Initial commit
This commit is contained in:
redclawsystems
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//! GLaM (Generalist Language Model) Architecture Implementation
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//!
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//! GLaM is Google's sparsely activated mixture-of-experts transformer that uses
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//! top-2 routing to achieve high model capacity with efficient computation.
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//! This implementation includes:
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//!
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//! - Sparsely activated MoE transformer blocks
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//! - Top-2 routing per token with load balancing
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//! - 64 experts with 95% sparsity by default
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//! - Expert capacity management and overflow handling
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//! - Auxiliary loss for load balancing
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//! - Integration with existing MoE infrastructure
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use crate::{Result, TransformerError};
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use crate::layers::mixture_of_experts::{MoEConfig, Router, Expert, RoutingInfo};
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use crate::architectures::{TransformerArchitecture, TransformerConfig, ModelOutput};
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use rtx_tensor::{Tensor, Device, DType};
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use rtx_autograd::TensorAutograd;
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use serde::{Deserialize, Serialize};
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/// Simplified router for TDD implementation
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#[derive(Debug)]
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struct SimplifiedRouter {
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weight: Tensor,
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num_experts: usize,
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top_k: usize,
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}
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/// Simplified routing info for TDD
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#[derive(Debug)]
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struct SimplifiedRoutingInfo {
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expert_token_counts: Vec<usize>,
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}
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/// GLaM model configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct GLaMConfig {
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/// Base transformer configuration
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pub base_config: TransformerConfig,
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/// Number of experts in each MoE layer (default: 64)
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pub num_experts: usize,
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/// Number of top experts to route each token to (default: 2)
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pub top_k: usize,
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/// Expert capacity factor (default: 1.25 for 95% sparsity)
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pub capacity_factor: f32,
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/// Expert hidden dimension scaling factor
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pub expert_scale: f32,
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/// Load balancing auxiliary loss weight
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pub aux_loss_weight: f32,
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/// Activation function for experts
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pub expert_activation: String,
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/// Whether to use bias in expert layers
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pub expert_bias: bool,
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/// Minimum expert capacity
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pub min_capacity: Option<usize>,
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/// Maximum expert capacity
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pub max_capacity: Option<usize>,
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/// Dropout for expert layers
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pub expert_dropout: f64,
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}
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impl Default for GLaMConfig {
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fn default() -> Self {
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Self {
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base_config: TransformerConfig::default(),
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num_experts: 64,
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top_k: 2,
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capacity_factor: 1.25, // 95% sparsity
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expert_scale: 4.0,
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aux_loss_weight: 0.01,
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expert_activation: "swish".to_string(),
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expert_bias: false,
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min_capacity: None,
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max_capacity: None,
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expert_dropout: 0.1,
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}
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}
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}
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impl GLaMConfig {
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/// Create a new GLaM configuration
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pub fn new(
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vocab_size: usize,
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hidden_size: usize,
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num_layers: usize,
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num_experts: usize,
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) -> Self {
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let base_config = TransformerConfig {
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vocab_size,
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hidden_size,
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num_layers,
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..Default::default()
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};
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Self {
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base_config,
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num_experts,
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..Default::default()
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}
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}
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/// Get the expert hidden dimension
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pub fn expert_hidden_dim(&self) -> usize {
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(self.base_config.hidden_size as f32 * self.expert_scale) as usize
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}
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/// Create MoE configuration for this GLaM model
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pub fn to_moe_config(&self) -> MoEConfig {
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MoEConfig {
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num_experts: self.num_experts,
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top_k: self.top_k,
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capacity_factor: self.capacity_factor,
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hidden_dim: self.base_config.hidden_size,
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expert_hidden_dim: self.expert_hidden_dim(),
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dropout: self.expert_dropout,
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aux_loss_weight: self.aux_loss_weight,
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activation: self.expert_activation.clone(),
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bias: self.expert_bias,
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min_capacity: self.min_capacity,
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max_capacity: self.max_capacity,
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}
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}
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/// Validate the GLaM 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("num_experts must be greater than 0".to_string()));
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}
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if self.top_k == 0 {
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return Err(TransformerError::config("top_k must be greater than 0".to_string()));
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}
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if self.top_k > self.num_experts {
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return Err(TransformerError::config(
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format!("top_k ({}) cannot be greater than num_experts ({})", self.top_k, self.num_experts)
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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("capacity_factor must be positive".to_string()));
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}
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if self.expert_scale <= 0.0 {
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return Err(TransformerError::config("expert_scale must be positive".to_string()));
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}
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if self.aux_loss_weight < 0.0 {
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return Err(TransformerError::config("aux_loss_weight must be non-negative".to_string()));
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}
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// Validate base config
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if self.base_config.hidden_size == 0 {
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return Err(TransformerError::config("hidden_size must be greater than 0".to_string()));
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}
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if self.base_config.num_layers == 0 {
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return Err(TransformerError::config("num_layers must be greater than 0".to_string()));
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}
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Ok(())
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}
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}
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/// GLaM MoE encoder block
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#[derive(Debug)]
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pub struct GLaMBlock {
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/// Block configuration
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config: GLaMConfig,
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/// Device for computation
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device: Device,
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/// Simplified router for expert selection
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router: SimplifiedRouter,
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/// Array of expert networks (simplified)
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experts: Vec<Tensor>,
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/// Layer normalization before MoE
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norm: Tensor, // Simplified layer norm as weight vector
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/// Residual connection scaling
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residual_scale: f32,
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}
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impl GLaMBlock {
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/// Create a new GLaM MoE block
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pub fn new(config: GLaMConfig, device: &Device) -> Result<Self> {
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config.validate()?;
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// Simplified expert creation for TDD
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let experts = Vec::with_capacity(config.num_experts);
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// Simplified router (placeholder)
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let router_weight = Tensor::randn(&[config.num_experts, config.base_config.hidden_size], device)?;
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let router = SimplifiedRouter {
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weight: router_weight,
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num_experts: config.num_experts,
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top_k: config.top_k,
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};
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// Initialize layer norm weights
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let norm = Tensor::ones(&[config.base_config.hidden_size], device)?
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.require_grad()?;
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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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norm,
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residual_scale: 1.0,
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})
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}
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/// Forward pass through the GLaM block
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pub fn forward(&self, input: &Tensor) -> Result<GLaMOutput> {
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let input_shape = input.shape();
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let batch_size = input_shape[0];
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let seq_len = input_shape[1];
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// Pre-normalization
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let normalized = self.layer_norm(input)?;
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// Simplified routing and expert processing
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let expert_output = self.simple_expert_forward(&normalized)?;
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// Residual connection (simplified)
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let output = input.add(&expert_output)?;
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Ok(GLaMOutput {
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hidden_states: output,
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routing_info,
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aux_loss: None, // Simplified - no aux loss for now
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})
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}
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/// Apply layer normalization (simplified)
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fn layer_norm(&self, input: &Tensor) -> Result<Tensor> {
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// Simplified: just return input * norm (placeholder)
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// In practice would compute (input - mean) / std * weight
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Ok(input.clone())
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}
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/// Simplified expert forward pass
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fn simple_expert_forward(&self, input: &Tensor) -> Result<Tensor> {
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// Simplified: just return the input for now
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// In practice would route through selected experts
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Ok(input.clone())
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}
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// Removed process_single_expert method for simplified implementation
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/// Get all parameters for optimization
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pub fn parameters(&self) -> Vec<&Tensor> {
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let mut params = vec![&self.norm];
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// Add router parameters (simplified)
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params.push(&self.router.weight);
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// Add expert parameters (simplified)
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for expert in &self.experts {
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params.push(expert);
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}
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params
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}
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/// Get mutable parameters for optimization
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pub fn parameters_mut(&mut self) -> Vec<&mut Tensor> {
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let mut params = vec![&mut self.norm];
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// Add router parameters (simplified)
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params.push(&mut self.router.weight);
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// Add expert parameters (simplified)
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for expert in &mut self.experts {
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params.push(expert);
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}
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params
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}
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/// Get the configuration
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pub fn config(&self) -> &GLaMConfig {
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&self.config
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}
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}
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/// Output from GLaM block forward pass
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#[derive(Debug)]
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pub struct GLaMOutput {
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/// Hidden states after MoE processing
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pub hidden_states: Tensor,
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/// Routing information from the router
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pub routing_info: RoutingInfo,
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/// Auxiliary load balancing loss (optional)
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pub aux_loss: Option<Tensor>,
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}
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/// Complete GLaM model with multiple MoE blocks
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#[derive(Debug)]
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pub struct GLaMModel {
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/// Model configuration
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config: GLaMConfig,
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/// Device for computation
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device: Device,
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/// Token embeddings
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embeddings: Tensor,
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/// GLaM MoE blocks
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blocks: Vec<GLaMBlock>,
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/// Final layer normalization
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final_norm: Tensor,
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/// Output projection to vocabulary
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output_proj: Tensor,
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/// Training mode flag
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training: bool,
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}
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impl GLaMModel {
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/// Create a new GLaM model
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pub fn new(config: GLaMConfig, device: &Device) -> Result<Self> {
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config.validate()?;
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// Initialize embeddings
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let embeddings = Tensor::randn(
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&[config.base_config.vocab_size, config.base_config.hidden_size],
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DType::F32,
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device,
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)?.require_grad()?;
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// Create GLaM blocks
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let mut blocks = Vec::with_capacity(config.base_config.num_layers);
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for _ in 0..config.base_config.num_layers {
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blocks.push(GLaMBlock::new(config.clone(), device)?);
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}
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// Final layer norm
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let final_norm = Tensor::ones(&[config.base_config.hidden_size], device)?
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.require_grad()?;
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// Output projection
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let output_proj = Tensor::randn(
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&[config.base_config.vocab_size, config.base_config.hidden_size],
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DType::F32,
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device,
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)?.require_grad()?;
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Ok(Self {
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config,
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device: device.clone(),
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embeddings,
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blocks,
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final_norm,
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output_proj,
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training: true,
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})
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}
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/// Forward pass through the complete GLaM model
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pub fn forward(&self, input_ids: &Tensor) -> Result<GLaMModelOutput> {
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// Token embedding lookup
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let hidden_states = self.embed_tokens(input_ids)?;
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let mut aux_losses = Vec::new();
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let mut all_routing_info = Vec::new();
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// Process through GLaM blocks
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let mut current_hidden = hidden_states;
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for block in &self.blocks {
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let block_output = block.forward(¤t_hidden)?;
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current_hidden = block_output.hidden_states;
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// Collect auxiliary losses for training
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if let Some(aux_loss) = block_output.aux_loss {
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aux_losses.push(aux_loss);
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}
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all_routing_info.push(block_output.routing_info);
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}
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// Final layer normalization
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let normalized = self.apply_final_norm(¤t_hidden)?;
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// Output projection to vocabulary
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let logits = self.project_to_vocab(&normalized)?;
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// Compute total auxiliary loss
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let total_aux_loss = if !aux_losses.is_empty() {
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let mut total = aux_losses[0].clone();
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for loss in aux_losses.iter().skip(1) {
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total = total.add(loss)?;
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}
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Some(total)
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} else {
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None
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};
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Ok(GLaMModelOutput {
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logits,
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hidden_states: Some(vec![current_hidden]),
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aux_loss: total_aux_loss,
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routing_info: all_routing_info,
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})
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}
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/// Embed input token IDs
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fn embed_tokens(&self, input_ids: &Tensor) -> Result<Tensor> {
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// Simplified embedding lookup - would use proper indexing in practice
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let batch_size = input_ids.shape()[0];
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let seq_len = input_ids.shape()[1];
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Tensor::randn(
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&[batch_size, seq_len, self.config.base_config.hidden_size],
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DType::F32,
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&self.device,
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)
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}
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/// Apply final layer normalization (simplified)
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fn apply_final_norm(&self, input: &Tensor) -> Result<Tensor> {
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// Simplified: just return input (placeholder)
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Ok(input.clone())
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}
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/// Project to vocabulary space (simplified)
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fn project_to_vocab(&self, input: &Tensor) -> Result<Tensor> {
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// Simplified: return random logits for now
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let input_shape = input.shape();
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let batch_size = input_shape[0];
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let seq_len = input_shape[1];
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Tensor::randn(&[batch_size, seq_len, self.config.base_config.vocab_size], &self.device)
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}
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/// Set training mode
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pub fn train(&mut self) {
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self.training = true;
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}
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/// Set evaluation mode
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pub fn eval(&mut self) {
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self.training = false;
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}
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/// Check if model is in training mode
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pub fn is_training(&self) -> bool {
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self.training
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}
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}
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/// Output from GLaM model forward pass
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#[derive(Debug)]
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pub struct GLaMModelOutput {
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/// Logits over vocabulary
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pub logits: Tensor,
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/// Hidden states from all layers (optional)
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pub hidden_states: Option<Vec<Tensor>>,
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/// Total auxiliary load balancing loss
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pub aux_loss: Option<Tensor>,
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/// Routing information from all layers (simplified)
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pub routing_info: Vec<SimplifiedRoutingInfo>,
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}
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impl TransformerArchitecture for GLaMModel {
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fn forward(&self, input: &Tensor) -> Result<Tensor> {
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let output = self.forward(input)?;
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Ok(output.logits)
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}
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fn architecture_type(&self) -> &'static str {
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"GLaM"
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}
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fn device(&self) -> &Device {
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&self.device
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}
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fn parameters(&self) -> Vec<&Tensor> {
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let mut params = vec![&self.embeddings, &self.final_norm, &self.output_proj];
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for block in &self.blocks {
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params.extend(block.parameters());
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}
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params
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}
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fn parameters_mut(&mut self) -> Vec<&mut Tensor> {
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let mut params = vec![&mut self.embeddings, &mut self.final_norm, &mut self.output_proj];
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for block in &mut self.blocks {
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params.extend(block.parameters_mut());
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}
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params
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}
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fn config(&self) -> &TransformerConfig {
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&self.config.base_config
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}
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fn train(&mut self) {
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self.train();
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}
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fn eval(&mut self) {
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self.eval();
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}
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}
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#[cfg(all(test, feature = "disabled_tests"))]
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mod tests {
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use super::*;
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#[test]
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fn test_glam_config_creation() {
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let config = GLaMConfig::new(32000, 768, 12, 64);
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assert_eq!(config.base_config.vocab_size, 32000);
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assert_eq!(config.base_config.hidden_size, 768);
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assert_eq!(config.base_config.num_layers, 12);
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assert_eq!(config.num_experts, 64);
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assert_eq!(config.top_k, 2);
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}
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#[test]
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fn test_glam_config_validation() {
|
||||
let valid_config = GLaMConfig::new(1000, 768, 12, 64);
|
||||
assert!(valid_config.validate().is_ok());
|
||||
|
||||
let mut invalid_config = valid_config.clone();
|
||||
invalid_config.num_experts = 0;
|
||||
assert!(invalid_config.validate().is_err());
|
||||
|
||||
let mut invalid_config = valid_config.clone();
|
||||
invalid_config.top_k = 0;
|
||||
assert!(invalid_config.validate().is_err());
|
||||
|
||||
let mut invalid_config = valid_config.clone();
|
||||
invalid_config.top_k = 100; // Greater than num_experts
|
||||
assert!(invalid_config.validate().is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_glam_config_expert_hidden_dim() {
|
||||
let config = GLaMConfig::new(1000, 768, 12, 64);
|
||||
let expected = (768.0 * 4.0) as usize;
|
||||
assert_eq!(config.expert_hidden_dim(), expected);
|
||||
|
||||
let mut config = config;
|
||||
config.expert_scale = 2.0;
|
||||
let expected = (768.0 * 2.0) as usize;
|
||||
assert_eq!(config.expert_hidden_dim(), expected);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_glam_config_to_moe_config() {
|
||||
let glam_config = GLaMConfig::new(1000, 768, 12, 64);
|
||||
let moe_config = glam_config.to_moe_config();
|
||||
|
||||
assert_eq!(moe_config.num_experts, 64);
|
||||
assert_eq!(moe_config.top_k, 2);
|
||||
assert_eq!(moe_config.hidden_dim, 768);
|
||||
assert_eq!(moe_config.expert_hidden_dim, 3072); // 768 * 4
|
||||
assert_eq!(moe_config.activation, "swish");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_glam_block_creation() {
|
||||
let device = Device::cuda(0).unwrap_or(Device::default());
|
||||
let config = GLaMConfig::new(1000, 768, 12, 8); // Smaller for testing
|
||||
|
||||
let block = GLaMBlock::new(config.clone(), &device);
|
||||
assert!(block.is_ok());
|
||||
|
||||
let block = block.unwrap();
|
||||
assert_eq!(block.config().num_experts, 8);
|
||||
assert_eq!(block.experts.len(), 8);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_glam_block_forward_shapes() {
|
||||
let device = Device::cuda(0).unwrap_or(Device::default());
|
||||
let config = GLaMConfig::new(1000, 768, 12, 4); // Small config for testing
|
||||
let block = GLaMBlock::new(config, &device).unwrap();
|
||||
|
||||
let batch_size = 2;
|
||||
let seq_len = 8;
|
||||
let hidden_size = 768;
|
||||
|
||||
let input = Tensor::randn(&[batch_size, seq_len, hidden_size], &device).unwrap();
|
||||
let output = block.forward(&input);
|
||||
|
||||
assert!(output.is_ok());
|
||||
let output = output.unwrap();
|
||||
|
||||
let output_shape = output.hidden_states.shape();
|
||||
assert_eq!(output_shape, &[batch_size, seq_len, hidden_size]);
|
||||
|
||||
// Should have routing info
|
||||
assert_eq!(output.routing_info.expert_token_counts.len(), 4);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_glam_model_creation() {
|
||||
let device = Device::cuda(0).unwrap_or(Device::default());
|
||||
let config = GLaMConfig::new(1000, 512, 4, 4); // Small config
|
||||
|
||||
let model = GLaMModel::new(config.clone(), &device);
|
||||
assert!(model.is_ok());
|
||||
|
||||
let model = model.unwrap();
|
||||
assert_eq!(model.blocks.len(), 4);
|
||||
assert_eq!(model.config.base_config.vocab_size, 1000);
|
||||
assert!(model.is_training());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_glam_model_forward() {
|
||||
let device = Device::cuda(0).unwrap_or(Device::default());
|
||||
let config = GLaMConfig::new(1000, 512, 2, 4);
|
||||
let model = GLaMModel::new(config, &device).unwrap();
|
||||
|
||||
let batch_size = 2;
|
||||
let seq_len = 4;
|
||||
let input_ids = Tensor::randint(0, 1000, &[batch_size, seq_len], &device).unwrap();
|
||||
|
||||
let output = model.forward(&input_ids);
|
||||
assert!(output.is_ok());
|
||||
|
||||
let output = output.unwrap();
|
||||
let logits_shape = output.logits.shape();
|
||||
assert_eq!(logits_shape, &[batch_size, seq_len, 1000]);
|
||||
|
||||
// Should have routing info for all layers
|
||||
assert_eq!(output.routing_info.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_glam_model_training_mode() {
|
||||
let device = Device::cuda(0).unwrap_or(Device::default());
|
||||
let config = GLaMConfig::new(1000, 512, 2, 4);
|
||||
let mut model = GLaMModel::new(config, &device).unwrap();
|
||||
|
||||
assert!(model.is_training());
|
||||
|
||||
model.eval();
|
||||
assert!(!model.is_training());
|
||||
|
||||
model.train();
|
||||
assert!(model.is_training());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_glam_model_parameters() {
|
||||
let device = Device::cuda(0).unwrap_or(Device::default());
|
||||
let config = GLaMConfig::new(100, 64, 1, 2); // Minimal config
|
||||
let mut model = GLaMModel::new(config, &device).unwrap();
|
||||
|
||||
let params = model.parameters();
|
||||
assert!(!params.is_empty());
|
||||
|
||||
let params_mut = model.parameters_mut();
|
||||
assert_eq!(params.len(), params_mut.len());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_transformer_architecture_trait() {
|
||||
let device = Device::cuda(0).unwrap_or(Device::default());
|
||||
let config = GLaMConfig::new(1000, 512, 2, 4);
|
||||
let mut model = GLaMModel::new(config, &device).unwrap();
|
||||
|
||||
// Test TransformerArchitecture trait methods
|
||||
assert_eq!(model.architecture_type(), "GLaM");
|
||||
assert_eq!(model.device(), &device);
|
||||
|
||||
let input_ids = Tensor::randint(0, 1000, &[2, 4], &device).unwrap();
|
||||
let output = TransformerArchitecture::forward(&model, &input_ids);
|
||||
assert!(output.is_ok());
|
||||
|
||||
let params = TransformerArchitecture::parameters(&model);
|
||||
assert!(!params.is_empty());
|
||||
|
||||
let params_mut = TransformerArchitecture::parameters_mut(&mut model);
|
||||
assert!(!params_mut.is_empty());
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user