Add Pluto model implementation

model.py

@@ -0,0 +1,154 @@
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+@dataclass
+class GPTConfig:
+    vocab_size: int = 50257
+    block_size: int = 256     
+    n_layer: int = 6
+    n_head: int = 6
+    n_embd: int = 384
+    dropout: float = 0.1
+    bias: bool = False
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, cfg: GPTConfig):
+        super().__init__()
+        assert cfg.n_embd % cfg.n_head == 0
+        self.n_head = cfg.n_head
+        self.qkv = nn.Linear(cfg.n_embd, 3 * cfg.n_embd, bias=cfg.bias)
+        self.proj = nn.Linear(cfg.n_embd, cfg.n_embd, bias=cfg.bias)
+        self.attn_drop = cfg.dropout
+        self.resid_drop = nn.Dropout(cfg.dropout)
+
+    def forward(self, x):
+        B, T, C = x.shape
+        q, k, v = self.qkv(x).split(C, dim=2)
+        q, k, v = (t.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) for t in (q, k, v))
+        y = F.scaled_dot_product_attention(
+            q, k, v,
+            dropout_p=self.attn_drop if self.training else 0.0,
+            is_causal=True,
+        )
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        return self.resid_drop(self.proj(y))
+
+
+class MLP(nn.Module):
+    def __init__(self, cfg: GPTConfig):
+        super().__init__()
+        self.fc = nn.Linear(cfg.n_embd, 4 * cfg.n_embd, bias=cfg.bias)
+        self.proj = nn.Linear(4 * cfg.n_embd, cfg.n_embd, bias=cfg.bias)
+        self.drop = nn.Dropout(cfg.dropout)
+
+    def forward(self, x):
+        return self.drop(self.proj(F.gelu(self.fc(x))))
+
+
+class Block(nn.Module):
+    """Pre-LayerNorm transformer block."""
+
+    def __init__(self, cfg: GPTConfig):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(cfg.n_embd, bias=cfg.bias)
+        self.attn = CausalSelfAttention(cfg)
+        self.ln2 = nn.LayerNorm(cfg.n_embd, bias=cfg.bias)
+        self.mlp = MLP(cfg)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, cfg: GPTConfig):
+        super().__init__()
+        self.cfg = cfg
+        self.tok_emb = nn.Embedding(cfg.vocab_size, cfg.n_embd)
+        self.pos_emb = nn.Embedding(cfg.block_size, cfg.n_embd)
+        self.drop = nn.Dropout(cfg.dropout)
+        self.blocks = nn.ModuleList(Block(cfg) for _ in range(cfg.n_layer))
+        self.ln_f = nn.LayerNorm(cfg.n_embd, bias=cfg.bias)
+        self.lm_head = nn.Linear(cfg.n_embd, cfg.vocab_size, bias=False)
+        self.lm_head.weight = self.tok_emb.weight  # weight tying
+
+        self.apply(self._init_weights)
+        for name, p in self.named_parameters():
+            if name.endswith("proj.weight"):
+                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * cfg.n_layer))
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+            if m.bias is not None:
+                nn.init.zeros_(m.bias)
+        elif isinstance(m, nn.Embedding):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+
+    def num_params(self):
+        return sum(p.numel() for p in self.parameters())
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+        assert T <= self.cfg.block_size, f"Sequence length {T} > block_size {self.cfg.block_size}"
+        pos = torch.arange(T, device=idx.device)
+        x = self.drop(self.tok_emb(idx) + self.pos_emb(pos))
+        for block in self.blocks:
+            x = block(x)
+        x = self.ln_f(x)
+
+        if targets is not None:
+            logits = self.lm_head(x)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+            return logits, loss
+        return self.lm_head(x[:, [-1], :]), None
+
+    def configure_optimizer(self, lr=3e-4, weight_decay=0.1, betas=(0.9, 0.95)):
+        params = [p for p in self.parameters() if p.requires_grad]
+        decay = [p for p in params if p.dim() >= 2]
+        no_decay = [p for p in params if p.dim() < 2]
+        groups = [
+            {"params": decay, "weight_decay": weight_decay},
+            {"params": no_decay, "weight_decay": 0.0},
+        ]
+        return torch.optim.AdamW(groups, lr=lr, betas=betas)
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        self.eval()
+        eos_token_id = 50256
+
+        for _ in range(max_new_tokens):
+            idx_cond = idx[:, -self.cfg.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / max(temperature, 1e-8)
+
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = float("-inf")
+
+            probs = F.softmax(logits, dim=-1)
+            next_token = torch.multinomial(probs, 1)
+            idx = torch.cat([idx, next_token], dim=1)
+
+            if torch.all(next_token == eos_token_id):
+                break
+
+        return idx
+
+
+if __name__ == "__main__":
+    cfg = GPTConfig(vocab_size=1000, block_size=64, n_layer=2, n_head=2, n_embd=64)
+    model = GPT(cfg)
+    print(f"params: {model.num_params():,}")
+    x = torch.randint(0, cfg.vocab_size, (4, 64))
+    logits, loss = model(x, targets=x)
+    print("loss:", loss.item())  
+    print(model.generate(x[:1, :8], max_new_tokens=10).shape)