# Copyright © 2023-2024 Apple Inc. import math from dataclasses import dataclass from typing import Any, Dict, Optional import mlx.core as mx import mlx.nn as nn from mlx.nn.layers.distributed import shard_inplace, shard_linear, sum_gradients from .activations import swiglu from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention from .pipeline import PipelineMixin from .switch_layers import SwitchGLU @dataclass class ModelArgs(BaseModelArgs): model_type: str = "deepseek_v2" vocab_size: int = 102400 hidden_size: int = 4096 intermediate_size: int = 11008 moe_intermediate_size: int = 1407 num_hidden_layers: int = 30 num_attention_heads: int = 32 num_key_value_heads: int = 32 n_shared_experts: Optional[int] = None n_routed_experts: Optional[int] = None routed_scaling_factor: float = 1.0 kv_lora_rank: int = 512 q_lora_rank: int = 1536 qk_rope_head_dim: int = 64 v_head_dim: int = 128 qk_nope_head_dim: int = 128 topk_method: str = "gready" n_group: Optional[int] = None topk_group: Optional[int] = None num_experts_per_tok: Optional[int] = None moe_layer_freq: int = 1 first_k_dense_replace: int = 0 max_position_embeddings: int = 2048 rms_norm_eps: float = 1e-6 rope_theta: float = 10000.0 rope_scaling: Dict = None attention_bias: bool = False def yarn_find_correction_dim( num_rotations, dim, base=10000, max_position_embeddings=2048 ): return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / ( 2 * math.log(base) ) def yarn_find_correction_range( low_rot, high_rot, dim, base=10000, max_position_embeddings=2048 ): low = math.floor( yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings) ) high = math.ceil( yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings) ) return max(low, 0), min(high, dim - 1) def yarn_get_mscale(scale=1, mscale=1): if scale <= 1: return 1.0 return 0.1 * mscale * math.log(scale) + 1.0 def yarn_linear_ramp_mask(min_val, max_val, dim): if min_val == max_val: max_val += 0.001 # Prevent singularity linear_func = (mx.arange(dim, dtype=mx.float32) - min_val) / (max_val - min_val) return mx.clip(linear_func, 0, 1) class DeepseekV2YarnRotaryEmbedding(nn.Module): def __init__( self, dim, max_position_embeddings=2048, base=10000, scaling_factor=1.0, original_max_position_embeddings=4096, beta_fast=32, beta_slow=1, mscale=1, mscale_all_dim=0, ): super().__init__() self.mscale = yarn_get_mscale(scaling_factor, mscale) / yarn_get_mscale( scaling_factor, mscale_all_dim ) freq_extra = base ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim) freq_inter = scaling_factor * base ** ( mx.arange(0, dim, 2, dtype=mx.float32) / dim ) low, high = yarn_find_correction_range( beta_fast, beta_slow, dim, base, original_max_position_embeddings, ) freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2) self._freqs = (freq_inter * freq_extra) / ( freq_inter * freq_mask + freq_extra * (1 - freq_mask) ) def __call__(self, x, offset=0): if self.mscale != 1.0: x = self.mscale * x return mx.fast.rope( x, x.shape[-1], traditional=True, base=None, scale=1.0, offset=offset, freqs=self._freqs, ) class DeepseekV2Attention(nn.Module): def __init__(self, config: ModelArgs): super().__init__() self.config = config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.q_lora_rank = config.q_lora_rank self.qk_rope_head_dim = config.qk_rope_head_dim self.kv_lora_rank = config.kv_lora_rank self.v_head_dim = config.v_head_dim self.qk_nope_head_dim = config.qk_nope_head_dim self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim self.scale = self.q_head_dim**-0.5 if self.q_lora_rank is None: self.q_proj = nn.Linear( self.hidden_size, self.num_heads * self.q_head_dim, bias=False ) else: self.q_a_proj = nn.Linear( self.hidden_size, self.q_lora_rank, bias=config.attention_bias ) self.q_a_layernorm = nn.RMSNorm(self.q_lora_rank, eps=1e-6) self.q_b_proj = nn.Linear( self.q_lora_rank, self.num_heads * self.q_head_dim, bias=False ) self.kv_a_proj_with_mqa = nn.Linear( self.hidden_size, self.kv_lora_rank + self.qk_rope_head_dim, bias=config.attention_bias, ) self.kv_a_layernorm = nn.RMSNorm(self.kv_lora_rank, eps=1e-6) self.kv_b_proj = nn.Linear( self.kv_lora_rank, self.num_heads * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim), bias=False, ) self.o_proj = nn.Linear( self.num_heads * self.v_head_dim, self.hidden_size, bias=config.attention_bias, ) mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0) scaling_factor = self.config.rope_scaling["factor"] if mscale_all_dim: mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) self.scale = self.scale * mscale * mscale rope_kwargs = { key: self.config.rope_scaling[key] for key in [ "original_max_position_embeddings", "beta_fast", "beta_slow", "mscale", "mscale_all_dim", ] if key in self.config.rope_scaling } self.rope = DeepseekV2YarnRotaryEmbedding( dim=self.qk_rope_head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, **rope_kwargs, ) def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None, ) -> mx.array: B, L, D = x.shape if self.q_lora_rank is None: q = self.q_proj(x) else: q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(x))) q = q.reshape(B, L, self.num_heads, self.q_head_dim).transpose(0, 2, 1, 3) q_nope, q_pe = mx.split(q, [self.qk_nope_head_dim], axis=-1) compressed_kv = self.kv_a_proj_with_mqa(x) compressed_kv, k_pe = mx.split(compressed_kv, [self.kv_lora_rank], axis=-1) k_pe = k_pe.reshape(B, L, 1, self.qk_rope_head_dim).transpose(0, 2, 1, 3) kv = self.kv_b_proj(self.kv_a_layernorm(compressed_kv)) kv = kv.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3) k_nope, values = mx.split(kv, [self.qk_nope_head_dim], axis=-1) if cache is not None: q_pe = self.rope(q_pe, cache.offset) k_pe = self.rope(k_pe, cache.offset) k_pe = mx.repeat(k_pe, self.num_heads, axis=1) keys, values = cache.update_and_fetch( mx.concatenate([k_nope, k_pe], axis=-1), values ) else: q_pe = self.rope(q_pe) k_pe = self.rope(k_pe) k_pe = mx.repeat(k_pe, self.num_heads, axis=1) keys = mx.concatenate([k_nope, k_pe], axis=-1) queries = mx.concatenate([q_nope, q_pe], axis=-1) 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 DeepseekV2MLP(nn.Module): def __init__( self, config: ModelArgs, hidden_size: int = None, intermediate_size: int = None ): super().__init__() self.config = config self.hidden_size = config.hidden_size if hidden_size is None else hidden_size self.intermediate_size = ( config.intermediate_size if intermediate_size is None else intermediate_size ) self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) def __call__(self, x): down_proj = self.down_proj(swiglu(self.gate_proj(x), self.up_proj(x))) return down_proj class MoEGate(nn.Module): def __init__(self, config: ModelArgs): super().__init__() self.config = config self.top_k = config.num_experts_per_tok self.n_routed_experts = config.n_routed_experts self.routed_scaling_factor = config.routed_scaling_factor self.topk_method = config.topk_method self.n_group = config.n_group self.topk_group = config.topk_group self.weight = mx.zeros((self.n_routed_experts, config.hidden_size)) def __call__(self, x): gates = x @ self.weight.T scores = mx.softmax(gates, axis=-1, precise=True) if self.topk_method == "group_limited_greedy": bsz, seq_len = x.shape[:2] scores = scores.reshape(bsz, seq_len, self.n_group, -1) group_scores = scores.max(axis=-1, keepdims=True) k = self.n_group - self.topk_group group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-2)[..., :k, :] scores = mx.put_along_axis( scores, group_idx, mx.array(0.0, scores.dtype), axis=-2 ) scores = scores.reshape(bsz, seq_len, -1) k = self.top_k inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k] scores = mx.take_along_axis(scores, inds, axis=-1) scores = scores * self.routed_scaling_factor return inds, scores class DeepseekV2MoE(nn.Module): def __init__(self, config: ModelArgs): super().__init__() self.config = config self.num_experts_per_tok = config.num_experts_per_tok self.switch_mlp = SwitchGLU( config.hidden_size, config.moe_intermediate_size, config.n_routed_experts ) self.gate = MoEGate(config) if config.n_shared_experts is not None: intermediate_size = config.moe_intermediate_size * config.n_shared_experts self.shared_experts = DeepseekV2MLP( config=config, intermediate_size=intermediate_size ) self.sharding_group = None def __call__(self, x): if self.sharding_group is not None: x = sum_gradients(self.sharding_group)(x) inds, scores = self.gate(x) y = self.switch_mlp(x, inds) y = (y * scores[..., None]).sum(axis=-2) if self.config.n_shared_experts is not None: y = y + self.shared_experts(x) if self.sharding_group is not None: y = mx.distributed.all_sum(y, group=self.sharding_group) return y class DeepseekV2DecoderLayer(nn.Module): def __init__(self, config: ModelArgs, layer_idx: int): super().__init__() self.self_attn = DeepseekV2Attention(config) self.mlp = ( DeepseekV2MoE(config) if ( config.n_routed_experts is not None and layer_idx >= config.first_k_dense_replace and layer_idx % config.moe_layer_freq == 0 ) else DeepseekV2MLP(config) ) 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 ) def __call__( self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = 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 DeepseekV2Model(PipelineMixin, nn.Module): def __init__(self, config: ModelArgs): super().__init__() self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) self.layers = [ DeepseekV2DecoderLayer(config, idx) for idx in range(config.num_hidden_layers) ] self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def __call__( self, x: mx.array, cache: Optional[Any] = None, ) -> mx.array: h = self.embed_tokens(x) pipeline_rank = self.pipeline_rank pipeline_size = self.pipeline_size if cache is None: cache = [None] * len(self.pipeline_layers) mask = create_attention_mask(h, cache[0]) # 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): h = l(h, 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, config: ModelArgs): super().__init__() self.args = config self.model_type = config.model_type self.model = DeepseekV2Model(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) def __call__( self, inputs: mx.array, cache: Optional[Any] = None, ): out = self.model(inputs, cache) return self.lm_head(out) def sanitize(self, weights): for l in range(self.args.num_hidden_layers): prefix = f"model.layers.{l}" for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]: for k in ["weight", "scales", "biases"]: if f"{prefix}.mlp.experts.0.{m}.{k}" in weights: to_join = [ weights.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}") for e in range(self.args.n_routed_experts) ] weights[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join) 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 if layer.self_attn.q_lora_rank is None: layer.self_attn.q_proj = shard_linear( layer.self_attn.q_proj, "all-to-sharded", group=group ) else: layer.self_attn.q_b_proj = shard_linear( layer.self_attn.q_b_proj, "all-to-sharded", group=group ) layer.self_attn.kv_b_proj = shard_linear( layer.self_attn.kv_b_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.num_heads //= N # Shard the MLP if isinstance(layer.mlp, DeepseekV2MLP): 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 ) # Shard the MoE. Shard in place since the MoE should be responsible # for aggregating the results. else: layer.mlp.sharding_group = group shard_inplace( layer.mlp.shared_experts.gate_proj, "all-to-sharded", group=group ) shard_inplace( layer.mlp.shared_experts.down_proj, "sharded-to-all", group=group ) shard_inplace( layer.mlp.shared_experts.up_proj, "all-to-sharded", group=group ) shard_inplace( layer.mlp.switch_mlp.gate_proj, "all-to-sharded", group=group ) shard_inplace( layer.mlp.switch_mlp.down_proj, "sharded-to-all", group=group ) shard_inplace( layer.mlp.switch_mlp.up_proj, "all-to-sharded", group=group ) @property def layers(self): return self.model.pipeline_layers