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"""
Comprehensive evaluation script for Quillan-Ronin v5.3.1
Tests generation quality, perplexity, and reasoning capabilities.
"""
import sys, os, json
from pathlib import Path
import torch
import torch.nn.functional as F
import time
import math
BASE = Path(__file__).parent.parent
sys.path.insert(0, str(BASE / '_dev'))
from quillan_v8_saturated import QuillanRoninSovereign, QuillanArchConfig
from quillan_bpe_tokenizer import QuillanBPETokenizer
def load_tokenizer():
"""Load GPT-2 tokenizer to match model checkpoint (50257 vocab)."""
try:
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
print(f'[OK] Loaded GPT-2 tokenizer: {tokenizer.vocab_size} vocab')
return tokenizer
except ImportError:
print('[WARN] transformers not installed, using fallback')
return None
def load_model(ckpt_path, tokenizer, device='cuda'):
"""Load model from checkpoint."""
print(f'Loading checkpoint: {ckpt_path}')
ckpt = torch.load(ckpt_path, map_location=device, weights_only=False)
# Handle different checkpoint formats
if isinstance(ckpt, dict):
if 'state_dict' in ckpt:
state = ckpt['state_dict']
elif 'model_state_dict' in ckpt:
state = ckpt['model_state_dict']
else:
state = ckpt
config = ckpt.get('config', {})
else:
state = ckpt
config = {}
# Use SFT checkpoint configuration (hidden_dim=1024, ffn_dim=2048, eggroll_rank=16)
# Initialize with checkpoint vocab size (50257) to load successfully
cfg = QuillanArchConfig(
text_only=True,
hidden_dim=config.get('hidden_dim', 1024),
ffn_dim=config.get('ffn_dim', 2048),
vocab_size=config.get('vocab_size', 50257), # Match checkpoint vocab size first
num_experts=config.get('num_experts', 34),
top_k=config.get('top_k', 4),
eggroll_rank=16, # SFT checkpoint uses rank=16 for expert swarms
e_ice_limit_ms=100,
device=device
)
model = QuillanRoninSovereign(cfg).to(device)
missing, unexpected = model.load_state_dict(state, strict=False)
model.eval()
print(f'[OK] Model loaded')
print(f' Missing keys: {len(missing)}, Unexpected: {len(unexpected)}')
print(f' Step: {ckpt.get("step", "unknown")}, Loss: {ckpt.get("loss", "unknown")}')
return model, cfg
def calculate_perplexity(model, data, tokenizer, device='cuda', max_batches=10):
"""Calculate perplexity on validation data."""
print('\n--- Perplexity Evaluation ---')
model.eval()
total_loss = 0.0
total_tokens = 0
with torch.no_grad():
for i, text in enumerate(data[:max_batches]):
tokens = tokenizer.encode(text) if tokenizer else [ord(c) % 50257 for c in text]
if len(tokens) < 10:
continue
# Truncate to max sequence length (1024)
max_len = 1024
if len(tokens) > max_len:
tokens = tokens[:max_len]
x = torch.tensor([tokens[:-1]], dtype=torch.long, device=device)
y = torch.tensor([tokens[1:]], dtype=torch.long, device=device)
out = model(x)
logits = out['logits'] if isinstance(out, dict) else out
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), y.view(-1))
total_loss += loss.item() * y.numel()
total_tokens += y.numel()
if i % 5 == 0:
print(f' Batch {i+1}/{min(max_batches, len(data))}: Loss={loss.item():.4f}')
avg_loss = total_loss / total_tokens if total_tokens > 0 else float('inf')
perplexity = math.exp(avg_loss)
print(f'[OK] Perplexity: {perplexity:.2f}')
return perplexity
def evaluate_generation(model, tokenizer, prompts, device='cuda', max_tokens=50, temperature=0.8):
"""Evaluate text generation quality with improved sampling."""
print('\n--- Generation Evaluation ---')
results = []
for i, prompt in enumerate(prompts):
print(f'\nPrompt {i+1}: {prompt}')
tokens = tokenizer.encode(prompt) if tokenizer else [ord(c) % 50257 for c in prompt]
x = torch.tensor([tokens], dtype=torch.long, device=device)
generated = []
start_time = time.time()
with torch.no_grad():
for step in range(max_tokens):
out = model(x, recursive_depth=0) # Disable recursive consciousness loop
logits = out['logits'] if isinstance(out, dict) else out
# Apply repetition penalty (2.0 as per antigravity walkthrough)
for token in set(generated):
logits[0, -1, token] /= 2.0
# Nucleus sampling (top-p=0.85)
probs = F.softmax(logits[0, -1] / temperature, dim=-1)
sorted_probs, sorted_indices = torch.sort(probs, descending=True)
cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
sorted_indices_to_remove = cumulative_probs > 0.85
sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].clone()
sorted_indices_to_remove[0] = False
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[0, -1, indices_to_remove] = float('-inf')
next_token = torch.multinomial(F.softmax(logits[0, -1], dim=-1), 1)
generated.append(next_token.item())
x = torch.cat([x, next_token.unsqueeze(0)], dim=1)
elapsed = time.time() - start_time
tokens_per_sec = len(generated) / elapsed if elapsed > 0 else 0
full_tokens = tokens + generated
generated_text = tokenizer.decode(full_tokens) if tokenizer else ''.join(chr(t % 128) for t in full_tokens)
try:
print(f'Generated: {generated_text}')
except UnicodeEncodeError:
print(f'Generated: [Unicode error - text contains unsupported characters]')
print(f'Speed: {tokens_per_sec:.2f} tokens/sec')
results.append({
'prompt': prompt,
'generated': generated_text,
'tokens_per_sec': tokens_per_sec,
'time': elapsed
})
return results
def evaluate_reasoning(model, tokenizer, device='cuda'):
"""Evaluate reasoning capabilities with simple logic tasks."""
print('\n--- Reasoning Evaluation ---')
reasoning_prompts = [
"If all cats are animals and Fluffy is a cat, then",
"The sum of 5 and 7 is",
"To bake a cake, first you need to",
"The opposite of hot is"
]
results = evaluate_generation(model, tokenizer, reasoning_prompts, device, max_tokens=20, temperature=0.6)
# Simple coherence check
coherent = 0
for r in results:
gen = r['generated'].lower()
if any(word in gen for word in ['is', 'then', 'the', 'you', 'need', 'cold']):
coherent += 1
coherence_rate = coherent / len(results) if results else 0
print(f'[OK] Coherence rate: {coherence_rate:.2%}')
return results, coherence_rate
def check_vram_usage(model, device='cuda'):
"""Check VRAM usage."""
if device == 'cuda' and torch.cuda.is_available():
vram_allocated = torch.cuda.memory_allocated(device) / 1024**3
vram_reserved = torch.cuda.memory_reserved(device) / 1024**3
vram_total = torch.cuda.get_device_properties(device).total_memory / 1024**3
print(f'\n--- VRAM Usage ---')
print(f'Allocated: {vram_allocated:.2f} GB')
print(f'Reserved: {vram_reserved:.2f} GB')
print(f'Total: {vram_total:.2f} GB')
print(f'Usage: {vram_allocated/vram_total*100:.1f}%')
return {
'allocated_gb': vram_allocated,
'reserved_gb': vram_reserved,
'total_gb': vram_total,
'usage_percent': vram_allocated/vram_total*100
}
return None
def main():
device = 'cpu' # Force CPU - GTX 1050 incompatible with current PyTorch and insufficient VRAM
print(f'Using device: {device}')
# Find latest checkpoint - use checkpoints_sft (completed SFT training)
ckpt_dir = BASE / 'checkpoints_sft'
ckpt_files = list(ckpt_dir.glob('*.pt'))
if not ckpt_files:
print(f'ERROR: No checkpoints found in {ckpt_dir}')
return
latest = max(ckpt_files, key=lambda p: p.stat().st_mtime)
print(f'Latest checkpoint: {latest.name}')
# Load model and tokenizer
tokenizer = load_tokenizer()
model, cfg = load_model(str(latest), tokenizer, device)
# Check VRAM
vram_info = check_vram_usage(device)
# Evaluation prompts
test_prompts = [
"The future of artificial intelligence is",
"Once upon a time",
"In a world where technology advances rapidly",
"The most important lesson I learned"
]
# Run evaluations
gen_results = evaluate_generation(model, tokenizer, test_prompts, device, max_tokens=30)
reason_results, coherence = evaluate_reasoning(model, tokenizer, device)
# Load actual JSONL training datasets used in SFT training
validation_texts = []
jsonl_files = [
BASE / 'training_data' / 'instruct_train.jsonl',
BASE / 'training_data' / 'GPT_5.5_Distilled.jsonl',
BASE / 'training_data' / 'code_train.jsonl',
BASE / 'training_data' / 'quillan_12mb_training_dataset.jsonl',
BASE / 'training_data' / 'quillan_science_absolute.jsonl',
BASE / 'training_data' / 'quillan_science_additional.jsonl'
]
for jsonl_file in jsonl_files:
if jsonl_file.exists():
try:
with open(jsonl_file, 'r', encoding='utf-8') as f:
count = 0
for line in f:
if count >= 50: # Take first 50 samples per file
break
try:
data = json.loads(line)
# Extract text content from different formats
if 'messages' in data:
# Chat format - extract assistant responses
for msg in data['messages']:
if msg.get('role') == 'assistant':
validation_texts.append(msg.get('content', ''))
break
elif 'text' in data:
validation_texts.append(data['text'])
elif 'content' in data:
validation_texts.append(data['content'])
elif 'prompt' in data and 'completion' in data:
validation_texts.append(data['prompt'] + ' ' + data['completion'])
count += 1
except:
continue
print(f'Loaded {count} samples from {jsonl_file.name}')
except Exception as e:
print(f'Failed to load {jsonl_file.name}: {e}')
# Also load pre-train blend data
pretrain_file = BASE / 'training_data' / 'quillan_corpus_CLEAN_V7.pt'
if pretrain_file.exists():
try:
data = torch.load(pretrain_file, map_location='cpu')
if isinstance(data, list):
validation_texts.extend(data[:50])
print(f'Loaded 50 samples from {pretrain_file.name}')
except Exception as e:
print(f'Failed to load {pretrain_file.name}: {e}')
if not validation_texts:
# Fallback to hardcoded samples
validation_texts = [
"The quick brown fox jumps over the lazy dog.",
"Machine learning is a subset of artificial intelligence.",
"Python is a popular programming language for data science.",
"The model architecture uses mixture of experts for efficiency."
]
print('Using fallback validation texts')
print(f'Total validation samples: {len(validation_texts)}')
if tokenizer:
perplexity = calculate_perplexity(model, validation_texts, tokenizer, device)
else:
perplexity = None
# Summary
print('\n' + '='*50)
print('EVALUATION SUMMARY')
print('='*50)
print(f'Checkpoint: {latest.name}')
print(f'Device: {device}')
print(f'Coherence Rate: {coherence:.2%}')
if perplexity:
print(f'Perplexity: {perplexity:.2f}')
if vram_info:
print(f'VRAM Usage: {vram_info["usage_percent"]:.1f}%')
print(f'Average Generation Speed: {sum(r["tokens_per_sec"] for r in gen_results)/len(gen_results):.2f} tokens/sec')
print('='*50)
# Save results
results = {
'checkpoint': str(latest),
'device': device,
'coherence_rate': coherence,
'perplexity': perplexity,
'vram_usage': vram_info,
'generation_results': gen_results,
'reasoning_results': reason_results
}
out_path = BASE / 'evaluation_results.json'
with open(out_path, 'w') as f:
json.dump(results, f, indent=2)
print(f'\n[OK] Results saved to {out_path}')
if __name__ == '__main__':
main()
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