model.py

"""
AAC Micro Brain — 16M parameter conversational flow model.
Tiny transformer that only knows how humans talk in everyday situations.
No world knowledge. No encyclopedia. Just conversation patterns.

Architecture: ~16M params
- vocab_size: 8192
- d_model: 512
- n_heads: 8
- n_layers: 6
- d_ff: 1024
- max_seq_len: 128
"""

import mlx.core as mx
import mlx.nn as nn
import math


class MultiHeadAttention(nn.Module):
    def __init__(self, d_model: int, n_heads: int):
        super().__init__()
        self.n_heads = n_heads
        self.d_head = d_model // n_heads
        self.qkv = nn.Linear(d_model, 3 * d_model, bias=False)
        self.out = nn.Linear(d_model, d_model, bias=False)

    def __call__(self, x, mask=None):
        B, T, C = x.shape
        qkv = self.qkv(x)
        q, k, v = mx.split(qkv, 3, axis=-1)

        q = q.reshape(B, T, self.n_heads, self.d_head).transpose(0, 2, 1, 3)
        k = k.reshape(B, T, self.n_heads, self.d_head).transpose(0, 2, 1, 3)
        v = v.reshape(B, T, self.n_heads, self.d_head).transpose(0, 2, 1, 3)

        scale = math.sqrt(self.d_head)
        attn = (q @ k.transpose(0, 1, 3, 2)) / scale

        if mask is not None:
            attn = attn + mask

        attn = mx.softmax(attn, axis=-1)
        out = (attn @ v).transpose(0, 2, 1, 3).reshape(B, T, C)
        return self.out(out)


class TransformerBlock(nn.Module):
    def __init__(self, d_model: int, n_heads: int, d_ff: int):
        super().__init__()
        self.attn = MultiHeadAttention(d_model, n_heads)
        self.ff = nn.Sequential(
            nn.Linear(d_model, d_ff, bias=False),
            nn.GELU(),
            nn.Linear(d_ff, d_model, bias=False),
        )
        self.ln1 = nn.RMSNorm(d_model)
        self.ln2 = nn.RMSNorm(d_model)

    def __call__(self, x, mask=None):
        x = x + self.attn(self.ln1(x), mask=mask)
        x = x + self.ff(self.ln2(x))
        return x


class MicroBrain(nn.Module):
    """16M param conversational flow predictor."""

    def __init__(
        self,
        vocab_size: int = 8192,
        d_model: int = 512,
        n_heads: int = 8,
        n_layers: int = 6,
        d_ff: int = 1024,
        max_seq_len: int = 128,
    ):
        super().__init__()
        self.d_model = d_model
        self.max_seq_len = max_seq_len

        self.token_emb = nn.Embedding(vocab_size, d_model)
        self.pos_emb = nn.Embedding(max_seq_len, d_model)

        self.layers = [TransformerBlock(d_model, n_heads, d_ff) for _ in range(n_layers)]
        self.ln_final = nn.RMSNorm(d_model)
        self.output = nn.Linear(d_model, vocab_size, bias=False)

    def __call__(self, tokens):
        B, T = tokens.shape
        positions = mx.arange(T)

        x = self.token_emb(tokens) + self.pos_emb(positions)

        # Causal mask
        mask = nn.MultiHeadAttention.create_additive_causal_mask(T)

        for layer in self.layers:
            x = layer(x, mask=mask)

        x = self.ln_final(x)
        logits = self.output(x)
        return logits

    def count_params(self):
        """Count total parameters."""
        from mlx.utils import tree_flatten
        return sum(v.size for _, v in tree_flatten(self.parameters()))


def create_model(**kwargs):
    model = MicroBrain(**kwargs)
    mx.eval(model.parameters())
    n_params = model.count_params()
    print(f"MicroBrain: {n_params:,} parameters ({n_params / 1e6:.1f}M)")
    return model


if __name__ == "__main__":
    model = create_model()