training.py

# imports
import torch
import torch.nn as nn
from torch.nn import functional as F
import random
import mmap
import pickle
import argparse

# defining hyperparameters
block_size = 32
batch_size = 128
max_iters = 3000
learning_rate = 2e-5
eval_iters = 100
eval_interval = 100
n_embd = 384 # how long the embedded vector will be
n_layer = 8 # number of decoder blocks
n_head = 8
dropout = 0.2 # dropout random neurons at a rate of 20% so that the model doesnt overfit

# implementing parallelism
device = 'cuda' if torch.cuda.is_available() else 'cpu' #using mps due to apple silicon restrictions
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") # using Metal Performance Shaders(mps) for apple silicon devices
print(device)

# Adding the argparser to parse arguments
# parser = argparse.ArgumentParser(description='demo')
# parser.add_argument('-batch_size', type=str, required=True, help='Please provide a batch size')

# args = parser.parse_args()

# # now argument value is usable in our program
# print(f'batch size: {args.batch_size}')
# batch_size = args.batch_size

chars = ""

# Read the vocab file and collect characters
with open("data/vocab.txt", 'r', encoding='utf-8') as f:
    text = f.read()
    chars = sorted(list(set(text)))

# Ensure common special characters are included
# If you expect any additional special characters, add them here.
additional_chars = [' ', '\n', '\t']

# Merge additional characters with those found in vocab.txt
for char in additional_chars:
    if char not in chars:
        chars.append(char)

# Sort the characters list again after adding new characters due to errors
chars = sorted(chars)

vocab_size = len(chars)
print(chars)

string_to_int = { ch:i for i,ch in enumerate(chars) }
int_to_string = { i:ch for i,ch in enumerate(chars) }
encode = lambda s: [string_to_int[c] for c in s]
decode = lambda l: ''.join([int_to_string[i] for i in l])

# memory map for using small snippets of text from a single file of any size
def get_random_chunk(split):
    filename = "data/output_train_0.txt" if split == 'train' else "data/output_val_0.txt"
    with open(filename, 'rb') as f:
        with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:
            # Determine the file size and a random position to start reading
            file_size = len(mm)
            start_pos = random.randint(0, (file_size) - block_size*batch_size)

            # Seek to the random position and read the block of text
            mm.seek(start_pos)
            block = mm.read(block_size*batch_size-1)

            # Decode the block to a string, ignoring any invalid byte sequences
            decoded_block = block.decode(
                'utf-8', errors='ignore').replace('\r', '')

            # Train and test splits
            data = torch.tensor(encode(decoded_block), dtype=torch.long)

    return data

def get_batch(split):
    data = get_random_chunk(split)
    ix = torch.randint(len(data) - block_size, (batch_size,))
    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])
    x, y = x.to(device), y.to(device)
    return x, y

class Head(nn.Module):
    """ one head of self-attention """

    def __init__(self, head_size):
        super().__init__()
        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)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))

        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        # input of size (batch, time-step, channels)
        # output of size (batch, time-step, head size)
        B,T,C = x.shape
        k = self.key(x)   # (B,T,hs)
        q = self.query(x) # (B,T,hs)
        # compute attention scores ("affinities")
        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, 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 the weighted aggregation of the values
        v = self.value(x) # (B,T,hs)
        out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)
        return out
    
class MultiHeadAttention(nn.Module):
    """ multiple heads of self-attention in parallel """

    def __init__(self, num_heads, head_size):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
        self.proj = nn.Linear(head_size * num_heads, n_embd)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3])
        out = self.dropout(self.proj(out))
        return out
    
class FeedFoward(nn.Module):
    """ a simple linear layer followed by a non-linearity """

    def __init__(self, n_embd):
        super().__init__()
        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):
        return self.net(x)
    
class Block(nn.Module):
    """ Transformer block: communication followed by computation """

    def __init__(self, n_embd, n_head):
        # n_embd: embedding dimension, n_head: the number of heads we'd like
        super().__init__()
        head_size = n_embd // n_head
        self.sa = MultiHeadAttention(n_head, head_size)
        self.ffwd = FeedFoward(n_embd)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)

    def forward(self, x):
        y = self.sa(x)
        x = self.ln1(x + y)
        y = self.ffwd(x)
        x = self.ln2(x + y)
        return x
    
class GPTLanguageModel(nn.Module):
    def __init__(self, vocab_size):
        super().__init__()
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(
            block_size, n_embd)  # Adding positional embedding
        # Number of decoder blocks we have running sequentially
        self.blocks = nn.Sequential(
            *[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)  # final layer normalization
        # transforming the decoder output to make it workable with softmax
        self.lm_head = nn.Linear(n_embd, vocab_size)
        self.apply(self._init_weights)

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

    def forward(self, index, targets=None):
        B, T = index.shape

        # idx and targets are both (B, T) tensor of integers
        tok_emb = self.token_embedding_table(index)  # (B, T, C)
        pos_emb = self.position_embedding_table(
            torch.arange(T, device=device))  # (T, C)
        x = tok_emb + pos_emb  # (B, T, C)
        x = self.blocks(x)  # (B, T, C)
        logits = self.lm_head(x)  # (B, T, vocab_size)
        B, T, C = logits.shape

        if targets is None:
            loss = None
        else:
            logits = logits.view(B*T, C)
            targets = targets.view(B*T)
            loss = F.cross_entropy(logits, targets)

        return logits, loss  # Return both logits and loss

    def generate(self, index, max_new_tokens):
        # index is (B, T) array of indices in the current context
        for _ in range(max_new_tokens):
            # get the predictions
            logits, _ = self.forward(index)  # Unpack logits and ignore loss
            # focus only on the last time step
            logits = logits[:, -1, :]  # becomes (B, C)
            # apply softmax to get probabilities
            probs = F.softmax(logits, dim=-1)  # (B, C)
            # sample from the distribution
            index_next = torch.multinomial(probs, num_samples=1)  # (B, 1)
            # append sampled index to the running sequence
            index = torch.cat((index, index_next), dim=1)  # (B, T+1)

        return index
    
model = GPTLanguageModel(vocab_size)
print('loading model parameters...')
with open('model-01.pkl', 'rb') as f:
    model = pickle.load(f)
print('Loaded successfully')
m = model.to(device)

@torch.no_grad()
def estimate_loss():
    out = {}
    model.eval()
    for split in ['train', 'val']:
        losses = torch.zeros(eval_iters)
        for k in range(eval_iters):
            X, Y = get_batch(split)
            logits, loss = model(X, Y)
            losses[k] = loss.item()
        out[split] = losses.mean()
    model.train()
    return out

# create a PyTorch optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)

for iter in range(max_iters):
    print(iter)
    if iter % eval_iters == 0:
        losses = estimate_loss()
        print(f"step: {iter}, train loss: {losses['train']:.3f}, val loss: {losses['val']:.3f}")

    # sample a batch of data
    xb, yb = get_batch('train')

    # evaluate the loss
    logits, loss = model.forward(xb, yb)
    optimizer.zero_grad(set_to_none=True)
    loss.backward()
    optimizer.step()
print(loss.item())

# saving the trained model
with open('model-01.pkl', 'wb') as f:
    pickle.dump(model, f)
print('model saved')