import re import time import uuid import json import random from datetime import datetime from typing import List, Dict, Any from .schemas import BenchmarkTask, BenchmarkRunRequest, BenchmarkResult, BenchmarkReport from .model_manager import model_manager from .telemetry import telemetry_service from .schemas import GenerateRequest # Predefined suites — 4 categories as per spec BENCHMARK_SUITES: Dict[str, List[BenchmarkTask]] = { "reasoning": [ BenchmarkTask( id="reason-1", name="الاستدلال المنطقي - حسابي", category="reasoning", prompt="إذا كان لدينا 5 تفاحات وأخذ أحمد 2 ثم اشترى 4 تفاحات إضافية، كم تفاحة أصبح لديه؟ أجب برقم فقط.", expected="7", expected_regex=r"\b7\b", max_tokens=64 ), BenchmarkTask( id="reason-2", name="الاستدلال - متتالية", category="reasoning", prompt="ما هو الرقم التالي في المتتالية: 2, 4, 8, 16, ... ؟ أجب برقم فقط.", expected="32", expected_regex=r"\b32\b", max_tokens=64 ), BenchmarkTask( id="reason-3", name="Reasoning - Logic Puzzle", category="reasoning", prompt="All cats are animals. Whiskers is a cat. Is Whiskers an animal? Answer Yes or No.", expected="Yes", expected_regex=r"(?i)\byes\b", max_tokens=64 ), BenchmarkTask( id="reason-4", name="الاستدلال - مقارنة", category="reasoning", prompt="أيهما أكبر: 3/4 أم 2/3؟ أجب بالكسر الأكبر فقط.", expected="3/4", expected_regex=r"3\s*/\s*4", max_tokens=64 ), ], "coding": [ BenchmarkTask( id="code-1", name="كتابة دالة - فيبوناتشي", category="coding", prompt="اكتب دالة Python باسم fibonacci(n) ترجع الرقم n في متتالية فيبوناتشي بدون شرح إضافي، فقط الكود.", expected="def fibonacci", expected_regex=r"def\s+fibonacci", max_tokens=256 ), BenchmarkTask( id="code-2", name="تصحيح كود - حلقة", category="coding", prompt="ما ناتج هذا الكود؟\nfor i in range(3):\n print(i)\nأجب بالأرقام المطبوعة مفصولة بفواصل.", expected="0, 1, 2", expected_regex=r"0.*1.*2", max_tokens=64 ), BenchmarkTask( id="code-3", name="Coding - Reverse String", category="coding", prompt="Write a Python function to reverse a string. Only code, no explanation. Function name: reverse_string", expected="def reverse_string", expected_regex=r"def\s+reverse_string", max_tokens=200 ), BenchmarkTask( id="code-4", name="كود - فرز", category="coding", prompt="اكتب كود Python لفرز قائمة أرقام تصاعدياً باستخدام sorted(). فقط سطر واحد.", expected="sorted", expected_regex=r"sorted\s*\(", max_tokens=64 ), ], "arabic": [ BenchmarkTask( id="ar-1", name="جودة العربية - تصحيح إملائي", category="arabic", prompt="صحح الجملة التالية إملائياً: 'ذهبة الطالبة الى المدرسة صباحن'.", expected="ذهبت", expected_regex=r"ذهبت", max_tokens=128 ), BenchmarkTask( id="ar-2", name="جودة العربية - مرادف", category="arabic", prompt="ما مرادف كلمة 'سعيد'؟ أجب بكلمة واحدة.", expected="فرح", expected_regex=r"(فرح|مسرور|مبتهج|سعيد)", max_tokens=32 ), BenchmarkTask( id="ar-3", name="جودة العربية - إعراب", category="arabic", prompt="أعرب كلمة 'الكتاب' في جملة: 'قرأ الطالب الكتاب'.", expected="مفعول به", expected_regex=r"مفعول\s*به", max_tokens=128 ), BenchmarkTask( id="ar-4", name="جودة العربية - تلخيص", category="arabic", prompt="لخص الجملة: 'الذكاء الاصطناعي هو مجال من مجالات علوم الحاسب يهدف إلى إنشاء أنظمة قادرة على محاكاة الذكاء البشري.' في 10 كلمات.", expected="الذكاء الاصطناعي", expected_regex=r"الذكاء\s*الاصطناعي", max_tokens=64 ), ], "summarization": [ BenchmarkTask( id="sum-1", name="تلخيص - نص تقني", category="summarization", prompt="لخص النص التالي في جملتين: 'تم إطلاق نموذج لغوي جديد يدعم اللغة العربية بشكل ممتاز. النموذج يحتوي على 7 مليار معامل وتم تدريبه على 2 تريليون توكن. يحقق النموذج نتائج ممتازة في اختبارات الفهم والتلخيص والبرمجة.'", expected="7 مليار", expected_regex=r"7\s*مليار", max_tokens=128 ), BenchmarkTask( id="sum-2", name="Summarization - English", category="summarization", prompt="Summarize in one sentence: 'The Transformer architecture, introduced in 2017, revolutionized NLP by using self-attention mechanisms instead of recurrence, enabling parallel training and better long-range dependencies handling.'", expected="Transformer", expected_regex=r"(?i)transformer", max_tokens=128 ), BenchmarkTask( id="sum-3", name="تلخيص - نقاط", category="summarization", prompt="حول النص إلى 3 نقاط: 'الطاقة المتجددة تشمل الشمس والرياح والمياه. هي صديقة للبيئة وتقلل الانبعاثات. الاستثمار فيها ينمو سنوياً بنسبة 10%.'", expected="الشمس", expected_regex=r"الشمس|الرياح|المياه", max_tokens=128 ), ], } reports_store: Dict[str, BenchmarkReport] = {} def evaluate_answer(generation: str, task: BenchmarkTask, mode: str = "regex") -> tuple[bool, float, str]: gen = generation.strip() if mode == "exact" and task.expected: passed = task.expected.strip().lower() in gen.lower() return passed, 1.0 if passed else 0.0, "Exact match" elif mode == "regex" and task.expected_regex: try: passed = bool(re.search(task.expected_regex, gen, re.MULTILINE | re.UNICODE)) return passed, 1.0 if passed else 0.0, f"Regex: {task.expected_regex} -> {'match' if passed else 'no match'}" except re.error as e: return False, 0.0, f"Regex error: {e}" elif mode == "llm": # Simulate LLM-as-judge: heuristic length + keyword check # In real would call model again if task.expected and task.expected.lower() in gen.lower(): return True, 0.85, "LLM-judge: keyword found" # fallback to regex if task.expected_regex and re.search(task.expected_regex, gen, re.MULTILINE | re.UNICODE | re.IGNORECASE): return True, 0.8, "LLM-judge: pattern match" # Simulate judge giving partial score = 0.3 if len(gen) > 10 else 0.0 return score > 0.5, score, "LLM-judge: heuristic" return False, 0.0, "No evaluation method" def run_benchmark(req: BenchmarkRunRequest, model_path: str = "") -> BenchmarkReport: suites = req.suites if "all" in suites: suites = ["reasoning", "coding", "arabic", "summarization"] tasks: List[BenchmarkTask] = [] for s in suites: lst = BENCHMARK_SUITES.get(s, []) if req.max_tasks_per_suite: lst = lst[:req.max_tasks_per_suite] tasks.extend(lst) results: List[BenchmarkResult] = [] total_start = time.time() vram_peak = 0 # Auto speed-up on CPU (no GPU) — cap tokens to avoid 2+ minute per task is_cpu = not telemetry_service._has_gpu or model_manager._device == "cpu" for task in tasks: start = time.time() snap_before = telemetry_service.get_snapshot() # On CPU, cap to 32 tokens (~15s per task instead of 120s) effective_tokens = min(task.max_tokens, 32) if is_cpu else task.max_tokens # Generate gen_req = GenerateRequest( prompt=task.prompt, max_new_tokens=effective_tokens, temperature=req.temperature, stream=False ) # Use blocking generation try: out = model_manager.generate_blocking(gen_req) generation = out["text"] stats = out["stats"] tokens_per_sec = stats.get("tokens_per_sec", 0) if isinstance(stats, dict) else 0 ttft = stats.get("ttft_ms", 0) if isinstance(stats, dict) else 0 latency = stats.get("total_time_ms", (time.time()-start)*1000) if isinstance(stats, dict) else (time.time()-start)*1000 except Exception as e: generation = f"[خطأ في التوليد: {str(e)[:100]}]" tokens_per_sec = 0 ttft = 0 latency = (time.time()-start)*1000 passed, score, reason = evaluate_answer(generation, task, req.judge_mode) # VRAM peak tracking snap_after = telemetry_service.get_snapshot(tokens_per_sec=tokens_per_sec) vram_used = snap_after.vram_used_mb or snap_after.vram_peak_mb or 0 if vram_used > vram_peak: vram_peak = vram_used # In demo without GPU, simulate if vram_used == 0: vram_used = 3500 + random.uniform(-300, 800) if vram_used > vram_peak: vram_peak = vram_used results.append(BenchmarkResult( task_id=task.id, name=task.name, category=task.category, prompt=task.prompt, expected=task.expected, generation=generation, passed=passed, score=score, latency_ms=round(latency,1), tokens_per_sec=round(tokens_per_sec,1), ttft_ms=round(ttft,1), vram_peak_mb=round(vram_used,1), judge_reason=reason )) total_time = (time.time() - total_start) * 1000 passed_count = sum(1 for r in results if r.passed) accuracy = passed_count / len(results) if results else 0 avg_tps = sum(r.tokens_per_sec for r in results) / len(results) if results else 0 avg_ttft = sum(r.ttft_ms for r in results) / len(results) if results else 0 avg_lat = sum(r.latency_ms for r in results) / len(results) if results else 0 # By category by_cat = {} for cat in ["reasoning", "coding", "arabic", "summarization"]: cat_results = [r for r in results if r.category == cat] if cat_results: c_passed = sum(1 for r in cat_results if r.passed) by_cat[cat] = { "total": len(cat_results), "passed": c_passed, "accuracy": round(c_passed/len(cat_results), 3), "avg_tps": round(sum(r.tokens_per_sec for r in cat_results)/len(cat_results),1), "avg_ttft": round(sum(r.ttft_ms for r in cat_results)/len(cat_results),1), } report_id = str(uuid.uuid4())[:8] report = BenchmarkReport( id=report_id, model_path=model_path or model_manager.info.model_path or "demo-model", timestamp=datetime.utcnow(), total_tasks=len(results), passed=passed_count, accuracy=round(accuracy,3), avg_tokens_per_sec=round(avg_tps,1), avg_ttft_ms=round(avg_ttft,1), avg_latency_ms=round(avg_lat,1), vram_peak_mb=round(vram_peak,1), results=results, by_category=by_cat ) reports_store[report_id] = report return report def get_report(report_id: str) -> BenchmarkReport | None: return reports_store.get(report_id) def list_reports(): return list(reports_store.values())