82. Quantization Artifact Evaluation Project | 量化产物评估项目

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本章节的实战代码可以点击以下链接在免费 GPU 算力平台上直接运行：

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量化得到的 checkpoint、配置和量化参数并不自动构成可部署产物。这个项目先审计 artifact 是否可识别、校准与评测是否隔离、质量是否达到门槛、目标 backend 是否兼容；通过这些门槛后，才把候选交给 67 做部署对照、交给 83 做多 profile Serving 测量。

关键词：artifact manifest、calibration、evaluation split、backend compatibility、evidence level。

前置阅读

• 40 GPTQ / AWQ Quantization：理解校准数据、误差和 weight-only 量化。
• 41 FP8 运行时低精度路径：核对格式、缩放与 runtime kernel 证据。
• 66 Inference Performance Comparison：了解部署 benchmark 的浮点 baseline 契约。
• 67 Quantized Inference and Deployment：在 artifact gate 之后做 backend 与 workload 对照。

本节的 CPU 实验不加载模型；它验证产物与证据的结构。GPU/backend 单元默认关闭，只有配置了本地 artifact 与 loader 命令才会执行。

Step 1：把量化结果写成可审计的 artifact 候选

一个候选至少要能回答四件事：它来自哪个 base model revision，采用何种量化格式与参数，校准数据是什么，以及哪些文件共同构成可加载产物。把这些字段写进 manifest 后，格式不同的 GPTQ、AWQ、GGUF 或低比特训练导出才能进入同一套检查。

manifest 区域	必填信息	作用
model	id、revision、架构族	识别量化前的基座
quantization	format、bits、group size、scheme	解释数值表示与适用 loader
calibration	dataset、revision、sample count	追溯量化时使用的数据
files	权重、config、tokenizer 相对路径	检查产物是否完整
evidence	quality、compatibility、failure	防止把“文件存在”误当作可部署结论

Step 2：校准误差、任务质量与加载兼容性是三类证据

校准集帮助估计低比特表示误差，评测集检查任务能力是否下降，loader smoke 则确认目标 backend 能读到同一组文件。三者的输入和结论不同：校准误差低不保证任务质量，质量达标也不保证 artifact 能由目标 backend 加载。

证据	最小记录	回答的问题
校准	数据 revision、样本数、误差摘要	数值近似是否在预期范围
质量	独立评测集、指标、baseline、阈值	候选是否保留目标能力
兼容性	backend、版本、加载结果、失败日志	目标执行路径能否识别 artifact
复测	hardware、workload、结果 JSON	记录是否可重复和可交接

Step 3：先过 artifact gate，再进入部署 benchmark

本节只做采用前的 gate：manifest 完整、校准与评测集隔离、质量达到阈值，并且没有未解释的 loader 失败。满足这些条件的候选仍需要在 66 的浮点 baseline 下进入 67，测量 TTFT、TPOT、吞吐、显存和目标 backend 路径；如果同一 artifact 要服务多个 workload，再由 83 比较 profile。

阶段	输入	输出	不在本节完成的事
82 artifact gate	manifest、质量记录、兼容性记录	accept / tune / reject 与可追溯 JSON	服务吞吐结论
66 / 67 部署对照	浮点 baseline + 已通过候选	backend、质量与性能对照	多 workload 选型
83 Serving profile	已加载 artifact + 多 workload	profile 选择与容量建议	重新定义 artifact 格式

Step 4：代码设计——manifest 契约、证据隔离与采用建议

题目区把“文件是否存在”与“是否值得部署”分开。TODO 1 校验 artifact 的结构字段；TODO 2 检查校准与评测数据不重合；TODO 3 组合质量与兼容性记录；TODO 4 在预算和证据等级下给出决策。测试分别覆盖四项责任，避免把缺文件、数据泄漏、质量不达标和 loader 失败混成一个错误。

TODO	学习者实现的机制	关键不变量
1	manifest 准入	模型、量化、文件和证据字段完整
2	数据隔离检查	calibration 与 evaluation 不能共享样本 id
3	质量与兼容性 gate	两类记录独立完整，失败可追溯
4	采用决策	质量、兼容性、显存预算和证据等级同时满足

from typing import Any, Dict, List

import json
from pathlib import Path

# TODO 1：校验量化 artifact 的 manifest。
def validate_artifact_manifest(manifest: Dict[str, Any]) -> Dict[str, Any]:
    """Return a ready flag for a deployable quantization artifact candidate.

    Variables to determine:
    # model = ???          # id and revision of the base model
    # quantization = ???   # format, bits and group_size
    # files = ???          # weights, config and tokenizer paths
    """
    raise NotImplementedError('请先完成 TODO 1')


# TODO 2：检查校准集与独立评测集是否隔离。
def validate_data_separation(calibration_ids: List[str], evaluation_ids: List[str]) -> Dict[str, Any]:
    """Return overlap ids; quality evidence is invalid when any ids overlap.

    Variables to determine:
    # calibration_set = ???
    # evaluation_set = ???
    # overlap = ???
    """
    raise NotImplementedError('请先完成 TODO 2')


# TODO 3：聚合质量与兼容性 gate。
def build_artifact_gate(manifest: Dict[str, Any], quality: Dict[str, Any], compatibility: Dict[str, Any]) -> Dict[str, Any]:
    """Combine manifest readiness with independent quality and loader records.

    Variables to determine:
    # quality_ready = ???        # metric, baseline_metric and threshold are present
    # compatibility_ready = ???  # backend, loader_status and version are present
    # issues = ???
    """
    raise NotImplementedError('请先完成 TODO 3')


# TODO 4：在质量、显存与证据等级下形成采用建议。
def decide_artifact_adoption(gate: Dict[str, Any], *, max_memory_mb: float) -> Dict[str, str]:
    """Return accept/tune/reject before the artifact enters deployment profiling.

    Variables to determine:
    # quality_ok = ???       # metric meets threshold
    # compatibility_ok = ??? # loader_status is loaded
    # decision = ???         # accept only for real loader evidence with no failures
    """
    raise NotImplementedError('请先完成 TODO 4')

# Tests are split by responsibility: manifest, split isolation, artifact gate, and adoption.
def test_manifest_contract():
    manifest = {
        'model': {'id': 'toy/base', 'revision': 'r1'},
        'quantization': {'format': 'awq', 'bits': 4, 'group_size': 128},
        'calibration': {'dataset': 'toy-cal', 'revision': 'v1', 'sample_count': 128},
        'files': {'weights': 'model.safetensors', 'config': 'config.json', 'tokenizer': 'tokenizer.json'},
        'evidence': {'evidence_level': 'artifact_manifest_and_eval', 'failure': None},
    }
    assert validate_artifact_manifest(manifest)['ready']
    assert not validate_artifact_manifest({**manifest, 'files': {'weights': 'model.safetensors'}})['ready']
    return manifest


def test_data_split_isolation():
    assert validate_data_separation(['c1', 'c2'], ['e1', 'e2'])['ready']
    overlap = validate_data_separation(['c1', 'c2'], ['e2', 'c1'])
    assert not overlap['ready'] and overlap['overlap'] == ['c1']


def test_artifact_gate(manifest):
    quality = {'metric': 0.89, 'baseline_metric': 0.91, 'threshold': 0.87, 'eval_split': 'heldout-v1', 'peak_memory_mb': 1200}
    compatibility = {'backend': 'vllm', 'version': 'x.y', 'loader_status': 'loaded', 'failure': None}
    gate = build_artifact_gate(manifest, quality, compatibility)
    assert gate['ready'] and gate['quality']['metric'] == 0.89
    failed = build_artifact_gate(manifest, quality, {**compatibility, 'loader_status': 'failed', 'failure': 'unsupported format'})
    assert not failed['ready']
    return gate


def test_artifact_decision(gate):
    accepted = decide_artifact_adoption(gate, max_memory_mb=1500)
    assert accepted['decision'] == 'accept'
    observed = decide_artifact_adoption({**gate, 'evidence_level': 'cpu_manifest_gate'}, max_memory_mb=1500)
    assert observed['decision'] == 'tune'


_manifest = test_manifest_contract()
test_data_split_isolation()
_gate = test_artifact_gate(_manifest)
test_artifact_decision(_gate)
print('测试通过：artifact manifest、数据隔离、质量/兼容性 gate 与决策一致。')

---

参考代码与解析

from typing import Any, Dict, List


def validate_artifact_manifest(manifest: Dict[str, Any]) -> Dict[str, Any]:
    """TODO 1：检查模型、量化、校准、文件与证据字段。"""
    issues = []
    model = manifest.get('model', {})
    quantization = manifest.get('quantization', {})
    calibration = manifest.get('calibration', {})
    files = manifest.get('files', {})
    evidence = manifest.get('evidence', {})
    for section, values, required in (
        ('model', model, ('id', 'revision')),
        ('quantization', quantization, ('format', 'bits', 'group_size')),
        ('calibration', calibration, ('dataset', 'revision', 'sample_count')),
        ('files', files, ('weights', 'config', 'tokenizer')),
        ('evidence', evidence, ('evidence_level', 'failure')),
    ):
        if not isinstance(values, dict):
            issues.append(f'{section} must be a mapping')
            continue
        issues.extend(
            f'missing {section}.{key}'
            for key in required
            if key not in values or (values.get(key) in (None, '') and not (section == 'evidence' and key == 'failure'))
        )
    if isinstance(quantization, dict) and quantization:
        if int(quantization.get('bits', 0)) <= 0:
            issues.append('quantization.bits must be positive')
        if int(quantization.get('group_size', 0)) <= 0:
            issues.append('quantization.group_size must be positive')
    return {'ready': not issues, 'issues': issues}


def validate_data_separation(calibration_ids: List[str], evaluation_ids: List[str]) -> Dict[str, Any]:
    """TODO 2：校准与评测样本必须无重叠。"""
    overlap = sorted(set(calibration_ids) & set(evaluation_ids))
    return {'ready': not overlap, 'overlap': overlap, 'issues': ['calibration/evaluation overlap'] if overlap else []}


def build_artifact_gate(manifest: Dict[str, Any], quality: Dict[str, Any], compatibility: Dict[str, Any]) -> Dict[str, Any]:
    """TODO 3：合并 manifest、独立质量与 loader 兼容性证据。"""
    manifest_check = validate_artifact_manifest(manifest)
    issues = list(manifest_check['issues'])
    for key in ('metric', 'baseline_metric', 'threshold', 'eval_split', 'peak_memory_mb'):
        if key not in quality:
            issues.append(f'missing quality.{key}')
    for key in ('backend', 'version', 'loader_status', 'failure'):
        if key not in compatibility:
            issues.append(f'missing compatibility.{key}')
    if not issues and str(compatibility['loader_status']) != 'loaded':
        issues.append(f"loader status: {compatibility['loader_status']}")
    if not issues and compatibility.get('failure'):
        issues.append(f"loader failure: {compatibility['failure']}")
    return {
        'ready': not issues,
        'issues': issues,
        'quality': quality,
        'compatibility': compatibility,
        'evidence_level': manifest.get('evidence', {}).get('evidence_level', 'cpu_manifest_gate'),
    }


def decide_artifact_adoption(gate: Dict[str, Any], *, max_memory_mb: float) -> Dict[str, str]:
    """TODO 4：质量、兼容性、显存和证据等级共同决定是否进入部署 benchmark。"""
    if not gate.get('ready'):
        return {'decision': 'reject', 'reason': 'artifact gate 未通过：' + '; '.join(gate.get('issues', []))}
    quality = gate['quality']
    quality_ok = float(quality['metric']) >= float(quality['threshold'])
    memory_ok = float(quality['peak_memory_mb']) <= max_memory_mb
    compatibility_ok = gate['compatibility'].get('loader_status') == 'loaded' and not gate['compatibility'].get('failure')
    if not (quality_ok and memory_ok and compatibility_ok):
        return {'decision': 'reject', 'reason': '质量、显存或 loader 兼容性未达到门槛'}
    if gate.get('evidence_level') != 'artifact_manifest_and_eval':
        return {'decision': 'tune', 'reason': '仍需真实 artifact manifest、独立评测和 loader 记录'}
    return {'decision': 'accept', 'reason': 'artifact 可进入 67 的部署 benchmark；多 workload 再进入 83'}

答案解析

• manifest 把基座、量化参数、校准来源、文件集合和证据等级放在同一可追溯记录中；任一文件缺失都不能假设 loader 会推断出来。
• 校准与独立评测回答不同问题，因此必须检查样本重叠。质量记录至少保留 baseline、阈值和 held-out split。
• accept 的含义是“可以进入部署 benchmark”，而不是“已经获得最佳吞吐”。67 仍需要同 workload 的浮点 baseline，83 才比较多个服务 profile。

Step 5：可选 GPU/backend 实验——加载 artifact 并保存 gate 记录

在本地已有量化 artifact 时，配置 loader 命令、manifest 路径和独立评测结果。单元不会下载模型；执行后将 loader 返回码、环境、manifest 和质量记录写到 JSON，供 67/83 复用。

# 默认关闭：填写本地 artifact 与明确的 loader 命令后再开启。
RUN_ARTIFACT_GATE = False
ARTIFACT_GATE_CONFIG = {
    'artifact_dir': '',
    'manifest_path': '',
    'backend': 'vllm',
    'loader_command': '',  # 例如实际项目的 load/smoke command；不要把示例当作可执行命令。
    'quality_record_path': '',
    'max_memory_mb': 0.0,  # 填入部署预算；0 表示只记录，不在 82 以显存门槛拒绝。
    'result_dir': 'benchmarks/results/82_quantization_artifact_evaluation',
}
print('Artifact gate disabled. Provide local paths and loader_command before enabling.')

5.2 环境、manifest 与评测记录预检

import json
from pathlib import Path
import platform
import subprocess

if RUN_ARTIFACT_GATE:
    artifact_dir = Path(ARTIFACT_GATE_CONFIG['artifact_dir'])
    manifest_path = Path(ARTIFACT_GATE_CONFIG['manifest_path'])
    quality_path = Path(ARTIFACT_GATE_CONFIG['quality_record_path'])
    missing = [str(path) for path in (artifact_dir, manifest_path, quality_path) if not path.exists()]
    if missing:
        raise FileNotFoundError(f'请先提供本地 artifact、manifest 和质量记录：{missing}')
    if not ARTIFACT_GATE_CONFIG['loader_command'].strip():
        raise ValueError('请配置实际 loader_command；82 不猜测 backend 启动方式。')
    print({'platform': platform.platform(), 'backend': ARTIFACT_GATE_CONFIG['backend'], 'artifact_dir': str(artifact_dir)})

5.3 执行 loader smoke 并保存 JSON

if RUN_ARTIFACT_GATE:
    manifest = json.loads(manifest_path.read_text(encoding='utf-8'))
    quality = json.loads(quality_path.read_text(encoding='utf-8'))
    compatibility = {
        'backend': ARTIFACT_GATE_CONFIG['backend'],
        'version': 'record-in-loader-output',
        'loader_status': 'pending',
        'failure': None,
    }
    completed = subprocess.run(ARTIFACT_GATE_CONFIG['loader_command'], shell=True, text=True, capture_output=True)
    compatibility['loader_status'] = 'loaded' if completed.returncode == 0 else 'failed'
    compatibility['failure'] = None if completed.returncode == 0 else completed.stderr[-1000:]
    gate = build_artifact_gate(manifest, quality, compatibility)
    result_dir = Path(ARTIFACT_GATE_CONFIG['result_dir'])
    result_dir.mkdir(parents=True, exist_ok=True)
    ARTIFACT_GATE_RESULT_PATH = result_dir / '82_quantization_artifact_gate.json'
    payload = {'config': ARTIFACT_GATE_CONFIG, 'manifest': manifest, 'quality': quality, 'compatibility': compatibility, 'gate': gate}
    # 原始 JSON 保留 backend 与 loader 细节；标准记录只用于跨项目衔接。
    from tools.quantization_result_schema import artifact_gate_record, save_record
    payload['normalized_result'] = artifact_gate_record(payload)
    ARTIFACT_GATE_RESULT_PATH.write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding='utf-8')
    normalized_path = result_dir / '82_quantization_artifact_gate_normalized.json'
    save_record(normalized_path, payload['normalized_result'])
    print(f'written: {ARTIFACT_GATE_RESULT_PATH}')
    print(f'normalized: {normalized_path}')

5.4 读取记录并决定下一步

result_path = globals().get('ARTIFACT_GATE_RESULT_PATH')
if result_path is None:
    print('尚未生成 artifact gate 记录。完成 5.2–5.3 后，此处将读取 JSON 并给出进入 67 或返回修复的建议。')
else:
    payload = json.loads(Path(result_path).read_text(encoding='utf-8'))
    budget_mb = float(ARTIFACT_GATE_CONFIG['max_memory_mb']) or float(payload['quality']['peak_memory_mb'])
    decision = decide_artifact_adoption(payload['gate'], max_memory_mb=budget_mb)
    print({'loader_status': payload['compatibility']['loader_status'], 'gate_ready': payload['gate']['ready'], **decision})

相关阅读

• GPTQ: Accurate Post-Training Quantization：校准与逐层误差控制。
• AWQ: Activation-aware Weight Quantization：以激活重要性决定权重保护策略。
• vLLM Quantization：量化格式与 loader/backend 支持需要以实际版本为准。
• Safetensors format：权重文件与 metadata 的可追溯存储格式。