Create model.py

model.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from dataclasses import dataclass
+import numpy as np
+from tqdm import tqdm
+from contextlib import nullcontext
+import os
+
+@dataclass
+class SLMConfig:
+    block_size: int = 256
+    vocab_size: int = 16834
+    n_layer: int = 10
+    n_head: int = 8
+    n_embd: int = 512
+    dropout: float = 0.0
+    bias: bool = True
+
+class LayerNorm(nn.Module):
+    def __init__(self, ndim, bias=True, eps=1e-5):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+        self.eps = eps
+
+    def forward(self, x):
+        return F.layer_norm(x, x.shape[-1:], self.weight, self.bias, self.eps)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.flash = hasattr(F, 'scaled_dot_product_attention')
+        if not self.flash:
+            self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
+                                        .view(1, 1, config.block_size, config.block_size))
+
+    def forward(self, x):
+        B, T, C = x.size()
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+
+        if self.flash:
+            y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.attn_dropout.p if self.training else 0.0, is_causal=True)
+        else:
+            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+            att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
+            att = F.softmax(att, dim=-1)
+            att = self.attn_dropout(att)
+            y = att @ v
+
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = self.resid_dropout(self.c_proj(y))
+        return y
+
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        # SwiGLU typically keeps the hidden dimension at 4 * n_embd (like LLaMA), 
+        # or uses 8/3 * n_embd to maintain the same parameter count as a standard MLP.
+        # Here we stick to 4 * n_embd for maximum capacity.
+        hidden_dim = 4 * config.n_embd
+        
+        # w1: Gate Projection
+        self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
+        # w2: Value Projection
+        self.w2 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
+        # c_proj: Output Projection (Down projection)
+        self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)
+        
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        # SwiGLU Logic: (SiLU(Gate) * Value) -> Projection
+        # 1. Gate path: w1(x) -> SiLU
+        # 2. Value path: w2(x)
+        # 3. Element-wise multiply
+        x = F.silu(self.w1(x)) * self.w2(x)
+        
+        # 4. Output projection
+        return self.dropout(self.c_proj(x))
+
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln1 = LayerNorm(config.n_embd, config.bias)
+        self.attn = CausalSelfAttention(config)
+        self.ln2 = LayerNorm(config.n_embd, config.bias)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+@dataclass
+class SLMConfig:
+    block_size: int
+    vocab_size: int
+    n_layer: int
+    n_head: int
+    n_embd: int
+    dropout: float = 0.0
+    bias: bool = True
+
+class SLM(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.transformer = nn.ModuleDict(dict(
+            wte=nn.Embedding(config.vocab_size, config.n_embd),
+            wpe=nn.Embedding(config.block_size, config.n_embd),
+            drop=nn.Dropout(config.dropout),
+            h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f=LayerNorm(config.n_embd, config.bias),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.transformer.wte.weight = self.lm_head.weight  # weight tying
+
+        self.apply(self._init_weights)
+        # Apply special scaled init to the residual projections, c_proj
+        for pn, p in self.named_parameters():
+            if pn.endswith('c_proj.weight'):
+                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
+
+    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)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        device = idx.device
+        b, t = idx.size()
+        assert t <= self.config.block_size
+        pos = torch.arange(0, t, dtype=torch.long, device=device)
+
+        tok_emb = self.transformer.wte(idx)
+        pos_emb = self.transformer.wpe(pos)
+        x = self.transformer.drop(tok_emb + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.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
+        else:
+            logits = self.lm_head(x[:, [-1], :])
+            return logits, None
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        """
+        Generate tokens given a conditioning sequence.
+        idx: Tensor of shape (B, T)
+        """
+        for _ in range(max_new_tokens):
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            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)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx