134 lines
4.6 KiB
Python
134 lines
4.6 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
from torch.nn import functional as F
|
|
|
|
# hyperparameters
|
|
batch_size = 32 # how many independent sequences will we process in parallel?
|
|
block_size = 8 # what is the maximum context length for predictions?
|
|
max_iters = 3000
|
|
eval_interval = 300
|
|
learning_rate = 1e-2
|
|
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
|
eval_iters = 200
|
|
n_embd = 16
|
|
# ------------
|
|
|
|
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:]
|
|
|
|
# data loading
|
|
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) - 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
|
|
|
|
@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
|
|
|
|
# super simple bigram model
|
|
class BigramLanguageModel(nn.Module):
|
|
|
|
# 我是孙悟空
|
|
# 空是我悟孙
|
|
|
|
def __init__(self, vocab_size):
|
|
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) # (B,T) --> (B,T,n_embd)
|
|
self.pos_embedding_table = nn.Embedding(block_size, n_embd) # (B,T) --> (B,T,)
|
|
self.head = nn.Linear(n_embd,vocab_size) # (B,T,n_embd) @ (vocab_size,vocab_size) ---> (B,T,vocab_size)
|
|
def forward(self, idx, targets=None):
|
|
|
|
# idx and targets are both (B,T) tensor of integers
|
|
x_emb = self.token_embedding_table(idx) # (B,T) --> (B,T,n_embd)
|
|
# torch.arange(T,device=device) T:8 1,2,3,4,5,6,7,8
|
|
p_emb = self.pos_embedding_table(torch.arange(T,device=device))# (T -> T,n_embd)
|
|
|
|
x = x_emb + p_emb # (B,T,n_embd)
|
|
|
|
logits = self.head(x) # (B,T,n_embd) -> (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):
|
|
# get the predictions
|
|
logits, loss = self(idx)
|
|
# 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
|
|
|
|
model = BigramLanguageModel(vocab_size)
|
|
m = model.to(device)
|
|
|
|
# create a PyTorch optimizer
|
|
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
|
|
|
|
for iter in range(max_iters):
|
|
|
|
# every once in a while evaluate the loss on train and val sets
|
|
if iter % eval_interval == 0:
|
|
losses = estimate_loss()
|
|
print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
|
|
|
|
# sample a batch of data
|
|
xb, yb = get_batch('train')
|
|
|
|
# evaluate the loss
|
|
logits, loss = model(xb, yb)
|
|
optimizer.zero_grad(set_to_none=True)
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
# generate from the model
|
|
context = torch.zeros((1, 1), dtype=torch.long, device=device)
|
|
print(decode(m.generate(context, max_new_tokens=500)[0].tolist()))
|