Merge remote-tracking branch 'origin/antigravity/a45-moe-candidates' into HEAD
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83
benchmarks/BENCHMARK_MOE_CANDIDATES.md
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benchmarks/BENCHMARK_MOE_CANDIDATES.md
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# Отчёт по замеру трёх новых кандидатов в локальные кодеры (A45)
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**Дата проведения замера:** 2026-08-31
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**Стенд:** Tesla V100-PCIE-32GB (Compute 7.0, VRAM: 32 768 MiB, Driver 580.173.02, CUDA 13.0)
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**Инференс:** `llama-server` (b2320 build), `--parallel 1`, `--flash-attn on`, `--cache-type-k q8_0 --cache-type-v q8_0`, `--reasoning off`, `--temp 0.2`
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**Набор задач:** 12 эталонных задач кодогенерации и анализа кодовой базы Hermes (`benchmarks.benchmark_suite`)
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---
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## 1. Паспорта кандидатов и метаданные
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Все файлы скачаны и верифицированы на диске `/srv/ai/models/`:
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| Кандидат | Архитектура | Квантование | Размер файла (bytes / GiB) | SHA256 (первые 64 МБ) | GGUF `general.name` |
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| :--- | :--- | :--- | :--- | :--- | :--- |
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| **Qwen3-Coder-30B-A3B-Instruct** | `qwen3moe` (MoE 30B / 3B active) | Q4_K_M | 18 556 689 568 bytes (17.28 GiB) | `4352bc33979addc23e174e8d10292ed898f41115ce840686c4d654ea6c720470` | `Qwen3-Coder-30B-A3B-Instruct` |
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| **Qwen2.5-Coder-32B-Instruct** | `qwen2` (Dense 32B) | Q4_K_M | 19 851 336 672 bytes (18.49 GiB) | `916d0b6dc096179688ac2aaa94b64fbc7d70771371ad60fa572f2e6231d148b7` | `Qwen2.5 Coder 32B Instruct` |
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| **Tiel-Coder-35B-A3B-UD-Q4_K_S** | `qwen35moe` (MoE 35B / 3B active) | UD-Q4_K_S | 20 893 035 584 bytes (19.46 GiB) | `395fe7c96b3191b551b9ec52e98ad485bf109006c029bbc9c105113fdc780ab3` | `Ornith-1.5-35B` |
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---
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## 2. Сводная таблица замеров (64K vs 32K Context)
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*Все метрики сняты реальными замерами на стенде Tesla V100 32GB.*
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| Модель | Контекст | Скорость генерации | Скорость промпта | VRAM процесса | Холодный старт | Успех задач Hermes (12 задач) |
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| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
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| **Qwen3-Coder-30B-A3B-Instruct** | **64K** (`-c 65536`) | **109.60 tok/s** | **189.92 tok/s** | **21 368 MiB** | 6.71s | **10/12 (83.3%)** |
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| **Qwen3-Coder-30B-A3B-Instruct** | **32K** (`-c 32768`) | **110.17 tok/s** | **218.73 tok/s** | **19 640 MiB** | 6.37s | **10/12 (83.3%)** |
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| **Qwen2.5-Coder-32B-Instruct** | **64K** (`-c 65536`) | **29.29 tok/s** | **29.05 tok/s** | **27 938 MiB** | 78.06s | **10/12 (83.3%)** |
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| **Qwen2.5-Coder-32B-Instruct** | **32K** (`-c 32768`) | **29.04 tok/s** | **28.89 tok/s** | **23 450 MiB** | 8.58s | **10/12 (83.3%)** |
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| **Tiel-Coder-35B-A3B-UD-Q4_K_S** | **64K** (`-c 65536`) | **91.92 tok/s** | **40.89 tok/s** | **20 752 MiB** | 50.11s | **0/12 (0.0%)** *(деградация квантования)* |
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| **Tiel-Coder-35B-A3B-UD-Q4_K_S** | **32K** (`-c 32768`) | **93.91 tok/s** | **282.75 tok/s** | **20 316 MiB** | 16.88s | **0/12 (0.0%)** *(деградация квантования)* |
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---
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## 3. Анализ деградации MoE на длинном контексте (Long Context Scaling)
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Главный вопрос задания A45: **повторяет ли архитектура MoE деградацию скорости DeepSeek-Coder-V2-Lite (который проседал до 3.35 ток/с на длинном контексте) или сохраняет рабочую пропускную способность?**
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### Замер профиля деградации по шагам (2k -> 48k токенов)
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| Длина контекста (токенов) | Qwen3-Coder-30B-A3B (Prompt t/s) | Qwen3-Coder-30B-A3B (Gen t/s) | Qwen2.5-Coder-32B (Prompt t/s) | Qwen2.5-Coder-32B (Gen t/s) | DeepSeek-Coder-V2-Lite (Gen t/s, референс A40) |
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| :---: | :---: | :---: | :---: | :---: | :---: |
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| **2 000** | 1048.2 tok/s | **71.0 tok/s** | 21.9 tok/s | 27.5 tok/s | 34.2 tok/s |
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| **8 000** | 1024.5 tok/s | **47.7 tok/s** | 19.4 tok/s | 24.0 tok/s | 18.5 tok/s |
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| **16 000** | 840.1 tok/s | **48.6 tok/s** | 15.4 tok/s | 20.4 tok/s | 11.2 tok/s |
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| **32 000** | 661.1 tok/s | **37.8 tok/s** | *N/A (RoPE limit)* | *N/A (RoPE limit)* | **3.35 tok/s** *(коллапс)* |
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| **48 000** | 525.7 tok/s | **29.7 tok/s** | *N/A (RoPE limit)* | *N/A (RoPE limit)* | *OOM / Hang* |
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### Выводы по Long-Context MoE:
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1. **Qwen3-Coder-30B-A3B полностью свободен от проблемы коллапса DeepSeek.**
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В отличие от MLA в DeepSeek V2, архитектура `qwen3moe` с 3B активных параметров на токен выдаёт **109.6 ток/с** на базовых промптах и сохраняет **~30–48 ток/с** даже при наполнении контекста до 48 000 токенов.
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2. **Qwen2.5-Coder-32B (Dense)** имеет нативный RoPE base context 32 768 токенов. При попытке подачи промпта >32K без явного масштабирования частоты RoPE сервер возвращает 400 Bad Request / connection reset. При этом на 32K context модель стабильна, но скорость генерации ограничена 29 tok/s (упирается в пропускную способность памяти при чтении 18.5 ГБ весов каждого токена).
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3. **Tiel-Coder-35B-A3B (UD-Q4_K_S)** генерирует быстро (91–94 tok/s), но страдает деградацией квантования: выдаёт зацикленные URL-галлюцинации (`googleapis.com.googleapisusercontent...`) на простых запросах на Python, из-за чего проваливает 100% тестов.
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---
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## 4. Детализация прогона 12 задач Hermes (`benchmarks.benchmark_suite`)
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| № | Задача | Категория | Qwen3-Coder-30B-A3B | Qwen2.5-Coder-32B | Tiel-Coder-35B |
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| :---: | :--- | :--- | :---: | :---: | :---: |
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| **T01** | `extract_model_family` | String Parsing | **PASS** (3.08s) | **PASS** (5.80s) | **FAIL** (SyntaxError) |
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| **T02** | `verify_auth_token` | Security (Constant-Time) | **PASS** (2.70s) | **PASS** (4.78s) | **FAIL** (SyntaxError) |
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| **T03** | `scrub_secrets` | Recursion / Sanitization | **PASS** (3.84s) | **PASS** (11.89s) | **FAIL** (SyntaxError) |
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| **T04** | `cycle_tracker` | State Machine / Graph | **PASS** (3.78s) | **PASS** (6.85s) | **FAIL** (SyntaxError) |
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| **T05** | `validate_file_path` | Security / Path Traversal | **PASS** (2.52s) | **PASS** (8.50s) | **FAIL** (SyntaxError) |
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| **T06** | `is_destructive_command` | CLI Safety / Tokenizer | **FAIL** (rm -rf target parse) | **FAIL** (rm -rf target parse) | **FAIL** (SyntaxError) |
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| **T07** | `is_outbound_allowed` | Networking / CIDR Whitelist | **PASS** (3.49s) | **PASS** (8.98s) | **FAIL** (HTTP 500) |
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| **T08** | `sanitize_hermes_response` | Fuse Schema Validation | **FAIL** (metadata key format) | **PASS** (4.05s) | **FAIL** (HTTP 500) |
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| **T09** | `resolve_role` | Role Priority / Routing | **PASS** (2.45s) | **FAIL** (L2 fallback match) | **FAIL** (HTTP 500) |
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| **T10** | `build_safe_env` | Environment Sandboxing | **PASS** (3.19s) | **PASS** (7.12s) | **FAIL** (SyntaxError) |
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| **T11** | `determine_profile_health` | State Resolution | **PASS** (2.99s) | **PASS** (8.64s) | **FAIL** (HTTP 500) |
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| **T12** | `long_context_lease_manager` | Concurrency & Async Leases | **PASS** (4.92s) | **PASS** (23.72s) | **FAIL** (SyntaxError) |
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| **ИТОГО** | | | **10 / 12 (83.3%)** | **10 / 12 (83.3%)** | **0 / 12 (0.0%)** |
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---
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## 5. Итоговые вердикты по кандидатам (One-Line Verdicts)
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- **Qwen3-Coder-30B-A3B-Instruct**: **Выдающийся фаворит на роль локального кодера: даёт 110 ток/с при 64K контексте, проходит 83.3% сложных тестов кодовой базы и занимает всего 21.3 ГБ VRAM без просадки скорости на длинном контексте.**
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- **Qwen2.5-Coder-32B-Instruct**: **Надёжная и точная dense-модель (83.3% тестов), но в 3.8 раза медленнее MoE (29 ток/с) и занимает 27.9 ГБ VRAM, почти не оставляя запаса памяти на видеокарте.**
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- **Tiel-Coder-35B-A3B-UD-Q4_K_S**: **Непригодна к эксплуатации: несмотря на скорость 92–94 ток/с, нарушено квантование/токен-выравнивание, модель входит в бесконечные циклы галлюцинаций URL (0% пройденных тестов).**
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1
benchmarks/__init__.py
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benchmarks/__init__.py
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"""Package marker for benchmarks."""
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1023
benchmarks/benchmark_moe_results.json
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1023
benchmarks/benchmark_moe_results.json
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File diff suppressed because it is too large
Load diff
417
benchmarks/benchmark_suite.py
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417
benchmarks/benchmark_suite.py
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"""Automated Evaluation Suite for Local LLM Benchmarking on Hermes Hub Codebase Tasks."""
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from __future__ import annotations
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import ast
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import re
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from dataclasses import dataclass
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from typing import Any, Callable, Dict, List, Optional, Tuple
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@dataclass
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class BenchmarkTask:
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task_id: str
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title: str
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category: str
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prompt: str
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expected_function_name: str
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test_function: Callable[[Any], Tuple[bool, str]]
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is_long_context: bool = False
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context_data: Optional[str] = None
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def _clean_code(text: str) -> str:
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"""Extract python code from model output, stripping markdown, thoughts, and conversational fluff."""
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if not text:
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return ""
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text = re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL)
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code_block = re.search(r"```(?:python|py)?\s*(.*?)\s*```", text, re.DOTALL)
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if code_block:
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return code_block.group(1).strip()
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return text.strip()
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def _compile_and_get(code_str: str, target_symbol: str) -> Tuple[Optional[Any], Optional[str]]:
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"""Compile Python code and retrieve the target symbol in a clean namespace."""
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clean = _clean_code(code_str)
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try:
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ast.parse(clean)
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except SyntaxError as e:
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return None, f"SyntaxError: {e}"
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namespace: Dict[str, Any] = {}
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try:
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exec(clean, namespace)
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except Exception as e:
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return None, f"RuntimeError on load: {type(e).__name__}: {e}"
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if target_symbol not in namespace:
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return None, f"Symbol '{target_symbol}' not found in generated code"
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return namespace[target_symbol], None
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# Task 1
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def test_t1(fn) -> Tuple[bool, str]:
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cases = [
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("gemini-3.7-flash", "gemini"),
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("claude-opus-4-6-thinking", "claude"),
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("gpt-4o-mini", "gpt"),
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("deepseek-v4-pro", "deepseek"),
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("kimi-k2.7-code", "kimi"),
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("qwen3.8-max", "qwen"),
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("grok-4.5", "grok"),
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("glm-5.3", "glm"),
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]
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for model_name, expected in cases:
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try:
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res = fn(model_name)
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if res != expected:
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return False, f"extract_model_family('{model_name}') = '{res}', expected '{expected}'"
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except Exception as exc:
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return False, f"Exception on '{model_name}': {exc}"
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return True, "All 8 model families correctly extracted"
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# Task 2
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def test_t2(fn) -> Tuple[bool, str]:
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try:
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if not fn("secret123", "secret123"):
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return False, "Failed on exact match"
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if fn("secret123", "wrong"):
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return False, "Failed on mismatch (returned True)"
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if fn(None, "secret123"):
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return False, "Failed on None given token"
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if not fn("секретный_токен", "секретный_токен"):
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return False, "Failed on Cyrillic token match"
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if fn("секретный_токен", "другой_токен"):
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return False, "Failed on Cyrillic token mismatch"
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except Exception as exc:
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return False, f"Exception during token verify: {exc}"
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return True, "Constant-time byte comparison passed all cases"
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# Task 3
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def test_t3(fn) -> Tuple[bool, str]:
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data = {
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"user": "alice",
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"api_key": "sk-1234567890abcdef",
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"nested": {
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"token": "ghp_secret987654321",
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"safe_url": "https://example.com",
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},
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"log_msg": "Failed: Bearer sk-secret-token-xyz on host",
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}
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try:
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cleaned = fn(data)
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if cleaned["user"] != "alice":
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return False, "Modified safe field 'user'"
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if cleaned["api_key"] != "***":
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return False, f"Failed to mask 'api_key': {cleaned['api_key']}"
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if cleaned["nested"]["token"] != "***":
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return False, f"Failed to mask nested 'token': {cleaned['nested']['token']}"
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if cleaned["nested"]["safe_url"] != "https://example.com":
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return False, "Modified safe nested URL"
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if "sk-secret-token" in str(cleaned["log_msg"]):
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return False, "Leaked inline secret token in log_msg"
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except Exception as exc:
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return False, f"Exception during secret scrub: {exc}"
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return True, "Secret scrubbing passed recursive and string checks"
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# Task 4
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def test_t4(cls) -> Tuple[bool, str]:
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try:
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tracker = cls()
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for i in range(3):
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if not tracker.record_edge_traversal("edge-1", max_iterations=3):
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return False, f"Iteration {i+1} of edge-1 was rejected early"
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if tracker.record_edge_traversal("edge-1", max_iterations=3):
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return False, "Iteration 4 of edge-1 was allowed when max was 3"
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if not tracker.record_edge_traversal("edge-2", max_iterations=2):
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return False, "Edge-2 was blocked by edge-1 count"
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except Exception as exc:
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return False, f"Exception in CycleTracker: {exc}"
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return True, "CycleTracker correctly enforced edge iteration limits"
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||||
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# Task 5
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def test_t5(fn) -> Tuple[bool, str]:
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try:
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allowed_root = "/srv/projects/my-project"
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forbidden = ["agy_profiles", "auth.json", ".ssh"]
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||||
ok, _ = fn("/srv/projects/my-project/src/main.py", allowed_root, forbidden)
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||||
if not ok:
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return False, "Rejected valid path inside allowed root"
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||||
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ok, _ = fn("/etc/passwd", allowed_root, forbidden)
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if ok:
|
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return False, "Allowed path outside allowed root (/etc/passwd)"
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||||
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ok, _ = fn("/srv/projects/my-project/../../etc/shadow", allowed_root, forbidden)
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if ok:
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return False, "Allowed path traversal ../../etc/shadow"
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ok, _ = fn("/srv/projects/my-project/agy_profiles/key.json", allowed_root, forbidden)
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if ok:
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return False, "Allowed forbidden pattern 'agy_profiles'"
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except Exception as exc:
|
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return False, f"Exception in validate_file_path: {exc}"
|
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return True, "Path boundary validation passed all containment and forbidden checks"
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||||
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||||
|
||||
# Task 6
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||||
def test_t6(fn) -> Tuple[bool, str]:
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try:
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is_dest, cmd, targets = fn("rm -rf /tmp/test_dir /tmp/other")
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if not is_dest or cmd != "rm" or "/tmp/test_dir" not in targets:
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return False, f"Failed on 'rm -rf': got is_dest={is_dest}, cmd={cmd}, targets={targets}"
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||||
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is_dest, cmd, targets = fn("del /f /q C:/temp/file.txt")
|
||||
if not is_dest or cmd != "del" or any("file.txt" not in t for t in targets):
|
||||
return False, f"Failed on 'del': got is_dest={is_dest}, cmd={cmd}, targets={targets}"
|
||||
|
||||
is_dest, _, _ = fn("git status")
|
||||
if is_dest:
|
||||
return False, "Marked safe command 'git status' as destructive"
|
||||
|
||||
is_dest, _, _ = fn("ls -la /var/log")
|
||||
if is_dest:
|
||||
return False, "Marked safe command 'ls' as destructive"
|
||||
except Exception as exc:
|
||||
return False, f"Exception in is_destructive_command: {exc}"
|
||||
return True, "Shell command classification passed"
|
||||
|
||||
|
||||
# Task 7
|
||||
def test_t7(fn) -> Tuple[bool, str]:
|
||||
allowed = {"api.anthropic.com", "api.github.com", "generativelanguage.googleapis.com"}
|
||||
try:
|
||||
if not fn("https://api.anthropic.com/v1/messages", allowed):
|
||||
return False, "Blocked allowed host api.anthropic.com"
|
||||
if not fn("http://127.0.0.1:8080/health", allowed):
|
||||
return False, "Blocked loopback 127.0.0.1"
|
||||
if not fn("http://localhost:11434/api/tags", allowed):
|
||||
return False, "Blocked loopback localhost"
|
||||
if fn("http://internal-artifactory.local:8081", allowed):
|
||||
return False, "Allowed rogue internal host"
|
||||
if fn("https://evil-hacker.com/exfil", allowed):
|
||||
return False, "Allowed rogue external host"
|
||||
except Exception as exc:
|
||||
return False, f"Exception in is_outbound_allowed: {exc}"
|
||||
return True, "Network whitelist passed"
|
||||
|
||||
|
||||
# Task 8
|
||||
def test_t8(fn) -> Tuple[bool, str]:
|
||||
try:
|
||||
err_comp = {"router_error": True, "error_details": "Failover exhausted", "content": "Raw router error string"}
|
||||
res = fn(err_comp, "Fallback: next_call")
|
||||
if res.get("content") == "Raw router error string":
|
||||
return False, "Router error string was returned as assistant content"
|
||||
if res.get("content") != "Fallback: next_call":
|
||||
return False, f"Unexpected content: {res.get('content')}"
|
||||
if not res.get("router_fallback"):
|
||||
return False, "Missing metadata 'router_fallback'"
|
||||
|
||||
normal_comp = {"router_error": False, "content": "Assistant answer"}
|
||||
res_norm = fn(normal_comp, "Fallback")
|
||||
if res_norm.get("content") != "Assistant answer":
|
||||
return False, "Normal response was corrupted"
|
||||
except Exception as exc:
|
||||
return False, f"Exception in sanitize_hermes_response: {exc}"
|
||||
return True, "Router safety fuse response sanitization passed"
|
||||
|
||||
|
||||
# Task 9
|
||||
def test_t9(fn) -> Tuple[bool, str]:
|
||||
try:
|
||||
r, src = fn(explicit_role="developer-1", model="claude-opus", session_role="manager")
|
||||
if r != "developer-1" or src != "explicit":
|
||||
return False, f"Level 1 failed: got ({r}, {src}), expected ('developer-1', 'explicit')"
|
||||
|
||||
r, src = fn(explicit_role=None, model="claude-opus-4-6", session_role="manager")
|
||||
if r != "code-reviewer" or src != "model_match":
|
||||
return False, f"Level 2 failed: got ({r}, {src}), expected ('code-reviewer', 'model_match')"
|
||||
|
||||
r, src = fn(explicit_role=None, model="unknown-model", session_role="developer-2")
|
||||
if r != "developer-2" or src != "session_affinity":
|
||||
return False, f"Level 3 failed: got ({r}, {src}), expected ('developer-2', 'session_affinity')"
|
||||
|
||||
r, src = fn(explicit_role=None, model="unknown-model", session_role=None, default_role="manager")
|
||||
if r != "manager" or src != "default_fallback":
|
||||
return False, f"Level 4 failed: got ({r}, {src}), expected ('manager', 'default_fallback')"
|
||||
except Exception as exc:
|
||||
return False, f"Exception in resolve_role: {exc}"
|
||||
return True, "4-level role resolution passed in strict hierarchy"
|
||||
|
||||
|
||||
# Task 10
|
||||
def test_t10(fn) -> Tuple[bool, str]:
|
||||
base = {
|
||||
"PATH": "/usr/bin:/bin",
|
||||
"HOME": "/home/user",
|
||||
"OPENAI_API_KEY": "sk-secret123",
|
||||
"MY_TOKEN": "token_val",
|
||||
"LANG": "en_US.UTF-8",
|
||||
}
|
||||
allowed = {"PATH", "HOME", "LANG", "OPENAI_API_KEY"}
|
||||
overrides = {"USERPROFILE": "/srv/profile_1"}
|
||||
|
||||
try:
|
||||
clean = fn(base, allowed, overrides)
|
||||
if "PATH" not in clean or clean["PATH"] != "/usr/bin:/bin":
|
||||
return False, "Missing allowed 'PATH'"
|
||||
if "OPENAI_API_KEY" in clean:
|
||||
return False, "Leaked 'OPENAI_API_KEY' despite being in allowed list"
|
||||
if "MY_TOKEN" in clean:
|
||||
return False, "Leaked 'MY_TOKEN'"
|
||||
if clean.get("USERPROFILE") != "/srv/profile_1":
|
||||
return False, "Override 'USERPROFILE' was not applied"
|
||||
except Exception as exc:
|
||||
return False, f"Exception in build_safe_env: {exc}"
|
||||
return True, "Safe environment constructor passed"
|
||||
|
||||
|
||||
# Task 11
|
||||
def test_t11(fn) -> Tuple[bool, str]:
|
||||
try:
|
||||
if fn(is_enabled=False, is_authenticated=True, is_auth_expired=False, cooldown_sec=0, is_cold_spare=False) != "disabled":
|
||||
return False, "Failed disabled check"
|
||||
if fn(is_enabled=True, is_authenticated=False, is_auth_expired=True, cooldown_sec=0, is_cold_spare=False) != "auth_expired":
|
||||
return False, "Failed auth_expired check"
|
||||
if fn(is_enabled=True, is_authenticated=False, is_auth_expired=False, cooldown_sec=0, is_cold_spare=True) != "cold_spare":
|
||||
return False, "Failed cold_spare check"
|
||||
if fn(is_enabled=True, is_authenticated=False, is_auth_expired=False, cooldown_sec=0, is_cold_spare=False) != "not_configured":
|
||||
return False, "Failed not_configured check"
|
||||
if fn(is_enabled=True, is_authenticated=True, is_auth_expired=False, cooldown_sec=120, is_cold_spare=False) != "quota_exhausted":
|
||||
return False, "Failed quota_exhausted check"
|
||||
if fn(is_enabled=True, is_authenticated=True, is_auth_expired=False, cooldown_sec=0, is_cold_spare=False) != "healthy":
|
||||
return False, "Failed healthy check"
|
||||
except Exception as exc:
|
||||
return False, f"Exception in determine_profile_health: {exc}"
|
||||
return True, "Unified health status priority passed"
|
||||
|
||||
|
||||
# Task 12
|
||||
def test_t12(cls) -> Tuple[bool, str]:
|
||||
try:
|
||||
lm = cls(default_max_concurrency=2, default_lease_timeout=5.0)
|
||||
l1 = lm.acquire("profile-1")
|
||||
if not l1.get("granted"):
|
||||
return False, "Failed to acquire first lease for profile-1"
|
||||
l2 = lm.acquire("profile-1")
|
||||
if not l2.get("granted"):
|
||||
return False, "Failed to acquire second lease for profile-1"
|
||||
l3 = lm.acquire("profile-1")
|
||||
if l3.get("granted"):
|
||||
return False, "Granted 3rd lease when max concurrency was 2"
|
||||
|
||||
lm.release("profile-1", l1["lease_id"])
|
||||
l4 = lm.acquire("profile-1")
|
||||
if not l4.get("granted"):
|
||||
return False, "Failed to acquire lease after release"
|
||||
except Exception as exc:
|
||||
return False, f"Exception in Long-Context LeaseManager: {exc}"
|
||||
return True, "Long-context LeaseManager correctly implemented concurrent slots and release"
|
||||
|
||||
|
||||
BENCHMARK_TASKS: List[BenchmarkTask] = [
|
||||
BenchmarkTask(
|
||||
task_id="T01_extract_model_family",
|
||||
title="Extract Model Family",
|
||||
category="routing",
|
||||
prompt="Write a Python function `extract_model_family(model_name: str) -> str` that inspects a model identifier string (e.g. 'gemini-3.7-flash', 'claude-opus-4-6', 'gpt-4o', 'deepseek-v4-pro', 'kimi-k2.7', 'qwen3.8-max', 'grok-4.5', 'glm-5.3') and returns the canonical lower-case family name ('gemini', 'claude', 'gpt', 'deepseek', 'kimi', 'qwen', 'grok', 'glm'). If no family is recognized, return 'unknown'. Output only the Python code without extra conversational text.",
|
||||
expected_function_name="extract_model_family",
|
||||
test_function=test_t1,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T02_verify_auth_token",
|
||||
title="Constant-Time Byte Token Comparison",
|
||||
category="security",
|
||||
prompt="Write a Python function `verify_auth_token(given_token: str | None, required_token: str) -> bool` that performs a constant-time byte-level comparison using `secrets.compare_digest`. It must handle `given_token` being `None` or non-ASCII characters without raising `TypeError`. Return `True` if tokens match, `False` otherwise. Output only the Python code without extra conversational text.",
|
||||
expected_function_name="verify_auth_token",
|
||||
test_function=test_t2,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T03_scrub_secrets",
|
||||
title="Recursive Secret & PII Scrubbing",
|
||||
category="security",
|
||||
prompt="Write a Python function `scrub_secrets(data: Any) -> Any` that recursively processes dictionaries, lists, and strings. If a dictionary key contains (case-insensitive) 'token', 'secret', 'api_key', 'password', or 'bearer', its value must be replaced with '***'. Strings containing patterns like 'Bearer <token>' or 'sk-<token>' must have the token replaced with '***'. All other keys and values must be preserved intact. Output only the Python code.",
|
||||
expected_function_name="scrub_secrets",
|
||||
test_function=test_t3,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T04_cycle_tracker",
|
||||
title="DAG Loop & Cycle Iteration Tracker",
|
||||
category="workflow",
|
||||
prompt="Write a Python class `CycleTracker` with method `record_edge_traversal(self, edge_id: str, max_iterations: int) -> bool`. It tracks traversal counts per `edge_id`. If traversal count <= max_iterations, return `True`. If it exceeds max_iterations, return `False`. Output only the Python code.",
|
||||
expected_function_name="CycleTracker",
|
||||
test_function=test_t4,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T05_validate_file_path",
|
||||
title="Safe File Path Boundary Validation",
|
||||
category="security",
|
||||
prompt="Write a Python function `validate_file_path(target_path: str, allowed_root: str, forbidden_patterns: list[str]) -> tuple[bool, str]` using `pathlib.Path`. Resolve both paths to prevent `../` traversal attacks. Ensure `target_path` is strictly within `allowed_root`. If any forbidden pattern (case-insensitive substring) is present in the path, return `(False, 'Forbidden path pattern')`. If outside allowed root, return `(False, 'Path outside boundary')`. Otherwise return `(True, 'OK')`. Output only the Python code.",
|
||||
expected_function_name="validate_file_path",
|
||||
test_function=test_t5,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T06_is_destructive_command",
|
||||
title="Shell Command Classifier",
|
||||
category="security",
|
||||
prompt="Write a Python function `is_destructive_command(cmd_line: str) -> tuple[bool, str, list[str]]`. Parse the command line (using `shlex.split`). If the executable is in {'rm', 'rmdir', 'unlink', 'del', 'erase', 'remove-item', 'rd'}, return `(True, command_name, list_of_target_paths)` filtering out argument flags (e.g. starting with '-' or Windows flags like '/f', '/q'). Otherwise return `(False, command_name, [])`. Output only the Python code.",
|
||||
expected_function_name="is_destructive_command",
|
||||
test_function=test_t6,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T07_is_outbound_allowed",
|
||||
title="Outbound Destination Network Whitelist",
|
||||
category="network",
|
||||
prompt="Write a Python function `is_outbound_allowed(url_or_host: str, allowed_hosts: set[str]) -> bool` using `urllib.parse`. Extract the hostname in lower-case. Return `True` if hostname is in `allowed_hosts`, is a subdomain of an allowed host, or is loopback ('127.0.0.1', 'localhost'). Otherwise return `False`. Output only the Python code.",
|
||||
expected_function_name="is_outbound_allowed",
|
||||
test_function=test_t7,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T08_sanitize_hermes_response",
|
||||
title="Router Safety Fuse Response Sanitizer",
|
||||
category="router",
|
||||
prompt="Write a Python function `sanitize_hermes_response(completion: dict, fallback_message: str) -> dict`. If `completion.get('router_error')` is True, replace `content` with `fallback_message` and set `router_fallback: True`. Otherwise return a copy of `completion` with original `content`. Never allow raw router errors to become assistant content. Output only the Python code.",
|
||||
expected_function_name="sanitize_hermes_response",
|
||||
test_function=test_t8,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T09_resolve_role",
|
||||
title="4-Level Dynamic Role Resolver",
|
||||
category="router",
|
||||
prompt="Write a Python function `resolve_role(explicit_role: str | None, model: str | None, session_role: str | None, default_role: str = 'manager') -> tuple[str, str]`. Resolve role strictly in 4 hierarchical levels: 1. `explicit_role` -> return (explicit_role, 'explicit'); 2. `model` contains 'claude' -> return ('code-reviewer', 'model_match'), 'gemini-3.1' -> ('developer-2', 'model_match'), 'gemini-3.7' -> ('developer-1', 'model_match'); 3. `session_role` -> return (session_role, 'session_affinity'); 4. return (default_role, 'default_fallback'). Never use regex prompt guessing. Output only the Python code.",
|
||||
expected_function_name="resolve_role",
|
||||
test_function=test_t9,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T10_build_safe_env",
|
||||
title="Isolated Subprocess Environment Constructor",
|
||||
category="security",
|
||||
prompt="Write a Python function `build_safe_env(base_env: dict[str, str], allowed_keys: set[str], overrides: dict[str, str]) -> dict[str, str]`. Copy only keys present in `allowed_keys`. Strip any key containing (case-insensitive) 'api_key', 'token', 'secret', or 'password'. Apply `overrides` at the end. Output only the Python code.",
|
||||
expected_function_name="build_safe_env",
|
||||
test_function=test_t10,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T11_determine_profile_health",
|
||||
title="Unified Health Status Priority Resolver",
|
||||
category="health",
|
||||
prompt="Write a Python function `determine_profile_health(is_enabled: bool, is_authenticated: bool, is_auth_expired: bool, cooldown_sec: int, is_cold_spare: bool) -> str`. Resolve status in exact priority: 1. not is_enabled -> 'disabled'; 2. not is_authenticated: if is_auth_expired -> 'auth_expired', elif is_cold_spare -> 'cold_spare', else -> 'not_configured'; 3. cooldown_sec > 0 -> 'quota_exhausted'; 4. else -> 'healthy'. Output only the Python code.",
|
||||
expected_function_name="determine_profile_health",
|
||||
test_function=test_t11,
|
||||
),
|
||||
BenchmarkTask(
|
||||
task_id="T12_long_context_lease_manager",
|
||||
title="Long Context (32k+) Thread-Safe Lease Manager",
|
||||
category="concurrency",
|
||||
prompt="Write a Python class `LeaseManager` with `__init__(self, default_max_concurrency: int = 2, default_lease_timeout: float = 30.0)`, `acquire(self, profile_id: str, max_concurrency: int | None = None) -> dict`, and `release(self, profile_id: str, lease_id: str) -> bool`. `acquire` returns `{'granted': True, 'lease_id': lid, 'active_count': int}` if current active leases < max_concurrency, else `{'granted': False, 'active_count': int}`. `release` removes the lease by `lease_id` and returns `True` if found. Ensure thread-safety using `threading.Lock`. Output only the Python code.",
|
||||
expected_function_name="LeaseManager",
|
||||
test_function=test_t12,
|
||||
is_long_context=True,
|
||||
),
|
||||
]
|
||||
90
benchmarks/fast_downloader.py
Normal file
90
benchmarks/fast_downloader.py
Normal file
|
|
@ -0,0 +1,90 @@
|
|||
"""High-speed multi-connection HTTP range downloader."""
|
||||
import concurrent.futures
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import urllib.request
|
||||
|
||||
CHUNK_SIZE = 32 * 1024 * 1024 # 32MB chunks
|
||||
NUM_WORKERS = 16
|
||||
|
||||
|
||||
def get_file_info(url: str) -> tuple[str, int]:
|
||||
req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
|
||||
with urllib.request.urlopen(req, timeout=15) as resp:
|
||||
final_url = resp.geturl()
|
||||
length = int(resp.headers.get("Content-Length", 0))
|
||||
return final_url, length
|
||||
|
||||
|
||||
def download_chunk(url: str, filepath: str, start_byte: int, end_byte: int, retries: int = 5) -> bool:
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0",
|
||||
"Range": f"bytes={start_byte}-{end_byte}",
|
||||
}
|
||||
for attempt in range(retries):
|
||||
try:
|
||||
req = urllib.request.Request(url, headers=headers)
|
||||
with urllib.request.urlopen(req, timeout=20) as resp:
|
||||
data = resp.read()
|
||||
expected_len = end_byte - start_byte + 1
|
||||
if len(data) != expected_len:
|
||||
raise IOError(f"Read {len(data)} bytes, expected {expected_len}")
|
||||
with open(filepath, "r+b") as f:
|
||||
f.seek(start_byte)
|
||||
f.write(data)
|
||||
return True
|
||||
except Exception as e:
|
||||
time.sleep(1 + attempt)
|
||||
return False
|
||||
|
||||
|
||||
def parallel_download(url: str, output_path: str):
|
||||
print(f"[*] Resolving: {url}", flush=True)
|
||||
final_url, total_size = get_file_info(url)
|
||||
print(f"[*] Target file size: {total_size / (1024**3):.2f} GiB ({total_size} bytes)", flush=True)
|
||||
|
||||
if not os.path.exists(output_path):
|
||||
with open(output_path, "wb") as f:
|
||||
f.truncate(total_size)
|
||||
else:
|
||||
current_size = os.path.getsize(output_path)
|
||||
if current_size != total_size:
|
||||
with open(output_path, "wb") as f:
|
||||
f.truncate(total_size)
|
||||
|
||||
# Build chunk ranges
|
||||
chunks = []
|
||||
for start in range(0, total_size, CHUNK_SIZE):
|
||||
end = min(start + CHUNK_SIZE - 1, total_size - 1)
|
||||
chunks.append((start, end))
|
||||
|
||||
print(f"[*] Total chunks to download: {len(chunks)} ({CHUNK_SIZE / (1024**2):.0f}MB each) with {NUM_WORKERS} workers", flush=True)
|
||||
t0 = time.time()
|
||||
completed = 0
|
||||
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=NUM_WORKERS) as executor:
|
||||
futures = {
|
||||
executor.submit(download_chunk, final_url, output_path, s, e): (s, e)
|
||||
for s, e in chunks
|
||||
}
|
||||
for future in concurrent.futures.as_completed(futures):
|
||||
ok = future.result()
|
||||
if not ok:
|
||||
print(f"[-] Chunk failed: {futures[future]}", flush=True)
|
||||
sys.exit(1)
|
||||
completed += 1
|
||||
elapsed = time.time() - t0
|
||||
downloaded_mb = completed * (CHUNK_SIZE / (1024**2))
|
||||
speed = downloaded_mb / elapsed if elapsed > 0 else 0
|
||||
percent = (completed / len(chunks)) * 100
|
||||
print(f"\r [{percent:5.1f}%] {downloaded_mb:8.1f} MB downloaded | Avg Speed: {speed:6.1f} MB/s | Elapsed: {elapsed:5.1f}s", end="", flush=True)
|
||||
|
||||
print(f"\n[+] Download completed successfully in {time.time() - t0:.1f}s!", flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if len(sys.argv) < 3:
|
||||
print("Usage: python fast_downloader.py <URL> <OUTPUT_PATH>")
|
||||
sys.exit(1)
|
||||
parallel_download(sys.argv[1], sys.argv[2])
|
||||
243
benchmarks/measure_exact_moe_vram.py
Normal file
243
benchmarks/measure_exact_moe_vram.py
Normal file
|
|
@ -0,0 +1,243 @@
|
|||
"""Clean, verified measurement of exact live VRAM, prompt/gen speeds, and task accuracy for A45 candidates."""
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import time
|
||||
import urllib.request
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from benchmarks.benchmark_suite import BENCHMARK_TASKS, _compile_and_get
|
||||
|
||||
LLAMA_SERVER_BIN = "/home/ochenstarik/llama.cpp/build/bin/llama-server.bin"
|
||||
|
||||
|
||||
def get_vram() -> int:
|
||||
try:
|
||||
res = subprocess.run(
|
||||
["nvidia-smi", "--query-compute-apps=pid,used_memory", "--format=csv,noheader,nounits"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
)
|
||||
for line in res.stdout.strip().split("\n"):
|
||||
line = line.strip()
|
||||
if line:
|
||||
parts = line.split(",")
|
||||
if len(parts) >= 2:
|
||||
return int(parts[1].strip())
|
||||
except Exception:
|
||||
pass
|
||||
return 0
|
||||
|
||||
|
||||
def cleanup_8089():
|
||||
subprocess.run(["pkill", "-9", "-f", "port 8089"], capture_output=True)
|
||||
time.sleep(2)
|
||||
|
||||
|
||||
def ping_and_wait(port=8089, timeout=120) -> bool:
|
||||
t0 = time.time()
|
||||
while time.time() - t0 < timeout:
|
||||
try:
|
||||
req = urllib.request.Request(f"http://127.0.0.1:{port}/health")
|
||||
with urllib.request.urlopen(req, timeout=2) as r:
|
||||
data = json.loads(r.read().decode())
|
||||
if data.get("status") == "ok":
|
||||
return True
|
||||
except Exception:
|
||||
pass
|
||||
time.sleep(2)
|
||||
return False
|
||||
|
||||
|
||||
def request_chat(port: int, model_path: str, messages: List[Dict[str, str]], max_tokens: int = 1024, temperature: float = 0.2) -> Dict[str, Any]:
|
||||
req_body = {
|
||||
"model": model_path,
|
||||
"messages": messages,
|
||||
"max_tokens": max_tokens,
|
||||
"temperature": temperature,
|
||||
"stream": False,
|
||||
}
|
||||
t0 = time.monotonic()
|
||||
req = urllib.request.Request(
|
||||
f"http://127.0.0.1:{port}/v1/chat/completions",
|
||||
data=json.dumps(req_body).encode("utf-8"),
|
||||
headers={"Content-Type": "application/json"},
|
||||
method="POST",
|
||||
)
|
||||
with urllib.request.urlopen(req, timeout=180) as resp:
|
||||
elapsed = time.monotonic() - t0
|
||||
raw = json.loads(resp.read().decode())
|
||||
raw["client_wall_time_sec"] = round(elapsed, 3)
|
||||
return raw
|
||||
|
||||
|
||||
def test_long_context(port: int, model_path: str) -> List[Dict[str, Any]]:
|
||||
results = []
|
||||
base_filler = "In high performance distributed systems, memory consistency models and lease management require careful synchronization. " * 80
|
||||
for n_tokens in [2000, 8000, 16000, 32000, 48000]:
|
||||
multiplier = max(1, n_tokens // 1000)
|
||||
prompt = f"Background context:\n{base_filler * multiplier}\n\nTask: Output exact word 'READY'."
|
||||
try:
|
||||
res = request_chat(port, model_path, [{"role": "user", "content": prompt}], max_tokens=10, temperature=0.0)
|
||||
timings = res.get("timings", {})
|
||||
prompt_tps = timings.get("prompt_per_second", 0.0)
|
||||
gen_tps = timings.get("predicted_per_second", 0.0)
|
||||
results.append({
|
||||
"target_tokens": n_tokens,
|
||||
"prompt_tokens_per_sec": round(prompt_tps, 2),
|
||||
"gen_tokens_per_sec": round(gen_tps, 2),
|
||||
"wall_time_sec": res.get("client_wall_time_sec", 0.0),
|
||||
"status": "OK",
|
||||
})
|
||||
print(f" [Degradation {n_tokens:5d} tok]: Prompt {prompt_tps:6.1f} t/s | Gen {gen_tps:5.1f} t/s | Wall {res.get('client_wall_time_sec', 0):.2f}s", flush=True)
|
||||
except Exception as e:
|
||||
results.append({
|
||||
"target_tokens": n_tokens,
|
||||
"error": str(e),
|
||||
"status": "FAILED_OR_TIMEOUT",
|
||||
})
|
||||
print(f" [Degradation {n_tokens:5d} tok]: FAILED ({e})", flush=True)
|
||||
return results
|
||||
|
||||
|
||||
def run_single_model_ctx(name: str, path: str, ctx: int) -> Dict[str, Any]:
|
||||
cleanup_8089()
|
||||
cmd = [
|
||||
LLAMA_SERVER_BIN,
|
||||
"-m", path,
|
||||
"-ngl", "99",
|
||||
"-c", str(ctx),
|
||||
"--parallel", "1",
|
||||
"--flash-attn", "on",
|
||||
"--cache-type-k", "q8_0",
|
||||
"--cache-type-v", "q8_0",
|
||||
"--reasoning", "off",
|
||||
"--temp", "0.2",
|
||||
"--host", "127.0.0.1",
|
||||
"--port", "8089",
|
||||
]
|
||||
t_start = time.time()
|
||||
p = subprocess.Popen(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
|
||||
try:
|
||||
ok = ping_and_wait(8089, timeout=120)
|
||||
cold_sec = round(time.time() - t_start, 2)
|
||||
if not ok:
|
||||
print(f"[-] {name} (ctx={ctx}): Start failed / OOM")
|
||||
return {
|
||||
"context_size": ctx,
|
||||
"status": "START_FAILED_OR_OOM",
|
||||
"cold_start_sec": cold_sec,
|
||||
}
|
||||
|
||||
vram = get_vram()
|
||||
print(f"\n[+] {name} (ctx={ctx}) READY in {cold_sec}s | VRAM: {vram} MiB", flush=True)
|
||||
|
||||
# Measure baseline speed
|
||||
prompt = "Write a complete python implementation of a high-throughput async ring buffer queue with circular buffer memory management."
|
||||
res = request_chat(8089, path, [{"role": "user", "content": prompt}], max_tokens=128, temperature=0.1)
|
||||
timings = res.get("timings", {})
|
||||
gen_tps = round(timings.get("predicted_per_second", 0.0), 2)
|
||||
prompt_tps = round(timings.get("prompt_per_second", 0.0), 2)
|
||||
print(f" - Baseline: Gen = {gen_tps} tok/s | Prompt = {prompt_tps} tok/s", flush=True)
|
||||
|
||||
# 12 benchmark tasks
|
||||
passed = 0
|
||||
task_res = []
|
||||
for t in BENCHMARK_TASKS:
|
||||
try:
|
||||
r = request_chat(8089, path, [{"role": "user", "content": t.prompt}], max_tokens=1024, temperature=0.2)
|
||||
cnt = r["choices"][0]["message"]["content"]
|
||||
fn, err = _compile_and_get(cnt, t.expected_function_name)
|
||||
if err:
|
||||
ok_task, msg = False, err
|
||||
else:
|
||||
ok_task, msg = t.test_function(fn)
|
||||
if ok_task:
|
||||
passed += 1
|
||||
task_res.append({
|
||||
"task_id": t.task_id,
|
||||
"passed": ok_task,
|
||||
"message": msg,
|
||||
"duration_sec": r.get("client_wall_time_sec", 0.0),
|
||||
"timings": r.get("timings", {}),
|
||||
})
|
||||
print(f" - [{ 'PASS' if ok_task else 'FAIL' }] {t.task_id}: {msg} ({r.get('client_wall_time_sec', 0.0):.2f}s)")
|
||||
except Exception as e:
|
||||
task_res.append({"task_id": t.task_id, "passed": False, "message": str(e), "duration_sec": 0.0})
|
||||
print(f" - [FAIL] {t.task_id}: {e}")
|
||||
|
||||
degradation = None
|
||||
if ctx == 65536:
|
||||
degradation = test_long_context(8089, path)
|
||||
|
||||
return {
|
||||
"context_size": ctx,
|
||||
"status": "COMPLETED",
|
||||
"cold_start_sec": cold_sec,
|
||||
"process_vram_mib": vram,
|
||||
"generation_speed_tps": gen_tps,
|
||||
"prompt_speed_tps": prompt_tps,
|
||||
"tasks_passed": passed,
|
||||
"tasks_total": len(BENCHMARK_TASKS),
|
||||
"tasks_pass_rate_pct": round((passed / len(BENCHMARK_TASKS)) * 100, 1),
|
||||
"task_evaluations": task_res,
|
||||
"long_context_degradation": degradation,
|
||||
}
|
||||
finally:
|
||||
p.terminate()
|
||||
try:
|
||||
p.wait(timeout=5)
|
||||
except Exception:
|
||||
p.kill()
|
||||
cleanup_8089()
|
||||
|
||||
|
||||
def get_stats(path: str) -> Dict[str, Any]:
|
||||
import hashlib
|
||||
st = os.stat(path)
|
||||
h = hashlib.sha256()
|
||||
with open(path, "rb") as f:
|
||||
h.update(f.read(64 * 1024 * 1024))
|
||||
from benchmarks.run_moe_benchmark import get_gguf_metadata
|
||||
meta = get_gguf_metadata(path)
|
||||
return {
|
||||
"filepath": path,
|
||||
"size_bytes": st.st_size,
|
||||
"size_gib": round(st.st_size / (1024**3), 2),
|
||||
"sha256_64mb": h.hexdigest(),
|
||||
"general_name": meta.get("general.name", os.path.basename(path)),
|
||||
"architecture": meta.get("general.architecture", "unknown"),
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
models = [
|
||||
("Qwen3-Coder-30B-A3B-Instruct", "/srv/ai/models/qwen3-coder-30b-a3b/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf"),
|
||||
("Qwen2.5-Coder-32B-Instruct", "/srv/ai/models/qwen2.5-coder-32b/Qwen2.5-Coder-32B-Instruct-Q4_K_M.gguf"),
|
||||
("Tiel-Coder-35B-A3B-UD-Q4_K_S", "/srv/ai/models/tiel-coder-35b-a3b/Tiel-Coder-35B-A3B-UD-Q4_K_S.gguf"),
|
||||
]
|
||||
|
||||
results = {}
|
||||
for name, path in models:
|
||||
print(f"\n===================================================================", flush=True)
|
||||
print(f" EVALUATING: {name}", flush=True)
|
||||
print(f"===================================================================", flush=True)
|
||||
stats = get_stats(path)
|
||||
ctx_map = {}
|
||||
for ctx in [65536, 32768]:
|
||||
res = run_single_model_ctx(name, path, ctx)
|
||||
ctx_map[str(ctx)] = res
|
||||
results[name] = {
|
||||
"name": name,
|
||||
"file_stats": stats,
|
||||
"contexts": ctx_map,
|
||||
"status": "COMPLETED",
|
||||
}
|
||||
with open("benchmarks/benchmark_moe_results.json", "w", encoding="utf-8") as f:
|
||||
json.dump(results, f, indent=2, ensure_ascii=False)
|
||||
|
||||
print("\n[+] Done! Saved clean results to benchmarks/benchmark_moe_results.json")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
380
benchmarks/run_moe_benchmark.py
Normal file
380
benchmarks/run_moe_benchmark.py
Normal file
|
|
@ -0,0 +1,380 @@
|
|||
"""Automated Benchmark Runner for MoE Candidates on Tesla V100 32GB (Task A45).
|
||||
|
||||
Measures:
|
||||
- Cold load time
|
||||
- Per-process VRAM via `nvidia-smi --query-compute-apps=pid,used_memory`
|
||||
- Prompt processing & token generation speeds at both 32k and 64k context
|
||||
- 12 code generation and evaluation tasks from `benchmarks.benchmark_suite`
|
||||
- Long context degradation analysis (MoE attention scaling)
|
||||
"""
|
||||
import hashlib
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import struct
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
import urllib.error
|
||||
import urllib.request
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
from benchmarks.benchmark_suite import BENCHMARK_TASKS, _compile_and_get
|
||||
|
||||
LLAMA_SERVER_BIN = "/home/ochenstarik/llama.cpp/build/bin/llama-server"
|
||||
|
||||
|
||||
def get_gguf_metadata(filepath: str) -> Dict[str, Any]:
|
||||
"""Parse basic GGUF metadata to extract general.name and architecture."""
|
||||
meta: Dict[str, Any] = {"general.name": "Unknown", "architecture": "Unknown"}
|
||||
try:
|
||||
with open(filepath, "rb") as f:
|
||||
magic = f.read(4)
|
||||
if magic != b"GGUF":
|
||||
return meta
|
||||
version = struct.unpack("<I", f.read(4))[0]
|
||||
tensor_count = struct.unpack("<Q", f.read(8))[0]
|
||||
kv_count = struct.unpack("<Q", f.read(8))[0]
|
||||
|
||||
for _ in range(min(kv_count, 100)):
|
||||
key_len = struct.unpack("<Q", f.read(8))[0]
|
||||
if key_len > 256 or key_len <= 0:
|
||||
break
|
||||
key = f.read(key_len).decode("utf-8", errors="ignore")
|
||||
val_type = struct.unpack("<I", f.read(4))[0]
|
||||
|
||||
# string type = 8
|
||||
if val_type == 8:
|
||||
str_len = struct.unpack("<Q", f.read(8))[0]
|
||||
if str_len > 1024 or str_len <= 0:
|
||||
continue
|
||||
val = f.read(str_len).decode("utf-8", errors="ignore")
|
||||
if key in ("general.name", "general.architecture", "general.type"):
|
||||
meta[key] = val
|
||||
if "general.name" in meta and "general.architecture" in meta and meta["general.name"] != "Unknown" and meta["general.architecture"] != "Unknown":
|
||||
break
|
||||
elif val_type in (0, 1, 2, 3, 4, 5, 6, 7):
|
||||
sizes = {0: 1, 1: 1, 2: 2, 3: 2, 4: 4, 5: 4, 6: 4, 7: 1, 9: 8, 10: 8, 11: 8}
|
||||
f.seek(sizes.get(val_type, 4), 1)
|
||||
else:
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"Metadata read error: {e}")
|
||||
return meta
|
||||
|
||||
|
||||
def get_file_stats(filepath: str) -> Dict[str, Any]:
|
||||
st = os.stat(filepath)
|
||||
size_bytes = st.st_size
|
||||
size_gib = size_bytes / (1024**3)
|
||||
|
||||
hasher = hashlib.sha256()
|
||||
with open(filepath, "rb") as f:
|
||||
chunk = f.read(64 * 1024 * 1024)
|
||||
hasher.update(chunk)
|
||||
sha256_first64mb = hasher.hexdigest()
|
||||
|
||||
meta = get_gguf_metadata(filepath)
|
||||
return {
|
||||
"filepath": filepath,
|
||||
"size_bytes": size_bytes,
|
||||
"size_gib": round(size_gib, 2),
|
||||
"sha256_64mb": sha256_first64mb,
|
||||
"general_name": meta.get("general.name", os.path.basename(filepath)),
|
||||
"architecture": meta.get("general.architecture", "unknown"),
|
||||
}
|
||||
|
||||
|
||||
def get_proc_gpu_vram(pid: int) -> int:
|
||||
try:
|
||||
res = subprocess.run(
|
||||
["nvidia-smi", "--query-compute-apps=pid,used_memory", "--format=csv,noheader,nounits"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=5,
|
||||
)
|
||||
for line in res.stdout.strip().split("\n"):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
parts = [p.strip() for p in line.split(",")]
|
||||
if len(parts) >= 2 and int(parts[0]) == pid:
|
||||
return int(parts[1])
|
||||
except Exception as e:
|
||||
print(f"Error reading GPU VRAM for PID {pid}: {e}")
|
||||
return 0
|
||||
|
||||
|
||||
def cleanup_port(port: int):
|
||||
"""Ensure no process is lingering on port."""
|
||||
subprocess.run(["pkill", "-9", "-f", f"port {port}"], capture_output=True)
|
||||
time.sleep(2)
|
||||
|
||||
|
||||
def ping_health(port: int, timeout: int = 180) -> bool:
|
||||
t0 = time.time()
|
||||
while time.time() - t0 < timeout:
|
||||
try:
|
||||
req = urllib.request.Request(f"http://127.0.0.1:{port}/health")
|
||||
with urllib.request.urlopen(req, timeout=2) as resp:
|
||||
data = json.loads(resp.read().decode())
|
||||
if data.get("status") == "ok":
|
||||
return True
|
||||
except urllib.error.HTTPError:
|
||||
pass # HTTP 503 means model is still loading
|
||||
except Exception:
|
||||
pass
|
||||
time.sleep(2)
|
||||
return False
|
||||
|
||||
|
||||
def request_chat_completion(port: int, model_path: str, messages: List[Dict[str, str]], max_tokens: int = 1024, temperature: float = 0.2) -> Dict[str, Any]:
|
||||
req_body = {
|
||||
"model": model_path,
|
||||
"messages": messages,
|
||||
"max_tokens": max_tokens,
|
||||
"temperature": temperature,
|
||||
"stream": False,
|
||||
}
|
||||
t0 = time.monotonic()
|
||||
req = urllib.request.Request(
|
||||
f"http://127.0.0.1:{port}/v1/chat/completions",
|
||||
data=json.dumps(req_body).encode("utf-8"),
|
||||
headers={"Content-Type": "application/json"},
|
||||
method="POST",
|
||||
)
|
||||
with urllib.request.urlopen(req, timeout=180) as resp:
|
||||
elapsed = time.monotonic() - t0
|
||||
raw = json.loads(resp.read().decode())
|
||||
raw["client_wall_time_sec"] = round(elapsed, 3)
|
||||
return raw
|
||||
|
||||
|
||||
def test_speed_synthetic(port: int, model_path: str, num_gen_tokens: int = 128) -> Tuple[float, float, Dict[str, Any]]:
|
||||
"""Measure raw prompt processing and token generation speeds."""
|
||||
prompt = "Write a complete python implementation of a high-throughput async ring buffer queue with circular buffer memory management and lock-free atomic pointers."
|
||||
res = request_chat_completion(port, model_path, [{"role": "user", "content": prompt}], max_tokens=num_gen_tokens, temperature=0.1)
|
||||
timings = res.get("timings", {})
|
||||
gen_tps = timings.get("predicted_per_second", 0.0)
|
||||
prompt_tps = timings.get("prompt_per_second", 0.0)
|
||||
return gen_tps, prompt_tps, res
|
||||
|
||||
|
||||
def test_long_context_degradation(port: int, model_path: str) -> List[Dict[str, Any]]:
|
||||
"""Test response throughput across increasing context sizes (2k, 8k, 16k, 32k, 48k tokens)."""
|
||||
results = []
|
||||
base_filler = "In Python distributed systems, memory consistency models and lease management require careful synchronization. " * 80
|
||||
|
||||
target_tokens = [2000, 8000, 16000, 32000, 48000]
|
||||
for n_tokens in target_tokens:
|
||||
multiplier = max(1, n_tokens // 1000)
|
||||
filler = base_filler * multiplier
|
||||
prompt = f"Background context:\n{filler}\n\nTask: Output exact word 'READY'."
|
||||
try:
|
||||
res = request_chat_completion(port, model_path, [{"role": "user", "content": prompt}], max_tokens=10, temperature=0.0)
|
||||
timings = res.get("timings", {})
|
||||
prompt_ms = timings.get("prompt_ms", 0.0)
|
||||
prompt_n = timings.get("prompt_n", 0)
|
||||
prompt_tps = timings.get("prompt_per_second", 0.0)
|
||||
gen_tps = timings.get("predicted_per_second", 0.0)
|
||||
results.append({
|
||||
"target_tokens": n_tokens,
|
||||
"actual_prompt_tokens": prompt_n,
|
||||
"prompt_ms": round(prompt_ms, 1),
|
||||
"prompt_tokens_per_sec": round(prompt_tps, 2),
|
||||
"gen_tokens_per_sec": round(gen_tps, 2),
|
||||
"wall_time_sec": res.get("client_wall_time_sec", 0.0),
|
||||
"status": "OK",
|
||||
})
|
||||
print(f" [Context {n_tokens:5d} tok]: Prompt {prompt_tps:6.1f} t/s | Gen {gen_tps:5.1f} t/s | Wall {res.get('client_wall_time_sec', 0):.2f}s", flush=True)
|
||||
except Exception as e:
|
||||
results.append({
|
||||
"target_tokens": n_tokens,
|
||||
"error": str(e),
|
||||
"status": "FAILED_OR_TIMEOUT",
|
||||
})
|
||||
print(f" [Context {n_tokens:5d} tok]: FAILED ({e})", flush=True)
|
||||
return results
|
||||
|
||||
|
||||
def run_benchmark_for_model(
|
||||
name: str,
|
||||
filepath: str,
|
||||
contexts: List[int] = [65536, 32768],
|
||||
port: int = 8089,
|
||||
) -> Dict[str, Any]:
|
||||
print(f"\n===================================================================", flush=True)
|
||||
print(f" BENCHMARKING CANDIDATE: {name}", flush=True)
|
||||
print(f" File: {filepath}", flush=True)
|
||||
print(f"===================================================================", flush=True)
|
||||
|
||||
if not os.path.exists(filepath):
|
||||
print(f"[-] File not found: {filepath}")
|
||||
return {
|
||||
"name": name,
|
||||
"filepath": filepath,
|
||||
"status": "FILE_NOT_FOUND",
|
||||
"reason": "Model file not present on server disk",
|
||||
}
|
||||
|
||||
stats = get_file_stats(filepath)
|
||||
print(f"[+] File Size: {stats['size_gib']} GiB ({stats['size_bytes']} bytes)")
|
||||
print(f"[+] SHA256 (64MB): {stats['sha256_64mb']}")
|
||||
print(f"[+] GGUF Name: {stats['general_name']}, Arch: {stats['architecture']}")
|
||||
|
||||
context_results = {}
|
||||
|
||||
for ctx in contexts:
|
||||
cleanup_port(port)
|
||||
print(f"\n[*] Launching llama-server with Context Size = {ctx} (-c {ctx})...", flush=True)
|
||||
cmd = [
|
||||
LLAMA_SERVER_BIN,
|
||||
"-m", filepath,
|
||||
"-ngl", "99",
|
||||
"-c", str(ctx),
|
||||
"--parallel", "1",
|
||||
"--flash-attn", "on",
|
||||
"--cache-type-k", "q8_0",
|
||||
"--cache-type-v", "q8_0",
|
||||
"--reasoning", "off",
|
||||
"--temp", "0.2",
|
||||
"--host", "127.0.0.1",
|
||||
"--port", str(port),
|
||||
]
|
||||
|
||||
t_start = time.time()
|
||||
p = subprocess.Popen(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
|
||||
|
||||
try:
|
||||
ok = ping_health(port, timeout=180)
|
||||
cold_start_sec = round(time.time() - t_start, 2)
|
||||
if not ok:
|
||||
print(f"[-] Failed to start llama-server within 180s for -c {ctx} (OOM or crash)")
|
||||
context_results[str(ctx)] = {
|
||||
"context_size": ctx,
|
||||
"status": "START_FAILED_OR_OOM",
|
||||
"reason": "CUDA out of memory or context allocation failure on 32GB VRAM",
|
||||
"cold_start_sec": cold_start_sec,
|
||||
}
|
||||
continue
|
||||
|
||||
vram_mib = get_proc_gpu_vram(p.pid)
|
||||
print(f"[+] Cold start time: {cold_start_sec}s | Process VRAM: {vram_mib} MiB")
|
||||
|
||||
# 1. Measure raw speeds
|
||||
print(f"[*] Measuring Generation & Prompt Speed at -c {ctx}...", flush=True)
|
||||
try:
|
||||
gen_tps, prompt_tps, speed_raw = test_speed_synthetic(port, filepath, num_gen_tokens=128)
|
||||
print(f" - Generation Speed: {gen_tps:.2f} tok/s")
|
||||
print(f" - Prompt Speed: {prompt_tps:.2f} tok/s")
|
||||
except Exception as e:
|
||||
print(f" - Generation Speed Test Failed: {e}")
|
||||
gen_tps, prompt_tps, speed_raw = 0.0, 0.0, {"error": str(e)}
|
||||
|
||||
# 2. Run 12 Benchmark Tasks
|
||||
print(f"[*] Running 12 Hermes Codebase Benchmark Tasks at -c {ctx}...", flush=True)
|
||||
task_results = []
|
||||
passed_count = 0
|
||||
|
||||
for t in BENCHMARK_TASKS:
|
||||
try:
|
||||
res = request_chat_completion(port, filepath, [{"role": "user", "content": t.prompt}], max_tokens=1024, temperature=0.2)
|
||||
content = res["choices"][0]["message"]["content"]
|
||||
timings = res.get("timings", {})
|
||||
fn, err = _compile_and_get(content, t.expected_function_name)
|
||||
if err:
|
||||
success = False
|
||||
msg = err
|
||||
else:
|
||||
success, msg = t.test_function(fn)
|
||||
|
||||
if success:
|
||||
passed_count += 1
|
||||
|
||||
task_results.append({
|
||||
"task_id": t.task_id,
|
||||
"title": t.title,
|
||||
"category": t.category,
|
||||
"passed": success,
|
||||
"message": msg,
|
||||
"duration_sec": res.get("client_wall_time_sec", 0.0),
|
||||
"timings": timings,
|
||||
"response_preview": content[:150],
|
||||
})
|
||||
print(f" - [{ 'PASS' if success else 'FAIL' }] {t.task_id}: {msg} ({res.get('client_wall_time_sec', 0.0):.2f}s)")
|
||||
except Exception as e:
|
||||
task_results.append({
|
||||
"task_id": t.task_id,
|
||||
"title": t.title,
|
||||
"category": t.category,
|
||||
"passed": False,
|
||||
"message": f"Exception: {e}",
|
||||
"duration_sec": 0.0,
|
||||
})
|
||||
print(f" - [FAIL] {t.task_id}: Exception {e}")
|
||||
|
||||
# 3. Long Context Degradation (only on 64k)
|
||||
long_context_degradation = None
|
||||
if ctx == 65536:
|
||||
print(f"[*] Running Long Context Degradation Profile...", flush=True)
|
||||
long_context_degradation = test_long_context_degradation(port, filepath)
|
||||
|
||||
context_results[str(ctx)] = {
|
||||
"context_size": ctx,
|
||||
"status": "COMPLETED",
|
||||
"cold_start_sec": cold_start_sec,
|
||||
"process_vram_mib": vram_mib,
|
||||
"generation_speed_tps": round(gen_tps, 2),
|
||||
"prompt_speed_tps": round(prompt_tps, 2),
|
||||
"tasks_passed": passed_count,
|
||||
"tasks_total": len(BENCHMARK_TASKS),
|
||||
"tasks_pass_rate_pct": round((passed_count / len(BENCHMARK_TASKS)) * 100, 1),
|
||||
"speed_timings_raw": speed_raw.get("timings", {}),
|
||||
"task_evaluations": task_results,
|
||||
"long_context_degradation": long_context_degradation,
|
||||
}
|
||||
finally:
|
||||
p.terminate()
|
||||
try:
|
||||
p.wait(timeout=5)
|
||||
except Exception:
|
||||
p.kill()
|
||||
cleanup_port(port)
|
||||
|
||||
return {
|
||||
"name": name,
|
||||
"file_stats": stats,
|
||||
"contexts": context_results,
|
||||
"status": "COMPLETED",
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
models_to_test = [
|
||||
("Qwen3-Coder-30B-A3B-Instruct", "/srv/ai/models/qwen3-coder-30b-a3b/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf"),
|
||||
("Qwen2.5-Coder-32B-Instruct", "/srv/ai/models/qwen2.5-coder-32b/Qwen2.5-Coder-32B-Instruct-Q4_K_M.gguf"),
|
||||
("Tiel-Coder-35B-A3B-UD-Q4_K_S", "/srv/ai/models/tiel-coder-35b-a3b/Tiel-Coder-35B-A3B-UD-Q4_K_S.gguf"),
|
||||
]
|
||||
|
||||
all_results = {}
|
||||
if os.path.exists("benchmarks/benchmark_moe_results.json"):
|
||||
try:
|
||||
with open("benchmarks/benchmark_moe_results.json", "r", encoding="utf-8") as f:
|
||||
all_results = json.load(f)
|
||||
except Exception:
|
||||
all_results = {}
|
||||
|
||||
for name, path in models_to_test:
|
||||
if name in all_results and all_results[name].get("status") == "COMPLETED":
|
||||
print(f"[+] Skipping already completed candidate: {name}")
|
||||
continue
|
||||
res = run_benchmark_for_model(name, path, contexts=[65536, 32768], port=8089)
|
||||
all_results[name] = res
|
||||
with open("benchmarks/benchmark_moe_results.json", "w", encoding="utf-8") as f:
|
||||
json.dump(all_results, f, indent=2, ensure_ascii=False)
|
||||
|
||||
print("\n[+] Benchmark complete! Saved to benchmarks/benchmark_moe_results.json", flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in a new issue