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Local inference script for MicroGPT.
Loads a trained checkpoint and generates text.
Usage:
# Interactive mode (type prompts)
python inference.py
# Generate from a prompt
python inference.py --prompt "Once upon a time"
# Use a specific checkpoint
python inference.py --checkpoint runs_out/micro_gpt_ckpt_step_100000.pt --prompt "Hello"
"""
import os
import sys
import argparse
from pathlib import Path
import torch
import torch.nn.functional as F
# Add current directory for imports
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from models import get_model
def load_model_and_meta(checkpoint_path: str, device: str = 'cpu'):
"""Load a trained model checkpoint and return model + metadata."""
print(f'Loading checkpoint: {checkpoint_path}')
ckpt = torch.load(checkpoint_path, map_location=device, weights_only=False)
config = ckpt['config']
meta = ckpt['meta']
step = ckpt['step']
best_val_loss = ckpt.get('best_val_loss', 'N/A')
print(f' Architecture : {config["arch"]}')
print(f' Dataset : {config["dataset"]}')
print(f' Vocab size : {meta["vocab_size"]}')
print(f' Block size : {config["block_size"]}')
print(f' Layers/Heads : {config["n_layer"]}/{config["n_head"]}')
print(f' Embed dim : {config["n_embd"]}')
print(f' Parameters : {ckpt["n_params"]:,}')
print(f' Training step: {step}')
print(f' Best val loss: {best_val_loss}')
# Build model
ModelClass = get_model(config['arch'])
model = ModelClass(
vocab_size=meta['vocab_size'],
block_size=config['block_size'],
n_layer=config['n_layer'],
n_head=config['n_head'],
n_embd=config['n_embd'],
dropout=config.get('dropout', 0.1),
)
model.load_state_dict(ckpt['model'])
model.to(device)
model.eval()
return model, meta, config
def encode_text(text: str, stoi: dict) -> torch.Tensor:
"""Encode a string into token indices using the char-level tokenizer."""
indices = [stoi.get(c, 0) for c in text] # 0 = newline (fallback)
return torch.tensor([indices], dtype=torch.long)
def decode_tokens(tokens: list, itos: dict) -> str:
"""Decode token indices back into a string."""
return ''.join(itos.get(i, '?') for i in tokens)
@torch.no_grad()
def generate_text(model, prompt: str, meta: dict, max_new_tokens: int = 256,
temperature: float = 0.9, top_k: int = 40, device: str = 'cpu'):
"""Generate text continuation from a prompt."""
stoi = meta['stoi']
itos = meta['itos']
# Encode prompt
prompt_ids = encode_text(prompt, stoi)
prompt_ids = prompt_ids.to(device)
# Generate
output_ids = model.generate(
prompt_ids,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_k=top_k,
)
# Decode
full_text = decode_tokens(output_ids[0].tolist(), itos)
return full_text
def interactive_mode(model, meta, device: str):
"""Interactive chat-like generation."""
print('\n' + '=' * 60)
print(' MicroGPT Interactive Mode')
print(' Type your prompt and press Enter.')
print(' Type "quit", "exit", or "q" to stop.')
print('=' * 60)
while True:
try:
prompt = input('\nPrompt: ').strip()
except (EOFError, KeyboardInterrupt):
print()
break
if prompt.lower() in ('quit', 'exit', 'q'):
break
if not prompt:
continue
print('\nGenerating...\n')
result = generate_text(model, prompt, meta, device=device)
print(result)
print()
def main():
parser = argparse.ArgumentParser(description='MicroGPT Local Inference')
parser.add_argument('--checkpoint', type=str,
default='runs_out/micro_gpt_ckpt_step_100000.pt',
help='Path to model checkpoint')
parser.add_argument('--prompt', type=str, default=None,
help='Text prompt for generation')
parser.add_argument('--max_new_tokens', type=int, default=256,
help='Maximum tokens to generate')
parser.add_argument('--temperature', type=float, default=0.9,
help='Sampling temperature (higher = more random)')
parser.add_argument('--top_k', type=int, default=40,
help='Top-k sampling threshold')
parser.add_argument('--device', type=str, default='cpu',
help='Device to run on (cpu, cuda, mps)')
args = parser.parse_args()
# Resolve device
if args.device == 'cuda' and not torch.cuda.is_available():
print('CUDA not available, falling back to CPU')
args.device = 'cpu'
elif args.device == 'mps' and not hasattr(torch.backends, 'mps') or not torch.backends.mps.is_available():
print('MPS not available, falling back to CPU')
args.device = 'cpu'
# Load model
model, meta, config = load_model_and_meta(args.checkpoint, args.device)
if args.prompt:
# Single generation
print(f'\nPrompt: {args.prompt}')
print(f'Generating (temp={args.temperature}, top_k={args.top_k}, max_tokens={args.max_new_tokens})...\n')
result = generate_text(
model, args.prompt, meta,
max_new_tokens=args.max_new_tokens,
temperature=args.temperature,
top_k=args.top_k,
device=args.device,
)
print(result)
else:
# Interactive mode
interactive_mode(model, meta, args.device)
if __name__ == '__main__':
main() |