Keras 3 backend-agnostic custom gradient parity

Custom Gradient Consistency Across Backends

Keras 3 enables a unified API that allows models to execute on TensorFlow, PyTorch, or JAX by setting the KERAS_BACKEND environment variable. While standard layers are mapped to framework primitives, implementing custom gradient calculations for user-defined layers introduces potential behavioral discrepancies.

The goal is to ensure that complex custom gradients maintain mathematical parity and numerical stability regardless of the underlying execution engine. Because each backend handles automatic differentiation and gradient accumulation differently, it is unclear if a single custom gradient implementation will yield identical results across all three frameworks.

• Does Keras 3 provide a standardized interface for custom gradients that guarantees identical behavior across JAX, PyTorch, and TensorFlow?
• What are the constraints when defining custom gradients to avoid backend-specific tensor behavior?