231 lines
8.1 KiB
Python
231 lines
8.1 KiB
Python
import torch
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import torch.nn as nn
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from torch.nn import functional as F
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# hyperparameters
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batch_size = 64 # how many independent sequences will we process in parallel?
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block_size = 256 # what is the maximum context length for predictions?
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max_iters = 5000
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eval_interval = 10
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learning_rate = 3e-4
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device = 'cuda' #if torch.cuda.is_available() else 'cpu'
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eval_iters = 200
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n_embd = 384
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n_head = 6
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n_layer = 6
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dropout = 0.2
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# ------------
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torch.manual_seed(1337)
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# wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
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with open('input.txt', 'r', encoding='utf-8') as f:
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text = f.read()
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# here are all the unique characters that occur in this text
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chars = sorted(list(set(text)))
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vocab_size = len(chars)
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# create a mapping from characters to integers
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stoi = { ch:i for i,ch in enumerate(chars) }
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itos = { i:ch for i,ch in enumerate(chars) }
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encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
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decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
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# Train and test splits
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data = torch.tensor(encode(text), dtype=torch.long)
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n = int(0.9*len(data)) # first 90% will be train, rest val
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train_data = data[:n]
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val_data = data[n:]
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# data loading
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def get_batch(split):
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# generate a small batch of data of inputs x and targets y
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data = train_data if split == 'train' else val_data
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ix = torch.randint(len(data) - block_size, (batch_size,))
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x = torch.stack([data[i:i+block_size] for i in ix])
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y = torch.stack([data[i+1:i+block_size+1] for i in ix])
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x, y = x.to(device), y.to(device)
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return x, y
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@torch.no_grad()
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def estimate_loss():
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out = {}
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model.eval()
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for split in ['train', 'val']:
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losses = torch.zeros(eval_iters)
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for k in range(eval_iters):
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X, Y = get_batch(split)
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logits, loss = model(X, Y)
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losses[k] = loss.item()
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out[split] = losses.mean()
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model.train()
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return out
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class Head(nn.Module):
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""" one head of self-attention """
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def __init__(self, head_size):
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super().__init__()
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self.key = nn.Linear(n_embd, head_size, bias=False)
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self.query = nn.Linear(n_embd, head_size, bias=False)
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self.value = nn.Linear(n_embd, head_size, bias=False)
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self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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# input of size (batch, time-step, channels)
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# output of size (batch, time-step, head size)
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B,T,C = x.shape
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k = self.key(x) # (B,T,hs)
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q = self.query(x) # (B,T,hs)
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# compute attention scores ("affinities")
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wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)
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wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
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wei = F.softmax(wei, dim=-1) # (B, T, T)
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wei = self.dropout(wei)
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# perform the weighted aggregation of the values
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v = self.value(x) # (B,T,hs)
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out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)
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return out
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class MultiHeadAttention(nn.Module):
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""" multiple heads of self-attention in parallel """
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def __init__(self, num_heads, head_size):
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super().__init__()
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self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
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self.proj = nn.Linear(head_size * num_heads, n_embd)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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out = torch.cat([h(x) for h in self.heads], dim=-1)
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out = self.dropout(self.proj(out))
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return out
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class FeedFoward(nn.Module):
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""" a simple linear layer followed by a non-linearity """
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def __init__(self, n_embd):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(n_embd, 4 * n_embd),
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nn.ReLU(),
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nn.Linear(4 * n_embd, n_embd),
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nn.Dropout(dropout),
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)
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def forward(self, x):
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return self.net(x)
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class Block(nn.Module):
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""" Transformer block: communication followed by computation """
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def __init__(self, n_embd, n_head):
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# n_embd: embedding dimension, n_head: the number of heads we'd like
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super().__init__()
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head_size = n_embd // n_head
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self.sa = MultiHeadAttention(n_head, head_size)
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self.ffwd = FeedFoward(n_embd)
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self.ln1 = nn.LayerNorm(n_embd)
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self.ln2 = nn.LayerNorm(n_embd)
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def forward(self, x):
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x = x + self.sa(self.ln1(x))
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x = x + self.ffwd(self.ln2(x))
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return x
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class GPTLanguageModel(nn.Module):
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def __init__(self):
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super().__init__()
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# each token directly reads off the logits for the next token from a lookup table
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self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
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self.position_embedding_table = nn.Embedding(block_size, n_embd)
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self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
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self.ln_f = nn.LayerNorm(n_embd) # final layer norm
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self.lm_head = nn.Linear(n_embd, vocab_size)
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# better init, not covered in the original GPT video, but important, will cover in followup video
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self.apply(self._init_weights)
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def _init_weights(self, module):
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if isinstance(module, nn.Linear):
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torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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if module.bias is not None:
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torch.nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Embedding):
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torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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def forward(self, idx, targets=None):
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B, T = idx.shape
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# idx and targets are both (B,T) tensor of integers
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tok_emb = self.token_embedding_table(idx) # (B,T,C)
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pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
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x = tok_emb + pos_emb # (B,T,C)
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x = self.blocks(x) # (B,T,C)
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x = self.ln_f(x) # (B,T,C)
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logits = self.lm_head(x) # (B,T,vocab_size)
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if targets is None:
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loss = None
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else:
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B, T, C = logits.shape
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logits = logits.view(B*T, C)
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targets = targets.view(B*T)
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loss = F.cross_entropy(logits, targets)
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return logits, loss
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def generate(self, idx, max_new_tokens):
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# idx is (B, T) array of indices in the current context
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for _ in range(max_new_tokens):
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# crop idx to the last block_size tokens
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idx_cond = idx[:, -block_size:]
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# get the predictions
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logits, loss = self(idx_cond)
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# focus only on the last time step
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logits = logits[:, -1, :] # becomes (B, C)
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# apply softmax to get probabilities
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probs = F.softmax(logits, dim=-1) # (B, C)
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# sample from the distribution
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idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
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# append sampled index to the running sequence
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idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
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return idx
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model = GPTLanguageModel()
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m = model.to(device)
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# print the number of parameters in the model
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print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')
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# create a PyTorch optimizer
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optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
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for iter in range(max_iters):
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#print((iter / max_iters * 100) + "%")
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# every once in a while evaluate the loss on train and val sets
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""" if iter > 0:
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if iter % eval_interval == 0 or iter == max_iters - 1:
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losses = estimate_loss()
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print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}") """
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if iter % eval_interval == 0 or iter == max_iters - 1:
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print(f"{(iter / max_iters * 100)}%")
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# sample a batch of data
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xb, yb = get_batch('train')
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# evaluate the loss
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logits, loss = model(xb, yb)
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optimizer.zero_grad(set_to_none=True)
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loss.backward()
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optimizer.step()
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# generate from the model
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context = torch.zeros((1, 1), dtype=torch.long, device=device)
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print(decode(m.generate(context, max_new_tokens=500)[0].tolist()))
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#open('more.txt', 'w').write(decode(m.generate(context, max_new_tokens=10000)[0].tolist()))
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