# 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 mlx.nn.layers.distributed import shard_linear from .activations import swiglu from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention from .cache import KVCache, RotatingKVCache from .pipeline import PipelineMixin from .rope_utils import initialize_rope @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 head_dim: Optional[int] = None max_position_embeddings: Optional[int] = None num_key_value_heads: Optional[int] = None rope_parameters: Optional[Dict[str, Union[float, str]]] = None tie_word_embeddings: bool = True layer_types: Optional[List[str]] = None sliding_window: Optional[int] = None def __post_init__(self): if self.num_key_value_heads is None: self.num_key_value_heads = self.num_attention_heads if self.layer_types is None: self.layer_types = ["full_attention"] * self.num_hidden_layers def _get_llama_4_attn_scale(size, offset, beta: float, max_position_embeddings: int): if isinstance(offset, mx.array) and offset.ndim > 0: offset = offset[:, None] scaling = 1 + beta * mx.log( 1 + mx.floor((mx.arange(size) + offset) / max_position_embeddings) ) if scaling.ndim == 2: return scaling[:, None, :, None] else: return 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.head_dim = head_dim = args.head_dim or args.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) self.rope = initialize_rope( self.head_dim, args.rope_parameters["rope_theta"], False, args.rope_parameters, args.max_position_embeddings, ) def __call__( self, x: mx.array, attn_scale: 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) # 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) offset = 0 if cache is not None: offset = cache.offset queries = self.rope(queries, offset=offset) keys = self.rope(keys, offset=offset) keys, values = cache.update_and_fetch(keys, values) else: queries = self.rope(queries) keys = self.rope(keys) queries = queries * attn_scale 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(swiglu(self.gate_proj(x), self.up_proj(x))) class TransformerBlock(nn.Module): def __init__(self, args: ModelArgs, use_sliding: bool = False): super().__init__() self.num_attention_heads = args.num_attention_heads self.hidden_size = args.hidden_size self.use_sliding = use_sliding 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, attn_scale: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None, ) -> mx.array: r = self.self_attn(self.input_layernorm(x), attn_scale, mask, cache) h = x + r r = self.mlp(self.post_attention_layernorm(h)) out = h + r return out class LanguageModel(PipelineMixin, 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.layer_types = args.layer_types self.sliding_window = args.sliding_window self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) self.layers = [ TransformerBlock(args=args, use_sliding=layer_type == "sliding_attention") for layer_type in self.layer_types ] self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps) self.fa_idx = self.layer_types.index("full_attention") self.swa_idx = None for e, l in enumerate(self.layers): if l.use_sliding: self.swa_idx = e break def pipeline(self, group): super().pipeline(group) self.fa_idx = None self.swa_idx = None for e, l in enumerate(self.pipeline_layers): if self.swa_idx is None and l.use_sliding: self.swa_idx = e elif self.fa_idx is None and not l.use_sliding: self.fa_idx = e if self.fa_idx is not None and self.swa_idx is not None: break def __call__( self, inputs: mx.array, cache=None, input_embeddings: Optional[mx.array] = None, ): if input_embeddings is not None: h = input_embeddings else: h = self.embed_tokens(inputs) pipeline_rank = self.pipeline_rank pipeline_size = self.pipeline_size if cache is None: cache = [None] * len(self.pipeline_layers) offset = 0 else: offset = cache[0].offset swa_mask = fa_mask = None if self.fa_idx is not None: fa_mask = create_attention_mask(h, cache[self.fa_idx]) if self.swa_idx is not None: swa_mask = create_attention_mask( h, cache[self.swa_idx], window_size=self.sliding_window ) attn_scale = _get_llama_4_attn_scale( inputs.shape[1], offset, self.args.rope_parameters["llama_4_scaling_beta"], self.args.rope_parameters["original_max_position_embeddings"], ).astype(h.dtype) # Receive from the previous process in the pipeline if pipeline_rank < pipeline_size - 1: h = mx.distributed.recv_like(h, (pipeline_rank + 1)) for l, c in zip(self.pipeline_layers, cache): mask = swa_mask if l.use_sliding else fa_mask h = l(h, attn_scale, mask, cache=c) # Send to the next process in the pipeline if pipeline_rank != 0: h = mx.distributed.send(h, (pipeline_rank - 1) % pipeline_size) if cache[-1] is not None: cache[-1].keys = mx.depends(cache[-1].keys, h) # Broadcast h while keeping it in the graph if pipeline_size > 1: h = mx.distributed.all_gather(h)[: h.shape[0]] 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 = LanguageModel(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, input_embeddings: Optional[mx.array] = None, ): out = self.model(inputs, cache, input_embeddings) if self.args.tie_word_embeddings: out = self.model.embed_tokens.as_linear(out) else: out = self.lm_head(out) return out def sanitize(self, weights): # Remove unused precomputed rotary freqs weights = { k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k } if self.args.tie_word_embeddings: weights.pop("lm_head.weight", None) new_weights = {} for k, v in weights.items(): if "weight_scale_inv" in k: scale_inv = v wk = k.replace("_scale_inv", "") weight = weights[wk] new_weights[wk] = weight * scale_inv elif "activation_scale" in k: continue elif k not in new_weights: new_weights[k] = v weights = new_weights return weights def shard(self, group: Optional[mx.distributed.Group] = None): group = group or mx.distributed.init() N = group.size() for layer in self.model.layers: # Shard the self attention layer.self_attn.q_proj = shard_linear( layer.self_attn.q_proj, "all-to-sharded", group=group ) layer.self_attn.k_proj = shard_linear( layer.self_attn.k_proj, "all-to-sharded", group=group ) layer.self_attn.v_proj = shard_linear( layer.self_attn.v_proj, "all-to-sharded", group=group ) layer.self_attn.o_proj = shard_linear( layer.self_attn.o_proj, "sharded-to-all", group=group ) layer.self_attn.n_heads //= N layer.self_attn.n_kv_heads //= N # Shard the MLP layer.mlp.gate_proj = shard_linear( layer.mlp.gate_proj, "all-to-sharded", group=group ) layer.mlp.down_proj = shard_linear( layer.mlp.down_proj, "sharded-to-all", group=group ) layer.mlp.up_proj = shard_linear( layer.mlp.up_proj, "all-to-sharded", group=group ) @property def layers(self): return self.model.pipeline_layers def make_cache(self): return [ ( RotatingKVCache(max_size=self.model.sliding_window) if layer.use_sliding else KVCache() ) for layer in self.layers ]