# Copyright © 2023-2024 Apple Inc. from dataclasses import dataclass from typing import Any, Dict, List, 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 head_dim: int num_transformer_layers: int model_dim: int vocab_size: int ffn_dim_divisor: int num_query_heads: List num_kv_heads: List ffn_multipliers: List ffn_with_glu: bool = True normalize_qk_projections: bool = True share_input_output_layers: bool = True rms_norm_eps: float = 1e-6 rope_freq_constant: float = 10000 def make_divisible( v: Union[float, int], divisor: Optional[int] = 8, min_value: Optional[Union[float, int]] = None, ) -> Union[float, int]: """ This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by the divisor It can be seen at: https://github.com/tensorflow/models/blob/2cfc99eff5e5eb729c6793d2f3d03aa1c9be2b15/research/slim/nets/mobilenet/mobilenet.py#L62 Args: v: input value divisor: default to 8 min_value: minimum divisor value Returns: new_v: new divisible value """ if min_value is None: min_value = divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_v < 0.9 * v: new_v += divisor return new_v class Attention(nn.Module): def __init__(self, args: ModelArgs, layer_id: int): super().__init__() self.head_dim = head_dim = args.head_dim self.layer_id = layer_id self.model_dim = model_dim = args.model_dim self.n_heads = n_heads = args.num_query_heads[layer_id] self.n_kv_heads = n_kv_heads = args.num_kv_heads[layer_id] self.scale = head_dim**-0.5 op_size = (n_heads + (n_kv_heads * 2)) * head_dim self.qkv_proj = nn.Linear(model_dim, op_size, bias=False) self.out_proj = nn.Linear(n_heads * head_dim, model_dim, bias=False) self.normalize_qk_projections = args.normalize_qk_projections if self.normalize_qk_projections: self.q_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps) self.k_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps) self.rope = nn.RoPE(head_dim, traditional=False, base=args.rope_freq_constant) def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None, ) -> mx.array: B, L, D = x.shape qkv = self.qkv_proj(x) qkv = qkv.reshape( B, L, self.n_heads + (self.n_kv_heads * 2), self.head_dim ).transpose(0, 2, 1, 3) queries, keys, values = mx.split( qkv, [self.n_heads, self.n_heads + self.n_kv_heads], axis=1 ) # Prepare the queries, keys and values for the attention computation if self.normalize_qk_projections: queries = self.q_norm(queries) keys = self.k_norm(keys) 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 = 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.out_proj(output) class MLP(nn.Module): def __init__(self, args: ModelArgs, layer_id: int): super().__init__() self.args = args dim = args.model_dim ffn_multiplier = args.ffn_multipliers[layer_id] intermediate_dim = int( make_divisible( ffn_multiplier * args.model_dim, divisor=args.ffn_dim_divisor, ) ) self.proj_1 = nn.Linear(dim, 2 * intermediate_dim, bias=False) self.proj_2 = nn.Linear(intermediate_dim, dim, bias=False) def __call__(self, x) -> mx.array: x = self.proj_1(x) gate, x = mx.split(x, 2, axis=-1) return self.proj_2(nn.silu(gate) * x) class TransformerBlock(nn.Module): def __init__(self, args: ModelArgs, layer_id: int): super().__init__() dim = args.model_dim self.attn = Attention(args, layer_id=layer_id) self.ffn = MLP(args, layer_id=layer_id) self.ffn_norm = nn.RMSNorm(dim, eps=args.rms_norm_eps) self.attn_norm = nn.RMSNorm(dim, eps=args.rms_norm_eps) def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None, ) -> mx.array: r = self.attn(self.attn_norm(x), mask, cache) h = x + r r = self.ffn(self.ffn_norm(h)) out = h + r return out class OpenELMModel(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.args = args self.vocab_size = args.vocab_size self.num_transformer_layers = args.num_transformer_layers assert self.vocab_size > 0 self.token_embeddings = nn.Embedding(args.vocab_size, args.model_dim) self.layers = [ TransformerBlock(args, layer_id=layer_id) for layer_id in range(self.num_transformer_layers) ] self.norm = nn.RMSNorm(args.model_dim, eps=args.rms_norm_eps) def __call__( self, inputs: mx.array, cache=None, ): h = self.token_embeddings(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, cache=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.transformer = OpenELMModel(args) if not args.share_input_output_layers: self.lm_head = nn.Linear(args.model_dim, args.vocab_size, bias=False) def __call__( self, inputs: mx.array, cache=None, ): out = self.transformer(inputs, cache) if self.args.share_input_output_layers: out = self.transformer.token_embeddings.as_linear(out) else: out = self.lm_head(out) return out @property def layers(self): return self.transformer.layers