DOC, MAINT: Improve GPU docs, remove references to dpctl arrays

doc/sources/algorithms.rst

@@ -21,7 +21,7 @@ Supported Algorithms
 
 .. note::
    To verify that oneDAL is being used for these algorithms, you can enable verbose mode. 
-   See :ref:`verbose mode documentation <verbose>` for details.
+   See :doc:`verbose` for details.
 
 Applying |sklearnex| impacts the following |sklearn| estimators:
 

doc/sources/array_api.rst

@@ -23,17 +23,16 @@ Overview
 
 Many estimators from the |sklearnex| support passing data classes that conform to the
 `Array API <https://data-apis.org/array-api/>`_ specification as inputs to methods like ``.fit()``
-and ``.predict()``, such as :external+dpnp:doc:`dpnp.ndarray <reference/ndarray>` or
-`torch.tensor <https://docs.pytorch.org/docs/stable/tensors.html>`__. This is particularly
-useful for GPU computations, as it allows performing operations on inputs that are already
+and ``.predict()``, such as |dpnp_array| or `torch.tensor <https://docs.pytorch.org/docs/stable/tensors.html>`__.
+This is particularly useful for GPU computations, as it allows performing operations on inputs that are already
 on GPU without moving the data from host to device.
 
 .. important::
     Array API is disabled by default in |sklearn|. In order to get array API support in the |sklearnex|, it must
     be :external+sklearn:doc:`enabled in scikit-learn <modules/array_api>`, which requires either changing
     global settings or using a ``config_context``, plus installing additional dependencies such as ``array-api-compat``.
 
-When passing array API inputs whose data is on a SyCL-enabled device (e.g. an Intel GPU), as
+When passing array API inputs whose data is on a SYCL-enabled device (e.g. an Intel GPU), as
 supported for example by `PyTorch <https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html>`__
 and |dpnp|, if array API support is enabled and the requested operation (e.g. call to ``.fit()`` / ``.predict()``
 on the estimator class being used) is :ref:`supported on device/GPU <sklearn_algorithms_gpu>`, computations
@@ -51,10 +50,10 @@ through options ``allow_sklearn_after_onedal`` (default is ``True``) and ``allow
 
 If array API is enabled for |sklearn| and the estimator being used has array API support on |sklearn| (which can be
 verified by attribute ``array_api_support`` from :obj:`sklearn.utils.get_tags`), then array API inputs whose data
-is allocated neither on CPU nor on a SyCL device will be forwarded directly to the unpatched methods from |sklearn|,
+is allocated neither on CPU nor on a SYCL device will be forwarded directly to the unpatched methods from |sklearn|,
 without using the accelerated versions from this library, regardless of option ``allow_sklearn_after_onedal``.
 
-While other array API inputs (e.g. torch arrays with data allocated on a non-SyCL device) might be supported
+While other array API inputs (e.g. torch arrays with data allocated on a non-SYCL device) might be supported
 by the |sklearnex| in cases where the same class from |sklearn| doesn't support array API, note that the data will
 be transferred to host if it isn't already, and the computations will happen on CPU.
 
@@ -80,6 +79,7 @@ in many cases they are.
     classes that have :external+dpctl:doc:`USM data <api_reference/dpctl/memory>`. In order to ensure that computations
     happen on the intended device under array API, make sure that the data is already on the desired device.
 
+.. _array_api_estimators:
 
 Supported classes
 =================
@@ -98,11 +98,10 @@ The following patched classes have support for array API inputs:
 - :obj:`sklearnex.linear_model.IncrementalRidge`
 
 .. note::
-    While full array API support is currently not implemented for all classes, :external+dpnp:doc:`dpnp.ndarray <reference/ndarray>`
-    and :external+dpctl:doc:`dpctl.tensor <api_reference/dpctl/tensor>` inputs are supported by all the classes
-    that have :ref:`GPU support <oneapi_gpu>`. Note however that if array API support is not enabled in |sklearn|,
-    when passing these classes as inputs, data will be transferred to host and then back to device instead of being
-    used directly.
+    While full array API support is currently not implemented for all classes, |dpnp_array| inputs are supported
+    by all the classes that have :ref:`GPU support <oneapi_gpu>`. Note however that if array API support is not
+    enabled in |sklearn|, when passing these classes as inputs, data will be transferred to host and then back to
+    device instead of being used directly.
 
 
 Example usage
@@ -111,52 +110,102 @@ Example usage
 GPU operations on GPU arrays
 ----------------------------
 
-.. code-block:: python
-
-    # Array API support from sklearn requires enabling it on SciPy too
-    import os
-    os.environ["SCIPY_ARRAY_API"] = "1"
-
-    import numpy as np
-    import dpnp
-    from sklearnex import config_context
-    from sklearnex.linear_model import LinearRegression
-
-    # Random data for a regression problem
-    rng = np.random.default_rng(seed=123)
-    X_np = rng.standard_normal(size=(100, 10), dtype=np.float32)
-    y_np = rng.standard_normal(size=100, dtype=np.float32)
-
-    # DPNP offers an array-API-compliant class where data can be on GPU
-    X = dpnp.array(X_np, device="gpu")
-    y = dpnp.array(y_np, device="gpu")
-
-    # Important to note again that array API must be enabled on scikit-learn
-    model = LinearRegression()
-    with config_context(array_api_dispatch=True):
-        model.fit(X, y)
-
-    # Fitted attributes are now of the same class as inputs
-    assert isinstance(model.coef_, X.__class__)
-
-    # Predictions are also of the same class
-    with config_context(array_api_dispatch=True):
-        pred = model.predict(X[:5])
-    assert isinstance(pred, X.__class__)
-
-    # Fitted models can be passed array API inputs of a different class
-    # than the training data, as long as their data resides in the same
-    # device. This now fits a model using a non-NumPy class whose data is on CPU.
-    X_cpu = dpnp.array(X_np, device="cpu")
-    y_cpu = dpnp.array(y_np, device="cpu")
-    model_cpu = LinearRegression()
-    with config_context(array_api_dispatch=True):
-        model_cpu.fit(X_cpu, y_cpu)
-        pred_dpnp = model_cpu.predict(X_cpu[:5])
-        pred_np = model_cpu.predict(X_cpu[:5].asnumpy())
-    assert isinstance(pred_dpnp, X_cpu.__class__)
-    assert isinstance(pred_np, np.ndarray)
-    assert pred_dpnp.__class__ != pred_np.__class__
+.. tabs::
+    .. tab:: With Torch tensors
+       .. code-block:: python
+
+           # Array API support from sklearn requires enabling it on SciPy too
+           import os
+           os.environ["SCIPY_ARRAY_API"] = "1"
+
+           import numpy as np
+           import torch
+           from sklearnex import config_context
+           from sklearnex.linear_model import LinearRegression
+
+           # Random data for a regression problem
+           rng = np.random.default_rng(seed=123)
+           X_np = rng.standard_normal(size=(100, 10), dtype=np.float32)
+           y_np = rng.standard_normal(size=100, dtype=np.float32)
+
+           # Torch offers an array-API-compliant class where data can be on GPU (referred to as 'xpu')
+           X = torch.tensor(X_np, device="xpu")
+           y = torch.tensor(y_np, device="xpu")
+
+           # Important to note again that array API must be enabled on scikit-learn
+           model = LinearRegression()
+           with config_context(array_api_dispatch=True):
+               model.fit(X, y)
+
+           # Fitted attributes are now of the same class as inputs
+           assert isinstance(model.coef_, torch.Tensor)
+
+           # Predictions are also of the same class
+           with config_context(array_api_dispatch=True):
+               pred = model.predict(X[:5])
+           assert isinstance(pred, torch.Tensor)
+
+           # Fitted models can be passed array API inputs of a different class
+           # than the training data, as long as their data resides in the same
+           # device. This now fits a model using a non-NumPy class whose data is on CPU.
+           X_cpu = torch.tensor(X_np, device="cpu")
+           y_cpu = torch.tensor(y_np, device="cpu")
+           model_cpu = LinearRegression()
+           with config_context(array_api_dispatch=True):
+               model_cpu.fit(X_cpu, y_cpu)
+               pred_torch = model_cpu.predict(X_cpu[:5])
+               pred_np = model_cpu.predict(X_cpu[:5].numpy())
+           assert isinstance(pred_torch, X_cpu.__class__)
+           assert isinstance(pred_np, np.ndarray)
+           assert pred_torch.__class__ != pred_np.__class__
+
+    .. tab:: With DPNP arrays
+       .. code-block:: python
+
+           # Array API support from sklearn requires enabling it on SciPy too
+           import os
+           os.environ["SCIPY_ARRAY_API"] = "1"
+
+           import numpy as np
+           import dpnp
+           from sklearnex import config_context
+           from sklearnex.linear_model import LinearRegression
+
+           # Random data for a regression problem
+           rng = np.random.default_rng(seed=123)
+           X_np = rng.standard_normal(size=(100, 10), dtype=np.float32)
+           y_np = rng.standard_normal(size=100, dtype=np.float32)
+
+           # DPNP offers an array-API-compliant class where data can be on GPU
+           X = dpnp.array(X_np, device="gpu")
+           y = dpnp.array(y_np, device="gpu")
+
+           # Important to note again that array API must be enabled on scikit-learn
+           model = LinearRegression()
+           with config_context(array_api_dispatch=True):
+               model.fit(X, y)
+
+           # Fitted attributes are now of the same class as inputs
+           assert isinstance(model.coef_, X.__class__)
+
+           # Predictions are also of the same class
+           with config_context(array_api_dispatch=True):
+               pred = model.predict(X[:5])
+           assert isinstance(pred, X.__class__)
+
+           # Fitted models can be passed array API inputs of a different class
+           # than the training data, as long as their data resides in the same
+           # device. This now fits a model using a non-NumPy class whose data is on CPU.
+           X_cpu = dpnp.array(X_np, device="cpu")
+           y_cpu = dpnp.array(y_np, device="cpu")
+           model_cpu = LinearRegression()
+           with config_context(array_api_dispatch=True):
+               model_cpu.fit(X_cpu, y_cpu)
+               pred_dpnp = model_cpu.predict(X_cpu[:5])
+               pred_np = model_cpu.predict(X_cpu[:5].asnumpy())
+           assert isinstance(pred_dpnp, X_cpu.__class__)
+           assert isinstance(pred_np, np.ndarray)
+           assert pred_dpnp.__class__ != pred_np.__class__
 
 
 ``array-api-strict``

doc/sources/config-contexts.rst

@@ -0,0 +1,91 @@
+.. Copyright contributors to the oneDAL project
+..
+.. Licensed under the Apache License, Version 2.0 (the "License");
+.. you may not use this file except in compliance with the License.
+.. You may obtain a copy of the License at
+..
+..     http://www.apache.org/licenses/LICENSE-2.0
+..
+.. Unless required by applicable law or agreed to in writing, software
+.. distributed under the License is distributed on an "AS IS" BASIS,
+.. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. See the License for the specific language governing permissions and
+.. limitations under the License.
+.. include:: substitutions.rst
+.. _config_contexts:
+
+=========================================
+Configuration Contexts and Global Options
+=========================================
+
+Overview
+========
+
+Just like |sklearn|, the |sklearnex| offers configurable options which can be managed
+locally through a configuration context, or globally through process-wide settings,
+by extending the configuration-related functions from |sklearn| (see :obj:`sklearn.config_context`
+for details).
+
+Configurations in the |sklearnex| are particularly useful for :doc:`GPU functionalities <oneapi-gpu>`
+and :doc:`SMPD mode <distributed-mode>`, and are necessary to modify for enabling :doc:`array API <array_api>`.
+
+Configuration context and global options manager for the |sklearnex| can either be imported directly
+from the module ``sklearnex``, or can be imported from the ``sklearn`` module after applying patching.
+
+Note that options in the |sklearnex| are a superset of options from |sklearn|, and options passed to
+the configuration contexts and global settings of the |sklearnex| will also affect |sklearn| if the
+option is supported by it - meaning: the same context manager  or global option setter is used for
+both libraries.
+
+Example usage
+=============
+
+Example using the ``target_offload`` option to make computations run on a GPU:
+
+With a local context
+--------------------
+
+Here, only the operations from |sklearn| and from the |sklearnex| that happen within the 'with'
+block will be affected by the options:
+
+.. code:: python
+
+    import numpy as np
+    from sklearnex import config_context
+    from sklearnex.cluster import DBSCAN
+
+    X = np.array([[1., 2.], [2., 2.], [2., 3.],
+                  [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+    with config_context(target_offload="gpu"):
+        clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+
+As a global option
+------------------
+
+Here, all computations from |sklearn| and from the |sklearnex| that happen after the option
+is modified are affected:
+
+.. code:: python
+
+    import numpy as np
+    from sklearnex import set_config
+    from sklearnex.cluster import DBSCAN
+
+    X = np.array([[1., 2.], [2., 2.], [2., 3.],
+                  [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32)
+
+    set_config(target_offload="gpu") # set it globally
+    clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+    set_config(target_offload="auto") # restore it back
+
+API Reference
+=============
+
+Note that all of the options accepted by these functions in |sklearn| are also accepted
+here - these just list the additional options offered by the |sklearnex|.
+
+.. autofunction:: sklearnex.config_context
+
+.. autofunction:: sklearnex.get_config
+
+.. autofunction:: sklearnex.set_config

doc/sources/distributed-mode.rst

@@ -85,7 +85,7 @@ data on device without this may lead to a runtime error): ::
     export I_MPI_OFFLOAD=1
 
 SMPD-aware versions of estimators can be imported from the ``sklearnex.spmd`` module. Data should be distributed across multiple nodes as
-desired, and should be transferred to a |dpctl| or `dpnp <https://github.com/IntelPython/dpnp>`__ array before being passed to the estimator.
+desired, and should be transferred to a |dpnp_array| before being passed to the estimator.
 
 Note that SPMD estimators allow an additional argument ``queue`` in their ``.fit`` / ``.predict`` methods, which accept :obj:`dpctl.SyclQueue` objects. For example, while the signature for :obj:`sklearn.linear_model.LinearRegression.predict` would be