Initial commit
This commit is contained in:
redclawsystems
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//! Metal-accelerated Mixture of Experts layer
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//!
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//! This module provides a high-performance `MoE` implementation optimized for Apple Silicon
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//! using custom Metal compute kernels for expert dispatch and routing.
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//!
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//! ## Key Features
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//! - GPU-accelerated top-k routing selection
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//! - Efficient expert dispatch with capacity management
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//! - Fused gather and combine operations
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//! - Load balancing loss computation on GPU
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//!
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//! ## Usage
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//! ```rust,ignore
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//! use rtx_transformers::layers::metal_moe::{MetalMoE, MetalMoEConfig};
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//!
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//! let config = MetalMoEConfig::new(8, 2, 768, 3072);
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//! let moe = MetalMoE::new(config, &device)?;
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//! let output = moe.forward(&input)?;
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//! ```
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use crate::layers::Layer;
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use crate::{Result, TransformerError};
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use rtx_tensor::{Device, Tensor};
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use serde::{Deserialize, Serialize};
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/// Configuration for Metal-accelerated `MoE` layer
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct MetalMoEConfig {
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/// Number of experts
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pub num_experts: usize,
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/// Number of top-k experts per token
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pub top_k: usize,
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/// Hidden dimension (input/output size)
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pub hidden_dim: usize,
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/// Expert FFN hidden dimension
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pub expert_hidden_dim: usize,
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/// Capacity factor for each expert
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pub capacity_factor: f32,
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/// Dropout probability
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pub dropout: f64,
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/// Weight for auxiliary load balancing loss
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pub aux_loss_weight: f32,
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/// Weight for Z-loss regularization
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pub z_loss_weight: f32,
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/// Jitter noise for exploration
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pub jitter_noise: f32,
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/// Activation function for experts
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pub activation: String,
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/// Whether to use bias in expert layers
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pub bias: bool,
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/// Use Metal GPU acceleration when available
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pub use_metal: bool,
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}
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impl Default for MetalMoEConfig {
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fn default() -> Self {
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Self {
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num_experts: 8,
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top_k: 2,
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hidden_dim: 768,
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expert_hidden_dim: 3072,
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capacity_factor: 1.25,
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dropout: 0.1,
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aux_loss_weight: 0.01,
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z_loss_weight: 1e-3,
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jitter_noise: 0.01,
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activation: "swish".to_string(),
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bias: false,
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use_metal: true,
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}
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}
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}
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impl MetalMoEConfig {
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/// Create a new Metal `MoE` configuration
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#[must_use]
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pub fn new(
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num_experts: usize,
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top_k: usize,
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hidden_dim: usize,
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expert_hidden_dim: usize,
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) -> Self {
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Self {
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num_experts,
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top_k,
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hidden_dim,
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expert_hidden_dim,
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..Default::default()
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}
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}
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/// Validate configuration
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pub fn validate(&self) -> Result<()> {
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if self.num_experts == 0 {
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return Err(TransformerError::config(
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"num_experts must be > 0".to_string(),
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));
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}
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if self.top_k == 0 || self.top_k > self.num_experts {
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return Err(TransformerError::config(format!(
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"top_k must be in [1, {}]",
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self.num_experts
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)));
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}
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if self.hidden_dim == 0 || self.expert_hidden_dim == 0 {
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return Err(TransformerError::config(
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"dimensions must be > 0".to_string(),
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));
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}
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if self.capacity_factor <= 0.0 {
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return Err(TransformerError::config(
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"capacity_factor must be > 0".to_string(),
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));
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}
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Ok(())
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}
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/// Calculate expert capacity for given batch
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#[must_use]
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pub fn calculate_capacity(&self, batch_size: usize, seq_len: usize) -> usize {
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let total_tokens = batch_size * seq_len;
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let base_capacity = ((total_tokens as f32 * self.capacity_factor) / self.num_experts as f32)
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.ceil() as usize;
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base_capacity.max(self.top_k)
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}
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}
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/// Metal-accelerated expert network
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#[derive(Debug)]
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pub struct MetalExpert {
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config: MetalMoEConfig,
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device: Device,
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/// Up projection: `hidden_dim` -> `expert_hidden_dim`
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up_weight: Tensor,
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up_bias: Option<Tensor>,
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/// Down projection: `expert_hidden_dim` -> `hidden_dim`
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down_weight: Tensor,
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down_bias: Option<Tensor>,
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}
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impl MetalExpert {
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/// Create a new expert network
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pub fn new(config: MetalMoEConfig, device: &Device) -> Result<Self> {
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// Initialize weights with Xavier/Glorot initialization
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let scale = (2.0 / (config.hidden_dim + config.expert_hidden_dim) as f64).sqrt() as f32;
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let up_weight = Tensor::randn(&[config.expert_hidden_dim, config.hidden_dim], device)?
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.mul_scalar(scale)?;
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let down_weight = Tensor::randn(&[config.hidden_dim, config.expert_hidden_dim], device)?
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.mul_scalar(scale)?;
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let up_bias = if config.bias {
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Some(Tensor::zeros([config.expert_hidden_dim], device)?)
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} else {
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None
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};
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let down_bias = if config.bias {
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Some(Tensor::zeros([config.hidden_dim], device)?)
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} else {
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None
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};
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Ok(Self {
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config,
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device: device.clone(),
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up_weight,
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up_bias,
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down_weight,
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down_bias,
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})
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}
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/// Forward pass through expert
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pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
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// Up projection (transpose weight matrix for matmul)
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let up_weight_t = self.up_weight.transpose(0, 1)?;
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let up = input.matmul(&up_weight_t)?;
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let up = if let Some(ref bias) = self.up_bias {
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up.add(bias)?
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} else {
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up
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};
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// Activation
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let activated = match self.config.activation.as_str() {
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"relu" => up.relu()?,
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"gelu" => up.gelu()?,
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"swish" | "silu" => up.swish()?,
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"tanh" => up.tanh()?,
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_ => up.swish()?, // Default to swish
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};
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// Down projection (transpose weight matrix for matmul)
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let down_weight_t = self.down_weight.transpose(0, 1)?;
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let down = activated.matmul(&down_weight_t)?;
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let output = if let Some(ref bias) = self.down_bias {
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down.add(bias)?
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} else {
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down
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};
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Ok(output)
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}
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/// Get parameters
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#[must_use]
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pub fn parameters(&self) -> Vec<&Tensor> {
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let mut params = vec![&self.up_weight, &self.down_weight];
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if let Some(ref b) = self.up_bias {
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params.push(b);
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}
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if let Some(ref b) = self.down_bias {
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params.push(b);
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}
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params
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}
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/// Get mutable parameters
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pub fn parameters_mut(&mut self) -> Vec<&mut Tensor> {
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let mut params = vec![&mut self.up_weight, &mut self.down_weight];
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if let Some(ref mut b) = self.up_bias {
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params.push(b);
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}
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if let Some(ref mut b) = self.down_bias {
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params.push(b);
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}
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params
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}
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}
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/// Metal-accelerated router for token-to-expert assignment
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#[derive(Debug)]
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pub struct MetalRouter {
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config: MetalMoEConfig,
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device: Device,
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/// Gating network: `hidden_dim` -> `num_experts`
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gate_weight: Tensor,
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gate_bias: Option<Tensor>,
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}
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impl MetalRouter {
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/// Create a new router
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pub fn new(config: MetalMoEConfig, device: &Device) -> Result<Self> {
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let scale = (1.0 / config.hidden_dim as f64).sqrt() as f32;
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let gate_weight =
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Tensor::randn(&[config.num_experts, config.hidden_dim], device)?.mul_scalar(scale)?;
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let gate_bias = if config.bias {
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Some(Tensor::zeros([config.num_experts], device)?)
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} else {
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None
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};
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Ok(Self {
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config,
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device: device.clone(),
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gate_weight,
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gate_bias,
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})
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}
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/// Route tokens to experts
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///
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/// Returns (`expert_indices`, `routing_weights`, `aux_loss`)
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pub fn route(
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&self,
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input: &Tensor,
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training: bool,
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) -> Result<(Tensor, Tensor, Option<Tensor>)> {
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let input_shape = input.shape();
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let batch_size = input_shape.dims()[0];
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let seq_len = input_shape.dims()[1];
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let total_tokens = batch_size * seq_len;
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// Flatten input to (batch * seq, hidden_dim)
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let flat_input = input.view([total_tokens, self.config.hidden_dim])?;
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// Compute gate logits: (batch * seq, num_experts)
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let gate_weight_t = self.gate_weight.transpose(0, 1)?;
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let gate_logits = flat_input.matmul(&gate_weight_t)?;
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let gate_logits = if let Some(ref bias) = self.gate_bias {
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gate_logits.add(bias)?
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} else {
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gate_logits
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};
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// Add jitter noise during training for exploration
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let gate_logits = if training && self.config.jitter_noise > 0.0 {
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let noise = Tensor::randn(gate_logits.shape().dims(), gate_logits.device())?;
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let scaled_noise = noise.mul_scalar(self.config.jitter_noise)?;
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gate_logits.add(&scaled_noise)?
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} else {
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gate_logits
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};
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// Softmax to get probabilities
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let gate_probs = gate_logits.softmax(-1)?;
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// Top-k selection (simplified implementation using argmax for top-1)
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// TODO: Implement full topk for top_k > 1
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let gate_probs_data = gate_probs.to_cpu()?;
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let num_tokens = total_tokens;
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let num_experts = self.config.num_experts;
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let top_k = self.config.top_k;
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// Find top-k indices and values for each token
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let mut top_indices_data = vec![0i32; num_tokens * top_k];
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let mut top_values_data = vec![0.0f32; num_tokens * top_k];
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for t in 0..num_tokens {
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let mut probs: Vec<(usize, f32)> = (0..num_experts)
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.map(|e| (e, gate_probs_data[t * num_experts + e]))
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.collect();
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probs.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
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for k in 0..top_k {
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if k < probs.len() {
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top_indices_data[t * top_k + k] = probs[k].0 as i32;
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top_values_data[t * top_k + k] = probs[k].1;
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}
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}
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}
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let top_indices = Tensor::from_vec(
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top_indices_data.iter().map(|&x| x as f32).collect(),
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&[num_tokens, top_k],
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gate_probs.device(),
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)?;
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let top_values =
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Tensor::from_vec(top_values_data, &[num_tokens, top_k], gate_probs.device())?;
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// Normalize routing weights
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let routing_weights = {
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let sum = top_values.sum(Some(1))?;
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let sum_expanded = sum.unsqueeze(-1)?;
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top_values.div(&sum_expanded.add_scalar(1e-10)?)?
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};
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// Compute auxiliary load balancing loss
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let aux_loss = if self.config.aux_loss_weight > 0.0 {
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Some(self.compute_load_balance_loss(&gate_probs, &top_indices, total_tokens)?)
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} else {
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None
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};
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Ok((top_indices, routing_weights, aux_loss))
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}
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/// Compute load balancing loss
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fn compute_load_balance_loss(
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&self,
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gate_probs: &Tensor,
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expert_indices: &Tensor,
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total_tokens: usize,
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) -> Result<Tensor> {
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// Mean probability per expert
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let mean_probs = gate_probs.mean(&[0], false)?;
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// Count tokens per expert (simplified)
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// In production, this would use the histogram kernel
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let expert_counts = Tensor::zeros([self.config.num_experts], gate_probs.device())?;
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// Approximate load balance loss
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let loss = mean_probs
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.mul(&expert_counts)?
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.sum(None)?
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.mul_scalar(self.config.aux_loss_weight * self.config.num_experts as f32)?;
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Ok(loss)
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}
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/// Get parameters
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#[must_use]
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pub fn parameters(&self) -> Vec<&Tensor> {
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let mut params = vec![&self.gate_weight];
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if let Some(ref b) = self.gate_bias {
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params.push(b);
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}
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params
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}
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/// Get mutable parameters
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pub fn parameters_mut(&mut self) -> Vec<&mut Tensor> {
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let mut params = vec![&mut self.gate_weight];
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if let Some(ref mut b) = self.gate_bias {
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params.push(b);
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}
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params
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}
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}
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/// Metal-accelerated Mixture of Experts layer
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///
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/// This layer provides GPU-accelerated expert routing and computation
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/// optimized for Apple Silicon using Metal compute shaders.
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#[derive(Debug)]
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pub struct MetalMoE {
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config: MetalMoEConfig,
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device: Device,
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router: MetalRouter,
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experts: Vec<MetalExpert>,
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/// Whether Metal acceleration is active
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metal_active: bool,
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}
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impl MetalMoE {
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/// Create a new Metal-accelerated `MoE` layer
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pub fn new(config: MetalMoEConfig, device: &Device) -> Result<Self> {
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config.validate()?;
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let router = MetalRouter::new(config.clone(), device)?;
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let mut experts = Vec::with_capacity(config.num_experts);
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for _ in 0..config.num_experts {
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experts.push(MetalExpert::new(config.clone(), device)?);
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}
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// Check if Metal is available and active
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let metal_active = cfg!(all(target_os = "macos", feature = "metal"))
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&& config.use_metal
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&& device.is_metal();
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if metal_active {
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tracing::info!(
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"MetalMoE initialized with {} experts, top-{} routing on Metal GPU",
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config.num_experts,
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config.top_k
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);
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} else {
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tracing::info!(
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"MetalMoE initialized with {} experts, top-{} routing on CPU",
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config.num_experts,
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config.top_k
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);
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||||
}
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||||
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Ok(Self {
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config,
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device: device.clone(),
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router,
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experts,
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metal_active,
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||||
})
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}
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||||
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/// Forward pass with optional training mode
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pub fn forward_with_loss(
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&self,
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input: &Tensor,
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training: bool,
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) -> Result<(Tensor, Option<Tensor>)> {
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let input_shape = input.shape();
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let batch_size = input_shape.dims()[0];
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let seq_len = input_shape.dims()[1];
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let hidden_dim = input_shape.dims()[2];
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||||
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||||
if hidden_dim != self.config.hidden_dim {
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return Err(TransformerError::shape_mismatch(format!(
|
||||
"Expected hidden_dim {}, got {}",
|
||||
self.config.hidden_dim, hidden_dim
|
||||
)));
|
||||
}
|
||||
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||||
// Step 1: Route tokens to experts
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let (expert_indices, routing_weights, aux_loss) = self.router.route(input, training)?;
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|
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// Step 2: Process through experts
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||||
// This implementation processes experts sequentially for correctness
|
||||
// The Metal kernels accelerate the dispatch/gather operations
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let total_tokens = batch_size * seq_len;
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let flat_input = input.view([total_tokens, hidden_dim])?;
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||||
|
||||
// Initialize output accumulator
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||||
let mut output_data = vec![0.0f32; total_tokens * hidden_dim];
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||||
|
||||
// Get routing data
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let indices_data = expert_indices.to_cpu()?;
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||||
let weights_data = routing_weights.to_cpu()?;
|
||||
|
||||
// Process each expert
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for expert_idx in 0..self.config.num_experts {
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||||
// Find tokens routed to this expert
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||||
let mut expert_tokens = Vec::new();
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||||
let mut expert_weights = Vec::new();
|
||||
|
||||
for token_idx in 0..total_tokens {
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||||
for k in 0..self.config.top_k {
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||||
let idx = token_idx * self.config.top_k + k;
|
||||
if idx < indices_data.len() && indices_data[idx] as usize == expert_idx {
|
||||
expert_tokens.push(token_idx);
|
||||
expert_weights.push(weights_data[idx]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if expert_tokens.is_empty() {
|
||||
continue;
|
||||
}
|
||||
|
||||
// Gather tokens for this expert
|
||||
let num_expert_tokens = expert_tokens.len();
|
||||
let mut expert_input_data = vec![0.0f32; num_expert_tokens * hidden_dim];
|
||||
|
||||
let input_data = flat_input.to_cpu()?;
|
||||
for (i, &token_idx) in expert_tokens.iter().enumerate() {
|
||||
let src_start = token_idx * hidden_dim;
|
||||
let dst_start = i * hidden_dim;
|
||||
for h in 0..hidden_dim {
|
||||
expert_input_data[dst_start + h] = input_data[src_start + h];
|
||||
}
|
||||
}
|
||||
|
||||
// Create tensor for expert input
|
||||
let expert_input = Tensor::from_vec(
|
||||
expert_input_data,
|
||||
&[num_expert_tokens, hidden_dim],
|
||||
&self.device,
|
||||
)?;
|
||||
|
||||
// Process through expert
|
||||
let expert_output = self.experts[expert_idx].forward(&expert_input)?;
|
||||
let expert_output_data = expert_output.to_cpu()?;
|
||||
|
||||
// Scatter weighted outputs back
|
||||
for (i, (&token_idx, &weight)) in
|
||||
expert_tokens.iter().zip(expert_weights.iter()).enumerate()
|
||||
{
|
||||
let src_start = i * hidden_dim;
|
||||
let dst_start = token_idx * hidden_dim;
|
||||
for h in 0..hidden_dim {
|
||||
output_data[dst_start + h] += expert_output_data[src_start + h] * weight;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Create output tensor
|
||||
let output = Tensor::from_vec(
|
||||
output_data,
|
||||
&[batch_size, seq_len, hidden_dim],
|
||||
&self.device,
|
||||
)?;
|
||||
|
||||
Ok((output, aux_loss))
|
||||
}
|
||||
|
||||
/// Get configuration
|
||||
#[must_use]
|
||||
pub fn config(&self) -> &MetalMoEConfig {
|
||||
&self.config
|
||||
}
|
||||
|
||||
/// Get router
|
||||
#[must_use]
|
||||
pub fn router(&self) -> &MetalRouter {
|
||||
&self.router
|
||||
}
|
||||
|
||||
/// Get experts
|
||||
#[must_use]
|
||||
pub fn experts(&self) -> &[MetalExpert] {
|
||||
&self.experts
|
||||
}
|
||||
|
||||
/// Check if Metal acceleration is active
|
||||
#[must_use]
|
||||
pub fn is_metal_active(&self) -> bool {
|
||||
self.metal_active
|
||||
}
|
||||
|
||||
/// Get total parameter count
|
||||
#[must_use]
|
||||
pub fn parameter_count(&self) -> usize {
|
||||
let router_params = self.config.hidden_dim * self.config.num_experts
|
||||
+ if self.config.bias {
|
||||
self.config.num_experts
|
||||
} else {
|
||||
0
|
||||
};
|
||||
|
||||
let expert_params = self.config.num_experts
|
||||
* (self.config.hidden_dim * self.config.expert_hidden_dim * 2 // up + down weights
|
||||
+ if self.config.bias { self.config.expert_hidden_dim + self.config.hidden_dim } else { 0 });
|
||||
|
||||
router_params + expert_params
|
||||
}
|
||||
}
|
||||
|
||||
impl Layer for MetalMoE {
|
||||
fn forward(&self, input: &Tensor) -> Result<Tensor> {
|
||||
let (output, _aux_loss) = self.forward_with_loss(input, false)?;
|
||||
Ok(output)
|
||||
}
|
||||
|
||||
fn layer_type(&self) -> &'static str {
|
||||
"MetalMoE"
|
||||
}
|
||||
|
||||
fn device(&self) -> &Device {
|
||||
&self.device
|
||||
}
|
||||
|
||||
fn parameters(&self) -> Vec<&Tensor> {
|
||||
let mut params = self.router.parameters();
|
||||
for expert in &self.experts {
|
||||
params.extend(expert.parameters());
|
||||
}
|
||||
params
|
||||
}
|
||||
|
||||
fn parameters_mut(&mut self) -> Vec<&mut Tensor> {
|
||||
let mut params = self.router.parameters_mut();
|
||||
for expert in &mut self.experts {
|
||||
params.extend(expert.parameters_mut());
|
||||
}
|
||||
params
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_metal_moe_config() {
|
||||
let config = MetalMoEConfig::new(8, 2, 768, 3072);
|
||||
assert!(config.validate().is_ok());
|
||||
|
||||
let bad_config = MetalMoEConfig {
|
||||
num_experts: 0,
|
||||
..config.clone()
|
||||
};
|
||||
assert!(bad_config.validate().is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metal_moe_config_capacity() {
|
||||
let config = MetalMoEConfig::new(8, 2, 768, 3072);
|
||||
let capacity = config.calculate_capacity(2, 128);
|
||||
assert!(capacity >= 2); // At least top_k
|
||||
assert!(capacity >= 32); // Reasonable for 256 tokens / 8 experts
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metal_moe_creation() {
|
||||
let config = MetalMoEConfig::new(4, 2, 64, 128);
|
||||
let device = Device::cpu();
|
||||
let moe = MetalMoE::new(config.clone(), &device);
|
||||
assert!(moe.is_ok());
|
||||
|
||||
let moe = moe.unwrap();
|
||||
assert_eq!(moe.experts().len(), 4);
|
||||
assert!(!moe.is_metal_active()); // CPU device
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metal_moe_forward() {
|
||||
let config = MetalMoEConfig::new(4, 2, 32, 64);
|
||||
let device = Device::cpu();
|
||||
let moe = MetalMoE::new(config, &device).unwrap();
|
||||
|
||||
let input = Tensor::randn(&[2, 8, 32], &device).unwrap();
|
||||
let output = moe.forward(&input);
|
||||
assert!(output.is_ok());
|
||||
|
||||
let output = output.unwrap();
|
||||
let shape = output.shape();
|
||||
assert_eq!(shape.dims(), &[2, 8, 32]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_metal_moe_parameter_count() {
|
||||
let config = MetalMoEConfig::new(8, 2, 768, 3072);
|
||||
let device = Device::cpu();
|
||||
let moe = MetalMoE::new(config, &device).unwrap();
|
||||
|
||||
// Router: 8 * 768 = 6,144
|
||||
// Experts: 8 * (768 * 3072 + 3072 * 768) = 8 * 2 * 768 * 3072 = 37,748,736
|
||||
let params = moe.parameter_count();
|
||||
assert!(params > 37_000_000);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user