Upload PresGPT2.py

PresGPT2.py

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+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+# parameters for GPT-2 
+class GPTConfig:
+    def __init__(self, block_size, vocab_size, n_layers, n_heads, n_embd):
+        self.block_size = block_size
+        self.vocab_size = vocab_size
+        self.n_layers = n_layers
+        self.n_heads = n_heads
+        self.n_embd = n_embd
+    
+""" one head of self-attention """
+class Head(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        
+        head_size = config.n_embd // config.n_heads
+        
+        self.key = nn.Linear(config.n_embd, head_size, bias=False)
+        self.query = nn.Linear(config.n_embd, head_size, bias=False)
+        self.value = nn.Linear(config.n_embd, head_size, bias=False)
+        self.register_buffer('tril', torch.tril(torch.ones(config.block_size, config.block_size)))  
+        
+    def forward(self, x):
+        _,T,_ = x.shape
+
+        k = self.key(x)
+        q = self.query(x)
+        
+        # transpose last two dimensions
+        wei = q @ k.transpose(-2,-1) * (k.shape[-1]**-0.5)
+
+        # mask out bottom half
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
+
+        # softmax each column
+        wei = F.softmax(wei, dim=-1)
+
+        v = self.value(x)
+
+        return wei @ v
+    
+class MultiHeadAttention(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        self.heads = nn.ModuleList([Head(config) for _ in range(config.n_heads)])
+        self.proj = nn.Linear(config.n_embd,config. n_embd)
+        self.proj.STD_SCALE_INIT = 1
+        
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1)
+        return self.proj(out)
+        
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.n_embd, 4*config.n_embd)
+        self.gelu = nn.GELU(approximate='tanh')
+        self.c_proj = nn.Linear(4*config.n_embd, config.n_embd)
+        self.c_proj.STD_SCALE_INIT = 1
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        return self.c_proj(x)
+
+class Block(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = MultiHeadAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+        
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+        
+class PresGPT2(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        self.config = config
+        
+        # stop immediately if not a multiple
+        assert config.n_embd % config.n_heads == 0
+        
+        self.transformer = nn.ModuleDict(
+            dict(
+                wte = nn.Embedding(config.vocab_size, config.n_embd),
+                wpe = nn.Embedding(config.block_size, config.n_embd),
+                h = nn.ModuleList([Block(config) for _ in range(config.n_layers)]),
+                ln_f = nn.LayerNorm(config.n_embd)
+            )
+        )
+        
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        
+        # weight tying intuition: if inputs are encoded similar, outputs should be similar
+        self.lm_head.weight = self.transformer.wte.weight
+        
+        self.apply(self.__init_weights)
+        
+    def __init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            std = 0.02
+            
+            if hasattr(module, 'STD_SCALE_INIT'):
+                # 2 times since each Block has two residual sums
+                std *= (2*self.config.n_layers)**-0.5
+            
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        
+    def forward(self, X, y=None):
+        B,T = X.shape
+        
+        tok_emb = self.transformer.wte(X) # -> (B, T, config.n_embd)
+        
+        pos = torch.arange(0, T, 1, dtype=torch.long, device=X.device) # put it on the same device as X
+        
+        pos_emb = self.transformer.wpe(pos)
+        
+        x = pos_emb + tok_emb
+        
+        for b in self.transformer.h:
+            x = b(x)
+            
+        x = self.transformer.ln_f(x)
+        
+        loss = None
+        logits = self.lm_head(x) # B x T x vocab_size
+        
+        if y is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), y.view(-1))
+            
+        return logits, loss