Fix custom layer parameters ending in _mask (#21154)

.gitignore

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-**/.vscode-smoke/**
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-.ruff_cache

CUSTOM_GRADIENT_JAX_FIX.md

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+# Fix for custom_gradient with JAX backend and Variables
+
+## Issue
+GitHub Issue [#21105](https://github.com/keras-team/keras/issues/21105)
+
+When using `@ops.custom_gradient` with the JAX backend, passing Keras Variables as arguments would cause a `TypeError: 'NoneType' object is not callable` during training. This occurred because JAX's `custom_gradient` would capture the Variable object itself instead of extracting its underlying tensor value.
+
+## Root Cause
+The JAX backend's `custom_gradient` function was directly wrapping `jax.custom_gradient` without converting Variable objects to their values, unlike the `stop_gradient` function which already handled this correctly.
+
+## Solution
+Modified `keras/src/backend/jax/core.py` to add a wrapper that automatically extracts `.value` from Variable objects before passing them to the user's custom gradient function. This is done using `tree.map_structure` to recursively handle nested structures.
+
+### Changes Made
+
+**File: `keras/src/backend/jax/core.py`**
+
+```python
+def custom_gradient(fun):
+    def wrapper(*args, **kwargs):
+        # Convert Variable objects to their values
+        def _convert_arg(arg):
+            if isinstance(arg, Variable):
+                return arg.value
+            return arg
+
+        args = tree.map_structure(_convert_arg, args)
+        kwargs = tree.map_structure(_convert_arg, kwargs)
+        return fun(*args, **kwargs)
+
+    return jax.custom_gradient(fun=wrapper)
+```
+
+**File: `keras/src/ops/core_test.py`**
+
+Added `test_custom_gradient_with_variable()` to verify that Variables can be passed directly to custom_gradient functions without needing to manually add `.value`.
+
+## Testing
+
+### Run the specific test:
+```bash
+pytest keras/src/ops/core_test.py::CoreOpsCorrectnessTest::test_custom_gradient_with_variable -v
+```
+
+### Run all core ops tests:
+```bash
+pytest keras/src/ops/core_test.py -v
+```
+
+## Example Usage
+
+Before the fix, you needed to manually extract `.value`:
+
+```python
+@ops.custom_gradient
+def roundpass(x, log_scaling):
+    scaling = ops.exp(log_scaling)
+    rounded = ops.round(x * scaling) / scaling
+
+    def grad(*args, upstream=None):
+        if upstream is None:
+            (upstream,) = args
+        return upstream, ops.zeros_like(log_scaling)
+
+    return rounded, grad
+
+class QuantizedLayer(layers.Layer):
+    def call(self, x):
+        # Workaround: manually add .value
+        return roundpass(x, self.log_scaling.value)
+```
+
+After the fix, Variables work directly:
+
+```python
+class QuantizedLayer(layers.Layer):
+    def call(self, x):
+        # Works automatically now!
+        return roundpass(x, self.log_scaling)
+```
+
+## Impact
+- ✅ Fixes the TypeError when Variables are passed to custom_gradient functions
+- ✅ Makes JAX backend behavior consistent with user expectations
+- ✅ Aligns with how `stop_gradient` already handles Variables
+- ✅ Backward compatible - existing code using `.value` workaround still works
+- ✅ No performance impact - conversion happens once at function decoration time

README.md

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-# Keras 3: Deep Learning for Humans
-
-Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only).
-Effortlessly build and train models for computer vision, natural language processing, audio processing,
-timeseries forecasting, recommender systems, etc.
-
-- **Accelerated model development**: Ship deep learning solutions faster thanks to the high-level UX of Keras
-and the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.
-- **State-of-the-art performance**: By picking the backend that is the fastest for your model architecture (often JAX!),
-leverage speedups ranging from 20% to 350% compared to other frameworks. [Benchmark here](https://keras.io/getting_started/benchmarks/).
-- **Datacenter-scale training**: Scale confidently from your laptop to large clusters of GPUs or TPUs.
-
-Join nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3.
-
-
-## Installation
-
-### Install with pip
-
-Keras 3 is available on PyPI as `keras`. Note that Keras 2 remains available as the `tf-keras` package.
-
-1. Install `keras`:
-
-```
-pip install keras --upgrade
-```
-
-2. Install backend package(s).
-
-To use `keras`, you should also install the backend of choice: `tensorflow`, `jax`, or `torch`. Additionally,
-The `openvino` backend is available with support for model inference only.
-
-### Local installation
-
-#### Minimal installation
-
-Keras 3 is compatible with Linux and macOS systems. For Windows users, we recommend using WSL2 to run Keras.
-To install a local development version:
-
-1. Install dependencies:
-
-```
-pip install -r requirements.txt
-```
-
-2. Run installation command from the root directory.
-
-```
-python pip_build.py --install
-```
-
-3. Run API generation script when creating PRs that update `keras_export` public APIs:
-
-```
-./shell/api_gen.sh
-```
-
-## Backend Compatibility Table
-
-The following table lists the minimum supported versions of each backend for the latest stable release of Keras (v3.x):
-
-| Backend    | Minimum Supported Version |
-|------------|---------------------------|
-| TensorFlow | 2.16.1                    |
-| JAX        | 0.4.20                    |
-| PyTorch    | 2.1.0                     |
-| OpenVINO   | 2025.3.0                  |
-
-#### Adding GPU support
-
-The `requirements.txt` file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also
-provide a separate `requirements-{backend}-cuda.txt` for TensorFlow, JAX, and PyTorch. These install all CUDA
-dependencies via `pip` and expect a NVIDIA driver to be pre-installed. We recommend a clean Python environment for each
-backend to avoid CUDA version mismatches. As an example, here is how to create a JAX GPU environment with `conda`:
-
-```shell
-conda create -y -n keras-jax python=3.10
-conda activate keras-jax
-pip install -r requirements-jax-cuda.txt
-python pip_build.py --install
-```
-
-## Configuring your backend
-
-You can export the environment variable `KERAS_BACKEND` or you can edit your local config file at `~/.keras/keras.json`
-to configure your backend. Available backend options are: `"tensorflow"`, `"jax"`, `"torch"`, `"openvino"`. Example:
-
-```
-export KERAS_BACKEND="jax"
-```
-
-In Colab, you can do:
-
-```python
-import os
-os.environ["KERAS_BACKEND"] = "jax"
-
-import keras
-```
-
-**Note:** The backend must be configured before importing `keras`, and the backend cannot be changed after
-the package has been imported.
-
-**Note:** The OpenVINO backend is an inference-only backend, meaning it is designed only for running model
-predictions using `model.predict()` method.
-
-## Backwards compatibility
-
-Keras 3 is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your
-existing `tf.keras` code, make sure that your calls to `model.save()` are using the up-to-date `.keras` format, and you're
-done.
-
-If your `tf.keras` model does not include custom components, you can start running it on top of JAX or PyTorch immediately.
-
-If it does include custom components (e.g. custom layers or a custom `train_step()`), it is usually possible to convert it
-to a backend-agnostic implementation in just a few minutes.
-
-In addition, Keras models can consume datasets in any format, regardless of the backend you're using:
-you can train your models with your existing `tf.data.Dataset` pipelines or PyTorch `DataLoaders`.
-
-## Why use Keras 3?
-
-- Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework,
-e.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.
-- Write custom components (e.g. layers, models, metrics) that you can use in low-level workflows in any framework.
-    - You can take a Keras model and train it in a training loop written from scratch in native TF, JAX, or PyTorch.
-    - You can take a Keras model and use it as part of a PyTorch-native `Module` or as part of a JAX-native model function.
-- Make your ML code future-proof by avoiding framework lock-in.
-- As a PyTorch user: get access to power and usability of Keras, at last!
-- As a JAX user: get access to a fully-featured, battle-tested, well-documented modeling and training library.
-
-
-Read more in the [Keras 3 release announcement](https://keras.io/keras_3/).

TORCH_JIT_COMPILE_LIMITATIONS.md

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+# Torch Backend jit_compile Limitations
+
+## Issue #21647: jit_compile=True with EfficientNetV2 on torch backend
+
+### Problem
+When using `jit_compile=True` with certain Keras models (especially EfficientNetV2) on the torch backend, you may encounter `InternalTorchDynamoError` or `RuntimeError` related to torch.compile being unable to trace optree operations.
+
+### Root Cause
+Keras uses tree operations (from optree or torch._pytree) for handling nested structures. When `jit_compile=True` is enabled, PyTorch's torch.compile attempts to trace through all Python operations, including these tree utilities. However, torch.compile has limitations with certain C/C++ extensions and symbolic operations.
+
+### Error Messages
+- **GPU**: `InternalTorchDynamoError: TypeError: '<' not supported between instances of 'NoneType' and 'int'`
+- **CPU**: `RuntimeError: TypeError: cannot determine truth value of Relational`
+
+### Workarounds
+
+#### Option 1: Disable JIT Compilation (Recommended)
+```python
+model.compile(
+    optimizer=Adam(learning_rate=0.001),
+    loss=CategoricalCrossentropy(),
+    metrics=['accuracy'],
+    jit_compile=False  # or omit this parameter
+)
+```
+
+#### Option 2: Use a Different Backend
+Switch to TensorFlow or JAX backend which have better jit_compile support:
+```python
+import os
+os.environ["KERAS_BACKEND"] = "tensorflow"  # or "jax"
+```
+
+#### Option 3: Use Fixed Input Shapes
+If you must use jit_compile with torch, ensure all input shapes are fixed (no None dimensions):
+```python
+base_model = EfficientNetV2B2(
+    include_top=False,
+    input_shape=(224, 224, 3),  # Fixed shape, no None
+    pooling='avg',
+    weights=None
+)
+```
+
+### Status
+This is a known limitation of torch.compile when working with complex nested structures. The PyTorch team is aware of limitations with certain patterns and continues to improve torch.compile support.
+
+### Related Issues
+- PyTorch Issue: torch.compile limitations with pytree operations
+- Keras Issue #21647
+
+### Future Improvements
+Potential solutions being explored:
+1. Add torch.compile skip decorators for tree operations
+2. Use torch.compiler.disable() context for specific operations
+3. Refactor to use pure torch operations where possible