Merge branch 'main' into jt-diag

.github/workflows/test.yml

@@ -9,7 +9,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        BACKEND: ['numpy', 'pytorch']
+        BACKEND: ['numpy', 'pytorch', 'paddle']
         python-version: ['3.12']
         include:
           - BACKEND: 'numpy'
@@ -52,6 +52,10 @@ jobs:
         elif [[ "${{matrix.BACKEND}}" == "jax" ]]; then
           echo "Installing JAX";
           pip install jax jaxlib;
+        elif [[ "${{matrix.BACKEND}}" == "paddle" ]]; then
+          echo "Installing PaddlePaddle";
+          pip install 'setuptools>=65.0';
+          pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/;
         fi
 
     - name: Install package
@@ -61,7 +65,7 @@ jobs:
     - name: Test with backend ${{matrix.BACKEND}} pytest and coverage
       run: |
         TENSORLY_BACKEND=${{matrix.BACKEND}} pytest -vv --cov tensorly --cov-report xml --durations=10 tensorly
-    
+
     - name: Check coverage with CodeCov
       uses: codecov/codecov-action@v3
       with:

README.rst

@@ -19,7 +19,7 @@ TensorLy
 ========
 
 
-TensorLy is a Python library that aims at making tensor learning simple and accessible. It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, PyTorch, JAX, TensorFlow or CuPy, and run methods at scale on CPU or GPU.
+TensorLy is a Python library that aims at making tensor learning simple and accessible. It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, PyTorch, JAX, TensorFlow, CuPy or Paddle, and run methods at scale on CPU or GPU.
 
 - **Website:** https://tensorly.org
 - **Source-code:**  https://github.com/tensorly/tensorly
@@ -96,7 +96,7 @@ You can also create tensors in TT-format, Tucker, etc, see `random tensors <http
 Setting the backend
 -------------------
 
-You can change the backend to perform computation with a different framework. By default, the backend is NumPy, but you can also perform the computation using  PyTorch, TensorFlow, JAX or CuPy (requires to have installed them first). For instance, after setting the backend to PyTorch, all the computation is done by PyTorch, and tensors can be created on GPU:
+You can change the backend to perform computation with a different framework. By default, the backend is NumPy, but you can also perform the computation using  PyTorch, TensorFlow, JAX, CuPy or Paddle (requires to have installed them first). For instance, after setting the backend to PyTorch, all the computation is done by PyTorch, and tensors can be created on GPU:
 
 .. code:: python
 

doc/about.rst

@@ -13,7 +13,7 @@ later published as a `JMLR paper <http://jmlr.org/papers/v20/18-277.html>`_ titl
 by `Jean Kossaifi`_, `Yannis Panagakis`_, `Anima Anandkumar`_ and `Maja Pantic`_.
 
 Originally, TensorLy was built on top of NumPy and SciPy only. In order to combine tensor methods with deep learning and run them on multiple devices, CPU and GPU, a flexible backend system was added.
-This allows algorithms written in TensorLy to be ran with any major framework such as PyTorch, TensorFlow, CuPy and JAX.
+This allows algorithms written in TensorLy to be ran with any major framework such as PyTorch, TensorFlow, CuPy, JAX and Paddle.
 
 Core developers
 -----------------