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A custom GPT based on Zero To Hero utilizing tiktoken with the intent to augment AI Transformer-model education and reverse engineer GPT models from scratch.
python -m venv venv
pip install torch
NOTE: ensure you visit pytorch.org and get your specific configuration where the below is simply an example
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121
pip install tiktoken
import torch
import torch.nn as nn
from torch.nn import functional as F
import tiktoken
import warnings
# ignore cuda warnings
warnings.filterwarnings('ignore')
# the batch_size parameter determines how many independent sequences will be
# processed in parallel during training and increasing the batch size allows
# for more efficient computation and parallelization but may require more memory
# and a larger batch size can also provide a more stable gradient estimation
# but might lead to slower convergence or generalization issues
batch_size = 8 # how many independent sequences will we process in parallel?
# the block_size parameter defines the maximum context length for predictions
# and it determines the number of tokens from the input sequence that the model
# considers when making predictions and if the context length exceeds the block_size,
# the model will only consider the most recent block_size tokens and
# when you change this parameter you can affect the model's ability to capture long-range
# dependencies in the input sequences and a larger block_size allows for more context but
# may also increase computational requirements
block_size = 64 # what is the maximum context length for predictions?
# the max_iters parameter represents the maximum number of iterations or steps during the
# training process and it determines how many times the model will update its parameters
# based on the training data and increasing max_iters allows for more training iterations,
# potentially leading to better model performance, however, it may also increase the training
# time and the risk of overfitting if the model starts memorizing the training data
max_iters = 500
# the eval_interval parameter specifies the frequency at which the model's performance
# is evaluated on the training and validation sets and it determines how often the loss
# values are printed or logged during training and a smaller eval_interval provides more
# frequent updates on the model's progress but can increase the computational overhead
# and adjusting this parameter depends on the desired level of monitoring and the trade-off
# between evaluation frequency and training efficiency
eval_interval = 100
# the learning_rate parameter controls the step size at each iteration during the model's
# parameter update using gradient descent optimization and it determines how much the model's
# parameters are adjusted based on the computed gradients and a higher learning rate allows
# for larger updates, potentially leading to faster convergence, however, using a very high
# learning rate can cause the optimization process to become unstable or prevent
# convergence, on the other hand, a lower learning rate may require more iterations for
# convergence but can provide more precise parameter updates
learning_rate = 1e-3
# the device parameter specifies the device on which the model and tensors are placed for
# computation and if CUDA is available and enabled, the model will be placed on the GPU ('cuda'),
# which can significantly accelerate training and if CUDA is not available or enabled,
# the model will be placed on the CPU ('cpu') and consider when choosing the appropriate device depends
# on the availability of compatible hardware and the memory requirements of the model
device = 'cuda' if torch.backends.cuda.is_built() else 'cpu'
# the eval_iters parameter determines the number of iterations used to estimate the loss on the
# training and validation sets during evaluation and it represents the number of iterations used
# to compute the average loss value and a larger eval_iters value provides a more accurate estimation
# of the loss but can increase the evaluation time and adjusting this parameter depends on the
# desired level of accuracy in the loss estimation and the trade-off between evaluation time and accuracy
eval_iters = 200
# the n_embd parameter represents the embedding dimension or size of the token embeddings
# in the model and it determines the dimensionality of the learned token representations and
# changing this parameter can affect the model's capacity to capture and encode information from
# the input tokens and a larger n_embd value allows for a higher capacity model but may increase the
# number of parameters and computational requirements, conversely, decreasing n_embd can result in a model
# with lower capacity and less expressive power
n_embd = 64
# the n_head parameter determines the number of attention heads used in the multi-head attention
# mechanism of the model and attention heads allow the model to attend to different parts of the input
# sequence simultaneously capturing different dependencies and patterns and increasing n_head allows
# for more fine-grained attention and enhances the model's ability to capture complex relationships,
# however, it also increases the computational cost and the number of parameters in the model
n_head = 4
# the n_layer parameter specifies the number of transformer blocks or layers in the model and
# each transformer block consists of attention mechanisms and feed-forward neural networks and
# increasing n_layer allows for a deeper model with more complex representations and increased
# modeling capacity, however, a higher number of layers may increase the computational requirements
# and the risk of overfitting if the model becomes too complex for the available training data
n_layer = 4
# the dropout parameter represents the dropout probability, which determines the probability
# of randomly setting inputs to zero during training and dropout is a regularization technique
# that helps prevent overfitting by reducing co-adaptation between neurons and a dropout value
# of 0.0 means no dropout is applied, while a value of 1.0 means all inputs are set to zero and
# adjusting the dropout value can influence the model's generalization ability and higher dropout
# values introduce more regularization, which can be useful when dealing with limited training
# data or to prevent overfitting, however, too much dropout may lead to underfitting, and too
# little dropout may result in overfitting
dropout = 0.0
# print device either cuda or cpu
print(device)
# torch.manual_seed(1337) # if you want to have reproducibility
# open dataset and create text object
with open('data.txt', 'r', encoding='utf-8') as f:
text = f.read()
# create a mapping from subwords to integers
enc = tiktoken.get_encoding("gpt2")
# train and test splits
data = torch.tensor(enc.encode(text), dtype=torch.long)
n = int(0.8*len(data)) # first 80% will be train, rest val
train_data = data[:n]
val_data = data[n:]
def get_batch(split):
"""
Retrieves a batch of data for a given split.
Args:
split (str): Specifies the split of the data to retrieve ('train' or 'val').
Returns:
tuple: A tuple containing the input data and corresponding target data.
- x (torch.Tensor): Input data of shape (batch_size, block_size).
- y (torch.Tensor): Target data of shape (batch_size, block_size).
Raises:
ValueError: If an invalid split value is provided.
Notes:
- The function assumes the existence of the variables `train_data`, `val_data`,
`block_size`, `batch_size`, and `device` in the global scope.
- `train_data` and `val_data` are expected to be PyTorch tensors containing the
complete training and validation datasets, respectively.
- `block_size` specifies the length of each sequence block in the data.
- `batch_size` determines the number of sequences to include in each batch.
- `device` specifies the device on which the tensors will be placed.
Example:
# Retrieve a training batch
x_train, y_train = get_batch('train')
# Retrieve a validation batch
x_val, y_val = get_batch('val')
"""
data = train_data if split == 'train' else val_data
# randomly select starting indices for the batch
ix = torch.randint(len(data) - block_size, (batch_size,))
# retrieve the input and target sequences for each starting index
x = torch.stack([data[i:i+block_size] for i in ix])
y = torch.stack([data[i+1:i+block_size+1] for i in ix])
# move the tensors to the specified device
x, y = x.to(device), y.to(device)
return x, y
@torch.no_grad()
def estimate_loss():
"""
Estimates the average loss on the training and validation datasets.
Returns:
dict: A dictionary containing the average loss for each dataset split.
- 'train' (float): Average loss on the training dataset.
- 'val' (float): Average loss on the validation dataset.
Notes:
- The function assumes the existence of the variables `eval_iters` and `model`
in the global scope.
- `eval_iters` specifies the number of iterations to perform for loss estimation.
- `model` is the PyTorch model object to evaluate.
- The `get_batch` function is expected to be defined and accessible.
Example:
# Estimate the losses
loss_estimation = estimate_loss()
# Access the average loss on the training dataset
train_loss = loss_estimation['train']
# Access the average loss on the validation dataset
val_loss = loss_estimation['val']
"""
out = {}
model.eval()
# estimate losses for each dataset split
for split in ['train', 'val']:
losses = torch.zeros(eval_iters)
for k in range(eval_iters):
X, Y = get_batch(split)
_, loss = model(X, Y)
losses[k] = loss.item()
# calculate the average loss
out[split] = losses.mean()
model.train()
return out
class Head(nn.Module):
"""
A single head of self-attention.
Args:
head_size (int): The size of the attention head.
Attributes:
key (nn.Linear): Linear layer for computing the 'key' projection.
query (nn.Linear): Linear layer for computing the 'query' projection.
value (nn.Linear): Linear layer for computing the 'value' projection.
tril (torch.Tensor): Lower triangular mask for masking attention scores.
dropout (nn.Dropout): Dropout layer for regularization.
Methods:
forward(x): Performs the forward pass of the attention head.
Example:
# Create an attention head
head = Head(head_size=128)
# Perform the forward pass
output = head(x)
"""
def __init__(self, head_size):
super().__init__()
# linear layers for key, query, and value projections
self.key = nn.Linear(n_embd, head_size, bias=False)
self.query = nn.Linear(n_embd, head_size, bias=False)
self.value = nn.Linear(n_embd, head_size, bias=False)
# lower triangular mask for masking attention scores
self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
# dropout layer for regularization
self.dropout = nn.Dropout(dropout)
def forward(self, x):
"""
Performs the forward pass of the attention head.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, sequence_length, embedding_size).
Returns:
torch.Tensor: Output tensor after attention computation of shape (batch_size, sequence_length, embedding_size).
"""
_, T, C = x.shape
# compute key, query, and value projections
k = self.key(x) # (B, T, C)
q = self.query(x) # (B, T, C)
# compute attention scores ("affinities")
wei = q @ k.transpose(-2, -1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T)
wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
wei = F.softmax(wei, dim=-1) # (B, T, T)
wei = self.dropout(wei)
# perform weighted aggregation of the values
v = self.value(x) # (B, T, C)
out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)
return out
class MultiHeadAttention(nn.Module):
"""
Multi-head self-attention module.
Args:
num_heads (int): The number of attention heads.
head_size (int): The size of each attention head.
Attributes:
heads (nn.ModuleList): List of attention heads.
proj (nn.Linear): Linear layer for projecting the concatenated heads.
dropout (nn.Dropout): Dropout layer for regularization.
Methods:
forward(x): Performs the forward pass of the multi-head attention module.
Example:
# Create a multi-head attention module
attention = MultiHeadAttention(num_heads=8, head_size=64)
# Perform the forward pass
output = attention(x)
"""
def __init__(self, num_heads, head_size):
"""
Initializes a multi-head attention module.
Args:
num_heads (int): The number of attention heads.
head_size (int): The size of each attention head.
"""
super().__init__()
# list of attention heads
self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
# linear layer for projecting the concatenated heads
self.proj = nn.Linear(n_embd, n_embd)
# dropout layer for regularization
self.dropout = nn.Dropout(dropout)
def forward(self, x):
"""
Performs the forward pass of the multi-head attention module.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, sequence_length, embedding_size).
Returns:
torch.Tensor: Output tensor after the multi-head attention computation of shape (batch_size, sequence_length, embedding_size).
"""
out = torch.cat([h(x) for h in self.heads], dim=-1)
out = self.dropout(self.proj(out))
return out
class FeedForward(nn.Module):
"""
Feed-forward module consisting of linear layers followed by a non-linearity and dropout.
Args:
n_embd (int): The input and output embedding size.
Attributes:
net (nn.Sequential): Sequential module containing linear layers, ReLU activation, and dropout.
Methods:
forward(x): Performs the forward pass of the feed-forward module.
Example:
# Create a feed-forward module
ff_module = FeedForward(n_embd=512)
# Perform the forward pass
output = ff_module(x)
"""
def __init__(self, n_embd):
"""
Initializes a feed-forward module.
Args:
n_embd (int): The input and output embedding size.
"""
super().__init__()
# sequential module containing linear layers, ReLU activation, and dropout
self.net = nn.Sequential(
nn.Linear(n_embd, 4 * n_embd),
nn.ReLU(),
nn.Linear(4 * n_embd, n_embd),
nn.Dropout(dropout),
)
def forward(self, x):
"""
Performs the forward pass of the feed-forward module.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, sequence_length, embedding_size).
Returns:
torch.Tensor: Output tensor after the feed-forward computation of shape (batch_size, sequence_length, embedding_size).
"""
return self.net(x)
class Block(nn.Module):
"""
Transformer block consisting of self-attention and feed-forward layers.
Args:
n_embd (int): The embedding dimension.
n_head (int): The number of attention heads.
Attributes:
sa (MultiHeadAttention): Multi-head self-attention module.
ffwd (FeedForward): Feed-forward module.
ln1 (nn.LayerNorm): Layer normalization module after the self-attention layer.
ln2 (nn.LayerNorm): Layer normalization module after the feed-forward layer.
Methods:
forward(x): Performs the forward pass of the transformer block.
Example:
# Create a transformer block
block = Block(n_embd=512, n_head=8)
# Perform the forward pass
output = block(x)
"""
def __init__(self, n_embd, n_head):
"""
Initializes a Transformer block.
Args:
n_embd (int): The embedding dimension.
n_head (int): The number of attention heads.
"""
super().__init__()
head_size = n_embd // n_head
# multi-head self-attention module
self.sa = MultiHeadAttention(n_head, head_size)
# feed-forward module
self.ffwd = FeedForward(n_embd)
# layer normalization modules
self.ln1 = nn.LayerNorm(n_embd)
self.ln2 = nn.LayerNorm(n_embd)
def forward(self, x):
"""
Performs the forward pass of the transformer block.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, sequence_length, embedding_size).
Returns:
torch.Tensor: Output tensor after the transformer block computation of shape (batch_size, sequence_length, embedding_size).
"""
x = x + self.sa(self.ln1(x))
x = x + self.ffwd(self.ln2(x))
return x
class BigramLanguageModel(nn.Module):
"""
A simple bigram language model based on the Transformer architecture.
Args:
None
Attributes:
token_embedding_table (nn.Embedding): Lookup table for token embeddings.
position_embedding_table (nn.Embedding): Lookup table for position embeddings.
blocks (nn.Sequential): Sequence of Transformer blocks.
ln_f (nn.LayerNorm): Final layer normalization.
lm_head (nn.Linear): Linear layer for language model prediction.
Methods:
__init__():
Initializes the BigramLanguageModel class.
forward(idx, targets=None):
Performs forward pass through the model.
generate(idx, max_new_tokens):
Generates new tokens based on the given context.
"""
def __init__(self):
"""
Initializes the BigramLanguageModel class by setting up the model architecture.
Args:
None
Returns:
None
"""
super().__init__()
self.token_embedding_table = nn.Embedding(enc.n_vocab, n_embd)
self.position_embedding_table = nn.Embedding(block_size, n_embd)
self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
self.ln_f = nn.LayerNorm(n_embd)
self.lm_head = nn.Linear(n_embd, enc.n_vocab)
def forward(self, idx, targets=None):
"""
Performs forward pass through the model.
Args:
idx (torch.Tensor): Input indices tensor of shape (B, T).
targets (torch.Tensor): Target indices tensor of shape (B, T).
Returns:
logits (torch.Tensor): Output logits tensor of shape (B, T, vocab_size).
loss (torch.Tensor or None): Optional loss tensor if targets are provided.
"""
B, T = idx.shape
tok_emb = self.token_embedding_table(idx)
pos_emb = self.position_embedding_table(torch.arange(T, device=device))
x = tok_emb + pos_emb
x = self.blocks(x)
x = self.ln_f(x)
logits = self.lm_head(x)
if targets is None:
loss = None
else:
B, T, C = logits.shape
logits = logits.view(B*T, C)
targets = targets.view(B*T)
loss = F.cross_entropy(logits, targets)
return logits, loss
def generate(self, idx, max_new_tokens):
"""
Generates new tokens based on the given context.
Args:
idx (torch.Tensor): Input indices tensor of shape (B, T).
max_new_tokens (int): Maximum number of new tokens to generate.
Returns:
idx (torch.Tensor): Generated indices tensor of shape (B, T+max_new_tokens).
"""
for _ in range(max_new_tokens):
idx_cond = idx[:, -block_size:]
logits, loss = self(idx_cond)
logits = logits[:, -1, :]
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx
# create instance of the BigramLanguageModel class and assign it to the variable model with default settings
model = BigramLanguageModel()
# move the model to the specified device to ensure that the model and its parameters are stored and
# operated on using the appropriate hardware (e.g., GPU if available) and the modified model is assigned
# to the variable m.
m = model.to(device)
# print the number of parameters in the model
print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')
# create a PyTorch optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
# loop iterates over a specified number of iterations (max_iters)
# used for training the model and performing updates on the parameters
for iter in range(max_iters):
# checks if it's time to evaluate the loss on the training and
# validation sets and it is determined by the value of eval_interval
# or if it's the last iteration (iter == max_iters - 1) and
# the estimate_loss() function is called to compute the losses, and
# then the losses are printed to provide feedback on the model's performance
if iter % eval_interval == 0 or iter == max_iters - 1:
losses = estimate_loss()
print(f'step {iter}: train loss {losses["train"]:.4f}, val loss {losses["val"]:.4f}')
# sample a batch of data (xb) and its corresponding targets (yb)
# from the training set using the get_batch() function
# the returned tensors represent inputs and targets for the model
xb, yb = get_batch('train')
# evaluate the loss with the batch of inputs and targets (xb, yb) is passed
# to the model (model) to obtain the predicted logits and the computed loss
# and the logits represent the model's output probabilities for the next token
# prediction
logits, loss = model(xb, yb)
# set all the gradients of the optimizer's parameters to zero and
# prepare the optimizer for the next iteration to avoid accumulating gradients
# from previous iterations
optimizer.zero_grad(set_to_none=True)
# compute the gradients of the loss with respect to the model's parameters using
# backpropagation and the gradients are used to update the parameters during the
# optimizer's step() operation
loss.backward()
# update the model's parameters based on the computed gradients and the optimization
# algorithm implemented by the optimizer and it performs a step of gradient descent
# to minimize the loss and improve the model's performance
optimizer.step()
# initialize a tensor context with shape (1, 1) filled with zeros and the tensor is
# of type torch.long (representing integer values) and is placed on the specified
# device (such as CPU or GPU) then this tensor is used as the initial context for
# generating new tokens
context = torch.zeros((1, 1), dtype=torch.long, device=device)
# generate a sequence of tokens using the generate method of the BigramLanguageModel
# instance (m) and the method takes the context tensor and a maximum number of new tokens
# to generate (max_new_tokens=2000) and the generated sequence is obtained as a tensor of shape
# (1, T+1) where T is the number of tokens in the generated sequence and the .tolist() method
# converts the tensor to a Python list and the enc.decode() function is then used to decode the
# list of tokens back into human-readable text then it maps the token indices to their
# corresponding string representations based on the encoding used by the model and finally
# the resulting decoded text is printed to the console, displaying the generated sequence of
# tokens as text
print(enc.decode(m.generate(context, max_new_tokens=2000)[0].tolist()))python kgpt.py
cuda
6.686545 M parameters
step 0: train loss 10.9858, val loss 11.0051
step 100: train loss 5.3582, val loss 6.1447
step 200: train loss 4.3341, val loss 5.5531
step 300: train loss 3.4520, val loss 5.3926
step 400: train loss 2.8158, val loss 5.3509
step 499: train loss 2.3219, val loss 5.4989
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