model file

model.py

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+import time
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+# hyperparameters
+batch_size = 8
+block_size = 2048
+eval_interval = 500
+learning_rate = 3e-4
+device = 'cuda:1' if torch.cuda.is_available() else 'cpu'
+eval_iters = 200
+n_embd = 784
+n_head = 12
+n_layer = 12
+dropout = 0.1
+
+# Reserved memory allocation for H100 GPU
+if torch.cuda.is_available():
+    torch.cuda.set_device(device)
+    torch.cuda.empty_cache()
+
+# Mixed precision training setup
+scaler = torch.cuda.amp.GradScaler()
+
+torch.manual_seed(1337)
+
+with open('input.txt', 'r', encoding='utf-8') as f:
+    text = f.read()
+
+chars = sorted(list(set(text)))
+vocab_size = 50257
+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]
+decode = lambda l: ''.join([itos[i] for i in l])
+
+data = torch.tensor(encode(text), dtype=torch.long)
+n = int(0.9 * len(data))
+train_data = data[:n]
+val_data = data[n:]
+
+def get_batch(split):
+    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()
+    eval_start_time = time.time()
+    for split in ['train', 'val']:
+        losses = torch.zeros(eval_iters)
+        for k in range(eval_iters):
+            X, Y = get_batch(split)
+            with torch.cuda.amp.autocast():
+                logits, loss = model(X, Y)
+            losses[k] = loss.item()
+        out[split] = losses.mean()
+    eval_time = time.time() - eval_start_time
+    print(f"Evaluation time: {eval_time:.2f} seconds")
+    model.train()
+    return out
+
+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)
+        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
+
+class GPTLanguageModel(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)
+
+        # better init, not covered in the original GPT video, but important, will cover in followup video
+        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.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    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
+
+model = GPTLanguageModel()
+m = model.to(device)
+# print the number of parameters in the model
+print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')
+
+# optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
+
+# training_start_time = time.time()
+
+# iter = 0
+# print("Initializing training...")
+
+# while True:
+
+#     # Evaluate losses at evaluation intervals
+#     if iter % eval_interval == 0:
+#         losses = estimate_loss()
+#         print(f"Step {iter}: train loss = {losses['train']:.4f}, val loss = {losses['val']:.4f}")
+
+#     # Stop training if train loss is below the threshold
+#     if losses['train'] < 0.099999:
+#         print(f"Step {iter}: train loss = {losses['train']:.4f}, val loss = {losses['val']:.4f}")
+#         print("Training Loss is less than 0.099999. Stopping training.")
+
+#         model_save_path = 'model.pth'
+#         torch.save(model.state_dict(), model_save_path)
+#         print(f"Model saved to {model_save_path}")
+
+#         torch.save(optimizer.state_dict(), 'optimizer.pth')
+#         print("Optimizer state saved.")
+    
+#         break
+
+#     # Fetch training batch
+#     xb, yb = get_batch('train')
+
+#     # Start iteration timing
+#     iter_start_time = time.time()
+
+#     # Forward pass with mixed precision
+#     with torch.amp.autocast('cuda'):
+#         logits, loss = model(xb, yb)
+
+#     # Backward pass and optimization
+#     optimizer.zero_grad()
+#     scaler.scale(loss).backward()
+#     scaler.step(optimizer)
+#     scaler.update()
+
+#     # Log every 50 iterations
+#     if iter % 50 == 0:
+#         iter_time = time.time() - iter_start_time
+#         print(f"Iteration {iter}: loss = {loss.item():.4f}, time = {iter_time:.2f} seconds")
+
+#     # Increment iteration counter
+#     iter += 1
+
+# # Log total training time
+# training_time = time.time() - training_start_time
+# print(f"Total training time: {training_time:.2f} seconds")
+
+# Generate text from the model
+context = torch.zeros((1, 1), dtype=torch.long, device=device)
+# print("Generated text:")
+# print(decode(model.generate(context, max_new_tokens=500)[0].tolist()))