c645e6222d
CRITICAL FINDINGS from 3-trial validation: - 85,120 gradient clipping warnings (81.6% of logs) - REGRESSION - Rainbow features DISABLED: use_dueling=false, use_distributional=false, use_noisy_nets=false - Negative Q-values confirmed: HOLD -1000 to -3250 - Performance: Sharpe 0.29 (target 0.77) Changes: - Fixed N-Step compilation (7/7 tests passing) - Fixed Distributional compilation (6/6 tests passing) - Fixed Dueling CUDA errors (10/10 tests passing) - Added TDD validation for state_dim=225 - Total: 23/23 Wave 11 tests passing (100%) Issues requiring investigation: 1. Why are Dueling/Distributional/Noisy disabled in hyperopt? 2. Why gradient explosion despite previous fixes? 3. Test coverage gaps - unit tests pass but integration fails 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
64 lines
2.3 KiB
Rust
64 lines
2.3 KiB
Rust
//! Gradient utilities for Candle framework
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//!
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//! Provides gradient clipping and other gradient-related operations
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//! that are missing from candle_nn::optim
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use candle_core::{Error, backprop::GradStore, Var};
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/// Clip gradients by global L2 norm (similar to PyTorch's clip_grad_norm_)
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///
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/// This function computes the L2 norm of all gradients and scales them
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/// proportionally if the total norm exceeds max_norm.
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///
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/// # Arguments
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/// * `vars` - Slice of Var containing model parameters
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/// * `grads` - Mutable reference to GradStore from loss.backward()
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/// * `max_norm` - Maximum allowed gradient norm
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///
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/// # Returns
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/// A tuple of (actual_norm, clipped_norm) where:
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/// - actual_norm: The total gradient norm before clipping
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/// - clipped_norm: The gradient norm after clipping (≤ max_norm)
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///
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/// # Example
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/// ```no_run
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/// use ml::gradient_utils::clip_grad_norm;
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/// use candle_core::Var;
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///
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/// let vars = vec![]; // Your model variables
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/// // ... compute loss ...
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/// let mut grads = loss.backward()?;
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/// let (actual_norm, clipped_norm) = clip_grad_norm(&vars, &mut grads, 10.0)?;
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/// println!("Gradient norm: {} -> {}", actual_norm, clipped_norm);
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/// # Ok::<(), candle_core::Error>(())
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/// ```
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pub fn clip_grad_norm(vars: &[Var], grads: &mut GradStore, max_norm: f64) -> Result<(f64, f64), Error> {
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let mut total_norm_sq = 0.0f64;
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// First pass: Calculate the total L2 norm of all gradients
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for var in vars.iter() {
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if let Some(grad) = grads.get(var) {
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let norm_sq = grad.sqr()?.sum_all()?.to_scalar::<f32>()? as f64;
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total_norm_sq += norm_sq;
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}
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}
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let total_norm = total_norm_sq.sqrt();
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// Second pass: Scale gradients if the norm exceeds the maximum
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if total_norm > max_norm {
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let scale_factor = max_norm / (total_norm + 1e-6); // Add epsilon for numerical stability
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for var in vars.iter() {
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// Remove gradient, scale it, and insert back
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if let Some(grad) = grads.remove(var) {
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let scaled_grad = (grad * scale_factor)?;
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grads.insert(var, scaled_grad);
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}
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}
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// Return both actual and clipped norms
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Ok((total_norm, max_norm))
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} else {
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// Norm is already below max_norm, no clipping needed
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Ok((total_norm, total_norm))
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}
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}
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