# Copyright © 2023-2025 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 @dataclass class ModelArgs(BaseModelArgs): model_type: str vocab_size: int hidden_size: int num_hidden_layers: int intermediate_size: int num_attention_heads: int num_key_value_heads: int rms_norm_eps: float rope_theta: float = 10000 max_position_embeddings: int = 32768 attention_bias: bool = False use_qk_norm: bool = True rope_scaling: Optional[Dict[str, Union[float, str]]] = None tie_word_embeddings: bool = False head_dim: Optional[int] = None def __post_init__(self): if self.rope_scaling: required_keys = {"alpha", "factor", "type"} if not all(key in self.rope_scaling for key in required_keys): raise ValueError(f"rope_scaling must contain keys {required_keys}") class DynamicNTKAlphaRoPE(nn.Module): def __init__( self, dims: int, base: float = 10000, scaling_alpha: float = 1.0, ): super().__init__() self.dims = dims base = base * scaling_alpha ** (dims / (dims - 2)) self._freqs = base ** (mx.arange(0, self.dims, 2) / self.dims) def __call__(self, x, offset: int = 0): return mx.fast.rope( x, self.dims, traditional=False, base=None, scale=1.0, offset=offset, freqs=self._freqs, ) class Attention(nn.Module): def __init__(self, args: ModelArgs): super().__init__() dim = args.hidden_size self.n_heads = n_heads = args.num_attention_heads self.n_kv_heads = n_kv_heads = args.num_key_value_heads head_dim = ( args.head_dim if args.head_dim is not None else args.hidden_size // n_heads ) self.head_dim = head_dim self.scale = head_dim**-0.5 self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=args.attention_bias) self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=args.attention_bias) self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=args.attention_bias) self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=args.attention_bias) self.use_qk_norm = args.use_qk_norm if self.use_qk_norm: self.query_layernorm = nn.RMSNorm(head_dim, args.rms_norm_eps) self.key_layernorm = nn.RMSNorm(head_dim, args.rms_norm_eps) scaling_alpha = 1.0 if args.rope_scaling and "alpha" in args.rope_scaling: scaling_alpha = args.rope_scaling["alpha"] self.rope = DynamicNTKAlphaRoPE( head_dim, base=args.rope_theta, scaling_alpha=scaling_alpha, ) 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 = queries.reshape(B, L, self.n_heads, self.head_dim).transpose( 0, 2, 1, 3 ) keys = keys.reshape(B, L, self.n_kv_heads, self.head_dim).transpose(0, 2, 1, 3) values = values.reshape(B, L, self.n_kv_heads, self.head_dim).transpose( 0, 2, 1, 3 ) if cache is not None: queries = self.rope(queries, offset=cache.offset) keys = self.rope(keys, offset=cache.offset) else: queries = self.rope(queries) keys = self.rope(keys) if self.use_qk_norm: queries = self.query_layernorm(queries) keys = self.key_layernorm(keys) if cache is not None: keys, values = cache.update_and_fetch(keys, values) 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 = args.hidden_size hidden_dim = args.intermediate_size self.gate_proj = nn.Linear(dim, hidden_dim, bias=False) self.down_proj = nn.Linear(hidden_dim, dim, bias=False) self.up_proj = nn.Linear(dim, hidden_dim, bias=False) 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.num_attention_heads = args.num_attention_heads self.hidden_size = args.hidden_size 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 ) self.args = args 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 HunyuanV1DenseModel(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.args = args 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 = [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, ): 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 = HunyuanV1DenseModel(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, ): out = self.model(inputs, cache) if self.args.tie_word_embeddings: return self.model.embed_tokens.as_linear(out) else: return self.lm_head(out) @property def layers(self): return self.model.layers def sanitize(self, weights): if self.args.tie_word_embeddings: weights.pop("lm_head.weight", None) return weights