# Copyright © 2025 Apple Inc. from dataclasses import dataclass from typing import Any, Dict, Optional import mlx.core as mx import mlx.nn as nn from .activations import swiglu from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention from .rope_utils import initialize_rope @dataclass class ModelArgs(BaseModelArgs): model_type: str = "youtu_llm" vocab_size: int = 128256 hidden_size: int = 2048 intermediate_size: int = 6144 num_hidden_layers: int = 32 num_attention_heads: int = 16 num_key_value_heads: int = 16 kv_lora_rank: int = 512 q_lora_rank: int = 1536 qk_rope_head_dim: int = 64 v_head_dim: int = 128 qk_nope_head_dim: int = 128 max_position_embeddings: int = 131072 rms_norm_eps: float = 1e-6 rope_theta: float = 1600000 rope_traditional: bool = True rope_scaling: Optional[Dict] = None attention_bias: bool = False mlp_bias: bool = False tie_word_embeddings: bool = True class YoutuLLMAttention(nn.Module): def __init__(self, config: ModelArgs): super().__init__() self.config = config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.q_lora_rank = config.q_lora_rank self.qk_rope_head_dim = config.qk_rope_head_dim self.kv_lora_rank = config.kv_lora_rank self.v_head_dim = config.v_head_dim self.qk_nope_head_dim = config.qk_nope_head_dim self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim self.scale = self.q_head_dim**-0.5 if self.q_lora_rank is None: self.q_proj = nn.Linear( self.hidden_size, self.num_heads * self.q_head_dim, bias=False ) else: self.q_a_proj = nn.Linear( self.hidden_size, self.q_lora_rank, bias=config.attention_bias ) self.q_a_layernorm = nn.RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps) self.q_b_proj = nn.Linear( self.q_lora_rank, self.num_heads * self.q_head_dim, bias=False ) self.kv_a_proj_with_mqa = nn.Linear( self.hidden_size, self.kv_lora_rank + self.qk_rope_head_dim, bias=config.attention_bias, ) self.kv_a_layernorm = nn.RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps) self.kv_b_proj = nn.Linear( self.kv_lora_rank, self.num_heads * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim), bias=False, ) self.o_proj = nn.Linear( self.num_heads * self.v_head_dim, self.hidden_size, bias=config.attention_bias, ) self.rope = initialize_rope( self.qk_rope_head_dim, base=self.rope_theta, traditional=config.rope_traditional, scaling_config=config.rope_scaling, max_position_embeddings=config.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 if self.q_lora_rank is None: q = self.q_proj(x) else: q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(x))) q = q.reshape(B, L, self.num_heads, self.q_head_dim).transpose(0, 2, 1, 3) q_nope, q_pe = mx.split(q, [self.qk_nope_head_dim], axis=-1) compressed_kv = self.kv_a_proj_with_mqa(x) compressed_kv, k_pe = mx.split(compressed_kv, [self.kv_lora_rank], axis=-1) k_pe = k_pe.reshape(B, L, 1, self.qk_rope_head_dim).transpose(0, 2, 1, 3) kv = self.kv_b_proj(self.kv_a_layernorm(compressed_kv)) kv = kv.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3) k_nope, values = mx.split(kv, [self.qk_nope_head_dim], axis=-1) if cache is not None: q_pe = self.rope(q_pe, cache.offset) k_pe = self.rope(k_pe, cache.offset) k_pe = mx.repeat(k_pe, self.num_heads, axis=1) keys, values = cache.update_and_fetch( mx.concatenate([k_nope, k_pe], axis=-1), values ) else: q_pe = self.rope(q_pe) k_pe = self.rope(k_pe) k_pe = mx.repeat(k_pe, self.num_heads, axis=1) keys = mx.concatenate([k_nope, k_pe], axis=-1) queries = mx.concatenate([q_nope, q_pe], axis=-1) 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 YoutuLLMMLP(nn.Module): def __init__(self, config: ModelArgs): super().__init__() self.gate_proj = nn.Linear( config.hidden_size, config.intermediate_size, bias=config.mlp_bias ) self.up_proj = nn.Linear( config.hidden_size, config.intermediate_size, bias=config.mlp_bias ) self.down_proj = nn.Linear( config.intermediate_size, config.hidden_size, bias=config.mlp_bias ) def __call__(self, x) -> mx.array: return self.down_proj(swiglu(self.gate_proj(x), self.up_proj(x))) class YoutuLLMDecoderLayer(nn.Module): def __init__(self, config: ModelArgs): super().__init__() self.hidden_size = config.hidden_size self.self_attn = YoutuLLMAttention(config) self.mlp = YoutuLLMMLP(config) self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = nn.RMSNorm( config.hidden_size, eps=config.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)) out = h + r return out class YoutuLLMModel(nn.Module): def __init__(self, config: ModelArgs): super().__init__() self.config = config self.vocab_size = config.vocab_size self.num_hidden_layers = config.num_hidden_layers self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) self.layers = [ YoutuLLMDecoderLayer(config=config) for _ in range(config.num_hidden_layers) ] self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def __call__( self, inputs: mx.array, cache=None, ): 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, config: ModelArgs): super().__init__() self.config = config self.model_type = config.model_type self.model = YoutuLLMModel(config) if not config.tie_word_embeddings: self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) def __call__( self, inputs: mx.array, cache=None, ): out = self.model(inputs, cache) if self.config.tie_word_embeddings: out = self.model.embed_tokens.as_linear(out) else: out = self.lm_head(out) return out def sanitize(self, weights): if self.config.tie_word_embeddings: weights.pop("lm_head.weight", None) return weights @property def layers(self): return self.model.layers