Merge branch 'main' into jt-diag

Author: Aaron Meyer

.github/workflows/deploy_pypi.yml

@@ -1,4 +1,4 @@
-name: Deploy to Pypi
+name: Deploy 📦 to Test-Pypi and Pypi 🐍
 
 on:
   push:
@@ -23,47 +23,87 @@ jobs:
           python -m pip install -r requirements.txt
           python -m pip install -r doc/requirements_doc.txt
           pip install setuptools wheel
+      - name: Install package
+        run: |
+          python -m pip install -e .
       - name: Make doc
         run: |
           cd doc
           python minify.py
           make html
           cd ..
-      - name: Install package
-        run: |
-          python -m pip install -e .
       - name: Build a binary wheel and a source tarball
         run: |
           python setup.py sdist bdist_wheel
-      - name: Publish package to TestPyPI
-        uses: pypa/gh-action-pypi-publish@master
-        with:
-          user: __token__
-          password: ${{ secrets.TEST_PYPI_PASSWORD }}
-          repository_url: https://test.pypi.org/legacy/
-      - name: Publish package to PyPI
-        uses: pypa/gh-action-pypi-publish@master
+      - name: Store the distribution packages
+        uses: actions/upload-artifact@v4
         with:
-          user: __token__
-          password: ${{ secrets.PYPI_PASSWORD }}
-      - name: Push doc to stable
-        if: success()
-        run: |
-          # Add deploy key and clone through ssh
-          eval "$(ssh-agent -s)"
-          mkdir ~/.ssh
-          echo "${{ secrets.DEPLOY_DOC_KEY }}" > ~/.ssh/id_rsa
-          chmod 600 ~/.ssh/id_rsa
-          ssh-keyscan -t rsa github.com
-          echo 'Documentation was successfully built, updating the website.'
-          # See https://github.community/t/github-actions-bot-email-address/17204/5
-          git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com"
-          git config --global user.name "github-actions"
-          git clone "git@github.com:tensorly/tensorly.github.io.git" doc_folder
-          echo "-- Updating the content"
-          cd doc_folder
-          cp -r dev/* stable/
-          echo "Pushing to git"
-          git add stable
-          git commit -m "Github action: new release."
-          git push --force origin main
+          name: python-package-distributions
+          path: dist/
+
+  publish-to-testpypi:
+    name: Publish Python 🐍 distribution 📦 to TestPyPI
+    needs:
+    - build
+    runs-on: ubuntu-latest
+
+    environment:
+      name: testpypi
+      url: https://test.pypi.org/p/tensorly
+
+    permissions:
+      id-token: write
+
+    steps:
+    - name: Download all the dists
+      uses: actions/download-artifact@v4
+      with:
+        name: python-package-distributions
+        path: dist/
+    - name: Publish distribution 📦 to TestPyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        repository-url: https://test.pypi.org/legacy/
+
+  publish-to-pypi:
+    name: >-
+      Publish Python 🐍 distribution 📦 to PyPI
+    if: startsWith(github.ref, 'refs/tags/')  # only publish to PyPI on tag pushes
+    needs:
+    - build
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/tensorly  # Replace <package-name> with your PyPI project name
+    permissions:
+      id-token: write  # IMPORTANT: mandatory for trusted publishing
+
+    steps:
+    - name: Download all the dists
+      uses: actions/download-artifact@v4
+      with:
+        name: python-package-distributions
+        path: dist/
+    - name: Publish distribution 📦 to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+    - name: Push doc to stable if upload was successful
+      if: success()
+      run: |
+        # Add deploy key and clone through ssh
+        eval "$(ssh-agent -s)"
+        mkdir ~/.ssh
+        echo "${{ secrets.DEPLOY_DOC_KEY }}" > ~/.ssh/id_rsa
+        chmod 600 ~/.ssh/id_rsa
+        ssh-keyscan -t rsa github.com
+        echo 'Documentation was successfully built, updating the website.'
+        # See https://github.community/t/github-actions-bot-email-address/17204/5
+        git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com"
+        git config --global user.name "github-actions"
+        git clone "git@github.com:tensorly/tensorly.github.io.git" doc_folder
+        echo "-- Updating the content"
+        cd doc_folder
+        cp -r dev/* stable/
+        echo "Pushing to git"
+        git add stable
+        git commit -m "Github action: new release."
+        git push --force origin main

doc/modules/api.rst

@@ -290,14 +290,14 @@ Other tensor algebraic functionalities:
     kronecker
     mode_dot
     multi_mode_dot
-    proximal.soft_thresholding
-    proximal.svd_thresholding
-    proximal.procrustes
     inner
     outer
     batched_outer
     tensordot
     higher_order_moment
+    proximal.soft_thresholding
+    proximal.svd_thresholding
+    proximal.procrustes
 
 Tensor Algebra Backend
 ----------------------
@@ -411,6 +411,26 @@ Tensor Regression (:mod:`tensorly.regression`)
     CP_PLSR
 
 
+Solvers (:mod:`tensorly.solvers`)
+=================================
+
+Tensorly provides with efficient solvers for nonnegative least squares problems which are crucial to nonnegative tensor decomposition, as well as a generic admm solver useful for constrained decompositions. Several proximal (projection) operators are located in tenalg.
+
+.. automodule:: tensorly.solvers
+    :no-members:
+    :no-inherited-members:
+
+.. currentmodule:: tensorly.solvers
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    nnls.hals_nnls
+    nnls.fista
+    nnls.active_set_nnls
+    admm.admm
+
 Performance measures (:mod:`tensorly.metrics`)
 ==============================================
 

examples/applications/plot_image_compression.py

@@ -8,7 +8,7 @@
 import matplotlib.pyplot as plt
 import tensorly as tl
 import numpy as np
-from scipy.misc import face
+from scipy.datasets import face
 from scipy.ndimage import zoom
 from tensorly.decomposition import parafac
 from tensorly.decomposition import tucker

examples/decomposition/plot_guide_for_constrained_cp.py

@@ -80,7 +80,7 @@
 # Using one constraint for all modes
 # --------------------------------------------
 # Constraints are inputs of the constrained_parafac function, which itself uses the
-# ``tensorly.tenalg.proximal.validate_constraints`` function in order to process the input
+# ``tensorly.solver.proximal.validate_constraints`` function in order to process the input
 # of the user. If a user wants to use the same constraint for all modes, an
 # input (bool or a scalar value or list of scalar values) should be given to this constraint.
 # Assume, one wants to use unimodality constraint for all modes. Since it does not require

tensorly/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.8.1"
+__version__ = "0.9.0"
 
 import sys
 
@@ -89,6 +89,7 @@
     norm,
     dot,
     kron,
+    einsum,
     solve,
     lstsq,
     qr,
@@ -139,6 +140,7 @@
 from . import tenalg
 from . import random
 from . import datasets
+from . import solvers
 
 
 # Add Backend functions, dynamically dispatched

tensorly/backend/paddle_backend.py

@@ -2,6 +2,7 @@
 from typing import Sequence
 
 from packaging.version import Version
+import warnings
 
 try:
     import paddle
@@ -23,14 +24,14 @@
 )
 
 
-if Version(paddle.__version__) < Version("2.6.0"):
-    raise RuntimeError("TensorLy only supports paddle v2.6.0 and above.")
+if paddle.__version__ != "0.0.0" and Version(paddle.__version__) < Version("2.6.0"):
+    raise RuntimeError(
+        "TensorLy only supports paddle v2.6.0 and above, or the develop version, "
+        f"but got {paddle.__version__}"
+    )
 
 
 class PaddleBackend(Backend, backend_name="paddle"):
-    # set default device to cpu
-    place = paddle.device.set_device("cpu")
-
     @staticmethod
     def context(tensor: paddle.Tensor):
         return {
@@ -67,15 +68,7 @@ def tensor(
             # If source is a tensor, use clone-detach as suggested by Paddle
             tensor = data.clone().detach()
         else:
-            # Else, use Paddle's tensor constructor
-            if place is None:
-                # set default device to cpu when place is not specified
-                # and  gpu is avaiable
-                current_device = paddle.device.get_device()
-                if current_device.startswith("gpu"):
-                    place = "cpu"
-
-            tensor = paddle.to_tensor(data, place=place)
+            tensor = paddle.to_tensor(data)
 
         # Set dtype/place/stop_gradient if specified
         if dtype is not None:
@@ -249,9 +242,32 @@ def lstsq(a: paddle.Tensor, b: paddle.Tensor, rcond=None, driver="gelsd"):
             b = b.unsqueeze(-1)
 
         m, n = a.shape[-2], a.shape[-1]
-        sol, res, rank, single_value = paddle.linalg.lstsq(
-            a, b, rcond=rcond, driver=driver
-        )
+        if driver != "gels" and paddle.device.get_device() != "cpu":
+            fallback_device = "cpu"
+            # NOTE: Paddle only support 'gels' on CUDA, so we use a.cpu() and b.cpu()
+            # as input, then fall back to CPU lstsq implementation and show warnings.
+            with paddle.base.dygraph.guard(fallback_device):
+                sol, res, rank, single_value = paddle.linalg.lstsq(
+                    a.to(fallback_device),
+                    b.to(fallback_device),
+                    rcond=rcond,
+                    driver=driver,
+                )
+            # copy result back to current device
+            current_device = paddle.device.get_device()
+            sol = sol.to(current_device)
+            res = res.to(current_device)
+            rank = rank.to(current_device)
+            single_value = single_value.to(current_device)
+            warnings.warn(
+                f"lstsq is falling back to {fallback_device} as the"
+                f" specified driver '{driver}' is only supported on {fallback_device}, "
+                "which may result in additional overhead."
+            )
+        else:
+            sol, res, rank, single_value = paddle.linalg.lstsq(
+                a, b, rcond=rcond, driver=driver
+            )
         if m > n and driver != "gelsy":
             compute_residuals = True
             if driver in ["gelss", "gelsd"]:

tensorly/backend/tensorflow_backend.py

@@ -85,7 +85,7 @@ def norm(tensor, order=2, axis=None):
     @staticmethod
     def solve(lhs, rhs):
         squeeze = False
-        if rhs.ndim == 1:
+        if tnp.ndim(rhs) == 1:
             squeeze = [-1]
             rhs = tf.reshape(rhs, (-1, 1))
         res = tf.linalg.solve(lhs, rhs)
@@ -117,9 +117,11 @@ def lstsq(a, b):
         return x, residuals if tf.linalg.matrix_rank(a) == n else tf.constant([])
 
     def svd(self, matrix, full_matrices):
-        """Correct for the atypical return order of tf.linalg.svd."""
+        """Correct for the atypical return order of tf.linalg.svd.
+        According to the https://www.tensorflow.org/api_docs/python/tf/linalg/svd#expandable-2
+        not only the order is changed, but V is returned instead of V^H"""
         S, U, V = tf.linalg.svd(matrix, full_matrices=full_matrices)
-        return U, S, tf.transpose(a=V)
+        return U, S, tfm.conj(tf.transpose(a=V))
 
     def index_update(self, tensor, indices, values):
         if not isinstance(tensor, tf.Variable):

tensorly/base.py

@@ -113,7 +113,7 @@ def partial_unfold(tensor, mode=0, skip_begin=1, skip_end=0, ravel_tensors=False
         new_shape = [tensor.shape[i] for i in range(skip_begin)] + new_shape
 
     if skip_end:
-        new_shape += [tensor.shape[-i] for i in range(1, 1 + skip_end)]
+        new_shape += [tensor.shape[-i] for i in range(skip_end, 0, -1)]
 
     return tl.reshape(tl.moveaxis(tensor, mode + skip_begin, skip_begin), new_shape)
 

tensorly/contrib/decomposition/_tt_cross.py

@@ -452,18 +452,15 @@ def maxvol(A):
 
         # Find the row of max norm
         max_row_idx = tl.argmax(rows_norms, axis=0)
-        max_row = A[rest_of_rows[max_row_idx], :]
+        max_row = A_new[max_row_idx, :]
 
         # Compute the projection of max_row to other rows
-        # projection a to b is computed as: <a,b> / sqrt(|a|*|b|)
+        # projection = <b, a>/|a|^2
         projection = tl.dot(A_new, tl.transpose(max_row))
-        normalization = tl.sqrt(rows_norms[max_row_idx] * rows_norms)
-        # make sure normalization vector is of the same shape of projection
-        normalization = tl.reshape(normalization, tl.shape(projection))
-        projection = projection / normalization
+        projection = projection / (tl.sum(max_row**2))
 
         # Subtract the projection from A_new:  b <- b - a * projection
-        A_new = A_new - A_new * tl.reshape(projection, (tl.shape(A_new)[0], 1))
+        A_new = A_new - tl.tenalg.outer((projection, max_row))
 
         # Delete the selected row
         mask.pop(tl.to_numpy(max_row_idx))

tensorly/decomposition/_constrained_cp.py

@@ -6,7 +6,8 @@
 from ._base_decomposition import DecompositionMixin
 from ..base import unfold
 from ..cp_tensor import CPTensor, cp_norm, validate_cp_rank
-from ..tenalg.proximal import admm, proximal_operator, validate_constraints
+from ..solvers.admm import admm
+from ..tenalg.proximal import proximal_operator, validate_constraints
 from ..tenalg.svd import svd_interface
 from ..tenalg import unfolding_dot_khatri_rao