File size: 9,217 Bytes
a683148 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 |
# generate_text.py - Improved text generation with advanced sampling
import torch
from transformers import AutoTokenizer
from model_neo import NeoMini, NeoMiniConfig
import json
import os
from pathlib import Path
def load_model(checkpoint_path="checkpoints/extended_context_model.pt"):
"""Load trained model and tokenizer"""
print(f"Loading model from {checkpoint_path}...")
# Check if checkpoint exists
if not os.path.exists(checkpoint_path):
print(f"Error: Checkpoint not found at {checkpoint_path}")
print("Available checkpoints:")
checkpoint_dir = Path("checkpoints")
if checkpoint_dir.exists():
for ckpt in sorted(checkpoint_dir.glob("checkpoint_step_*.pt")):
print(f" - {ckpt}")
return None, None
# Load checkpoint
checkpoint = torch.load(checkpoint_path, map_location="cuda" if torch.cuda.is_available() else "cpu")
# Create model with same config
config = NeoMiniConfig()
model = NeoMini(config)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
# Move to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
print(f"Model loaded on {device}")
# Load tokenizer
tokenizer_path = "data/tokenizer"
if os.path.exists(tokenizer_path):
tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
else:
print("Using GPT-2 tokenizer as fallback...")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
print(f"Tokenizer vocab size: {tokenizer.vocab_size}")
print(f"Model parameters: {model.get_num_params():,}")
return model, tokenizer
def generate_text(model, tokenizer, prompt, max_length=100,
temperature=0.7, # Lower = more focused
top_k=50, # Only consider top 50 tokens
top_p=0.9, # Nucleus sampling
repetition_penalty=1.1): # Penalize repetition
"""Generate text with advanced sampling techniques"""
device = next(model.parameters()).device
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
original_length = input_ids.size(1)
print(f"Generating with: temp={temperature}, top_k={top_k}, top_p={top_p}")
with torch.no_grad():
for step in range(max_length):
# Forward pass
logits = model(input_ids)
next_token_logits = logits[0, -1, :] / temperature
# Apply repetition penalty
if repetition_penalty != 1.0:
for token_id in set(input_ids[0].tolist()):
if next_token_logits[token_id] < 0:
next_token_logits[token_id] *= repetition_penalty
else:
next_token_logits[token_id] /= repetition_penalty
# Top-k filtering
if top_k > 0:
top_k_logits, _ = torch.topk(next_token_logits, top_k)
min_top_k = top_k_logits[-1]
next_token_logits[next_token_logits < min_top_k] = float('-inf')
# Top-p (nucleus) sampling
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Keep at least one token
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# Convert back to original indices
indices_to_remove = sorted_indices[sorted_indices_to_remove]
next_token_logits[indices_to_remove] = float('-inf')
# Sample next token
probs = torch.softmax(next_token_logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
# Append to sequence
input_ids = torch.cat([input_ids, next_token.unsqueeze(0)], dim=1)
# Check for EOS token
if next_token.item() == tokenizer.eos_token_id:
print(f" → Stopped at EOS token (step {step+1})")
break
# Check for max context length
if input_ids.size(1) >= 1024: # Model's max context
print(f" → Stopped at max context length (step {step+1})")
break
return tokenizer.decode(input_ids[0], skip_special_tokens=True)
def compare_generation_settings(model, tokenizer, prompt):
"""Compare different generation settings"""
print(f"\n{'='*80}")
print(f"COMPARING GENERATION SETTINGS")
print(f"Prompt: '{prompt}'")
print(f"{'='*80}")
settings = [
{"name": "Conservative", "temp": 0.5, "top_k": 20, "top_p": 0.8},
{"name": "Balanced", "temp": 0.7, "top_k": 50, "top_p": 0.9},
{"name": "Creative", "temp": 0.9, "top_k": 100, "top_p": 0.95},
{"name": "Focused", "temp": 0.3, "top_k": 10, "top_p": 0.7}
]
for setting in settings:
print(f"\n--- {setting['name']} Generation ---")
generated = generate_text(
model, tokenizer, prompt, max_length=80,
temperature=setting['temp'],
top_k=setting['top_k'],
top_p=setting['top_p']
)
# Only show the generated part (after prompt)
generated_only = generated[len(prompt):].strip()
print(f"Output: {generated_only}")
def interactive_mode(model, tokenizer):
"""Interactive text generation"""
print(f"\n{'='*60}")
print("INTERACTIVE MODE - Enter prompts (or 'quit' to exit)")
print(f"{'='*60}")
while True:
try:
prompt = input("\nEnter your prompt: ").strip()
if prompt.lower() in ['quit', 'exit', 'q']:
break
if not prompt:
continue
# Get generation parameters
try:
temp = float(input("Temperature (0.1-1.5, default 0.7): ") or "0.7")
top_k = int(input("Top-K (1-100, default 50): ") or "50")
top_p = float(input("Top-P (0.1-1.0, default 0.9): ") or "0.9")
max_len = int(input("Max length (10-200, default 100): ") or "100")
except ValueError:
print("Using default parameters...")
temp, top_k, top_p, max_len = 0.7, 50, 0.9, 100
print(f"\nGenerating...")
generated = generate_text(
model, tokenizer, prompt,
max_length=max_len, temperature=temp,
top_k=top_k, top_p=top_p
)
print(f"\nFull Output:\n{'-'*40}")
print(generated)
print(f"{'-'*40}")
except KeyboardInterrupt:
break
print("\nExiting interactive mode...")
def main():
print("MAP-NEO Mini Text Generator")
print("=" * 50)
# Load model
model, tokenizer = load_model()
if model is None or tokenizer is None:
print("Failed to load model. Exiting.")
return
# Test prompts
test_prompts = [
"The future of artificial intelligence",
"In a world where technology",
"Scientists have discovered",
"The key to success is",
"Climate change is",
"The importance of education",
"Once upon a time, there was",
"To solve this problem, we need to"
]
print(f"\n{'='*60}")
print("BASIC GENERATION TEST")
print(f"{'='*60}")
# Test basic generation
for i, prompt in enumerate(test_prompts[:3], 1):
print(f"\n--- Test {i}/3 ---")
print(f"Prompt: {prompt}")
print("-" * 50)
generated = generate_text(
model, tokenizer, prompt,
max_length=80, temperature=0.7,
top_k=50, top_p=0.9
)
# Show only generated part
generated_only = generated[len(prompt):].strip()
print(f"Generated: {generated_only}")
# Compare settings
compare_generation_settings(
model, tokenizer,
"The most important discovery in science was"
)
# Interactive mode
print(f"\n{'='*60}")
choice = input("Start interactive mode? (y/n): ").lower().strip()
if choice in ['y', 'yes']:
interactive_mode(model, tokenizer)
print("\nText generation complete!")
if __name__ == "__main__":
main()
|