model.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class ModelConfig:
    vocab_size = 32000
    d_model = 348
    n_layer = 4
    n_head = 6
    d_ff = 1392
    max_len = 256
    dropout = 0.3

class CausalSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        assert config.d_model % config.n_head == 0

        self.c_attn = nn.Linear(config.d_model, 3 * config.d_model)
        self.c_proj = nn.Linear(config.d_model, config.d_model)

        self.n_head = config.n_head
        self.d_model = config.d_model
        self.max_len = config.max_len
        self.dropout = nn.Dropout(config.dropout)

        self.register_buffer("bias", torch.tril(torch.ones(config.max_len, config.max_len))
                                     .view(1, 1, config.max_len, config.max_len))

    def forward(self, x):
        B, T, C = x.size()

        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.d_model, 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)

        #att = (q @ k.transpose(-2, -1)) * (1.0 / k.size(-1)**2)
        att = (q @ k.transpose(-2, -1)) * (1.0 / (k.size(-1)**0.5))

        att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))

        att = F.softmax(att, dim=-1)
        att = self.dropout(att)

        y = att @ v
        y = y.transpose(1, 2).contiguous().view(B, T, C)

        return self.c_proj(y)

class FeedForward(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(config.d_model, config.d_ff),
            nn.GELU(),
            nn.Linear(config.d_ff, config.d_model),
            nn.Dropout(config.dropout),
        )

    def forward(self, x):
        return self.net(x)

class TransformerBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln1 = nn.LayerNorm(config.d_model)
        self.attn = CausalSelfAttention(config)
        self.ln2 = nn.LayerNorm(config.d_model)
        self.mlp = FeedForward(config)

    def forward(self, x):
        x = x + self.attn(self.ln1(x))
        x = x + self.mlp(self.ln2(x))
        return x

class CodeCompletionTransformer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config

        self.token_embedding = nn.Embedding(config.vocab_size, config.d_model)
        self.position_embedding = nn.Embedding(config.max_len, config.d_model)

        self.dropout = nn.Dropout(config.dropout)

        self.blocks = nn.Sequential(*[TransformerBlock(config) for _ in range(config.n_layer)])

        self.ln_f = nn.LayerNorm(config.d_model)

        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        self.token_embedding.weight = self.lm_head.weight

        self.apply(self._init_weights)

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
        elif isinstance(module, nn.LayerNorm):
            torch.nn.init.zeros_(module.bias)
            torch.nn.init.ones_(module.weight)

    def forward(self, idx):
        B, T = idx.size()
        assert T <= self.config.max_len, f"Sequence length {T} exceeds model max_len {self.config.max_len}"

        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)

        tok_emb = self.token_embedding(idx)
        pos_emb = self.position_embedding(pos)
        x = self.dropout(tok_emb + pos_emb)

        x = self.blocks(x)
        x = self.ln_f(x)

        logits = self.lm_head(x)

        return logits

if __name__ == "__main__":
    # Quick sanity check: run a dummy forward pass
    config = ModelConfig()
    model = CodeCompletionTransformer(config)
    dummy_input = torch.randint(0, config.vocab_size, (4, config.max_len))
    print(model(dummy_input).shape)