Upload tiny_gpt2.py

model/tiny_gpt2.py

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+# Minimal GPT-2-ish decoder-only LM, written for clarity.
+from dataclasses import dataclass
+import math
+import torch
+import torch.nn as nn
+
+@dataclass
+class GPTConfig:
+    vocab_size: int = 16000
+    n_layer: int = 6
+    n_head: int = 6
+    n_embed: int = 384
+    block_size: int = 256
+    attn_pdrop: float = 0.0
+    resid_pdrop: float = 0.0
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, cfg: GPTConfig):
+        super().__init__()
+        assert cfg.n_embed % cfg.n_head == 0
+        self.n_head = cfg.n_head
+        self.key = nn.Linear(cfg.n_embed, cfg.n_embed, bias=False)
+        self.query = nn.Linear(cfg.n_embed, cfg.n_embed, bias=False)
+        self.value = nn.Linear(cfg.n_embed, cfg.n_embed, bias=False)
+        self.proj = nn.Linear(cfg.n_embed, cfg.n_embed, bias=False)
+        self.attn_drop = nn.Dropout(cfg.attn_pdrop)
+        self.resid_drop = nn.Dropout(cfg.resid_pdrop)
+        self.register_buffer("mask",
+            torch.tril(torch.ones(cfg.block_size, cfg.block_size)).view(1,1,cfg.block_size,cfg.block_size)
+        )
+
+    def forward(self, x):
+        B,T,C = x.size()
+        H = self.n_head
+        k = self.key(x).view(B,T,H,C//H).transpose(1,2)
+        q = self.query(x).view(B,T,H,C//H).transpose(1,2)
+        v = self.value(x).view(B,T,H,C//H).transpose(1,2)
+        att = (q @ k.transpose(-2,-1)) / math.sqrt(k.size(-1))
+        att = att.masked_fill(self.mask[:,:,:T,:T]==0, float("-inf"))
+        att = torch.softmax(att, dim=-1)
+        att = self.attn_drop(att)
+        y = att @ v
+        y = y.transpose(1,2).contiguous().view(B,T,C)
+        y = self.resid_drop(self.proj(y))
+        return y
+
+class Block(nn.Module):
+    def __init__(self, cfg: GPTConfig):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(cfg.n_embed)
+        self.attn = CausalSelfAttention(cfg)
+        self.ln2 = nn.LayerNorm(cfg.n_embed)
+        self.mlp = nn.Sequential(
+            nn.Linear(cfg.n_embed, 4*cfg.n_embed),
+            nn.GELU(),
+            nn.Linear(4*cfg.n_embed, cfg.n_embed),
+            nn.Dropout(cfg.resid_pdrop),
+        )
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+class TinyGPT2(nn.Module):
+    def __init__(self, cfg: GPTConfig):
+        super().__init__()
+        self.cfg = cfg
+        self.tok_emb = nn.Embedding(cfg.vocab_size, cfg.n_embed)
+        self.pos_emb = nn.Embedding(cfg.block_size, cfg.n_embed)
+        self.drop = nn.Dropout(cfg.resid_pdrop)
+        self.blocks = nn.ModuleList([Block(cfg) for _ in range(cfg.n_layer)])
+        self.ln_f = nn.LayerNorm(cfg.n_embed)
+        self.head = nn.Linear(cfg.n_embed, cfg.vocab_size, bias=False)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        if isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens=64, top_k=50, top_p=0.95, temperature=1.0):
+        self.eval()
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -self.cfg.block_size:]
+            logits = self(idx_cond)[:, -1, :] / max(temperature, 1e-5)
+            logits = self._top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
+            probs = torch.softmax(logits, dim=-1)
+            next_id = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat([idx, next_id], dim=1)
+        return idx
+
+    @staticmethod
+    def _top_k_top_p_filtering(logits, top_k=0, top_p=1.0):
+        if top_k and top_k > 0:
+            v, _ = torch.topk(logits, top_k)
+            logits[logits < v[:, [-1]]] = -float("inf")
+        if top_p < 1.0:
+            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+            cumprobs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
+            idx = cumprobs > top_p
+            idx[..., 1:] = idx[..., :-1].clone()
+            idx[..., 0] = 0
+            sorted_logits[idx] = -float("inf")
+            logits.scatter_(1, sorted_indices, sorted_logits)
+        return logits
+
+    def forward(self, idx):
+        B,T = idx.size()
+        pos = torch.arange(0, T, device=idx.device).unsqueeze(0)
+        x = self.tok_emb(idx) + self.pos_emb(pos)
+        x = self.drop(x)
+        for block in self.blocks:
+            x = block(x)
+        x = self.ln_f(x)
+        return self.head(x)