diff --git a/docs/assets/simulator-fidelity/data.json b/docs/assets/simulator-fidelity/data.json new file mode 100644 index 0000000..6df95a9 --- /dev/null +++ b/docs/assets/simulator-fidelity/data.json @@ -0,0 +1,60 @@ +{ + "schema": "simulator-fidelity-figure-data-v1", + "objective": "maximum_tested_slo_feasible_offered_request_rate_per_gpu", + "qwen30_mixed": { + "sources": { + "real": "recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-mixed-qwen30b-tp-mns-surface-high1-dash1-d8899c5-20260701T095858Z", + "comparison": "/home/gahow/phd/replayserve/runs/simfid_s2rb/results/metrics.json" + }, + "configs": [ + {"name": "tp1_mns8", "tp": 1, "mns": 8, "real": 2.1, "frontier_profile_only": 1.1, "frontier_calibrated": 1.7166666666666666}, + {"name": "tp1_mns16", "tp": 1, "mns": 16, "real": 2.35, "frontier_profile_only": 1.1, "frontier_calibrated": 2.3833333333333333}, + {"name": "tp1_mns32", "tp": 1, "mns": 32, "real": 2.283333333333333, "frontier_profile_only": 1.1, "frontier_calibrated": 2.3833333333333333}, + {"name": "tp1_mns64", "tp": 1, "mns": 64, "real": 2.283333333333333, "frontier_profile_only": 1.1, "frontier_calibrated": 2.3833333333333333}, + {"name": "tp2_mns8", "tp": 2, "mns": 8, "real": 2.275, "frontier_profile_only": 0.0, "frontier_calibrated": 1.7416666666666667}, + {"name": "tp2_mns16", "tp": 2, "mns": 16, "real": 2.275, "frontier_profile_only": 1.1916666666666667, "frontier_calibrated": 2.3}, + {"name": "tp2_mns32", "tp": 2, "mns": 32, "real": 3.283333333333333, "frontier_profile_only": 0.0, "frontier_calibrated": 3.75}, + {"name": "tp2_mns64", "tp": 2, "mns": 64, "real": 3.2583333333333333, "frontier_profile_only": 0.0, "frontier_calibrated": 3.75}, + {"name": "tp4_mns8", "tp": 4, "mns": 8, "real": 1.2833333333333334, "frontier_profile_only": 0.0, "frontier_calibrated": 1.3208333333333333}, + {"name": "tp4_mns16", "tp": 4, "mns": 16, "real": 2.441666666666667, "frontier_profile_only": 0.0, "frontier_calibrated": 2.5}, + {"name": "tp4_mns32", "tp": 4, "mns": 32, "real": 2.441666666666667, "frontier_profile_only": 1.3208333333333333, "frontier_calibrated": 2.5}, + {"name": "tp4_mns64", "tp": 4, "mns": 64, "real": 2.441666666666667, "frontier_profile_only": 1.3208333333333333, "frontier_calibrated": 2.5} + ], + "profile_only_metrics": { + "kendall_tau_b": 0.0, + "pairwise_exact_sign_accuracy": 0.3787878787878788, + "simulator_top_set": ["tp4_mns32", "tp4_mns64"], + "real_top_set": ["tp2_mns32"], + "top1_regret_worst": 0.25634517766497456 + }, + "calibrated_metrics": { + "kendall_tau_b": 0.9668009539030813, + "pairwise_exact_sign_accuracy": 0.9393939393939394, + "simulator_top_set": ["tp2_mns32", "tp2_mns64"], + "real_top_set": ["tp2_mns32"], + "top1_regret_best": 0.0, + "top1_regret_worst": 0.0076142131979695165 + } + }, + "qwen235_prefill": { + "source": "runs/frontier-multicase-sufficiency-v0/best_effort/fixed_cohort_evidence/v2_refined_comparison.json", + "configs": [ + {"name": "tp4_mns64_mbt8192", "tp": 4, "mns": 64, "mbt": 8192, "expert_parallel": false, "real": 0.05, "frontier": 0.0375}, + {"name": "tp4_mns128_mbt8192", "tp": 4, "mns": 128, "mbt": 8192, "expert_parallel": false, "real": 0.05, "frontier": 0.0375}, + {"name": "tp4_mns64_mbt16384", "tp": 4, "mns": 64, "mbt": 16384, "expert_parallel": false, "real": 0.075, "frontier": 0.0625}, + {"name": "tp4_mns128_mbt16384", "tp": 4, "mns": 128, "mbt": 16384, "expert_parallel": false, "real": 0.075, "frontier": 0.0625}, + {"name": "tp8_mns64_mbt8192", "tp": 8, "mns": 64, "mbt": 8192, "expert_parallel": true, "real": 0.05625, "frontier": 0.05}, + {"name": "tp8_mns128_mbt8192", "tp": 8, "mns": 128, "mbt": 8192, "expert_parallel": true, "real": 0.05625, "frontier": 0.05}, + {"name": "tp8_mns64_mbt16384", "tp": 8, "mns": 64, "mbt": 16384, "expert_parallel": true, "real": 0.05625, "frontier": 0.05625}, + {"name": "tp8_mns128_mbt16384", "tp": 8, "mns": 128, "mbt": 16384, "expert_parallel": true, "real": 0.05625, "frontier": 0.05625} + ], + "metrics": { + "spearman_rank_correlation": 0.9486832980505138, + "pairwise_non_tied_accuracy": 1.0, + "comparable_non_tied_pairs": 20, + "simulator_top_set": ["tp4_mns64_mbt16384", "tp4_mns128_mbt16384"], + "real_top_set": ["tp4_mns64_mbt16384", "tp4_mns128_mbt16384"], + "top1_regret_worst": 0.0 + } + } +} diff --git a/docs/assets/simulator-fidelity/qwen235-prefill-config-ranking.png b/docs/assets/simulator-fidelity/qwen235-prefill-config-ranking.png new file mode 100644 index 0000000..34b4045 Binary files /dev/null and b/docs/assets/simulator-fidelity/qwen235-prefill-config-ranking.png differ diff --git a/docs/assets/simulator-fidelity/qwen235-t0-fixed-shape-ranking.png b/docs/assets/simulator-fidelity/qwen235-t0-fixed-shape-ranking.png new file mode 100644 index 0000000..317b418 Binary files /dev/null and b/docs/assets/simulator-fidelity/qwen235-t0-fixed-shape-ranking.png differ diff --git a/docs/assets/simulator-fidelity/qwen30-mixed-config-ranking.png b/docs/assets/simulator-fidelity/qwen30-mixed-config-ranking.png new file mode 100644 index 0000000..df77685 Binary files /dev/null and b/docs/assets/simulator-fidelity/qwen30-mixed-config-ranking.png differ diff --git a/docs/assets/simulator-fidelity/qwen30-vllm020-profile-ablation.png b/docs/assets/simulator-fidelity/qwen30-vllm020-profile-ablation.png new file mode 100644 index 0000000..598a1a3 Binary files /dev/null and b/docs/assets/simulator-fidelity/qwen30-vllm020-profile-ablation.png differ diff --git a/runs/fidelity-headroom/run_pilot_simulator.py b/runs/fidelity-headroom/run_pilot_simulator.py index 6db18b4..d1833ec 100644 --- a/runs/fidelity-headroom/run_pilot_simulator.py +++ b/runs/fidelity-headroom/run_pilot_simulator.py @@ -1,5 +1,5 @@ #!/usr/bin/env python3 -"""Run and score the 12 frozen Frontier P1 primary probes, CPU only.""" +"""Run and score a frozen Frontier probe manifest, CPU only.""" from __future__ import annotations @@ -54,9 +54,16 @@ def git_capture(root: Path, *arguments: str) -> str: def execute(args: argparse.Namespace) -> dict[str, Any]: + args.output = args.output.resolve() prepared = json.loads(args.prepared_manifest.read_text(encoding="utf-8")) if prepared["status"] != "PASS": raise RuntimeError("prepared simulator manifest did not pass") + expected_runs = int(prepared.get("expected_runs", len(prepared["entries"]))) + if expected_runs != len(prepared["entries"]): + raise RuntimeError( + f"prepared manifest expected {expected_runs} runs but contains " + f"{len(prepared['entries'])} entries" + ) driver = load_module( "simfid_execution_driver", args.replayserve_root @@ -71,7 +78,7 @@ def execute(args: argparse.Namespace) -> dict[str, Any]: failures = [] gpu_visibility_disabled = True for sequence, entry in enumerate(prepared["entries"]): - run_root = args.output / f"{sequence:02d}_{entry['fixture_id']}" + run_root = args.output / f"{sequence:03d}_{entry['fixture_id']}" scorer_path = run_root / "scorer_output.json" if scorer_path.is_file() and args.resume: scorer = json.loads(scorer_path.read_text(encoding="utf-8")) @@ -205,8 +212,8 @@ def execute(args: argparse.Namespace) -> dict[str, Any]: red_flags = [] if failures: red_flags.append("frontier_run_failure") - if len(results) != 12: - red_flags.append("runs_not_12") + if len(results) != expected_runs: + red_flags.append("runs_not_expected") if any(not 0.0 <= value <= 1.0 for value in pass_rates): red_flags.append("pass_rate_out_of_range") if any(value <= 0 for value in throughputs): @@ -251,7 +258,8 @@ def execute(args: argparse.Namespace) -> dict[str, Any]: "distinct_n": len(set(runtimes)), }, "invariants": { - "runs_12": len(results) == 12, + "runs_expected": len(results) == expected_runs, + "expected_runs": expected_runs, "zero_failures": not failures, "ratios_bounded": all(0.0 <= value <= 1.0 for value in pass_rates), "nonnegative_metrics": all(value > 0 for value in throughputs), diff --git a/runs/frontier-qwen30-vllm020-profile-v1/analyze_op_trace_delta.py b/runs/frontier-qwen30-vllm020-profile-v1/analyze_op_trace_delta.py new file mode 100644 index 0000000..07883e4 --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/analyze_op_trace_delta.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python3 +"""Compare Frontier operator predictions at the identical initial scheduler state.""" + +from __future__ import annotations + +import argparse +import json +import re +from pathlib import Path + + +PATTERN = re.compile( + r"\[OP-TRACE\]\[MONOLITHIC\]\[(?:ATTENTION|MOE)\]\[([^]]+)\] " + r"batch_id=(\d+), layer_id=(\d+), predicted_time_ms=([0-9.eE+-]+)" +) +COMPONENTS = ( + "input_layernorm", + "attn_pre_proj", + "attn_rope", + "attn_kv_cache_save", + "attn_prefill", + "attn_decode", + "attn_post_proj", + "post_attention_layernorm", + "moe_gating_linear", + "moe_gating_routing_topk", + "moe_shuffling", + "moe_grouped_gemm", +) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--old", type=Path, required=True) + parser.add_argument("--new", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + return parser.parse_args() + + +def first_state(path: Path) -> dict[str, float]: + values: dict[str, set[float]] = {} + with path.open(errors="replace") as handle: + for line in handle: + match = PATTERN.search(line) + if match and int(match[2]) == 0 and int(match[3]) == 0: + values.setdefault(match[1], set()).add(float(match[4])) + ambiguous = {name: sorted(items) for name, items in values.items() if len(items) != 1} + if ambiguous: + raise ValueError(f"ambiguous initial predictions in {path}: {ambiguous}") + result = {name: next(iter(items)) for name, items in values.items()} + result.setdefault("attn_decode", 0.0) + missing = sorted(set(COMPONENTS) - set(result)) + if missing: + raise ValueError(f"missing initial predictions in {path}: {missing}") + return result + + +def main() -> None: + args = parse_args() + old = first_state(args.old) + new = first_state(args.new) + rows = [] + for component in COMPONENTS: + rows.append( + { + "component": component, + "historical_profile_ms": old[component], + "vllm020_profile_ms": new[component], + "new_over_old": new[component] / old[component] + if old[component] != 0 + else None, + } + ) + old_total = sum(old[name] for name in COMPONENTS) + new_total = sum(new[name] for name in COMPONENTS) + output = { + "schema": "frontier-initial-op-trace-delta.v1", + "comparison_contract": { + "fixture": "fidelity_p1_tp1_mns64_low1", + "scheduler_state": "batch_id=0, layer_id=0 before profile-dependent trajectories diverge", + "calibration_a_tp": 1.0, + }, + "layer_component_sum_ms": { + "historical_profile": old_total, + "vllm020_profile": new_total, + "new_over_old": new_total / old_total, + }, + "rows": rows, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n") + print(args.output) + + +if __name__ == "__main__": + main() diff --git a/runs/frontier-qwen30-vllm020-profile-v1/analyze_p1_profile_ablation.py b/runs/frontier-qwen30-vllm020-profile-v1/analyze_p1_profile_ablation.py new file mode 100644 index 0000000..67ec218 --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/analyze_p1_profile_ablation.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +"""Analyze real, calibrated, old-profile, and new-profile P1 probe outcomes.""" + +from __future__ import annotations + +import argparse +import csv +import json +import statistics +from pathlib import Path +from typing import Any + + +MODES = ("historical-calibrated", "historical-profile-only", "vllm020-profile-only") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--controller-state", type=Path, required=True) + parser.add_argument("--calibrated", type=Path, required=True) + parser.add_argument("--old-profile-only", type=Path, required=True) + parser.add_argument("--new-profile-only", type=Path, required=True) + parser.add_argument("--output-json", type=Path, required=True) + parser.add_argument("--output-csv", type=Path, required=True) + return parser.parse_args() + + +def load_results(path: Path) -> dict[tuple[str, str], dict[str, Any]]: + payload = json.loads(path.read_text()) + if payload["status"] != "PASS" or len(payload["results"]) != 12: + raise ValueError(f"expected 12 passing simulator probes in {path}") + return {(row["cell"], row["role"]): row for row in payload["results"]} + + +def main() -> None: + args = parse_args() + controller = json.loads(args.controller_state.read_text()) + real: dict[tuple[str, str], dict[str, Any]] = {} + for cell, value in controller["cells"].items(): + tp = int(value["tp"]) + for run in value["runs"]: + if run["role"] in ("low1", "high1"): + real[(cell, run["role"])] = { + **run, + "tp": tp, + "offered_req_s_per_gpu": int(run["selected_count"]) / 60 / tp, + } + if len(real) != 12: + raise ValueError(f"expected 12 real P1 probes, found {len(real)}") + + modes = { + "historical-calibrated": load_results(args.calibrated), + "historical-profile-only": load_results(args.old_profile_only), + "vllm020-profile-only": load_results(args.new_profile_only), + } + rows: list[dict[str, Any]] = [] + summaries: dict[str, Any] = {} + for mode in MODES: + predicted = modes[mode] + false_feasible = 0 + false_infeasible = 0 + pass_errors: list[float] = [] + capacity_lower_bounds: dict[str, float] = {} + for key in sorted(real): + real_row = real[key] + sim_row = predicted[key] + scorer = sim_row["scorer"] + sim_feasible = bool(scorer["slo"]["feasible"]) + real_feasible = bool(real_row["feasible"]) + false_feasible += int(sim_feasible and not real_feasible) + false_infeasible += int(real_feasible and not sim_feasible) + pass_error = abs(float(scorer["slo"]["pass_rate"]) - float(real_row["pass_rate"])) + pass_errors.append(pass_error) + rows.append( + { + "mode": mode, + "cell": key[0], + "role": key[1], + "real_feasible": real_feasible, + "sim_feasible": sim_feasible, + "real_pass_rate": float(real_row["pass_rate"]), + "sim_pass_rate": float(scorer["slo"]["pass_rate"]), + "pass_rate_absolute_error": pass_error, + "offered_req_s_per_gpu": float(real_row["offered_req_s_per_gpu"]), + "sim_throughput_req_s_per_gpu": float( + scorer["throughput_requests_per_second_per_gpu"] + ), + } + ) + if sim_feasible: + capacity_lower_bounds[key[0]] = max( + capacity_lower_bounds.get(key[0], 0.0), + float(real_row["offered_req_s_per_gpu"]), + ) + agreement = 12 - false_feasible - false_infeasible + summaries[mode] = { + "probe_classification": { + "agreement": agreement, + "accuracy": agreement / 12, + "false_feasible": false_feasible, + "false_infeasible": false_infeasible, + }, + "pass_rate_mae": statistics.mean(pass_errors), + "feasible_probe_count": sum( + bool(row["scorer"]["slo"]["feasible"]) for row in predicted.values() + ), + "p1_capacity_lower_bounds_req_s_per_gpu": { + cell: capacity_lower_bounds.get(cell, 0.0) + for cell in sorted(controller["cells"]) + }, + "rank_identifiable": bool(capacity_lower_bounds), + } + + output = { + "schema": "frontier-qwen30-p1-profile-ablation.v1", + "scope": { + "cells": 6, + "probes_per_cell": 2, + "roles": ["low1", "high1"], + "reading": "held-out boundary classification, not a complete capacity sweep", + }, + "sources": { + "controller_state": str(args.controller_state.resolve()), + "historical_calibrated": str(args.calibrated.resolve()), + "historical_profile_only": str(args.old_profile_only.resolve()), + "vllm020_profile_only": str(args.new_profile_only.resolve()), + }, + "summaries": summaries, + "rows": rows, + } + args.output_json.parent.mkdir(parents=True, exist_ok=True) + args.output_json.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n") + args.output_csv.parent.mkdir(parents=True, exist_ok=True) + with args.output_csv.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + print(args.output_json) + + +if __name__ == "__main__": + main() diff --git a/runs/frontier-qwen30-vllm020-profile-v1/analyze_routing_mismatch.py b/runs/frontier-qwen30-vllm020-profile-v1/analyze_routing_mismatch.py new file mode 100644 index 0000000..56d8492 --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/analyze_routing_mismatch.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 +"""Compare captured vLLM expert loads with Frontier's fixed routing prior.""" + +from __future__ import annotations + +import argparse +import json +import math +import statistics +from pathlib import Path +from typing import Any + +import numpy as np + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--routing", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--seed", type=int, default=42) + return parser.parse_args() + + +def gini(values: np.ndarray) -> float: + values = np.asarray(values, dtype=np.float64) + if values.sum() == 0: + return 0.0 + ordered = np.sort(values) + n = len(ordered) + indices = np.arange(1, n + 1, dtype=np.float64) + return float((2 * np.sum(indices * ordered) / np.sum(ordered) - (n + 1)) / n) + + +def stats(values: np.ndarray) -> dict[str, float]: + values = np.asarray(values, dtype=np.float64) + mean = float(np.mean(values)) + return { + "load_cv": float(np.std(values) / mean), + "load_gini": gini(values), + "max_load_ratio": float(np.max(values) / mean), + "expert_utilization": float(np.count_nonzero(values) / len(values)), + } + + +def proportional_counts(total: int, ratios: np.ndarray) -> np.ndarray: + exact = total * ratios / ratios.sum() + counts = np.floor(exact).astype(np.int64) + remainder = total - int(counts.sum()) + order = sorted( + range(len(ratios)), + key=lambda i: (-(exact[i] - counts[i]), -(ratios[i] / ratios.sum()), i), + ) + for index in range(remainder): + counts[order[index % len(order)]] += 1 + assert int(counts.sum()) == total + return counts + + +def correlation(left: np.ndarray, right: np.ndarray) -> float: + value = float(np.corrcoef(left, right)[0, 1]) + return value if math.isfinite(value) else 0.0 + + +def distribution(values: list[float]) -> dict[str, float]: + return { + "min": min(values), + "median": statistics.median(values), + "max": max(values), + } + + +def main() -> None: + args = parse_args() + routing = json.loads(args.routing.read_text()) + phases = routing["phases"] + layer_count = len(phases["prefill"]["per_layer"]) + expert_count = len(phases["prefill"]["per_layer"][0]["counts"]) + + rows: list[dict[str, Any]] = [] + for phase in ("prefill", "decode"): + for layer, actual in enumerate(phases[phase]["per_layer"]): + rng = np.random.RandomState(args.seed + layer) + ratios = rng.uniform(0.1, 1.0, expert_count) + synthetic = proportional_counts(int(actual["total_routed_tokens"]), ratios) + synthetic_stats = stats(synthetic) + for name in synthetic_stats: + if not math.isclose( + stats(np.asarray(actual["counts"]))[name], + float(actual[name]), + rel_tol=0, + abs_tol=1e-12, + ): + raise ValueError(f"captured metric mismatch: {phase} layer {layer} {name}") + rows.append( + { + "phase": phase, + "layer": layer, + "total_routed_tokens": int(actual["total_routed_tokens"]), + "actual": {name: float(actual[name]) for name in synthetic_stats}, + "frontier_simulation": synthetic_stats, + "actual_vs_frontier_pearson": correlation( + np.asarray(actual["counts"], dtype=np.float64), synthetic + ), + } + ) + + phase_summary: dict[str, Any] = {} + for phase in ("prefill", "decode"): + selected = [row for row in rows if row["phase"] == phase] + phase_summary[phase] = { + "token_count": int(phases[phase]["token_count"]), + "actual": { + name: distribution([row["actual"][name] for row in selected]) + for name in selected[0]["actual"] + }, + "frontier_simulation": { + name: distribution([row["frontier_simulation"][name] for row in selected]) + for name in selected[0]["frontier_simulation"] + }, + "actual_vs_frontier_pearson": distribution( + [row["actual_vs_frontier_pearson"] for row in selected] + ), + } + + phase_correlations = [] + for layer in range(layer_count): + prefill = np.asarray(phases["prefill"]["per_layer"][layer]["counts"]) + decode = np.asarray(phases["decode"]["per_layer"][layer]["counts"]) + phase_correlations.append(correlation(prefill, decode)) + + output = { + "schema": "frontier-routing-mismatch.v1", + "source": str(args.routing.resolve()), + "frontier_contract": { + "mode": "simulation", + "seed": args.seed, + "allocation": "per-layer fixed Uniform(0.1, 1.0), normalized once and reused for every batch and phase", + "layer_count": layer_count, + "expert_count": expert_count, + }, + "phase_summary": phase_summary, + "actual_prefill_vs_decode_pearson": distribution(phase_correlations), + "frontier_prefill_vs_decode_pearson": 1.0, + "rows": rows, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n") + print(args.output) + + +if __name__ == "__main__": + main() diff --git a/runs/frontier-qwen30-vllm020-profile-v1/analyze_s2_profile_ablation.py b/runs/frontier-qwen30-vllm020-profile-v1/analyze_s2_profile_ablation.py new file mode 100644 index 0000000..ac4dfec --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/analyze_s2_profile_ablation.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python3 +"""Score a replacement-profile S2 sweep against the frozen real oracle.""" + +from __future__ import annotations + +import argparse +import importlib.util +import json +import math +import sys +from pathlib import Path +from typing import Any + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--shard-metrics", type=Path, action="append", required=True) + parser.add_argument("--ground-truth", type=Path, required=True) + parser.add_argument("--historical-metrics", type=Path, required=True) + parser.add_argument("--historical-analyzer", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + return parser.parse_args() + + +def load_module(path: Path): + spec = importlib.util.spec_from_file_location("simfid_s2_analyzer", path) + if spec is None or spec.loader is None: + raise ImportError(path) + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + spec.loader.exec_module(module) + return module + + +def ranking(scores: dict[str, float]) -> list[dict[str, Any]]: + return [ + {"rank": index + 1, "cell": cell, "score": score} + for index, (cell, score) in enumerate( + sorted(scores.items(), key=lambda item: (-item[1], item[0])) + ) + ] + + +def robust_loao( + analyzer: Any, + runs: list[dict[str, Any]], + mode: str, + reading: str, + real_scores: dict[str, float], +) -> dict[str, Any]: + """Historical LOAO with an explicit null range for all-tied concordance.""" + anchors = sorted( + {(row["cell_id"], int(row["probe_index"])) for row in runs}, + key=lambda item: (analyzer.CELL_ORDER.index(item[0]), item[1]), + ) + replicates = [] + undefined = [] + for cell, probe in anchors: + scores, detail = analyzer.cell_scores( + runs, mode, reading, removed=(cell, probe) + ) + if scores is None: + undefined.append( + {"removed_cell_id": cell, "removed_probe_index": probe, **detail} + ) + continue + metric = analyzer.rank_metrics(real_scores, scores) + replicates.append( + { + "removed_cell_id": cell, + "removed_probe_index": probe, + "top1_optimistic_regret": metric["top1"]["optimistic_regret"], + "top1_worst_case_regret": metric["top1"]["worst_case_regret"], + "top5_minimum_exact_five_overlap": metric["top5"]["minimum_exact_five_overlap"], + "top5_maximum_exact_five_overlap": metric["top5"]["maximum_exact_five_overlap"], + "top5_optimistic_regret": metric["top5"]["optimistic_regret"], + "top5_worst_case_regret": metric["top5"]["worst_case_regret"], + "tau_b": metric["kendall_tau_b"]["tau_b"], + "pairwise_exact_sign_accuracy": metric["pairwise_direction"]["exact_sign_accuracy"], + "pairwise_non_tied_concordance": metric["pairwise_direction"]["non_tied_concordance"], + "trap_reproduced": metric["named_interactions"]["trap_reproduced"], + "tp2_mns32_unique_global_best": metric["named_interactions"]["tp2_mns32_unique_global_best"], + } + ) + scalar_keys = ( + "top1_optimistic_regret", + "top1_worst_case_regret", + "top5_minimum_exact_five_overlap", + "top5_maximum_exact_five_overlap", + "top5_optimistic_regret", + "top5_worst_case_regret", + "tau_b", + "pairwise_exact_sign_accuracy", + "pairwise_non_tied_concordance", + ) + ranges = {} + for key in scalar_keys: + values = [row[key] for row in replicates if row[key] is not None] + ranges[key] = { + "min": min(values) if values else None, + "max": max(values) if values else None, + } + return { + "replicate_count": len(anchors), + "defined_replicates": len(replicates), + "undefined_replicates": len(undefined), + "undefined": undefined, + "ranges": ranges, + "trap_reproduced_count": sum(row["trap_reproduced"] for row in replicates), + "tp2_mns32_unique_global_best_count": sum( + row["tp2_mns32_unique_global_best"] for row in replicates + ), + "replicates": replicates, + "range_semantics": "deterministic LOAO sensitivity ranges, not confidence intervals", + } + + +def main() -> None: + args = parse_args() + analyzer = load_module(args.historical_analyzer) + ground = json.loads(args.ground_truth.read_text()) + cells = {str(cell["cell_id"]): cell for cell in ground["cells"]} + if set(cells) != set(analyzer.CELL_ORDER): + raise ValueError("ground-truth cells differ from the frozen 3x4 surface") + + result_rows: list[dict[str, Any]] = [] + sources = [] + for path in args.shard_metrics: + payload = json.loads(path.read_text()) + if payload["status"] != "PASS": + raise ValueError(f"shard did not pass: {path}") + sources.append({"path": str(path.resolve()), "runs": len(payload["results"])}) + result_rows.extend(payload["results"]) + if len(result_rows) != 92: + raise ValueError(f"expected 92 replacement-profile probes, found {len(result_rows)}") + + seen: set[tuple[str, int]] = set() + runs = [] + for row in result_rows: + cell_id = str(row["cell"]) + probe_index = int(row["probe_index"]) + key = (cell_id, probe_index) + if key in seen: + raise ValueError(f"duplicate probe {key}") + seen.add(key) + real_probe = cells[cell_id]["probe_history"][probe_index] + if not math.isclose( + float(real_probe["sampling_u"]), float(row["sampling_u"]), rel_tol=0, abs_tol=1e-15 + ): + raise ValueError(f"sampling_u mismatch for {key}") + if int(real_probe["request_count"]) != int(row["selected_count"]): + raise ValueError(f"request count mismatch for {key}") + runs.append( + { + **row, + "cell_id": cell_id, + "mode": "vllm020-profile-only", + "probe_index": probe_index, + "sampling_u": float(row["sampling_u"]), + "request_count": int(row["selected_count"]), + "tensor_parallel_size": int(row["tensor_parallel_size"]), + "real_anchor": { + "feasible": bool(real_probe["feasible"]), + "pass_rate": float(real_probe["pass_rate"]), + "request_count": int(real_probe["request_count"]), + }, + } + ) + + historical = json.loads(args.historical_metrics.read_text()) + real_scores = {cell: float(value) for cell, value in historical["real_scores"].items()} + analyses = {} + for reading in analyzer.READINGS: + scores, detail = analyzer.cell_scores(runs, "vllm020-profile-only", reading) + if scores is None: + raise ValueError(f"undefined {reading} scores: {detail}") + analysis = { + "reading": reading, + "simulated_scores": scores, + "ranking": ranking(scores), + "cell_score_details": detail["cells"], + "metrics": analyzer.rank_metrics(real_scores, scores), + "loao": robust_loao( + analyzer, runs, "vllm020-profile-only", reading, real_scores + ), + } + if reading == "SLO-gated": + analysis["false_feasibility"] = analyzer.false_feasibility( + runs, "vllm020-profile-only" + ) + analyses[reading] = analysis + + historical_modes = {} + for label, key in ( + ("historical-profile-only", "uncalibrated/SLO-gated"), + ("historical-per-tp-calibration", "frozen-calibrated/SLO-gated"), + ): + value = historical["analyses"][key] + historical_modes[label] = { + "simulated_scores": value["simulated_scores"], + "ranking": ranking(value["simulated_scores"]), + "metrics": value["metrics"], + "false_feasibility": value["false_feasibility"], + } + + output = { + "schema": "frontier-qwen30-s2-profile-ablation.v1", + "scope": { + "cells": len(analyzer.CELL_ORDER), + "probes": len(runs), + "trace_horizon_seconds": 60, + "calibration_a_tp": 1.0, + }, + "sources": { + "replacement_profile_shards": sources, + "ground_truth": str(args.ground_truth.resolve()), + "historical_metrics": str(args.historical_metrics.resolve()), + }, + "real_scores": real_scores, + "historical_modes": historical_modes, + "vllm020_profile_only": analyses, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n") + print(args.output) + + +if __name__ == "__main__": + main() diff --git a/runs/frontier-qwen30-vllm020-profile-v1/artifacts/qwen30-initial-op-trace-delta.json b/runs/frontier-qwen30-vllm020-profile-v1/artifacts/qwen30-initial-op-trace-delta.json new file mode 100644 index 0000000..58346dc --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/artifacts/qwen30-initial-op-trace-delta.json @@ -0,0 +1,87 @@ +{ + "comparison_contract": { + "calibration_a_tp": 1.0, + "fixture": "fidelity_p1_tp1_mns64_low1", + "scheduler_state": "batch_id=0, layer_id=0 before profile-dependent trajectories diverge" + }, + "layer_component_sum_ms": { + "historical_profile": 1.148334, + "new_over_old": 2.700194368537377, + "vllm020_profile": 3.1007249999999997 + }, + "rows": [ + { + "component": "input_layernorm", + "historical_profile_ms": 0.010619, + "new_over_old": 1.955833882663151, + "vllm020_profile_ms": 0.020769 + }, + { + "component": "attn_pre_proj", + "historical_profile_ms": 0.204389, + "new_over_old": 1.0199276869107439, + "vllm020_profile_ms": 0.208462 + }, + { + "component": "attn_rope", + "historical_profile_ms": 0.018908, + "new_over_old": 7.394171779141103, + "vllm020_profile_ms": 0.139809 + }, + { + "component": "attn_kv_cache_save", + "historical_profile_ms": 0.014226, + "new_over_old": 1.427386475467454, + "vllm020_profile_ms": 0.020306 + }, + { + "component": "attn_prefill", + "historical_profile_ms": 0.107759, + "new_over_old": 2.670709639102071, + "vllm020_profile_ms": 0.287793 + }, + { + "component": "attn_decode", + "historical_profile_ms": 0.0, + "new_over_old": null, + "vllm020_profile_ms": 0.0 + }, + { + "component": "attn_post_proj", + "historical_profile_ms": 0.134153, + "new_over_old": 0.967559428413826, + "vllm020_profile_ms": 0.129801 + }, + { + "component": "post_attention_layernorm", + "historical_profile_ms": 0.011481, + "new_over_old": 0.9000087100426792, + "vllm020_profile_ms": 0.010333 + }, + { + "component": "moe_gating_linear", + "historical_profile_ms": 0.011802, + "new_over_old": 2.2642772411455687, + "vllm020_profile_ms": 0.026723 + }, + { + "component": "moe_gating_routing_topk", + "historical_profile_ms": 0.009662, + "new_over_old": 2.2922790312564687, + "vllm020_profile_ms": 0.022148 + }, + { + "component": "moe_shuffling", + "historical_profile_ms": 0.030431, + "new_over_old": 0.0, + "vllm020_profile_ms": 0.0 + }, + { + "component": "moe_grouped_gemm", + "historical_profile_ms": 0.594904, + "new_over_old": 3.756204362384519, + "vllm020_profile_ms": 2.234581 + } + ], + "schema": "frontier-initial-op-trace-delta.v1" +} diff --git a/runs/frontier-qwen30-vllm020-profile-v1/artifacts/qwen30-p1-profile-ablation.csv b/runs/frontier-qwen30-vllm020-profile-v1/artifacts/qwen30-p1-profile-ablation.csv new file mode 100644 index 0000000..2750398 --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/artifacts/qwen30-p1-profile-ablation.csv @@ -0,0 +1,37 @@ +mode,cell,role,real_feasible,sim_feasible,real_pass_rate,sim_pass_rate,pass_rate_absolute_error,offered_req_s_per_gpu,sim_throughput_req_s_per_gpu +historical-calibrated,tp1_mns64,high1,False,True,0.24581005586592178,0.9888268156424581,0.7430167597765364,2.9833333333333334,2.8823345959582403 +historical-calibrated,tp1_mns64,low1,True,True,1.0,1.0,0.0,2.033333333333333,1.9806287931485522 +historical-calibrated,tp1_mns8,high1,False,False,0.20670391061452514,0.16759776536312848,0.03910614525139666,2.9833333333333334,2.162661799726284 +historical-calibrated,tp1_mns8,low1,True,True,1.0,0.9754098360655737,0.024590163934426257,2.033333333333333,1.9806313066623409 +historical-calibrated,tp2_mns64,high1,True,True,1.0,1.0,0.0,2.875,2.809583898291986 +historical-calibrated,tp2_mns64,low1,True,True,1.0,1.0,0.0,1.9583333333333333,1.911740196570019 +historical-calibrated,tp2_mns8,high1,False,False,0.20238095238095238,0.15476190476190477,0.047619047619047616,2.8,2.083217332903509 +historical-calibrated,tp2_mns8,low1,True,True,1.0,0.9868995633187773,0.013100436681222738,1.9083333333333334,1.8637394408880168 +historical-calibrated,tp4_mns16,high1,False,False,0.10666666666666667,0.2693333333333333,0.16266666666666663,3.125,2.6829294600763345 +historical-calibrated,tp4_mns16,low1,False,True,0.6196078431372549,1.0,0.3803921568627451,2.125,2.0865667413849516 +historical-calibrated,tp4_mns64,high1,True,True,1.0,1.0,0.0,3.125,3.0718260341342285 +historical-calibrated,tp4_mns64,low1,True,True,1.0,1.0,0.0,2.125,2.0967062495193307 +historical-profile-only,tp1_mns64,high1,False,False,0.24581005586592178,0.24581005586592178,0.0,2.9833333333333334,2.82546810606982 +historical-profile-only,tp1_mns64,low1,True,False,1.0,0.7377049180327869,0.2622950819672131,2.033333333333333,1.9427723213024963 +historical-profile-only,tp1_mns8,high1,False,False,0.20670391061452514,0.09497206703910614,0.111731843575419,2.9833333333333334,1.5728783582441022 +historical-profile-only,tp1_mns8,low1,True,False,1.0,0.1721311475409836,0.8278688524590164,2.033333333333333,1.5629164685645112 +historical-profile-only,tp2_mns64,high1,True,False,1.0,0.2608695652173913,0.7391304347826086,2.875,2.7121507155597584 +historical-profile-only,tp2_mns64,low1,True,False,1.0,0.8382978723404255,0.16170212765957448,1.9583333333333333,1.849101894293262 +historical-profile-only,tp2_mns8,high1,False,False,0.20238095238095238,0.041666666666666664,0.16071428571428573,2.8,0.9859710483293953 +historical-profile-only,tp2_mns8,low1,True,False,1.0,0.0611353711790393,0.9388646288209607,1.9083333333333334,0.9771135206177947 +historical-profile-only,tp4_mns16,high1,False,False,0.10666666666666667,0.028,0.07866666666666668,3.125,0.9604809617478992 +historical-profile-only,tp4_mns16,low1,False,False,0.6196078431372549,0.058823529411764705,0.5607843137254902,2.125,0.9495221159963836 +historical-profile-only,tp4_mns64,high1,True,False,1.0,0.0026666666666666666,0.9973333333333333,3.125,2.281094127221267 +historical-profile-only,tp4_mns64,low1,True,False,1.0,0.4627450980392157,0.5372549019607843,2.125,1.9793960278798877 +vllm020-profile-only,tp1_mns64,high1,False,False,0.24581005586592178,0.0,0.24581005586592178,2.9833333333333334,2.074240230678028 +vllm020-profile-only,tp1_mns64,low1,True,False,1.0,0.0,1.0,2.033333333333333,1.6189525827713862 +vllm020-profile-only,tp1_mns8,high1,False,False,0.20670391061452514,0.0,0.20670391061452514,2.9833333333333334,0.5271192714936628 +vllm020-profile-only,tp1_mns8,low1,True,False,1.0,0.0,1.0,2.033333333333333,0.5225514701978402 +vllm020-profile-only,tp2_mns64,high1,True,False,1.0,0.0,1.0,2.875,1.7237810220598582 +vllm020-profile-only,tp2_mns64,low1,True,False,1.0,0.0,1.0,1.9583333333333333,1.608919184557297 +vllm020-profile-only,tp2_mns8,high1,False,False,0.20238095238095238,0.0,0.20238095238095238,2.8,0.39315152956277477 +vllm020-profile-only,tp2_mns8,low1,True,False,1.0,0.0,1.0,1.9083333333333334,0.3894201655965407 +vllm020-profile-only,tp4_mns16,high1,False,False,0.10666666666666667,0.0,0.10666666666666667,3.125,0.43700447014112276 +vllm020-profile-only,tp4_mns16,low1,False,False,0.6196078431372549,0.0,0.6196078431372549,2.125,0.43475717185302865 +vllm020-profile-only,tp4_mns64,high1,True,False,1.0,0.0,1.0,3.125,1.2598449015844073 +vllm020-profile-only,tp4_mns64,low1,True,False,1.0,0.0,1.0,2.125,1.2025831680037373 diff --git a/runs/frontier-qwen30-vllm020-profile-v1/artifacts/qwen30-p1-profile-ablation.json b/runs/frontier-qwen30-vllm020-profile-v1/artifacts/qwen30-p1-profile-ablation.json new file mode 100644 index 0000000..0bf3077 --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/artifacts/qwen30-p1-profile-ablation.json @@ -0,0 +1,511 @@ +{ + "rows": [ + { + "cell": "tp1_mns64", + "mode": "historical-calibrated", + "offered_req_s_per_gpu": 2.9833333333333334, + "pass_rate_absolute_error": 0.7430167597765364, + "real_feasible": false, + "real_pass_rate": 0.24581005586592178, + "role": "high1", + "sim_feasible": true, + "sim_pass_rate": 0.9888268156424581, + "sim_throughput_req_s_per_gpu": 2.8823345959582403 + }, + { + "cell": "tp1_mns64", + "mode": "historical-calibrated", + "offered_req_s_per_gpu": 2.033333333333333, + "pass_rate_absolute_error": 0.0, + "real_feasible": true, + "real_pass_rate": 1.0, + "role": "low1", + "sim_feasible": true, + "sim_pass_rate": 1.0, + "sim_throughput_req_s_per_gpu": 1.9806287931485522 + }, + { + "cell": "tp1_mns8", + "mode": "historical-calibrated", + "offered_req_s_per_gpu": 2.9833333333333334, + "pass_rate_absolute_error": 0.03910614525139666, + "real_feasible": false, + "real_pass_rate": 0.20670391061452514, + "role": "high1", + "sim_feasible": false, + "sim_pass_rate": 0.16759776536312848, + "sim_throughput_req_s_per_gpu": 2.162661799726284 + }, + { + "cell": "tp1_mns8", + "mode": "historical-calibrated", + "offered_req_s_per_gpu": 2.033333333333333, + "pass_rate_absolute_error": 0.024590163934426257, + "real_feasible": true, + "real_pass_rate": 1.0, + "role": "low1", + "sim_feasible": true, + "sim_pass_rate": 0.9754098360655737, + "sim_throughput_req_s_per_gpu": 1.9806313066623409 + }, + { + "cell": "tp2_mns64", + "mode": "historical-calibrated", + "offered_req_s_per_gpu": 2.875, + "pass_rate_absolute_error": 0.0, + "real_feasible": true, + "real_pass_rate": 1.0, + "role": "high1", + "sim_feasible": true, + "sim_pass_rate": 1.0, + "sim_throughput_req_s_per_gpu": 2.809583898291986 + }, + { + "cell": "tp2_mns64", + "mode": "historical-calibrated", + "offered_req_s_per_gpu": 1.9583333333333333, + 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EXP-SIMFID-Q30-P020：同栈 per-TP operator profile 能否恢复 Frontier ranking？ -> **状态：** 运行中（用户已批准 5-step campaign） +> **状态：** 完成；H1 rejected，结果支持 H2（2026-07-16） ## Claim 与决策 @@ -22,8 +22,8 @@ ## 预期产物与 review -- **预期数据：** TP1/2/4 attention、KV-cache update、linear、MoE、collective raw profiles；冻结 manifest/sha256；12-cell simulation outputs；三 baseline comparison table。 -- **Figure prototype：** `mock-profile-ablation.png`；x 为 12 configs，y 为 normalized capacity；series 为 real、old profile-only、new profile-only、calibrated。图中数值明确标为 mock，不进入结论。 +- **实际数据：** TP1/2/4 attention、KV-cache update、linear、MoE、collective raw profiles；冻结 manifest/sha256；92/92 new profile-only runs；old/new/calibrated comparison；scheduler graph-mode 与 native MoE routing diagnostics。 +- **Figure：** `../../docs/assets/simulator-fidelity/qwen30-vllm020-profile-ablation.png`；四个 panels 分别展示完整 ranking、routing skew、phase-dependent graph mode 和 held-out P1 boundary accuracy。图中只使用实测或冻结分析结果。 - **人工 review：** 已批准。用户明确要求推进 5 个步骤，并要求 smoke 通过后直接完整运行。 - **Review 意见：** 不能把 TP–MNS 耦合拆成互不相关问题；profile 只冻结 execution counterfactual 的证据，最终仍以完整 config ranking 判断。 @@ -32,12 +32,27 @@ - **Code：** AITuner 当前 branch `codex/fidelity-prefix-pilot-20260714`；Frontier canonical commit `d9cfeb6d8791fbf2f295dd9744c56a666171776e`；vLLM commit/tag `88d34c6409e9fb3c7b8ca0c04756f061d2099eb1` / `0.20.0`。 - **Environment：** `/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1`；hardware/driver/package freeze 写入每个 profile artifact。 - **产物路径：** remote `/home/admin/cpfs/wjh/frontier_qwen30_vllm020_profiles/`; local harvest `runs/frontier-qwen30-vllm020-profile-v1/fleet-artifacts/`。 -- **已知 deviation：** 历史 real ground truth 来自 dash1；本次 profile 按用户要求在 dash0 生成。driver 也已从历史 570.133.20 漂移到 dash0 当前 580.95.05。结果只能归因于“profile stack alignment 在当前 dash0 上的效果”，不能声称严格复现旧 dash1 execution latency。 +- **已知 deviation：** 历史 real ground truth 与本次 profile 都来自 dash0；早期文档中的主机 provenance 标注错误。driver 从历史 570.133.20 漂移到当前 580.95.05，因此结果仍不能解释为严格的逐 kernel latency 复现，只能作为同一 H20 主机上 profile-stack alignment 的对照。 ## 结果 -- **观察事实：** 待完整 profile 与 simulation。 -- **异常：** Frontier 原生 attention profiler 只实现 `FLASHINFER`/`NO_OP`，不能调用真实 serving 的 `FLASH_ATTN`；其 MoE wrapper声明并硬编码 vLLM 0.10.x API。vLLM 0.20.0 自带的 attention benchmark可调用实际 FlashAttention backend，但 mixed prefill+decode 在 FA3 中是一个 fused varlen call，而 Frontier schema将其拆成 `attn_prefill` 与 `attn_decode` 两项。 -- **Interpretation 与剩余 alternatives：** 这既是 profiler compatibility gap，也是潜在 representational gap。先用真实 kernel smoke 判断能否无损物化 Frontier profile；不能无损时同时保留 fused total 与 schema projection，避免误把 projection error 当 kernel error。 -- **Claim update：** unchanged。 -- **下一步：** 通过 vLLM 0.20 exact-kernel smoke；冻结 trace-derived shape support；完整 TP1/2/4 profiling；profile-only rerun。 +- **Step 1—同栈固定：** dash0 8×H20；Qwen3-30B-A3B BF16；community vLLM `0.20.0+cu129` / source commit `88d34c6409e9fb3c7b8ca0c04756f061d2099eb1`。真实 serving 与 profiles 使用同一主机和 model family；历史 driver 570.133.20 到当前 580.95.05 的漂移保留为限制。 +- **Step 2—per-TP profiles：** profile-v2 冻结 attention 132 rows、直接测量 fused-mixed diagnostic 30 rows、linear 36 rows、MoE 72 rows、all-reduce 24 rows。attention/linear/MoE 通过单 H20 local-shard shape 表达 TP；all-reduce 使用真实 TP2/4 ranks。base comparison 保持 historical analytical CC backend，measured all-reduce 仅作独立诊断。 +- **Step 3—trace execution coverage：** 6 个 P1 cells 共 161,161 scheduler/model steps：pure decode 151,471（100% FULL graph），pure prefill 224（192 NONE、30 PIECEWISE、2 FULL），true mixed 9,466（8,623 NONE、842 PIECEWISE、1 FULL）。exact-trace 8-request native routing probe 表明实际 median max/mean expert load 为 prefill 6.12、decode 5.64，而 Frontier fixed prior 为 1.84；actual-vs-prior Pearson median 接近 0。该小 cohort 用于机制否证，不作为 routing population estimate。 +- **Step 4—冻结 profile-only：** profile-v1 因 true-mixed schema 缺少 `attn_decode_in_mixed` 在第一个 mixed batch fail。profile-v2 使用 same-TP pure prefill/decode ratio 做 total-conserving compatibility split，同时保留直接实测 fused total。两个 46-run CPU shards 均 PASS，92/92 runs 无 crash，总 simulator runtime 2,394.9 s（0.665 CPU-hour）。 +- **Step 5—ranking comparison：** real optimum 为 TP2/MNS32。old profile-only top set 为 TP4/MNS32/64，τ-b=0、worst regret=25.63%；new vLLM 0.20 profile-only 在所有 92 anchors 上都 SLO-infeasible，12 configs 全并列，τ-b=0、exact pair sign=7.58%、worst tie-break regret=60.91%；historical frozen per-TP calibration top set 为 TP2/MNS32/64，τ-b=0.9668、worst regret=0.76%。在 held-out P1 12 boundary labels 上，new profile-only accuracy 41.67%、pass-rate MAE 69.84%，也没有优于 old profile-only；calibration 分别为 83.33% 和 11.75%。 +- **Same-state delta：** 在 `tp1_mns64` 的 batch 0/layer 0、profile-dependent trajectories 尚未分叉时，new/old layer component sum 已为 2.70×；MoE grouped GEMM 3.76×、RoPE 7.39×、attention prefill 2.67×。误差不是可由一个 version correction 解释的一致 scale。 +- **异常与 schema gap：** Frontier 原生 attention profiler 只实现 `FLASHINFER`/`NO_OP`，不能调用真实 serving 的 `FLASH_ATTN`；其 MoE wrapper声明并硬编码 vLLM 0.10.x API。vLLM 0.20.0 的 FA3 mixed prefill+decode 是一个 fused varlen call，而 Frontier schema要求 `attn_prefill` 和 `attn_decode_in_mixed` 两项。兼容 split 是归因，不是观测。 +- **Interpretation：** H1 被否证。同栈 isolated operator profile 不等价于真实 execution counterfactual。TP 与 MNS 共同改变 local shape、collective、queue/batch composition、phase mixture、routing 和 CUDA graph regime；static operator sum 在 scheduler 推进后产生 feedback amplification。历史 per-TP calibration 是 composition 之后的外部 E2E scale，能吸收这些残差，但不是 Frontier 原生 per-TP profiling 或机制解释。 +- **Claim update：** 支持 H2。新的 research target 是验证 execution-context-conditioned composition，而不是继续把更多 isolated kernel rows 当作 fidelity 的充分条件。 +- **下一步：** 分别做 graph-conditioned step profile、fused mixed total、trace-conditioned routing 和 measured collective 四个单变量消融，再回到完整 TP×MNS joint surface 评测；frozen calibration 仅作上界，不参与 mechanism fitting。 + +## 产物索引 + +- Frozen profile：`frozen/profile-v2/manifest.json` +- Full-surface comparison：`artifacts/qwen30-s2-profile-ablation.json` +- P1 boundary comparison：`artifacts/qwen30-p1-profile-ablation.json` +- Scheduler/graph-mode summary：`artifacts/qwen30-p1b-opprof-summary.json` +- Native routing mismatch：`artifacts/qwen30-routing-mismatch.json` +- Same-state operator delta：`artifacts/qwen30-initial-op-trace-delta.json` +- Figure script：`plot_profile_ablation.py` diff --git a/runs/frontier-qwen30-vllm020-profile-v1/frozen/profile-v1/allreduce.json b/runs/frontier-qwen30-vllm020-profile-v1/frozen/profile-v1/allreduce.json new file mode 100644 index 0000000..aae1f0a --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/frozen/profile-v1/allreduce.json @@ -0,0 +1,824 @@ +{ + "environment": [ + { + "backend_env": { + "VLLM_ALLREDUCE_USE_FLASHINFER": "1", + "VLLM_FLASHINFER_ALLREDUCE_BACKEND": "trtllm" + }, + "gpu": "NVIDIA H20", + "model": "/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B", + "torch_cuda": "12.9", + "torch_version": "2.11.0+cu129", + "vllm_source_commit": "88d34c6409e9fb3c7b8ca0c04756f061d2099eb1", + "vllm_version": "0.20.0" + }, + { + "backend_env": { + "VLLM_ALLREDUCE_USE_FLASHINFER": "1", + 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0000000..3abcf44 --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/frozen/profile-v2/manifest.json @@ -0,0 +1,53 @@ +{ + "attention_tp_coverage": [ + 1, + 2, + 4 + ], + "environment_contract": { + "dtype": "bfloat16", + "frontier_commit": "d9cfeb6d8791fbf2f295dd9744c56a666171776e", + "hardware": "NVIDIA H20", + "model": "Qwen3-30B-A3B", + "tensor_parallel_sizes": [ + 1, + 2, + 4 + ], + "vllm_source_commit": "88d34c6409e9fb3c7b8ca0c04756f061d2099eb1", + "vllm_version": "0.20.0" + }, + "inputs": { + "/home/gahow/phd/aituner/runs/frontier-qwen30-vllm020-profile-v1/fleet-artifacts/qwen30-vllm020-allreduce-full-tp2-20260716-v1-dispatch-aware-20260716T140743025781Z/artifacts/artifacts/allreduce-full-tp2-v1/raw/allreduce-tp2.json": "97c3c76b5a04e95bd9192423c2b891667c668f39cc0dfecbd097d749939f2d0a", + 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"/home/gahow/phd/aituner/runs/frontier-qwen30-vllm020-profile-v1/fleet-artifacts/qwen30-vllm020-router-full-20260716-v3-tp-context-20260716T145446098505Z/artifacts/artifacts/router-full-v3/raw/router.json": "1962972e983bff3e06a721ef4ae4ec65728ff669681497a4a7e7f769b88b4931" + }, + "outputs": { + "allreduce.json": "b38d14f990578d668523d25b107aceed433da5020d8ada3b6e44d3562261a3b3", + "attention.csv": "76dcb767cebb4ec1c4e24bd04d93ddd48b5d271986ebfb51a197ab33e1b3d87d", + "attention_true_mixed_fused.csv": "43ef4be90bddc9aeac6dbbe339feec24162cd1f2129a08fbd959e6ee4eaf5f60", + "linear_op.csv": "67666cb0a4901b74599d468df2e31bcaa2a11a7842cc0cefba24ffce62508e0c", + "moe.csv": "0e4dcba72918a1c4cf4e96ced31ee3829248a19ad54553cebef14417725808b0" + }, + "profile_id": "qwen3-30b-a3b-bf16-vllm020-h20-tp1-2-4-fused-mixed-total-conserving", + "projection_contract": { + "allreduce": "Frozen exact runtime measurements; base profile-only comparison keeps the historical Frontier CC backend fixed to isolate compute profile fidelity", + "attention": "Pure prefill/extend/decode FA3 core plus separately measured KV update; input/output reshape assumed zero; exported mean is used as median target; true mixed rows use a total-conserving compatibility projection", + "attention_true_mixed": "The directly measured fused total is preserved in diagnostics. Frontier's two targets are projected by the same-TP pure prefill/decode reference ratio, with projected prefill + decode exactly equal to the fused total; the split is a schema compatibility attribution, not an observation", + "linear": "Frontier profiler using vLLM 0.20 CUDA operators", + "moe": "Replicated gate and fused top-k plus TP-local modular expert kernel; expert measurement already includes prepare/finalize so shuffling is zero" + }, + "row_counts": { + "allreduce": 24, + "attention_frontier_compatible": 132, + "attention_true_mixed_fused_diagnostic": 30, + "linear": 36, + "moe": 72 + }, + "schema_version": "frontier_qwen30_vllm020_frozen_profile.v2" +} diff --git a/runs/frontier-qwen30-vllm020-profile-v1/frozen/profile-v2/moe.csv b/runs/frontier-qwen30-vllm020-profile-v1/frozen/profile-v2/moe.csv new file mode 100644 index 0000000..e5bf507 --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/frozen/profile-v2/moe.csv @@ -0,0 +1,73 @@ 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+#!/usr/bin/env python3 +"""Render the profile ablation and execution-context diagnostics.""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--s2", type=Path, required=True) + parser.add_argument("--routing", type=Path, required=True) + parser.add_argument("--opprof", type=Path, required=True) + parser.add_argument("--p1", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + s2 = json.loads(args.s2.read_text()) + routing = json.loads(args.routing.read_text()) + opprof = json.loads(args.opprof.read_text()) + p1 = json.loads(args.p1.read_text()) + + plt.rcParams.update({"font.size": 9, "axes.titlesize": 10, "axes.labelsize": 9}) + fig, axes = plt.subplots(2, 2, figsize=(13.2, 8.2), constrained_layout=True) + + real = s2["real_scores"] + cells = sorted(real, key=lambda cell: (-real[cell], cell)) + x = np.arange(len(cells)) + series = ( + ("Real", real, "#111111", "o"), + ( + "Old profile-only", + s2["historical_modes"]["historical-profile-only"]["simulated_scores"], + "#d95f02", + "s", + ), + ( + "vLLM 0.20 profile-only", + s2["vllm020_profile_only"]["SLO-gated"]["simulated_scores"], + "#7570b3", + "^", + ), + ( + "Frozen per-TP calibration", + s2["historical_modes"]["historical-per-tp-calibration"]["simulated_scores"], + "#1b9e77", + "D", + ), + ) + ax = axes[0, 0] + for label, values, color, marker in series: + ax.plot(x, [values[cell] for cell in cells], label=label, color=color, marker=marker, lw=1.7) + ax.set_xticks(x, [cell.replace("_", "\n") for cell in cells]) + ax.set_ylabel("SLO-feasible req/s/GPU") + ax.set_title("(a) Full 92-probe config ranking") + ax.grid(axis="y", alpha=0.25) + ax.legend(fontsize=8, ncol=2, loc="upper right") + + ax = axes[0, 1] + categories = ("Actual prefill", "Actual decode", "Frontier prior") + cv = [ + routing["phase_summary"]["prefill"]["actual"]["load_cv"]["median"], + routing["phase_summary"]["decode"]["actual"]["load_cv"]["median"], + routing["phase_summary"]["prefill"]["frontier_simulation"]["load_cv"]["median"], + ] + max_ratio = [ + routing["phase_summary"]["prefill"]["actual"]["max_load_ratio"]["median"], + routing["phase_summary"]["decode"]["actual"]["max_load_ratio"]["median"], + routing["phase_summary"]["prefill"]["frontier_simulation"]["max_load_ratio"]["median"], + ] + bx = np.arange(len(categories)) + width = 0.34 + bars1 = ax.bar(bx - width / 2, cv, width, label="Load CV", color="#66c2a5") + bars2 = ax.bar(bx + width / 2, max_ratio, width, label="Max/mean load", color="#fc8d62") + ax.bar_label(bars1, fmt="%.2f", fontsize=8) + ax.bar_label(bars2, fmt="%.2f", fontsize=8) + ax.set_xticks(bx, categories) + ax.set_ylim(0, max(max_ratio) * 1.2) + ax.set_title("(b) Per-layer MoE routing skew") + ax.legend(fontsize=8) + ax.grid(axis="y", alpha=0.25) + + graph_counts = {phase: Counter() for phase in ("pure_decode", "pure_prefill", "true_mixed")} + for cell in opprof["cells"]: + for group in cell["groups"]: + graph_counts[group["phase"]][group["cudagraph_runtime_mode"]] += int(group["steps"]) + ax = axes[1, 0] + phases = tuple(graph_counts) + bottoms = np.zeros(len(phases)) + for mode, color in (("FULL", "#1b9e77"), ("PIECEWISE", "#e6ab02"), ("NONE", "#d95f02")): + values = np.asarray( + [100 * graph_counts[phase][mode] / sum(graph_counts[phase].values()) for phase in phases] + ) + ax.bar(np.arange(len(phases)), values, bottom=bottoms, label=mode, color=color) + bottoms += values + ax.set_xticks(np.arange(len(phases)), [phase.replace("_", " ") for phase in phases]) + ax.set_ylabel("Observed scheduler steps (%)") + ax.set_ylim(0, 100) + ax.set_title("(c) Real vLLM execution mode is phase-dependent") + ax.legend(fontsize=8, ncol=3, loc="lower left") + + ax = axes[1, 1] + mode_order = ("historical-calibrated", "historical-profile-only", "vllm020-profile-only") + labels = ("Per-TP\ncalibration", "Old\nprofile-only", "vLLM 0.20\nprofile-only") + accuracy = [100 * p1["summaries"][mode]["probe_classification"]["accuracy"] for mode in mode_order] + mae = [100 * p1["summaries"][mode]["pass_rate_mae"] for mode in mode_order] + px = np.arange(len(labels)) + bars1 = ax.bar(px - width / 2, accuracy, width, label="Label accuracy (higher better)", color="#1b9e77") + bars2 = ax.bar(px + width / 2, mae, width, label="Pass-rate MAE (lower better)", color="#d95f02") + ax.bar_label(bars1, fmt="%.1f", fontsize=8) + ax.bar_label(bars2, fmt="%.1f", fontsize=8) + ax.set_xticks(px, labels) + ax.set_ylabel("Percent") + ax.set_ylim(0, 105) + ax.set_title("(d) Held-out P1 boundary probes (12 labels)") + ax.legend(fontsize=8, loc="upper right") + ax.grid(axis="y", alpha=0.25) + + fig.suptitle( + "Qwen3-30B-A3B / vLLM 0.20 / BF16 / dash0 H20: operator provenance is not execution-context fidelity", + fontsize=12, + ) + args.output.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(args.output, dpi=180) + fig.savefig(args.output.with_suffix(".svg")) + print(args.output) + + +if __name__ == "__main__": + main() diff --git a/runs/frontier-qwen30-vllm020-profile-v1/prepare_s2_profile_comparison.py b/runs/frontier-qwen30-vllm020-profile-v1/prepare_s2_profile_comparison.py new file mode 100644 index 0000000..962952a --- /dev/null +++ b/runs/frontier-qwen30-vllm020-profile-v1/prepare_s2_profile_comparison.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +"""Prepare the 92 frozen S2-R-b probes with a replacement profile bundle.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path +from typing import Any + + +PROFILE_KEYS = { + "linear_op_input_file": "linear_op.csv", + "atten_input_file": "attention.csv", + "moe_input_file": "moe.csv", +} + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1 << 20), b""): + digest.update(chunk) + return digest.hexdigest() + + +def write_json(path: Path, payload: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--resolved-plan", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--profile-root", type=Path, required=True) + parser.add_argument("--cache-root", type=Path) + parser.add_argument("--shards", type=int, default=1) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + if args.shards < 1: + raise SystemExit("--shards must be positive") + source = json.loads(args.resolved_plan.read_text()) + source_rows = [row for row in source["runs"] if row["mode"] == "uncalibrated"] + if len(source_rows) != 92: + raise SystemExit(f"expected 92 uncalibrated probes, found {len(source_rows)}") + + output = args.output.resolve() + profile_root = args.profile_root.resolve() + cache_root = (args.cache_root or (output / "prediction-cache")).resolve() + profile_hashes: dict[str, str] = {} + for filename in PROFILE_KEYS.values(): + path = profile_root / filename + if not path.is_file(): + raise SystemExit(f"missing frozen profile: {path}") + profile_hashes[str(path)] = sha256(path) + + entries: list[dict[str, Any]] = [] + for row in source_rows: + fixture_dir = Path(row["fixture_dir"]).resolve() + fixture_manifest_path = fixture_dir / "fixture_manifest.json" + fixture = json.loads(fixture_manifest_path.read_text()) + config = json.loads(Path(row["config_path"]).read_text()) + config["mode"] = "new-profile-only" + config["config_id"] = f"{row['cell_id']}__new-profile-only" + config["calibration"]["a_tp"] = 1.0 + knobs = config["frontier"]["knobs"] + knobs["cache_dir"] = str(cache_root) + knobs["no_cache"] = False + for key, filename in PROFILE_KEYS.items(): + knobs[key] = str(profile_root / filename) + + target_config = output / "configs" / f"{row['fixture_id']}.json" + write_json(target_config, config) + entries.append( + { + "fixture_id": row["fixture_id"], + "cell": row["cell_id"], + "role": f"probe-{int(row['probe_index']):02d}", + "anchor": float(row["sampling_u"]), + "selected_count": int(row["request_count"]), + "probe_index": int(row["probe_index"]), + "sampling_u": float(row["sampling_u"]), + "tensor_parallel_size": int(row["tensor_parallel_size"]), + "config": str(target_config), + "fixture_manifest": str(fixture_manifest_path), + "frontier_csv": str(fixture_dir / "frontier.csv"), + "sidecar": str(fixture_dir / "sidecar.jsonl"), + "calibration_scale": 1.0, + "request_count": int(fixture["request_count"]), + } + ) + + base = { + "schema": "frontier-qwen30-s2-profile-comparison-prepared.v1", + "status": "PASS", + "mode": "new-profile-only", + "source": { + "resolved_plan": str(args.resolved_plan.resolve()), + "sha256": sha256(args.resolved_plan), + }, + "profile_hashes": profile_hashes, + "isolation": { + "calibration_a_tp": 1.0, + "prediction_cache": str(cache_root), + "all_non_profile_knobs_inherited": True, + }, + "suite_total_runs": len(entries), + } + write_json( + output / "prepared-manifest.json", + {**base, "expected_runs": len(entries), "entries": entries}, + ) + for shard in range(args.shards): + shard_entries = entries[shard :: args.shards] + write_json( + output / f"prepared-manifest-shard-{shard:02d}-of-{args.shards:02d}.json", + { + **base, + "shard": {"index": shard, "count": args.shards}, + "expected_runs": len(shard_entries), + "entries": shard_entries, + }, + ) + print(output / "prepared-manifest.json") + + +if __name__ == "__main__": + main() diff --git a/simulator-fidelity.md b/simulator-fidelity.md new file mode 100644 index 0000000..728ce14 --- /dev/null +++ b/simulator-fidelity.md @@ -0,0 +1,360 @@ +# Frontier simulator fidelity：以 config ranking 为目标的阶段性评测 + +更新日期：2026-07-16。 + +统一实验平台：所有新增与重跑实验只使用 `dash0` 的 8×NVIDIA H20。Qwen30B 的真实 P1 artifacts 也来自 `dash0`；早期文档中的主机 provenance 标注错误，本版已按实验主机和远端 artifact 路径更正。 + +## 结论摘要 + +本文统一使用同一个优化目标：**在给定 workload 和 SLO 下，最大被测试可行 offered request throughput/GPU**。我们关心 simulator 是否保持 config 的相对排序、是否覆盖真机最优集合，以及部署 simulator 所选 config 后的真实 regret；绝对 latency/throughput 误差不是首要判据。 + +当前证据不支持“Frontier 普遍找不到最优 config”这一判断： + +- 在 Qwen3-30B mixed serving artifact 上，未经端到端校准的 Frontier 排序失败，选择 regret 为 `25.63%`；但使用独立 workload 上冻结的 per-TP calibration 后，Frontier 的候选 top set 包含真机最优 config，Kendall τ-b 为 `0.9668`，最坏 tie-break regret 为 `0.76%`。真实 serving 与本轮补测 profiles 均运行在 `dash0`。 +- 将 operator profile 更新为与真实 serving 一致的 community vLLM `0.20.0`、BF16、H20、TP1/2/4 栈后，新的 profile-only Frontier **没有恢复排序**：92 个真实 anchors 在 simulator 中全部 SLO-infeasible，12 个 config 因而全部并列，最坏 tie-break regret 为 `60.91%`。这否证了“旧 profile 版本不一致是主要原因”这一简单解释，并把问题收敛到 execution context、operator composition 与 mixed-state schema。 +- 在 Qwen3-235B-A22B-FP8 prefill-only 上，补齐 FP8/MoE profile 与 serving semantics、但不做端到端 action calibration 后，Frontier 的最优集合与真机完全一致，Spearman ρ 为 `0.9487`，20/20 个可比较非 tie config pair 同序，选择 regret 为 `0`。 +- 在 Qwen3-235B-A22B-FP8 fixed-shape mixed 上，Frontier 同样给出了与真机完全相同的 TP4 top set，Kendall τ-b=`0.8944`、worst tie-break regret=`0`。但这次成功掩盖了一个明确的机制错误：Frontier 认为同一 TP family 内四个 MNS/MBT config 完全等价，真机 TP8 capacity 却形成对角为 0.30、非对角为 0.20 req/s/GPU 的 checkerboard interaction。34 个 config-load labels 中有 10 个 false-infeasible；20 个 real non-tie pairs 中只保持 16 个方向。 +- 现有结果表明：**在已对齐的受控 case 中，绝对 capacity 有 gap、甚至 action differential 错误，都不必然妨碍 Frontier 找到当前 surface 的最优 config。** 统一 dash0 campaign 已完整覆盖 Qwen235 FP8 prefill-only 与 fixed-shape mixed，但仍不能外推到 trace-faithful mixed 或 decode-only，也不能忽略达到这种 fidelity 所需的真机 profile、KV capacity、runtime-semantic patch 或 calibration 成本。 + +因此，现阶段最准确的 research statement 不是“simulator 排序一定错误”，而是： + +> Simulator 的 usefulness 取决于 compatibility envelope。问题是需要多少、什么类型的真机信息，才能使其 config ranking 可被信任；以及当 workload、runtime 或 execution topology 变化时，如何低成本判断该 envelope 是否仍成立。 + +## 评测口径 + +对系统 `s ∈ {real, sim}` 和 config `c`，定义： + +```text +capacity_s(c) = max { tested offered rate r | empirical SLO pass rate(c, r) >= 0.95 } +``` + +所有结果使用 `capacity/实际占用 GPU 数` 作为 primary score。我们只报告实际测试到的最大可行点，不对未测试区间做连续插值，也不因观察到局部非单调就强制单调化。 + +核心指标如下： + +- **Top set**：达到各自最大 capacity 的全部 config，保留并列。 +- **Top-set hit/match**：simulator top set 是否与 real top set 有交集/完全相同。 +- **Worst tie-break regret**：若 simulator 给出多个并列最优，任取其中最差一个在真机上的损失： + + ```text + max[c in SimTop] (RealBest - capacity_real(c)) / RealBest + ``` + +- **Rank correlation**：有并列时优先使用 Kendall τ-b；Qwen235B 已有冻结分析使用 Spearman ρ，同时报告非 tie pair direction。 +- **Pairwise direction**：两两 config 的 `> / = / <` 是否一致。只报告 non-tied accuracy 时，会同时给出可比较 pair 数量。 + +这个定义也修正了之前的口径混用：内部 `throughput proxy` 不是“满足 SLO 的最大 throughput”，不能用来决定部署 config。本文的 Qwen30B 主结论因此取代 [旧 simulator fidelity 总结](docs/simulator-fidelity-frontier-20260711.md) 中基于 throughput-proxy 的 config-selection 结论；该旧读法仍可作为 simulator 内部机制误差的诊断证据。 + +## 实验 setup + +### Case A：Qwen3-30B-A3B mixed serving + +| 项目 | 设置 | +|---|---| +| 真机 | `dash0`，8×NVIDIA H20 | +| model | `Qwen/Qwen3-30B-A3B` | +| serving runtime | community vLLM `0.20.0`，CUDA 12.9 环境 | +| simulator | Frontier commit `d9cfeb6d8791fbf2f295dd9744c56a666171776e` | +| trace | `chat_w20260311_1000`，replay time scale=`0.1`，input 0--8192 tokens，output override=128 tokens，max concurrency=64 | +| SLO | 至少 95% requests 同时满足 TTFT 阶梯阈值（input ≤4096: 2s；≤32768: 4s；其余 6s）和 TPOT ≤50ms | +| config surface | `TP∈{1,2,4} × MNS∈{8,16,32,64}`，MBT=8192，共 12 cells | +| ground truth | 每个 cell 最多 8 个搜索 probes；92 个实测 anchors，共 27,583 个 request observations | +| simulator workload | 对每个 real anchor 重放同一冻结 request cohort 的 token length、arrival、request count 与 prefix block identities；old profile-only、calibrated、vLLM 0.20 profile-only 各 92 runs，合计 276/276 成功 | +| primary objective | SLO-feasible request throughput/GPU | + +我们区分两个 Frontier 条件： + +1. **Profile-only**：直接使用已有 H20 operator profiles，不用 serving E2E 数据修正时间尺度。 +2. **Frozen per-TP calibration**：使用 `TP1=0.723481, TP2=0.468089, TP4=0.352137` 三个 execution-time scales。它们在独立的 `coder_200_ts2` workload 上拟合，并在 `coder_200_ts3` 上冻结验证；本 `chat` surface 不重新拟合。该 scale 由外部 hook 在 Frontier 组成 batch execution time 后注入，并不是 Frontier 原生的 per-TP operator profiling 功能。 + +Frontier 原生 profile schema 可以按 `num_tensor_parallel_workers` 选择 TP-specific kernel rows；这与上面的 calibration 不同。第二个条件不是 zero-measurement prediction：它使用了同 model/hardware/runtime family 的真机 serving 数据，并能同时吸收 CUDA graph、fusion、routing、CPU launch 和 profile provenance 等残差。另一个限制是已有 H20 profiles 的生成栈与 ground-truth vLLM 0.20.0 并非严格同版本；per-TP calibration 吸收了相当一部分 execution-scale mismatch。 + +### Case B：Qwen3-235B-A22B-FP8 prefill-only + +| 项目 | 设置 | +|---|---| +| 真机 | `dash0`，8×NVIDIA H20（97,871 MiB/GPU，全互联 NV18），driver `580.95.05` | +| model | `Qwen/Qwen3-235B-A22B-FP8`，block-wise FP8 weights，BF16 KV cache | +| serving runtime | community vLLM `0.10.2`；Python 3.12.3、torch 2.8.0+cu128、FlashInfer 0.3.1.post1、Transformers 4.55.2；eager execution | +| simulator | Frontier commit `d9cfeb6d8791fbf2f295dd9744c56a666171776e` + 冻结的 FP8/MoE best-effort patches | +| source trace | `thinking_w20260327_1000`；从 15,409 个 eligible requests 中固定选择 64 个 length-stratified prompts | +| fixed cohort | seed=`2026071501`；input p50=1478、p95=18,439、max=29,385、mean=3511.55 tokens；output override=1 token | +| offered rates | 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.75, 1.00, 1.50 req/s；只缩放同一 cohort 的 arrival timeline | +| SLO | 至少 61/64 requests 满足 `TTFT ≤ 1000ms + input_tokens/8000 tokens/s` | +| config surface | `TP∈{4,8} × MNS∈{64,128} × MBT∈{8192,16384}`；实际 TP4 为 EP off，TP8 为 EP on | +| serving controls | prefix cache/speculative decoding off，chunked prefill on；每个 config fresh server，第二轮反转 config/rate 顺序 | +| primary objective | 最大被测试可行 offered rate / TP | + +这里比较的不是 stock Frontier。为建立公平的 FP8 compatibility envelope，我们先用同一 community-vLLM 0.10.2 栈在 H20 上测量 FP8 linear/attention/MoE/collective profiles，修复了 MoE FP8 tuning-key、Qwen MoE serving plan、TP/EP-aware cache key 与 critical-lane prediction，并输入真机测得的 KV block capacity（TP4=26,101；TP8=62,351）。Frontier refined outputs 在真机 v2/v3 ranking 运行前冻结，**没有使用本 case 的 serving capacity 做 end-to-end calibration**。 + +该 case 是有意设计的 prefill mechanism-isolation workload：output 固定为 1，prefix cache 关闭，并使用固定 64-request cohort。它不保持原 trace 的 output-length distribution 和 prefix reuse，因此不能被称为 trace-faithful workload，也不能代表 mixed serving。 + +## Workload fidelity 修正 + +`thinking_w20260327_1000` 的 600 秒原始窗口包含 15,479 requests，字段包括 exact prompt、arrival、`input_length`、`output_length`、session parent/turn 和 block-size=64 的 `hash_ids`。只读审计得到： + +| 属性 | 数值 | +|---|---:| +| natural offered rate | 25.798 req/s | +| input tokens | mean=3,660.0，p50=1,491，p95=19,610.6，p99=29,944.6，max=51,827 | +| output tokens | mean=3,924.6，p50=3,435，p95=8,945.1，p99=17,705.5，max=74,867 | +| total context | mean=7,584.6，p50=5,317，p95=25,102.1，p99=37,110.5，max=74,973 | +| rows with `hash_ids` | 15,479/15,479 | +| `len(hash_ids) == ceil(input_length/64)` | 15,479/15,479 | +| arrival-order repeated block ratio | 40.88% | +| input+output 超过 community model 40,960 context | 72 requests | +| output length=0 | 6 requests | + +因此，input/output/hash filtering 不是 trace fidelity 的默认要求。此前 fixed output length 是为了分别隔离 prefill 或 decode 机制；如果用它回答 production-trace fidelity，就属于错误 benchmark。新的实验矩阵固定为： + +| 层次 | Workload contract | Prefix contract | 回答的问题 | +|---|---|---|---| +| T0 fixed-shape | 固定 ISL、OSL、uniform QPS；所有 config 使用相同 request IDs/order | prefix off，使用等长但不同 token 的 prompts | 最简单条件下 execution/scheduler composition 是否匹配 | +| T1 trace-faithful mixed | 保留 trace 的 arrival、input、output、session；不使用 completion override | real 由 exact prompt tokens 自然计算 cache key；Frontier 消费等价 `block_hash_ids` | 在真实 joint distribution 下能否保持 config rank | +| T2 decode-only | output 保留 trace 或受控固定值；input length 表示 request 到达时已存在的 KV residency | initial-KV state 单独冻结；不得用一次 1-token prefill 冒充 | KV residency、decode batching、TP/DP/EP coupling 下能否保持 rank | + +T1 中只允许两类有记录的排除：模型 context 上限不支持的 72 requests，以及 API 无法形成 completion 的 6 个 zero-output rows；二者不重叠，最终 eligible universe 为 15,401/15,479 requests（99.50%）。排除后 input/output/total 的均值分别为 3,575.0/3,823.3/7,398.2 tokens，p95 分别为 18,887/8,768/23,697，说明没有把 workload 人为变成 short-request case。其余请求不能因为“跑得慢”或“方便形成 cohort”被选择性移除。capacity search 只通过 trace 已有、同 session 共享的 `sampling_u` 做与长度无关的 session-coherent thinning；每个被选 request 的 arrival、input、output、prompt、hash 和相对次序均保持不变。完整 eligible universe 是抽样母体和最终 workload 声明，不用 length-stratified cohort 代替。 + +全量 tokenizer audit 进一步验证了 15,401/15,401 个 eligible prompts、共 55,057,919 tokens 的实际 community-Qwen token 数与 trace `input_length` 完全一致。在 867,538 个 source blocks 上，`hash_id` 与 `(parent_hash_id, 64-token chunk)` 的双向映射也没有发现冲突。因此 exact prompt 和 source hash 足以保留该 trace 的 prefix-equivalence relation，不需要用 synthetic length-only prompt 替代。 + +trace 的 source hash block size 为 64，而 community vLLM 0.10.2 的 CUDA KV block size 不能直接设置为 64。下一轮实验冻结 real 与 Frontier 的 block size 都为 16：从 exact prompt token IDs 重新生成 block-16 parent/content identities，并用 source `hash_ids` 验证每四个完整 block-16 所对应的 64-token equality/reuse relation。tokenized length、request order、cache query/hit/allocated blocks 仍必须在两侧逐请求核对；上面的离线 audit 不能代替 runtime counter parity。 + +## 总体结果 + +| Case / simulator 条件 | configs | real optimum | simulator top set | top-set 结论 | rank / pairwise | worst regret | 判断 | +|---|---:|---|---|---|---|---:|---| +| Qwen30 mixed / old profile-only | 12 | TP2,MNS32 | TP4,MNS32/64 | miss | τ-b=0.000；exact sign=37.88% | 25.63% | 排序错误 | +| Qwen30 mixed / vLLM 0.20 profile-only | 12 | TP2,MNS32 | 全部 12 configs | 不可辨识 | τ-b=0.000；exact sign=7.58% | 60.91% | 同栈 raw profile 仍不足 | +| Qwen30 mixed / frozen per-TP calibration | 12 | TP2,MNS32 | TP2,MNS32/64 | hit，非 exact | τ-b=0.9668；exact sign=93.94% | 0.76% | dash0 | +| Qwen235 FP8 prefill / best-effort | 8 | TP4,MBT16K,MNS64/128 | 与 real 完全相同 | exact match | ρ=0.9487；non-tied 20/20 | 0 | 足以选最优 config | +| Qwen235 FP8 fixed-shape mixed / frozen full profile | 8 | 四个 TP4 configs | 与 real 完全相同 | exact match | τ-b=0.8944；exact sign=24/28；real non-tie=16/20 | 0 | 选对 topology；漏掉 TP8 MNS×MBT interaction | + +最重要的区别是：Qwen30 的 high-fidelity 结果依赖 per-TP E2E calibration；Qwen235 的 high-fidelity 结果不依赖本 case 的 E2E calibration，但依赖同 runtime/hardware 的 operator profile、真实 KV capacity 和多处 compatibility fixes。二者都不能表述为“拿 stock Frontier 零成本预测即可”。 + +## Case A baseline 结果：Qwen30 mixed serving + +![Qwen3-30B mixed serving simulator-vs-real config ranking](docs/assets/simulator-fidelity/qwen30-mixed-config-ranking.png) + +图中 config 按真机 capacity 从高到低排列。上图显示 profile-only Frontier 选错 TP family；下图显示冻结 per-TP calibration 后，simulator 基本恢复了有用排序。 + +| Config | Real | Frontier profile-only | Frontier calibrated | Calibrated − real | +|---|---:|---:|---:|---:| +| TP1, MNS8 | 2.1000 | 1.1000 | 1.7167 | -0.3833 | +| TP1, MNS16 | 2.3500 | 1.1000 | 2.3833 | +0.0333 | +| TP1, MNS32 | 2.2833 | 1.1000 | 2.3833 | +0.1000 | +| TP1, MNS64 | 2.2833 | 1.1000 | 2.3833 | +0.1000 | +| TP2, MNS8 | 2.2750 | 0.0000 | 1.7417 | -0.5333 | +| TP2, MNS16 | 2.2750 | 1.1917 | 2.3000 | +0.0250 | +| **TP2, MNS32** | **3.2833** | 0.0000 | **3.7500** | +0.4667 | +| TP2, MNS64 | 3.2583 | 0.0000 | **3.7500** | +0.4917 | +| TP4, MNS8 | 1.2833 | 0.0000 | 1.3208 | +0.0375 | +| TP4, MNS16 | 2.4417 | 0.0000 | 2.5000 | +0.0583 | +| TP4, MNS32 | 2.4417 | **1.3208** | 2.5000 | +0.0583 | +| TP4, MNS64 | 2.4417 | **1.3208** | 2.5000 | +0.0583 | + +单位均为 SLO-feasible req/s/GPU。`0.0000` 表示该 config 在复用的 tested anchors 中没有被 Frontier 判为 SLO-feasible，不代表硬件在零负载下也无法运行。 + +相对排序方面： + +- Profile-only 的 top set 为 TP4/MNS32、TP4/MNS64；两者真机 capacity 都是 2.4417，而 real optimum TP2/MNS32 是 3.2833，因此 regret 为 `1−2.4417/3.2833=25.63%`。 +- Calibrated Frontier 把 TP2/MNS32 和 TP2/MNS64 判为并列。前者是真机唯一最优，后者低 `0.025 req/s/GPU`；因此 optimistic regret=0，worst tie-break regret=`0.76%`。 +- Calibrated absolute error 并不是一个全局常数：TP2/MNS8 被低估 0.5333，而 TP2/MNS32/64 被高估约 0.47--0.49。**选对 config 不代表 simulator 已正确解释每个 state/action 的性能。** +- 92 个 anchor-level SLO 判定中，calibrated Frontier 仍有 21 个 false-feasible、7 个 false-infeasible；aggregate top set 正确部分来自 cell boundary 上误差抵消。因此该条件足以做本 surface 的 near-optimal selection，但不能替代真机 SLO certification。 + +### 同栈 vLLM 0.20 per-TP profile 消融 + +为了判断上述差异是否只是旧 operator profile 与真实 serving 版本不一致，我们在 `dash0` 上使用 ground-truth 相同的 community vLLM `0.20.0` source commit `88d34c6409e9fb3c7b8ca0c04756f061d2099eb1`、Qwen3-30B-A3B BF16 与 H20，重新测量 TP1/2/4 profiles。Frontier code、92 个 trace fixtures、SLO、KV capacity、config surface、communication backend 与随机种子保持不变；这轮只替换 compute profiles，不使用本 surface 的 serving latency 拟合 scale。 + +| Profile component | 冻结行数 | 测量与使用方式 | +|---|---:|---| +| attention + KV | 132 | 102 个 pure prefill/decode rows；30 个 true-mixed rows由同 TP pure profile 比例做 total-conserving split | +| fused mixed diagnostic | 30 | 保存 vLLM FA3 实测 fused total；不作为 Frontier 的第三个 target | +| linear / norm / RoPE / router | 36 | vLLM 0.20 CUDA operator | +| MoE | 72 | TP1/2/4 local-shard shapes × 12 token sizes × uniform/random/hotset8 routing | +| all-reduce | 24 | TP2/4 实测；本 base comparison 不注入，用来诊断 communication，保持历史 analytical CC backend 不变 | + +attention、linear 和 MoE 的 `TP` 字段改变的是单张 H20 上的 local-shard shape；它们不是一次 distributed full-engine graph replay。只有 all-reduce 使用真实 TP2/4 ranks 测量。该限制是实验设计的一部分：本消融回答“把相同 runtime 的 isolated operator profiles 换进去是否足够”，而不是预先假设 isolated profile 已等价于真实 execution counterfactual。 + +![Qwen3-30B vLLM 0.20 profile-only ablation and execution-context diagnostics](docs/assets/simulator-fidelity/qwen30-vllm020-profile-ablation.png) + +完整 92-probe 结果如下。`0` 表示复用的 anchors 中没有一个被 simulator 判为 SLO-feasible；它既不表示零负载硬件 capacity，也不能被解释成“因为真实最优 config 位于并列集合中所以 top-set hit”。当所有 config 都并列时 simulator 没有选择信息，故 primary 指标是 worst tie-break regret。 + +| Frontier 条件 | Sim top set | τ-b | Exact pair sign | Worst regret | Anchor labels：agree / false feasible / false infeasible | +|---|---|---:|---:|---:|---:| +| old profile-only | TP4,MNS32/64 | 0.0000 | 37.88% | 25.63% | 37 / 0 / 55 | +| vLLM 0.20 profile-only | 全部 12 configs | 0.0000 | 7.58% | 60.91% | 30 / 0 / 62 | +| frozen per-TP calibration | TP2,MNS32/64 | 0.9668 | 93.94% | 0.76% | 64 / 21 / 7 | + +若忽略 SLO，仅按 simulator 完成 throughput proxy 排序，新 profile 的 top-1 是 TP1/MNS64，真机 regret 仍为 `30.46%`，τ-b=`0.3940`，non-tied pair concordance=`70.49%`。这只是 failure diagnosis，不能替代本文定义的 SLO-feasible capacity objective。两路 CPU shard 都是 46/46 PASS，合计 simulator runtime 为 2,394.9 s（0.665 CPU-hour）；所以结果不是 crash、missing row 或未完成运行造成的。 + +#### 为什么相同 operator provenance 仍不等于 execution fidelity + +证据把 mismatch 收敛到以下三个相互耦合的层面： + +1. **Execution regime 随 scheduler state 改变。** 6 个真实 P1 cells 共记录 161,161 个 model steps。151,471 个 pure-decode steps 全部使用 FULL CUDA graph；224 个 pure-prefill steps 中 192 个为 NONE、30 个 PIECEWISE、仅 2 个 FULL；9,466 个 true-mixed steps 中 8,623 个为 NONE、842 个 PIECEWISE、仅 1 个 FULL。本轮 isolated profiles 则关闭 CUDA graph，Frontier comparison 也固定 `decode_cuda_graph_mode=none`。同一个 nominal operator 在不同 phase/state 下并不是同一个 launch/fusion regime。 +2. **Mixed attention schema 不保真。** vLLM 0.20 的 FA3 对 true-mixed batch 执行一个 fused varlen call；Frontier 必须分别得到 `attn_prefill` 与 `attn_decode_in_mixed`。不做 split 时 simulator 在第一个 mixed batch 明确报 missing prediction；采用 pure-profile ratio split 虽保持 `prefill + decode = fused total`，但 split 是兼容性归因而非真实观测。scheduler 使用这个虚构分解推进后续状态，误差会累积。 +3. **MoE routing 不是固定先验。** 对 exact trace 的 8 个 requests、48 层 native routing capture，实际 median load CV 在 prefill/decode 分别为 `1.076/1.107`，median max/mean expert load 为 `6.12/5.64`；Frontier 固定的 per-layer Uniform prior 分别只有 `0.485` 与 `1.84`。实际与 Frontier expert allocation 的逐层 Pearson median 接近 0（prefill `-0.017`、decode `-0.024`），而 Frontier 在所有 phase 复用相同 allocation，prefill/decode correlation 被固定为 1.0；真实 median correlation 为 `0.667`。8-request capture 是 mechanism probe，不是 workload population estimate，但足以否证“固定 routing vector 等价于该 trace execution”的假设。 + +更直接地，在同一个 `tp1_mns64` fixture、`batch_id=0/layer_id=0`、两条 trajectory 尚未分叉时，新 profile 的 layer component sum 已是旧 profile 的 `2.70×`（1.148→3.101 ms）：MoE grouped GEMM 为 `3.76×`、RoPE `7.39×`、attention prefill `2.67×`。这说明版本更新没有产生一个小而一致的 correction；不同 components 的误差方向和幅度不同，随后又通过 scheduler queue、batch composition、graph mode 与 routing 反馈到下一个 state。 + +因此本实验否证 H1：**Frontier 原生支持按 `num_tensor_parallel_workers` 选择 per-TP profile rows，而且 attention/MoE kernel 也确实可以逐 TP 测量；真正缺失的是 scheduler state 与 execution regime 条件化的 joint counterfactual。** TP 和 MNS 不能拆开：TP 改变 shard、collective、replica capacity、routing placement 与 graph shape，MNS 改变 batching/queue evolution；两者共同决定 phase mixture 和 execution mode。历史 per-TP calibration 是 composition 之后的外部 E2E scale，因此能吸收这些残差，但它不是机制解释，也不是 Frontier 原生 profiling。 + +下一轮不应继续盲目加 isolated rows，而应依次做可证伪的 context ablation：按 FULL pure-decode、NONE/PIECEWISE prefill/mixed 建立 graph-conditioned step profiles；保留 fused mixed layer/step total 而不是要求不可观测的拆分；用 trace-conditioned per-layer/request/phase routing histogram 替换固定先验；再单独注入 measured collective。每次只改变一个条件，最终仍在完整 `TP×MNS` joint surface 上以 SLO ranking 验证，frozen calibration 只作为可达到的上界。 + +本次 accepted profile jobs 使用约 0.118 H20-GPU-hour；包括 smoke 和失败 attempt 的完整 campaign 为 0.410 H20-GPU-hour。冻结 profile、manifest、condensed artifacts 与作图脚本位于 `runs/frontier-qwen30-vllm020-profile-v1/`。 + +## Case B 结果：Qwen235 FP8 prefill-only + +![Qwen3-235B FP8 prefill-only simulator-vs-real config ranking](docs/assets/simulator-fidelity/qwen235-prefill-config-ranking.png) + +| TP | EP | MNS | MBT | Real | Frontier | Frontier − real | +|---:|---:|---:|---:|---:|---:|---:| +| 4 | off | 64 | 8192 | 0.05000 | 0.03750 | -0.01250 | +| 4 | off | 128 | 8192 | 0.05000 | 0.03750 | -0.01250 | +| 4 | off | 64 | 16384 | **0.07500** | **0.06250** | -0.01250 | +| 4 | off | 128 | 16384 | **0.07500** | **0.06250** | -0.01250 | +| 8 | on | 64 | 8192 | 0.05625 | 0.05000 | -0.00625 | +| 8 | on | 128 | 8192 | 0.05625 | 0.05000 | -0.00625 | +| 8 | on | 64 | 16384 | 0.05625 | 0.05625 | 0.00000 | +| 8 | on | 128 | 16384 | 0.05625 | 0.05625 | 0.00000 | + +单位均为 SLO-feasible req/s/GPU。这里 Frontier 对 capacity 有明显、且依赖 topology/action family 的绝对 gap： + +- TP4/MBT8K 与 TP4/MBT16K 都低估 0.0125 req/s/GPU，但正确预测了 MBT8K→16K 的相对增益。 +- TP8/MBT8K 低估 0.00625，而 TP8/MBT16K 恰好匹配；Frontier 因而预测 MBT 在 TP8 上有收益，真机中四个 TP8 config 实际并列。 +- 尽管这个 action differential 是错的，TP4/MBT16K 相对其余 config 有足够 margin，所以 simulator top set 仍与 real top set 完全相同，最终 regret=0。 + +细化边界实验中，Frontier/real 的 33 个 config-load labels 有 6 个 false-infeasible；这些误差在 fresh-server v3 复测中稳定重复。因此这个 case 的结论应严格限定为：**Frontier 是有效的 config ranker / conservative screener，但不是精确的 capacity estimator，也没有完全学对 topology-dependent MBT effect。** + +## Case C 结果：Qwen235 FP8 fixed-shape mixed + +2026-07-16 在 `dash0` GPU 0--3 上完成了 community vLLM 0.10.2、TP4/DP1、MNS64、MBT8192、eager、BF16 KV、prefix/spec/CUDA graph off 的 pilot。所有请求实际 usage 和 streaming token count 都严格为 ISL=2,048、OSL=128： + +| load | requests | TTFT mean/p95 | TPOT mean/p95 | 40 ms joint SLO | +|---|---:|---:|---:|---:| +| single | 1 | 586.2 / 586.2 ms | 136.2 / 136.2 ms | 0/1 | +| concurrency=2 | 2 | 310.2 / 434.3 ms | 124.3 / 124.9 ms | 0/2 | + +Frontier 使用与成功 prefill ranking 相同的 frozen source fingerprint、TP4 KV blocks=26,101 和 block size=16。第一次运行完成 prefill 并提交第一个 decode token后，在纯 decode batch 抛出 `attention decode prediction cache not found for cluster monolithic`。初始化日志给出直接原因：`no standard decode rows`，而 final attention CSV 的 726 rows 全部是 prefill rows。该 crash 没有被记为 SLO fail，也没有使用 dummy prediction。 + +随后在 `dash0` 单张 H20 上用同一 community-vLLM/FlashInfer 栈补测 TP4、batch `{1,2}`、KV `{2048,2176}` 的 4 个 CUDA-event decode points。profile-closed Frontier smoke 精确完成了相同请求： + +| load | Real TTFT | Frontier TTFT | Real TPOT | Frontier TPOT | +|---|---:|---:|---:|---:| +| single | 586.2 ms | 267.7 ms | 136.2 ms | 88.3 ms | +| concurrency=2 | 186.1--434.3 ms | 470.7 ms | 123.8--124.9 ms | 90.5 ms | + +因此 missing-row blocker 已关闭，但 absolute error 明显且不是固定比例；这正是需要 config surface 而不能只看单点 latency 的原因。steady-QPS 下还会出现 prefill+decode true-mixed batches，最小 4-row profile 不足以支持完整 sweep。完整 closure 另外测量了 TP4/TP8 各 81 个 standard-decode points 和 108 个 true-mixed points，覆盖 decode batch 到 128、KV 2,048--2,175；合并 root 共 1,104 rows。Frontier 已确认用 108 个 TP-specific samples 训练 `attn_decode_in_mixed`，没有使用本 case 的 E2E latency calibration。 + +完整 8 configs × 8 rates simulator surface 已在任何真机 surface cell 之前冻结，manifest SHA256 为 `801aa36451c8647f71cc87011144622d2203786e82f189ed1375d964399b106a`。64/64 cells 都有 64 个 finite、非负、shape-exact request records，累计 simulator CPU time 为 11,675.2 s。冻结结果如下，单位为最大被测试可行 req/s/GPU： + +| config family | TPOT 40 ms | TPOT 120 ms | TPOT 150 ms | TPOT 180 ms | +|---|---:|---:|---:|---:| +| TP4，任意 MNS/MBT | none | 0.20 | 0.40 | 0.60 | +| TP8，任意 MNS/MBT | none | 0.05 | 0.15 | 0.20 | + +Frontier 在同一 TP family 内给四个 MNS/MBT config 生成了逐 request 完全相同的结果，即它预测这些 controls 在当前 load range 都不 binding；150 ms sensitivity 下则预测 TP4 family 的 per-GPU capacity 是 TP8 的 2.67×。这两个判断均已用下面的完整真机 surface 检验。 + +第一次真机 surface attempt 还暴露了一个 measurement-contract 问题：一个 fresh server 内连续测多个 rates 会把前一 anchor 的 execution warm state 带入后一 anchor。两个 TP4/MNS64 config 在正向 `0.10→1.60→2.40` 的 1.60 点分别只有 36/64、41/64 requests 通过 150 ms SLO；反向先运行 2.40 后，同一 1.60 点都变成 64/64。逐段看，正向 1.60 的前 24 requests TPOT 从约 234 ms 递减到 169 ms，后半段才稳定到 114--134 ms；反向 1.60 从第一个 request 起就在 113--128 ms。这个 attempt 整体排除，不能用“两个 round 都通过”的保守聚合掩盖 real 与 simulator 的 state contract 不同。 + +修正后的 ground-truth contract 对每个 `(config, rate, round)` 独立启动 server，并在目标 rate 下丢弃 `min(32, max(4, ceil(rate×20)))` 个 exact-shape warmup requests。anchor-isolation smoke 在两个 config、两个 fresh-server repeats 上均为 64/64 completed、0 failure、150 ms SLO 64/64；四次 p95 TPOT 为 127.8--138.0 ms，p95 TTFT 为 295.8--305.5 ms。只有这个通过 gate 的 contract 会进入最终 real capacity/rank。 + +### 完整评测 setup + +| 项目 | 设置 | +|---|---| +| 真机 | `dash0`，8×NVIDIA H20（97,871 MiB/GPU、driver 580.95.05、NV18）；TP4 cells 各独占 4 GPU，TP8 cells 独占全部 8 GPU | +| model/runtime | `Qwen/Qwen3-235B-A22B-FP8`；community vLLM 0.10.2、Python 3.12.3、torch 2.8.0+cu128、FlashInfer 0.3.1.post1；eager、FP8 weights、BF16 KV | +| workload | 64 个不同 token 的固定长度 requests；ISL=2,048、OSL=128、uniform QPS、prefix/speculative decoding/CUDA graph off | +| config surface | `TP∈{4,8} × MNS∈{64,128} × MBT∈{8192,16384}`；TP4 为 EP off，TP8 为 EP on | +| simulator | Frontier commit `d9cfeb6d8791fbf2f295dd9744c56a666171776e` + frozen best-effort patches；1,104-row attention profile；无本 case E2E calibration | +| simulator anchors | 每个 config 都运行 `{0.10,0.20,0.40,0.80,1.20,1.60,2.40,3.20}` req/s；64/64 cells 在真机 surface 前冻结 | +| real anchors | 围绕冻结 simulator boundary 运行并自适应补齐相邻点；共 34 config-rate cells × 2 independent rounds | +| 每个 real anchor | fresh vLLM server；先按目标 rate 丢弃 `min(32,max(4,ceil(rate×20)))` 个 warmup requests，再从空 scheduler queue 测 64 requests | +| primary SLO | ≥61/64 requests 同时满足 TTFT≤1,256 ms、TPOT≤150 ms；两个 rounds 均通过才判定 feasible | +| primary objective | 最大被测试可行 offered req/s / 实际占用 GPU 数 | + +150 ms 是看到最低负载真实 TPOT 已达 136.2 ms 后公开记录的 post-pilot amendment；原 40 ms 阈值仍保留，但所有 config 在最低 anchor 都不可行。120 ms sensitivity 的 lower boundary 尚未补齐，180 ms sensitivity 在最高 3.20 anchor 仍有 censored cells，因此本文只对 150 ms primary SLO 报告完整 ranking。 + +### 完整结果 + +![Qwen3-235B fixed-shape mixed simulator-vs-real config ranking](docs/assets/simulator-fidelity/qwen235-t0-fixed-shape-ranking.png) + +| TP | MNS | MBT | Real | Frontier | Real boundary：pass → fail（两个 rounds） | +|---:|---:|---:|---:|---:|---| +| 4 | 64 | 8192 | **0.60** | **0.40** | 2.40: 64/64,64/64 → 3.20: 57/64,59/64 | +| 4 | 64 | 16384 | **0.60** | **0.40** | 2.40: 64/64,64/64 → 3.20: 62/64,57/64 | +| 4 | 128 | 8192 | **0.60** | **0.40** | 2.40: 64/64,64/64 → 3.20: 53/64,55/64 | +| 4 | 128 | 16384 | **0.60** | **0.40** | 2.40: 64/64,64/64 → 3.20: 52/64,54/64 | +| 8 | 64 | 8192 | 0.30 | 0.15 | 2.40: 64/64,64/64 → 3.20: 46/64,48/64 | +| 8 | 64 | 16384 | 0.20 | 0.15 | 1.60: 64/64,64/64 → 2.40: 45/64,53/64 | +| 8 | 128 | 8192 | 0.20 | 0.15 | 1.60: 64/64,64/64 → 2.40: 40/64,56/64 | +| 8 | 128 | 16384 | 0.30 | 0.15 | 2.40: 64/64,64/64 → 3.20: 50/64,46/64 | + +Real 与 Frontier 单位均为 SLO-feasible req/s/GPU；boundary 的 offered rate 单位为 system req/s。所有 8 个 real boundaries 都由相邻 lattice points 闭合。四个 TP4 configs 是 real 与 simulator 的共同、完全相同的 top set，因此 optimistic/worst tie-break regret 都是 0。Kendall τ-b=`0.8944`；28 个 config pairs 中 exact `>/< /=` sign 命中 24 个，20 个 real non-tie pairs 中命中 16 个方向。34 个实际测量的 config-load labels 中，Frontier 命中 24 个，有 10 个 false-infeasible、0 个 false-feasible。 + +这个结果同时给出一个比“absolute error”更重要的反例。TP8 真机在 2.40 system req/s 上的 feasibility matrix 是： + +```text + MBT 8K MBT 16K +MNS 64 pass fail +MNS 128 fail pass +``` + +换成 capacity，`ΔMBT | MNS64 = -0.10`，而 `ΔMBT | MNS128 = +0.10 req/s/GPU`，difference-in-differences 为 `+0.20 req/s/GPU`。也就是说，**MBT 的作用方向取决于 MNS；它们不能被看成两个独立、可加的 controls。** Frontier 对四个 TP8 cells 都预测 0.15，interaction 恰好为 0。相同四个 configs 在 1.60 及以下都通过，差异只在持续高负载下出现，说明问题不只是单个 operator 在空载下的固定时间偏差，而发生在 batching/scheduling state 与 execution topology 的组合上。现有数据还不能区分具体是 batch composition、MoE EP communication、memory/KV pressure，还是 runtime 的其他动态状态；下一步应对此边界做 scheduler batch trace、per-step kernel/NCCL timeline 与 KV/queue counters，而不是先给出未经验证的根因。 + +因此本 case 的严格结论是：**Frontier 足以在这个 8-config surface 上选择全局最优 topology，但不足以解释或优化 TP8 family 内的 MNS/MBT controls。** 成功来自 TP4-vs-TP8 的真实 margin 足够大，能够容忍 TP4 33.3%、TP8 25%--50% 的 capacity 低估和内部 action-ordering error；它不是 simulator 已获得 scheduling/execution 深层认知的证据。 + +接受的 ground-truth 共 18 个 fleet jobs、68 个 fresh-server anchors、4,352 个 measured requests 和 1,664 个 warmup requests，消耗 36.26 H20-GPU-hours；从第一个 accepted job 到最后一个完成的墙钟跨度为 5.74 小时。增量 full-attention closure 在单张 H20 上运行 12 秒（0.0033 H20-GPU-hours），但它复用了此前已有的 726-row prefill profile，因此这个数字不是构建全部 compatibility envelope 的总成本。另有共享 GPU/endpoint 的污染 attempts 和暴露 cross-anchor warm-state leakage 的 diagnostic attempts 被整目录隔离，不进入上述统计。Frontier 64-cell surface 消耗 11,675.2 CPU-seconds。完整 hash、逐 anchor input 与排除 ledger 见本节末的复现链接。 + +完整证据见 [T0 smoke report](runs/frontier-multicase-sufficiency-v1/t0-smoke-report.md) 与 [预注册协议](runs/frontier-multicase-sufficiency-v1/protocol.md)。 + +原预注册的 TPOT 40 ms 因最低负载已不可行而失去 capacity-ranking 可辨识性；本文已把 150 ms 明确记录为 post-pilot protocol amendment，并保留 40 ms 全部失败的 sensitivity 结果，没有把 SLO change 隐藏成预注册结论。 + +## 尚不能纳入 simulator ranking 的 case + +### 原 internal-runtime Qwen235 prefill-only + +原实验有 8/8 valid real cells，但使用不同的 serving/runtime contract，real top set 是四个 TP8 config。它没有与本文 best-effort Frontier 使用同一个 community-vLLM FP8 profile/serving contract，不能和新 simulator 数值直接拼接。本文的 community-vLLM Case B 是目前可辩护的 aligned prefill comparison。 + +### 原 Qwen235 decode-only + +真实 surface 有 7/8 valid cells，且 capacity 搜索区间大量重叠：经验 top set 为 5/8，而考虑 bracket uncertainty 时 8/8 都可能最优。更关键的是，该 case 依赖 initial KV state、DecodeBenchConnector、EAGLE3、FP8 KV、DeepEP/NVSHMEM 与 decode CUDA graphs；Frontier 当前没有等价的 initial-KV/EP8 execution contract。因此现在给出 simulator-vs-real rank 或 regret 都会把 contract mismatch 误写成 fidelity 结果。 + +下一步不再用 decode-dominant mixed workload 回避问题，而是在 community vLLM 0.10.2 与 Frontier 两侧增加显式、相同的 initial-KV contract：request 到达时分配与 input length 对应的 KV blocks、标记 prefill complete，再从第一步 decode 开始调度。首先关闭 speculative decode、prefix cache 和 decode CUDA graph，建立 fixed-shape decode-only baseline；随后按单变量顺序启用 trace lengths/prefix、EP8、EAGLE3 和 FULL_DECODE_ONLY graph。每一层都重新报告 ranking，不能把后一层的 mismatch 归因给前一层。 + +## 当前判断与下一步 gate + +当前证据的结论是： + +1. **绝对 gap 不是否决 simulator 的理由。** Qwen235 中 11%--50% 的 capacity 低估仍可保持 zero-regret config selection。 +2. **但 rank 成功不能证明模型机制正确。** Qwen235 prefill 的 TP8 MBT differential 错误，fixed-shape mixed 又漏掉 TP8 的 MNS×MBT 非加性交互；Qwen30 还有 28/92 anchor SLO labels 错误。只是当前 decision margin 足以容忍这些 residual。 +3. **alignment 是结果的一部分。** Qwen30 需要 per-TP E2E calibration 才能恢复排序；Qwen235 不需要本 case E2E calibration，但需要同栈 FP8 profiles、真机 KV capacity 和 simulator patches。论文必须报告这部分真机成本，不能把它隐藏在“offline profiles”中。 +4. **目前不能建立“simulator 全局选优普遍失败”的 premise。** 受控 prefill、fixed-shape mixed 和历史 mixed surface 上，经过 alignment 的 Frontier 已经能选到最优或 0.76% 内的近最优 config。更有证据的 research premise 是：aggregate selection success 会掩盖 action interaction 与 state-transition model 的错误；若要声称全局选优失败，仍必须在 dash0 的 trace-faithful mixed、decode-only、prefix cache 或跨 topology case 上得到稳定反例。 + +后续每个 case 建议使用同一 gate：worst selected-config regret ≤5%、tie-aware rank correlation ≥0.8、足够数量的 informative pairs、ground-truth bracket 不足以反转最优决策，并单独报告达到该结果所需的 real-GPU profiling/calibration cost。 + +## 数据与复现 + +- 图表输入：[data.json](docs/assets/simulator-fidelity/data.json) +- 画图脚本：[plot_simulator_fidelity.py](scripts/plot_simulator_fidelity.py) +- Qwen30 audit：[report.md](runs/frontier-multicase-sufficiency-v0/results/qwen30-baseline/report.md) +- Qwen30 aligned metrics：[metrics.json](runs/frontier-slo-alignment-v0/results/metrics.json) +- Qwen235 fixed-cohort comparison：[v2_refined_comparison.json](runs/frontier-multicase-sufficiency-v0/best_effort/fixed_cohort_evidence/v2_refined_comparison.json) +- Qwen235 full report：[report.md](runs/frontier-multicase-sufficiency-v0/best_effort/fixed_cohort_evidence/report.md) +- Fixed-shape mixed comparison：[comparison.json](runs/frontier-multicase-sufficiency-v1/results/t0-final/comparison.json) +- Fixed-shape mixed capacity table：[capacity.csv](runs/frontier-multicase-sufficiency-v1/results/t0-final/capacity.csv) +- Frozen Frontier surface：[frontier_surface_frozen.json](runs/frontier-multicase-sufficiency-v1/artifacts/frontier-t0-surface-v1/frontier_surface_frozen.json) +- Executed real plan：[real-plan-executed.json](runs/frontier-multicase-sufficiency-v1/artifacts/t0-real-surface-v1/real-plan-executed.json) +- Real exclusions/acceptance ledger：[exclusions.json](runs/frontier-multicase-sufficiency-v1/artifacts/t0-real-surface-v1/exclusions.json) +- Fixed-shape / trace-faithful / decode-only 分阶段协议：[protocol.md](runs/frontier-multicase-sufficiency-v1/protocol.md) +- Trace tokenizer/hash contract audit：[trace-contract-audit.json](runs/frontier-multicase-sufficiency-v1/trace-contract-audit.json) +- Trace contract audit reproducer：[audit_trace_contract.py](runs/frontier-multicase-sufficiency-v1/audit_trace_contract.py) +- T0 real smoke 与 Frontier decode-profile failure：[t0-smoke-report.md](runs/frontier-multicase-sufficiency-v1/t0-smoke-report.md) +- 尚未对齐 case 的 real-only audit：[report.md](runs/frontier-multicase-sufficiency-v0/results/ground-truth/report.md) + +重新生成 PNG/SVG： + +```bash +python3 scripts/plot_simulator_fidelity.py +```