can1357
570 lines
20 KiB
Python
570 lines
20 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = ["pillow"]
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# ///
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"""exp14: best-of-round-1 combination for gpt-5.5.
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Combines the validated levers from round 1:
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- 8x13 glyphs on a patch-aligned 8x16 cell (exp01: 8on16-sent .918@150)
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- two-column document layout (exp04: +F1 / -cost / -read-tax at 6x10)
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- per-model variant: gpt-5.5 prefers bw (exp10), sent is the runner-up
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Conditions (gpt-5.5 only, 1568px):
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img-doc-8on16-bw doc layout, near-black ink (the combination)
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img-doc-8on16-sent doc layout, sentence-hue glyphs (variant probe)
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img-8on16-bw plain grid, missing round-1 cell (8on16 ran only as sent)
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Phased: screen all three at length 150, confirm the winner at 50/250,
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optional effort=none probe at 50. Records merge across runs (cells keyed by
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model/length/condition/effort are replaced when re-run, kept otherwise).
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Usage:
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uv run exp14_bestgpt.py --render-only # capacity + sample PNGs
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uv run exp14_bestgpt.py # screen @150 (default cells)
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uv run exp14_bestgpt.py --cells img-doc-8on16-bw@50,img-doc-8on16-bw@250
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uv run exp14_bestgpt.py --cells img-doc-8on16-bw@50 --effort none
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uv run exp14_bestgpt.py --report # re-aggregate, no API
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"""
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import argparse
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import csv
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import json
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import sys
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from concurrent.futures import ThreadPoolExecutor
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from pathlib import Path
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from PIL import Image
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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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import squad # noqa: E402
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from bdf import _DARK, FontCfg, capacity, ensure_font, parse_bdf, render # noqa: E402
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from providers import llm_complete, load_env_key # noqa: E402
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from run import CACHE, QA_CACHE, RESULTS, load_prompt, sha8 # noqa: E402
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EXP = "exp14"
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OUT_DIR = RESULTS / f"{EXP}-bestgpt"
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MODEL = "gpt-5.5"
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PRICE_IN, PRICE_OUT = 2.0, 16.0
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FONT = FontCfg(
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"8on16", "8x13", 8, 16
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) # exp01 winner: 8x13 glyphs, 16px patch-aligned pitch
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GUTTER = 3 # char cells between doc columns (as exp04)
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SCREEN_CELLS = "img-doc-8on16-bw@150,img-doc-8on16-sent@150,img-8on16-bw@150"
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_WHITE = (255, 255, 255)
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_BLACK = (0, 0, 0)
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_INK = (24, 24, 24) # exp04 body ink
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def cached(model: str, tag: str, payload: object, fn, fresh: bool) -> dict:
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"""Disk-cache `fn() -> dict` keyed by (model, tag, payload). Truncations are not cached."""
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key = sha8(model, tag, json.dumps(payload, sort_keys=True, default=str))
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path = QA_CACHE / f"{key}.json"
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if path.exists() and not fresh:
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hit = json.loads(path.read_text())
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if hit.get("stop") != "max_tokens":
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return hit
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out = fn()
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if out.get("stop") == "max_tokens":
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print(f" WARN truncated, not cached: {model} {tag} {key}")
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else:
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path.write_text(json.dumps(out))
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return out
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# --- document layout (ported from exp04, parameterized for FONT) ------------
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def wrap(text: str, width: int) -> list[str]:
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"""Greedy word-wrap, no mid-word breaks (hard split only for width+ words)."""
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lines: list[str] = []
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cur = ""
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for word in text.split():
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while len(word) > width: # pathological; never hit on SQuAD prose
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if cur:
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lines.append(cur)
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cur = ""
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lines.append(word[:width])
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word = word[width:]
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if not cur:
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cur = word
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elif len(cur) + 1 + len(word) <= width:
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cur += " " + word
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else:
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lines.append(cur)
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cur = word
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if cur:
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lines.append(cur)
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return lines
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def layout_page(paras: list[dict], col_w: int) -> list[dict]:
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"""Typeset paragraphs into lines: [{kind: heading|body|blank, text}].
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Article title changes become headings (repeated at the top of a page even
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when the article continues, since each page is read in isolation).
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Paragraphs are separated by one blank line.
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"""
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lines: list[dict] = []
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prev_title = None
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for p in paras:
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if p["title"] != prev_title:
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if lines:
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lines.append({"kind": "blank", "text": ""})
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for hl in wrap(p["title"].replace("_", " ").upper(), col_w):
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lines.append({"kind": "heading", "text": hl})
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prev_title = p["title"]
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elif lines:
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lines.append({"kind": "blank", "text": ""})
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for bl in wrap(p["ctx"], col_w):
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lines.append({"kind": "body", "text": bl})
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return lines
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def pack_pages(paras: list[dict], col_w: int, max_lines: int) -> list[tuple[int, int]]:
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"""Greedy paragraph-aligned packing: [(i, j)] para ranges, one per page."""
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pages = []
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i = 0
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while i < len(paras):
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j = i + 1
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while j < len(paras) and len(layout_page(paras[i : j + 1], col_w)) <= max_lines:
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j += 1
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pages.append((i, j))
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i = j
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return pages
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def _sentence_colors(lines: list[dict]) -> list[list[tuple[int, int, int]]]:
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"""Per-line per-char glyph color cycling hue per sentence across the page."""
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joined = "\n".join(ln["text"] for ln in lines)
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idx, out_idx = 0, []
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for i, ch in enumerate(joined):
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out_idx.append(idx)
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if ch in ".!?" and i + 1 < len(joined) and joined[i + 1] in " \n":
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idx += 1
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colors, pos = [], 0
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for ln in lines:
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n = len(ln["text"])
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colors.append([_DARK[out_idx[pos + k] % 6] for k in range(n)])
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pos += n + 1 # the joining newline
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return colors
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def render_doc(lines: list[dict], size: int, variant: str, cache: Path) -> Image.Image:
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"""Two-column page: left column rows top-to-bottom, then right column."""
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glyphs, font_ascent = parse_bdf(ensure_font(FONT, cache))
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ascent = FONT.ascent if FONT.ascent is not None else font_ascent
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cols, rows, _ = capacity(FONT, size)
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col_w = (cols - GUTTER) // 2
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sent_colors = _sentence_colors(lines) if variant == "sent" else None
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img = Image.new("RGB", (size, size), _WHITE)
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px = img.load()
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for li, ln in enumerate(lines):
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column, row = divmod(li, rows)
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if column > 1:
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break # overflow guard; pack_pages should prevent this
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x_origin = column * (col_w + GUTTER) * FONT.adv
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y0 = row * FONT.pitch
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for ci, ch in enumerate(ln["text"]):
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glyph = glyphs.get(ord(ch))
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if glyph is None:
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continue
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if ln["kind"] == "heading":
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fg = _BLACK
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elif sent_colors is not None:
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fg = sent_colors[li][ci]
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else:
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fg = _INK
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w, h, xoff, yoff = glyph["bbx"]
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top = y0 + ascent - h - yoff
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shift = 0x80 if w <= 8 else 0x8000
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strikes = (0, 1) if ln["kind"] == "heading" else (0,)
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for dx in strikes:
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for r, bits in enumerate(glyph["rows"]):
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y = top + r
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if not 0 <= y < size:
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continue
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for b in range(w):
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if bits & (shift >> b):
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x = x_origin + ci * FONT.adv + xoff + b + dx
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if 0 <= x < size:
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px[x, y] = fg
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return img
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# --- runner -----------------------------------------------------------------
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def atomic_save(img: Image.Image, png: Path) -> None:
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tmp = png.with_suffix(".tmp.png")
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img.save(tmp)
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tmp.replace(png)
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def qa_call(
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messages: list[dict],
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questions: list[dict],
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length: int,
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cond: str,
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start: int,
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ctx: dict,
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) -> list[dict]:
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"""One QA call + scoring; shared by doc and grid paths."""
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args, keys = ctx["args"], ctx["keys"]
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qa = cached(
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MODEL,
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f"{EXP}-qa",
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{"messages": messages, "effort": args.effort},
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lambda: dict(
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zip(
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("text", "usage", "stop"),
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llm_complete(
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keys,
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MODEL,
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messages,
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max_tokens=args.max_tokens,
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effort=args.effort,
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),
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)
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),
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args.fresh,
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)
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answers = squad.parse_numbered(qa["text"], len(questions))
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records = []
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for q, a in zip(questions, answers):
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records.append(
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{
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"model": MODEL,
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"length": length,
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"cond": cond,
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"effort": args.effort,
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"chunk": start,
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"pos_rel": q["pos_rel"],
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"q": q["q"],
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"answer": a,
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"golds": q["golds"],
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"em": squad.exact_match(a, q["golds"]),
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"f1": squad.f1(a, q["golds"]),
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"abstained": "unreadable" in a.lower(),
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}
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)
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records[0]["usage"] = [{"phase": "qa", **qa["usage"]}]
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return records
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def run_doc_page(
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cond: str, length: int, page: tuple[int, int], ctx: dict
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) -> list[dict]:
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args, paras, offsets = ctx["args"], ctx["paras"], ctx["offsets"]
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i, j = page
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start = offsets[i]
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end = offsets[j - 1] + len(paras[j - 1]["ctx"])
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questions = squad.sample_chunk_questions(
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paras, offsets, start, end, args.qpc, args.seed
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)
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if not questions:
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return []
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variant = cond.removeprefix("img-doc-8on16-")
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lines = ctx["lines"][page]
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page_key = sha8(
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cond, json.dumps([(p["title"], p["ctx"]) for p in paras[i:j]]), str(args.size)
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)
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png = CACHE / f"{EXP}-doc-{variant}-{page_key}.png"
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if not png.exists() or png.stat().st_size == 0:
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atomic_save(render_doc(lines, args.size, variant, CACHE), png)
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cols, rows, _ = capacity(FONT, args.size)
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col_w = (cols - GUTTER) // 2
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q_block = "\n".join(f"{k + 1}. {q['q']}" for k, q in enumerate(questions))
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messages = [
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{
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"role": "user",
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"content": [
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{
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"text": load_prompt("exp04-qa-image.md").format(
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col_w=col_w, rows=rows
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)
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},
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{"image_path": png},
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{"text": q_block},
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],
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}
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]
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return qa_call(messages, questions, length, cond, start, ctx)
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def run_grid_chunk(
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cond: str, length: int, start: int, end: int, ctx: dict
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) -> list[dict]:
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args, flow, paras, offsets = ctx["args"], ctx["flow"], ctx["paras"], ctx["offsets"]
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questions = squad.sample_chunk_questions(
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paras, offsets, start, end, args.qpc, args.seed
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)
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if not questions:
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return []
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chunk_text = flow[start:end]
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variant = cond.removeprefix("img-8on16-")
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png = CACHE / f"{EXP}-8on16-{variant}-{sha8(chunk_text, str(args.size))}.png"
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if not png.exists() or png.stat().st_size == 0:
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atomic_save(render(chunk_text, FONT, CACHE, args.size, variant), png)
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cols, rows, _ = capacity(FONT, args.size)
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q_block = "\n".join(f"{k + 1}. {q['q']}" for k, q in enumerate(questions))
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messages = [
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{
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"role": "user",
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"content": [
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{"text": load_prompt("qa-image.md").format(cols=cols, rows=rows)},
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{"image_path": png},
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{"text": q_block},
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],
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}
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]
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return qa_call(messages, questions, length, cond, start, ctx)
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def aggregate(records: list[dict]) -> dict:
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n = len(records)
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f1s = [r["f1"] for r in records]
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mean_f1 = sum(f1s) / n
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se = (sum((x - mean_f1) ** 2 for x in f1s) / (n * (n - 1))) ** 0.5 if n > 1 else 0.0
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us = [u for r in records if "usage" in r for u in r["usage"]]
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tok = {
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k: sum(u.get(k, 0) for u in us)
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for k in ("in", "out", "cache_w", "cache_r", "reasoning")
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}
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cost_in = (
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(tok["in"] + 1.25 * tok["cache_w"] + 0.1 * tok["cache_r"]) / 1e6 * PRICE_IN
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)
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cost_out = tok["out"] / 1e6 * PRICE_OUT
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return {
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"n": n,
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"em": sum(r["em"] for r in records) / n,
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"f1": mean_f1,
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"f1_se": se,
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"abstained": sum(r["abstained"] for r in records),
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**{f"tok_{k}": v for k, v in tok.items()},
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"cost_in_usd": round(cost_in, 4),
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"cost_out_usd": round(cost_out, 4),
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"cost_usd": round(cost_in + cost_out, 4),
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}
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def cell_label(cond: str, effort: str | None) -> str:
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return f"{cond}+eff-{effort}" if effort else cond
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def write_outputs(
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records: list[dict], capacity_stats: dict, args_dict: dict
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) -> list[dict]:
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with (OUT_DIR / "records.jsonl").open("w") as fh:
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for r in records:
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fh.write(json.dumps(r) + "\n")
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cell_keys = sorted(
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{(r["length"], r["cond"], r.get("effort")) for r in records},
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key=lambda k: (k[0], k[1], k[2] or ""),
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)
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cells = []
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for length, cond, effort in cell_keys:
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sub = [
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r
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for r in records
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if r["length"] == length and r["cond"] == cond and r.get("effort") == effort
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]
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cells.append(
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{
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"model": MODEL,
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"length": length,
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"condition": cell_label(cond, effort),
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**aggregate(sub),
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}
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)
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(OUT_DIR / "summary.json").write_text(
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json.dumps(
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{"args": args_dict, "capacity": capacity_stats, "cells": cells}, indent=1
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)
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)
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with (OUT_DIR / "matrix.csv").open("w", newline="") as fh:
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writer = csv.DictWriter(fh, fieldnames=list(cells[0].keys()))
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writer.writeheader()
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writer.writerows(cells)
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return cells
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--cells", default=SCREEN_CELLS, help="comma list of cond@length")
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ap.add_argument("--qpc", type=int, default=30)
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ap.add_argument("--seed", type=int, default=42)
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ap.add_argument("--size", type=int, default=1568)
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ap.add_argument("--workers", type=int, default=3)
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ap.add_argument("--max-tokens", type=int, default=32768)
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ap.add_argument("--effort", default=None)
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ap.add_argument("--fresh", action="store_true")
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ap.add_argument("--render-only", action="store_true")
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ap.add_argument(
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"--report", action="store_true", help="re-aggregate existing records, no API"
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)
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ap.add_argument("--env", default="~/.env")
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args = ap.parse_args()
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CACHE.mkdir(exist_ok=True)
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QA_CACHE.mkdir(exist_ok=True)
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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cols, rows, grid_cap = capacity(FONT, args.size)
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col_w = (cols - GUTTER) // 2
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max_lines = 2 * rows
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print(
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f"8on16 @ {args.size}px: grid {cols}x{rows} = {grid_cap} chars; "
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f"doc 2 x {col_w} cols + gutter {GUTTER}, {max_lines} line slots"
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)
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rec_path = OUT_DIR / "records.jsonl"
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existing: list[dict] = []
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if rec_path.exists():
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existing = [
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json.loads(ln) for ln in rec_path.read_text().splitlines() if ln.strip()
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]
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cap_path = OUT_DIR / "capacity.json"
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capacity_stats: dict = json.loads(cap_path.read_text()) if cap_path.exists() else {}
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if args.report:
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cells = write_outputs(existing, capacity_stats, vars(args))
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for c in cells:
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print(
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f"len {c['length']:<4} {c['condition']:<28} n={c['n']:<4} EM {c['em']:.3f} "
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f"F1 {c['f1']:.3f} ±{c['f1_se']:.3f} ${c['cost_usd']:.3f} "
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f"out={c['tok_out']} rsn={c['tok_reasoning']}"
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)
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return
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cell_specs = []
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for spec in args.cells.split(","):
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spec = spec.strip()
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if not spec:
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continue
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cond, _, ln = spec.partition("@")
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cell_specs.append((cond, int(ln)))
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lengths = sorted({ln for _, ln in cell_specs})
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keys = {}
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if not args.render_only:
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keys["openai"] = load_env_key("OPENAI_API_KEY", args.env)
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all_paras = squad.load_paragraphs(CACHE)
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tasks = []
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for length in lengths:
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paras = all_paras[:length]
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flow, offsets = squad.build_flow(paras)
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pages = pack_pages(paras, col_w, max_lines)
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page_lines = {pg: layout_page(paras[pg[0] : pg[1]], col_w) for pg in pages}
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page_chars = [
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offsets[j - 1] + len(paras[j - 1]["ctx"]) - offsets[i] for i, j in pages
|
|
]
|
|
capacity_stats[str(length)] = {
|
|
"doc_pages": len(pages),
|
|
"mean_chars_page": round(sum(page_chars) / len(pages)),
|
|
"min_chars_page": min(page_chars),
|
|
"max_chars_page": max(page_chars),
|
|
"grid_chars_page": grid_cap,
|
|
"corpus_chars": len(flow),
|
|
"grid_pages": -(-len(flow) // grid_cap),
|
|
}
|
|
st = capacity_stats[str(length)]
|
|
print(
|
|
f" len {length}: {st['doc_pages']} doc pages (mean {st['mean_chars_page']} chars, "
|
|
f"{round(100 * st['mean_chars_page'] / grid_cap)}% of grid {grid_cap}); "
|
|
f"grid {st['grid_pages']} pages; corpus {st['corpus_chars']}"
|
|
)
|
|
ctx = {
|
|
"args": args,
|
|
"paras": paras,
|
|
"flow": flow,
|
|
"offsets": offsets,
|
|
"keys": keys,
|
|
"lines": page_lines,
|
|
}
|
|
for cond, ln in cell_specs:
|
|
if ln != length:
|
|
continue
|
|
if cond.startswith("img-doc-"):
|
|
for pg in pages:
|
|
tasks.append(("doc", cond, length, pg, ctx))
|
|
else:
|
|
for start in range(0, len(flow), grid_cap):
|
|
tasks.append(
|
|
(
|
|
"grid",
|
|
cond,
|
|
length,
|
|
(start, min(start + grid_cap, len(flow))),
|
|
ctx,
|
|
)
|
|
)
|
|
|
|
cap_path.write_text(json.dumps(capacity_stats, indent=1))
|
|
|
|
if args.render_only:
|
|
for kind, cond, length, unit, ctx in tasks:
|
|
if unit[0] != 0 and (kind == "grid" or unit != list(ctx["lines"])[0]):
|
|
continue
|
|
if kind == "doc":
|
|
variant = cond.removeprefix("img-doc-8on16-")
|
|
i, j = unit
|
|
key = sha8(
|
|
cond,
|
|
json.dumps([(p["title"], p["ctx"]) for p in ctx["paras"][i:j]]),
|
|
str(args.size),
|
|
)
|
|
png = CACHE / f"{EXP}-doc-{variant}-{key}.png"
|
|
atomic_save(
|
|
render_doc(ctx["lines"][unit], args.size, variant, CACHE), png
|
|
)
|
|
else:
|
|
variant = cond.removeprefix("img-8on16-")
|
|
chunk_text = ctx["flow"][unit[0] : unit[1]]
|
|
png = (
|
|
CACHE
|
|
/ f"{EXP}-8on16-{variant}-{sha8(chunk_text, str(args.size))}.png"
|
|
)
|
|
atomic_save(render(chunk_text, FONT, CACHE, args.size, variant), png)
|
|
print(f" sample: {png}")
|
|
return
|
|
|
|
print(f"{len(tasks)} page/chunk tasks")
|
|
new_records: list[dict] = []
|
|
done = 0
|
|
with ThreadPoolExecutor(args.workers) as pool:
|
|
futures = []
|
|
for kind, cond, length, unit, ctx in tasks:
|
|
if kind == "doc":
|
|
futures.append(pool.submit(run_doc_page, cond, length, unit, ctx))
|
|
else:
|
|
futures.append(
|
|
pool.submit(run_grid_chunk, cond, length, unit[0], unit[1], ctx)
|
|
)
|
|
for fut in futures:
|
|
new_records.extend(fut.result())
|
|
done += 1
|
|
print(f" {done}/{len(tasks)}", flush=True)
|
|
|
|
# merge: drop existing records for cells just re-run, keep everything else
|
|
rerun = {(ln, cond, args.effort) for cond, ln in cell_specs}
|
|
kept = [
|
|
r for r in existing if (r["length"], r["cond"], r.get("effort")) not in rerun
|
|
]
|
|
records = kept + new_records
|
|
|
|
cells = write_outputs(records, capacity_stats, vars(args))
|
|
for c in cells:
|
|
print(
|
|
f"len {c['length']:<4} {c['condition']:<28} n={c['n']:<4} EM {c['em']:.3f} "
|
|
f"F1 {c['f1']:.3f} ±{c['f1_se']:.3f} ${c['cost_usd']:.3f} "
|
|
f"out={c['tok_out']} rsn={c['tok_reasoning']}"
|
|
)
|
|
print(f"\n-> {OUT_DIR}/records.jsonl, matrix.csv, summary.json")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|