Clean up stale documentation and references (#3106)

This PR cleans up stale documentation and references, one commit per
change:

1. **Quick start guide.** The testing section suggested `pytest -s
tests`, but there is no top-level `tests` directory; it now gives
`python -m pytest dpnp/tests` for the source tree and `python -m pytest
--pyargs dpnp.tests` for an installed package. The scikit-build recipes
now pass `--no-build-isolation` to `pip install -e .`, as
`scripts/build_locally.py` does, so pip doesn't rebuild the package in
an isolated environment with build dependencies from PyPI. The guide now
says the driver script works on Linux and Windows, and a `code-block`
that didn't render because it had no blank line before its content is
fixed.
2. **API reference toctree.** `doc/reference/index.rst` listed 14 pages
that don't exist (`special`, `sparse`, `ndimage`, `generic`, `memory`,
`cuda`, `memoize`, `kernel`, `optimize`, `interoperability`, `testing`,
`prof`, `environment`, `misc`); they are removed.
`doc/reference/polynomials.rst`, a placeholder whose entries are all
commented out and which `routines.rst` leaves out on purpose, is marked
`:orphan:`. Both produced Sphinx warnings.
3. **Stale files and references.**
- `.github/workflows/Windows-IntelLLVM_3.22.cmake` is removed: nothing
uses it, and `conda-recipe/bld.bat` applies only the 3.26 copy.
- `doc/docstring_template.py` described a fallback to NumPy and an array
printing format that no longer exist; it now shows the docstring layout
dpnp uses.
- The 110 `dpctl.tensor` references left in `dpnp/tensor` docstrings and
comments now say `dpnp.tensor`. Every referenced name exists in
`dpnp.tensor`; on the same lines, `:module:` is fixed to `:mod:` and a
misspelled `usm_ndarrays` to `usm_ndarray`. 30b894b

Author: github-actions[bot]

_sources/quick_start_guide.rst.txt

@@ -113,16 +113,16 @@ To build and install the package on Linux OS, run:
 .. code-block:: bash
 
     python setup.py build_ext --inplace -- -G Ninja -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icpx
-    python -m pip install -e .
+    python -m pip install -e . --no-build-isolation
 
 To build and install the package on Windows OS, run:
 
 .. code-block:: bash
 
     python setup.py build_ext --inplace -- -G Ninja -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icx
-    python -m pip install -e .
+    python -m pip install -e . --no-build-isolation
 
-Alternatively, to develop on Linux OS, you can use the driver script:
+Alternatively, to develop on Linux or Windows OS, you can use the driver script:
 
 .. code-block:: bash
 
@@ -200,6 +200,7 @@ You can then use one of them as the argument to ``--target-hip``.
 For example:
 
 .. code-block:: bash
+
     python scripts/build_locally.py --target-hip=gfx90a
 
 Multi-target build
@@ -235,12 +236,18 @@ These options are useful when building pybind11 extensions that use
 Testing
 =======
 
-If you want to execute the scope of Python test suites which are available
-by the source, you will need to run a command as below:
+To run the test suite from the source tree, after building the package as
+described above, run:
+
+.. code-block:: bash
+
+    python -m pytest dpnp/tests
+
+To run the tests of an installed ``dpnp`` package, run:
 
 .. code-block:: bash
 
-    pytest -s tests
+    python -m pytest --pyargs dpnp.tests
 
 Examples
 ========

_sources/reference/index.rst.txt

@@ -16,21 +16,7 @@ API reference of the Data Parallel Extension for NumPy*
    ndarray
    ufunc
    routines
-   special
    scipy
-   sparse
-   ndimage
-   generic
-   memory
-   cuda
-   memoize
-   kernel
-   optimize
-   interoperability
-   testing
-   prof
-   environment
    dtypes_table
    comparison
-   misc
    array_api

_sources/reference/polynomials.rst.txt

@@ -1,3 +1,7 @@
+:orphan:
+
+.. Not in any toctree until dpnp implements the polynomial routines.
+
 .. currentmodule:: dpnp
 
 Polynomials

quick_start_guide.html

@@ -1148,15 +1148,15 @@ <h3>Build and Install with scikit-build<a class="headerlink" href="#build-and-in
 </div>
 <p>To build and install the package on Linux OS, run:</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>setup.py<span class="w"> </span>build_ext<span class="w"> </span>--inplace<span class="w"> </span>--<span class="w"> </span>-G<span class="w"> </span>Ninja<span class="w"> </span>-DCMAKE_C_COMPILER:PATH<span class="o">=</span>icx<span class="w"> </span>-DCMAKE_CXX_COMPILER:PATH<span class="o">=</span>icpx
-python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.
+python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.<span class="w"> </span>--no-build-isolation
 </pre></div>
 </div>
 <p>To build and install the package on Windows OS, run:</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>setup.py<span class="w"> </span>build_ext<span class="w"> </span>--inplace<span class="w"> </span>--<span class="w"> </span>-G<span class="w"> </span>Ninja<span class="w"> </span>-DCMAKE_C_COMPILER:PATH<span class="o">=</span>icx<span class="w"> </span>-DCMAKE_CXX_COMPILER:PATH<span class="o">=</span>icx
-python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.
+python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.<span class="w"> </span>--no-build-isolation
 </pre></div>
 </div>
-<p>Alternatively, to develop on Linux OS, you can use the driver script:</p>
+<p>Alternatively, to develop on Linux or Windows OS, you can use the driver script:</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>scripts/build_locally.py
 </pre></div>
 </div>
@@ -1212,6 +1212,9 @@ <h4>AMD build<a class="headerlink" href="#amd-build" title="Link to this heading
 <p>This will print names like <code class="docutils literal notranslate"><span class="pre">gfx90a</span></code>, <code class="docutils literal notranslate"><span class="pre">gfx1030</span></code>, etc.
 You can then use one of them as the argument to <code class="docutils literal notranslate"><span class="pre">--target-hip</span></code>.</p>
 <p>For example:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>scripts/build_locally.py<span class="w"> </span>--target-hip<span class="o">=</span>gfx90a
+</pre></div>
+</div>
 </section>
 <section id="multi-target-build">
 <h4>Multi-target build<a class="headerlink" href="#multi-target-build" title="Link to this heading">¶</a></h4>
@@ -1240,9 +1243,13 @@ <h2>Command-Line Interface<a class="headerlink" href="#command-line-interface" t
 </section>
 <section id="testing">
 <h2>Testing<a class="headerlink" href="#testing" title="Link to this heading">¶</a></h2>
-<p>If you want to execute the scope of Python test suites which are available
-by the source, you will need to run a command as below:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pytest<span class="w"> </span>-s<span class="w"> </span>tests
+<p>To run the test suite from the source tree, after building the package as
+described above, run:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pytest<span class="w"> </span>dpnp/tests
+</pre></div>
+</div>
+<p>To run the tests of an installed <code class="docutils literal notranslate"><span class="pre">dpnp</span></code> package, run:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pytest<span class="w"> </span>--pyargs<span class="w"> </span>dpnp.tests
 </pre></div>
 </div>
 </section>

reference/comparison.html

@@ -1068,7 +1068,7 @@
 <h1>Comparison Table NumPy/ DPNP/ CuPy<a class="headerlink" href="#comparison-table-numpy-dpnp-cupy" title="Link to this heading">¶</a></h1>
 <p>Here is a list of NumPy and CuPy APIs and its corresponding DPNP implementations.</p>
 <p><code class="docutils literal notranslate"><span class="pre">-</span></code> in DPNP column means that DPNP implementation is not provided yet.</p>
-<p><strong>NumPy(v2.5.4) / DPNP(v0.21.0dev15+1.g674bc14fdfa) / CuPy(v14.2.0) APIs</strong></p>
+<p><strong>NumPy(v2.5.4) / DPNP(v0.21.0dev15+2.g30b894bdbc8) / CuPy(v14.2.0) APIs</strong></p>
 <section id="summary">
 <h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <div class="table-wrapper docutils container">