Karpathy GPT model on GitHub

Wednesday 8th December, 2021 - Bruce Sterling

*It’s a neural net architecture, but it’s so small and compact. I can put all its python code into one blog post. So, why not? At the Share Artmaker Blog, we’re here to serve!

Andrei Karpathy is the director of AI at Tesla, leading the Autopilot Vision team. Previously OpenAI, CS231n, PhD @ Stanford.


GPT model:
– the initial stem consists of a combination of token encoding and a positional encoding
– the meat of it is a uniform sequence of Transformer blocks
– each Transformer is a sequential combination of a 1-hidden-layer MLP block and a self-attention block
– all blocks feed into a central residual pathway similar to resnets
– the final decoder is a linear projection into a vanilla Softmax classifier

import math
import logging

import torch
import torch.nn as nn
from torch.nn import functional as F

logger = logging.getLogger(__name__)

class GPTConfig:
“”” base GPT config, params common to all GPT versions “””
embd_pdrop = 0.1
resid_pdrop = 0.1
attn_pdrop = 0.1

def __init__(self, vocab_size, block_size, **kwargs):
self.vocab_size = vocab_size
self.block_size = block_size
for k,v in kwargs.items():
setattr(self, k, v)

class GPT1Config(GPTConfig):
“”” GPT-1 like network roughly 125M params “””
n_layer = 12
n_head = 12
n_embd = 768

class CausalSelfAttention(nn.Module):
A vanilla multi-head masked self-attention layer with a projection at the end.
It is possible to use torch.nn.MultiheadAttention here but I am including an
explicit implementation here to show that there is nothing too scary here.

def __init__(self, config):
assert config.n_embd % config.n_head == 0
# key, query, value projections for all heads
self.key = nn.Linear(config.n_embd, config.n_embd)
self.query = nn.Linear(config.n_embd, config.n_embd)
self.value = nn.Linear(config.n_embd, config.n_embd)
# regularization
self.attn_drop = nn.Dropout(config.attn_pdrop)
self.resid_drop = nn.Dropout(config.resid_pdrop)
# output projection
self.proj = nn.Linear(config.n_embd, config.n_embd)
# causal mask to ensure that attention is only applied to the left in the input sequence
self.register_buffer(“mask”, 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

def forward(self, x, layer_past=None):
B, T, C = x.size()

# calculate query, key, values for all heads in batch and move head forward to be the batch dim
k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
v = self.value(x).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.mask[:,:,:T,:T] == 0, float(‘-inf’))
att = F.softmax(att, dim=-1)
att = self.attn_drop(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_drop(self.proj(y))
return y

class Block(nn.Module):
“”” an unassuming Transformer block “””

def __init__(self, config):
self.ln1 = nn.LayerNorm(config.n_embd)
self.ln2 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
self.mlp = nn.Sequential(
nn.Linear(config.n_embd, 4 * config.n_embd),
nn.Linear(4 * config.n_embd, config.n_embd),

def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x

class GPT(nn.Module):
“”” the full GPT language model, with a context size of block_size “””

def __init__(self, config):

# input embedding stem
self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
self.drop = nn.Dropout(config.embd_pdrop)
# transformer
self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
# decoder head
self.ln_f = nn.LayerNorm(config.n_embd)
self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

self.block_size = config.block_size

logger.info(“number of parameters: %e”, sum(p.numel() for p in self.parameters()))

def get_block_size(self):
return self.block_size

def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=0.02)
if isinstance(module, nn.Linear) and module.bias is not None:
elif isinstance(module, nn.LayerNorm):

def configure_optimizers(self, train_config):
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

if pn.endswith(‘bias’):
# all biases will not be decayed
elif pn.endswith(‘weight’) and isinstance(m, whitelist_weight_modules):
# weights of whitelist modules will be weight decayed
elif pn.endswith(‘weight’) and isinstance(m, blacklist_weight_modules):
# weights of blacklist modules will NOT be weight decayed

# special case the position embedding parameter in the root GPT module as not decayed

# 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”: train_config.weight_decay},
{“params”: [param_dict[pn] for pn in sorted(list(no_decay))], “weight_decay”: 0.0},
optimizer = torch.optim.AdamW(optim_groups, lr=train_config.learning_rate, betas=train_config.betas)
return optimizer

def forward(self, idx, targets=None):
b, t = idx.size()
assert t <= self.block_size, "Cannot forward, model block size is exhausted." # forward the GPT model token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector x = self.drop(token_embeddings + position_embeddings) x = self.blocks(x) x = self.ln_f(x) logits = self.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)) return logits, loss