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# nanoGPT
The cleanest, fastest repository for training/finetuning medium-sized GPTs.
This repo currently requires reading the code, but it's not that bad. work ongoing...
Getting started:
We need a few dependencies:
- [pytorch](https://pytorch.org), of course
- numpy
- `pip install datasets` for huggingface datasets
- `pip install tiktoken` for OpenAI's fast bpe code
- `pip install wandb` for optional logging
```
$ cd data/openwebtext
$ python prepare.py
```
To download and tokenize the [openwebtext](https://huggingface.co/datasets/openwebtext) dataset. It will create a `train.bin` and `val.bin` which holds the GPT2 BPE token ids in a massive sequence. Then we're ready to kick off training. First open up train.py and read it, make sure the settings look ok. Then:
```
$ python train.py
```
Once some checkpoints are written to the output directory `out`, we're ready to sample from the model:
```
$ python sample.py
```

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# saves the openwebtext dataset to a binary file for training. following was helpful:
# https://github.com/HazyResearch/flash-attention/blob/main/training/src/datamodules/language_modeling_hf.py
import mmap
import subprocess
import numpy as np
import tiktoken
from datasets import load_dataset # huggingface datasets
# number of workers in .map() calls
# good number to use is ~order num_cpu_cores()
num_proc = 16
# takes 54GB in huggingface .cache dir, about 8M documents (8,013,769)
dataset = load_dataset("openwebtext")
# owt by default only contains the 'train' split, so create a test split
split_dataset = dataset["train"].train_test_split(test_size=0.0005, seed=2357, shuffle=True)
split_dataset['val'] = split_dataset.pop('test') # rename the test split to val
# this results in:
# >>> split_dataset
# DatasetDict({
# train: Dataset({
# features: ['text'],
# num_rows: 8009762
# })
# val: Dataset({
# features: ['text'],
# num_rows: 4007
# })
# })
# we now want to tokenize the dataset. first define the encoding function (gpt2 bpe)
enc = tiktoken.get_encoding("gpt2")
def process(example):
ids = enc.encode_ordinary(example['text']) # encode_ordinary ignores any special tokens
ids.append(enc.eot_token) # add the end of text token, e.g. 50256 for gpt2 bpe
out = {'ids': ids, 'len': len(ids)}
return out
# tokenize the dataset
tokenized = split_dataset.map(
process,
remove_columns=['text'],
desc="tokenizing the splits",
num_proc=num_proc,
)
# concatenate all the ids in each dataset into one large file we can use for training
for split, dset in tokenized.items():
offset = np.cumsum(dset['len']).tolist()
total = offset[-1] # total number of tokens in the dataset
dset = dset.add_column('offset', offset)
# preallocate space in a temporary file to store the concatenated ids
filename = f'{split}.bin'
dtype = np.uint16 # (can do since enc.max_token_value == 50256 is < 2**16)
bytes_per_token = 2 # i.e. np.dtype(dtype).itemsize
subprocess.run(['truncate', '-s', str(total * bytes_per_token), filename], check=True)
# write the ids to the file
def write_to_file(example):
with open(filename, 'r+b') as f:
arr_len = len(example['ids'])
start = example['offset'] - arr_len
mm = mmap.mmap(f.fileno(), 0)
arr = np.ndarray((arr_len,), dtype=dtype, buffer=mm, offset=bytes_per_token * start)
arr[:] = example['ids']
mm.flush()
dset.map(
write_to_file,
desc=f"writing {split} split to file {filename}",
num_proc=num_proc,
)
# train.bin is ~17GB, val.bin ~8.5MB
# train has ~9B tokens (9,035,582,198)
# val has ~4M tokens (4,434,897)
# to read the bin files later, e.g. with numpy:
# m = np.memmap('train.bin', dtype=np.uint16, mode='r')

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## openwebtext dataset
after running `prepare.py` (preprocess) we get:
- train.bin is ~17GB, val.bin ~8.5MB
- train has ~9B tokens (9,035,582,198)
- val has ~4M tokens (4,434,897)
this came from 8,013,769 documents in total.
references:
- OpenAI's WebText dataset is discussed in [GPT-2 paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
- [OpenWebText](https://skylion007.github.io/OpenWebTextCorpus/) dataset

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"""
Full definition of a GPT Language Model, all of it in this single file.
References:
1) the official GPT-2 TensorFlow implementation released by OpenAI:
https://github.com/openai/gpt-2/blob/master/src/model.py
2) huggingface/transformers PyTorch implementation:
https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py
"""
import math
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
@torch.jit.script
def fused_gelu(x):
"""
Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT).
Reference: Gaussian Error Linear Units (GELU) paper: https://arxiv.org/abs/1606.08415
"""
return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_head == 0
# key, query, value projections for all heads, but in a batch
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
# output projection
self.c_proj = nn.Linear(config.n_embd, config.n_embd)
# regularization
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
# causal mask to ensure that attention is only applied to the left in the input sequence
self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
.view(1, 1, config.block_size, config.block_size))
self.n_head = config.n_head
self.n_embd = config.n_embd
def forward(self, x):
B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
q, k ,v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
# output projection
y = self.resid_dropout(self.c_proj(y))
return y
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
x = self.c_fc(x)
x = fused_gelu(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd)
self.mlp = MLP(config)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
@dataclass
class GPTConfig:
block_size: int = 1024
vocab_size: int = 50257
n_layer: int = 12
n_head: int = 12
n_embd: int = 768
dropout: float = 0.1
class GPT(nn.Module):
def __init__(self, config):
super().__init__()
assert config.vocab_size is not None
assert config.block_size is not None
self.block_size = config.block_size
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(config.vocab_size, config.n_embd),
wpe = nn.Embedding(config.block_size, config.n_embd),
drop = nn.Dropout(config.dropout),
h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
ln_f = nn.LayerNorm(config.n_embd),
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# report number of parameters (note we don't count the decoder parameters in lm_head)
n_params = sum(p.numel() for p in self.transformer.parameters())
print("number of parameters: %.2fM" % (n_params/1e6,))
def forward(self, idx, targets=None):
device = idx.device
b, t = idx.size()
assert t <= self.block_size, f"Cannot forward sequence of length {t}, block size is only {self.block_size}"
pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0) # shape (1, t)
# forward the GPT model itself
tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
pos_emb = self.transformer.wpe(pos) # position embeddings of shape (1, t, n_embd)
x = self.transformer.drop(tok_emb + pos_emb)
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
logits = self.lm_head(x)
# if we are given some desired targets also calculate the loss
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
return logits, loss
def crop_block_size(self, block_size):
# model surgery to decrease the block size if necessary
# e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
# but want to use a smaller block size for some smaller, simpler model
assert block_size <= self.block_size
self.block_size = block_size
self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
for block in self.transformer.h:
block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]
@classmethod
def from_pretrained(cls, model_type):
assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
from transformers import GPT2LMHeadModel
print("loading weights from pretrained gpt: %s" % model_type)
layer_config = {
'gpt2': dict(n_layer=12, n_head=12, n_embd=768), # 124M params
'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
}[model_type]
# create a from-scratch initialized minGPT model
config = GPTConfig(block_size=1024, **layer_config)
model = GPT(config)
sd = model.state_dict()
# init a huggingface/transformers model
model_hf = GPT2LMHeadModel.from_pretrained(model_type)
sd_hf = model_hf.state_dict()
# copy while ensuring all of the parameters are aligned and match in names and shapes
keys = [k for k in sd_hf if not k.endswith('attn.masked_bias')] # ignore these
transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
# basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
# this means that we have to transpose these weights when we import them
assert len(keys) == len(sd)
for k in keys:
if any(k.endswith(w) for w in transposed):
# special treatment for the Conv1D weights we need to transpose
assert sd_hf[k].shape[::-1] == sd[k].shape
with torch.no_grad():
sd[k].copy_(sd_hf[k].t())
else:
# vanilla copy over the other parameters
assert sd_hf[k].shape == sd[k].shape
with torch.no_grad():
sd[k].copy_(sd_hf[k])
return model
def configure_optimizers(self, weight_decay, learning_rate, betas):
"""
This long function is unfortunately doing something very simple and is being very defensive:
We are separating out all parameters of the model into two buckets: those that will experience
weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
We are then returning the PyTorch optimizer object.
"""
# separate out all parameters to those that will and won't experience regularizing weight decay
decay = set()
no_decay = set()
whitelist_weight_modules = (torch.nn.Linear, )
blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
for mn, m in self.named_modules():
for pn, p in m.named_parameters():
fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
# random note: because named_modules and named_parameters are recursive
# we will see the same tensors p many many times. but doing it this way
# allows us to know which parent module any tensor p belongs to...
if pn.endswith('bias'):
# all biases will not be decayed
no_decay.add(fpn)
elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
# weights of whitelist modules will be weight decayed
decay.add(fpn)
elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
# weights of blacklist modules will NOT be weight decayed
no_decay.add(fpn)
# validate that we considered every parameter
param_dict = {pn: p for pn, p in self.named_parameters()}
inter_params = decay & no_decay
union_params = decay | no_decay
assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
% (str(param_dict.keys() - union_params), )
# create the pytorch optimizer object
optim_groups = [
{"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": weight_decay},
{"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
]
optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas)
return optimizer
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
"""
Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
the sequence max_new_tokens times, feeding the predictions back into the model each time.
Most likely you'll want to make sure to be in model.eval() mode of operation for this.
"""
for _ in range(max_new_tokens):
# if the sequence context is growing too long we must crop it at block_size
idx_cond = idx if idx.size(1) <= self.block_size else idx[:, -self.block_size:]
# forward the model to get the logits for the index in the sequence
logits, _ = self(idx_cond)
# pluck the logits at the final step and scale by desired temperature
logits = logits[:, -1, :] / temperature
# optionally crop the logits to only the top k options
if top_k is not None:
v, _ = torch.topk(logits, top_k)
logits[logits < v[:, [-1]]] = -float('Inf')
# apply softmax to convert logits to (normalized) probabilities
probs = F.softmax(logits, dim=-1)
# sample from the distribution
idx_next = torch.multinomial(probs, num_samples=1)
# append sampled index to the running sequence and continue
idx = torch.cat((idx, idx_next), dim=1)
return idx

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"""
Sample from a trained model
"""
import os
import torch
import tiktoken
from model import GPTConfig, GPT
device = 'cuda:2'
torch.manual_seed(1337)
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
out_dir = 'out'
ckpt_path = os.path.join(out_dir, 'ckpt.pt')
checkpoint = torch.load(ckpt_path, map_location=device)
# model
gptconf = GPTConfig(**checkpoint['model_args'])
model = GPT(gptconf)
model.load_state_dict(checkpoint['model'])
model.eval()
model.to(device)
enc = tiktoken.get_encoding("gpt2")
#start = enc.encode("\n")
start = [enc.eot_token]
x = (torch.tensor(start, dtype=torch.long, device=device)[None, ...])
for k in range(1):
with torch.no_grad():
with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
y = model.generate(x, 300, temperature=0.8, top_k=200)
print(enc.decode(y[0].tolist()))
print('---------------')

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"""
Train a GPT model on a dataset of text. One GPU version.
The text is assumed to pre-tokenized and inside files train.pt and val.pt
"""
import os
import time
import math
import numpy as np
import torch
import wandb
from model import GPTConfig, GPT
# -----------------------------------------------------------------------------
# settings, todo argparse or something
# I/O
out_dir = 'out'
eval_interval = 500
log_interval = 1
# wandb logging
wandb_log = False
wandb_entity = 'karpathy'
wandb_project = 'owt'
wandb_run_name = 'owt1' # 'run' + str(time.time())
# data
dataset = 'openwebtext'
batch_size = 32
block_size = 512
# model
device = 'cuda:0'
init_from = 'scratch' # 'scratch' or 'resume' or 'gpt2*'
dropout = 0.1
n_layer = 12
n_head = 12
n_embd = 768
# adamw optimizer
learning_rate = 2.5e-4 # max learning rate
max_iters = 500000 # total number of training iterations
weight_decay = 1e-2
betas = (0.9, 0.95)
# learning rate decay settings
decay_lr = True # whether to decay the learning rate
warmup_iters = 2000 # how many steps to warm up for
lr_decay_iters = 320000 # how many steps to decay the learning rate for
min_lr = 1e-5 # minimum learning rate
# -----------------------------------------------------------------------------
os.makedirs(out_dir, exist_ok=True)
torch.manual_seed(1337)
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
# poor man's data loader, TODO use real DataLoader...
data_dir = os.path.join('data', dataset)
train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
val_data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')
def get_batch(split):
data = train_data if split == 'train' else val_data
ix = torch.randint(len(data) - block_size, (batch_size,))
x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
x, y = x.to(device), y.to(device)
return x, y
# model init
# TODO I don't love this whole part/API yet
model_args = dict(n_layer = n_layer, n_head = n_head, n_embd = n_embd, block_size = block_size, dropout = dropout)
if init_from == 'scratch':
# init a new model from scratch
gptconf = GPTConfig(**model_args)
model = GPT(gptconf)
elif init_from == 'resume':
# resume training from a checkpoint. TODO: do we resume iter_num etc too? (yes...)
ckpt_path = os.path.join(out_dir, 'ckpt.pt')
checkpoint = torch.load(ckpt_path)
checkpoint_model_args = checkpoint['model_args']
for k, v in model_args.items():
assert checkpoint_model_args[k] == v, "for now"
gptconf = GPTConfig(**model_args)
model = GPT(gptconf)
model.load_state_dict(checkpoint['model'])
elif init_from.startswith('gpt2'):
# initialize from OpenAI GPT-2 weights
model = GPT.from_pretrained(init_from)
if block_size < model.block_size:
model.crop_block_size(block_size)
model.to(device)
@torch.no_grad()
def estimate_loss(eval_iters=50):
out = {}
model.eval()
for split in ['train', 'val']:
losses = torch.zeros(eval_iters)
for k in range(eval_iters):
X, Y = get_batch(split)
with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
logits, loss = model(X, Y)
losses[k] = loss.item()
out[split] = losses.mean()
model.train()
return out
# optimizer
optimizer = model.configure_optimizers(weight_decay, learning_rate, betas)
if init_from == 'resume':
optimizer.load_state_dict(checkpoint['optimizer'])
# learning rate decay scheduler (cosine with warmup)
def get_lr(iter):
# 1) linear warmup for warmup_iters steps
if iter < warmup_iters:
return learning_rate * iter / warmup_iters
# 2) if iter > lr_decay_iters, return min learning rate
if iter > lr_decay_iters:
return min_lr
# 3) in between, use cosine decay down to min learning rate
decay_ratio = (iter - warmup_iters) / (lr_decay_iters - warmup_iters)
assert 0 <= decay_ratio <= 1
coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # ranges 0..1
return min_lr + coeff * (learning_rate - min_lr)
# logging
if wandb_log:
wandb.init(project=wandb_project, entity=wandb_entity, name=wandb_run_name)
wandb.config = {
"batch_size": batch_size,
"block_size": block_size,
"learning_rate": learning_rate, # TODO log everything else too
}
# training loop
iter_num = 0
num_tokens = 0
best_val_loss = 1e9
t0 = time.time()
while True:
# determine the learning rate for this iteration
if decay_lr:
lr = get_lr(iter_num)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
else:
lr = learning_rate
if iter_num % eval_interval == 0:
losses = estimate_loss()
print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
if wandb_log:
wandb.log({
"iter": iter_num,
"num_tokens": num_tokens,
"train/loss": losses['train'],
"val/loss": losses['val'],
"lr": lr,
})
if losses['val'] < best_val_loss:
best_val_loss = losses['val']
if iter_num > 0: # don't save checkpoints on very first iteration...
checkpoint = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'model_args': model_args,
'iter_num': iter_num,
}
torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt'))
X, Y = get_batch('train')
with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
logits, loss = model(X, Y)
optimizer.zero_grad(set_to_none=True)
loss.backward()
# TODO: gradient clipping
optimizer.step()
t1 = time.time()
dt = t1 - t0
t0 = t1
if iter_num % log_interval == 0:
lossf = loss.item() # loss as float. TODO CPU-GPU sync: profile, make sure not slow af
print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms")
iter_num += 1
num_tokens += X.numel()
# termination conditions
if iter_num >= max_iters:
break