# Copyright © 2024 Apple Inc. import inspect from dataclasses import dataclass from typing import Dict, Optional, Tuple, Union import mlx.core as mx import mlx.nn as nn @dataclass class TextConfig: model_type: str hidden_size: int = 4096 num_hidden_layers: int = 32 intermediate_size: int = 11008 num_attention_heads: int = 32 rms_norm_eps: float = 1e-6 vocab_size: int = 32000 num_key_value_heads: int = None rope_theta: float = 10000 rope_traditional: bool = False rope_scaling: Optional[Dict[str, Union[float, str]]] = None @classmethod def from_dict(cls, params): return cls( **{ k: v for k, v in params.items() if k in inspect.signature(cls).parameters } ) def __post_init__(self): if self.num_key_value_heads is None: self.num_key_value_heads = self.num_attention_heads if self.rope_scaling: required_keys = {"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}") if self.rope_scaling["type"] != "linear": raise ValueError("rope_scaling 'type' currently only supports 'linear'") class Attention(nn.Module): def __init__(self, config: TextConfig): super().__init__() dim = config.hidden_size self.n_heads = n_heads = config.num_attention_heads self.n_kv_heads = n_kv_heads = config.num_key_value_heads self.repeats = n_heads // n_kv_heads head_dim = config.hidden_size // n_heads self.scale = head_dim**-0.5 self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False) self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False) rope_scale = ( 1 / config.rope_scaling["factor"] if config.rope_scaling is not None and config.rope_scaling["type"] == "linear" else 1 ) self.rope = nn.RoPE( head_dim, traditional=config.rope_traditional, base=config.rope_theta, scale=rope_scale, ) def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Tuple[mx.array, mx.array]] = 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.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: key_cache, value_cache = cache queries = self.rope(queries, offset=key_cache.shape[2]) keys = self.rope(keys, offset=key_cache.shape[2]) keys = mx.concatenate([key_cache, keys], axis=2) values = mx.concatenate([value_cache, values], axis=2) else: queries = self.rope(queries) keys = self.rope(keys) output = mx.fast.scaled_dot_product_attention( queries, keys, values, scale=self.scale, mask=mask ) output = output.transpose(0, 2, 1, 3).reshape(B, L, -1) return self.o_proj(output), (keys, values) class MLP(nn.Module): def __init__(self, dim, hidden_dim): super().__init__() 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, config: TextConfig): super().__init__() self.num_attention_heads = config.num_attention_heads self.hidden_size = config.hidden_size self.self_attn = Attention(config) self.mlp = MLP(config.hidden_size, config.intermediate_size) 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 ) self.config = config def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Tuple[mx.array, mx.array]] = None, ) -> mx.array: r, cache = 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, cache class Llama(nn.Module): def __init__(self, config: TextConfig): super().__init__() self.config = config self.vocab_size = config.vocab_size self.num_hidden_layers = config.num_hidden_layers assert self.vocab_size > 0 self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) self.layers = [ TransformerBlock(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, inputs_embeds=None, ): # for passing merged input embeddings if inputs_embeds is None: h = self.embed_tokens(inputs) else: h = inputs_embeds mask = None if h.shape[1] > 1: mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1]) mask = mask.astype(h.dtype) if cache is None: cache = [None] * len(self.layers) for e, layer in enumerate(self.layers): h, cache[e] = layer(h, mask, cache[e]) return self.norm(h), cache class LanguageModel(nn.Module): def __init__(self, config: TextConfig): super().__init__() self.model_type = config.model_type if self.model_type != "llama": raise ValueError( f"Model type {self.model_type} not supported. Currently only 'llama' is supported" ) self.model = Llama(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) def __call__( self, inputs: mx.array, cache=None, inputs_embeds=None, ): out, cache = self.model(inputs, cache, inputs_embeds) return self.lm_head(out), cache @staticmethod def sanitize(weights): # Remove unused precomputed rotary freqs return { k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k }