77. Long Sequence Memory Architecture Benchmark | 长序列记忆架构基准
难度： Hard | 环境： CPU-first；GPU 可选 | 标签： 长上下文, Attention, SSM, 架构基准 | 目标人群： 架构选型与长序列学习者
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本节导读
长序列模型的差别不只在“上下文窗口有多大”，还在历史以什么形式保存：显式 KV Attention 保留可检索的 token 状态，递推模型把历史压缩为固定形状状态，混合架构则让不同层承担不同记忆职责。本节从状态大小、历史读取能力、长距离质量与服务成本四个维度比较三类候选，并把结果整理为可复查的架构选型记录。
不同公开模型的数字通常属于观察性证据：模型规模、训练数据、tokenizer 与实现路径都可能不同，不能把差异全部归因于记忆机制。
关键词： KV Cache, Recurrent State, Hybrid Architecture, Long Context, Evidence Level
前置阅读
导语： 先区分完整 KV 历史、局部/稀疏访问和递推状态，再用同一组长度与任务条件比较它们的质量和成本。
- 45. Linear Attention and Recurrent State | 线性 Attention 与递推状态
- ARCH-HYBRID-MEMORY. Attention / SSM Hybrid | Attention 与 SSM 混合记忆
- 大模型架构 · 线性 Attention、SSM 与混合记忆
- 61. Model Architecture Exploration | 模型架构探索
Step 1：把“历史如何保存”变成可比较的候选
三类候选应使用相同长度阶梯、任务、batch、dtype、backend 与硬件。显式 KV Attention 保存每个历史 token 的 K/V；递推模型持续更新固定形状状态；混合架构还要显式记录 layer_schedule、Attention 层数和 SSM 层数。先对齐输入与层级安排，才能把状态、质量与成本放在同一张证据表中。
Step 2：状态账本与历史读取是两件事
设 batch 为 B、序列长度为 L、注意力层数为 N_a、递推层数为 N_r、KV heads 为 H_kv、head dimension 为 D_h。显式 KV 的状态量近似随 2 × B × L × N_a × H_kv × D_h 增长；递推层的状态由 B × N_r × state_dim × value_dim 决定，不随 L 线性增长。状态更小只说明保存成本不同；是否还能读取任务需要的远程信息，要由独立质量检查回答。
| 候选 | 保存什么 | 如何读取历史 | 状态随长度变化 |
|---|---|---|---|
explicit_kv | 每 token 的 K/V | Query 可对保存的历史做内容相关检索 | 线性增长 |
recurrent_state | 固定形状累计状态 | 通过当前状态间接读取已压缩历史 | 近似固定 |
hybrid | 部分层 K/V + 部分层递推状态 | 全局读取与扫描式状态由层分工共同承担 | 随 Attention 层数线性增长 |
Step 3：质量、成本与可比性分别记录
递推状态可能降低状态成本，却在精确远程读取上失败；显式 KV 可能保留更多信息，却提高长输入的 prefill、decode 与显存代价。真实比较应把质量、成本和可比性拆开记录，而不是把它们压成单一分数。
| 证据维度 | 最少记录 | 用途 |
|---|---|---|
| 质量 | 任务、训练/评测长度、needle 或长依赖指标、失败样例 | 判断压缩后的历史是否仍支持目标任务 |
| 成本 | prefill、TPOT、吞吐、模型常驻显存、运行期增量峰值、KV/state memory | 判断长度增长暴露在哪个阶段 |
| 可比性 | checkpoint、tokenizer、revision、dtype、backend、hardware、对照说明 | 区分同家族近似对照与跨模型观察 |
| 决策 | evidence level、accept/tune/reject、下一步 | 让质量和成本结论可回查、可复测 |
Step 4：代码设计——记忆接口、状态账本与证据门槛
题目区先把三类候选的层数与状态维度写成可检查的结构契约，再分别验证状态增长与一个简化的远程读取行为，最后检查真实结果记录并形成决策。CPU 样例只演示记忆接口的差异，不把简化状态机当作真实 SSM 的任务质量结论。
| TODO | 代码契约与机制责任 | 关键约束 | 测试证据 |
|---|---|---|---|
| TODO 1 | 验证显式 KV、递推或混合候选的层数与维度 | attention_layers + recurrent_layers = num_layers；类型与层数匹配 | 三类合法候选与非法层数 |
| TODO 2 | 建立 KV/state memory 账本 | KV 仅由 Attention 层按 L 增长；递推状态不随 L 增长 | 长度翻倍、三类候选比较 |
| TODO 3 | 用简化历史接口比较精确读取与摘要读取 | 显式历史可按位置读取；递推状态只保留摘要；混合保留局部窗口 | 远程 token、近邻 token 与摘要状态 |
| TODO 4 | 检查真实结果是否具备质量、成本、证据和失败字段 | 不用单个 score 代替任务与长度信息 | 完整记录与缺失质量记录 |
| TODO 5 | 按质量门槛、显存预算和证据等级给出决策 | 观察性证据只能进入扩展评测，不能直接宣称因果收益 | accept、tune、reject 与 evidence gate |
python
from typing import Dict, Listpython
def validate_memory_architecture(spec: Dict[str, object]) -> Dict[str, object]:
"""校验 explicit_kv、recurrent_state 或 hybrid 的层数与状态维度契约。"""
# TODO 1：结构契约——检查 architecture_type、层数分配与各类状态维度。
# architecture_type = ???
# issues = ???
raise NotImplementedError("请先完成 TODO 1")
def estimate_memory_state(spec: Dict[str, object], seq_len: int, batch_size: int = 1) -> Dict[str, object]:
"""估算固定 workload 下的 KV 与递推状态字节数；结果是结构账本而非峰值显存。"""
# TODO 2：状态账本——KV 仅由 attention_layers 随 seq_len 增长；递推状态由 recurrent_layers 与 state/value 维度决定。
# kv_bytes = ???
# recurrent_state_bytes = ???
raise NotImplementedError("请先完成 TODO 2")
def simulate_memory_read(spec: Dict[str, object], tokens: List[str], target_index: int, local_window: int = 2) -> Dict[str, object]:
"""用玩具记忆接口比较精确历史读取、摘要读取与局部窗口保留。"""
# TODO 3：历史读取——explicit_kv 返回 target token；recurrent_state 只返回是否见过的摘要；hybrid 只精确保留尾部 local_window。
# read_mode = ???
# recovered = ???
raise NotImplementedError("请先完成 TODO 3")
def validate_memory_evidence(record: Dict[str, object]) -> Dict[str, object]:
"""检查真实模型记录是否同时具备质量、成本、证据等级与失败状态。"""
# TODO 4：证据字段——quality 应包含 task、eval_length、metric；cost 应包含 prefill_ms、decode_tpot_ms、peak_memory_mb。
# issues = ???
raise NotImplementedError("请先完成 TODO 4")
def recommend_memory_architecture(
candidates: List[Dict[str, object]], *, min_quality: float, max_peak_memory_mb: float
) -> Dict[str, object]:
"""按质量、显存和证据等级选择候选；返回 accept/tune/reject。"""
# TODO 5：决策门槛——先检查证据，再用 quality.metric 与 cost.peak_memory_mb 筛选候选。
# feasible = ???
# decision = ???
raise NotImplementedError("请先完成 TODO 5")python
# 每个测试函数只验证一个机制责任，便于定位失败原因。
def test_architecture_contract():
explicit = {
'name': 'kv', 'architecture_type': 'explicit_kv', 'num_layers': 4,
'attention_layers': 4, 'recurrent_layers': 0, 'kv_heads': 2,
'head_dim': 8, 'state_dim': 0, 'value_dim': 0, 'dtype_bytes': 2,
}
recurrent = {
'name': 'ssm', 'architecture_type': 'recurrent_state', 'num_layers': 4,
'attention_layers': 0, 'recurrent_layers': 4, 'kv_heads': 0,
'head_dim': 0, 'state_dim': 8, 'value_dim': 8, 'dtype_bytes': 2,
}
hybrid = {
'name': 'hybrid', 'architecture_type': 'hybrid', 'num_layers': 4,
'attention_layers': 2, 'recurrent_layers': 2, 'kv_heads': 2,
'head_dim': 8, 'state_dim': 8, 'value_dim': 8, 'dtype_bytes': 2,
}
assert validate_memory_architecture(explicit)['ready']
assert validate_memory_architecture(recurrent)['ready']
assert validate_memory_architecture(hybrid)['ready']
assert not validate_memory_architecture({**hybrid, 'attention_layers': 3})['ready']
return explicit, recurrent, hybrid
def test_state_growth(explicit, recurrent, hybrid):
kv_short, kv_long = estimate_memory_state(explicit, 16), estimate_memory_state(explicit, 32)
state_short, state_long = estimate_memory_state(recurrent, 16), estimate_memory_state(recurrent, 32)
hybrid_state = estimate_memory_state(hybrid, 32)
assert kv_long['total_state_bytes'] == 2 * kv_short['total_state_bytes']
assert state_long['total_state_bytes'] == state_short['total_state_bytes']
assert state_long['total_state_bytes'] < hybrid_state['total_state_bytes'] < kv_long['total_state_bytes']
def test_memory_read_contract(explicit, recurrent, hybrid):
tokens = ['A', 'needle', 'C', 'D', 'E']
assert simulate_memory_read(explicit, tokens, 1)['recovered'] == 'needle'
assert simulate_memory_read(recurrent, tokens, 1)['read_mode'] == 'summary_only'
assert simulate_memory_read(hybrid, tokens, 4, local_window=2)['recovered'] == 'E'
assert simulate_memory_read(hybrid, tokens, 1, local_window=2)['read_mode'] == 'summary_only'
def test_evidence_and_decision():
baseline = {
'name': 'kv', 'quality': {'task': 'needle', 'eval_length': 4096, 'metric': 0.90},
'cost': {'prefill_ms': 12.0, 'decode_tpot_ms': 2.0, 'peak_memory_mb': 1800},
'evidence': {'evidence_level': 'same_family_variant', 'failure': None},
}
candidate = {
'name': 'hybrid', 'quality': {'task': 'needle', 'eval_length': 4096, 'metric': 0.88},
'cost': {'prefill_ms': 10.0, 'decode_tpot_ms': 1.8, 'peak_memory_mb': 1300},
'evidence': {'evidence_level': 'same_family_variant', 'failure': None},
}
incomplete = {'name': 'missing_quality', 'cost': candidate['cost'], 'evidence': candidate['evidence']}
assert validate_memory_evidence(baseline)['ready']
assert not validate_memory_evidence(incomplete)['ready']
result = recommend_memory_architecture([candidate, incomplete], min_quality=0.85, max_peak_memory_mb=1500)
assert result['decision'] == 'accept' and result['recommended_name'] == 'hybrid'
observed = {**candidate, 'name': 'observed', 'evidence': {'evidence_level': 'cross_model_observational', 'failure': None}}
assert recommend_memory_architecture([observed], min_quality=0.85, max_peak_memory_mb=1500)['decision'] == 'tune'
explicit_spec, recurrent_spec, hybrid_spec = test_architecture_contract()
test_state_growth(explicit_spec, recurrent_spec, hybrid_spec)
test_memory_read_contract(explicit_spec, recurrent_spec, hybrid_spec)
test_evidence_and_decision()
print('✅ 长序列记忆架构项目 CPU 机制测试通过。')参考代码与解析
python
def validate_memory_architecture(spec: Dict[str, object]) -> Dict[str, object]:
"""校验 explicit_kv、recurrent_state 或 hybrid 的层数与状态维度契约。"""
# TODO 1：结构契约。
architecture_type = str(spec.get('architecture_type', ''))
issues = []
if architecture_type not in {'explicit_kv', 'recurrent_state', 'hybrid'}:
issues.append('unsupported architecture_type')
required = ('num_layers', 'attention_layers', 'recurrent_layers', 'dtype_bytes')
issues.extend(f'missing: {key}' for key in required if key not in spec)
if not issues:
if any(int(spec[key]) < 0 for key in ('attention_layers', 'recurrent_layers')) or int(spec['num_layers']) <= 0:
issues.append('layer counts must be non-negative and num_layers positive')
if int(spec['attention_layers']) + int(spec['recurrent_layers']) != int(spec['num_layers']):
issues.append('attention_layers + recurrent_layers must equal num_layers')
if int(spec['dtype_bytes']) <= 0:
issues.append('dtype_bytes must be positive')
if architecture_type == 'explicit_kv' and int(spec['recurrent_layers']) != 0:
issues.append('explicit_kv cannot contain recurrent layers')
if architecture_type == 'recurrent_state' and int(spec['attention_layers']) != 0:
issues.append('recurrent_state cannot contain attention layers')
if int(spec['attention_layers']) and (int(spec.get('kv_heads', 0)) <= 0 or int(spec.get('head_dim', 0)) <= 0):
issues.append('attention layers need positive kv_heads and head_dim')
if int(spec['recurrent_layers']) and (int(spec.get('state_dim', 0)) <= 0 or int(spec.get('value_dim', 0)) <= 0):
issues.append('recurrent layers need positive state_dim and value_dim')
return {'ready': not issues, 'issues': issues}
def estimate_memory_state(spec: Dict[str, object], seq_len: int, batch_size: int = 1) -> Dict[str, object]:
"""估算固定 workload 下的 KV 与递推状态字节数；结果是结构账本而非峰值显存。"""
# TODO 2：状态账本。
validation = validate_memory_architecture(spec)
if not validation['ready']:
raise ValueError(f"非法候选：{validation['issues']}")
if seq_len <= 0 or batch_size <= 0:
raise ValueError('seq_len 和 batch_size 必须为正整数')
dtype_bytes = int(spec['dtype_bytes'])
kv_bytes = (2 * batch_size * seq_len * int(spec['attention_layers']) * int(spec.get('kv_heads', 0)) * int(spec.get('head_dim', 0)) * dtype_bytes)
recurrent_state_bytes = (batch_size * int(spec['recurrent_layers']) * int(spec.get('state_dim', 0)) * int(spec.get('value_dim', 0)) * dtype_bytes)
return {
'kv_bytes': float(kv_bytes),
'recurrent_state_bytes': float(recurrent_state_bytes),
'total_state_bytes': float(kv_bytes + recurrent_state_bytes),
'evidence_level': 'cpu_state_ledger',
}
def simulate_memory_read(spec: Dict[str, object], tokens: List[str], target_index: int, local_window: int = 2) -> Dict[str, object]:
"""用玩具记忆接口比较精确历史读取、摘要读取与局部窗口保留。"""
# TODO 3：历史读取接口。
if not tokens or not 0 <= target_index < len(tokens) or local_window <= 0:
raise ValueError('tokens、target_index 与 local_window 不合法')
architecture_type = str(spec['architecture_type'])
target = tokens[target_index]
if architecture_type == 'explicit_kv':
return {'read_mode': 'exact_history', 'recovered': target, 'summary': tuple(tokens)}
if architecture_type == 'recurrent_state':
return {'read_mode': 'summary_only', 'recovered': None, 'summary': {'seen_tokens': len(tokens), 'contains_target': target in tokens}}
exact_window = tokens[-local_window:]
if target_index >= len(tokens) - local_window:
return {'read_mode': 'local_exact', 'recovered': target, 'summary': tuple(exact_window)}
return {'read_mode': 'summary_only', 'recovered': None, 'summary': {'local_window': tuple(exact_window), 'contains_target': target in tokens}}
def validate_memory_evidence(record: Dict[str, object]) -> Dict[str, object]:
"""检查真实模型记录是否同时具备质量、成本、证据等级与失败状态。"""
# TODO 4：证据字段。
issues = []
for section in ('quality', 'cost', 'evidence'):
if not isinstance(record.get(section), dict):
issues.append(f'missing section: {section}')
if not issues:
quality, cost, evidence = record['quality'], record['cost'], record['evidence']
for key in ('task', 'eval_length', 'metric'):
if key not in quality:
issues.append(f'missing quality.{key}')
for key in ('prefill_ms', 'decode_tpot_ms', 'peak_memory_mb'):
if key not in cost:
issues.append(f'missing cost.{key}')
for key in ('evidence_level', 'failure'):
if key not in evidence:
issues.append(f'missing evidence.{key}')
return {'ready': not issues, 'issues': issues}
def recommend_memory_architecture(candidates: List[Dict[str, object]], *, min_quality: float, max_peak_memory_mb: float) -> Dict[str, object]:
"""按质量、显存和证据等级选择候选；返回 accept/tune/reject。"""
# TODO 5：决策门槛。
checked = [(candidate, validate_memory_evidence(candidate)) for candidate in candidates]
valid = [candidate for candidate, check in checked if check['ready']]
feasible = [candidate for candidate in valid if float(candidate['quality']['metric']) >= min_quality and float(candidate['cost']['peak_memory_mb']) <= max_peak_memory_mb]
if not feasible:
return {'decision': 'reject', 'recommended_name': None, 'reason': '没有候选同时满足质量门槛和显存预算', 'next_action': 'collect_or_tune_candidates'}
best = min(feasible, key=lambda item: (float(item['cost']['peak_memory_mb']), float(item['cost']['prefill_ms'])))
if best['evidence']['evidence_level'] == 'same_family_variant':
return {'decision': 'accept', 'recommended_name': best['name'], 'reason': '质量、显存与近似结构对照证据均达标', 'next_action': 'run_length_ladder'}
return {'decision': 'tune', 'recommended_name': best['name'], 'reason': '质量和显存达标，但跨模型结果只能作为观察性证据', 'next_action': 'find_matched_variant_or_expand_eval'}答案解析
- TODO 1： 结构契约先排除不成立的候选：显式 KV 不能同时声明递推层，纯递推不能声明 Attention 层，混合候选必须说明两类层各占多少。
- TODO 2： KV 状态随
seq_len增长，递推状态只由层数和状态维度决定。这个账本只解释状态趋势，不等于 allocator、activation 或真实峰值显存。 - TODO 3： 玩具接口把“是否精确保留历史”写成可测行为：显式历史可直接读取目标 token；递推状态只表示曾经见过；混合结构只精确保留局部窗口。真实 SSM 的质量仍需任务评测。
- TODO 4： 真实记录必须同时有任务与评测长度、prefill/TPOT/峰值显存、证据等级和失败状态。只写一个
score无法判断长上下文能力。 - TODO 5： 同家族变体在质量和预算达标后可进入长度阶梯；跨模型观察即使数值更好，也只应进入
tune，继续寻找可比变体或扩大评测。
Step 5：可选 GPU 实验——真实模型长度阶梯对照
GPU 实验默认关闭。它在短、中、长三个长度档记录模型结构、远程读取 smoke、prefill、TPOT、模型常驻显存、运行期增量峰值和可见 KV/state 大小，并保存为独立 JSON。先用 Transformers 固定 tokenizer、输入和模型输出语义；只有候选均受支持时，才进入 vLLM 主基线或 SGLang 同口径对照。跨模型比较仍标记为观察性证据。环境与版本口径见模型、执行栈与环境资产表。
5.1 环境、模型与固定条件
| 条目 | 统一条件 | 记录目的 |
|---|---|---|
| 模型候选 | model_id、revision、声明的架构类型、比较说明 | 追踪显式 KV / 递推 / 混合候选的来源与可比性 |
| 长度阶梯 | seq_lens、相同 prompt 模板、needle 位置 | 观察长度对质量与成本的影响 |
| 生成条件 | batch、dtype、max_new_tokens、warmup、repeats | 固定服务侧测量口径 |
| 质量 smoke | 输入中已验证的远程 needle 与失败输出 | 发现明显的长距离读取失败 |
| 结果位置 | 独立时间戳 JSON | 保留 workload、hardware、metrics、failure、evidence、decision |
python
# GPU 配置：默认关闭；至少配置两个模型候选后才运行。
import json
import os
from datetime import datetime, timezone
from pathlib import Path
from typing import Dict
RUN_MEMORY_ARCH_GPU = os.environ.get('RUN_MEMORY_ARCH_GPU', '0') == '1'
MEMORY_ARCH_GPU_CONFIG = {
'models': [
{'name': 'baseline', 'model_id': os.environ.get('MEMORY_ARCH_BASELINE_MODEL', ''), 'architecture_type': 'explicit_kv'},
{'name': 'candidate', 'model_id': os.environ.get('MEMORY_ARCH_CANDIDATE_MODEL', ''), 'architecture_type': os.environ.get('MEMORY_ARCH_CANDIDATE_TYPE', 'recurrent_state')},
],
'comparison_design': os.environ.get('MEMORY_ARCH_COMPARISON_DESIGN', 'cross_model_observational'),
'comparison_notes': os.environ.get('MEMORY_ARCH_COMPARISON_NOTES', ''),
'revision': os.environ.get('MEMORY_ARCH_REVISION', 'main'),
'seq_lens': [256, 512, 1024],
'batch_size': 1,
'dtype': 'float16',
'max_new_tokens': 16,
'warmup': 2,
'repeats': 3,
'needle_phrase': 'MEMORY_ARCH_NEEDLE_77',
}
def resolve_tutorial_root() -> Path:
"""在真正写入结果时解析本地仓库或标准 Colab 克隆目录。"""
configured = os.environ.get('LLM_ALGO_REPO_DIR')
candidates = [Path(configured).expanduser()] if configured else []
candidates.extend([Path.cwd(), Path('/content/llm-algo-leetcode')])
for candidate in candidates:
if (candidate / '02_PyTorch_Algorithms').is_dir():
return candidate
raise RuntimeError('未找到教程根目录；请在仓库根目录运行，或设置 LLM_ALGO_REPO_DIR。')
MEMORY_ARCH_RESULTS_DIR = None
print('Memory architecture GPU benchmark is', 'enabled' if RUN_MEMORY_ARCH_GPU else 'disabled')
print('Set MEMORY_ARCH_BASELINE_MODEL, MEMORY_ARCH_CANDIDATE_MODEL and RUN_MEMORY_ARCH_GPU=1 to run it.')5.2 backend、模型与记忆接口预检
预检确认 CUDA、Transformers、模型 ID、架构类型和比较设计。代码会从真实 config 推断已知的递推或混合模型，并在声明与可识别结构冲突时停止；无法识别的模型会保留为声明值，但结果只能作为观察性记录。
python
if not RUN_MEMORY_ARCH_GPU:
print('GPU benchmark skipped. Enable RUN_MEMORY_ARCH_GPU after configuring model IDs.')
else:
import torch
import transformers
if not torch.cuda.is_available():
raise RuntimeError('RUN_MEMORY_ARCH_GPU=1 requires a CUDA-enabled PyTorch runtime.')
missing = [item['name'] for item in MEMORY_ARCH_GPU_CONFIG['models'] if not item['model_id']]
if missing:
raise ValueError(f'请先设置模型 ID：{missing}')
if MEMORY_ARCH_GPU_CONFIG['comparison_design'] not in {'same_family_variant', 'cross_model_observational'}:
raise ValueError('comparison_design 必须是 same_family_variant 或 cross_model_observational')
if MEMORY_ARCH_GPU_CONFIG['comparison_design'] == 'same_family_variant' and not MEMORY_ARCH_GPU_CONFIG['comparison_notes']:
raise ValueError('same_family_variant 需要在 MEMORY_ARCH_COMPARISON_NOTES 说明训练、tokenizer 与版本匹配依据。')
valid_types = {'explicit_kv', 'recurrent_state', 'hybrid'}
invalid_types = [item['architecture_type'] for item in MEMORY_ARCH_GPU_CONFIG['models'] if item['architecture_type'] not in valid_types]
if invalid_types:
raise ValueError(f'不支持的 architecture_type：{invalid_types}')
print('CUDA:', torch.cuda.get_device_name(0), '| transformers:', transformers.__version__)
print('comparison design:', MEMORY_ARCH_GPU_CONFIG['comparison_design'])5.3 配置长度阶梯与质量探针
每个长度档使用相同 filler、needle、问题与生成长度。构造 prompt 时会先为 needle 和问题预留 token，并断言 needle 仍位于输入中；这样失败才可以被解释为模型的读取失败，而不是截断错误。单个 needle 只用于 smoke，后续应把独立任务集分数补入 JSON。
python
if RUN_MEMORY_ARCH_GPU:
def build_memory_arch_prompt(tokenizer, target_tokens: int, needle_phrase: str) -> str:
"""构造含远程 needle 的近似目标长度 prompt，并为结尾问题预留 token。"""
suffix = f'\nHidden memory token: {needle_phrase}.\nQuestion: What is the hidden memory token?'
suffix_tokens = len(tokenizer(suffix, add_special_tokens=False).input_ids)
if suffix_tokens >= target_tokens:
raise ValueError('目标长度不足以容纳 needle 和问题；请增大 seq_lens。')
filler = 'The archive contains routine records with no special instruction. '
chunks = []
while len(tokenizer(' '.join(chunks), add_special_tokens=False).input_ids) < target_tokens - suffix_tokens:
chunks.append(filler)
return ' '.join(chunks) + suffix
print('length ladder:', MEMORY_ARCH_GPU_CONFIG['seq_lens'], '| repeats:', MEMORY_ARCH_GPU_CONFIG['repeats'])5.4 执行长度阶梯并保存 JSON
每个模型在每个长度档重复测量 prefill 与单步 decode，记录中位数；显存拆为模型常驻、运行期峰值和相对当前基线的增量峰值。代码优先读取 past_key_values、cache_params、cache 或 state；若模型没有暴露可读取状态接口，会写入 failure，不把空值误作可比的 KV/state 证据。
python
if RUN_MEMORY_ARCH_GPU:
from statistics import median
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
device = torch.device('cuda')
dtype = getattr(torch, MEMORY_ARCH_GPU_CONFIG['dtype'])
def infer_architecture_type(config) -> str:
"""从已知 config 名称识别递推或混合模型；其余默认为显式 KV Decoder。"""
model_type = str(getattr(config, 'model_type', '')).lower()
if any(name in model_type for name in ('zamba', 'jamba')):
return 'hybrid'
if 'mamba' in model_type or 'ssm' in model_type:
return 'recurrent_state'
return 'explicit_kv'
def tensor_bytes(value, seen=None) -> int | None:
"""递归汇总 cache/state 对象中的 tensor；未知对象返回 None。"""
seen = set() if seen is None else seen
if id(value) in seen:
return 0
seen.add(id(value))
if isinstance(value, torch.Tensor):
return value.numel() * value.element_size()
if hasattr(value, 'to_legacy_cache'):
return tensor_bytes(value.to_legacy_cache(), seen)
if isinstance(value, (tuple, list)):
parts = [tensor_bytes(item, seen) for item in value]
return None if any(part is None for part in parts) else sum(parts)
if isinstance(value, dict):
parts = [tensor_bytes(item, seen) for item in value.values()]
return None if any(part is None for part in parts) else sum(parts)
if hasattr(value, '__dict__'):
fields = [item for key, item in vars(value).items() if not key.startswith('_')]
parts = [tensor_bytes(item, seen) for item in fields]
return None if not parts or any(part is None for part in parts) else sum(parts)
return None
def extract_runtime_state(outputs):
"""返回模型公开的 cache/state 字段名与字节数；没有公开接口时显式标记缺失。"""
for field in ('past_key_values', 'cache_params', 'cache', 'state'):
value = getattr(outputs, field, None)
if value is not None:
return field, tensor_bytes(value)
return None, None
def run_decode_step(model, next_token, state_field, state_value):
"""按公开 cache/state 参数做单步 decode；接口不支持时抛出可记录的错误。"""
kwargs = {'use_cache': True}
if state_field is not None:
kwargs[state_field] = state_value
return model(next_token, **kwargs)
def measure_memory_model(model_spec: Dict[str, str]) -> Dict[str, object]:
"""运行一个 checkpoint 的长度阶梯，返回结构、质量 smoke、成本与失败记录。"""
model_id = model_spec['model_id']
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=MEMORY_ARCH_GPU_CONFIG['revision'])
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
config = AutoConfig.from_pretrained(model_id, revision=MEMORY_ARCH_GPU_CONFIG['revision'])
inferred_type = infer_architecture_type(config)
if inferred_type != model_spec['architecture_type']:
raise ValueError(f"{model_spec['name']} 的声明类型 {model_spec['architecture_type']} 与 config 推断 {inferred_type} 不一致。")
model = AutoModelForCausalLM.from_pretrained(model_id, revision=MEMORY_ARCH_GPU_CONFIG['revision'], torch_dtype=dtype).to(device).eval()
torch.cuda.synchronize(device)
resident_mb = torch.cuda.memory_allocated(device) / 2**20
rows = []
for seq_len in MEMORY_ARCH_GPU_CONFIG['seq_lens']:
prompt = build_memory_arch_prompt(tokenizer, seq_len, MEMORY_ARCH_GPU_CONFIG['needle_phrase'])
encoded = tokenizer(prompt, return_tensors='pt', truncation=True, max_length=seq_len, add_special_tokens=False)
input_ids = encoded.input_ids.to(device)
needle_ids = tokenizer(MEMORY_ARCH_GPU_CONFIG['needle_phrase'], add_special_tokens=False).input_ids
input_list = input_ids[0].tolist()
needle_present = any(input_list[idx:idx + len(needle_ids)] == needle_ids for idx in range(len(input_list) - len(needle_ids) + 1))
if not needle_present:
raise RuntimeError('needle 在截断后不在输入中；请检查 prompt 构造与 seq_len。')
prefill_runs, decode_runs = [], []
outputs = None
state_field, state_size = None, None
failure = None
with torch.inference_mode():
for _ in range(MEMORY_ARCH_GPU_CONFIG['warmup']):
model(input_ids, use_cache=True)
for _ in range(MEMORY_ARCH_GPU_CONFIG['repeats']):
torch.cuda.synchronize(device)
before_mb = torch.cuda.memory_allocated(device) / 2**20
torch.cuda.reset_peak_memory_stats(device)
start, end = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
start.record(); outputs = model(input_ids, use_cache=True); end.record(); torch.cuda.synchronize(device)
prefill_runs.append(start.elapsed_time(end))
state_field, state_size = extract_runtime_state(outputs)
next_token = outputs.logits[:, -1:].argmax(dim=-1)
try:
decode_start, decode_end = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
decode_start.record(); run_decode_step(model, next_token, state_field, getattr(outputs, state_field) if state_field else None); decode_end.record(); torch.cuda.synchronize(device)
decode_runs.append(decode_start.elapsed_time(decode_end))
except Exception as exc:
failure = f'decode_interface_error: {type(exc).__name__}: {exc}'
break
peak_mb = torch.cuda.max_memory_allocated(device) / 2**20
generated = model.generate(input_ids, max_new_tokens=MEMORY_ARCH_GPU_CONFIG['max_new_tokens'], do_sample=False, use_cache=True)
completion = tokenizer.decode(generated[0, input_ids.shape[1]:], skip_special_tokens=True)
if failure is None and state_field is None:
failure = 'state_interface_not_exposed'
if failure is None and MEMORY_ARCH_GPU_CONFIG['needle_phrase'] not in completion:
failure = 'needle_not_recalled'
rows.append({
'seq_len': int(input_ids.shape[1]),
'quality': {'task': 'single_prompt_needle_smoke', 'eval_length': int(input_ids.shape[1]), 'metric': float(MEMORY_ARCH_GPU_CONFIG['needle_phrase'] in completion), 'failure': failure},
'cost': {
'prefill_ms': float(median(prefill_runs)),
'decode_tpot_ms': None if not decode_runs else float(median(decode_runs)),
'model_resident_memory_mb': resident_mb,
'runtime_peak_memory_mb': peak_mb,
'incremental_peak_memory_mb': max(0.0, peak_mb - before_mb),
'peak_memory_mb': peak_mb,
'kv_or_state_memory_mb': None if state_size is None else state_size / 2**20,
},
'state_interface': state_field,
})
del model
torch.cuda.empty_cache()
return {
'name': model_spec['name'], 'model': model_id, 'architecture_type': inferred_type,
'architecture': {'model_type': getattr(config, 'model_type', None), 'kv_heads': getattr(config, 'num_key_value_heads', None), 'rope_scaling': getattr(config, 'rope_scaling', None), 'max_position_embeddings': getattr(config, 'max_position_embeddings', None)},
'rows': rows,
}
model_records = [measure_memory_model(model_spec) for model_spec in MEMORY_ARCH_GPU_CONFIG['models']]
MEMORY_ARCH_RESULTS_DIR = resolve_tutorial_root() / 'benchmarks' / 'results' / '77_memory_architecture'
MEMORY_ARCH_RESULTS_DIR.mkdir(parents=True, exist_ok=True)
MEMORY_ARCH_RESULT_PATH = MEMORY_ARCH_RESULTS_DIR / f"gpu_memory_arch_{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%SZ')}.json"
MEMORY_ARCH_RECORD = {
'workload': MEMORY_ARCH_GPU_CONFIG,
'hardware': {'gpu': torch.cuda.get_device_name(device), 'torch': torch.__version__, 'cuda': torch.version.cuda},
'models': model_records,
'failure': None,
'evidence_level': 'single_gpu_length_ladder_observational',
'decision': 'inspect',
'note': 'Needle smoke is not an independent long-context benchmark; cross-model results are observational.',
}
MEMORY_ARCH_RESULT_PATH.write_text(json.dumps(MEMORY_ARCH_RECORD, indent=2, default=str), encoding='utf-8')
print(f'Saved memory architecture benchmark: {MEMORY_ARCH_RESULT_PATH}')5.5 读取结果、检查长度趋势与失败记录
先比较同一 seq_len 下的 quality metric 与 failure，再看 prefill、TPOT、模型常驻显存、运行期增量峰值和 KV/state memory 如何随长度变化。没有公开状态接口、needle 不在输入中或 decode 接口不兼容，都应保留为 failure；这些记录说明当前模型或适配器尚不支持此项对照。
python
result_path = globals().get('MEMORY_ARCH_RESULT_PATH')
if result_path is None and MEMORY_ARCH_RESULTS_DIR is not None:
candidates = sorted(MEMORY_ARCH_RESULTS_DIR.glob('gpu_memory_arch_*.json'))
result_path = candidates[-1] if candidates else None
if result_path is None:
print('No memory architecture result found. Run 5.4 after configuring models.')
else:
record = json.loads(Path(result_path).read_text(encoding='utf-8'))
print(f"Result: {result_path} | evidence: {record['evidence_level']}")
for model in record['models']:
print(f"[{model['name']}] {model['architecture_type']} | {model['model']}")
for row in model['rows']:
quality, cost = row['quality'], row['cost']
print(
f" L={row['seq_len']}: quality={quality['metric']:.0f}, failure={quality['failure']}; "
f"prefill={cost['prefill_ms']:.2f}ms, TPOT={cost['decode_tpot_ms']}, "
f"resident={cost['model_resident_memory_mb']:.1f}MB, incremental={cost['incremental_peak_memory_mb']:.1f}MB, "
f"state={cost['kv_or_state_memory_mb']}, interface={row['state_interface']}"
)5.6 形成决策并进入扩展评测
只有候选在目标长度档保持质量门槛、满足成本预算、没有接口或读取 failure，并且证据设计足够可比时，才进入 accept。跨模型观察或单个 needle smoke 即使数值优异，也应先标记为 tune，再补独立任务集、更多长度档和可比变体。
python
if 'MEMORY_ARCH_RECORD' in globals():
all_rows = [row for model in MEMORY_ARCH_RECORD['models'] for row in model['rows']]
has_failure = any(row['quality']['failure'] for row in all_rows)
decision = 'tune'
reason = (
'存在 quality 或接口 failure，需要先修复候选并扩大评测'
if has_failure
else '当前只有长度阶梯与单个 needle smoke，仍需独立长上下文质量集'
)
MEMORY_ARCH_RECORD['decision'] = decision
MEMORY_ARCH_RECORD['decision_reason'] = reason
MEMORY_ARCH_RESULT_PATH.write_text(json.dumps(MEMORY_ARCH_RECORD, indent=2, default=str), encoding='utf-8')
import sys
repository_root = resolve_tutorial_root()
if str(repository_root) not in sys.path:
sys.path.insert(0, str(repository_root))
from tools.architecture_result_schema import make_producer_record, save_record
baseline_model = MEMORY_ARCH_RECORD['models'][0]
candidate_models = MEMORY_ARCH_RECORD['models'][1:]
comparison_mode = (
'controlled'
if MEMORY_ARCH_GPU_CONFIG['comparison_design'] == 'same_family_variant'
else 'observational'
)
failure_rows = {
model['name']: [
{'seq_len': row['seq_len'], 'failure': row['quality']['failure']}
for row in model['rows']
if row['quality']['failure']
]
for model in MEMORY_ARCH_RECORD['models']
}
failure_rows = {name: rows for name, rows in failure_rows.items() if rows}
companion = make_producer_record(
semantic_id='ARCH-LONG-SEQUENCE-MEMORY',
comparison_mode=comparison_mode,
source={
'notebook': '02_PyTorch_Algorithms/77_Long_Sequence_Memory_Architecture_Benchmark.ipynb',
'raw_result_path': str(MEMORY_ARCH_RESULT_PATH),
},
runtime={
'framework': 'transformers',
'hardware': MEMORY_ARCH_RECORD['hardware'],
'dtype': MEMORY_ARCH_GPU_CONFIG['dtype'],
'model_revision': MEMORY_ARCH_GPU_CONFIG['revision'],
},
workload={
key: MEMORY_ARCH_GPU_CONFIG[key]
for key in ('seq_lens', 'batch_size', 'max_new_tokens', 'warmup', 'repeats', 'needle_phrase')
},
baseline={
'name': baseline_model['name'],
'model': baseline_model['model'],
'state_representation': baseline_model['architecture_type'],
},
candidates=[
{
'name': model['name'],
'model': model['model'],
'state_representation': model['architecture_type'],
}
for model in candidate_models
],
quality={
model['name']: [row['quality'] for row in model['rows']]
for model in MEMORY_ARCH_RECORD['models']
},
cost={
model['name']: [dict(seq_len=row['seq_len'], **row['cost']) for row in model['rows']]
for model in MEMORY_ARCH_RECORD['models']
},
mechanism={
'length_ladder': MEMORY_ARCH_GPU_CONFIG['seq_lens'],
'comparison_design': MEMORY_ARCH_GPU_CONFIG['comparison_design'],
'comparison_notes': MEMORY_ARCH_GPU_CONFIG['comparison_notes'],
'models': {
model['name']: {
'state_representation': model['architecture_type'],
'architecture': model['architecture'],
'state_interfaces': [row['state_interface'] for row in model['rows']],
}
for model in MEMORY_ARCH_RECORD['models']
},
},
evidence_level='real_benchmark',
failure=failure_rows,
decision={
'status': decision,
'reason': reason,
'next_action': 'add an independent long-context quality set and matched architecture variants',
},
)
companion_path = MEMORY_ARCH_RESULT_PATH.with_name(
MEMORY_ARCH_RESULT_PATH.stem + '_architecture.json'
)
if companion_path.exists():
print(f'Companion already exists and was not overwritten: {companion_path}')
else:
save_record(companion_path, companion)
print(f'Saved companion: {companion_path}')
print(f'Decision: {decision} — {reason}')
else:
print('Run 5.4 before forming a decision.')