sample.py

"""
Sample from a trained model

REQUIRED:
  1. You must specify a config file from the config/ directory
  2. All configuration must be in the config file. No CLI overrides allowed

Usage:
  python sample.py <config_file>

Examples:
  python sample.py config/sample_gpt2.py
"""
import sys

# -----------------------------------------------------------------------------
# Configuration loading (BEFORE imports to validate config first)
# Usage:
#   python sample.py <config_file>
# Note: All configuration must be specified in the config file.
# -----------------------------------------------------------------------------

# Parse command line - only accept config file, no --key=value allowed
if len(sys.argv) != 2:
    print("ERROR: Invalid arguments!")
    print("Usage: python sample.py <config_file>")
    print("Available configs in config/:")
    print("  - sample_gpt2.py")
    sys.exit(1)

config_file = sys.argv[1]

# Disallow --key=value arguments
for arg in sys.argv[1:]:
    if arg.startswith('--'):
        print(f"ERROR: CLI overrides are not supported. All config must be in file: {config_file}")
        sys.exit(1)

# Load the specified config file
print(f"Loading config from: {config_file}")
exec(open(config_file).read())

# Validate required config keys
required_keys = ['out_dir', 'init_from', 'model_config']
missing_keys = [k for k in required_keys if k not in globals()]
if missing_keys:
    print(f"ERROR: Missing required config keys: {missing_keys}")
    sys.exit(1)

# Load model configuration
model_config = globals()['model_config']
model_file = f"models/{model_config}.py"
try:
    exec(open(model_file).read())
except FileNotFoundError:
    print(f"ERROR: Model file not found: {model_file}")
    print(f"Available models in models/:")
    import os
    for f in os.listdir('models'):
        if f.endswith('.py') and not f.startswith('_'):
            print(f"  - {f[:-3]}")
    sys.exit(1)

# Get model-specific required config keys from GPTConfig
model_required_keys = []
if 'GPTConfig' in globals():
    config_class = globals()['GPTConfig']
    import dataclasses
    for field in dataclasses.fields(config_class):
        model_required_keys.append(field.name)

# Validate model-specific config keys
# Skip validation for 'resume' mode (loads from checkpoint) and 'gpt2*' mode (loads pretrained)
# Only require model config when init_from='scratch'
if init_from == 'scratch':
    missing_model_keys = [k for k in model_required_keys if k not in globals()]
    if missing_model_keys:
        print(f"ERROR: Missing required model config keys for {model_config}: {missing_model_keys}")
        print(f"Required keys: {model_required_keys}")
        sys.exit(1)

# Print configuration (exclude internal variables)
exclude_keys = {'config_file', 'model_file', 'model_config', 'model_required_keys', 'config_class'}
print("\n" + "=" * 60)
print("SAMPLE CONFIGURATION")
print("=" * 60)
for key in sorted(globals().keys()):
    val = globals().get(key)
    if isinstance(val, (int, float, bool, str)) and key not in exclude_keys and not key.startswith('_'):
        print(f"  {key:30s} = {val}")
print("=" * 60 + "\n")

# Now import dependencies
import os
import pickle
from contextlib import nullcontext
import torch
import tiktoken

# Import GPTConfig and GPT from the model file
GPTConfig = globals()['GPTConfig']
GPT = globals()['GPT']

# Auto-detect dtype
if dtype == 'bfloat16' and not (torch.cuda.is_available() and torch.cuda.is_bf16_supported()):
    dtype = 'float16'

torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
device_type = 'cuda' if 'cuda' in device else 'cpu'
ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)

# model
checkpoint = None
if init_from == 'resume':
    # init from a model saved in a specific directory
    ckpt_path = os.path.join(out_dir, 'ckpt.pt')
    checkpoint = torch.load(ckpt_path, map_location=device)
    gptconf = GPTConfig(**checkpoint['model_args'])
    model = GPT(gptconf)
    state_dict = checkpoint['model']
    unwanted_prefix = '_orig_mod.'
    for k,v in list(state_dict.items()):
        if k.startswith(unwanted_prefix):
            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
    model.load_state_dict(state_dict)
elif init_from.startswith('gpt2'):
    # init from a given GPT-2 model
    model = GPT.from_pretrained(init_from, dict(dropout=0.0))

model.eval()
model.to(device)
if compile:
    model = torch.compile(model)

# look for the meta pickle in case it is available in the dataset folder
load_meta = False
if init_from == 'resume' and checkpoint is not None and 'config' in checkpoint and 'dataset' in checkpoint['config']:
    meta_path = os.path.join('data', checkpoint['config']['dataset'], 'meta.pkl')
    load_meta = os.path.exists(meta_path)
if load_meta:
    print(f"Loading meta from {meta_path}...")
    with open(meta_path, 'rb') as f:
        meta = pickle.load(f)
    stoi, itos = meta['stoi'], meta['itos']
    encode = lambda s: [stoi[c] for c in s]
    decode = lambda l: ''.join([itos[i] for i in l])
else:
    print("No meta.pkl found, assuming GPT-2 encodings...")
    enc = tiktoken.get_encoding("gpt2")
    encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"})
    decode = lambda l: enc.decode(l)

# encode the beginning of the prompt
if start.startswith('FILE:'):
    with open(start[5:], 'r', encoding='utf-8') as f:
        start = f.read()
start_ids = encode(start)
x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])

# run generation
with torch.no_grad():
    with ctx:
        for k in range(num_samples):
            y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
            print(decode(y[0].tolist()))
            print('---------------')