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data/meta.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6ee5a37533af83b67fcbe6b93705fde9e15e78bafe895f54b2cb2cb32534526c
+size 703

model.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dataclasses import dataclass
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+
+class Head(nn.Module):
+    """ one head of self-attention """
+    def __init__(self, config, head_size):
+        super().__init__()
+        self.key = nn.Linear(config.n_embed, head_size, bias=False)
+        self.query = nn.Linear(config.n_embed, head_size, bias=False)
+        self.value = nn.Linear(config.n_embed, head_size, bias=False)
+        self.register_buffer('tril', torch.tril(torch.ones(config.block_size, config.block_size)))
+
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        B, T, C = x.shape
+        k = self.key(x) #(B, T, head_size)
+        q = self.query(x) #(B, T, head_size)
+        wei = q @ k.transpose(-2, -1) * C**-0.5 #
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
+        wei = F.softmax(wei, dim=-1)
+        wei = self.dropout(wei)
+        v = self.value(x)
+        out = wei @ v
+        return out
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, config, head_size):
+        super().__init__()
+        self.heads = nn.ModuleList([Head(config, head_size) for _ in range(config.n_head)])
+        self.proj = nn.Linear(config.n_embed, config.n_embed)
+        self.dropout = nn.Dropout(config.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 FeedForward(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(config.n_embed, 4 * config.n_embed),
+            nn.ReLU(),
+            nn.Linear(4 * config.n_embed, config.n_embed),
+            nn.Dropout(config.dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        head_size = config.n_embed // config.n_head
+        self.sa = MultiHeadAttention(config, head_size)
+        self.ffwd = FeedForward(config)
+        self.ln1 = nn.LayerNorm(config.n_embed)
+        self.ln2 = nn.LayerNorm(config.n_embed)
+
+    def forward(self, x):
+        x = x + self.sa(self.ln1(x))
+        x = x + self.ffwd(self.ln2(x))
+        return x
+
+@dataclass
+class ModelConfig:
+    block_size: int = 256
+    vocab_size: int = 50304
+    n_layer: int = 6
+    n_head: int = 6
+    n_embed: int = 384
+    dropout: float = 0.2
+
+class BigramLanguageModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.vocab_size is not None
+        assert config.block_size is not None
+        self.config = config
+        
+        self.token_embedding_table = nn.Embedding(config.vocab_size, config.n_embed)
+        self.position_embedding_table = nn.Embedding(config.block_size, config.n_embed)
+        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
+        # nn.Sequential(
+        #     Block(n_embed, n_head=4),
+        #     Block(n_embed, n_head=4),
+        #     Block(n_embed, n_head=4),
+        #     nn.LayerNorm(n_embed),
+        # )
+        self.ln_f = nn.LayerNorm(config.n_embed) # final layer norm
+        # self.sa_heads = MultiHeadAttention(4, n_embed//4) # 4 of 8 dimensional self attention
+        # self.ffwd = FeedForward(n_embed)
+        self.lm_head = nn.Linear(config.n_embed, config.vocab_size)
+
+    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 = channels)
+        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) #(T, C)
+        x = tok_emb + pos_emb #(B, T, C)
+        # x = self.sa_heads(x) #apply one self attention head
+        # x = self.ffwd(x)
+        x = self.blocks(x)
+        x = self.ln_f(x)
+        logits = self.lm_head(x) #(B, T, Cw)
+
+        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
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        # idx is (B, T) array of indices in the current context
+        for _ in range(max_new_tokens):
+            # crop idx to last block_size token
+            idx_cond = idx[:, -self.config.block_size:]
+            # get the predictions
+            logits, loss = self(idx_cond)
+            # focus only on the last time step
+            logits = logits[:, -1, :] / temperature # becomes (B, C)
+            # optionally crop the logits to only the top k options
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')            
+            # apply softmax to get probabilities
+            probs = F.softmax(logits, dim=-1)
+            # sample from the distribution
+            idx_next = torch.multinomial(probs, num_samples=1)
+            # append sampled index to the running sequence
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx

saved_model/ckpt.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dd5c2d495d9f48692ebd0620d2b419c537dcb04fac36ddd62b36dc7f25debd9e
+size 161902