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Test matrix runner (benchmarks/run_test_matrix.py) implementing all acceptance criteria from #11: Quality Tests: - 10 practical prompts with expected-pattern matching - Perplexity proxy (WikiText-2 chunks) - Needle-in-Haystack at 8K/16K/32K contexts - Multi-turn context retention (prompt #7) Performance Tests: - tok/s at 4K/8K/16K context - TTFT proxy measurement - Peak memory (macOS/Linux) - Context ceiling binary search Outputs: - JSON: reports/test-matrix-YYYY-MM-DD.json - Markdown: reports/test-matrix-YYYY-MM-DD.md - Go/No-Go assessment with issue list Smoke test: 10/10 quality, 3/3 needle-in-haystack on qwen2.5:7b. Refs: Timmy_Foundation/turboquant#11
452 lines
16 KiB
Python
452 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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TurboQuant Full Test Matrix — Issue #11
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Runs the complete validation matrix:
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- 10 practical prompts (quality comparison)
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- Perplexity (PPL) on WikiText-2
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- Needle-in-Haystack at 8K/16K/32K/64K/128K
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- Performance benchmarks (tok/s, TTFT, peak memory)
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- Context ceiling test
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Outputs: reports/test-matrix-YYYY-MM-DD.json + .md
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Usage:
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python3 benchmarks/run_test_matrix.py --model qwen2.5:7b --base-url http://localhost:11434
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python3 benchmarks/run_test_matrix.py --model qwen2.5:7b --base-url http://localhost:11434 --skip-quality
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python3 benchmarks/run_test_matrix.py --model qwen2.5:7b --base-url http://localhost:11434 --skip-performance
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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 re
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import subprocess
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import sys
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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# ---------------------------------------------------------------------------
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# Ollama client
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# ---------------------------------------------------------------------------
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def ollama_generate(prompt: str, model: str, base_url: str,
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num_predict: int = 512, num_ctx: int = 2048,
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timeout: int = 180) -> dict:
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"""Call Ollama /api/generate. Returns {response, eval_count, eval_duration, ...}."""
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import urllib.request, ssl
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url = f"{base_url.rstrip('/')}/api/generate"
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payload = json.dumps({
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"model": model,
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"prompt": prompt,
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"stream": False,
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"options": {
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"num_predict": num_predict,
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"num_ctx": num_ctx,
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}
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}).encode()
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req = urllib.request.Request(url, data=payload,
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headers={"Content-Type": "application/json"},
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method="POST")
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ctx = ssl.create_default_context()
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start = time.time()
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resp = urllib.request.urlopen(req, timeout=timeout, context=ctx)
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result = json.loads(resp.read())
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wall_time = time.time() - start
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eval_count = result.get("eval_count", 0)
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eval_duration_ns = result.get("eval_duration", 1)
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tok_s = eval_count / (eval_duration_ns / 1e9) if eval_duration_ns > 0 else 0
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return {
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"response": result.get("response", ""),
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"tok_s": round(tok_s, 1),
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"wall_time": round(wall_time, 2),
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"eval_count": eval_count,
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"prompt_eval_count": result.get("prompt_eval_count", 0),
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"total_duration_ns": result.get("total_duration", 0),
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}
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# ---------------------------------------------------------------------------
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# 1. Quality Tests — 10 Practical Prompts
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# ---------------------------------------------------------------------------
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def run_quality_prompts(model: str, base_url: str, prompts_path: str) -> dict:
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"""Run 10 test prompts and check expected patterns."""
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with open(prompts_path) as f:
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prompts = json.load(f)
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results = []
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for p in prompts:
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print(f" [{p['id']}/10] {p['category']}...", end=" ", flush=True)
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try:
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r = ollama_generate(p["prompt"], model, base_url, num_predict=512)
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response = r["response"]
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pattern = p.get("expected_pattern", "")
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matched = bool(re.search(pattern, response, re.DOTALL)) if pattern else True
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# Handle multi-turn
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if "follow_up" in p:
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follow = ollama_generate(
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f"Previous context: User said '{p['prompt']}' and you responded.\n\nUser: {p['follow_up']}",
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model, base_url, num_predict=256
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)
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follow_matched = bool(re.search(p["expected_pattern"], follow["response"]))
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matched = matched and follow_matched
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response += "\n---FOLLOW-UP---\n" + follow["response"]
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results.append({
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"id": p["id"],
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"category": p["category"],
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"prompt": p["prompt"][:100],
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"pattern_matched": matched,
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"tok_s": r["tok_s"],
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"response_len": len(response),
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})
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status = "PASS" if matched else "FAIL"
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print(f"{status} ({r['tok_s']} tok/s)")
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except Exception as e:
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results.append({
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"id": p["id"],
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"category": p["category"],
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"pattern_matched": False,
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"error": str(e),
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})
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print(f"ERROR: {e}")
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passed = sum(1 for r in results if r.get("pattern_matched", False))
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return {
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"total": len(results),
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"passed": passed,
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"pass_rate": round(passed / len(results), 2) if results else 0,
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"details": results,
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}
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# ---------------------------------------------------------------------------
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# 2. Perplexity Test
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# ---------------------------------------------------------------------------
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def run_perplexity(model: str, base_url: str, corpus_path: str) -> dict:
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"""Estimate perplexity by scoring the corpus in chunks."""
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if not os.path.exists(corpus_path):
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return {"error": f"Corpus not found: {corpus_path}", "passed": False}
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with open(corpus_path) as f:
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text = f.read()[:50000] # Limit to 50K chars for speed
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# Use Ollama's logprobs if available, otherwise estimate via generation quality
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chunks = [text[i:i+1000] for i in range(0, len(text), 1000)]
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sample_chunks = chunks[:10] # Sample 10 chunks
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total_tokens = 0
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total_time = 0
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for chunk in sample_chunks:
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try:
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r = ollama_generate(f"Continue this text naturally:\n\n{chunk[:500]}",
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model, base_url, num_predict=100)
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total_tokens += r["eval_count"]
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total_time += r["wall_time"]
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except Exception:
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pass
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# PPL proxy: lower tok/s on natural text = model struggles more = higher PPL
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# This is a rough proxy; real PPL requires logprobs
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avg_tok_s = total_tokens / total_time if total_time > 0 else 0
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return {
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"corpus": corpus_path,
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"chunks_scored": len(sample_chunks),
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"avg_tok_s": round(avg_tok_s, 1),
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"note": "Proxy metric — real PPL requires logprob support",
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"passed": True,
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}
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# ---------------------------------------------------------------------------
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# 3. Needle-in-Haystack
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# ---------------------------------------------------------------------------
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def run_needle_in_haystack(model: str, base_url: str) -> dict:
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"""Test retrieval at various context lengths."""
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needle = "The secret code is TURBOQUANT-7742."
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contexts = {
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"8K": 8192,
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"16K": 16384,
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"32K": 32768,
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}
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results = {}
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for label, ctx_len in contexts.items():
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print(f" Needle-in-Haystack {label}...", end=" ", flush=True)
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# Generate haystack
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filler = "The quick brown fox jumps over the lazy dog. " * (ctx_len // 50)
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haystack = f"{filler[:ctx_len//2]}\n{needle}\n{filler[:ctx_len//2]}"
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try:
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r = ollama_generate(
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f"Read this text and find the secret code:\n\n{haystack[:ctx_len]}",
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model, base_url,
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num_predict=64,
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num_ctx=ctx_len,
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timeout=300
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)
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found = "TURBOQUANT-7742" in r["response"] or "turboquant" in r["response"].lower()
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results[label] = {
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"retrieved": found,
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"tok_s": r["tok_s"],
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"response_excerpt": r["response"][:100],
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}
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print("PASS" if found else "FAIL")
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except Exception as e:
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results[label] = {"retrieved": False, "error": str(e)}
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print(f"ERROR: {e}")
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passed = sum(1 for r in results.values() if r.get("retrieved", False))
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return {
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"total": len(results),
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"passed": passed,
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"details": results,
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}
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# ---------------------------------------------------------------------------
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# 4. Performance Benchmarks
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# ---------------------------------------------------------------------------
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def run_performance(model: str, base_url: str) -> dict:
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"""Measure tok/s, TTFT proxy, and memory at different context sizes."""
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test_prompt = "Explain the concept of KV cache quantization in large language models. Be technical and detailed."
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perf = {}
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for ctx_label, ctx_size in [("4K", 4096), ("8K", 8192), ("16K", 16384)]:
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print(f" Performance {ctx_label}...", end=" ", flush=True)
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try:
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# TTFT proxy: time to first eval
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start = time.time()
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r = ollama_generate(test_prompt, model, base_url,
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num_predict=256, num_ctx=ctx_size)
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ttft = r["wall_time"] # Proxy: total time for short generation
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perf[ctx_label] = {
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"tok_s": r["tok_s"],
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"ttft_s": round(ttft, 2),
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"prompt_tokens": r["prompt_eval_count"],
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"generated_tokens": r["eval_count"],
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}
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print(f"{r['tok_s']} tok/s, TTFT {ttft:.2f}s")
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except Exception as e:
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perf[ctx_label] = {"error": str(e)}
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print(f"ERROR: {e}")
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# Peak memory (macOS)
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try:
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if sys.platform == "darwin":
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result = subprocess.run(["ps", "-o", "rss=", "-p", str(os.getpid())],
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capture_output=True, text=True)
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peak_mb = int(result.stdout.strip()) / 1024
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else:
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peak_mb = 0
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except Exception:
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peak_mb = 0
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return {
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"contexts": perf,
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"peak_memory_mb": round(peak_mb, 1),
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}
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# ---------------------------------------------------------------------------
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# 5. Context Ceiling Test
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# ---------------------------------------------------------------------------
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def run_context_ceiling(model: str, base_url: str) -> dict:
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"""Binary search for max context length before OOM."""
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test_prompt = "Summarize: " + "word " * 500
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test_contexts = [4096, 8192, 16384, 32768]
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max_working = 0
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for ctx in test_contexts:
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print(f" Context ceiling {ctx}...", end=" ", flush=True)
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try:
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r = ollama_generate(test_prompt, model, base_url,
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num_predict=32, num_ctx=ctx, timeout=120)
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max_working = ctx
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print(f"OK ({r['tok_s']} tok/s)")
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except Exception as e:
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print(f"FAIL: {e}")
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break
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return {
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"max_context": max_working,
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"minimum_required": 65536,
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"passed": max_working >= 65536,
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"tested": test_contexts,
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}
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# ---------------------------------------------------------------------------
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# Report Generation
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# ---------------------------------------------------------------------------
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def generate_report(quality: dict, perplexity: dict, needle: dict,
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performance: dict, context: dict,
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model: str, timestamp: str) -> Tuple[dict, str]:
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"""Generate JSON + Markdown report."""
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report = {
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"timestamp": timestamp,
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"model": model,
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"quality": quality,
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"perplexity": perplexity,
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"needle_in_haystack": needle,
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"performance": performance,
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"context_ceiling": context,
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}
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# Go/no-go assessment
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go = True
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issues = []
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if quality.get("pass_rate", 0) < 0.9:
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go = False
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issues.append(f"Quality: {quality.get('passed', 0)}/10 passed (need >=9)")
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if not needle.get("passed", 0) == needle.get("total", 0):
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issues.append(f"Needle-in-Haystack: {needle.get('passed', 0)}/{needle.get('total', 0)}")
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if context.get("max_context", 0) < 65536:
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issues.append(f"Context ceiling: {context.get('max_context', 0)} < 64K required")
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report["go_no_go"] = "GO" if go and not issues else "NO-GO"
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report["issues"] = issues
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# Markdown
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md = f"""# TurboQuant Test Matrix Report
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**Generated:** {timestamp}
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**Model:** {model}
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## Go/No-Go: {report['go_no_go']}
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{chr(10).join('- ' + i for i in issues) if issues else 'All criteria met.'}
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## Quality (10 Practical Prompts)
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| # | Category | Pattern Match | tok/s |
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|---|----------|--------------|-------|
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"""
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for r in quality.get("details", []):
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status = "PASS" if r.get("pattern_matched") else "FAIL"
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md += f"| {r.get('id','')} | {r.get('category','')} | {status} | {r.get('tok_s','')} |\n"
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md += f"\n**Pass rate:** {quality.get('passed',0)}/{quality.get('total',0)} ({quality.get('pass_rate',0)*100:.0f}%)\n"
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md += f"""
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## Perplexity
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- Chunks scored: {perplexity.get('chunks_scored', 'N/A')}
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- Avg tok/s: {perplexity.get('avg_tok_s', 'N/A')}
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- Note: {perplexity.get('note', '')}
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## Needle-in-Haystack
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| Context | Retrieved | tok/s |
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|---------|-----------|-------|
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"""
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for label, detail in needle.get("details", {}).items():
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md += f"| {label} | {'PASS' if detail.get('retrieved') else 'FAIL'} | {detail.get('tok_s','')} |\n"
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md += f"\n**Retrieved:** {needle.get('passed',0)}/{needle.get('total',0)}\n"
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md += f"""
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## Performance
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| Context | tok/s | TTFT (s) | Prompt Tokens | Generated |
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|---------|-------|----------|---------------|-----------|
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"""
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for label, perf in performance.get("contexts", {}).items():
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md += f"| {label} | {perf.get('tok_s','')} | {perf.get('ttft_s','')} | {perf.get('prompt_tokens','')} | {perf.get('generated_tokens','')} |\n"
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md += f"\nPeak memory: {performance.get('peak_memory_mb', 'N/A')} MB\n"
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md += f"""
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## Context Ceiling
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- Max working context: {context.get('max_context', 'N/A')}
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- Minimum required: 65536
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- Passed: {'YES' if context.get('passed') else 'NO'}
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---
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*Generated by run_test_matrix.py. Ref: #11.*
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"""
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return report, md
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def main():
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parser = argparse.ArgumentParser(description="TurboQuant Full Test Matrix")
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parser.add_argument("--model", default="qwen2.5:7b")
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parser.add_argument("--base-url", default="http://localhost:11434")
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parser.add_argument("--prompts", default="benchmarks/test_prompts.json")
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parser.add_argument("--corpus", default="corpora/wiki.test.raw")
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parser.add_argument("--output-dir", default="reports")
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parser.add_argument("--skip-quality", action="store_true")
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parser.add_argument("--skip-performance", action="store_true")
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args = parser.parse_args()
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timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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date_str = datetime.now().strftime("%Y-%m-%d")
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print(f"=== TurboQuant Test Matrix ===")
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print(f"Model: {args.model}")
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print(f"Backend: {args.base_url}")
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print(f"Time: {timestamp}")
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print()
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quality = {}
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perplexity = {}
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needle = {}
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performance = {}
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context = {}
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if not args.skip_quality:
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print("[1/5] Quality — 10 Practical Prompts")
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quality = run_quality_prompts(args.model, args.base_url, args.prompts)
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print()
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print("[2/5] Perplexity — WikiText-2 proxy")
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perplexity = run_perplexity(args.model, args.base_url, args.corpus)
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print()
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print("[3/5] Needle-in-Haystack")
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needle = run_needle_in_haystack(args.model, args.base_url)
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print()
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if not args.skip_performance:
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print("[4/5] Performance — tok/s, TTFT, memory")
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performance = run_performance(args.model, args.base_url)
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print()
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print("[5/5] Context Ceiling")
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context = run_context_ceiling(args.model, args.base_url)
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print()
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# Generate report
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report, md = generate_report(quality, perplexity, needle, performance, context,
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args.model, timestamp)
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os.makedirs(args.output_dir, exist_ok=True)
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json_path = os.path.join(args.output_dir, f"test-matrix-{date_str}.json")
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md_path = os.path.join(args.output_dir, f"test-matrix-{date_str}.md")
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with open(json_path, "w") as f:
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json.dump(report, f, indent=2)
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with open(md_path, "w") as f:
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f.write(md)
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print(f"=== Results ===")
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print(f"Go/No-Go: {report['go_no_go']}")
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print(f"Quality: {quality.get('passed', 0)}/{quality.get('total', 0)}")
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print(f"Needle: {needle.get('passed', 0)}/{needle.get('total', 0)}")
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print(f"Context ceiling: {context.get('max_context', 0)}")
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print(f"Reports: {json_path}, {md_path}")
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if __name__ == "__main__":
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main()
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