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config.json

@@ -3,8 +3,8 @@
     "SimpleStories4MModel"
   ],
   "auto_map": {
-    "AutoConfig": "config_4m.SimpleStories4MConfig",
-    "AutoModelForCausalLM": "model_4m.SimpleStories4MModel"
+    "AutoConfig": "configuration_ss4m.SimpleStories4MConfig",
+    "AutoModelForCausalLM": "modeling_ss4m.SimpleStories4MModel"
   },
   "block_size": 1080,
   "dropout": 0.1,

nano_gpt_model.py

@@ -0,0 +1,134 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# model architecture
+class AttentionHead(nn.Module):
+  """a single head of self attention"""
+  
+  def __init__(self, n_embed, head_size, block_size, dropout):
+    super().__init__()
+    self.key = nn.Linear(n_embed, head_size, bias=False)
+    self.query = nn.Linear(n_embed, head_size, bias=False)
+    self.value = nn.Linear(n_embed, 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):
+    B, T, C = x.shape
+    K = self.key(x) # (B, T, C)
+    Q = self.query(x) # (B, T, C)
+    
+    wei = Q @ K.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, H, C) -> (B, T, T)
+    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) # (B, T, C)
+    out = wei @ V # (B, T, T) @ (B, T, C) -> (B, T, C)
+    return out
+  
+class MultiHeadAttention(nn.Module):
+  """a multi-head self attention layer"""
+  
+  def __init__(self, n_embed, n_heads, head_size, block_size, dropout):
+    super().__init__()
+    self.heads = nn.ModuleList([AttentionHead(n_embed, head_size, block_size, dropout) for _ in range(n_heads)])
+    self.fc = nn.Linear(head_size * n_heads, n_embed)
+    self.dropout = nn.Dropout(dropout)
+    
+  def forward(self, x):
+    out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, n_heads*C)    
+    out = self.fc(out) # (B, T, C)
+    out = self.dropout(out) 
+    return out
+  
+class FeedForward(nn.Module):
+  def __init__(self, n_embed, n_hidden, dropout):
+    super().__init__()
+    self.net = nn.Sequential(
+      nn.Linear(n_embed, n_hidden),
+      nn.ReLU(),
+      nn.Linear(n_hidden, n_embed),
+      nn.Dropout(dropout)
+    )
+    
+  def forward(self, x):
+   return self.net(x)
+  
+class Block(nn.Module):
+  def __init__(self, n_embed, n_heads, block_size, dropout):
+    super().__init__()
+    self.sa_heads = MultiHeadAttention(n_embed, n_heads, n_embed // n_heads, block_size, dropout)
+    self.ffwd = FeedForward(n_embed, n_embed*4, dropout)
+    self.ln1 = nn.LayerNorm(n_embed)
+    self.ln2 = nn.LayerNorm(n_embed)
+    
+    
+  def forward(self, x):
+    x = x + self.sa_heads(self.ln1(x)) #  [batch_size, block_size, n_embed]
+    x = x + self.ffwd(self.ln2(x)) # [batch_size, block_size, n_embed]
+    return x
+
+class NanoGPT(nn.Module):
+  def __init__(self, hyperparameters, device="cpu"):
+    super().__init__()
+    
+    # hyperparameters
+    vocab_size = hyperparameters['vocab_size']
+    block_size = hyperparameters['block_size']
+    n_embed = hyperparameters['n_embed']
+    n_heads = hyperparameters['n_heads']
+    n_layers = hyperparameters['n_layers']
+    dropout = hyperparameters['dropout']
+    
+    self.token_embedding_table = nn.Embedding(vocab_size, n_embed)
+    self.position_embedding_table = nn.Embedding(block_size, n_embed)
+    self.blocks = nn.Sequential(*[Block(n_embed, n_heads, block_size, dropout) for _ in range(n_layers)])
+    self.ln_f = nn.LayerNorm(n_embed)
+    self.lm_head = nn.Linear(n_embed, vocab_size)
+    
+    self.device = device
+    self.block_size = block_size
+      
+  def forward(self, idx, targets=None):
+    # idx and target are both [batch_size, block_size]
+    B, T = idx.shape
+    
+    tok_emb = self.token_embedding_table(idx) # [batch_size, block_size, n_embed]
+    pos_emb = self.position_embedding_table(torch.arange(T, device=self.device)) # [block_size, n_embed]
+    x = tok_emb + pos_emb # [batch_size, block_size, n_embed]
+    x = self.blocks(x)
+    x = self.ln_f(x)
+    logits = self.lm_head(x) # [batch_size, block_size, 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
+    # return 0, 0
+      
+  def generate(self, idx, max_new_tokens=100):
+    # idx is (B, T)
+    for _ in range(max_new_tokens):
+      # get the last block_size tokens
+      idx_cond = idx[:, -self.block_size:] # (B, T)
+      # get the predictions
+      logits, _ = 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