# Copyright © 2026 Apple Inc. 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 .switch_layers import SwitchGLU @dataclass class ModelArgs(BaseModelArgs): model_type: str vocab_size: int = 32000 hidden_size: int = 4096 intermediate_size: int = 14336 num_hidden_layers: int = 32 num_attention_heads: int = 32 num_experts_per_tok: int = 2 num_key_value_heads: int = 8 num_local_experts: int = 8 rms_norm_eps: float = 1e-5 rope_theta: float = 1e6 rope_traditional: bool = False rope_scaling: Optional[Dict[str, Union[float, str]]] = None tie_word_embeddings: bool = False def __post_init__(self): if self.num_key_value_heads is None: self.num_key_value_heads = self.num_attention_heads class MixtralAttention(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.hidden_size = args.hidden_size self.num_heads = args.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = args.num_key_value_heads self.rope_theta = args.rope_theta self.scale = self.head_dim**-0.5 self.q_proj = nn.Linear( self.hidden_size, self.num_heads * self.head_dim, bias=False ) self.k_proj = nn.Linear( self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False ) self.v_proj = nn.Linear( self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False ) self.o_proj = nn.Linear( self.num_heads * self.head_dim, self.hidden_size, bias=False ) self.rope = nn.RoPE( self.head_dim, traditional=args.rope_traditional, base=args.rope_theta, ) def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None, ) -> mx.array: B, L, D = x.shape queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x) # Prepare the queries, keys and values for the attention computation queries = queries.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3) keys = keys.reshape(B, L, self.num_key_value_heads, -1).transpose(0, 2, 1, 3) values = values.reshape(B, L, self.num_key_value_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 MixtralSparseMoeBlock(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.hidden_dim = args.hidden_size self.ffn_dim = args.intermediate_size self.num_experts = args.num_local_experts self.num_experts_per_tok = args.num_experts_per_tok # gating self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False) self.switch_mlp = SwitchGLU(self.hidden_dim, self.ffn_dim, self.num_experts) def __call__(self, x: mx.array) -> mx.array: gates = self.gate(x) k = self.num_experts_per_tok inds = mx.stop_gradient(mx.argpartition(-gates, kth=k - 1, axis=-1)[..., :k]) scores = mx.take_along_axis(gates, inds, axis=-1) scores = mx.softmax(scores, axis=-1, precise=True) y = self.switch_mlp(x, inds) y = (y * scores[..., None]).sum(axis=-2) return y class MixtralDecoderLayer(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.hidden_size = args.hidden_size self.self_attn = MixtralAttention(args) self.block_sparse_moe = MixtralSparseMoeBlock(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: Optional[mx.array] = None, cache: Optional[Any] = None, ) -> mx.array: r = self.self_attn(self.input_layernorm(x), mask, cache) h = x + r r = self.block_sparse_moe(self.post_attention_layernorm(h)) out = h + r return out class MixtralModel(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.vocab_size = args.vocab_size self.num_hidden_layers = args.num_hidden_layers self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) self.layers = [ MixtralDecoderLayer(args=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: Optional[mx.array] = None, ) -> mx.array: if input_embeddings is not None: h = input_embeddings else: h = self.embed_tokens(inputs) if cache is None: cache = [None] * len(self.layers) mask = create_attention_mask(h, cache[0]) for layer, c in zip(self.layers, cache): h = layer(h, mask, c) return self.norm(h) class Model(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.args = args self.model_type = args.model_type self.model = MixtralModel(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: Optional[mx.array] = None, ) -> mx.array: out = self.model(inputs, cache, input_embeddings) if self.args.tie_word_embeddings: return self.model.embed_tokens.as_linear(out) else: return self.lm_head(out) def sanitize(self, weights): if self.args.tie_word_embeddings: weights.pop("lm_head.weight", None) if "model.layers.0.block_sparse_moe.experts.0.w1.weight" not in weights: return weights for l in range(self.args.num_hidden_layers): prefix = f"model.layers.{l}" for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]: for k in ["weight", "scales", "biases"]: if f"{prefix}.block_sparse_moe.experts.0.{n}.{k}" in weights: to_join = [ weights.pop( f"{prefix}.block_sparse_moe.experts.{e}.{n}.{k}" ) for e in range(self.args.num_local_experts) ] weights[f"{prefix}.block_sparse_moe.switch_mlp.{m}.{k}"] = ( mx.stack(to_join) ) return weights @property def layers(self): return self.model.layers