model.py

"""
Full definition of a GPT Language Model, all of it in this single file.
"""

import math
import inspect
from dataclasses import dataclass

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



# hyperparameters
batch_size = 16 # how many independent sequences will we process in parallel?
block_size = 32 # what is the maximum context length for predictions?
max_iters = 5000
eval_interval = 100
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_embd = 64
n_head = 4
n_layer = 4
dropout = 0.0
# ------------

torch.manual_seed(1337)

# wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
with open('input.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# here are all the unique characters that occur in this text
chars = sorted(list(set(text)))
vocab_size = len(chars)
# create a mapping from characters to integers
stoi = { ch:i for i,ch in enumerate(chars) }
itos = { i:ch for i,ch in enumerate(chars) }
encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string

# Train and test splits
data = torch.tensor(encode(text), dtype=torch.long)
n = int(0.9*len(data)) # first 90% will be train, rest val
train_data = data[:n]
val_data = data[n:]

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):
          B,T,C = x.shape
          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 the 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):
      """ 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(n_embd, n_embd)
          self.dropout = nn.Dropout(dropout)

      def forward(self, x):
          out = torch.cat([h(x) for h in self.heads], dim=-1)
          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):
          x = x + self.sa(self.ln1(x))
          x = x + self.ffwd(self.ln2(x))
          return x

  # super simple bigram model
class BigramLanguageModel(nn.Module):

      def __init__(self):
          super().__init__()
          # each token directly reads off the logits for the next token from a lookup table
          self.token_embedding_table = nn.Embedding(vocab_size, 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) # final layer norm
          self.lm_head = nn.Linear(n_embd, vocab_size)

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

          # idx and targets are both (B,T) tensor of integers
          tok_emb = self.token_embedding_table(idx) # (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)
          x = self.ln_f(x) # (B,T,C)
          logits = self.lm_head(x) # (B,T,vocab_size)

          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):
          # idx is (B, T) array of indices in the current context
          for _ in range(max_new_tokens):
              # crop idx to the last block_size tokens
              idx_cond = idx[:, -block_size:]
              # get the predictions
              logits, loss = self(idx_cond)
              # 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
              idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
              # append sampled index to the running sequence
              idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
          return idx