# Copyright © 2026 Apple Inc. from dataclasses import dataclass from typing import Any, List, Optional import mlx.core as mx import mlx.nn as nn from mlx.nn.layers.distributed import shard_inplace, shard_linear, sum_gradients from .activations import swiglu from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention from .cache import KVCache, RotatingKVCache from .rope_utils import initialize_rope from .switch_layers import SwitchGLU @dataclass class ModelArgs(BaseModelArgs): model_type: str vocab_size: int hidden_size: int intermediate_size: int moe_intermediate_size: int num_hidden_layers: int num_attention_heads: int num_key_value_heads: int head_dim: int num_experts: int num_experts_per_tok: int num_shared_experts: int rms_norm_eps: float max_position_embeddings: int sliding_window: int layer_types: List[str] is_moe_layer: List[bool] n_group: int = 1 topk_group: int = 1 routed_scaling_factor: float = 2.5 norm_topk_prob: bool = True scoring_func: str = "sigmoid" topk_method: str = "noaux_tc" rope_theta: float = 1000000.0 rope_scaling: Optional[dict] = None rope_parameters: Optional[dict] = None tie_word_embeddings: bool = False def __post_init__(self): if self.rope_parameters is not None and "rope_theta" in self.rope_parameters: self.rope_theta = self.rope_parameters["rope_theta"] @mx.compile def group_expert_select( gates, e_score_correction_bias, top_k, n_group, topk_group, routed_scaling_factor, norm_topk_prob, ): scores = mx.sigmoid(gates.astype(mx.float32)) orig_scores = scores scores = scores + e_score_correction_bias if n_group > 1: scores = mx.unflatten(scores, axis=-1, shape=(n_group, -1)) group_scores = mx.topk(scores, 2, axis=-1).sum(axis=-1, keepdims=True) k = n_group - topk_group group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-2)[..., :k, :] scores = mx.put_along_axis( scores, mx.stop_gradient(group_idx), mx.array(0.0), axis=-2 ) scores = mx.flatten(scores, -2, -1) k = top_k inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k] scores = mx.take_along_axis(orig_scores, inds, axis=-1) if top_k > 1 and norm_topk_prob: denominator = scores.sum(axis=-1, keepdims=True) scores = scores / (denominator + 1e-20) scores = scores * routed_scaling_factor return inds, scores class MoEGate(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.top_k = args.num_experts_per_tok self.norm_topk_prob = args.norm_topk_prob self.n_routed_experts = args.num_experts self.routed_scaling_factor = args.routed_scaling_factor self.n_group = args.n_group self.topk_group = args.topk_group self.weight = mx.zeros((self.n_routed_experts, args.hidden_size)) self.e_score_correction_bias = mx.zeros((self.n_routed_experts,)) assert args.topk_method == "noaux_tc", "Unsupported topk method." def __call__(self, x): return group_expert_select( x @ self.weight.T, self.e_score_correction_bias, self.top_k, self.n_group, self.topk_group, self.routed_scaling_factor, self.norm_topk_prob, ) class MLP(nn.Module): def __init__(self, args: ModelArgs, intermediate_size: Optional[int] = None): super().__init__() hidden_size = args.hidden_size intermediate_size = intermediate_size or args.intermediate_size self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False) self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) def __call__(self, x): return self.down_proj(swiglu(self.gate_proj(x), self.up_proj(x))) class MoE(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.switch_mlp = SwitchGLU( args.hidden_size, args.moe_intermediate_size, args.num_experts, ) self.gate = MoEGate(args) self.shared_experts = ( MLP( args, intermediate_size=args.moe_intermediate_size * args.num_shared_experts, ) if args.num_shared_experts is not None and args.num_shared_experts > 0 else None ) self.sharding_group = None def __call__(self, x): if self.sharding_group is not None: x = sum_gradients(self.sharding_group)(x) inds, scores = self.gate(x) y = self.switch_mlp(x, inds) y = (y * scores[..., None]).sum(axis=-2).astype(y.dtype) if self.shared_experts is not None: y = y + self.shared_experts(x) if self.sharding_group is not None: y = mx.distributed.all_sum(y, group=self.sharding_group) return y class Attention(nn.Module): def __init__(self, args: ModelArgs, layer_idx: int): super().__init__() self.hidden_size = args.hidden_size self.n_heads = args.num_attention_heads self.n_kv_heads = args.num_key_value_heads self.head_dim = args.head_dim self.scale = self.head_dim**-0.5 self.q_proj = nn.Linear( self.hidden_size, self.n_heads * self.head_dim, bias=False ) self.k_proj = nn.Linear( self.hidden_size, self.n_kv_heads * self.head_dim, bias=False ) self.v_proj = nn.Linear( self.hidden_size, self.n_kv_heads * self.head_dim, bias=False ) self.o_proj = nn.Linear( self.n_heads * self.head_dim, self.hidden_size, bias=False ) self.q_norm = nn.RMSNorm(self.head_dim, eps=args.rms_norm_eps) self.k_norm = nn.RMSNorm(self.head_dim, eps=args.rms_norm_eps) self.is_sliding_window = args.layer_types[layer_idx] == "sliding_attention" self.apply_rope_all_layers = "sliding_attention" not in args.layer_types self.use_rope = self.is_sliding_window or self.apply_rope_all_layers if self.use_rope: self.rope = initialize_rope( self.head_dim, base=args.rope_theta, traditional=False, scaling_config=args.rope_scaling, max_position_embeddings=args.max_position_embeddings, ) 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) queries = self.q_norm(queries.reshape(B, L, self.n_heads, -1)).transpose( 0, 2, 1, 3 ) keys = self.k_norm(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: if self.use_rope: queries = self.rope(queries, offset=cache.offset) keys = self.rope(keys, offset=cache.offset) keys, values = cache.update_and_fetch(keys, values) elif self.use_rope: 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 DecoderLayer(nn.Module): def __init__(self, args: ModelArgs, layer_idx: int): super().__init__() self.self_attn = Attention(args, layer_idx) self.mlp = MoE(args) if args.is_moe_layer[layer_idx] else MLP(args) self.is_sliding_window = self.self_attn.is_sliding_window 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.mlp(self.post_attention_layernorm(h)) return h + r class ExaoneMoEModel(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.args = args self.vocab_size = args.vocab_size self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) self.layers = [DecoderLayer(args, idx) for idx in range(args.num_hidden_layers)] self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps) self.swa_idx = None self.ga_idx = None for i, layer in enumerate(self.layers): if layer.is_sliding_window and self.swa_idx is None: self.swa_idx = i if not layer.is_sliding_window and self.ga_idx is None: self.ga_idx = i self.window_size = args.sliding_window def __call__( self, inputs: mx.array, cache: Optional[Any] = None, ) -> mx.array: h = self.embed_tokens(inputs) if cache is None: cache = [None] * len(self.layers) global_mask = create_attention_mask( h, cache[self.ga_idx] if self.ga_idx is not None else cache[0] ) swa_mask = create_attention_mask( h, cache[self.swa_idx] if self.swa_idx is not None else cache[0], window_size=self.window_size, ) for layer, c in zip(self.layers, cache): mask = swa_mask if layer.is_sliding_window else global_mask 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 = ExaoneMoEModel(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: Optional[Any] = None, ): out = self.model(inputs, cache) 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): new_weights = {k: v for k, v in weights.items() if not k.startswith("mtp.")} weights = new_weights for l in range(self.args.num_hidden_layers): if not self.args.is_moe_layer[l]: continue prefix = f"model.layers.{l}" bias_key = f"{prefix}.mlp.e_score_correction_bias" if bias_key in weights: weights[f"{prefix}.mlp.gate.e_score_correction_bias"] = weights.pop( bias_key ) for m in ["gate_proj", "down_proj", "up_proj"]: for k in ["weight", "scales", "biases"]: first_key = f"{prefix}.mlp.experts.0.{m}.{k}" last_key = ( f"{prefix}.mlp.experts.{self.args.num_experts - 1}.{m}.{k}" ) if first_key in weights and last_key in weights: to_join = [ weights.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}") for e in range(self.args.num_experts) ] weights[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join) if self.args.tie_word_embeddings: weights.pop("lm_head.weight", None) return weights @property def layers(self): return self.model.layers @property def cast_predicate(self): def predicate(k): return "e_score_correction_bias" not in k return predicate def make_cache(self): caches = [] for layer in self.layers: if layer.is_sliding_window: caches.append( RotatingKVCache(max_size=self.args.sliding_window, keep=0) ) else: caches.append(KVCache()) return caches def shard(self, group: Optional[mx.distributed.Group] = None): group = group or mx.distributed.init() N = group.size() for layer in self.model.layers: layer.self_attn.q_proj = shard_linear( layer.self_attn.q_proj, "all-to-sharded", group=group ) layer.self_attn.k_proj = shard_linear( layer.self_attn.k_proj, "all-to-sharded", group=group ) layer.self_attn.v_proj = shard_linear( layer.self_attn.v_proj, "all-to-sharded", group=group ) layer.self_attn.o_proj = shard_linear( layer.self_attn.o_proj, "sharded-to-all", group=group ) layer.self_attn.n_heads //= N layer.self_attn.n_kv_heads //= N if isinstance(layer.mlp, MLP): layer.mlp.gate_proj = shard_linear( layer.mlp.gate_proj, "all-to-sharded", group=group ) layer.mlp.down_proj = shard_linear( layer.mlp.down_proj, "sharded-to-all", group=group ) layer.mlp.up_proj = shard_linear( layer.mlp.up_proj, "all-to-sharded", group=group ) else: layer.mlp.sharding_group = group if layer.mlp.shared_experts is not None: shard_inplace( layer.mlp.shared_experts.gate_proj, "all-to-sharded", group=group, ) shard_inplace( layer.mlp.shared_experts.down_proj, "sharded-to-all", group=group, ) shard_inplace( layer.mlp.shared_experts.up_proj, "all-to-sharded", group=group ) shard_inplace( layer.mlp.switch_mlp.gate_proj, "all-to-sharded", group=group ) shard_inplace( layer.mlp.switch_mlp.down_proj, "sharded-to-all", group=group ) shard_inplace( layer.mlp.switch_mlp.up_proj, "all-to-sharded", group=group )