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5997967 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | #!/usr/bin/env python3
"""
Frox AI Morph 1.1 — Evaluation Harness
Runs three checks any time you finish a training phase:
1. Perplexity on WikiText-2 (language modeling quality)
2. Sanity generations on a fixed prompt set (coherence spot-check)
3. Throughput benchmark (tokens/sec at a few sequence lengths)
Usage:
python scripts/evaluate.py --model ./frox-morph-1-1-output/sft_final
python scripts/evaluate.py --model ./frox-morph-1-1-output/sft_final --skip-generation
"""
from __future__ import annotations
import argparse
import json
import sys
import time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
import torch
from inference.engine.morph_engine import MorphInferenceEngine
from training.pipeline.trainer import evaluate_perplexity
from utils.common import print_banner, timer
SANITY_PROMPTS = [
"What is the capital of France?",
"Write a haiku about the ocean.",
"Explain what a neural network is in two sentences.",
"def fibonacci(n):\n # Complete this function",
"What's 17 times 23?",
"Give me three tips for staying focused while studying.",
]
def run_perplexity(engine: MorphInferenceEngine) -> float:
print("\n📊 Perplexity (WikiText-2)")
ppl = evaluate_perplexity(
engine.model.language_model, engine.tokenizer, engine.device,
max_samples=500, seq_len=512,
use_amp=engine.device.type == "cuda", amp_dtype=engine.dtype,
)
print(f" Perplexity: {ppl}")
return ppl
def run_sanity_generations(engine: MorphInferenceEngine) -> list:
print("\n🧪 Sanity Generations")
results = []
for prompt in SANITY_PROMPTS:
response = engine.generate(
[{"role": "user", "content": prompt}],
max_new_tokens=150, temperature=0.7,
)
has_content = len(response.strip()) > 5
has_repetition = _check_repetition(response)
status = "✓" if has_content and not has_repetition else "⚠"
print(f"\n {status} Q: {prompt}")
print(f" A: {response[:200]}{'...' if len(response) > 200 else ''}")
results.append({
"prompt": prompt, "response": response,
"has_content": has_content, "has_repetition": has_repetition,
})
return results
def _check_repetition(text: str, min_repeat: int = 4) -> bool:
"""Flag degenerate repetition (a common failure mode of undertrained models)."""
words = text.split()
if len(words) < min_repeat * 2:
return False
for i in range(len(words) - min_repeat):
window = tuple(words[i:i + min_repeat])
rest = words[i + min_repeat:i + min_repeat * 2]
if tuple(rest[:min_repeat]) == window:
return True
return False
def run_throughput_benchmark(engine: MorphInferenceEngine) -> dict:
print("\n⚡ Throughput Benchmark")
results = {}
for max_tokens in (50, 200, 500):
t0 = time.perf_counter()
_ = engine.generate(
[{"role": "user", "content": "Tell me a short story about a robot."}],
max_new_tokens=max_tokens, temperature=0.7,
)
elapsed = time.perf_counter() - t0
tok_s = max_tokens / elapsed
results[f"{max_tokens}_tokens"] = {"elapsed_s": round(elapsed, 2), "tok_per_s": round(tok_s, 1)}
print(f" {max_tokens:>4} tokens: {elapsed:.2f}s ({tok_s:.1f} tok/s)")
return results
def main():
parser = argparse.ArgumentParser(description="Evaluate Frox AI Morph 1.1")
parser.add_argument("--model", type=str, required=True)
parser.add_argument("--skip-perplexity", action="store_true")
parser.add_argument("--skip-generation", action="store_true")
parser.add_argument("--skip-throughput", action="store_true")
parser.add_argument("--output", type=str, default="./eval_results.json")
args = parser.parse_args()
print_banner()
engine = MorphInferenceEngine.from_pretrained(args.model)
report = {"model_path": args.model, "stats": engine.stats()}
if not args.skip_perplexity:
with timer("Perplexity eval"):
report["perplexity"] = run_perplexity(engine)
if not args.skip_generation:
with timer("Sanity generations"):
report["sanity_generations"] = run_sanity_generations(engine)
n_ok = sum(1 for r in report["sanity_generations"]
if r["has_content"] and not r["has_repetition"])
report["sanity_pass_rate"] = f"{n_ok}/{len(SANITY_PROMPTS)}"
if not args.skip_throughput:
with timer("Throughput benchmark"):
report["throughput"] = run_throughput_benchmark(engine)
Path(args.output).write_text(json.dumps(report, indent=2, default=str))
print(f"\n✅ Full report saved to {args.output}")
if "perplexity" in report:
print(f"\n{'='*50}")
print(f"SUMMARY: perplexity={report['perplexity']} | "
f"sanity={report.get('sanity_pass_rate', 'skipped')}")
print(f"{'='*50}")
if __name__ == "__main__":
main()
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