agentic-kvc/microbench/fresh_setup/plot_pd_crossover.py

"""Render the three PD-vs-colo crossover figures from fig_agg JSON dumps.

Inputs (produced by `fig_agg.py --json`):
  analysis/mb5_pd_ablation/fig1_reuse_fixed.json  reuse axis (N=8, FIXED real
        prefill delta=2048; vary cached prefix -> reuse = pfx/(pfx+delta).
        Controlled-variable: real new-prefill work is constant across the sweep,
        only the cached fraction (and total context) grows. Supersedes the old
        fig1.json, which held input=8192 and sliced prefix out of it so delta
        shrank 15x as reuse rose — a confound, not a pure reuse axis.)
  analysis/mb5_pd_ablation/fig2.json   shape axis   (N=8, reuse~70%)
  analysis/mb5_pd_ablation/fig3_conc32k.json  concurrency (in32768/out128,
        reuse~0.984 = 32256 resident + 512 real new-prefill per turn; retuned
        2026-05-31 to the agentic corner so PD pays the full-context per-turn
        KV-transfer tax while colo keeps it resident; vary N by step 8 up to the
        mean-E2E<=10s SLO ceiling)

Each figure overlays colo + the three PD ratios and marks the PD-best advantage.
All three share the corrected (uncontaminated, e13391e-gated-off) stack.
"""
from __future__ import annotations

import json
import re
from pathlib import Path

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

ROOT = Path(__file__).resolve().parents[2]
DATA = ROOT / "analysis" / "mb5_pd_ablation"
OUT = ROOT / "figs" / "mb5_pd_ablation"
OUT.mkdir(parents=True, exist_ok=True)

PD_ARMS = ["2P+6D", "4P+4D", "6P+2D"]
STYLE = {
    "colo":  dict(color="k",        marker="o", lw=2.4, ls="-",  label="colo (8×kv_both)"),
    "2P+6D": dict(color="#1f77b4",  marker="s", lw=1.6, ls="--", label="PD 2P+6D"),
    "4P+4D": dict(color="#2ca02c",  marker="^", lw=1.6, ls="--", label="PD 4P+4D"),
    "6P+2D": dict(color="#ff7f0e",  marker="v", lw=1.6, ls="--", label="PD 6P+2D"),
}


def load(name):
    return json.load(open(DATA / name))


def by_axis(rows, keyfn):
    """Group rows -> {axis_val: {arm: row}}."""
    out = {}
    for r in rows:
        k = keyfn(r["name"])
        if k is None:
            continue
        out.setdefault(k, {})[r["arm"]] = r
    return out


def pd_best(armmap, metric="e2e_p90"):
    vals = [(a, armmap[a][metric]) for a in PD_ARMS
            if a in armmap and armmap[a].get(metric) is not None]
    return min(vals, key=lambda t: t[1]) if vals else (None, None)


def series(grp, xs, arm, metric):
    return [grp[x][arm].get(metric) if arm in grp[x] else None for x in xs]


# ---------- Fig 1: reuse axis ----------
def _reuse_pct(name):
    """Reuse % from a `reuse_p{pfx}_d{delta}_{arm}` run name: pfx/(pfx+delta)."""
    m = re.search(r"_p(\d+)_d(\d+)", name)
    if not m:
        return None
    pfx, delta = int(m.group(1)), int(m.group(2))
    return round(pfx / (pfx + delta) * 100)


def fig_reuse():
    g = by_axis(load("fig1_reuse_fixed.json"), _reuse_pct)
    xs = sorted(g)
    reuse = xs
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.2))
    for arm in ["colo", *PD_ARMS]:
        ax1.plot(reuse, series(g, xs, arm, "e2e_p90"), **STYLE[arm])
    ax1.set_xlabel("intra-session KV reuse (%)  [fixed real prefill, delta=2048]")
    ax1.set_ylabel("E2E latency p90 (s)")
    ax1.set_title("(a) E2E-p90 vs reuse  (N=8, delta=2048/out256)")
    ax1.legend(fontsize=8); ax1.grid(alpha=.3)

    adv, putil = [], []
    for x in xs:
        co = g[x]["colo"]["e2e_p90"]; _, b = pd_best(g[x])
        adv.append(co / b if b else None)
        a = pd_best(g[x])[0]
        putil.append(g[x][a].get("pu") if a else None)
    ax2.plot(reuse, adv, color="purple", marker="D", lw=2, label="PD-best advantage (colo/PD)")
    ax2.axhline(1.0, color="grey", ls=":", lw=1)
    ax2.set_xlabel("intra-session KV reuse (%)"); ax2.set_ylabel("advantage  (>1 = PD wins)")
    ax2b = ax2.twinx()
    ax2b.plot(reuse, putil, color="brown", marker="x", lw=1.4, ls="-.", label="PD-best prefill-GPU util")
    ax2b.set_ylabel("prefill-GPU util (%)", color="brown"); ax2b.tick_params(axis="y", colors="brown")
    ax2.set_title("(b) advantage erodes; prefill GPUs go idle")
    l1, la1 = ax2.get_legend_handles_labels(); l2, la2 = ax2b.get_legend_handles_labels()
    ax2.legend(l1 + l2, la1 + la2, fontsize=8, loc="center right"); ax2.grid(alpha=.3)
    fig.suptitle("Fig 1 — Reuse axis (fixed real prefill delta=2048): PD's edge vs rising cache reuse",
                 fontsize=11, y=1.02)
    fig.tight_layout(); p = OUT / "fig1_reuse_axis.png"; fig.savefig(p, dpi=130, bbox_inches="tight")
    print("wrote", p)


# ---------- Fig 2: shape axis ----------
def fig_shape():
    g = by_axis(load("fig2.json"),
                lambda n: ((int(m.group(1)), int(m.group(2)))
                           if (m := re.search(r"_in(\d+)_out(\d+)_", n)) else None))
    xs = sorted(g, key=lambda t: t[0])  # ascending input
    labels = [f"in{i}\nout{o}" for i, o in xs]
    xi = list(range(len(xs)))
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.2))
    for arm in ["colo", *PD_ARMS]:
        ax1.plot(xi, series(g, xs, arm, "e2e_p90"), **STYLE[arm])
    ax1.set_xticks(xi); ax1.set_xticklabels(labels, fontsize=7)
    ax1.set_xlabel("shape  (decode-heavy → prefill-heavy)"); ax1.set_ylabel("E2E latency p90 (s)")
    ax1.set_title("(a) E2E-p90 vs shape  (N=8, reuse~70%)")
    ax1.legend(fontsize=8); ax1.grid(alpha=.3)

    adv, comp = [], []
    for x in xs:
        co = g[x]["colo"]["e2e_p90"]; a, b = pd_best(g[x])
        adv.append(co / b if b else None)
        # completion of the worst PD arm (exposes catastrophic ratio)
        worst = min((g[x][arm]["n"] / g[x][arm]["req"]) for arm in PD_ARMS if arm in g[x])
        comp.append(worst * 100)
    ax2.plot(xi, adv, color="purple", marker="D", lw=2, label="PD-best advantage (colo/PD)")
    ax2.axhline(1.0, color="grey", ls=":", lw=1)
    ax2.set_xticks(xi); ax2.set_xticklabels(labels, fontsize=7)
    ax2.set_xlabel("shape"); ax2.set_ylabel("advantage  (>1 = PD wins)")
    ax2b = ax2.twinx()
    ax2b.plot(xi, comp, color="red", marker="x", lw=1.4, ls="-.", label="worst-PD-arm completion %")
    ax2b.set_ylabel("worst PD completion (%)", color="red"); ax2b.tick_params(axis="y", colors="red")
    ax2b.set_ylim(80, 101)
    ax2.set_title("(b) advantage peaks mid-sweep; wrong ratio catastrophic at prefill extreme")
    l1, la1 = ax2.get_legend_handles_labels(); l2, la2 = ax2b.get_legend_handles_labels()
    ax2.legend(l1 + l2, la1 + la2, fontsize=8, loc="lower left"); ax2.grid(alpha=.3)
    fig.suptitle("Fig 2 — Shape axis: PD wins decode-heavy, ties prefill-heavy; optimal ratio rotates",
                 fontsize=11, y=1.02)
    fig.tight_layout(); p = OUT / "fig2_shape_axis.png"; fig.savefig(p, dpi=130, bbox_inches="tight")
    print("wrote", p)


# ---------- Fig 3: concurrency axis ----------
def fig_conc():
    g = by_axis(load("fig3_conc32k.json"),
                lambda n: (int(m.group(1)) if (m := re.search(r"_N(\d+)_", n)) else None))
    xs = sorted(g)
    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 4.2))

    # (a) request completion % — the headline (latency percentiles count successes
    # only, so they understate PD; completion is the honest collapse signal).
    for arm in ["colo", *PD_ARMS]:
        comp = [(g[x][arm]["n"] / g[x][arm]["req"] * 100) if arm in g[x] else None for x in xs]
        ax1.plot(xs, comp, **STYLE[arm])
    ax1.axhline(100, color="grey", ls=":", lw=1)
    ax1.set_xticks(xs); ax1.set_xticklabels(xs, fontsize=7)
    ax1.set_xlabel("concurrent sessions N"); ax1.set_ylabel("request completion (%)")
    ax1.set_title("(a) completion: colo 100%, PD collapses"); ax1.legend(fontsize=8); ax1.grid(alpha=.3)

    for arm in ["colo", *PD_ARMS]:
        ax2.plot(xs, series(g, xs, arm, "e2e_mean"), **STYLE[arm])
    ax2.axhline(10.0, color="red", ls=":", lw=1, label="SLO 10s")
    ax2.set_yscale("log"); ax2.set_xticks(xs); ax2.set_xticklabels(xs, fontsize=7)
    ax2.set_xlabel("concurrent sessions N"); ax2.set_ylabel("E2E latency mean (s, log)")
    ax2.set_title("(b) mean-E2E (successes only)"); ax2.legend(fontsize=8); ax2.grid(alpha=.3, which="both")

    for arm in ["colo", *PD_ARMS]:
        ax3.plot(xs, series(g, xs, arm, "tps"), **STYLE[arm])
    ax3.set_xticks(xs); ax3.set_xticklabels(xs, fontsize=7)
    ax3.set_xlabel("concurrent sessions N"); ax3.set_ylabel("throughput (tok/s)")
    ax3.set_title("(c) TPS"); ax3.legend(fontsize=8); ax3.grid(alpha=.3)
    fig.suptitle("Fig 3 — Concurrency axis (in32768/out128, reuse~0.984, PD capped 600s / colo uncapped): "
                 "colo degrades gracefully (100% completion), PD collapses earlier as N rises",
                 fontsize=10, y=1.02)
    fig.tight_layout(); p = OUT / "fig3_concurrency_axis.png"; fig.savefig(p, dpi=130, bbox_inches="tight")
    print("wrote", p)


if __name__ == "__main__":
    fig_reuse(); fig_shape(); fig_conc()