# Copyright © 2023-2024 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 hidden_size: int num_hidden_layers: int intermediate_size: int num_attention_heads: int rms_norm_eps: float vocab_size: int bias: bool = True max_position_embeddings: int = 32768 num_key_value_heads: int = None rope_theta: float = 10000 rope_traditional: bool = False rope_scaling: Optional[Dict[str, Union[float, str]]] = None tie_word_embeddings: bool = False 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"] not in ["linear", "dynamic"]: raise ValueError( "rope_scaling 'type' currently only supports 'linear' or 'dynamic" ) class DynamicNTKScalingRoPE(nn.Module): """Implements the rotary positional encoding with Dynamic NTK scaling.""" def __init__( self, dims: int, max_position_embeddings: int = 2048, traditional: bool = False, base: float = 10000, scale: float = 1.0, ): super().__init__() self.max_position_embeddings = max_position_embeddings self.original_base = base self.dims = dims self.traditional = traditional self.scale = scale def extra_repr(self): return f"{self.dims}, traditional={self.traditional}, max_position_embeddings={self.max_position_embeddings}, scaling_factor={self.scaling_factor}" def __call__(self, x, offset: int = 0): seq_len = x.shape[1] + offset if seq_len > self.max_position_embeddings: base = self.original_base * ( (self.scale * seq_len / self.max_position_embeddings) - (self.scale - 1) ) ** (self.dims / (self.dims - 2)) else: base = self.original_base return mx.fast.rope( x, self.dims, traditional=self.traditional, base=base, scale=self.scale, offset=offset, ) 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.n_kv_groups = n_heads // args.num_key_value_heads self.head_dim = head_dim = args.hidden_size // n_heads self.scale = head_dim**-0.5 self.wqkv = nn.Linear( dim, (n_heads + 2 * n_kv_heads) * head_dim, bias=args.bias ) self.wo = nn.Linear(n_heads * head_dim, dim, bias=args.bias) rope_scale = ( 1 / args.rope_scaling["factor"] if args.rope_scaling is not None and args.rope_scaling["type"] == "linear" else 2.0 ) self.rope = DynamicNTKScalingRoPE( head_dim, max_position_embeddings=args.max_position_embeddings, traditional=args.rope_traditional, base=args.rope_theta, scale=rope_scale, ) def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None, ) -> mx.array: B, L, D = x.shape qkv_states = self.wqkv(x) qkv_states = qkv_states.reshape(B, L, -1, 2 + self.n_kv_groups, self.head_dim) queries = qkv_states[..., : self.n_kv_groups, :] queries = queries.reshape(B, L, -1, self.head_dim) keys = qkv_states[..., -2, :] values = qkv_states[..., -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 = 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.wo(output) class MLP(nn.Module): def __init__(self, dim, hidden_dim): super().__init__() self.w1 = nn.Linear(dim, hidden_dim, bias=False) self.w2 = nn.Linear(hidden_dim, dim, bias=False) self.w3 = nn.Linear(dim, hidden_dim, bias=False) def __call__(self, x) -> mx.array: return self.w2(nn.silu(self.w1(x)) * self.w3(x)) class TransformerBlock(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.attention = Attention(args) self.feed_forward = MLP(args.hidden_size, args.intermediate_size) self.attention_norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps) self.ffn_norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps) def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None, ) -> mx.array: r = self.attention(self.attention_norm(x), mask, cache) h = x + r r = self.feed_forward(self.ffn_norm(h)) out = h + r return out class InternLM2Model(nn.Module): def __init__(self, args: ModelArgs): super().__init__() assert args.vocab_size > 0 self.tok_embeddings = 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.tok_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.model = InternLM2Model(args) if not args.tie_word_embeddings: self.output = 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: out = self.model.tok_embeddings.as_linear(out) else: out = self.output(out) return out def sanitize(self, weights): # Remove unused precomputed rotary freqs return {k: v for k, v in weights.items() if "attention.rope.inv_freq" not in k} @property def layers(self): return self.model.layers