model bilding function

model.py

@@ -0,0 +1,169 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+batch_size = 32
+block_size = 128
+max_iters = 1000
+learning_rate = 3e-4
+eval_steps = 200
+n_embd = 384
+n_head = 4
+n_layer = 4
+dropout = 0.2
+
+
+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):
+        y = self.sa(x)
+        x = self.ln1(x + y)
+        y = self.ffwd(x)
+        x = self.ln2(x + y)
+        return x
+
+
+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):
+        # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3])
+        out = torch.cat([h(x) for h in self.heads], dim=-1)
+        out = self.dropout(self.proj(out))
+        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))
+        )  # noqa 5501
+
+        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) # noqa 5501
+        wei = wei.masked_fill(
+            self.tril[:T, :T] == 0, float("-inf")
+        )  # (B, T, T) # noqa 5501
+        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 FeedFoward(nn.Module):
+    """Simple linear layer followed by non_linear layer"""
+
+    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 GPTLanguageModel(nn.Module):
+    def __init__(self, vocab_size, device):
+        super().__init__()
+        self.device = device
+        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)]
+        )  # noqa 5501
+        self.ln_f = nn.LayerNorm(n_embd)
+        self.lm_head = nn.Linear(n_embd, vocab_size)
+
+        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, index, targets=None):
+        B, T = index.shape
+
+        # idx and targets are both (B,T) tensor of integers
+        tok_emb = self.token_embedding_table(index)  # (B,T,C)
+        pos_emb = self.position_embedding_table(
+            torch.arange(T, device=self.device)
+        )  # (T,C) # noqa 5501
+        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, index, max_new_tokens):
+        # index is (B, T) array of indices in the current context
+        for _ in range(max_new_tokens):
+            # crop idx to the last block_size tokens
+            index_cond = index[:, -block_size:]
+            # get the predictions
+            logits, loss = self.forward(index_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
+            index_next = torch.multinomial(probs, num_samples=1)  # (B, 1)
+            # append sampled index to the running sequence
+            index = torch.cat((index, index_next), dim=1)  # (B, T+1)
+        return index
+
+
+def create_GPT_model(vocab_size, device):
+    model = GPTLanguageModel(vocab_size=vocab_size, device=device)
+    model = model.to(device)
+    return model