from dataclasses import dataclass from typing import Dict, Optional, Tuple, Union import mlx.core as mx import mlx.nn as nn from .base import BaseModelArgs from .su_rope import SuScaledRotaryEmbedding @dataclass class ModelArgs(BaseModelArgs): model_type: str hidden_size: int num_hidden_layers: int intermediate_size: int num_attention_heads: int rms_norm_eps: float vocab_size: int num_key_value_heads: int = None rope_theta: float = 10000 rope_traditional: bool = False rope_scaling: Optional[Dict[str, Union[float, str]]] = None max_position_embeddings: int = 131072 original_max_position_embeddings: int = 4096 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 = {"long_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"] not in ["su", "linear"]: print( "[WARNING] rope_scaling 'type' currently only supports 'linear' and 'su'; setting rope scaling to false." ) self.rope_scaling = None 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 self.num_hidden_layers = args.num_hidden_layers self.head_dim = head_dim = args.hidden_size // n_heads self.scale = head_dim**-0.5 op_size = n_heads * head_dim + 2 * (n_kv_heads * head_dim) self.qkv_proj = nn.Linear(dim, op_size, bias=False) self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False) rope_scale = 1.0 if args.rope_scaling and args.rope_scaling["type"] == "su": self.rope = SuScaledRotaryEmbedding( head_dim, traditional=False, base=args.rope_theta, scale=rope_scale, max_position_embeddings=args.max_position_embeddings, original_max_position_embeddings=args.original_max_position_embeddings, short_factor=args.rope_scaling["short_factor"], long_factor=args.rope_scaling["long_factor"], ) else: if args.rope_scaling and args.rope_scaling["type"] == "linear": rope_scale = 1 / args.rope_scaling["factor"] self.rope = nn.RoPE( head_dim, traditional=args.rope_traditional, base=args.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 qkv = self.qkv_proj(x) query_pos = self.n_heads * self.head_dim queries, keys, values = mx.split( qkv, [query_pos, query_pos + self.n_kv_heads * self.head_dim], axis=-1 ) # 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: 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 = 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) class MLP(nn.Module): def __init__(self, dim, hidden_dim): super().__init__() self.gate_up_proj = nn.Linear(dim, 2 * hidden_dim, bias=False) self.down_proj = nn.Linear(hidden_dim, dim, bias=False) def __call__(self, x) -> mx.array: x = self.gate_up_proj(x) gate, x = mx.split(x, 2, axis=-1) return self.down_proj(nn.silu(gate) * 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.hidden_size, args.intermediate_size) 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[Tuple[mx.array, mx.array]] = 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 Phi3Model(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 assert self.vocab_size > 0 self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) self.layers = [ TransformerBlock(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, ): h = self.embed_tokens(inputs) 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 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.model_type = args.model_type self.model = Phi3Model(args) self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False) self.args = args def __call__( self, inputs: mx.array, cache=None, ): out = self.model(inputs, cache) return self.lm_head(out) @property def layers(self): return self.model.layers @property def head_dim(self): return self.args.hidden_size // self.args.num_attention_heads @property def n_kv_heads(self): return self.args.num_key_value_heads