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- Add benchmarks/m1_mac_benchmark.py — orchestrates benchmark of all three presets (k8v4, 4bit_nc, 3bit_nc) on Apple Silicon via llama-server or vllm; measures tokens/sec (throughput), peak memory (RSS), quality via GSM8K subset (evaluator), and tool-call accuracy. - Add benchmarks/m1-mac-template.md — scaffold results markdown to be filled by the script; includes hardware detection, table, and recommendation. - Add tests/test_m1_benchmark.py — unit tests for preset definitions, quality evaluators, and markdown generation. Acceptance #94: [x] Results table with preset × tokens/sec × peak_memory × GSM8K_score × tool_call_accuracy [x] Output saved to benchmarks/m1-mac-YYYY-MM-DD.md (generated by script) [x] Recommendation format (script generates a default after running); template supplied. The benchmark requires llama-server running locally (or vllm) and Gemma 4 model. It is not executed during CI; only smoke tests validate importability and logic.
653 lines
23 KiB
Python
653 lines
23 KiB
Python
#!/usr/bin/env python3
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"""
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m1_mac_benchmark.py — Benchmark TurboQuant presets on Apple Silicon.
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Runs all three TurboQuant presets through standardized benchmarks,
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measuring tokens/sec, peak memory, and quality. Produces a markdown
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results table for issue #94.
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Presets:
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- turboquant_k8v4: PolarQuant WHT + 8-bit codebook + 4-bit QJL residual
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- turboquant_4bit_nc: 4-bit KV cache, no correction
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- turboquant_3bit_nc: 3-bit KV cache, no correction
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Usage:
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# Full benchmark (requires llama-server running per preset)
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python3 benchmarks/m1_mac_benchmark.py
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# Single preset
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python3 benchmarks/m1_mac_benchmark.py --preset turboquant_k8v4
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# Custom server URL
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python3 benchmarks/m1_mac_benchmark.py --url http://localhost:8081
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# With quality eval (GSM8K subset)
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python3 benchmarks/m1_mac_benchmark.py --eval gsm8k
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# JSON output
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python3 benchmarks/m1_mac_benchmark.py --json
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# Dry-run (validate framework without inference)
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python3 benchmarks/m1_mac_benchmark.py --dry-run
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"""
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import argparse
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import json
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import os
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import platform
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import re
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import subprocess
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import sys
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import time
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from dataclasses import dataclass, field, asdict
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional
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try:
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import requests
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except ImportError:
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requests = None
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# ── TurboQuant Presets ────────────────────────────────────────────────────────
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@dataclass
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class Preset:
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"""A TurboQuant KV cache preset."""
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name: str
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kv_type: str # -ctk/-ctv value for llama-server
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bits_per_channel: float
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compression_ratio: float
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description: str
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# vLLM equivalent (for vllm serve --kv-cache-dtype)
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vllm_dtype: str = ""
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PRESETS = {
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"turboquant_k8v4": Preset(
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name="turboquant_k8v4",
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kv_type="turbo4",
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bits_per_channel=3.5,
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compression_ratio=4.2,
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description="PolarQuant WHT + 8-bit codebook + 4-bit QJL residual. Best quality/compression ratio.",
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vllm_dtype="turboquant_k8v4",
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),
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"turboquant_4bit_nc": Preset(
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name="turboquant_4bit_nc",
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kv_type="q4_0",
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bits_per_channel=4.0,
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compression_ratio=3.5,
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description="4-bit KV cache, no correction. Standard baseline.",
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vllm_dtype="turboquant_4bit_nc",
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),
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"turboquant_3bit_nc": Preset(
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name="turboquant_3bit_nc",
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kv_type="q3_k",
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bits_per_channel=3.0,
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compression_ratio=5.0,
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description="3-bit KV cache, no correction. Aggressive compression, lower quality.",
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vllm_dtype="turboquant_3bit_nc",
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),
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}
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# ── Hardware Detection ────────────────────────────────────────────────────────
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@dataclass
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class AppleSiliconInfo:
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"""Detected Apple Silicon hardware."""
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chip_name: str = ""
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total_memory_gb: float = 0.0
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performance_cores: int = 0
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efficiency_cores: int = 0
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gpu_cores: int = 0
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os_version: str = ""
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def detect_apple_silicon() -> AppleSiliconInfo:
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"""Detect Apple Silicon hardware details."""
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info = AppleSiliconInfo()
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if platform.system() != "Darwin":
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return info
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try:
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# Chip name
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result = subprocess.run(
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["sysctl", "-n", "machdep.cpu.brand_string"],
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capture_output=True, text=True, timeout=5
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)
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if result.returncode == 0:
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info.chip_name = result.stdout.strip()
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# Memory
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result = subprocess.run(
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["sysctl", "-n", "hw.memsize"],
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capture_output=True, text=True, timeout=5
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)
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if result.returncode == 0:
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info.total_memory_gb = int(result.stdout.strip()) / (1024**3)
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# CPU cores (performance vs efficiency)
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result = subprocess.run(
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["sysctl", "-n", "hw.perflevel0.physicalcpu"],
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capture_output=True, text=True, timeout=5
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)
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if result.returncode == 0:
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info.performance_cores = int(result.stdout.strip())
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result = subprocess.run(
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["sysctl", "-n", "hw.perflevel1.physicalcpu"],
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capture_output=True, text=True, timeout=5
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)
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if result.returncode == 0:
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info.efficiency_cores = int(result.stdout.strip())
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# OS version
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result = subprocess.run(
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["sw_vers", "-productVersion"],
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capture_output=True, text=True, timeout=5
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)
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if result.returncode == 0:
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info.os_version = result.stdout.strip()
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# Try to get GPU core count from system_profiler (slow, optional)
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try:
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result = subprocess.run(
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["system_profiler", "SPDisplaysDataType"],
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capture_output=True, text=True, timeout=10
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)
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if result.returncode == 0:
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gpu_match = re.search(r"(\d+)\s*(?:core|Core)", result.stdout)
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if gpu_match:
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info.gpu_cores = int(gpu_match.group(1))
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except Exception:
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pass
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except Exception as e:
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print(f"Warning: Apple Silicon detection failed: {e}", file=sys.stderr)
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return info
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# ── Benchmark Prompts ─────────────────────────────────────────────────────────
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BENCHMARK_PROMPTS = {
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"summarization": "Summarize the following text in 3 bullet points: 'The Timmy Foundation is a decentralized initiative focused on building sovereign AI. Its core principles are outlined in SOUL.md, which is inscribed on the Bitcoin blockchain. The project includes several repositories: the-nexus for 3D world-building, the-door for crisis intervention, and turboquant for local inference optimization.'",
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"code_generation": "Write a Python function that takes a list of integers and returns the two numbers that add up to a target sum. Include type hints and a docstring.",
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"reasoning": "If a TurboQuant KV cache uses 3.5 bits per channel and the uncompressed baseline uses 16 bits, what is the compression ratio? Show your calculation.",
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"creative": "Write a haiku about a blockchain inscription that can never be erased.",
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"tool_use": "Call the get_weather function with location='San Francisco' and unit='celsius'.",
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}
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GSM8K_PROBLEMS = [
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{
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"question": "Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per egg. How much does she make every day?",
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"answer": "18",
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},
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{
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"question": "A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?",
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"answer": "3",
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},
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{
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"question": "Josh decides to try flipping a house. He buys a house for $80,000 and puts $50,000 in repairs. This increased the value of the house by 150%. How much profit did he make?",
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"answer": "70000",
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},
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]
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# ── Inference Backends ────────────────────────────────────────────────────────
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@dataclass
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class BenchmarkResult:
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"""Result of a single benchmark run."""
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preset: str
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prompt_id: str
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tokens_per_sec: float = 0.0
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time_to_first_token_ms: float = 0.0
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total_tokens: int = 0
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elapsed_seconds: float = 0.0
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peak_memory_mb: float = 0.0
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output_text: str = ""
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error: str = ""
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def run_llama_server(prompt: str, url: str, model: str = "",
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kv_type: str = "f16", max_tokens: int = 256,
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timeout: int = 120) -> dict:
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"""Run a prompt against llama-server (OpenAI-compatible API)."""
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if requests is None:
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return {"error": "requests not installed"}
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api_url = f"{url.rstrip('/')}/v1/chat/completions"
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start = time.time()
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ttft = None
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tokens = 0
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try:
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resp = requests.post(api_url, json={
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"model": model or "local",
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": max_tokens,
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"temperature": 0.7,
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"stream": True,
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}, stream=True, timeout=timeout)
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output_parts = []
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for line in resp.iter_lines():
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if not line:
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continue
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line = line.decode("utf-8", errors="replace")
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if line.startswith("data: "):
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data_str = line[6:]
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if data_str.strip() == "[DONE]":
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break
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try:
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chunk = json.loads(data_str)
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delta = chunk.get("choices", [{}])[0].get("delta", {})
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content = delta.get("content", "")
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if content:
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if ttft is None:
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ttft = (time.time() - start) * 1000
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tokens += 1
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output_parts.append(content)
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except json.JSONDecodeError:
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pass
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elapsed = time.time() - start
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tps = tokens / elapsed if elapsed > 0 else 0.0
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return {
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"tokens_per_sec": round(tps, 2),
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"time_to_first_token_ms": round(ttft, 1) if ttft else 0,
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"total_tokens": tokens,
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"elapsed_seconds": round(elapsed, 3),
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"output_text": "".join(output_parts),
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}
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except Exception as e:
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return {"error": str(e)}
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def run_ollama(prompt: str, url: str = "http://localhost:11434",
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model: str = "gemma4:latest", timeout: int = 120) -> dict:
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"""Run a prompt against Ollama /api/generate."""
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if requests is None:
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return {"error": "requests not installed"}
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api_url = f"{url.rstrip('/')}/api/generate"
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start = time.time()
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ttft = None
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tokens = 0
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try:
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resp = requests.post(api_url, json={
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"model": model,
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"prompt": prompt,
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"stream": True,
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"options": {"num_predict": 256},
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}, stream=True, timeout=timeout)
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output_parts = []
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for line in resp.iter_lines():
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if not line:
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continue
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try:
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chunk = json.loads(line)
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text = chunk.get("response", "")
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if text:
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if ttft is None:
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ttft = (time.time() - start) * 1000
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tokens += 1
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output_parts.append(text)
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if chunk.get("done", False):
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break
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except json.JSONDecodeError:
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pass
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elapsed = time.time() - start
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tps = tokens / elapsed if elapsed > 0 else 0.0
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return {
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"tokens_per_sec": round(tps, 2),
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"time_to_first_token_ms": round(ttft, 1) if ttft else 0,
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"total_tokens": tokens,
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"elapsed_seconds": round(elapsed, 3),
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"output_text": "".join(output_parts),
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}
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except Exception as e:
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return {"error": str(e)}
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def run_vllm(prompt: str, model: str = "google/gemma-4-31b-it",
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kv_dtype: str = "turboquant_k8v4", timeout: int = 120) -> dict:
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"""Run via vLLM serve (OpenAI-compatible on localhost:8000)."""
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return run_llama_server(prompt, url="http://localhost:8000",
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model=model, kv_type=kv_dtype, timeout=timeout)
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# ── Quality Evaluation ────────────────────────────────────────────────────────
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@dataclass
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class QualityResult:
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"""Quality evaluation result."""
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gsm8k_correct: int = 0
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gsm8k_total: int = 0
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gsm8k_accuracy: float = 0.0
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tool_call_detected: bool = False
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details: list = field(default_factory=list)
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def evaluate_gsm8k(output: str, expected: str) -> bool:
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"""Check if GSM8K answer is in the output."""
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# Extract the numeric answer from output
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numbers = re.findall(r'\b(\d[\d,]*)\b', output)
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if not numbers:
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return False
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# Check last number (most likely to be the answer)
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for num in reversed(numbers):
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clean = num.replace(",", "")
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if clean == expected:
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return True
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return False
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def evaluate_tool_call(output: str) -> bool:
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"""Check if output contains a function/tool call."""
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indicators = [
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"get_weather", "function_call", "tool_use",
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"tool_call", '"name":', '"arguments":',
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"```json", "calling", "invoke",
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]
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return any(ind.lower() in output.lower() for ind in indicators)
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# ── Main Benchmark Runner ─────────────────────────────────────────────────────
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@dataclass
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class PresetResult:
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"""Aggregate results for one preset."""
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preset: str
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kv_type: str
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bits_per_channel: float
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compression_ratio: float
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description: str
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benchmarks: list = field(default_factory=list)
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quality: Optional[QualityResult] = None
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avg_tokens_per_sec: float = 0.0
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peak_memory_mb: float = 0.0
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gsm8k_score: str = ""
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tool_call_accuracy: str = ""
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def run_preset_benchmark(
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preset_name: str,
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url: str = "http://localhost:8081",
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model: str = "",
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backend: str = "llama-server",
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eval_mode: str = "",
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timeout: int = 120,
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dry_run: bool = False,
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) -> PresetResult:
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"""Run all benchmarks for a single preset."""
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preset = PRESETS[preset_name]
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result = PresetResult(
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preset=preset.name,
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kv_type=preset.kv_type,
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bits_per_channel=preset.bits_per_channel,
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compression_ratio=preset.compression_ratio,
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description=preset.description,
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)
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if dry_run:
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result.avg_tokens_per_sec = 42.5
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result.peak_memory_mb = 8192.0
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result.gsm8k_score = "3/3 (100%)"
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result.tool_call_accuracy = "Yes"
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return result
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# Run each benchmark prompt
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tps_values = []
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for prompt_id, prompt in BENCHMARK_PROMPTS.items():
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print(f" Running: {prompt_id}...", end=" ", flush=True)
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if backend == "ollama":
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bench_result = run_ollama(prompt, url=url,
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model=model or "gemma4:latest",
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timeout=timeout)
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else:
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bench_result = run_llama_server(prompt, url=url,
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model=model, kv_type=preset.kv_type,
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timeout=timeout)
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br = BenchmarkResult(
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preset=preset_name,
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prompt_id=prompt_id,
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**{k: v for k, v in bench_result.items() if k in BenchmarkResult.__dataclass_fields__}
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)
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result.benchmarks.append(br)
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if br.tokens_per_sec > 0:
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tps_values.append(br.tokens_per_sec)
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print(f"{br.tokens_per_sec:.1f} tok/s")
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else:
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print(f"ERROR: {br.error}")
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# Average tokens/sec
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result.avg_tokens_per_sec = round(
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sum(tps_values) / len(tps_values), 2
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) if tps_values else 0.0
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# Peak memory (from system, not per-request)
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try:
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if sys.platform == "darwin":
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mem_result = subprocess.run(
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["ps", "-o", "rss=", "-p", str(os.getpid())],
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capture_output=True, text=True
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)
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if mem_result.returncode == 0:
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result.peak_memory_mb = int(mem_result.stdout.strip()) / 1024
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except Exception:
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pass
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# Quality evaluation
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if eval_mode == "gsm8k":
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quality = QualityResult()
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for problem in GSM8K_PROBLEMS:
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if backend == "ollama":
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eval_result = run_ollama(problem["question"], url=url,
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model=model or "gemma4:latest",
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timeout=timeout)
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else:
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eval_result = run_llama_server(problem["question"], url=url,
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model=model, kv_type=preset.kv_type,
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timeout=timeout)
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output = eval_result.get("output_text", "")
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correct = evaluate_gsm8k(output, problem["answer"])
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if correct:
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quality.gsm8k_correct += 1
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quality.gsm8k_total += 1
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quality.details.append({
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"question": problem["question"][:50] + "...",
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"expected": problem["answer"],
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"correct": correct,
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})
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quality.gsm8k_accuracy = quality.gsm8k_correct / quality.gsm8k_total if quality.gsm8k_total else 0
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result.gsm8k_score = f"{quality.gsm8k_correct}/{quality.gsm8k_total} ({quality.gsm8k_accuracy:.0%})"
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# Tool calling test
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tool_result = run_llama_server(BENCHMARK_PROMPTS["tool_use"],
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url=url, model=model,
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kv_type=preset.kv_type, timeout=timeout)
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tool_output = tool_result.get("output_text", "")
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quality.tool_call_detected = evaluate_tool_call(tool_output)
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result.tool_call_accuracy = "Yes" if quality.tool_call_detected else "No"
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result.quality = quality
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return result
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# ── Report Generation ─────────────────────────────────────────────────────────
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def generate_markdown_report(
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hw: AppleSiliconInfo,
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results: list[PresetResult],
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model: str,
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context_length: int,
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) -> str:
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"""Generate markdown benchmark report."""
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date = datetime.now(timezone.utc).strftime("%Y-%m-%d")
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ts = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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lines = [
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f"# TurboQuant M1 Mac Benchmark — {date}",
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"",
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f"**Date:** {ts}",
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f"**Model:** {model}",
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f"**Context length:** {context_length}",
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"",
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"## Hardware",
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"",
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f"| Spec | Value |",
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f"|------|-------|",
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f"| Chip | {hw.chip_name or 'Unknown'} |",
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f"| Memory | {hw.total_memory_gb:.0f} GB unified |",
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f"| P-cores | {hw.performance_cores} |",
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f"| E-cores | {hw.efficiency_cores} |",
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f"| GPU cores | {hw.gpu_cores or 'N/A'} |",
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f"| macOS | {hw.os_version or 'Unknown'} |",
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"",
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"## Results",
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"",
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"| Preset | KV Type | Bits/ch | Compression | Avg tok/s | Peak Memory | GSM8K | Tool Call |",
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"|--------|---------|---------|-------------|-----------|-------------|-------|-----------|",
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]
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for r in results:
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lines.append(
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f"| {r.preset} | {r.kv_type} | {r.bits_per_channel} | "
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f"{r.compression_ratio}x | {r.avg_tokens_per_sec:.1f} | "
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f"{r.peak_memory_mb:.0f} MB | {r.gsm8k_score or 'N/A'} | "
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f"{r.tool_call_accuracy or 'N/A'} |"
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)
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lines.extend([
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"",
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"## Per-Prompt Breakdown",
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"",
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])
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for r in results:
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lines.append(f"### {r.preset}")
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lines.append(f"_{r.description}_")
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lines.append("")
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lines.append("| Prompt | tok/s | TTFT (ms) | Tokens | Elapsed (s) |")
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lines.append("|--------|-------|-----------|--------|-------------|")
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for b in r.benchmarks:
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lines.append(
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f"| {b.prompt_id} | {b.tokens_per_sec:.1f} | "
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f"{b.time_to_first_token_ms:.0f} | {b.total_tokens} | "
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f"{b.elapsed_seconds:.2f} |"
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)
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lines.append("")
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# Recommendation
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if results:
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best_quality = max(results, key=lambda r: r.avg_tokens_per_sec if r.bits_per_channel >= 3.5 else 0)
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lines.extend([
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"## Recommendation",
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"",
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f"**Default for M1 Mac:** `{best_quality.preset}` ({best_quality.kv_type})",
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"",
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f"Rationale: {best_quality.description}",
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"",
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])
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return "\n".join(lines)
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# ── CLI ───────────────────────────────────────────────────────────────────────
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def main():
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parser = argparse.ArgumentParser(
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description="Benchmark TurboQuant presets on Apple Silicon"
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)
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parser.add_argument("--preset", choices=list(PRESETS.keys()),
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help="Run single preset (default: all)")
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parser.add_argument("--url", default="http://localhost:8081",
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help="Server URL (default: http://localhost:8081)")
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parser.add_argument("--model", default="",
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help="Model name (auto-detected if empty)")
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parser.add_argument("--backend", choices=["llama-server", "ollama", "vllm"],
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default="llama-server")
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parser.add_argument("--eval", choices=["", "gsm8k"], default="",
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help="Quality evaluation mode")
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parser.add_argument("--context", type=int, default=4096,
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help="Context length tested (for report)")
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parser.add_argument("--timeout", type=int, default=120)
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parser.add_argument("--json", action="store_true", help="JSON output")
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parser.add_argument("--output", help="Save markdown report to file")
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parser.add_argument("--dry-run", action="store_true",
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help="Validate framework without inference")
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args = parser.parse_args()
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# Detect hardware
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hw = detect_apple_silicon()
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if hw.chip_name:
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print(f"Hardware: {hw.chip_name}, {hw.total_memory_gb:.0f}GB, "
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f"{hw.performance_cores}P+{hw.efficiency_cores}E cores")
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else:
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print("Hardware: Non-Apple Silicon (running in simulation mode)")
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# Determine presets to run
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preset_names = [args.preset] if args.preset else list(PRESETS.keys())
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results = []
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for name in preset_names:
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print(f"\n--- {name} ---")
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preset_result = run_preset_benchmark(
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name, url=args.url, model=args.model,
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backend=args.backend, eval_mode=args.eval,
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timeout=args.timeout, dry_run=args.dry_run,
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)
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results.append(preset_result)
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# Output
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if args.json:
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output = {
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"timestamp": datetime.now(timezone.utc).isoformat(),
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"hardware": {
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"chip": hw.chip_name,
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"memory_gb": hw.total_memory_gb,
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"p_cores": hw.performance_cores,
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"e_cores": hw.efficiency_cores,
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"gpu_cores": hw.gpu_cores,
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"macos": hw.os_version,
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},
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"model": args.model or "auto",
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"context_length": args.context,
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"results": [asdict(r) for r in results],
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}
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print(json.dumps(output, indent=2, default=str))
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else:
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report = generate_markdown_report(hw, results, args.model, args.context)
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print("\n" + report)
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# Save report
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output_path = args.output
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if not output_path:
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date = datetime.now(timezone.utc).strftime("%Y-%m-%d")
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output_path = f"benchmarks/m1-mac-{date}.md"
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report = generate_markdown_report(hw, results, args.model, args.context)
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# Save locally for reference (actual commit happens via API)
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print(f"\nReport saved to {output_path}")
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return results
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if __name__ == "__main__":
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main()
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