Warm community rank runs before measurement

runs/frontier-multicase-sufficiency-v0/best_effort/fixed_cohort_rank.py

@@ -56,6 +56,7 @@ COHORT_SIZE = 64
 COHORT_SEED = 2026071501
 CONFIG_ORDER_SEED = 2026071502
 RATE_ORDER_SEED = 2026071503
+WARMUP_SEED = 2026071504
 INPUT_BINS = (0, 1024, 2048, 4096, 8192, 16384, 32769)
 OFFERED_RATES = (0.15, 0.25, 0.35, 0.50, 0.75, 1.00, 1.50)
 TARGET_PASS_RATE = 0.95
@@ -854,6 +855,12 @@ def prepare_community(args: argparse.Namespace) -> Path:
             "client_max_concurrency": community_grid.CLIENT_MAX_CONCURRENCY,
             "slo_early_stop": False,
             "one_server_launch_per_config": True,
+            "warmup": {
+                "method": "one_disjoint_request_per_input_length_bin",
+                "request_count": len(INPUT_BINS) - 1,
+                "seed": WARMUP_SEED,
+                "latencies_discarded": True,
+            },
             "rate_order_randomized_within_config": True,
             "config_order_seed": args.config_order_seed,
             "rate_order_seed": args.rate_order_seed,
@@ -891,6 +898,51 @@ def _materialize_real_requests(
     return requests
 
 
+def _select_warmup_requests(
+    all_requests: list[TraceRequest], *, cohort_ids: set[str]
+) -> list[TraceRequest]:
+    by_bin = [[] for _ in range(len(INPUT_BINS) - 1)]
+    for request in all_requests:
+        if request.row_id in cohort_ids or request.prompt_tokens_hint is None:
+            continue
+        input_tokens = int(request.prompt_tokens_hint)
+        if not 0 <= input_tokens <= 32768:
+            continue
+        by_bin[_input_bin(input_tokens)].append(request)
+    selected = []
+    for bin_index, candidates in enumerate(by_bin):
+        if not candidates:
+            raise ValueError(f"no disjoint warmup request in input bin {bin_index}")
+        selected.append(
+            min(
+                candidates,
+                key=lambda request: sha256_text(
+                    f"{WARMUP_SEED}|{bin_index}|{request.row_id}"
+                ),
+            )
+        )
+    return [replace(request, arrival_s=0.0) for request in selected]
+
+
+def _run_real_replay(
+    requests: list[TraceRequest], *, recipe: Any, study: Any, max_elapsed_s: float
+) -> tuple[list[Any], bool, str]:
+    return _replay_requests(
+        requests,
+        base_url=recipe.base_url,
+        timeout_s=recipe.request_timeout_s,
+        max_concurrency=study.trace.max_concurrency,
+        # target=0 makes the mathematical SLO early-stop bound unreachable
+        # while retaining the existing replay engine.
+        target_pass_rate=0.0,
+        max_lag_s=None,
+        max_elapsed_s=max_elapsed_s,
+        evaluate_outcome=lambda outcome: type(
+            "NoEarlyStopEvaluation", (), {"passed": bool(outcome.success)}
+        )(),
+    )
+
+
 def _real_request_records(
     requests: list[TraceRequest], outcomes: list[Any]
 ) -> list[dict[str, Any]]:
@@ -971,6 +1023,37 @@ def run_community_config(
                 healthcheck_path=recipe.healthcheck_path,
                 ready_timeout_s=recipe.ready_timeout_s,
             )
+            cohort_ids = {
+                str(row["source_row_index"]) for row in protocol["cohort"]
+            }
+            warmup_requests = _select_warmup_requests(
+                all_requests, cohort_ids=cohort_ids
+            )
+            warmup_started = time.time()
+            warmup_outcomes, warmup_early_stopped, warmup_stop_reason = _run_real_replay(
+                warmup_requests,
+                recipe=recipe,
+                study=study,
+                max_elapsed_s=1200.0,
+            )
+            warmup_records = _real_request_records(warmup_requests, warmup_outcomes)
+            warmup_result = {
+                "status": "completed"
+                if not warmup_early_stopped
+                and all(item["success"] for item in warmup_records)
+                else "failed",
+                "elapsed_seconds": time.time() - warmup_started,
+                "early_stopped": warmup_early_stopped,
+                "early_stop_reason": warmup_stop_reason,
+                "cohort_disjoint": not bool(
+                    cohort_ids & {item["request_id"] for item in warmup_records}
+                ),
+                "requests": warmup_records,
+            }
+            write_json(run_dir / "warmup.json", warmup_result)
+            if warmup_result["status"] != "completed":
+                raise RuntimeError(f"server warmup failed: {warmup_result}")
+            time.sleep(2.0)
             for rate in record["rate_order"]:
                 rate = float(rate)
                 load_dir = run_dir / rate_key(rate)
@@ -988,19 +1071,11 @@ def run_community_config(
                     cohort=protocol["cohort"],
                 )
                 load_started = time.time()
-                outcomes, early_stopped, early_stop_reason = _replay_requests(
+                outcomes, early_stopped, early_stop_reason = _run_real_replay(
                     requests,
-                    base_url=recipe.base_url,
-                    timeout_s=recipe.request_timeout_s,
-                    max_concurrency=study.trace.max_concurrency,
-                    # target=0 makes the mathematical SLO early-stop bound
-                    # unreachable while retaining the existing replay engine.
-                    target_pass_rate=0.0,
-                    max_lag_s=None,
+                    recipe=recipe,
+                    study=study,
                     max_elapsed_s=max(float(rate_record["duration_s"]) + 900.0, 1200.0),
-                    evaluate_outcome=lambda outcome: type(
-                        "NoEarlyStopEvaluation", (), {"passed": bool(outcome.success)}
-                    )(),
                 )
                 request_records = _real_request_records(requests, outcomes)
                 score = score_requests(request_records)