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TheTom
2e78787cb0
REFRACT is meant to be a general KV-quant quality oracle, applicable across model families (gemma, qwen, mistral, phi, llama, etc.) and not TurboQuant-specific. Top-level placement reflects that scope and gives a cleaner CLI: `python3 -m refract.cli` instead of nested under turboquant. Updates: - git mv turboquant/refract/ -> refract/ - All `from turboquant.refract` -> `from refract` imports rewritten - pyproject.toml: include both turboquant* and refract* in setuptools - All "turboquant/refract/" path references in README/tests updated Verified: 14/14 unit tests pass, CLI module path works.
194 lines
7.1 KiB
Python
194 lines
7.1 KiB
Python
"""REFRACT v0.1 CLI.
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Usage:
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python3 -m refract.cli score \\
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--model MODEL.gguf \\
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--reference "ctk=f16,ctv=f16" \\
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--candidate "ctk=q8_0,ctv=turbo4,attn_rot_v=0" \\
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--prompts refract/prompts/v0.1.jsonl \\
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--corpus ~/local_llms/llama.cpp/wikitext-2-raw/wiki.test.raw \\
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--chunks 32 -c 512 -ngl 99
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"""
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from __future__ import annotations
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import argparse
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import json
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import sys
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from pathlib import Path
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from .axes.gtm import run_gtm
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from .axes.kld import run_kld
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from .report import json_report, text_report, to_json_string
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from .runner import KVConfig
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from .score import MIN_FLOOR, composite_score
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def _add_score_parser(sub):
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p = sub.add_parser(
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"score",
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help="Run REFRACT v0.1 on a candidate KV config vs the reference.",
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)
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p.add_argument("--model", required=True, type=Path,
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help="Path to the GGUF model.")
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p.add_argument("--reference", default="ctk=f16,ctv=f16",
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help="Reference KV config (default: ctk=f16,ctv=f16).")
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p.add_argument("--candidate", required=True,
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help="Candidate KV config to score.")
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p.add_argument("--prompts", required=True, type=Path,
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help="Path to JSONL prompts file (e.g. prompts/v0.1.jsonl).")
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p.add_argument("--corpus", required=True, type=Path,
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help="Path to plain-text corpus for KLD axis "
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"(e.g. wiki.test.raw).")
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p.add_argument("--chunks", type=int, default=32,
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help="--chunks for llama-perplexity (default: 32).")
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p.add_argument("-c", "--ctx", type=int, default=512,
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help="Context size (default: 512).")
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p.add_argument("-ngl", "--n-gpu-layers", type=int, default=99,
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help="-ngl flag (default: 99).")
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p.add_argument("--n-predict", type=int, default=128,
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help="Tokens to greedy-decode per GTM prompt (default: 128).")
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p.add_argument("--seed", type=int, default=42,
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help="Greedy seed (default: 42).")
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p.add_argument("--measure-floor", action="store_true",
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help="Measure REFRACT(ref, ref) and abort if < %.1f."
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% MIN_FLOOR)
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p.add_argument("--skip-gtm", action="store_true",
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help="Skip Axis A. Composite uses KLD only (debug).")
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p.add_argument("--skip-kld", action="store_true",
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help="Skip Axis B. Composite uses GTM only (debug).")
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p.add_argument("--json-out", type=Path, default=None,
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help="Path to write the JSON report to.")
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p.add_argument("--no-progress", action="store_true",
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help="Suppress per-prompt progress output.")
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return p
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def _run_score(args) -> int:
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ref_kv = KVConfig.parse(args.reference)
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cand_kv = KVConfig.parse(args.candidate)
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print(f"REFRACT v0.1")
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print(f" model : {args.model}")
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print(f" reference : {ref_kv.label()}")
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print(f" candidate : {cand_kv.label()}")
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print()
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# ---- Floor check ------------------------------------------------------
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floor_score = None
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if args.measure_floor:
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print("Measuring noise floor: REFRACT(ref, ref) ...")
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floor_gtm = run_gtm(
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model=args.model, reference_kv=ref_kv, candidate_kv=ref_kv,
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prompts_path=args.prompts, n_predict=args.n_predict,
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ctx=args.ctx, n_gpu_layers=args.n_gpu_layers, seed=args.seed,
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progress=not args.no_progress,
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)
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floor_kld = run_kld(
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model=args.model, corpus=args.corpus,
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reference_kv=ref_kv, candidate_kv=ref_kv,
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chunks=args.chunks, ctx=args.ctx,
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n_gpu_layers=args.n_gpu_layers,
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progress=not args.no_progress,
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)
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floor_composite = composite_score(floor_gtm.score, floor_kld.score)
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floor_score = floor_composite.composite
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print(f" floor: {floor_score:.2f} (gtm={floor_gtm.score:.2f}, "
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f"kld={floor_kld.score:.2f}, kld nats={floor_kld.mean_kld:.6f})")
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if floor_score < MIN_FLOOR:
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print()
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print(f"ERROR: noise floor {floor_score:.2f} < {MIN_FLOOR}.")
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print("The reference itself is non-deterministic on this build.")
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print("KLD deltas vs this reference cannot be trusted. Aborting.")
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return 2
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print()
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# ---- GTM --------------------------------------------------------------
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if args.skip_gtm:
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gtm = _stub_gtm()
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else:
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print("Running Axis A (GTM)...")
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gtm = run_gtm(
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model=args.model, reference_kv=ref_kv, candidate_kv=cand_kv,
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prompts_path=args.prompts, n_predict=args.n_predict,
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ctx=args.ctx, n_gpu_layers=args.n_gpu_layers, seed=args.seed,
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progress=not args.no_progress,
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)
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print(f" GTM score: {gtm.score:.2f}")
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# ---- KLD --------------------------------------------------------------
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if args.skip_kld:
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kld = _stub_kld(args.chunks, args.ctx)
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else:
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print("Running Axis B (KLD@D, corpus proxy)...")
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kld = run_kld(
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model=args.model, corpus=args.corpus,
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reference_kv=ref_kv, candidate_kv=cand_kv,
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chunks=args.chunks, ctx=args.ctx,
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n_gpu_layers=args.n_gpu_layers,
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progress=not args.no_progress,
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)
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print(f" KLD score: {kld.score:.2f} (mean KLD = {kld.mean_kld:.6f} nats)")
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# ---- Composite --------------------------------------------------------
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composite = composite_score(gtm.score, kld.score, floor_score=floor_score)
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print()
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print(text_report(
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model=str(args.model),
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reference_label=ref_kv.label(),
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candidate_label=cand_kv.label(),
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composite=composite,
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gtm=gtm, kld=kld,
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))
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if args.json_out:
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rep = json_report(
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model=str(args.model),
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reference_label=ref_kv.label(),
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candidate_label=cand_kv.label(),
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composite=composite,
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gtm=gtm, kld=kld,
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)
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args.json_out.write_text(to_json_string(rep))
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print(f"\nJSON report written to {args.json_out}")
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return 0
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# Stubs for --skip-gtm / --skip-kld dev modes (composite still computes).
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def _stub_gtm():
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from .axes.gtm import GTMResult
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return GTMResult(
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score=100.0, full_match_rate=1.0,
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median_first_divergence=None, mean_prefix_agreement_length=0.0,
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n_prompts=0, n_tokens_each=0, per_prompt=[],
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)
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def _stub_kld(chunks: int, ctx: int):
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from .axes.kld import KLDResult
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return KLDResult(
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score=100.0, mean_kld=0.0, ppl=None,
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rms_dp_pct=None, same_topp_pct=None,
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base_path="", chunks=chunks, ctx=ctx, is_self_reference=False,
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)
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def main(argv=None) -> int:
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parser = argparse.ArgumentParser(
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prog="refract",
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description="REFRACT v0.1 — KV-cache quantization quality oracle.",
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)
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sub = parser.add_subparsers(dest="cmd", required=True)
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_add_score_parser(sub)
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args = parser.parse_args(argv)
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if args.cmd == "score":
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return _run_score(args)
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parser.error(f"unknown subcommand: {args.cmd}")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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