Adds the pieces needed to run the producer on dash1 and the consumer on
dash2 with the same shared cpfs venv:
start_vllm_single.sh
INSTANCE / GPU / PORT / BP / MASTER / ROLE env vars; brings up ONE
vLLM instance + applies the mooncake instrumentation patch (idempotent
since the venv is cpfs-shared, so the first invocation applies and the
second is a no-op). Per-instance MB2_LOG_DIR keeps producer/consumer
events separate even though both directories live on the same cpfs
path visible to both hosts.
mb2_kv_transfer.py
New --src-host / --dst-host args. Defaults stay 127.0.0.1 for
backward-compat with the intra-node sweep. /v1/completions URLs and
/query URLs now use the supplied hosts. remote_bootstrap_addr is
built as http://<src_host>:<src_bp> so the consumer's
do_remote_prefill request carries a routable address.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit closes the loop on the fresh-venv MB2 path. Three corrections
on top of the previous scaffold made the bench fire successfully on
dash1 GPU 0+1 with kv_both connector roles:
1. Re-target instrumentation patch to vLLM's shipped MooncakeConnector
(vllm/distributed/kv_transfer/kv_connector/v1/mooncake/mooncake_connector.py).
The mooncake-package's own mooncake_connector_v1.py turned out not to
be the implementation vLLM 0.18.1 loads — the
'{"kv_connector": "MooncakeConnector"}' config picks up the vLLM-shipped
one. Patches go at _send_blocks (P-side) and receive_kv_from_single_worker
(D-side, async, both entry and FINISH branch).
2. /query lives on the mooncake bootstrap port, not the vLLM HTTP port.
Add --src-bp / --dst-bp args; default 8998 / 8999.
3. kv_transfer_params schema for the vanilla connector:
do_remote_decode → {transfer_id}
do_remote_prefill → {transfer_id, remote_engine_id, remote_bootstrap_addr}
where remote_bootstrap_addr must include the http:// scheme. The dash0
smoke_test_migrate_cache.py was written for the patched build, which
used a different field-name set (remote_host, remote_port,
remote_block_ids); those are rejected here.
Also discovered (and worked around): vLLM 0.18.1 with kv_role=kv_consumer
raises AttributeError on `self.bootstrap_server` because that attribute
is only assigned conditionally inside `if not self.is_kv_consumer`. We
sidestep by running kv_both for the microbench — transfer mechanics are
identical (same batch_transfer_sync_write call); the role gate only
affects which request types each instance accepts. For §5 strict PD-disagg
baseline we'll need either to fix this bug or front the pair with a
role-aware proxy.
Sanity smoke (3 sizes × 2 repeats, dash1 GPU 0+1, kv_both intra-node):
input KV-MiB send_blocks_ms (P) receive_kv_ms (D) client_step2_ms
512 48 5–23 7–33 18–91
2048 192 21 23 37
8192 768 85 88 110
=> intra-node bandwidth ~9 GB/s on the actual transfer for 768 MiB,
which is well below NVLink p2p; likely PCIe-staged. Worth verifying.
Next step (in flight): full sweep 512..128k tokens × 5 repeats with
the per-stage analyzer.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
start_vllm_pair.sh
ROLE_A / ROLE_B env vars (default kv_producer / kv_consumer for strict
PD-disagg). Override to kv_both for the kv_both control. The role is
injected into --kv-transfer-config so vLLM imposes the role restriction.
mb2_kv_transfer.py
--skip-verify flag drops step 3 (the plain completion sanity-check on
the destination), required when the dst is kv_consumer-only since a
kv_consumer instance refuses to serve a request without
do_remote_prefill. The transfer-time itself is still measured from
step 2 (do_remote_prefill on the consumer).
Also: per-step client-side wall-clock timestamps (t_step1_client_unix,
t_step2_client_unix, t_step2_end_unix) are now captured so the
post-hoc breakdown analyzer can join with the per-instance JSONL logs
on absolute time.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Two new files prepare measurement of T_transfer(KV_size, network_path),
the gap §3.2's PD-disagg cost argument has had since day one.
microbench/fresh_setup/start_vllm_pair.sh
start | status | stop two vLLM 0.18.1 instances on local GPUs (A, B)
with --kv-transfer-config '{"kv_connector":"MooncakeConnector",
"kv_role":"kv_both"}' running off the fresh venv (vanilla wheel +
vanilla mooncake 0.3.11, NOT the dash0 patched build). GPU IDs and
ports are env-overridable so the same script drives the intra-node
pair (GPU_A=0 GPU_B=1 on one host) and the inter-node pair (GPU_A=0
on dash1, GPU_B=0 on dash2 — launched per host separately).
microbench/fresh_setup/mb2_kv_transfer.py
Three-step measurement borrowed from connector_tax/.../smoke_test_
migrate_cache.py:
1. do_remote_decode on A (compute & cache KV; max_tokens=1)
2. do_remote_prefill on B (pull KV from A — this is the timed step)
3. plain completion on B (sanity check: cached_tokens ≈ prompt len)
Sweeps input_tokens ∈ {512, 1k, 2k, 4k, 8k, 16k, 32k, 64k} with 5
repeats each; reports mean / p50 / p90 transfer time and a per-size
raw log. Per-token KV is 98304 B (Qwen3-Coder-30B-A3B), so the upper
end ≈ 6 GiB transfers — within the p99 11.5 GiB range from §2 but
below it (the model's max_model_len 200000 caps the absolute upper).
What we will NOT learn from this design:
- Bandwidth saturation when the system is loaded (single-request bench)
- vLLM-internal scheduling overhead vs pure transfer (the timed step
folds them together — but for the §3.2 argument that's the right
"what does PD-disagg actually pay" number)
Intentionally not committed yet: an orchestrator that loops over
intra-/inter-node configs. We start manual on dash1 intra-node to
verify the measurement is sane before scaling out.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
