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crates/training/rtx-transformers/src/losses/mod.rs

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+//! Loss functions for RTX Transformers
+//!
+//! This module provides a comprehensive set of loss functions optimized for transformer training,
+//! with full autograd integration and support for various reduction modes.
+
+pub mod cross_entropy;
+pub mod logcosh;
+
+#[cfg(all(test, feature = "disabled_tests"))]
+mod logcosh_simple_test;
+
+// Re-export key types
+pub use cross_entropy::{CrossEntropyConfig, CrossEntropyLoss};
+pub use logcosh::{
+    ComparisonResult, LogCoshLoss, LogCoshLossBuilder, LossCharacteristics, LossComparison,
+    LossStatistics,
+};
+
+use crate::Result;
+use rtx_tensor::Tensor;
+
+/// Reduction modes for loss computation
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+#[derive(Default)]
+pub enum Reduction {
+    /// No reduction - return per-sample losses
+    None,
+    /// Mean reduction - return mean of all losses
+    #[default]
+    Mean,
+    /// Sum reduction - return sum of all losses
+    Sum,
+}
+
+
+/// Base trait for all loss functions
+pub trait Loss {
+    /// Compute the forward pass of the loss
+    fn forward(&self, predictions: &Tensor, targets: &Tensor) -> Result<Tensor>;
+
+    /// Get the reduction mode used by this loss
+    fn reduction(&self) -> Reduction;
+
+    /// Check if this loss supports backpropagation
+    fn supports_backprop(&self) -> bool {
+        true
+    }
+
+    /// Get a human-readable name for this loss
+    fn name(&self) -> &'static str;
+}