Bigram Model with Linear layer & Token + Positional embeddings#
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Context window (block size) = 8
This model looks at 8 past tokens to predicts 1 future token
Two trainable embedding tables at input.
Token embedding table maps each token into a vector of size (32), giving a (8, 32) matrix
Position embedding table maps the positions 0-31 into a vector of size (32)
These are added together to informs the model of the positions of input tokens
import torch
import torch.nn as nn
from torch.nn import functional as F
Hyperparameters#
B = 32 # B (batch size): how many independent sequences will we process in parallel?
T = 1 # T (block size): what is the maximum context length for predictions?
C = 32 # C (channels) : dimensionality, also called d
max_iters = 3000
eval_interval = 300
learning_rate = 1e-2
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
torch.manual_seed(1337)
<torch._C.Generator at 0x7e71f812e250>
Dataset#
!wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
--2024-06-09 01:38:46-- https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1115394 (1.1M) [text/plain]
Saving to: ‘input.txt.4’
input.txt.4 0%[ ] 0 --.-KB/s
input.txt.4 100%[===================>] 1.06M --.-KB/s in 0.06s
2024-06-09 01:38:46 (18.6 MB/s) - ‘input.txt.4’ saved [1115394/1115394]
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
chars_str = ''.join(chars)
print(f'vocab_size: {vocab_size}')
print(f'vocabulary: {chars_str}')
# 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:]
def get_batch(split):
# generate a small batch of data of inputs x and targets y
data = train_data if split == 'train' else val_data
ix = torch.randint(len(data) - T, (T,))
x = torch.stack([data[i:i+T] for i in ix])
y = torch.stack([data[i+1:i+T+1] for i in ix])
x, y = x.to(device), y.to(device)
return x, y
vocab_size: 65
vocabulary:
!$&',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
Bigram Model with Linear layer & token/pos embeddings#
class BigramLanguageModel(nn.Module):
def __init__(self, B, T ,C):
super().__init__()
self.B, self.T, self.C = B, T, C
# each token directly reads off the logits for the next token from a lookup table
self.token_embedding_table = nn.Embedding(vocab_size, C) # for every possible token, weights for next token
self.position_embedding_table = nn.Embedding(B, C)
self.lm_head = nn.Linear(C, vocab_size)
def forward(self, idx, targets=None):
tok_emb = self.token_embedding_table(idx) # (B,T,C)
pos_emb = self.position_embedding_table(torch.arange(self.T, device=device)) # (T,C): [0,1,2..T-1]
'''
B - batch # of independant vectors processed
T - time/block/context # of tokens in a context
C - channels # of features
'''
x = tok_emb + pos_emb # (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):
for _ in range(max_new_tokens): # idx is (B, T) array of indices in the current context
idx_cond = idx[:, -self.B:] # crop the last block_size tokens for input
logits, loss = self(idx_cond) # get the predictions
logits = logits[:, -1, :] # (B,T,C) -> (B, C)
probs = F.softmax(logits, dim=-1) # (B, C)
idx_next = torch.multinomial(probs, num_samples=1) # sample from the distribution acc to prob (B, 1)
idx = torch.cat((idx, idx_next), dim=1) # New idx is concat (B, T+1)
return idx
model = BigramLanguageModel(B, T ,C)
m = model.to(device)
Training#
@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
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
for iter in range(max_iters):
if iter % eval_interval == 0: # every once in a while evaluate the loss on train and val sets
losses = estimate_loss()
print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
xb, yb = get_batch('train') # sample a batch of data
# evaluate the loss
logits, loss = model(xb, yb)
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
step 0: train loss 4.3337, val loss 4.4599
step 300: train loss 3.2863, val loss 3.2765
step 600: train loss 3.2917, val loss 3.0822
step 900: train loss 3.1424, val loss 3.0526
step 1200: train loss 3.0914, val loss 3.0241
step 1500: train loss 3.2469, val loss 2.9623
step 1800: train loss 2.8173, val loss 2.8259
step 2100: train loss 2.8679, val loss 2.9978
step 2400: train loss 2.9930, val loss 3.0948
step 2700: train loss 2.9934, val loss 2.9553
Inference#
context = torch.ones((1, B), dtype=torch.long, device=device) # start with '\n\n\n\n' as seed
out_ints = m.generate(context, max_new_tokens=500)[0].tolist() # output list of ints
print(decode(out_ints))
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