GPT in 60 Lines of NumPy

January 30, 2023

In this post, we'll implement a GPT from scratch in just 60 lines of numpy. We'll then load the trained GPT-2 model weights released by OpenAI into our implementation and generate some text.


EDIT (Feb 9th, 2023): Added a "What's Next" section and updated the intro with some notes.
EDIT (Feb 28th, 2023): Added some additional sections to "What's Next".

Table of Contents

What is a GPT?

GPT stands for Generative Pre-trained Transformer. It's a type of neural network architecture based on the Transformer. Jay Alammar's How GPT3 Works is an excellent introduction to GPTs at a high level, but here's the tl;dr:

Large Language Models (LLMs) like OpenAI's GPT-3 are just GPTs under the hood. What makes them special is they happen to be 1) very big (billions of parameters) and 2) trained on lots of data (hundreds of gigabytes of text).

Fundamentally, a GPT generates text given a prompt. Even with this very simple API (input = text, output = text), a well-trained GPT can do some pretty awesome stuff like write your emails, summarize a book, give you instagram caption ideas, explain black holes to a 5 year old, code in SQL, and even write your will.

So that's a high-level overview of GPTs and their capabilities. Let's dig into some more specifics.

Input / Output

The function signature for a GPT looks roughly like this:

def gpt(inputs: list[int]) -> list[list[float]]:
    # inputs has shape [n_seq]
    # output has shape [n_seq, n_vocab]
    output = # beep boop neural network magic
    return output


The input is some text represented by a sequence of integers that map to tokens in the text:

# integers represent tokens in our text, for example:
# text   = "not all heroes wear capes":
# tokens = "not"  "all" "heroes" "wear" "capes"
inputs =   [1,     0,    2,      4,     6]

Tokens are sub-pieces of the text, which are produced using some kind of tokenizer. We can map tokens to integers using a vocabulary:

# the index of a token in the vocab represents the integer id for that token
# i.e. the integer id for "heroes" would be 2, since vocab[2] = "heroes"
vocab = ["all", "not", "heroes", "the", "wear", ".", "capes"]

# a pretend tokenizer that tokenizes on whitespace
tokenizer = WhitespaceTokenizer(vocab)

# the encode() method converts a str -> list[int]
ids = tokenizer.encode("not all heroes wear") # ids = [1, 0, 2, 4]

# we can see what the actual tokens are via our vocab mapping
tokens = [tokenizer.vocab[i] for i in ids] # tokens = ["not", "all", "heroes", "wear"]

# the decode() method converts back a list[int] -> str
text = tokenizer.decode(ids) # text = "not all heroes wear"

In short:

In practice, we use more advanced methods of tokenization than simply splitting by whitespace, such as Byte-Pair Encoding or WordPiece, but the principle is the same:

  1. There is a vocab that maps string tokens to integer indices
  2. There is an encode method that converts str -> list[int]
  3. There is a decode method that converts list[int] -> str[1]


The output is a 2D array, where output[i][j] is the model's predicted probability that the token at vocab[j] is the next token inputs[i+1]. For example:

vocab = ["all", "not", "heroes", "the", "wear", ".", "capes"]
inputs = [1, 0, 2, 4] # "not" "all" "heroes" "wear"
output = gpt(inputs)
#              ["all", "not", "heroes", "the", "wear", ".", "capes"]
# output[0] =  [0.75    0.1     0.0       0.15    0.0   0.0    0.0  ]
# given just "not", the model predicts the word "all" with the highest probability

#              ["all", "not", "heroes", "the", "wear", ".", "capes"]
# output[1] =  [0.0     0.0      0.8     0.1    0.0    0.0   0.1  ]
# given the sequence ["not", "all"], the model predicts the word "heroes" with the highest probability

#              ["all", "not", "heroes", "the", "wear", ".", "capes"]
# output[-1] = [0.0     0.0     0.0     0.1     0.0    0.05  0.85  ]
# given the whole sequence ["not", "all", "heroes", "wear"], the model predicts the word "capes" with the highest probability

To get a next token prediction for the whole sequence, we simply take the token with the highest probability in output[-1]:

vocab = ["all", "not", "heroes", "the", "wear", ".", "capes"]
inputs = [1, 0, 2, 4] # "not" "all" "heroes" "wear"
output = gpt(inputs)
next_token_id = np.argmax(output[-1]) # next_token_id = 6
next_token = vocab[next_token_id] # next_token = "capes"

Taking the token with the highest probability as our prediction is known as greedy decoding or greedy sampling.

The task of predicting the next logical word in a sequence is called language modeling. As such, we can call a GPT a language model.

Generating a single word is cool and all, but what about entire sentences, paragraphs, etc ...?

Generating Text


We can generate full sentences by iteratively getting the next token prediction from our model. At each iteration, we append the predicted token back into the input:

def generate(inputs, n_tokens_to_generate):
    for _ in range(n_tokens_to_generate): # auto-regressive decode loop
        output = gpt(inputs) # model forward pass
        next_id = np.argmax(output[-1]) # greedy sampling
        inputs.append(int(next_id)) # append prediction to input
    return inputs[len(inputs) - n_tokens_to_generate :]  # only return generated ids

input_ids = [1, 0] # "not" "all"
output_ids = generate(input_ids, 3) # output_ids = [2, 4, 6]
output_tokens = [vocab[i] for i in output_ids] # "heroes" "wear" "capes"

This process of predicting a future value (regression), and adding it back into the input (auto), is why you might see a GPT described as autoregressive.


We can introduce some stochasticity (randomness) to our generations by sampling from the probability distribution instead of being greedy:

inputs = [1, 0, 2, 4] # "not" "all" "heroes" "wear"
output = gpt(inputs)
np.random.choice(np.arange(vocab_size), p=output[-1]) # capes
np.random.choice(np.arange(vocab_size), p=output[-1]) # hats
np.random.choice(np.arange(vocab_size), p=output[-1]) # capes
np.random.choice(np.arange(vocab_size), p=output[-1]) # capes
np.random.choice(np.arange(vocab_size), p=output[-1]) # pants

This allows us to generate different sentences given the same input. When combined with techniques like top-k, top-p, and temperature, which modify the distribution prior to sampling, the quality of our outputs is greatly increased. These techniques also introduce some hyperparameters that we can play around with to get different generation behaviors (for example, increasing temperature makes our model take more risks and thus be more "creative").


We train a GPT like any other neural network, using gradient descent with respect to some loss function. In the case of a GPT, we take the cross entropy loss over the language modeling task:

def lm_loss(inputs: list[int], params) -> float:
    # the labels y are just the input shifted 1 to the left
    # inputs = [not,     all,   heros,   wear,   capes]
    #      x = [not,     all,   heroes,  wear]
    #      y = [all,  heroes,     wear,  capes]
    # of course, we don't have a label for inputs[-1], so we exclude it from x
    # as such, for N inputs, we have N - 1 langauge modeling example pairs
    x, y = inputs[:-1], inputs[1:] # both have shape [num_tokens_in_seq - 1]

    # forward pass
    # all the predicted next token probability distributions at each position
    output = gpt(x, params) # has shape [num_tokens_in_seq - 1, num_tokens_in_vocab]

    # cross entropy loss
    # we take the average over all N-1 examples
    loss = np.mean(-np.log(output[y]))

    return loss

def train(texts: list[list[str]], params) -> float:
    for text in texts:
        inputs = tokenizer.encode(text)
        loss = lm_loss(inputs, params)
        gradients = compute_gradients_via_backpropagation(loss, params)
        params = gradient_descent_update_step(gradients, params)
    return params

This is a heavily simplified training setup, but it illustrates the point. Notice the addition of params to our gpt function signature (we left this out in the previous sections for simplicity). During each iteration of the training loop:

  1. We compute the language modeling loss for the given input text example
  2. The loss determines our gradients, which we compute via backpropagation
  3. We use the gradients to update our model parameters such that the loss is minimized (gradient descent)

Notice, we don't use explicitly labelled data. Instead, we are able to produce the input/label pairs from just the raw text itself. This is referred to as self-supervised learning.

Self-supervision enables us to massively scale training data. Just get our hands on as much raw text as possible and throw it at the model. For example, GPT-3 was trained on 300 billion tokens of text from the internet and books:

Table 2.2 from GPT-3 paper

Of course, you need a sufficiently large model to be able to learn from all this data, which is why GPT-3 has 175 billion parameters and probably cost between $1m-10m in compute cost to train.[2]

This self-supervised training step is called pre-training, since we can reuse the "pre-trained" models weights to further train the model on downstream tasks, such as classifying if a tweet is toxic or not. Pre-trained models are also sometimes called foundation models.

Training the model on downstream tasks is called fine-tuning, since the model weights have already been pre-trained to understand language, it's just being fine-tuned to the specific task at hand.

The "pre-training on a general task + fine-tuning on a specific task" strategy is called transfer learning.


In principle, the original GPT paper was only about the benefits of pre-training a transformer model for transfer learning. The paper showed that pre-training a 117M GPT achieved state-of-the-art performance on various NLP (natural language processing) tasks when fine-tuned on labelled datasets.

It wasn't until the GPT-2 and GPT-3 papers that we realized a GPT model pre-trained on enough data with enough parameters was capable of performing any arbitrary task by itself, no fine-tuning needed. Just prompt the model, perform autoregressive language modeling, and voila, the model magically gives us an appropriate response. This is referred to as in-context learning, because the model is using just the context of the prompt to perform the task. In-context learning can be zero shot, one shot, or few shot:

Figure 2.1 from the GPT-3 Paper

Generating text given a prompt is also sometimes referred to as conditional generation, since our model is generating some output conditioned on some input.

GPTs are not limited to NLP tasks. You can condition the model on anything you want. For example, you can turn a GPT into a chatbot (i.e. ChatGPT) by conditioning it on the conversation history. You can also further condition the chatbot to behave a certain way by prepending the prompt with some kind of description (i.e. "You are a chatbot. Be polite, speak in full sentences, don't say harmful things, etc ..."). Conditioning the model like this can even give your chatbot a persona. This is often referred to as a system prompt. However, this is not robust, you can still "jailbreak" the model and make it misbehave.

With that out of the way, let's finally get to the actual implementation.


Clone the repository for this tutorial:

git clone
cd picoGPT

Then let's install our dependencies:

pip install -r requirements.txt

Note: This code was tested with Python 3.9.10.

A quick breakdown of each of the files:

We'll be reimplementing from scratch, so let's delete it and recreate it as an empty file:


As a starting point, paste the following code into

import numpy as np

def gpt2(inputs, wte, wpe, blocks, ln_f, n_head):
    pass # TODO: implement this

def generate(inputs, params, n_head, n_tokens_to_generate):
    from tqdm import tqdm

    for _ in tqdm(range(n_tokens_to_generate), "generating"):  # auto-regressive decode loop
        logits = gpt2(inputs, **params, n_head=n_head)  # model forward pass
        next_id = np.argmax(logits[-1])  # greedy sampling
        inputs.append(int(next_id))  # append prediction to input

    return inputs[len(inputs) - n_tokens_to_generate :]  # only return generated ids

def main(prompt: str, n_tokens_to_generate: int = 40, model_size: str = "124M", models_dir: str = "models"):
    from utils import load_encoder_hparams_and_params

    # load encoder, hparams, and params from the released open-ai gpt-2 files
    encoder, hparams, params = load_encoder_hparams_and_params(model_size, models_dir)

    # encode the input string using the BPE tokenizer
    input_ids = encoder.encode(prompt)

    # make sure we are not surpassing the max sequence length of our model
    assert len(input_ids) + n_tokens_to_generate < hparams["n_ctx"]

    # generate output ids
    output_ids = generate(input_ids, params, hparams["n_head"], n_tokens_to_generate)

    # decode the ids back into a string
    output_text = encoder.decode(output_ids)

    return output_text

if __name__ == "__main__":
    import fire


Breaking down each of the 4 sections:

  1. The gpt2 function is the actual GPT code we'll be implementing. You'll notice that the function signature includes some extra stuff in addition to inputs:
    • wte, wpe, blocks, and ln_f are the parameters of our model.
    • n_head is a hyperparameter that is needed during the forward pass.
  2. The generate function is the autoregressive decoding algorithm we saw earlier. We use greedy sampling for simplicity. tqdm is a progress bar to help us visualize the decoding process as it generates tokens one at a time.
  3. The main function handles:
    1. Loading the tokenizer (encoder), model weights (params), and hyperparameters (hparams)
    2. Encoding the input prompt into token IDs using the tokenizer
    3. Calling the generate function
    4. Decoding the output IDs into a string
  4. fire.Fire(main) just turns our file into a CLI application, so we can eventually run our code with: python "some prompt here"

Let's take a closer look at encoder, hparams, and params, in a notebook, or an interactive python session, run:

from utils import load_encoder_hparams_and_params
encoder, hparams, params = load_encoder_hparams_and_params("124M", "models")

This will download the necessary model and tokenizer files to models/124M and load encoder, hparams, and params into our code.


encoder is the BPE tokenizer used by GPT-2:

>>> ids = encoder.encode("Not all heroes wear capes.")
>>> ids
[3673, 477, 10281, 5806, 1451, 274, 13]

>>> encoder.decode(ids)
"Not all heroes wear capes."

Using the vocabulary of the tokenizer (stored in encoder.decoder), we can take a peek at what the actual tokens look like:

>>> [encoder.decoder[i] for i in ids]
['Not', 'Ġall', 'Ġheroes', 'Ġwear', 'Ġcap', 'es', '.']

Notice, sometimes our tokens are words (e.g. Not), sometimes they are words but with a space in front of them (e.g. Ġall, the Ġ represents a space), sometimes there are part of a word (e.g. capes is split into Ġcap and es), and sometimes they are punctuation (e.g. .).

One nice thing about BPE is that it can encode any arbitrary string. If it encounters something that is not present in the vocabulary, it just breaks it down into substrings it does understand:

>>> [encoder.decoder[i] for i in encoder.encode("zjqfl")]
['z', 'j', 'q', 'fl']

We can also check the size of the vocabulary:

>>> len(encoder.decoder)

The vocabulary, as well as the byte-pair merges which determines how strings are broken down, is obtained by training the tokenizer. When we load the tokenizer, we're loading the already trained vocab and byte-pair merges from some files, which were downloaded alongside the model files when we ran load_encoder_hparams_and_params. See models/124M/encoder.json (the vocabulary) and models/124M/vocab.bpe (byte-pair merges).


hparams is a dictionary that contains the hyper-parameters of our model:

>>> hparams
  "n_vocab": 50257, # number of tokens in our vocabulary
  "n_ctx": 1024, # maximum possible sequence length of the input
  "n_embd": 768, # embedding dimension (determines the "width" of the network)
  "n_head": 12, # number of attention heads (n_embd must be divisible by n_head)
  "n_layer": 12 # number of layers (determines the "depth" of the network)

We'll use these symbols in our code's comments to show the underlying shape of things. We'll also use n_seq to denote the length of our input sequence (i.e. n_seq = len(inputs)).


params is a nested json dictionary that hold the trained weights of our model. The leaf nodes of the json are NumPy arrays. If we print params, replacing the arrays with their shapes, we get:

>>> import numpy as np
>>> def shape_tree(d):
>>>     if isinstance(d, np.ndarray):
>>>         return list(d.shape)
>>>     elif isinstance(d, list):
>>>         return [shape_tree(v) for v in d]
>>>     elif isinstance(d, dict):
>>>         return {k: shape_tree(v) for k, v in d.items()}
>>>     else:
>>>         ValueError("uh oh")
>>> print(shape_tree(params))
    "wpe": [1024, 768],
    "wte": [50257, 768],
    "ln_f": {"b": [768], "g": [768]},
    "blocks": [
            "attn": {
                "c_attn": {"b": [2304], "w": [768, 2304]},
                "c_proj": {"b": [768], "w": [768, 768]},
            "ln_1": {"b": [768], "g": [768]},
            "ln_2": {"b": [768], "g": [768]},
            "mlp": {
                "c_fc": {"b": [3072], "w": [768, 3072]},
                "c_proj": {"b": [768], "w": [3072, 768]},
        ... # repeat for n_layers

These are loaded from the original OpenAI tensorflow checkpoint:

>>> import tensorflow as tf
>>> tf_ckpt_path = tf.train.latest_checkpoint("models/124M")
>>> for name, _ in tf.train.list_variables(tf_ckpt_path):
>>>     arr = tf.train.load_variable(tf_ckpt_path, name).squeeze()
>>>     print(f"{name}: {arr.shape}")
model/h0/attn/c_attn/b: (2304,)
model/h0/attn/c_attn/w: (768, 2304)
model/h0/attn/c_proj/b: (768,)
model/h0/attn/c_proj/w: (768, 768)
model/h0/ln_1/b: (768,)
model/h0/ln_1/g: (768,)
model/h0/ln_2/b: (768,)
model/h0/ln_2/g: (768,)
model/h0/mlp/c_fc/b: (3072,)
model/h0/mlp/c_fc/w: (768, 3072)
model/h0/mlp/c_proj/b: (768,)
model/h0/mlp/c_proj/w: (3072, 768)
model/h1/attn/c_attn/b: (2304,)
model/h1/attn/c_attn/w: (768, 2304)
model/h9/mlp/c_proj/b: (768,)
model/h9/mlp/c_proj/w: (3072, 768)
model/ln_f/b: (768,)
model/ln_f/g: (768,)
model/wpe: (1024, 768)
model/wte: (50257, 768)

The following code converts the above tensorflow variables into our params dictionary.

For reference, here's the shapes of params but with the numbers replaced by the hparams they represent:

    "wpe": [n_ctx, n_embd],
    "wte": [n_vocab, n_embd],
    "ln_f": {"b": [n_embd], "g": [n_embd]},
    "blocks": [
            "attn": {
                "c_attn": {"b": [3*n_embd], "w": [n_embd, 3*n_embd]},
                "c_proj": {"b": [n_embd], "w": [n_embd, n_embd]},
            "ln_1": {"b": [n_embd], "g": [n_embd]},
            "ln_2": {"b": [n_embd], "g": [n_embd]},
            "mlp": {
                "c_fc": {"b": [4*n_embd], "w": [n_embd, 4*n_embd]},
                "c_proj": {"b": [n_embd], "w": [4*n_embd, n_embd]},
        ... # repeat for n_layers

You'll probably want to come back to reference this dictionary to check the shape of the weights as we implement our GPT. We'll match the variable names in our code with the keys of this dictionary for consistency.

Basic Layers

Last thing before we get into the actual GPT architecture itself, let's implement some of the more basic neural network layers that are non-specific to GPTs.


The non-linearity (activation function) of choice for GPT-2 is GELU (Gaussian Error Linear Units), an alternative for ReLU:

Figure 1 from the GELU paper

It is approximated by the following function:

def gelu(x):
    return 0.5 * x * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * x**3)))

Like ReLU, GELU operates element-wise on the input:

>>> gelu(np.array([[1, 2], [-2, 0.5]]))
array([[ 0.84119,  1.9546 ],
       [-0.0454 ,  0.34571]])


Good ole softmax:

\[ \text{softmax}(x)_i = \frac{e^{x_i}}{\sum_j e^{x_j}} \]

def softmax(x):
    exp_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
    return exp_x / np.sum(exp_x, axis=-1, keepdims=True)

We use the max(x) trick for numerical stability.

Softmax is used to a convert set of real numbers (between \(-\infty\) and \(\infty\)) to probabilities (between 0 and 1, with the numbers all summing to 1). We apply softmax over the last axis of the input.

>>> x = softmax(np.array([[2, 100], [-5, 0]]))
>>> x
array([[0.00034, 0.99966],
       [0.26894, 0.73106]])
>>> x.sum(axis=-1)
array([1., 1.])

Layer Normalization

Layer normalization standardizes values to have a mean of 0 and a variance of 1:

\[ \text{LayerNorm}(x) = \gamma \cdot \frac{x - \mu}{\sqrt{\sigma^2}} + \beta \]where \(\mu\) is the mean of \(x\), \(\sigma^2\) is the variance of \(x\), and \(\gamma\) and \(\beta\) are learnable parameters.

def layer_norm(x, g, b, eps: float = 1e-5):
    mean = np.mean(x, axis=-1, keepdims=True)
    variance = np.var(x, axis=-1, keepdims=True)
    x = (x - mean) / np.sqrt(variance + eps)  # normalize x to have mean=0 and var=1 over last axis
    return g * x + b  # scale and offset with gamma/beta params

Layer normalization ensures that the inputs for each layer are always within a consistent range, which is supposed to speed up and stabilize the training process. Like Batch Normalization, the normalized output is then scaled and offset with two learnable vectors gamma and beta. The small epsilon term in the denominator is used to avoid a division by zero error.

Layer norm is used instead of batch norm in the transformer for various reasons. The differences between various normalization techniques is outlined in this excellent blog post.

We apply layer normalization over the last axis of the input.

>>> x = np.array([[2, 2, 3], [-5, 0, 1]])
>>> x = layer_norm(x, g=np.ones(x.shape[-1]), b=np.zeros(x.shape[-1]))
>>> x
array([[-0.70709, -0.70709,  1.41418],
       [-1.397  ,  0.508  ,  0.889  ]])
>>> x.var(axis=-1)
array([0.99996, 1.     ]) # floating point shenanigans
>>> x.mean(axis=-1)
array([-0., -0.])


Your standard matrix multiplication + bias:

def linear(x, w, b):  # [m, in], [in, out], [out] -> [m, out]
    return x @ w + b

Linear layers are often referred to as projections (since they are projecting from one vector space to another vector space).

>>> x = np.random.normal(size=(64, 784)) # input dim = 784, batch/sequence dim = 64
>>> w = np.random.normal(size=(784, 10)) # output dim = 10
>>> b = np.random.normal(size=(10,))
>>> x.shape # shape before linear projection
(64, 784)
>>> linear(x, w, b).shape # shape after linear projection
(64, 10)

GPT Architecture

The GPT architecture follows that of the transformer:

Figure 1 from Attention is All You Need

But uses only the decoder stack (the right part of the diagram):

GPT Architecture

Note, the middle "cross-attention" layer is also removed since we got rid of the encoder.

At a high level, the GPT architecture has three sections:

In code, it looks like this:

def gpt2(inputs, wte, wpe, blocks, ln_f, n_head):  # [n_seq] -> [n_seq, n_vocab]
    # token + positional embeddings
    x = wte[inputs] + wpe[range(len(inputs))]  # [n_seq] -> [n_seq, n_embd]

    # forward pass through n_layer transformer blocks
    for block in blocks:
        x = transformer_block(x, **block, n_head=n_head)  # [n_seq, n_embd] -> [n_seq, n_embd]

    # projection to vocab
    x = layer_norm(x, **ln_f)  # [n_seq, n_embd] -> [n_seq, n_embd]
    return x @ wte.T  # [n_seq, n_embd] -> [n_seq, n_vocab]

Let's break down each of these three sections into more detail.


Token Embeddings

Token IDs by themselves are not very good representations for a neural network. For one, the relative magnitudes of the token IDs falsely communicate information (for example, if Apple = 5 and Table = 10 in our vocab, then we are implying that 2 * Table = Apple). Secondly, a single number is not a lot of dimensionality for a neural network to work with.

To address these limitations, we'll take advantage of word vectors, specifically via a learned embedding matrix:

wte[inputs] # [n_seq] -> [n_seq, n_embd]

Recall, wte is a [n_vocab, n_embd] matrix. It acts as a lookup table, where the \(i\)th row in the matrix corresponds to the learned vector for the \(i\)th token in our vocabulary. wte[inputs] uses integer array indexing to retrieve the vectors corresponding to each token in our input.

Like any other parameter in our network, wte is learned. That is, it is randomly initialized at the start of training and then updated via gradient descent.

Positional Embeddings

One quirk of the transformer architecture is that it doesn't take into account position. That is, if we randomly shuffled our input and then accordingly unshuffled the output, the output would be the same as if we never shuffled the input in the first place (the ordering of inputs doesn't have any effect on the output).

Of course, the ordering of words is a crucial part of language (duh), so we need some way to encode positional information into our inputs. For this, we can just use another learned embedding matrix:

wpe[range(len(inputs))] # [n_seq] -> [n_seq, n_embd]

Recall, wpe is a [n_ctx, n_embd] matrix. The \(i\)th row of the matrix contains a vector that encodes information about the \(i\)th position in the input. Similar to wte, this matrix is learned during gradient descent.

Notice, this restricts our model to a maximum sequence length of n_ctx.[3] That is, len(inputs) <= n_ctx must hold.


We can add our token and positional embeddings to get a combined embedding that encodes both token and positional information.

# token + positional embeddings
x = wte[inputs] + wpe[range(len(inputs))]  # [n_seq] -> [n_seq, n_embd]

# x[i] represents the word embedding for the ith word + the positional
# embedding for the ith position

Decoder Stack

This is where all the magic happens and the "deep" in deep learning comes in. We pass our embedding through a stack of n_layer transformer decoder blocks.

# forward pass through n_layer transformer blocks
for block in blocks:
    x = transformer_block(x, **block, n_head=n_head)  # [n_seq, n_embd] -> [n_seq, n_embd]

Stacking more layers is what allows us to control how deep our network is. GPT-3 for example, has a whopping 96 layers. On the other hand, choosing a larger n_embd value allows us to control how wide our network is (for example, GPT-3 uses an embedding size of 12288).

Projection to Vocab

In our final step, we project the output of the final transformer block to a probability distribution over our vocab:

# projection to vocab
x = layer_norm(x, **ln_f)  # [n_seq, n_embd] -> [n_seq, n_embd]
return x @ wte.T  # [n_seq, n_embd] -> [n_seq, n_vocab]

Couple things to note here:

  1. We first pass x through a final layer normalization layer before doing the projection to vocab. This is specific to the GPT-2 architecture (this is not present in the original GPT and Transformer papers).
  2. We are reusing the embedding matrix wte for the projection. Other GPT implementations may choose to use a separate learned weight matrix for the projection, however sharing the embedding matrix has a couple of advantages:
    • You save some parameters (although at GPT-3 scale, this is negligible).
    • Since the matrix is both responsible for mapping both to words and from words, in theory, it may learn a richer representation compared to having two separate matrixes.
  3. We don't apply softmax at the end, so our outputs will be logits instead of probabilities between 0 and 1. This is done for several reasons:
    • softmax is monotonic, so for greedy sampling np.argmax(logits) is equivalent to np.argmax(softmax(logits)) making softmax redundant
    • softmax is irreversible, meaning we can always go from logits to probabilities by applying softmax, but we can't go back to logits from probabilities, so for maximum flexibility, we output the logits
    • Numerically stability (for example, to compute cross entropy loss, taking log(softmax(logits)) is numerically unstable compared to log_softmax(logits)

The projection to vocab step is also sometimes called the language modeling head. What does "head" mean? Once your GPT is pre-trained, you can swap out the language modeling head with some other kind of projection, like a classification head for fine-tuning the model on some classification task. So your model can have multiple heads, kind of like a hydra.

So that's the GPT architecture at a high level, let's actually dig a bit deeper into what the decoder blocks are doing.

Decoder Block

The transformer decoder block consists of two sublayers:

  1. Multi-head causal self attention
  2. Position-wise feed forward neural network
def transformer_block(x, mlp, attn, ln_1, ln_2, n_head):  # [n_seq, n_embd] -> [n_seq, n_embd]
    # multi-head causal self attention
    x = x + mha(layer_norm(x, **ln_1), **attn, n_head=n_head)  # [n_seq, n_embd] -> [n_seq, n_embd]

    # position-wise feed forward network
    x = x + ffn(layer_norm(x, **ln_2), **mlp)  # [n_seq, n_embd] -> [n_seq, n_embd]

    return x

Each sublayer utilizes layer normalization on their inputs as well as a residual connection (i.e. add the input of the sublayer to the output of the sublayer).

Some things to note:

  1. Multi-head causal self attention is what facilitates the communication between the inputs. Nowhere else in the network does the model allow inputs to "see" each other. The embeddings, position-wise feed forward network, layer norms, and projection to vocab all operate on our inputs position-wise. Modeling relationships between inputs is tasked solely to attention.
  2. The Position-wise feed forward neural network is just a regular 2 layer fully connected neural network. This just adds a bunch of learnable parameters for our model to work with to facilitate learning.
  3. In the original transformer paper, layer norm is placed on the output layer_norm(x + sublayer(x)) while we place layer norm on the input x + sublayer(layer_norm(x)) to match GPT-2. This is referred to as pre-norm and has been shown to be important in improving the performance of the transformer.
  4. Residual connections (popularized by ResNet) serve a couple of different purposes:
    1. Makes it easier to optimize neural networks that are deep (i.e. networks that have lots of layers). The idea here is that we are providing "shortcuts" for the gradients to flow back through the network, making it easier to optimize the earlier layers in the network.
    2. Without residual connections, deeper models see a degradation in performance when adding more layers (possibly because it's hard for the gradients to flow all the way back through a deep network without losing information). Residual connections seem to give a bit of an accuracy boost for deeper networks.
    3. Can help with the vanishing/exploding gradients problem.

Let's dig a little deeper into the 2 sublayers.

Position-wise Feed Forward Network

This is just a simple multi-layer perceptron with 2 layers:

def ffn(x, c_fc, c_proj):  # [n_seq, n_embd] -> [n_seq, n_embd]
    # project up
    a = gelu(linear(x, **c_fc))  # [n_seq, n_embd] -> [n_seq, 4*n_embd]

    # project back down
    x = linear(a, **c_proj)  # [n_seq, 4*n_embd] -> [n_seq, n_embd]

    return x

Nothing super fancy here, we just project from n_embd up to a higher dimension 4*n_embd and then back down to n_embd[4].

Recall, from our params dictionary, that our mlp params look like this:

"mlp": {
    "c_fc": {"b": [4*n_embd], "w": [n_embd, 4*n_embd]},
    "c_proj": {"b": [n_embd], "w": [4*n_embd, n_embd]},

Multi-Head Causal Self Attention

This layer is probably the most difficult part of the transformer to understand. So let's work our way up to "Multi-Head Causal Self Attention" by breaking each word down into its own section:

  1. Attention
  2. Self
  3. Causal
  4. Multi-Head


I have another blog post on this topic, where we derive the scaled dot product equation proposed in the original transformer paper from the ground up:
\[\text{attention}(Q, K, V) = \text{softmax}(\frac{QK^T}{\sqrt{d_k}})V\]As such, I'm going to skip an explanation for attention in this post. You can also reference Lilian Weng's Attention? Attention! and Jay Alammar's The Illustrated Transformer which are also great explanations for attention.

We'll just adapt our attention implementation from my blog post:

def attention(q, k, v):  # [n_q, d_k], [n_k, d_k], [n_k, d_v] -> [n_q, d_v]
    return softmax(q @ k.T / np.sqrt(q.shape[-1])) @ v


When q, k, and v all come from the same source, we are performing self-attention (i.e. letting our input sequence attend to itself):

def self_attention(x): # [n_seq, n_embd] -> [n_seq, n_embd]
    return attention(q=x, k=x, v=x)

For example, if our input is "Jay went to the store, he bought 10 apples.", we would be letting the word "he" attend to all the other words, including "Jay", meaning the model can learn to recognize that "he" is referring to "Jay".

We can enhance self attention by introducing projections for q, k, v and the attention output:

def self_attention(x, w_k, w_q, w_v, w_proj): # [n_seq, n_embd] -> [n_seq, n_embd]
    # qkv projections
    q = x @ w_q # [n_seq, n_embd] @ [n_embd, n_embd] -> [n_seq, n_embd]
    k = x @ w_k # [n_seq, n_embd] @ [n_embd, n_embd] -> [n_seq, n_embd]
    v = x @ w_v # [n_seq, n_embd] @ [n_embd, n_embd] -> [n_seq, n_embd]

    # perform self attention
    x = attention(q, k, v) # [n_seq, n_embd] -> [n_seq, n_embd]

    # out projection
    x = x @ w_proj # [n_seq, n_embd] @ [n_embd, n_embd] -> [n_seq, n_embd]

    return x

This enables our model to learn a mapping for q, k, and v that best helps attention distinguish relationships between inputs.

We can reduce the number of matrix multiplication from 4 to just 2 if we combine w_q, w_k and w_v into a single matrix w_fc, perform the projection, and then split the result:

def self_attention(x, w_fc, w_proj): # [n_seq, n_embd] -> [n_seq, n_embd]
    # qkv projections
    x = x @ w_fc # [n_seq, n_embd] @ [n_embd, 3*n_embd] -> [n_seq, 3*n_embd]

    # split into qkv
    q, k, v = np.split(x, 3, axis=-1) # [n_seq, 3*n_embd] -> 3 of [n_seq, n_embd]

    # perform self attention
    x = attention(q, k, v) # [n_seq, n_embd] -> [n_seq, n_embd]

    # out projection
    x = x @ w_proj # [n_seq, n_embd] @ [n_embd, n_embd] = [n_seq, n_embd]

    return x

This is a bit more efficient as modern accelerators (GPUs) can take better advantage of one large matrix multiplication rather than 3 separate small ones happening sequentially.

Finally, we add bias vectors to match the implementation of GPT-2, use our linear function, and rename our parameters to match our params dictionary:

def self_attention(x, c_attn, c_proj): # [n_seq, n_embd] -> [n_seq, n_embd]
    # qkv projections
    x = linear(x, **c_attn) # [n_seq, n_embd] -> [n_seq, 3*n_embd]

    # split into qkv
    q, k, v = np.split(x, 3, axis=-1) # [n_seq, 3*n_embd] -> 3 of [n_seq, n_embd]

    # perform self attention
    x = attention(q, k, v) # [n_seq, n_embd] -> [n_seq, n_embd]

    # out projection
    x = linear(x, **c_proj) # [n_seq, n_embd] @ [n_embd, n_embd] = [n_seq, n_embd]

    return x

Recall, from our params dictionary, our attn params look like this:

"attn": {
    "c_attn": {"b": [3*n_embd], "w": [n_embd, 3*n_embd]},
    "c_proj": {"b": [n_embd], "w": [n_embd, n_embd]},


There is a bit of an issue with our current self-attention setup, our inputs can see into the future! For example, if our input is ["not", "all", "heroes", "wear", "capes"], during self attention we are allowing "wear" to see "capes". This means our output probabilities for "wear" will be biased since the model already knows the correct answer is "capes". This is no good since our model will just learn that the correct answer for input \(i\) can be taken from input \(i+1\).

To prevent this, we need to somehow modify our attention matrix to hide or mask our inputs from being able to see into the future. For example, let's pretend our attention matrix looks like this:

       not    all    heroes wear   capes
   not 0.116  0.159  0.055  0.226  0.443
   all 0.180  0.397  0.142  0.106  0.175
heroes 0.156  0.453  0.028  0.129  0.234
  wear 0.499  0.055  0.133  0.017  0.295
 capes 0.089  0.290  0.240  0.228  0.153

Each row corresponds to a query and the columns to a key. In this case, looking at the row for "wear", you can see that it is attending to "capes" in the last column with a weight of 0.295. To prevent this, we want to set that entry to 0.0:

      not    all    heroes wear   capes
   not 0.116  0.159  0.055  0.226  0.443
   all 0.180  0.397  0.142  0.106  0.175
heroes 0.156  0.453  0.028  0.129  0.234
  wear 0.499  0.055  0.133  0.017  0.
 capes 0.089  0.290  0.240  0.228  0.153

In general, to prevent all the queries in our input from looking into the future, we set all positions \(i, j\) where \(j > i\) to 0:

       not    all    heroes wear   capes
   not 0.116  0.     0.     0.     0.
   all 0.180  0.397  0.     0.     0.
heroes 0.156  0.453  0.028  0.     0.
  wear 0.499  0.055  0.133  0.017  0.
 capes 0.089  0.290  0.240  0.228  0.153

We call this masking. One issue with our above masking approach is our rows no longer sum to 1 (since we are setting them to 0 after the softmax has been applied). To make sure our rows still sum to 1, we need to modify our attention matrix before the softmax is applied.

This can be achieved by setting entries that are to be masked to \(-\infty\) prior to the softmax[5]:

def attention(q, k, v, mask):  # [n_q, d_k], [n_k, d_k], [n_k, d_v], [n_q, n_k] -> [n_q, d_v]
    return softmax(q @ k.T / np.sqrt(q.shape[-1]) + mask) @ v

where mask is the matrix (for n_seq=5):

0 -1e10 -1e10 -1e10 -1e10
0   0   -1e10 -1e10 -1e10
0   0     0   -1e10 -1e10
0   0     0     0   -1e10
0   0     0     0     0

We use -1e10 instead of -np.inf as -np.inf can cause nans.

Adding mask to our attention matrix instead of just explicitly setting the values to -1e10 works because practically, any number plus -inf is just -inf.

We can compute the mask matrix in NumPy with (1 - np.tri(n_seq)) * -1e10.

Putting it all together, we get:

def attention(q, k, v, mask):  # [n_q, d_k], [n_k, d_k], [n_k, d_v], [n_q, n_k] -> [n_q, d_v]
    return softmax(q @ k.T / np.sqrt(q.shape[-1]) + mask) @ v

def causal_self_attention(x, c_attn, c_proj): # [n_seq, n_embd] -> [n_seq, n_embd]
    # qkv projections
    x = linear(x, **c_attn) # [n_seq, n_embd] -> [n_seq, 3*n_embd]

    # split into qkv
    q, k, v = np.split(x, 3, axis=-1) # [n_seq, 3*n_embd] -> 3 of [n_seq, n_embd]

    # causal mask to hide future inputs from being attended to
    causal_mask = (1 - np.tri(x.shape[0], dtype=x.dtype)) * -1e10  # [n_seq, n_seq]

    # perform causal self attention
    x = attention(q, k, v, causal_mask) # [n_seq, n_embd] -> [n_seq, n_embd]

    # out projection
    x = linear(x, **c_proj) # [n_seq, n_embd] @ [n_embd, n_embd] = [n_seq, n_embd]

    return x


We can further improve our implementation by performing n_head separate attention computations, splitting our queries, keys, and values into heads:

def mha(x, c_attn, c_proj, n_head):  # [n_seq, n_embd] -> [n_seq, n_embd]
    # qkv projection
    x = linear(x, **c_attn)  # [n_seq, n_embd] -> [n_seq, 3*n_embd]

    # split into qkv
    qkv = np.split(x, 3, axis=-1)  # [n_seq, 3*n_embd] -> [3, n_seq, n_embd]

    # split into heads
    qkv_heads = list(map(lambda x: np.split(x, n_head, axis=-1), qkv))  # [3, n_seq, n_embd] -> [3, n_head, n_seq, n_embd/n_head]

    # causal mask to hide future inputs from being attended to
    causal_mask = (1 - np.tri(x.shape[0], dtype=x.dtype)) * -1e10  # [n_seq, n_seq]

    # perform attention over each head
    out_heads = [attention(q, k, v, causal_mask) for q, k, v in zip(*qkv_heads)]  # [3, n_head, n_seq, n_embd/n_head] -> [n_head, n_seq, n_embd/n_head]

    # merge heads
    x = np.hstack(out_heads)  # [n_head, n_seq, n_embd/n_head] -> [n_seq, n_embd]

    # out projection
    x = linear(x, **c_proj)  # [n_seq, n_embd] -> [n_seq, n_embd]

    return x

There are three steps added here:

  1. Split q, k, v into n_head heads:
# split into heads
qkv_heads = list(map(lambda x: np.split(x, n_head, axis=-1), qkv))  # [3, n_seq, n_embd] -> [n_head, 3, n_seq, n_embd/n_head]
  1. Compute attention for each head:
# perform attention over each head
out_heads = [attention(q, k, v) for q, k, v in zip(*qkv_heads)]  # [n_head, 3, n_seq, n_embd/n_head] -> [n_head, n_seq, n_embd/n_head]
  1. Merge the outputs of each head:
# merge heads
x = np.hstack(out_heads)  # [n_head, n_seq, n_embd/n_head] -> [n_seq, n_embd]

Notice, this reduces the dimension from n_embd to n_embd/n_head for each attention computation. This is a tradeoff. For reduced dimensionality, our model gets additional subspaces to work when modeling relationships via attention. For example, maybe one attention head is responsible for connecting pronouns to the person the pronoun is referencing. Maybe another might be responsible for grouping sentences by periods. Another could simply be identifying which words are entities, and which are not. Although, it's probably just another neural network black box.

The code we wrote performs the attention computations over each head sequentially in a loop (one at a time), which is not very efficient. In practice, you'd want to do these in parallel. For simplicity, we'll just leave this sequential.

With that, we're finally done our GPT implementation! Now, all that's left to do is put it all together and run our code.

Putting it All Together

Putting everything together, we get, which in its entirety is a mere 120 lines of code (60 lines if you remove comments and whitespace).

We can test our implementation with:

python \
    "Alan Turing theorized that computers would one day become" \
    --n_tokens_to_generate 8

which gives the output:

the most powerful machines on the planet.

It works!!!

We can test that our implementation gives identical results to OpenAI's official GPT-2 repo using the following Dockerfile (Note: this won't work on M1 Macbooks because of tensorflow shenanigans and also warning, it downloads all 4 GPT-2 model sizes, which is a lot of GBs of stuff to download):

docker build -t "openai-gpt-2" ""
docker run -dt "openai-gpt-2" --name "openai-gpt-2-app"
docker exec -it "openai-gpt-2-app" /bin/bash -c 'python3 src/ --length 8 --model_type 124M --top_k 1'
# paste "Alan Turing theorized that computers would one day become" when prompted

which should give an identical result:

the most powerful machines on the planet.

What Next?

This implementation is cool and all, but it's missing a ton of bells and whistles:

GPU/TPU Support

Replace NumPy with JAX:

import jax.numpy as np

That's it. You can now use the code with GPUs and even TPUs! Just make sure you install JAX correctly.


Again, if we replace NumPy with JAX:

import jax.numpy as np

Then computing the gradients is as easy as:

def lm_loss(params, inputs, n_head) -> float:
    x, y = inputs[:-1], inputs[1:]
    logits = gpt2(x, **params, n_head=n_head)
    loss = np.mean(-log_softmax(logits)[y])
    return loss

grads = jax.grad(lm_loss)(params, inputs, n_head)


Once again, if we replace NumPy with JAX[6]:

import jax.numpy as np

Then, making our gpt2 function batched is as easy as:

gpt2_batched = jax.vmap(gpt2, in_axes=[0, None, None, None, None, None])
gpt2_batched(batched_inputs) # [batch, seq_len] -> [batch, seq_len, vocab]

Inference Optimization

Our implementation is quite inefficient. The quickest and most impactful optimization you can make (outside of GPU + batching support) would be to implement a kv cache. Also, we implemented our attention head computations sequentially, when we should really be doing it in parallel[7].

There's many many more inference optimizations. I recommend Lillian Weng's Large Transformer Model Inference Optimization and Kipply's Transformer Inference Arithmetic as a starting point.


Training a GPT is pretty standard for a neural network (gradient descent w.r.t a loss function). Of course, you also need to use the standard bag of tricks when training a GPT (i.e. use the Adam optimizer, find the optimal learning rate, regularization via dropout and/or weight decay, use a learning rate scheduler, use the correct weight initialization, batching, etc ...).

The real secret sauce to training a good GPT model is the ability to scale the data and the model, which is where the real challenge is.

For scaling data, you'll want a corpus of text that is big, high quality, and diverse.

Scaling the model to billions of parameters involves a cr*p ton of engineering (and money lol). Training frameworks can get absurdly long and complex. A good place to start would be Lillian Weng's How to Train Really Large Models on Many GPUs. On the topic there's also the NVIDIA's Megatron Framework, Cohere's Training Framework, Google's PALM, the open source mesh-transformer-jax (used to train EleutherAI's open source models), and many many more.


Oh boy, how does one even evaluate LLMs? Honestly, it's really hard problem. HELM is pretty comprehensive and a good place to start, but you should always be skeptical of benchmarks and evaluation metrics.

Architecture Improvements

I recommend taking a look at Phil Wang's X-Transformer's. It has the latest and greatest research on the transformer architecture. This paper is also a pretty good summary (see Table 1). Facebook's recent LLaMA paper is also probably a good reference for standard architecture improvements (as of February 2023).

Stopping Generation

Our current implementation requires us to specify the exact number of tokens we'd like to generate ahead of time. This is not a very good approach as our generations end up being too long, too short, or cutoff mid-sentence.

To resolve this, we can introduce a special end of sentence (EOS) token. During pre-training, we append the EOS token to the end of our input (i.e. tokens = ["not", "all", "heroes", "wear", "capes", ".", "<|EOS|>"]). During generation, we simply stop whenever we encounter the EOS token (or if we hit some maximum sequence length):

def generate(inputs, eos_id, max_seq_len):
	prompt_len = len(inputs)
	while inputs[-1] != eos_id and len(inputs) < max_seq_len:
        output = gpt(inputs)
        next_id = np.argmax(output[-1])
    return inputs[prompt_len:]

GPT-2 was not pre-trained with an EOS token, so we can't use this approach in our code, but most LLMs nowadays use an EOS token.


We briefly touched on fine-tuning in the training section. Recall, fine-tuning is when we re-use the pre-trained weights to train the model on some downstream task. We call this process transfer-learning.

In theory, we could use zero-shot or few-shot prompting to get the model to complete our task, however, if you have access to a labelled dataset, fine-tuning a GPT is going to yield better results (results that can scale given additional data and higher quality data).

There are a couple different topics related to fine-tuning, I've broken them down below:

Classification Fine-tuning

In classification fine-tuning, we give the model some text and we ask it to predict which class it belongs to. For example, consider the IMDB dataset, which contains movie reviews that rate the movie as either good, or bad:

--- Example 1 ---
Text: I wouldn't rent this one even on dollar rental night.
Label: Bad
--- Example 2 ---
Text: I don't know why I like this movie so well, but I never get tired of watching it.
Label: Good
--- Example 3 ---

To fine-tune our model, we replace the language modeling head with a classification head, which we apply to the last token output:

def gpt2(inputs, wte, wpe, blocks, ln_f, cls_head, n_head):
    x = wte[inputs] + wpe[range(len(inputs))]
    for block in blocks:
        x = transformer_block(x, **block, n_head=n_head)
    x = layer_norm(x, **ln_f)

	# project to n_classes
	# [n_embd] @ [n_embd, n_classes] -> [n_classes]
    return x[-1] @ cls_head

We only use the last token output x[-1] because we only need to produce a single probability distribution for the entire input instead of n_seq distributions as in the case of language modeling. We take the last token in particular (instead of say the first token or a combination of all the tokens) because the last token is the only token that is allowed to attend to the entire sequence and thus has information about the input text as a whole.

As per usual, we optimize w.r.t. the cross entropy loss:

def singe_example_loss_fn(inputs: list[int], label: int, params) -> float:
    logits = gpt(inputs, **params)
    probs = softmax(logits)
    loss = -np.log(probs[label]) # cross entropy loss
    return loss

Generative Fine-tuning

Some tasks can't be neatly categorized into classes. For example, consider the task of summarization. We can fine-tune these types of task by simply performing language modeling on the input concatenated with the label. For example, here's what a single summarization training sample might look like:

--- Article ---
This is an article I would like to summarize.
--- Summary ---
This is the summary.

We train the model as we do during pre-training (optimize w.r.t language modeling loss).

At predict time, we feed the model the everything up to --- Summary --- and then perform auto-regressive language modeling to generate the summary.

The choice of the delimiters --- Article --- and --- Summary --- are arbitrary. How you choose to format the text is up to you, as long as it is consistent between training and inference.

Notice, we can also formulate classification tasks as generative tasks (for example with IMDB):

--- Text ---
I wouldn't rent this one even on dollar rental night.
--- Label ---

However, this will probably perform worse than doing classification fine-tuning directly (loss includes language modeling on the entire sequence, not just the final prediction, so the loss specific to the prediction will get diluted)

Instruction Fine-tuning

Most state-of-the-art large language models these days also undergo an additional instruction fine-tuning step after being pre-trained. In this step, the model is fine-tuned (generative) on thousands of instruction prompt + completion pairs that were human labeled. Instruction fine-tuning can also be referred to as supervised fine-tuning, since the data is human labelled (i.e. supervised).

So what's the benefit of instruction fine-tuning? While predicting the next word in a wikipedia article makes the model is good at continuing sentences, it doesn't make it particularly good at following instructions, or having a conversation, or summarizing a document (all the things we would like a GPT to do). Fine-tuning them on human labelled instruction + completion pairs is a way to teach the model how it can be more useful, and make them easier to interact with. This call this AI alignment, as we are aligning the model to do and behave as we want it to. Alignment is an active area of research, and includes more than just following instructions (bias, safety, intent, etc ...).

What does this instruction data look like exactly? Google's FLAN models were trained on various academic NLP datasets (which are already human labelled):

Figure 3 from FLAN paper

OpenAI's InstructGPT on the other hand was trained on prompts collected from their own API. They then paid workers to write completions for those prompts. Here's a breakdown of the data:

Table 1 and 2 from InstructGPT paper

Parameter Efficient Fine-tuning

When we talk about fine-tuning in the above sections, it is assumed that we are updating all of the model parameters. While this yields the best performance, it is costly both in terms of compute (need to back propagate over the entire model) and in terms of storage (for each fine-tuned model, you need to store a completely new copy of the parameters). For instruction fine-tuning, this is fine, we want maximum performance, but if you then wanted to fine-tune 100 different models for various downstream tasks, then you'd have a problem.

The most simple approach to this problem is to only update the head and freeze (i.e. make untrainable) the rest of the model. This would speed up training and greatly reduce the number of new parameters, however it would not perform nearly as well as a full fine-tune (we are lacking the deep in deep learning). We could instead selectively freeze specific layers (i.e. freeze all layers except the last 4, or freeze every other layer, or freeze all parameters except multi-head attention parameters), which would help restore some of the depth. This will perform a lot better, but we become a lot less parameter efficient and reduce our training speed ups.

Instead, we can utilize parameter-efficient fine-tuning (PEFT) methods. PEFT is active area of research, and there are lots of different methods to choose from.

As an example, take the Adapters paper. In this approach, we add an additional "adapter" layer after the FFN and MHA layers in the transformer block. The adapter layer is just a simple 2 layer fully connected neural network, where the input and output dimensions are n_embd, and the hidden dimension is smaller than n_embd:

Figure 2 from the Adapters paper

The size of the hidden dimension is a hyper-parameter that we can set, enabling us to tradeoff parameters for performance. For a BERT model, the paper showed that using this approach can reduce the number of trained parameters to 2% while only sustaining a small hit in performance (<1%) when compared to a full fine-tune.

  1. For certain applications, the tokenizer doesn't require a decode method. For example, if you want to classify if a movie review is saying the movie was good or bad, you only need to be able to encode the text and do a forward pass of the model, there is no need for decode. For generating text however, decode is a requirement. ↩︎

  2. Although, with the InstructGPT and Chinchilla papers, we've realized that we don't actually need to train models that big. An optimally trained and instruction fine-tuned GPT at 1.3B parameters can outperform GPT-3 at 175B parameters. ↩︎

  3. The original transformer paper used a calculated positional embedding which they found performed just as well as learned positional embeddings, but has the distinct advantage that you can input any arbitrarily long sequence (you are not restricted by a maximum sequence length). However, in practice, your model is only going to be as the good sequence lengths that it was trained on. You can't just train a GPT on sequences that are 1024 long and then expect it to perform well at 16k tokens long. Recently however, there has been some success with relative positional embeddings, such as Alibi and RoPE. ↩︎

  4. Different GPT models may choose a different hidden width that is not 4*n_embd, however this is the common practice for GPT models. Also, we give the multi-head attention layer a lot of attention (pun intended) for driving the success of the transformer, but at the scale of GPT-3, 80% of the model parameters are contained in the feed forward layer. Just something to think about. ↩︎

  5. If you're not convinced, stare at the softmax equation and convince yourself this is true (maybe even pull out a pen and paper):
    \[ \text{softmax}(\vec{x})_i=\frac{e^{x_i}}{\sum_je^{x_j}} \] ↩︎

  6. I love JAX ❤️. ↩︎

  7. Using JAX, this is as simple as heads = jax.vmap(attention, in_axes=(0, 0, 0, None))(q, k, v, causal_mask). ↩︎