add support for character-level language models, a new character-level shakespeare dataset, a new config file that shows how to train a character-level baby GPT on it, and adjust the sample function to figure out if it should decode with characters or GPT2 bpe tokens. The current implementation is a bit hacky and basically assumes just these two possibilities. In the future we may want to support more general encoders or decoders.

config/train_shakespeare_char.py

@@ -0,0 +1,36 @@
+# train a miniature character-level shakespeare model
+# good for debugging and playing on macbooks and such
+
+out_dir = 'out-shakespeare-char'
+eval_interval = 250 # keep frequent because we'll overfit
+eval_iters = 200
+log_interval = 10 # don't print too too often
+
+# we expect to overfit on this small dataset, so only save when val improves
+always_save_checkpoint = True
+
+wandb_log = False # override via command line if you like
+wandb_project = 'shakespeare-char'
+wandb_run_name = 'mini-gpt'
+
+dataset = 'shakespeare_char'
+batch_size = 64
+block_size = 128 # context of up to 128 previous characters
+
+# baby GPT model :)
+n_layer = 4
+n_head = 4
+n_embd = 128
+dropout = 0.0
+
+learning_rate = 1e-3 # with baby networks can afford to go a bit higher
+max_iters = 5000
+lr_decay_iters = 5000 # make equal to max_iters usually
+min_lr = 1e-4 # learning_rate / 10 usually
+beta2 = 0.99 # make a bit bigger because number of tokens per iter is small
+
+warmup_iters = 100 # not super necessary potentially
+
+# on macbook also add
+# device = 'cpu'  # run on cpu only
+# compile = False # do not torch compile the model

data/shakespeare_char/prepare.py

@@ -0,0 +1,67 @@
+"""
+Prepare the Shakespeare dataset for character-level language modeling.
+So instead of encoding with GPT-2 BPE tokens, we just map characters to ints.
+Will save train.bin, val.bin containing the ids, and meta.pkl containing the
+encoder and decoder and some other related info.
+"""
+import os
+import pickle
+import requests
+import numpy as np
+
+# download the tiny shakespeare dataset
+if not os.path.exists('input.txt'):
+    data_url = 'https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt'
+    with open('input.txt', 'w') as f:
+        f.write(requests.get(data_url).text)
+
+with open('input.txt', 'r') as f:
+    data = f.read()
+print("length of dataset in characters: ", len(data))
+
+# get all the unique characters that occur in this text
+chars = sorted(list(set(data)))
+vocab_size = len(chars)
+print("all the unique characters:", ''.join(chars))
+print("vocab size:", vocab_size)
+
+# create a mapping from characters to integers
+stoi = { ch:i for i,ch in enumerate(chars) }
+itos = { i:ch for i,ch in enumerate(chars) }
+def encode(s):
+    return [stoi[c] for c in s] # encoder: take a string, output a list of integers
+def decode(l):
+    ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
+
+# create the train and test splits
+n = len(data)
+train_data = data[:int(n*0.9)]
+val_data = data[int(n*0.9):]
+
+# encode both to integers
+train_ids = encode(train_data)
+val_ids = encode(val_data)
+print(f"train has {len(train_ids)} tokens")
+print(f"val has {len(val_ids)} tokens")
+
+# export to bin files
+train_ids = np.array(train_ids, dtype=np.uint16)
+val_ids = np.array(val_ids, dtype=np.uint16)
+train_ids.tofile('train.bin')
+val_ids.tofile('val.bin')
+
+# save the meta information as well, to help us encode/decode later
+meta = {
+    'vocab_size': vocab_size,
+    'itos': itos,
+    'stoi': stoi,
+}
+with open('meta.pkl', 'wb') as f:
+    pickle.dump(meta, f)
+
+# length of dataset in characters:  1115394
+# all the unique characters:
+#  !$&',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
+# vocab size: 65
+# train has 1003854 tokens
+# val has 111540 tokens

data/shakespeare_char/readme.md

@@ -0,0 +1,9 @@
+
+# tiny shakespeare, character-level
+
+Tiny shakespeare, of the good old char-rnn fame :) Treated on character-level.
+
+After running `prepare.py`:
+
+- train.bin has 1,003,854 tokens
+- val.bin has 111,540 tokens

sample.py

@@ -2,6 +2,7 @@
 Sample from a trained model
 """
 import os
+import pickle
 from contextlib import nullcontext
 import torch
 import tiktoken
@@ -45,9 +46,28 @@ model.to(device)
 if compile:
     model = torch.compile(model) # requires PyTorch 2.0 (optional)
 
+# look for the meta pickle in case it is available in the dataset folder
+load_meta = False
+if 'config' in checkpoint and 'dataset' in checkpoint['config']: # older checkpoints might not have these...
+    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)
+    # TODO want to make this more general to arbitrary encoder/decoder schemes
+    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:
+    # ok let's assume gpt-2 encodings by default
+    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
-enc = tiktoken.get_encoding("gpt2")
-start_ids = enc.encode(start, allowed_special={"<|endoftext|>"})
+start_ids = encode(start)
 x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
 
 # run generation
@@ -55,5 +75,5 @@ 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(enc.decode(y[0].tolist()))
-        print('---------------')
+            print(decode(y[0].tolist()))
+            print('---------------')

train.py

@@ -225,6 +225,7 @@ while True:
                     'model_args': model_args,
                     'iter_num': iter_num,
                     'best_val_loss': best_val_loss,
+                    'config': config,
                 }
                 print(f"saving checkpoint to {out_dir}")
                 torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt'))