nanbeige.py

# Copyright © 2024 Apple Inc.
# mlx-lm architecture module for Nanbeige4.2 (looped / recurrent-depth transformer).
#
# Nanbeige4.2 is Llama-style (GQA attention, SwiGLU MLP, RMSNorm, rotary embeddings)
# with one twist: the full decoder stack is executed `num_loops` times. Each loop pass
# keeps its own KV-cache slice, and (when skip_loop_final_norm is False) the final
# RMSNorm is applied at the end of every loop pass — the normalized output of one loop
# feeds the next loop as input.

from dataclasses import dataclass
from typing import Any, Dict, Optional, Union

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

from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
from .cache import KVCache
from .rope_utils import initialize_rope


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str
    hidden_size: int
    num_hidden_layers: int
    intermediate_size: int
    num_attention_heads: int
    rms_norm_eps: float
    vocab_size: int
    head_dim: Optional[int] = None
    max_position_embeddings: Optional[int] = None
    num_key_value_heads: Optional[int] = None
    attention_bias: bool = False
    mlp_bias: bool = False
    rope_theta: float = 10000.0
    rope_traditional: bool = False
    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
    tie_word_embeddings: bool = False
    num_loops: int = 1
    skip_loop_final_norm: bool = False

    def __post_init__(self):
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads
        if self.num_loops < 1:
            self.num_loops = 1


class Attention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        dim = args.hidden_size
        self.n_heads = args.num_attention_heads
        self.n_kv_heads = args.num_key_value_heads
        self.head_dim = head_dim = args.head_dim or (dim // self.n_heads)
        self.scale = head_dim**-0.5

        self.q_proj = nn.Linear(dim, self.n_heads * head_dim, bias=args.attention_bias)
        self.k_proj = nn.Linear(dim, self.n_kv_heads * head_dim, bias=args.attention_bias)
        self.v_proj = nn.Linear(dim, self.n_kv_heads * head_dim, bias=args.attention_bias)
        self.o_proj = nn.Linear(self.n_heads * head_dim, dim, bias=args.attention_bias)

        self.rope = initialize_rope(
            self.head_dim,
            args.rope_theta,
            args.rope_traditional,
            args.rope_scaling,
            args.max_position_embeddings,
        )

    def __call__(self, x: mx.array, mask=None, cache=None) -> mx.array:
        B, L, D = x.shape

        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)

        if cache is not None:
            queries = self.rope(queries, offset=cache.offset)
            keys = self.rope(keys, offset=cache.offset)
            keys, values = cache.update_and_fetch(keys, values)
        else:
            queries = self.rope(queries)
            keys = self.rope(keys)

        output = scaled_dot_product_attention(
            queries, keys, values, cache=cache, scale=self.scale, mask=mask
        )
        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
        return self.o_proj(output)


class MLP(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        dim, hidden = args.hidden_size, args.intermediate_size
        self.gate_proj = nn.Linear(dim, hidden, bias=args.mlp_bias)
        self.down_proj = nn.Linear(hidden, dim, bias=args.mlp_bias)
        self.up_proj = nn.Linear(dim, hidden, bias=args.mlp_bias)

    def __call__(self, x) -> mx.array:
        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))


class TransformerBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.self_attn = Attention(args)
        self.mlp = MLP(args)
        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
        self.post_attention_layernorm = nn.RMSNorm(
            args.hidden_size, eps=args.rms_norm_eps
        )

    def __call__(self, x: mx.array, mask=None, cache=None) -> mx.array:
        r = self.self_attn(self.input_layernorm(x), mask, cache)
        h = x + r
        r = self.mlp(self.post_attention_layernorm(h))
        return h + r


class NanbeigeModel(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.num_hidden_layers = args.num_hidden_layers
        self.num_loops = args.num_loops
        self.skip_loop_final_norm = args.skip_loop_final_norm
        assert args.vocab_size > 0
        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
        self.layers = [TransformerBlock(args) for _ in range(args.num_hidden_layers)]
        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)

    def __call__(self, inputs: mx.array, cache=None, input_embeddings=None):
        if input_embeddings is not None:
            h = input_embeddings
        else:
            h = self.embed_tokens(inputs)

        n = self.num_hidden_layers
        if cache is None:
            cache = [None] * (n * self.num_loops)

        # Each loop pass replays the whole stack against its own cache slice.
        for loop_idx in range(self.num_loops):
            loop_cache = cache[loop_idx * n : (loop_idx + 1) * n]
            mask = create_attention_mask(h, loop_cache[0])
            for layer, c in zip(self.layers, loop_cache):
                h = layer(h, mask, cache=c)
            if not self.skip_loop_final_norm:
                h = self.norm(h)

        if self.skip_loop_final_norm:
            h = self.norm(h)
        return h


class Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.model_type = args.model_type
        self.model = NanbeigeModel(args)
        if not args.tie_word_embeddings:
            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)

    def __call__(self, inputs: mx.array, cache=None, input_embeddings=None):
        out = self.model(inputs, cache, input_embeddings)
        if self.args.tie_word_embeddings:
            out = self.model.embed_tokens.as_linear(out)
        else:
            out = self.lm_head(out)
        return out

    def sanitize(self, weights):
        # Drop non-persistent rotary buffers if present in a checkpoint.
        weights = {
            k: v
            for k, v in weights.items()
            if "rotary_emb.inv_freq" not in k and ".rope." not in k
        }
        if self.args.tie_word_embeddings:
            weights.pop("lm_head.weight", None)
        return weights

    @property
    def layers(self):
        return self.model.layers

    def make_cache(self):
        # One KV cache per (loop, layer): the stack is executed num_loops times
        # and each pass must not see the other passes' keys/values.
        return [
            KVCache()
            for _ in range(self.args.num_hidden_layers * self.args.num_loops)
        ]