The DeepSeek V3 model file is ~450 lines of code in MLX LM.

Tuesday 28th January, 2025 - Bruce Sterling

*That’s not much code. I could blog that much code.

https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/models/deepseek_v3.py

# Copyright © 2024 Apple Inc.

import math
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple

import mlx.core as mx
import mlx.nn as nn

from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
from .switch_layers import SwitchGLU

@dataclass
class ModelArgs(BaseModelArgs):
model_type: str = “deepseek_v3”
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 = “noaux_tc”
scoring_func: str = “sigmoid”
norm_topk_prob: bool = True
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 DeepseekV3YarnRotaryEmbedding(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 DeepseekV3Attention(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) 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) 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 = DeepseekV3YarnRotaryEmbedding( 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 DeepseekV3MLP(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(nn.silu(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.norm_topk_prob = config.norm_topk_prob
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))
self.e_score_correction_bias = mx.zeros((self.n_routed_experts,))

def __call__(self, x):
gates = x @ self.weight.T

scores = mx.sigmoid(gates.astype(mx.float32))

assert self.topk_method == “noaux_tc”, “Unsupported topk method.”
bsz, seq_len = x.shape[:2]
scores = scores + self.e_score_correction_bias
scores = scores.reshape(bsz, seq_len, self.n_group, -1)
group_scores = mx.topk(scores, 2, axis=-1).sum(axis=-1)
k = self.n_group – self.topk_group
group_idx = mx.argpartition(group_scores, kth=k – 1, axis=-1)[…, :k]
batch_idx = mx.expand_dims(mx.arange(bsz), (1, 2))
seq_idx = mx.expand_dims(mx.arange(seq_len), (0, 2))
scores[batch_idx, seq_idx, group_idx] = 0.0
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)
if self.top_k > 1 and self.norm_topk_prob:
denominator = scores.sum(axis=-1, keepdims=True) + 1e-20
scores = scores / denominator
scores = scores * self.routed_scaling_factor

return inds, scores

class DeepseekV3MoE(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 = DeepseekV3MLP(
config=config, intermediate_size=intermediate_size
)

def __call__(self, x):
inds, scores = self.gate(x)
y = self.switch_mlp(x, inds)
y = (y * scores[…, None]).sum(axis=-2).astype(y.dtype)
if self.config.n_shared_experts is not None:
y = y + self.shared_experts(x)

return y

class DeepseekV3DecoderLayer(nn.Module):
def __init__(self, config: ModelArgs, layer_idx: int):
super().__init__()
self.self_attn = DeepseekV3Attention(config)
self.mlp = (
DeepseekV3MoE(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 DeepseekV3MLP(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
# Protect against overflow for fp16
if out.dtype == mx.float16:
out = mx.clip(out, a_min=None, a_max=mx.finfo(mx.float16).max – 1000)
return out

class DeepseekV3Model(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 = [
DeepseekV3DecoderLayer(config, idx)
for idx in range(config.num_hidden_layers)
]
self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.pipeline_rank = 0
self.pipeline_size = 1

def pipeline(self, group):
# Split layers in reverse so rank=0 gets the last layers and
# rank=pipeline_size-1 gets the first
self.pipeline_rank = group.rank()
self.pipeline_size = group.size()
layers_per_rank = (
len(self.layers) + self.pipeline_size – 1
) // self.pipeline_size
start = (self.pipeline_size – self.pipeline_rank – 1) * layers_per_rank
self.layers = self.layers[start : start + layers_per_rank]

def __call__(
self,
x: mx.array,
cache: Optional[Any] = None,
mask: Optional[mx.array] = None,
) -> mx.array:
h = self.embed_tokens(x)

pipeline_rank = self.pipeline_rank
pipeline_size = self.pipeline_size
# Hack to avoid time-outs during prompt-processing
dist_stream = mx.cpu if h.shape[1] > 1 else mx.gpu
if mask is None:
mask = create_attention_mask(h, cache)

if cache is None:
cache = [None] * len(self.layers)

# Receive from the previous process in the pipeline

if pipeline_rank < pipeline_size - 1: h = mx.distributed.recv_like(h, (pipeline_rank + 1), stream=dist_stream) for layer, c in zip(self.layers, cache): h = layer(h, mask, c) # Send to the next process in the pipeline if pipeline_rank != 0: h = mx.distributed.send( h, (pipeline_rank - 1) % pipeline_size, stream=dist_stream ) # Broadcast h while keeping it in the graph h = mx.distributed.all_gather(h, stream=dist_stream)[: 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 = DeepseekV3Model(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) def __call__( self, inputs: mx.array, cache: Optional[Any] = None, mask: Optional[mx.array] = None, ): out = self.model(inputs, cache, mask) 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) # Remove multi-token prediction layer return {k: v for k, v in weights.items() if not k.startswith("model.layers.61")} @property def layers(self): return self.model.layers