- window_summary.json SRR warmup/steady/drain aggregates
Closes the analyzer side of Phase A; replaces the
status: unavailable placeholders the existing scaffold emits when
join sources are missing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New replayer/srr.py drives a Poisson session-arrival load against the
existing proxy, with strict per-session turn sequentiality, explicit
warmup/steady/drain windows, and per-arrival fresh session_id +
request_id so APC/session-affinity counters are not contaminated by
repeated draws from the trace pool. Writes window_summary.json with
attempted/completed/errored split by window so latency tails can be
read on the steady-state window only.
Required by Batch 4 SRR sweep; trace-timestamp dispatch in replay.py
cannot drive arrival rate independently.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
When AGENTIC_STEP_LOG_PATH is set, the scheduler emits one JSONL line
per scheduler step with t_unix, worker_id, prefill/decode token
counts, n_running/n_waiting, preempted ids, and per-request phase
labels. No-op when the env var is unset, so production engines are
not impacted. bench.sh now threads AGENTIC_STEP_LOG_DIR through to
each per-engine launch so step logs end up at engine_${i}.jsonl.
Required by Batch 2 (PD-colo interference index) and Batch 5
(same-worker overlap attribution); engine /metrics polling cannot
provide per-step granularity.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Honor incoming X-Request-Id so replayer metrics and proxy breakdown
share a join key. Each route decision now captures session_id, the
full per-worker candidate-score snapshot (ongoing/pending/num_requests
/cached_blocks plus both linear and lmetric scores), the chosen score,
and unix timestamps for first-token and done events. A separate
_worker_state_log records one row per decision and is exposed via
GET /worker_state; GET /worker_state/latest returns a live snapshot
without recording it.
Required by Batch 3 (session hot-spot proof) and Batch 5 (failure
attribution); existing breakdown.json had no per-worker state at
decision time.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
RequestMetrics gains absolute unix timestamps (t_dispatch_unix,
t_first_token_unix, t_finish_unix), the proxy_request_id, the chosen
endpoint URL, and the trace hash_ids. Replayer sends
X-Request-Id: <session_id>:<turn_id>:<chat_id>:<idx> so proxy
breakdown rows can be joined to metrics by exact key.
Required by Batch 0 (online sequentiality proof) and Batch 1 reuse
decomposition; existing metrics.jsonl couldn't establish either.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Add Progress Snapshot table to the intern TODO so per-batch status
(DONE / partial / blocked-on-instrumentation) is visible at a glance.
- New analysis/claude_characterization_work_plan.md scopes the Phase A
instrumentation tasks (A1-A5) plus Window 1 (B1'+B2+B3) and Window 2
(B4+B5) on dash0, with locked decisions for model, topology, trace,
SLO style, and GPU phasing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds the experiment harness that gates the empirical claims (C2/C3/C4/C5)
in the PD-sep paper section. Three pieces:
1. scripts/bench.sh: new --mode pdsep with --pd-ratio P:D, and an
--eager flag to re-enable --enforce-eager for the cuda-graph
ablation. pdsep reuses the elastic-mode Mooncake kv_both launch and
swaps the proxy command from --combined to --prefill/--decode.
baseline and elastic flows are unchanged.
2. analysis/pd_sep_paper_section/scripts/bench_pd_matrix.sh: matrix
driver that runs {combined-ca, pdsep-4p4d, pdsep-6p2d} x cudagraph
x 3 seeds by default (~2 h on dash0). --with-rr adds combined-rr;
--with-eager doubles to ~5 h with the cuda-graph ablation. Skips
completed runs, captures per-instance vLLM logs (needed for C3
step-level KV-utilization mining).
3. fig_kv_memory_wall.pdf: empirical anchor (star) at REPORT.md §3.3's
observed 6P+2D 97% KV utilization. The marker lands on the model's
predicted curve at p90 input, confirming the steady-state analysis.
README updated with the run command, output layout, and the followup
plotters that consume outputs/pd_matrix/.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds the system-level argument resolving the roofline/PD-sep paradox.
Even at 95% cache reuse prefill stays compute-bound (the C6 roofline
fact), yet PD separation regresses TTFT 72%. The new system_analysis.md
walks through six layers showing why the roofline claim is necessary
but not sufficient, with the falsifiable condition being decode-side
KV memory budget: concurrent_decode * KV_per_req / (N_D * HBM_pool).
For chatbot this ratio is << 1 at any layout; for agentic at p90+
context it goes >> 1 under 4P+4D and 6P+2D, predicting the empirical
97% decode KV occupancy. fig_kv_memory_wall.pdf visualizes the model
with audit-able constants; fig_c1a/b ground the per-request KV-size
inputs in the actual sampled trace (input p50=33.5k, p90=101k,
intra-session reuse 79.2%).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds analysis/pd_sep_paper_section/ as the home for the "PD separation is
net negative under agentic workloads" paper section: plot scripts for C1
(workload chars), C6 (roofline), C7 (routing-vs-PD-sep lever), the C6/C7
PDFs already rendered, and a README mapping candidate claims to required
figures plus open re-run items.
Removes --enforce-eager from bench.sh and all active launch scripts so
cuda graphs are captured -- the prior methodology suppressed one of
PD-sep's structural advantages (D-node fixed-shape decode). Legacy
scripts under scripts/legacy/ are intentionally untouched as historical
records.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per analysis/unified_routing_fix_review.md #2, several docs still
presented the retired single-argmin + PUSH-migration design as the
final algorithm. Mark them superseded and document the current hybrid
direction (commit 255c8e6).
- REPORT.md §1.1 / §3.9: add errata callout and section header noting
the "Final Design" framing was retired after cc6e562 / 4c583f2;
point readers to docs/migration-policy-design.md.
- docs/migration-policy-design.md: rewrite. Opens with the current
hybrid algorithm (LMetric base + cache_ratio>0.5 affinity gate +
tie-breaker), then a "What Was Retired" commit table, then the old
Approach A numbers preserved as "Historical Baseline-Mode Comparison".
- analysis/research_findings.md §2.2 / §5: correct the LMetric framing.
LMetric isn't "neutralized by affinity constraints" (pure --policy
lmetric has no affinity at all); it converges to similar placements
because P_tokens includes new_uncached_tokens, giving it implicit
soft affinity.
- analysis/elastic_hypotheses.md: same LMetric correction in the
"DOESN'T work" summary, plus a footer cross-referencing the current
routing direction.
- analysis/unified_routing_fix_review.md: track this file (was
untracked); it is the review handoff cited from the updated docs.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Delete unreachable best_needs_push block in _handle_combined and the
four orphaned helpers (_handle_cached_prefill_offload,
_handle_direct_read_offload, _query_bootstrap_hit,
_get_bootstrap_client). Their only caller was the retired PUSH gate;
see REPORT §3.9 errata for the rejected experiments (cc6e562, 4c583f2).
- Extract pick_instance_unified_hybrid as a pure function returning
(chosen, idx, decision_dict). The decision dict carries the review #7
breakdown fields (decision, affinity_idx/chosen_idx, cache_hit/ratio,
avg_num_requests, fallback_score, tie_break_used).
- Add LMetric-fallback tie-breaker (primary score, then new_uncached,
num_requests, round-robin) so new sessions don't all pin to inst 0
when BS=0 across the board.
- Drop the lmetric-policy affinity write so --policy lmetric stays
affinity-free per review #3.
- Mark --max-offload-inflight / --offload-mode / --cache-gate-ratio /
--decode-iteration-s as [DEPRECATED] in --help; flags remain accepted
so scripts/bench.sh and legacy launchers don't break.
- Revert uncommitted overload_factor 2.0->1.5 default; H7 sweep already
rejected this knob (within noise). Future sweeps should go via CLI.
Tests: add 6 hybrid-policy tests in tests/test_proxy_pick.py covering
affinity-hit, overload break, low-cache fallback, tie-break rotation,
lmetric purity, and breakdown field shape. 19/19 pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Replace the full unified cost model with a simpler hybrid:
- If session has >50% cache on affinity instance AND instance not overloaded
(num_requests <= avg * overload_factor) → stick to affinity
- Otherwise → use LMetric (P × BS) for best load balance
This combines LMetric's superior load balance with explicit session
affinity for high-value sessions that have significant cache accumulation.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
PD-sep offload overhead (C queue + prefill + KV transfer + D schedule)
far exceeds any load balance benefit. With relaxed gate, cost model
triggered 134 offloads → E2E p90 went from 37s to 82s.
The proven winning configuration is Unified routing in baseline mode
(no Mooncake connector), which beats LMetric on E2E mean/p50/p90
purely through better routing (contention-aware + session affinity).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1. push_cost now models both C and D: max(c_cost, d_cost) where
c_cost includes C's queue + prefill, d_cost includes D's queue +
RDMA overhead. Old formula only had D's contention + RDMA.
2. Hard gate uses num_requests instead of ongoing_tokens, aligning
with the contention-based cost model.
3. Fix migration_discount: min(cap, 5) instead of hardcoded min(cap, 3).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
After _push_allowed was relaxed, the cost model correctly chose push
for high-cache sessions on overloaded instances. But a second gate at
execution time (push_new < heavy_threshold) blocked the actual offload,
downgrading to LOCAL on the target instance — which had no cache.
Worse, session affinity was already updated to the target, so all
subsequent turns also hit cold prefill.
This was the root cause of relaxed gate's performance regression:
affinity broken + push blocked = worst of both worlds.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The old gate blocked offload when push_new (= input - cache_hit) < 20K,
which prevented migration of high-cache sessions — exactly the ones
that benefit most. After PD-sep, the target receives full KV via RDMA
and has the same cache as the source, so cache_hit is irrelevant to
the offload decision.
New gate: only check input_length >= heavy_threshold (request must be
HEAVY) and max_offload_inflight (concurrency cap). Let the cost model
decide whether the contention difference justifies migration.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Reverts 3 commits: e991960, 5772149, 5b1d360.
57 migrations triggered but PD-sep overhead (C queue + KV transfer + D
cold start) caused HEAVY TTFT p90 to regress from 15.9s to 59.1s.
Migration mechanism needs fundamental rework before it can help.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The session migration path was calling _handle_cached_prefill_offload
with swapped c_inst/d_inst and missing cache_hit parameter, causing
TypeError on every migration attempt (13 of 41 errors in the test run).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace num_requests threshold with recent TTFT median as migration
trigger. Track per-instance rolling TTFT (last 8 requests) and trigger
migration when median > 5s (configurable). Target is the instance with
lowest recent TTFT, requiring > 2x improvement to justify migration.
This is more responsive than the instantaneous num_requests signal
because TTFT directly measures the user-facing impact of contention.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Approach A (contention-aware cost model): TTFT p90 -52% vs baseline.
Approach B (session migration): 0 triggers at 1.5x threshold — needs tuning.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When a request arrives for a session on an overloaded instance, force
migration if three conditions hold:
1. Instance busy: num_requests > avg * migration_request_factor (1.5x)
2. Session has cache value: cache_ratio > 50%
3. Request is HEAVY (>= heavy_threshold)
4. A meaningfully less-loaded target exists (num_requests gap > 2)
This bypasses the cost model for migration decisions — the cost model's
cache-inflated costs prevented migration even when instances had 150s
queue times with 99% cache hit.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
After a session migrates from C to D via offload, C's blocks were freed
to the LRU tail (most-recently-used position), making them the last to
be evicted. Since the session won't return to C, these blocks are dead
weight occupying cache capacity.
Now capture block IDs before _free_blocks and call evict_blocks to
remove them from the prefix cache hash table, so they can be reused
sooner for active sessions.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two bugs caused elastic to concentrate load on cached instances (10x token
imbalance vs 2.7x baseline):
1. _instance_cost queue only counted pending_prefill_tokens, missing
ongoing_decode_tokens entirely — instances with 50 decoding requests
appeared idle to the cost model.
2. Cache hits made overloaded instances look "cheap", creating a positive
feedback loop: more sessions → more cache → lower cost → more routing.
Added a hard gate (ongoing_tokens > avg * overload_factor) that breaks
affinity before the cost model runs, matching linear policy behavior.
Result: token imbalance 10.3x → 2.6x, TTFT p90 -37% vs baseline.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The replayer and proxy were building multi-turn prompts from trace tokens,
but the model generates different output tokens. Subsequent turns had wrong
prefix tokens, causing cache misses and invalid experimental measurements.
- replay.py: min_tokens=max_tokens for deterministic length, return_token_ids
to capture actual output, _apply_realized_prefix for next-turn correction
- proxy: extract output token_ids from SSE, record prompt+output as realized
prefix in shadow cache, extract _handle_local_request to deduplicate
- bench.sh/launch_elastic_p2p.sh: default elastic mode to unified policy
- mooncake_connector: only send prompt blocks (not stale output blocks),
track failed_recving_block_ids for error recovery
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The standalone hash computation in estimate_hit produced different hashes
than the hash_table (synced from scheduler). Root cause unclear (possibly
pickle serialization differences or hash chain state). Fix: delegate to
_lookup_by_tokens which is proven to work (push_blocks uses it).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Copies mooncake_connector.py, mooncake_utils.py, scheduler.py from
third_party/vllm to the pip-installed vllm's site-packages. C extensions
stay from the pip package; only Python files are overridden.
Usage: bash scripts/deploy_vllm_patches.sh [HOST]
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
A: Add /estimate_hit endpoint to bootstrap server for real-time cache
probing. Proxy queries this before committing to PUSH, eliminating
24% zero-match PUSH requests (shadow cache divergence).
C: Add _handle_cached_prefill_offload: C (cache source) does fast
cached prefill → KV to Mooncake → D pulls and decodes.
Replaces broken direct_read PUSH where D waited for RDMA transfer
while occupying KV blocks without doing compute.
Also: update §3.9 baseline to plain vLLM with full mean/p50/p90/p99.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Without affinity, all cached requests route to the same instance
(cache source always has lowest prefill cost), causing 149s queue.
Fix: if the session's last instance has cost <= 2x the global best,
use it (preserves cache locality). Only re-route when the affinity
instance is significantly more expensive (overloaded).
The 2x threshold is intentionally loose — it's not a hardcoded magic
number but a "prefer locality unless clearly worse" heuristic.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Test results:
- 640/640 blocks matched and pushed (ret=0)
- External prefix cache hit rate: 80.0% on D
- Turn 2 TTFT: inst_0 (cached) = 0.338s, inst_1 (RDMA push) = 0.367s
- C's scheduler was NOT involved (0 GPU compute on C)
The complete direct KV cache migration pipeline is working:
D → /push_blocks → C bootstrap matches tokens → C RDMA WRITE → D GPU
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
RDMA READ (batch_transfer_sync_read) fails on GPU memory because
batch_register_memory only sets IBV_ACCESS_REMOTE_WRITE.
New approach: D sends /push_blocks to C's bootstrap with token_ids
+ D's GPU addresses. C's bootstrap:
1. Looks up matching blocks in synced hash table (640/640 verified)
2. Uses C's TransferEngine.batch_transfer_sync_write to PUSH blocks
directly into D's GPU memory
3. Returns match count + push status
C's scheduler is still NOT involved (0 GPU compute on C).
The push uses C's worker thread + existing RDMA WRITE path (proven reliable).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Hash mismatch root cause: sha256_cbor vs sha256 (default) + NONE_HASH
from-import value binding. Both fixed. Now 640/640 blocks matched.
RDMA read (batch_transfer_sync_read) fails with ret=-1.
Likely cause: Mooncake TransferEngine may not support RDMA READ
to arbitrary registered memory without explicit permission setup.
The PUSH path (batch_transfer_sync_write) works because the sender
initiates, but PULL may need additional RDMA MR access flags.
Next: investigate Mooncake's RDMA read permission model, or
fall back to a two-step approach: D sends query → C responds
with blocks via batch_transfer_sync_write (existing PUSH path),
but triggered by the bootstrap server instead of the scheduler.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause confirmed: NONE_HASH = os.urandom(32) differs between
scheduler and bootstrap server even in the same process (init_none_hash
called separately by each import path). PYTHONHASHSEED makes it
deterministic: NONE_HASH = hash_fn(seed), same across all code paths.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: each vLLM instance has a random NONE_HASH (os.urandom(32))
when PYTHONHASHSEED is not set. All block hashes are chained from
NONE_HASH, so D's hashes never match C's hashes.
Fix: C's bootstrap server now accepts token_ids and does the prefix
cache lookup locally using C's own hash function and block pool.
No cross-instance hash matching needed.
New flow: D sends prompt token_ids → C computes hashes on C's side →
C looks up in C's own BlockPool → returns block_ids.
Also: module-level _shared_block_pool for scheduler→bootstrap bridge,
prompt_token_ids passed through PullReqMeta, test script added.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause of 0 cache hits on offloaded requests identified:
- Hash table sync IS working (scheduler→metadata→worker→bootstrap)
- But D's query_blocks returns no matches → hash format mismatch
between D's request.block_hashes and C's synced hashes
The gap: offloaded TTFT (12.4s) ≈ co-located TTFT (12.0s) because
D does FULL cold prefill (cache_hit=0), not partial prefill with
RDMA-read cached blocks.
Next: debug hash format mismatch between D and C.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Old cost model: offload_cost = colocated_cost + RDMA_overhead, so offload
was always 0.1s more expensive. Result: only 19/117 HEAVY offloaded.
New: colocated_cost includes interference penalty when C_s has decode
requests: penalty = prefill_time × min(num_requests, 3) × 0.3.
Offload now wins when C_s has 1+ concurrent request.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The assertion `assert RequestStatus.is_finished(req.status)` at
scheduler.py:2109 fires when a partial-remote-prefill request
receives `finished_recving` while in RUNNING state (local prefill
already started before RDMA read completed).
This was the root cause of 67% error rate: EngineCore crashed with
"fatal error" assertion, killing the vLLM instance.
Fix: Replace assertion with debug log for non-WAITING, non-finished
requests. kv_both no-offload baseline confirmed 0 errors, proving
the crash was from our scheduler patch, not kv_both instability.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
