Decoding the Transformers: A Dive into GPT with TensorFlow

The entire code for this guide, as well as some additional material, is available on the GitHub repository.

Transformers are a type of deep learning model that was introduced in the paper "Attention is All You Need" by Vaswani et al. (2017). They have been particularly successful in natural language processing tasks, and are behind state-of-the-art models such as GPT (Generative Pretrained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and others.

Transformers are based on the concept of self-attention, also known as transformer attention or scaled dot-product attention. Unlike traditional recurrent neural networks (RNNs) that process input sequences one element at a time, transformers process all elements of the input sequence in parallel, which allows them to learn long-range dependencies more effectively.

In order to achieve our goal, we're drawing inspiration from a comprehensive video series by Andrej Karpathy , where he skillfully constructs a GPT model from the ground up using PyTorch. However, our endeavor charts a slightly different course. We've opted to use TensorFlow, a robust open-source framework extensively used for machine learning applications, instead of PyTorch. To be more precise, we will be utilizing his nanoGPT repository built in PyTorch.

Nomenclature

Before we delve deeper, it's essential to familiarize ourselves with certain terminology that will be consistently used throughout this article. Understanding these terms will not only simplify the process but also enhance comprehension.

1. block_size: This parameter defines the sequence length that our model will consider for its tasks. It's essentially the 'context window' of our model. By adjusting the block size, we're tuning the amount of past information that the model considers when making predictions. It's a critical trade-off - larger block sizes provide more context, potentially improving model performance, but also require more computational resources and could lead to overfitting if not properly regularized. Conversely, smaller block sizes may limit the model's ability to understand context, but are faster to compute and less prone to overfitting. It's crucial to find a balance that suits the specific task and dataset at hand.
2. vocab_size: This is the total number of different words that are in the model's vocabulary. The model will only be able to understand and generate these words.
3. n_embd: This is the dimensionality of the embeddings. Each word from the vocabulary will be represented as a vector of this size. Larger dimensions allow more information to be stored about each word, but also increase the computational cost.
4. n_head: This parameter configures the number of transformer heads that are used in parallel. Each head will learn to pay attention to different parts of the input sequence, providing the model with multiple "perspectives". This concept is a part of the self-attention mechanism, which is central to the Transformer model.
5. n_layer: A set of parallel transformer heads are consolidated into blocks, and this parameter denotes the number of such blocks. Each block includes one multi-head self-attention mechanism and one position-wise feed-forward neural network. More layers generally allow the model to learn more complex patterns but also increase the risk of overfitting and the demand for computational resources.
6. dropout: This is the dropout rate, a regularization technique. A certain fraction of the input units to the transformer are randomly dropped during training, which helps to prevent overfitting.
7. bias: This is a flag indicating whether bias terms should be used in the dense layers. Bias allows the model to represent patterns that do not pass through the origin.
8. epsilon: This is a small constant used for numerical stability in the layer normalization layers. Layer normalization is a technique to standardize the inputs across the network, which helps in stabilizing the learning process and reducing the training time. We'll delve deeper into this in the subsequent sections.

We will capture all this inside the GPTconfig class.

@dataclas
class GPTConfig:
? ? block_size: int = 25
? ? vocab_size: int = 200 ?# GPT-2 vocab_size of 50257, padded up to the nearest multiple of 64 for efficiency
? ? n_layer: int = 12 # number of squential transformers
? ? n_head: int = 12 ?# number of attention heads
? ? n_embd: int = 768 ?# embedding size of the input
? ? dropout: float = 0.2 # dropout percentage
? ? bias: bool = True ?# True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
? ? epsilon: float = 1e-5 ?# epsilon value of layer normalizations        

Dataset

Our next objective is to procure the dataset we will be using for this project. For this purpose, we're following Andrej's footsteps and using the 'Tiny Shakespeare' dataset. We will download this dataset, encode it into numerical form, and then convert it into a batched dataset leveraging TensorFlow's in-built functionality. Encoding our data into a numerical format is crucial as it facilitates the computer's understanding and processing of the information. Here's the detailed breakdown of the provided code.

Function to download the dataset

# Function to download the datas
def text_extractor(url = "https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"):
??? # Request to fetch the tiny shakespeare dataset
??? response = requests.get(url)
??? # Checking if we got a valid response
??? if response.status_code == 200:
??????? # Opening a file and writing the content of the response
??????? with open('input.txt', 'w') as file:
??????????? file.write(response.text)
??? else:
??????? print(f"Failed to get file with status code: {response.status_code}")
??? # Reading the downloaded file
??? with open('input.txt', 'r', encoding='utf-8') as f:
??????? text = f.read()
??? return texte        

Function to encode the text into numbers

# Function to encode the text into number
def text_encoder(text):
    # Listing and sorting the unique characters in the text
    chars = sorted(list(set(text)))
    # Getting the total number of unique characters
    vocab_size = len(chars)
    print("".join(chars))
    print(vocab_size)
    # Creating mappings from characters to their corresponding numerical representations
    stoi = {ch:i for i, ch in enumerate(chars)}
    # Creating mappings from numbers to their corresponding characters
    itos = {i:ch for i, ch in enumerate(chars)}
    # Function to encode a string into a list of numbers
    encode = lambda s: [stoi[ch] for ch in s]
    # Function to decode a list of numbers back into a string
    decode = lambda l: "".join([itos[i] for i in l])
    print(encode("hii I am Krishna"))
    print("decoded: ", decode(encode("hii I am Krishna")))
    # Encoding the entire text into numbers
    series = encode(text)
    n = int(0.8*len(series))
    return series        

Function to create a windowed dataset

# Function to create a windowed datase
def windowed_dataset(series, window_size, batch_size, shuffle_buffer):
    # Creating a tensorflow dataset from the encoded series
    dataset = tf.data.Dataset.from_tensor_slices(series)
    # Creating a windowed dataset with each window of size window_size + 1 and shifting the window by 1 after each step
    dataset = dataset.window(size=window_size+1, shift = 1, drop_remainder=True)
    # Flattening the dataset
    dataset = dataset.flat_map(lambda window: window.batch(window_size+1))
    # Splitting each window into features (all elements except the last) and target (the last element)
    dataset = dataset.map(lambda x: (x[:-1], x[1:]))
    # Shuffling the dataset
    dataset = dataset.shuffle(shuffle_buffer)
    # Batching the dataset and prefetching 1 batch at a time to improve performance
    dataset = dataset.batch(batch_size).prefetch(1)
    return dataset        

Remember, the process of converting the text into numerical form is crucial because machine learning models do not understand text data in its raw form. Therefore, encoding the text data into numbers allows our model to comprehend and learn from the text data. Following the data extraction and encoding process, we can proceed to generate the training and testing data. Here's how to do it:

config = GPTConfi
text = text_extractor()
series = text_encoder(text)
n = len(series)

# Create the training dataset
train_dataset = windowed_dataset(series[:n], config.block_size, batch_size=250, shuffle_buffer=10)

# Create the testing dataset
test_dataset = windowed_dataset(series[n:], config.block_size, batch_size=250, shuffle_buffer=10)        

In this script, we start by initializing our GPT configuration object. We then extract the text data from the chosen dataset and encode this text data into numerical form. After getting the encoded data (series), we calculate its length. Next, we create the training dataset by taking the first n elements from the series. We specify the block size as defined in our GPT configuration, set a batch size of 250, and set a shuffle buffer size of 10. The shuffle buffer size determines the randomness of the shuffling process, which helps improve model generalization. The test dataset is created in a similar fashion, but this time, we take the elements from position n to the end of the series. By running this script, we will have our training and testing datasets ready for the model training phase.

Attention Mechanism

The attention mechanism forms the crux of the GPT model and many modern Transformer-based architectures. The goal of attention is to compute a context-aware representation of each word in our input sequence. Let's break down the steps involved in this process for better understanding:

1. Encoding: We begin with an input string, a sequence of words, with a length equivalent to the block_size. Each word in the input string is then encoded into a numerical value. This process transforms our data into a format that our model can understand and process more efficiently.
2. Embedding: Each encoded element in the input is further embedded into a vector within a higher-dimensional space of size n_embd. This process essentially represents our data in a much denser form, capturing the nuanced relationships and context between words in the process.
3. Key, Query, and Value Tables: We create three distinct tables known as Key, Query, and Value tables for these embedded vectors. These tables are essentially linear transformations of our input vector and are constructed in a higher dimensional space known as head_size. They are instrumental in understanding and capturing the semantic relationships between words in our sequence.
4. Weight Matrix Creation: A weight matrix (W) of dimensions block_size x block_size is created. Each Query in our previously constructed table is projected onto the Keys to compute similarity scores using cosine similarity. If the Query and Key are similar, the score will be positive. Conversely, if they are dissimilar, the score will be negative. A score of zero indicates no relation between them.
5. Future Word Masking: However, it's important to note that our Queries cannot 'see' the Keys corresponding to future words in our sequence. Therefore, to prevent leakage of information from the future, we set these future positions to zero in our weight matrix.
6. Normalization: We then convert this weight matrix into a probability matrix, often referred to as an adjacency matrix. This normalization process ensures that the sum of weights across each row equals one, making it possible to interpret these weights as probabilities.
7. Graph Creation: With the above steps, we can visualize our data as a directed graph. Each node in the graph corresponds to a word in our sequence, and the edges, defined by our adjacency matrix, represent the connections between words. The values at each node are provided by the Value array we created earlier.
8. Weighted Sum: Finally, we calculate a weighted sum of the values at every node. This weighted sum serves as the output for this stage of processing, providing a context-aware representation of each word, ready to be further processed or used in downstream tasks.

These steps are carried out for each word in the input sequence, and the process is repeated for each layer in the Transformer model. Through these layers, the model can learn to focus on different words at each layer, capturing more complex relationships and building a rich understanding of the input.

Code Walk Though: The MultiHeadAttention class is an integral part of the GPT model architecture. It helps achieve the attention mechanism we explained earlier but with a twist - it creates multiple attention networks in parallel. This allows the model to capture different types of information from the same input data.

class MultiHeadAttention(layers.Layer)
? ? def __init__(self, config):
? ? ? ? super(MultiHeadAttention, self).__init__()


? ? ? ? self.num_heads = config.n_head
? ? ? ? self.head_size = config.n_embd // config.n_head


? ? ? ? # Projecting input into key, query, and value for all attention heads, but in batch
? ? ? ? self.c_attn = layers.Dense(3 * config.n_embd, use_bias=config.bias)


? ? ? ? # Regularization
? ? ? ? self.attn_dropout = layers.Dropout(config.dropout)
? ? ? ? self.resid_dropout = layers.Dropout(config.dropout)


? ? def call(self, x):
? ? ? ? B, T, C = x.shape


? ? ? ? # Linear transformation for queries, keys, and values, note that C = n_embd
? ? ? ? qkv = self.c_attn(x) ?# Input shape: (B, T, C), Output shape: (B, T, 3 * n_embd)


? ? ? ? # Split the queries, keys, and values
? ? ? ? q, k, v = tf.split(qkv, 3, axis=-1) ?# Input shape: (B, T, 3 * n_embd), Output shapes: 3 * (B, T, n_embd)
? ? ? ? 
? ? ? ? 
? ? ? ? # Reshape queries, keys, and values for multi-head attention with head_size = n_embd // num_heads
? ? ? ? # BUG: possible issue with tensorflow, you can use tf.reshape(q, (B, T, self.num_heads, -1)), for tensorflow B is unknown: it will give an error
? ? ? ? q = tf.reshape(q, (-1, T, self.num_heads, self.head_size)) ?# Output shape: (B, T, num_heads, head_size)
? ? ? ? k = tf.reshape(k, (-1, T, self.num_heads, self.head_size)) ?# Output shape: (B, T, num_heads, head_size)
? ? ? ? v = tf.reshape(v, (-1, T, self.num_heads, self.head_size)) ?# Output shape: (B, T, num_heads, head_size)



? ? ? ? # Perform attention operations


? ? ? ? # Transpose queries, keys, and values for efficient matrix multiplication
? ? ? ? q = tf.transpose(q, perm=[0, 2, 1, 3]) ?# Output shape: (B, num_heads, T, head_size)
? ? ? ? k = tf.transpose(k, perm=[0, 2, 1, 3]) ?# Output shape: (B, num_heads, T, head_size)
? ? ? ? v = tf.transpose(v, perm=[0, 2, 1, 3]) ?# Output shape: (B, num_heads, T, head_size)


? ? ? ? # Compute attention scores ("affinities")
? ? ? ? wei = tf.matmul(q, k, transpose_b=True) * (self.head_size ** -0.5) ?# Output shape: (B, num_heads, T, T)


? ? ? ? mask = tf.linalg.band_part(tf.ones_like(wei), -1, 0) ?# Lower triangular matrix of ones
? ? ? ? wei = tf.where(mask == 1, wei, float("-inf")) ?# Set upper triangular part to -inf


? ? ? ? wei = tf.nn.softmax(wei, axis=-1) ?# Output shape: (B, num_heads, T, T)
? ? ? ? wei = self.attn_dropout(wei) ?# Regularization step 1


? ? ? ? # Perform the weighted aggregation of the values
? ? ? ? out = tf.matmul(wei, v) ?# Output shape: (B, num_heads, T, head_size)


? ? ? ? # Transpose and reshape the output to match the original shape
? ? ? ? out = tf.transpose(out, perm=[0, 2, 1, 3]) ?# Output shape: (B, T, num_heads, head_size)
? ? ? ? out = tf.reshape(out, (-1, T, C)) ?# Output shape: (B, T, C) - note that C = num_heads * head_size = n_embd
? ? ? ? out = self.resid_dropout(out) ?# Regularization step 2
? ? ? ? return out:        

Here's how the code works:

1. Initialization: The MultiHeadAttention layer initializes with the configuration provided, storing the number of attention heads (self.num_heads) and the size of each head (self.head_size). The head size is calculated by dividing the embedding dimension by the number of heads. It also defines a Dense layer for linear transformations of the inputs (self.c_attn) and two Dropout layers for regularization (self.attn_dropout, self.resid_dropout).
2. Call Method: This is where the attention mechanism takes place. The function takes the input tensor x as an argument.
3. Linear Transformation: The function applies a dense layer to the input tensor (self.c_attn(x)) to generate a combined QKV (Query, Key, Value) matrix.
4. Split QKV: The QKV matrix is then split into separate Query, Key, and Value matrices.
5. Reshaping for Multi-Head Attention: The Q, K, V matrices are reshaped to account for the multiple heads. Each matrix is reshaped from (Batch, Sequence, Embedding) to (Batch, Sequence, Num_heads, Head_size), essentially transforming the last dimension into two dimensions: number of heads and size of each head.
6. Transpose for Efficient Computation: The matrices are transposed to allow efficient matrix multiplication in the next steps.
7. Score Calculation: The dot product of Q and K matrices is calculated, scaled by dividing by the square root of head size. This results in a score matrix indicating the relevance of each word in the sequence to the current word.
8. Masking Future Information: A mask is created to avoid using future information during the attention process, effectively ensuring that the model is not 'peeking' into future tokens.
9. Softmax Application: The softmax function is applied to the scores to convert them into weights, which are then passed through a dropout layer for regularization.
10. Weighted Summation: The weighted sum of the Value matrix and the weights gives the output of the attention mechanism for each head.
11. Reshape and Regularization: The output is then transposed and reshaped back to its original dimensions. The reshaped output is passed through another dropout layer for regularization before being returned by the function.

By running this process in parallel for multiple attention heads, the model can focus on different features in the input data simultaneously, allowing it to better understand complex patterns in the data.

Final Missing pieces: MLP and Block

The MLP (Multi-Layer Perceptron) and Block classes in the code are vital components of the GPT model architecture. They help the model to learn complex representations and capture dependencies in the data. Let's take a closer look at both of them:

MLP Class:

class MLP(layers.Layer)
? ? def __init__(self, config):
? ? ? ? super(MLP, self).__init__()
? ? ? ? n_embed = config.n_embd
? ? ? ? self.c_fc = layers.Dense(4 * n_embed, use_bias=config.bias, activation=tf.keras.activations.gelu)
? ? ? ? self.c_proj = layers.Dense(config.n_embd, use_bias=config.bias)
? ? ? ? self.dropout = layers.Dropout(config.dropout)


? ? def call(self, x):
? ? ? ? x = self.c_fc(x)
? ? ? ? x = self.c_proj(x)
? ? ? ? x = self.dropout(x)
? ? ? ? return x:        

The MLP class is essentially a simple feed-forward neural network with one hidden layer and non-linear activation. It helps the model to learn and extract features from the inputs.

1. Initialization: The MLP layer initializes with the provided configuration, storing the Dense layers (self.c_fc and self.c_proj) for linear transformations of the inputs and a Dropout layer for regularization (self.dropout).
2. Call Method: This is where the feed-forward operation takes place. The function takes the input tensor x as an argument, passes it through the first Dense layer with GELU activation, then the second Dense layer, and finally a dropout layer. The output is then returned by the function.

Block Class:

class Block(layers.Layer)
? ? def __init__(self, config):
? ? ? ? super(Block, self).__init__()


? ? ? ? # Layer normalizing the input data as the number of features increases over time
? ? ? ? self.ln_1 = layers.LayerNormalization(epsilon=config.epsilon, center=False, scale=True)
? ? ? ? self.attn = MultiHeadAttention(config)
? ? ? ? self.ln_2 = layers.LayerNormalization(epsilon=config.epsilon, center=False, scale=True)
? ? ? ? self.mlp = MLP(config)


? ? def call(self, x):
? ? ? ? # 1. Input data is layer normalized: Layer normalizing the input data as the number of features increases over time
? ? ? ? x_normalized = self.ln_1(x)


? ? ? ? # 2. Fed through the attention network: We get the attention scores or weighted values
? ? ? ? attn_output = self.attn(x_normalized)


? ? ? ? # 3. Added to the input: Reduces vanishing gradient issues
? ? ? ? x = x + attn_output


? ? ? ? # 4. Layer normalized the data again
? ? ? ? x_normalized = self.ln_2(x)


? ? ? ? # 5. Final pass through a multi-layer perceptron: We are learning the features
? ? ? ? mlp_output = self.mlp(x_normalized)


? ? ? ? # 6. Added to the input again
? ? ? ? x = x + mlp_output


? ? ? ? return x:        

The Block class is essentially the building block of the Transformer architecture in the GPT model. It contains a multi-head self-attention mechanism followed by a position-wise feed-forward network (MLP).

1. Initialization: The Block layer initializes with two Layer Normalization layers (self.ln_1 and self.ln_2) that normalize the features across the feature dimension, a MultiHeadAttention layer (self.attn) that allows the model to focus on different parts of the sequence, and an MLP layer (self.mlp).
2. Call Method: This is where the operations within each Transformer block occur. The function takes the input tensor x as an argument and performs the following steps:

• Layer Normalization: The input data is first normalized. Layer Normalization and Batch Normalization are both normalization techniques widely used in deep learning, particularly in training deep neural networks. Normalizes over the feature dimension, making it suitable for recurrent networks and networks with variable batch sizes, as it's not dependent on the batch size. It's extensively used in Transformer-based models like GPT and BERT.
• Self-Attention: The normalized data is then passed through the attention network, producing the attention output.
• Add & Norm: The attention output is added to the original input (residual connection), helping mitigate the vanishing gradient problem. The sum is then normalized again.
• Feed-Forward Network: The data is passed through an MLP, enabling the model to learn complex representations.
• Add & Norm: The MLP output is added to the sum of the input and the attention output (another residual connection), and the result is the final output of the block.

These classes are the core building blocks of the GPT model, enabling it to process and learn from sequence data effectively.

Decoder Model

This segment of code outlines the construction of our GPT model, defining the structure and connections between the multiple components that make up the model's architecture. Here, we've harnessed the flexibility and modularity of the Keras API to construct a complex model with relative ease.

ef decoder(config)
? ? """
? ? Creates an decoder model based on the provided configuration.
? ? Args:config: An object specifying the configuration parameters.
? ? Returns:decoder: A Keras Model object representing the encoder model.
? ? """
? ? # create a dict with all the layers we need
? ? transformer_dict = {
? ? ? ? # input layer
? ? ? ? 'input': tf.keras.Input(shape=(config.block_size,)),
? ? ? ? # word token embeddings
? ? ? ? 'wte': tf.keras.layers.Embedding(config.vocab_size, config.n_embd, input_length=config.block_size),
? ? ? ? # word position embeddings
? ? ? ? 'wpe': tf.keras.layers.Embedding(config.block_size, config.n_embd),
? ? ? ? # dropout layer
? ? ? ? 'drop': tf.keras.layers.Dropout(config.dropout),
? ? ? ? # Transformer blocks
? ? ? ? 'h': tf.keras.Sequential([Block(config) for _ in range(config.n_layer)]),
? ? ? ? # layer normalization
? ? ? ? 'ln_f': tf.keras.layers.LayerNormalization(epsilon=config.epsilon, center=False, scale=True),
? ? ? ? # layer used to project the output of the GPT model to the vocabulary size
? ? ? ? 'lm_head': tf.keras.layers.Dense(config.vocab_size, use_bias=False)
? ? }
? ? # input
? ? idx = transformer_dict['input']
? ? pos = tf.range(0, config.block_size, dtype=tf.int64) ?# shape (t)

? ? # Forward the GPT model itself
? ? tok_emb = transformer_dict['wte'](idx) ?# token embeddings of shape (b, t, n_embd)
? ? pos_emb = transformer_dict['wpe'](pos) ?# position embeddings of shape (t, n_embd)
? ? x = transformer_dict['drop'](tok_emb + pos_emb)
? ? for block in transformer_dict['h'].layers:
? ? ? ? x = block(x)
? ? x = transformer_dict['ln_f'](x)

? ? # logit scores for each vocabulary word at each position in the input sequence.
? ? logits = transformer_dict['lm_head'](x) ?# shape (batch_size, sequence_length, vocab_size)

? ? # Create encoder model
? ? model = tf.keras.Model(inputs=idx, outputs=logits, name='encoder')

? ? return model        

Now, let's understand the purpose and function of each element of this code in detail:

1. tf.keras.Input(shape=(config.block_size,)): This denotes the input layer of our model, which expects input sequences of a length equal to the block_size defined in the configuration. block_size specifies how many tokens our model can consider at once for its tasks.
2. tf.keras.layers.Embedding(config.vocab_size, config.n_embd, input_length=config.block_size): This is the word token embedding layer of the model. It's responsible for converting each token in our input sequence into a corresponding high-dimensional vector representation. These vector embeddings are learned during the training phase and help capture the semantic properties of the tokens.
3. tf.keras.layers.Embedding(config.block_size, config.n_embd): This creates the positional embedding layer of the model. Positional embeddings are crucial for models like GPT to understand the order or position of words in the sequence, providing essential context.
4. tf.keras.layers.Dropout(config.dropout): This layer applies dropout regularization to the input embeddings. Dropout randomly sets a fraction of input units to 0 at each update during training, helping prevent overfitting.
5. tf.keras.Sequential([Block(config) for _ in range(config.n_layer)]): Here, we construct the transformer blocks that form the backbone of the GPT model. Each block contains a Multi-Head Attention layer and a feed-forward network, integrated with normalization and residual connections. The number of such blocks is dictated by our configuration.
6. tf.keras.layers.LayerNormalization(epsilon=config.epsilon, center=False, scale=True): Post-processing the output from the transformer blocks, we apply layer normalization. This technique normalizes the outputs across the features instead of the batch, enhancing model stability and performance.
7. tf.keras.layers.Dense(config.vocab_size, use_bias=False): The output from the GPT model is funneled into this linear layer (a dense layer with no bias), transforming the high-dimensional output vectors to match the size of our vocabulary. This outputs the logit scores for every word in our vocabulary for each input position.
8. tf.keras.Model(inputs=idx, outputs=logits, name='encoder'): With the defined layers and connections, we finally compile our GPT model as a Keras Model object. The inputs are the token indices for the input sequence, while the outputs are the computed logit scores for each possible word in our vocabulary at each input position.

In conclusion, once defined, we can proceed to train our GPT model with the given input text data and use it to generate contextually relevant and coherent text sequences. Each component in the model serves a distinct role in processing the input data, identifying dependencies among words, and producing the output sequence.

Training

if __name__ == '__main__'
? ? config = GPTConfig
? ? text = text_extractor()
? ? series = text_encoder(text)
? ? n = len(series)
? ? train_dataset = windowed_dataset(series[:n], config.block_size, batch_size=250, shuffle_buffer=10)
? ? test_dataset = windowed_dataset(series[n:], config.block_size, batch_size=250, shuffle_buffer=10)


? ? # Create the decoder model
? ? decoder_model = decoder(config)


? ? # Compile and train the model
? ? optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
? ? loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
? ? epochs = 10


? ? decoder_model.compile(optimizer=optimizer, loss=loss_fn)
? ? history = decoder_model.fit(train_dataset, epochs=epochs, validation_data=test_dataset):        

The provided code snippet demonstrates how to train the GPT model with our dataset:

1. config = GPTConfig sets up the configuration for the GPT model based on a predefined class GPTConfig where we have all our required model parameters defined.
2. text = text_extractor() extracts the raw text data from a source (the details of text_extractor function are not provided).
3. series = text_encoder(text) converts the raw text data into a numerical form (tokens) suitable for processing by the model (the specifics of the text_encoder function are not provided).
4. train_dataset = windowed_dataset(series[:n], config.block_size, batch_size=250, shuffle_buffer=10) and test_dataset = windowed_dataset(series[n:], config.block_size, batch_size=250, shuffle_buffer=10) split the encoded text into training and testing datasets. The windowed_dataset function (details not provided) presumably splits the data into sequences of length config.block_size, with a batch size of 250.
5. decoder_model = decoder(config) generates the GPT model according to the earlier discussed decoder function and the specified configuration.
6. optimizer = tf.keras.optimizers.Adam(learning_rate=0.001) sets up the Adam optimizer with a learning rate of 0.001.
7. loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) defines the loss function to be used during training. This specific loss function is suitable for multi-class classification problems like language modeling.
8. decoder_model.compile(optimizer=optimizer, loss=loss_fn) compiles the model with the specified optimizer and loss function, preparing it for training.
9. history = decoder_model.fit(train_dataset, epochs=epochs, validation_data=test_dataset) trains the model on the train_dataset for a predefined number of epochs, while also evaluating its performance on the test_dataset after each epoch.

Please make sure to modify the batch size, epochs, learning rate, and other parameters based on your specific data size, computational capacity, and requirements.

Conclusion

In conclusion, the Generative Pretrained Transformer (GPT) model, developed by OpenAI, is a potent tool for a wide array of tasks that involve generating human-like text. Through this guide, we have gone step by step through the construction of the GPT model, from the foundation of transformer blocks to multi-head attention, the MLP, layer normalization, and finally the assembling of these components into a complete model. We have also seen how to train this model using Keras and TensorFlow.

Understanding the inner workings of such a model provides insight into how it manages to generate coherent, contextually relevant text. It's a demonstration of how far we've come in natural language processing and understanding. This model finds its applications in several areas, from chatbots and virtual assistants to automated content generation and programming helpers.

The entire code for this guide, as well as some additional material, is available on the GitHub repository. I would like to extend a massive thank you to Andrey for his effort and dedication in sharing this outstanding resource. His hard work has made it possible for many to understand and implement this powerful model.

The power of GPT lies not just in its complexity but also in the broad applications it promises. It's an exciting time to be involved in AI and machine learning, and models like GPT offer a glimpse into the future of these technologies.

#transformers #machinelearning #nlp #gpt #deeplearning Andrej Karpathy