model.py

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