File size: 13,248 Bytes
9118991 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 | """Offline experiment catalog and argv compiler. No torch, network or GPU calls.
Unsupported paper cells are first-class blocked entries, never fake commands.
All enabled commands use existing validated entry points and unique outputs.
"""
from __future__ import annotations
import hashlib
import json
from pathlib import Path
MODELS = ("ouro_1_4b", "ouro_2_6b", "loopformer_3x8", "parcae_370m")
METHODS = ("bf16", "symmetric", "smoothquant", "quarot", "spinquant", "flatquant", "loopq")
FIG6_SIZES = (32, 64, 128, 256, 512, 1024, 2048)
def load_profile(path):
profile = json.loads(Path(path).read_text())
if profile.get("schema_version") != 1 or profile.get("exact_author_protocol") is not False:
raise ValueError("profile must explicitly disclose independent reproduction")
seeds = profile["seeds"]
if not seeds or len(seeds) != len(set(seeds)) or any(type(s) is not int or s < 0 for s in seeds):
raise ValueError("seeds must be distinct nonnegative integers")
tasks = profile["evaluation"]
if set(tasks) != {"hellaswag", "winogrande", "lambada_openai", "arc_challenge", "mmlu", "wikitext"}:
raise ValueError("all six evaluation tasks are required")
if any(type(t["fewshot"]) is not int or t["fewshot"] < 0 for t in tasks.values()) or tasks["wikitext"]["fewshot"] != 0:
raise ValueError("invalid few-shot protocol")
import math
if any(not math.isfinite(profile["parity"][k]) or profile["parity"][k] < 0
for k in ("max_logprob_error", "max_hidden_error")):
raise ValueError("finite nonnegative parity thresholds required")
return profile
def profile_digest(profile):
return hashlib.sha256(json.dumps(profile, sort_keys=True).encode()).hexdigest()
def catalog(profile):
jobs = {}
def add(identifier, kind, *, family, dependencies=(), blocked=(), **details):
if identifier in jobs:
raise ValueError(f"duplicate experiment {identifier}")
jobs[identifier] = dict(id=identifier, kind=kind, family=family,
dependencies=list(dependencies), blockers=list(blocked), **details)
return identifier
bfparity = add("gate_bf16_prefill", "parity", family="implementation", bits=16, seed=0)
for bits in (4, 8):
add(f"gate_smoke_a{bits}", "smoke", family="implementation", bits=bits, seed=0,
dependencies=(bfparity,))
def variant(bits, seed, *, ablation=None, samples=None, budget=4):
tag = f"a{bits}" + (f"_{ablation}" if ablation else "")
tag += f"_n{samples}" if samples is not None else ""
tag += f"_b{budget}" if budget != 4 else ""
tag += f"_s{seed}"
cal, parity = "cal_" + tag, "parity_" + tag
if cal not in jobs:
details = dict(bits=bits, seed=seed, ablation=ablation, samples=samples,
budget=0 if ablation == "no_slt" else budget)
add(cal, "calibration", family="calibration", dependencies=("gate_smoke_a4", "gate_smoke_a8"), **details)
add(parity, "parity", family="implementation", dependencies=(cal,), calibration=cal, **details)
return cal, parity
for model in MODELS:
for method in METHODS:
for bits in ((16,) if method == "bf16" else (4, 8)):
for seed in profile["seeds"]:
blockers = []
if model != "ouro_1_4b":
blockers.append("missing validated backbone/calibration/export/runtime adapter")
if method not in {"bf16", "loopq"}:
blockers.append("missing paper-matched baseline integration; direct RTN is not a substitute")
deps, cal = [bfparity, "gate_smoke_a4", "gate_smoke_a8"], None
if not blockers and method == "loopq":
cal, parity = variant(bits, seed)
deps = [parity]
add(f"table1_{model}_{method}_a{bits}_s{seed}", "evaluation", family="table1",
dependencies=deps if not blockers else (), blocked=blockers,
model=model, method=method, bits=bits, seed=seed, calibration=cal,
tasks=list(profile["evaluation"]))
for bits in (4, 8):
for ablation in ("no_las", "no_slt", "no_cta"):
for seed in profile["seeds"]:
cal, parity = variant(bits, seed, ablation=ablation)
add(f"table2_{ablation}_a{bits}_s{seed}", "evaluation", family="table2",
dependencies=(parity,), model="ouro_1_4b", method="loopq", ablation=ablation,
bits=bits, seed=seed, calibration=cal, tasks=list(profile["evaluation"]))
for axis, values in (("budget", (0, 2, 4, 8)), ("samples", FIG6_SIZES)):
for value in values:
for seed in profile["seeds"]:
kw = {axis: value}
cal, parity = variant(4, seed, **kw)
add(f"local_{axis}_{value}_s{seed}", "evaluation", family="local_sensitivity",
dependencies=(parity,), bits=4, seed=seed, method="loopq", model="ouro_1_4b",
study=(axis == "samples" or value != 4), calibration=cal,
tasks=["lambada_openai", "wikitext"], author_identity_unverified=True)
for bits in (4, 8):
for seed in profile["seeds"]:
add(f"control_direct_a{bits}_s{seed}", "evaluation", family="local_control",
dependencies=(bfparity, "gate_smoke_a4", "gate_smoke_a8"), bits=bits, seed=seed, method="direct",
tasks=list(profile["evaluation"]), calibration=None)
fixture = add("local_lambada_fixture", "fixture", family="local_analysis",
dependencies=(f"table1_ouro_1_4b_bf16_a16_s{profile['seeds'][0]}",))
for seed in profile["seeds"]:
cal, parity = variant(4, seed)
add(f"local_trajectory_s{seed}", "trajectory", family="local_analysis",
dependencies=(parity, fixture), calibration=cal, seed=seed, bits=4)
add(f"local_selection_s{seed}", "selection", family="local_analysis",
dependencies=(cal,), calibration=cal, seed=seed, bits=4)
# Explicit paper analysis cells; no hidden reduction to the Ouro-only subset.
add("figure1", "analysis", family="figure1", blocked=("P99/transition fixture and reduction protocol not frozen",))
for model in ("ouro_1_4b", "parcae_370m"):
for method in ("symmetric", "smoothquant", "quarot", "flatquant", "loopq"):
add(f"figure2_{model}_{method}", "analysis", family="figure2", model=model, method=method,
blocked=("requires frozen LAMBADA input fixture; Ouro LoopQ measurement script exists",
*(() if model == "ouro_1_4b" and method == "loopq" else ("missing model/baseline instrumentation",))))
add("figure3", "analysis", family="figure3", blocked=("scan logs available; author Score/Cost denominator unspecified",))
add("figure4", "analysis", family="figure4", blocked=("local budget sweep configured; exact author model/protocol identity unverified",))
add("figure6", "analysis", family="figure6", blocked=("local sample sweep configured; exact author model/protocol identity unverified",))
for method in ("bf16", "smoothquant", "quarot", "flatquant", "loopq"):
for loops in range(1, 17):
add(f"figure5_{method}_loops{loops}", "analysis", family="figure5", method=method,
loops=loops, bits=16 if method == "bf16" else 4,
tasks=["lambada_openai", "wikitext"],
blocked=("Parcae adapter missing; 8-loop calibration and LAS/SLT/CTA extrapolation policy unresolved",))
for model in MODELS:
for method in ("bf16", "loopq"):
add(f"table5_{model}_{method}", "memory", family="table5", model=model, method=method,
blocked=("requires validated deployment loader and measured resident/peak memory; analytic payload is not loaded memory",))
for gate in ("decode_kv", "batch_variable_length", "long_context", "real_resume", "packed_runtime"):
add(f"gate_{gate}", "validation", family="implementation", blocked=("real-model acceptance procedure still required on Pod",))
for job in jobs.values():
if any(dep not in jobs for dep in job["dependencies"]):
raise ValueError("missing dependency")
return jobs
def compile_commands(job, profile, *, data_root, gpu, python="python"):
if job["blockers"]:
raise ValueError("blocked experiment: " + "; ".join(job["blockers"]))
if gpu not in ("0", "1"):
raise ValueError("only physical GPU 0/1 permitted")
root = Path(data_root).resolve()
if root == Path("/"):
raise ValueError("data root cannot be filesystem root")
out = root / job["id"]
def script(name, *args):
return [python, f"loopQ/scripts/{name}.py", *map(str, args)]
def parity(artifact, destination, *, advisory=False):
args = ["--model", "ouro", "--gpu", gpu, "--prompts-file", "loopQ/configs/parity_prompts.json",
"--max-logprob-error", profile["parity"]["max_logprob_error"],
"--max-hidden-error", profile["parity"]["max_hidden_error"], "--output", destination]
if artifact is not None:
args += ["--artifact", artifact]
if advisory:
args += ["--acceptance-advisory"]
return script("verify_runtime_parity", *args)
if job["kind"] in {"calibration", "smoke"}:
opt, alg = profile["optimizer"], profile["algorithm"]
args = ["--gpu", gpu, "--seed", job["seed"], "--activation-bits", job["bits"],
"--optimizer", opt["name"], "--learning-rate", opt["learning_rate"],
"--cta-learning-rate", opt["cta_learning_rate"], "--weight-decay", opt["weight_decay"],
"--adam-betas", *opt["betas"], "--adam-epsilon", opt["epsilon"],
"--total-steps", 2 if job["kind"] == "smoke" else opt["final_steps"],
"--slt-round-steps", 1 if job["kind"] == "smoke" else opt["slt_round_steps"],
"--slt-budget", job.get("budget", 4), "--output", out / "components.pt"]
for key, value in alg.items():
args += ["--" + key.replace("_", "-"), value]
if job["kind"] == "smoke":
args += ["--smoke-small", "--smoke-samples", 2, "--smoke-max-length", 16]
if job.get("samples") is not None:
args += ["--study-samples", job["samples"]]
if job.get("ablation"):
args += ["--ablation", job["ablation"]]
commands = [script("calibrate_ouro", *args), script("export_ouro_artifact",
"--calibrated-components", out / "components.pt", "--activation-bits", job["bits"],
"--output", out / "artifact.pt")]
if job["kind"] == "smoke":
commands += [script("smoke_ouro_vllm", "--artifact", out / "artifact.pt", "--gpu", gpu,
"--output", out / "generation.json"),
parity(out / "artifact.pt", out / "parity.json", advisory=True)]
return commands
if job["kind"] == "parity":
artifact = root / job["calibration"] / "artifact.pt" if job.get("calibration") else None
return [parity(artifact, out / "parity.json")]
if job["kind"] == "fixture":
return [script("freeze_lambada_inputs", "--evaluation", root / job["dependencies"][0] / "lambada_openai",
"--samples", 32, "--output", out / "inputs.json")]
if job["kind"] == "trajectory":
return [script("measure_ouro_trajectory", "--artifact", root / job["calibration"] / "artifact.pt",
"--inputs", root / "local_lambada_fixture/inputs.json", "--gpu", gpu,
"--max-length", 256, "--output", out / "trajectory.json")]
if job["kind"] == "selection":
return [script("inspect_calibration", "--components", root / job["calibration"] / "components.pt",
"--output", out / "selection.json")]
if job["kind"] == "evaluation":
method, bits = job["method"], job["bits"]
configuration = ("bf16" if method == "bf16" else f"direct_w4a{bits}" if method == "direct"
else f"loopq_{job['ablation']}" if job.get("ablation") else f"loopq_w4a{bits}")
commands = []
for task in job["tasks"]:
args = ["--task", task, "--configuration", configuration, "--gpu", gpu,
"--fewshot", profile["evaluation"][task]["fewshot"], "--seed", job["seed"],
"--protocol-note", profile["protocol_note"], "--output", out / task]
if method == "loopq":
args += ["--artifact", root / job["calibration"] / "artifact.pt",
"--parity-report", root / job["dependencies"][0] / "parity.json"]
if job.get("ablation"):
args += ["--activation-bits", bits]
if job.get("study"):
args += ["--study-evaluation"]
commands.append(script("run_paper_benchmark", *args))
return commands
raise ValueError("no executor for this job kind")
|