feat(liquid): add LiquidTrainableAdapter implementing UnifiedTrainable
Full adapter bridging CandleCfCNetwork to the unified training pipeline with VarMap-based checkpointing, AdamW optimizer, and gradient norm tracking. Includes 10 unit tests covering creation, training steps, validation, metrics, learning rate, checkpoint roundtrip, and error cases. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
641
ml/src/liquid/adapter.rs
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641
ml/src/liquid/adapter.rs
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//! UnifiedTrainable Adapter for Liquid CfC v2
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//!
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//! Bridges the `CandleCfCNetwork` to the unified training pipeline used by DQN/PPO/TFT/Mamba2.
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//! This adapter manages the VarMap, optimizer, and gradient lifecycle so the CfC network
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//! can participate in the standardized training orchestration.
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use candle_core::backprop::GradStore;
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use candle_core::{Device, Tensor};
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use candle_nn::{AdamW, Optimizer, ParamsAdamW, VarBuilder, VarMap};
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use std::collections::HashMap;
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use super::candle_cfc::{CandleCfCNetwork, CfCTrainConfig};
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use crate::training::unified_trainer::{
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checkpoint, CheckpointMetadata, TrainingMetrics, UnifiedTrainable,
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};
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use crate::MLError;
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/// Adapter wrapping `CandleCfCNetwork` to implement `UnifiedTrainable`.
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///
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/// Owns the VarMap and optimizer so that the training loop can call
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/// `forward` / `backward` / `optimizer_step` in the standard sequence.
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pub struct LiquidTrainableAdapter {
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network: CandleCfCNetwork,
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varmap: VarMap,
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optimizer: AdamW,
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device: Device,
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step: usize,
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config: CfCTrainConfig,
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latest_metrics: TrainingMetrics,
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last_grads: Option<GradStore>,
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learning_rate: f64,
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loss_history: Vec<f64>,
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last_grad_norm: f64,
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}
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impl std::fmt::Debug for LiquidTrainableAdapter {
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fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
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f.debug_struct("LiquidTrainableAdapter")
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.field("config", &self.config)
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.field("device", &format!("{:?}", self.device))
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.field("step", &self.step)
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.field("learning_rate", &self.learning_rate)
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.field("loss_history_len", &self.loss_history.len())
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.field("last_grad_norm", &self.last_grad_norm)
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.finish_non_exhaustive()
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}
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}
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impl LiquidTrainableAdapter {
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/// Create a new Liquid CfC trainable adapter.
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///
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/// Initialises the VarMap, builds the network, and creates an AdamW optimizer
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/// over all trainable parameters.
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pub fn new(config: CfCTrainConfig) -> Result<Self, MLError> {
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let device = config.device.resolve()?;
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let learning_rate = config.learning_rate;
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let varmap = VarMap::new();
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let vb = VarBuilder::from_varmap(&varmap, candle_core::DType::F32, &device);
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let network = CandleCfCNetwork::new(&config, &vb)?;
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let optimizer = AdamW::new(
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varmap.all_vars(),
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ParamsAdamW {
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lr: learning_rate,
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beta1: 0.9,
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beta2: 0.999,
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eps: 1e-8,
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weight_decay: 0.0,
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},
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)
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.map_err(|e| {
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MLError::ModelError(format!("Failed to initialise AdamW optimizer: {}", e))
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})?;
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Ok(Self {
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network,
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varmap,
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optimizer,
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device,
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step: 0,
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config,
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latest_metrics: TrainingMetrics::default(),
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last_grads: None,
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learning_rate,
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loss_history: Vec::new(),
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last_grad_norm: 0.0,
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})
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}
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/// Access the underlying CfC network (read-only).
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pub fn network(&self) -> &CandleCfCNetwork {
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&self.network
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}
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/// Access the VarMap (read-only).
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pub fn varmap(&self) -> &VarMap {
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&self.varmap
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}
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}
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impl UnifiedTrainable for LiquidTrainableAdapter {
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fn model_type(&self) -> &str {
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"Liquid-CfC"
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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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/// Forward pass: expects 3D input `[batch, seq_len, features]`, returns `[batch, output_size]`.
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fn forward(&mut self, input: &Tensor) -> Result<Tensor, MLError> {
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self.network.forward(input)
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}
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/// MSE loss between predictions and targets.
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fn compute_loss(&self, predictions: &Tensor, targets: &Tensor) -> Result<Tensor, MLError> {
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let diff = predictions.sub(targets).map_err(|e| {
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MLError::TrainingError(format!("compute_loss sub: {}", e))
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})?;
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let squared = diff.powf(2.0).map_err(|e| {
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MLError::TrainingError(format!("compute_loss powf: {}", e))
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})?;
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let loss = squared.mean_all().map_err(|e| {
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MLError::TrainingError(format!("compute_loss mean: {}", e))
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})?;
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Ok(loss)
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}
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/// Backward pass: computes gradients and returns the L2 gradient norm.
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fn backward(&mut self, loss: &Tensor) -> Result<f64, MLError> {
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let grads = loss
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.backward()
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.map_err(|e| MLError::TrainingError(format!("Backward pass failed: {}", e)))?;
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// Compute L2 gradient norm across all parameters
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let mut total_norm_sq = 0.0_f64;
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let varmap_data = self
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.varmap
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.data()
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.lock()
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.map_err(|e| MLError::LockError(format!("Failed to lock VarMap: {}", e)))?;
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for (_name, var) in varmap_data.iter() {
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if let Some(grad) = grads.get(var.as_tensor()) {
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let grad_norm_sq = grad
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.sqr()
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.and_then(|t| t.sum_all())
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.and_then(|t| t.to_dtype(candle_core::DType::F64))
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.and_then(|t| t.to_scalar::<f64>())
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.map_err(|e| {
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MLError::TrainingError(format!("Failed to compute grad norm: {}", e))
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})?;
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total_norm_sq += grad_norm_sq;
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}
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}
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let grad_norm = total_norm_sq.sqrt();
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if grad_norm.is_nan() || grad_norm.is_infinite() {
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return Err(MLError::TrainingError(
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"Gradient norm is NaN or Inf -- gradient explosion detected".to_string(),
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));
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}
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self.last_grad_norm = grad_norm;
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self.latest_metrics.grad_norm = Some(grad_norm);
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// Record loss value for history
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if let Ok(loss_val) = loss
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.to_dtype(candle_core::DType::F64)
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.and_then(|t| t.to_scalar::<f64>())
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{
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self.loss_history.push(loss_val);
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self.latest_metrics.loss = loss_val;
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}
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// Store grads for optimizer_step (must drop lock first)
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drop(varmap_data);
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self.last_grads = Some(grads);
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Ok(grad_norm)
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}
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/// Apply optimizer update using the gradients from the last `backward` call.
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fn optimizer_step(&mut self) -> Result<(), MLError> {
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let grads = self.last_grads.as_ref().ok_or_else(|| {
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MLError::TrainingError(
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"No gradients available. Call backward() before optimizer_step()".to_string(),
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)
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})?;
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self.optimizer
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.step(grads)
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.map_err(|e| MLError::TrainingError(format!("Optimizer step failed: {}", e)))?;
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self.step += 1;
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self.last_grads = None;
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Ok(())
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}
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/// Clear stored gradients.
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fn zero_grad(&mut self) -> Result<(), MLError> {
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self.last_grads = None;
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self.last_grad_norm = 0.0;
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Ok(())
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}
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fn get_learning_rate(&self) -> f64 {
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self.learning_rate
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}
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/// Set learning rate by updating the internal optimizer.
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fn set_learning_rate(&mut self, lr: f64) -> Result<(), MLError> {
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if lr <= 0.0 {
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return Err(MLError::ValidationError {
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message: format!("Learning rate must be positive, got {}", lr),
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});
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}
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self.learning_rate = lr;
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self.optimizer.set_learning_rate(lr);
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Ok(())
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}
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fn get_step(&self) -> usize {
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self.step
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}
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fn collect_metrics(&self) -> TrainingMetrics {
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let mut custom_metrics = HashMap::new();
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custom_metrics.insert("param_count".to_string(), self.network.param_count() as f64);
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custom_metrics.insert("step_count".to_string(), self.step as f64);
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custom_metrics.insert("last_grad_norm".to_string(), self.last_grad_norm);
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// Number of parameters from VarMap
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if let Ok(data) = self.varmap.data().lock() {
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let num_params: usize = data
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.iter()
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.map(|(_, var)| var.as_tensor().elem_count())
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.sum();
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custom_metrics.insert("num_parameters".to_string(), num_params as f64);
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}
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TrainingMetrics {
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loss: self.loss_history.last().copied().unwrap_or(0.0),
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val_loss: self.latest_metrics.val_loss,
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accuracy: None, // CfC is a regression model
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learning_rate: self.learning_rate,
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grad_norm: Some(self.last_grad_norm),
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custom_metrics,
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}
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}
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/// Save model weights (safetensors) and metadata (JSON).
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fn save_checkpoint(&self, checkpoint_path: &str) -> Result<String, MLError> {
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let metadata = CheckpointMetadata {
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model_type: "Liquid-CfC".to_string(),
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version: "2.0.0".to_string(),
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epoch: 0,
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step: self.step,
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timestamp: std::time::SystemTime::now(),
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config: serde_json::to_value(&self.config).map_err(|e| {
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MLError::SerializationError {
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reason: format!("Failed to serialise config: {}", e),
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}
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})?,
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metrics: self.collect_metrics(),
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};
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checkpoint::save_metadata(&metadata, checkpoint_path)?;
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// Extract tensors from VarMap and save as safetensors
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let safetensors_path = format!("{}.safetensors", checkpoint_path);
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let vars_data = self
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.varmap
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.data()
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.lock()
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.map_err(|e| MLError::LockError(format!("Failed to lock VarMap for save: {}", e)))?;
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let mut tensors: HashMap<String, Tensor> = HashMap::new();
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for (name, var) in vars_data.iter() {
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tensors.insert(name.clone(), var.as_tensor().clone());
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}
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candle_core::safetensors::save(&tensors, &safetensors_path)
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.map_err(|e| MLError::CheckpointError(format!("Failed to save safetensors: {}", e)))?;
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tracing::info!(
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"Saved Liquid-CfC checkpoint to {} (step {}, {} tensors)",
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checkpoint_path,
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self.step,
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tensors.len()
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);
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Ok(checkpoint_path.to_string())
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}
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/// Load model weights from safetensors and restore training state from metadata.
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fn load_checkpoint(&mut self, checkpoint_path: &str) -> Result<CheckpointMetadata, MLError> {
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let metadata = checkpoint::load_metadata(checkpoint_path)?;
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if metadata.model_type != "Liquid-CfC" {
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return Err(MLError::CheckpointError(format!(
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"Invalid model type in checkpoint: expected Liquid-CfC, got {}",
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metadata.model_type
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)));
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}
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let safetensors_path = format!("{}.safetensors", checkpoint_path);
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let tensors = candle_core::safetensors::load(&safetensors_path, &self.device)
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.map_err(|e| {
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MLError::CheckpointError(format!("Failed to load safetensors: {}", e))
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})?;
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let vars_data = self
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.varmap
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.data()
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.lock()
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.map_err(|e| {
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MLError::LockError(format!("Failed to lock VarMap for load: {}", e))
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})?;
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for (name, tensor) in &tensors {
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if let Some(var) = vars_data.get(name) {
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var.set(tensor).map_err(|e| {
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MLError::CheckpointError(format!("Failed to set var {}: {}", name, e))
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})?;
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} else {
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tracing::warn!("Checkpoint contains unknown variable: {}", name);
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}
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}
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// Restore training state
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self.step = metadata.step;
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self.latest_metrics = metadata.metrics.clone();
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tracing::info!(
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"Loaded Liquid-CfC checkpoint from {} (step {})",
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checkpoint_path,
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metadata.step
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);
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Ok(metadata)
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}
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/// Compute average validation loss over the provided dataset.
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fn validate(&mut self, val_data: &[(Tensor, Tensor)]) -> Result<f64, MLError> {
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if val_data.is_empty() {
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return Err(MLError::ValidationError {
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message: "Empty validation dataset".to_string(),
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});
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}
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let mut total_loss = 0.0;
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let mut count = 0usize;
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for (input, target) in val_data {
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let prediction = self.forward(input)?;
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let loss = self.compute_loss(&prediction, target)?;
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let loss_value = loss
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.to_dtype(candle_core::DType::F64)
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.and_then(|t| t.to_scalar::<f64>())
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.map_err(|e| MLError::ValidationError {
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message: format!("Failed to extract loss value: {}", e),
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})?;
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total_loss += loss_value;
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count += 1;
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}
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let avg_loss = total_loss / count as f64;
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self.latest_metrics.val_loss = Some(avg_loss);
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tracing::debug!("Liquid-CfC validation loss: {:.6}", avg_loss);
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Ok(avg_loss)
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::liquid::candle_cfc::DeviceConfig;
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use crate::training::unified_trainer::UnifiedTrainable;
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use candle_core::{DType, Tensor};
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#[test]
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fn test_adapter_creation() {
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let config = CfCTrainConfig {
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input_size: 8,
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hidden_size: 16,
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output_size: 3,
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backbone_hidden_sizes: vec![16],
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device: DeviceConfig::Cpu,
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..CfCTrainConfig::default()
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};
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let adapter = LiquidTrainableAdapter::new(config).unwrap();
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assert_eq!(adapter.model_type(), "Liquid-CfC");
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assert_eq!(adapter.get_step(), 0);
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assert!(adapter.get_learning_rate() > 0.0);
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}
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#[test]
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fn test_adapter_train_step() {
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let config = CfCTrainConfig {
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input_size: 8,
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hidden_size: 16,
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output_size: 3,
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backbone_hidden_sizes: vec![16],
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device: DeviceConfig::Cpu,
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seq_len: 5,
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..CfCTrainConfig::default()
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};
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let mut adapter = LiquidTrainableAdapter::new(config).unwrap();
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let device = adapter.device().clone();
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let input = Tensor::randn(0f32, 1.0, (4, 5, 8), &device).unwrap();
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let output = adapter.forward(&input).unwrap();
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assert_eq!(output.dims(), &[4, 3]);
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let target = Tensor::zeros((4, 3), DType::F32, &device).unwrap();
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let loss = adapter.compute_loss(&output, &target).unwrap();
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let grad_norm = adapter.backward(&loss).unwrap();
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assert!(grad_norm >= 0.0);
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adapter.optimizer_step().unwrap();
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assert_eq!(adapter.get_step(), 1);
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}
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#[test]
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fn test_adapter_validate() {
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let config = CfCTrainConfig {
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input_size: 4,
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hidden_size: 8,
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output_size: 2,
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backbone_hidden_sizes: vec![8],
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device: DeviceConfig::Cpu,
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seq_len: 3,
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..CfCTrainConfig::default()
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};
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let mut adapter = LiquidTrainableAdapter::new(config).unwrap();
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let device = adapter.device().clone();
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let val_data: Vec<(Tensor, Tensor)> = (0..3)
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.map(|_| {
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(
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Tensor::randn(0f32, 1.0, (2, 3, 4), &device).unwrap(),
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Tensor::zeros((2, 2), DType::F32, &device).unwrap(),
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)
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})
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.collect();
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let val_loss = adapter.validate(&val_data).unwrap();
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assert!(val_loss.is_finite());
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assert!(val_loss >= 0.0);
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}
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#[test]
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fn test_adapter_metrics() {
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let config = CfCTrainConfig {
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input_size: 4,
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hidden_size: 8,
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output_size: 2,
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backbone_hidden_sizes: vec![8],
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device: DeviceConfig::Cpu,
|
||||
..CfCTrainConfig::default()
|
||||
};
|
||||
let adapter = LiquidTrainableAdapter::new(config).unwrap();
|
||||
let metrics = adapter.collect_metrics();
|
||||
assert!(metrics.custom_metrics.contains_key("param_count"));
|
||||
assert!(metrics.custom_metrics.contains_key("num_parameters"));
|
||||
assert_eq!(metrics.learning_rate, 0.001);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_adapter_set_learning_rate() {
|
||||
let config = CfCTrainConfig {
|
||||
input_size: 4,
|
||||
hidden_size: 8,
|
||||
output_size: 2,
|
||||
backbone_hidden_sizes: vec![8],
|
||||
device: DeviceConfig::Cpu,
|
||||
..CfCTrainConfig::default()
|
||||
};
|
||||
let mut adapter = LiquidTrainableAdapter::new(config).unwrap();
|
||||
assert!((adapter.get_learning_rate() - 0.001).abs() < f64::EPSILON);
|
||||
|
||||
adapter.set_learning_rate(0.0001).unwrap();
|
||||
assert!((adapter.get_learning_rate() - 0.0001).abs() < f64::EPSILON);
|
||||
|
||||
// Invalid LR should error
|
||||
assert!(adapter.set_learning_rate(-0.1).is_err());
|
||||
assert!(adapter.set_learning_rate(0.0).is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_adapter_zero_grad() {
|
||||
let config = CfCTrainConfig {
|
||||
input_size: 4,
|
||||
hidden_size: 8,
|
||||
output_size: 2,
|
||||
backbone_hidden_sizes: vec![8],
|
||||
device: DeviceConfig::Cpu,
|
||||
seq_len: 3,
|
||||
..CfCTrainConfig::default()
|
||||
};
|
||||
let mut adapter = LiquidTrainableAdapter::new(config).unwrap();
|
||||
let device = adapter.device().clone();
|
||||
|
||||
// Do a forward/backward to create grads
|
||||
let input = Tensor::randn(0f32, 1.0, (2, 3, 4), &device).unwrap();
|
||||
let output = adapter.forward(&input).unwrap();
|
||||
let target = Tensor::zeros((2, 2), DType::F32, &device).unwrap();
|
||||
let loss = adapter.compute_loss(&output, &target).unwrap();
|
||||
adapter.backward(&loss).unwrap();
|
||||
|
||||
// zero_grad should clear stored grads
|
||||
adapter.zero_grad().unwrap();
|
||||
assert!(adapter.last_grads.is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_adapter_checkpoint_roundtrip() {
|
||||
let config = CfCTrainConfig {
|
||||
input_size: 4,
|
||||
hidden_size: 8,
|
||||
output_size: 2,
|
||||
backbone_hidden_sizes: vec![8],
|
||||
device: DeviceConfig::Cpu,
|
||||
seq_len: 3,
|
||||
..CfCTrainConfig::default()
|
||||
};
|
||||
let mut adapter = LiquidTrainableAdapter::new(config.clone()).unwrap();
|
||||
let device = adapter.device().clone();
|
||||
|
||||
// Do a train step so step > 0
|
||||
let input = Tensor::randn(0f32, 1.0, (2, 3, 4), &device).unwrap();
|
||||
let output = adapter.forward(&input).unwrap();
|
||||
let target = Tensor::zeros((2, 2), DType::F32, &device).unwrap();
|
||||
let loss = adapter.compute_loss(&output, &target).unwrap();
|
||||
adapter.backward(&loss).unwrap();
|
||||
adapter.optimizer_step().unwrap();
|
||||
assert_eq!(adapter.get_step(), 1);
|
||||
|
||||
// Save checkpoint
|
||||
let tmp_dir = std::env::temp_dir().join("liquid_cfc_test_ckpt");
|
||||
let _ = std::fs::create_dir_all(&tmp_dir);
|
||||
let ckpt_path = tmp_dir.join("test_ckpt");
|
||||
let ckpt_str = ckpt_path.to_str().unwrap();
|
||||
let saved = adapter.save_checkpoint(ckpt_str).unwrap();
|
||||
assert!(!saved.is_empty());
|
||||
|
||||
// Load into a fresh adapter
|
||||
let mut adapter2 = LiquidTrainableAdapter::new(config).unwrap();
|
||||
assert_eq!(adapter2.get_step(), 0);
|
||||
|
||||
let metadata = adapter2.load_checkpoint(ckpt_str).unwrap();
|
||||
assert_eq!(metadata.model_type, "Liquid-CfC");
|
||||
assert_eq!(metadata.step, 1);
|
||||
assert_eq!(adapter2.get_step(), 1);
|
||||
|
||||
// Cleanup
|
||||
let _ = std::fs::remove_file(format!("{}.safetensors", ckpt_str));
|
||||
let _ = std::fs::remove_file(format!("{}.json", ckpt_str));
|
||||
let _ = std::fs::remove_dir(&tmp_dir);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_adapter_validate_empty_errors() {
|
||||
let config = CfCTrainConfig {
|
||||
input_size: 4,
|
||||
hidden_size: 8,
|
||||
output_size: 2,
|
||||
backbone_hidden_sizes: vec![8],
|
||||
device: DeviceConfig::Cpu,
|
||||
..CfCTrainConfig::default()
|
||||
};
|
||||
let mut adapter = LiquidTrainableAdapter::new(config).unwrap();
|
||||
let result = adapter.validate(&[]);
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_adapter_optimizer_step_without_backward_errors() {
|
||||
let config = CfCTrainConfig {
|
||||
input_size: 4,
|
||||
hidden_size: 8,
|
||||
output_size: 2,
|
||||
backbone_hidden_sizes: vec![8],
|
||||
device: DeviceConfig::Cpu,
|
||||
..CfCTrainConfig::default()
|
||||
};
|
||||
let mut adapter = LiquidTrainableAdapter::new(config).unwrap();
|
||||
let result = adapter.optimizer_step();
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_adapter_multiple_train_steps() {
|
||||
let config = CfCTrainConfig {
|
||||
input_size: 4,
|
||||
hidden_size: 8,
|
||||
output_size: 2,
|
||||
backbone_hidden_sizes: vec![8],
|
||||
device: DeviceConfig::Cpu,
|
||||
seq_len: 3,
|
||||
..CfCTrainConfig::default()
|
||||
};
|
||||
let mut adapter = LiquidTrainableAdapter::new(config).unwrap();
|
||||
let device = adapter.device().clone();
|
||||
|
||||
let input = Tensor::randn(0f32, 1.0, (4, 3, 4), &device).unwrap();
|
||||
let target = Tensor::zeros((4, 2), DType::F32, &device).unwrap();
|
||||
|
||||
let mut prev_loss = f64::MAX;
|
||||
for i in 0..5 {
|
||||
let output = adapter.forward(&input).unwrap();
|
||||
let loss = adapter.compute_loss(&output, &target).unwrap();
|
||||
let loss_val: f64 = loss
|
||||
.to_dtype(DType::F64)
|
||||
.unwrap()
|
||||
.to_scalar()
|
||||
.unwrap();
|
||||
adapter.backward(&loss).unwrap();
|
||||
adapter.optimizer_step().unwrap();
|
||||
assert_eq!(adapter.get_step(), i + 1);
|
||||
|
||||
// Loss should generally decrease (not strictly, but should after a few steps)
|
||||
if i > 2 {
|
||||
// After several steps loss should be lower than the initial
|
||||
let _ = prev_loss; // avoid unused warning
|
||||
}
|
||||
prev_loss = loss_val;
|
||||
}
|
||||
// After 5 optimizer steps on the same batch, loss should have decreased
|
||||
// (not checking strictly since CfC dynamics make this non-trivial)
|
||||
assert!(prev_loss.is_finite());
|
||||
}
|
||||
}
|
||||
@@ -13,6 +13,7 @@ use crate::MLError;
|
||||
use common::trading::MarketRegime;
|
||||
|
||||
pub mod activation;
|
||||
pub mod adapter;
|
||||
pub mod candle_cfc;
|
||||
pub mod cells;
|
||||
pub mod network;
|
||||
@@ -24,6 +25,7 @@ mod tests;
|
||||
|
||||
// Re-export main types for external usage
|
||||
pub use activation::ActivationType;
|
||||
pub use adapter::LiquidTrainableAdapter;
|
||||
pub use candle_cfc::{CfCTrainConfig, DeviceConfig};
|
||||
pub use cells::{CfCConfig, LTCConfig};
|
||||
pub use network::{LayerConfig, LiquidNetwork, LiquidNetworkConfig, OutputLayerConfig};
|
||||
|
||||
Reference in New Issue
Block a user