ndarray: fast views into existing arrays

CPU loops involving nanobind ndarrays weren't getting properly
vectorized. This commit adds *views*, which provide an efficient
abstraction that enables better code generation.

docs/api_extra.rst

@@ -565,6 +565,17 @@ section <ndarrays>`.
       Return a mutable pointer to the array data. Only enabled when `Scalar` is
       not itself ``const``.
 
+   .. cpp:function:: template <typename... Extra> auto view()
+
+      Returns an nd-array view that is optimized for fast array access on the
+      CPU. You may optionally specify additional ndarray constraints via the
+      `Extra` parameter (though a runtime check should first be performed to
+      ensure that the array possesses these properties).
+
+      The returned view provides the operations ``data()``, ``ndim()``,
+      ``shape()``, ``stride()``, and ``operator()`` following the conventions
+      of the `ndarray` type.
+
    .. cpp:function:: template <typename... Ts> auto& operator()(Ts... indices)
 
       Return a mutable reference to the element at stored at the provided

docs/ndarray.rst

@@ -83,13 +83,12 @@ should therefore prevent such undefined behavior.
 :cpp:class:`nb::ndarray\<...\> <ndarray>` accepts template arguments to
 specify such constraints. For example the function interface below
 guarantees that the implementation is only invoked when it is provided with
-a ``MxNx3`` array of 8-bit unsigned integers that is furthermore stored
-contiguously in CPU memory using a C-style array ordering convention.
+a ``MxNx3`` array of 8-bit unsigned integers.
 
 .. code-block:: cpp
 
    m.def("process", [](nb::ndarray<uint8_t, nb::shape<nb::any, nb::any, 3>,
-                                   nb::c_contig, nb::device::cpu> data) {
+                                   nb::device::cpu> data) {
        // Double brightness of the MxNx3 RGB image
        for (size_t y = 0; y < data.shape(0); ++y)
            for (size_t x = 0; x < data.shape(1); ++x)
@@ -100,15 +99,16 @@ contiguously in CPU memory using a C-style array ordering convention.
 
 The above example also demonstrates the use of
 :cpp:func:`nb::ndarray\<...\>::operator() <ndarray::operator()>`, which
-provides direct (i.e., high-performance) read/write access to the array
-data. Note that this function is only available when the underlying data
-type and ndarray rank are specified. It should only be used when the
-array storage is reachable via CPU’s virtual memory address space.
+provides direct read/write access to the array contents. Note that this
+function is only available when the underlying data type and ndarray dimension
+are specified via the :cpp:type:`ndarray\<..\> <ndarray>` template parameters.
+It should only be used when the array storage is accessible through the CPU's
+virtual memory address space.
 
 .. _ndarray-constraints-1:
 
 Constraint types
-~~~~~~~~~~~~~~~~
+----------------
 
 The following constraints are available
 
@@ -153,6 +153,94 @@ count until they go out of scope. It is legal call
 when the `GIL <https://wiki.python.org/moin/GlobalInterpreterLock>`__ is not
 held.
 
+.. _ndarray-views:
+
+Fast array views
+----------------
+
+The following advice applies to performance-sensitive CPU code that reads and
+writes arrays using loops that invoke :cpp:func:`nb::ndarray\<...\>::operator()
+<ndarray::operator()>`. It does not apply to GPU arrays because they are
+usually not accessed in this way.
+
+Consider the following snippet, which fills a 2D array with data:
+
+.. code-block:: cpp
+
+   void fill(nb::ndarray<float, nb::ndim<2>, nb::c_contig, nb::device::cpu> arg) {
+       for (size_t i = 0; i < array.shape(0); ++i)
+           for (size_t j = 0; j < array.shape(1); ++j)
+               arg(i, j) = /* ... */;
+   }
+
+While functional, this code is not perfect. The problem is that to compute the
+address of an entry, ``operator()`` accesses the DLPack array descriptor. This
+indirection can break certain compiler optimizations.
+
+nanobind provides the method :cpp:func:`ndarray\<...\>::view() <ndarray::view>`
+to fix this. It creates a tiny data structure that provides all information
+needed to access the array contents, and which can be held within CPU
+registers. All relevant compile-time information (:cpp:class:`nb::ndim <ndim>`,
+:cpp:class:`nb::shape <shape>`, :cpp:class:`nb::c_contig <c_contig>`,
+:cpp:class:`nb::f_contig <f_contig>`) is materialized in this view, which
+enables constant propagation, auto-vectorization, and loop unrolling.
+
+An improved version of the example using such a view is shown below:
+
+.. code-block:: cpp
+
+   void fill(nb::ndarray<float, nb::ndim<2>, nb::c_contig, nb::device::cpu> arg) {
+       auto v = array.view(); /// <-- new!
+
+       for (size_t i = 0; i < v.shape(0); ++i) // Important; use 'v' instead of 'arg' everywhere in loop
+           for (size_t j = 0; j < v.shape(1); ++j)
+               v(i, j) = /* ... */;
+   }
+
+Note that the view performs no reference counting. You may not store it in a way
+that exceeds the lifetime of the original array.
+
+When using OpenMP to parallelize expensive array operations, pass the
+``firstprivate(view_1, view_2, ...)`` so that each worker thread can copy the
+view into its register file.
+
+.. code-block:: cpp
+
+   auto v = array.view();
+   #pragma omp parallel for schedule(static) firstprivate(v)
+   for (...) { /* parallel loop */ }
+
+.. _ndarray-runtime-specialization:
+
+Specializing views at runtime
+-----------------------------
+
+As mentioned earlier, element access via ``operator()`` only works when both
+the array's scalar type and its dimension are specified within the type (i.e.,
+when they are known at compile time); the same is also true for array views.
+However, sometimes, it is useful that a function can be called with different
+array types.
+
+You may use the :cpp:func:`ndarray\<...\>::view() <ndarray::view>` method to
+create *specialized* views if a run-time check determines that it is safe to
+do so. For example, the function below accepts contiguous CPU arrays and
+performs a loop over a specialized 2D ``float`` view when the array is of
+this type.
+
+.. code-block:: cpp
+
+   void fill(nb::ndarray<nb::c_contig, nb::device::cpu> arg) {
+       if (arg.dtype() == nb::dtype<float>() && arg.ndim() == 2) {
+           auto v = array.view<float, nb::ndim<2>>(); // <-- new!
+
+           for (size_t i = 0; i < v.shape(0); ++i) {
+               for (size_t j = 0; j < v.shape(1); ++j) {
+                   v(i, j) = /* ... */;
+               }
+           }
+        } else { /* ... */ }
+   }
+
 Constraints in type signatures
 ------------------------------
 
@@ -364,6 +452,30 @@ interpreted as follows:
 - :cpp:enumerator:`rv_policy::move` is unsupported and demoted to
   :cpp:enumerator:`rv_policy::copy`.
 
+.. _ndarray_nonstandard_arithmetic:
+
+Nonstandard arithmetic types
+----------------------------
+
+Low or extended-precision arithmetic types (e.g., ``int128``, ``float16``,
+``bfloat``) are sometimes used but don't have standardized C++ equivalents. If
+you wish to exchange arrays based on such types, you must register a partial
+overload of ``nanobind::ndarray_traits`` to inform nanobind about it.
+
+For example, the following snippet makes ``__fp16`` (half-precision type on
+``aarch64``) available:
+
+.. code-block:: cpp
+
+   namespace nanobind {
+       template <> struct ndarray_traits<__fp16> {
+           static constexpr bool is_float  = true;
+           static constexpr bool is_bool   = false;
+           static constexpr bool is_int    = false;
+           static constexpr bool is_signed = true;
+       };
+   };
+
 Limitations
 -----------
 
@@ -383,30 +495,9 @@ internal representations (*dtypes*), including
 nanobind's :cpp:class:`nb::ndarray\<...\> <ndarray>` is based on the `DLPack
 <https://github.com/dmlc/dlpack>`__ array exchange protocol, which causes it to
 be more restrictive. Presently supported dtypes include signed/unsigned
-integers, floating point values, and boolean values.
+integers, floating point values, and boolean values. Some :ref:`nonstandard
+arithmetic types <ndarray_nonstandard_arithmetic>` can be supported as well.
 
 Nanobind can receive and return read-only arrays via the buffer protocol used
 to exchange data with NumPy. The DLPack interface currently ignores this
 annotation.
-
-Supporting nonstandard arithmetic types
----------------------------------------
-
-Low or extended-precision arithmetic types (e.g., ``int128``, ``float16``,
-``bfloat``) are sometimes used but don't have standardized C++ equivalents. If
-you wish to exchange arrays based on such types, you must register a partial
-overload of ``nanobind::ndarray_traits`` to inform nanobind about it.
-
-For example, the following snippet makes ``__fp16`` (half-precision type on
-``aarch64``) available:
-
-.. code-block:: cpp
-
-   namespace nanobind {
-       template <> struct ndarray_traits<__fp16> {
-           static constexpr bool is_float  = true;
-           static constexpr bool is_bool   = false;
-           static constexpr bool is_int    = false;
-           static constexpr bool is_signed = true;
-       };
-   };

include/nanobind/ndarray.h

@@ -247,6 +247,7 @@ template <typename... Ts> struct ndarray_info {
     using shape_type = void;
     constexpr static auto name = const_name("ndarray");
     constexpr static ndarray_framework framework = ndarray_framework::none;
+    constexpr static char order = '\0';
 };
 
 template <typename T, typename... Ts> struct ndarray_info<T, Ts...>  : ndarray_info<Ts...> {
@@ -259,6 +260,14 @@ template <size_t... Is, typename... Ts> struct ndarray_info<shape<Is...>, Ts...>
     using shape_type = shape<Is...>;
 };
 
+template <typename... Ts> struct ndarray_info<c_contig, Ts...> : ndarray_info<Ts...> {
+    constexpr static char order = 'C';
+};
+
+template <typename... Ts> struct ndarray_info<f_contig, Ts...> : ndarray_info<Ts...> {
+    constexpr static char order = 'F';
+};
+
 template <typename... Ts> struct ndarray_info<numpy, Ts...> : ndarray_info<Ts...> {
     constexpr static auto name = const_name("numpy.ndarray");
     constexpr static ndarray_framework framework = ndarray_framework::numpy;
@@ -282,6 +291,64 @@ template <typename... Ts> struct ndarray_info<jax, Ts...> : ndarray_info<Ts...>
 
 NAMESPACE_END(detail)
 
+template <typename Scalar, typename Shape, char Order> struct ndarray_view {
+    static constexpr size_t Dim = Shape::size;
+
+    ndarray_view() = default;
+    ndarray_view(const ndarray_view &) = default;
+    ndarray_view(ndarray_view &&) = default;
+    ndarray_view &operator=(const ndarray_view &) = default;
+    ndarray_view &operator=(ndarray_view &&) noexcept = default;
+    ~ndarray_view() noexcept = default;
+
+    template <typename... Ts> NB_INLINE Scalar &operator()(Ts... indices) const {
+        static_assert(
+            sizeof...(Ts) == Dim,
+            "ndarray_view::operator(): invalid number of arguments");
+
+        const int64_t indices_i64[] { (int64_t) indices... };
+        int64_t offset = 0;
+        for (size_t i = 0; i < Dim; ++i)
+            offset += indices_i64[i] * m_strides[i];
+
+        return *(m_data + offset);
+    }
+
+    size_t ndim() const { return Dim; }
+    size_t shape(size_t i) const { return m_shape[i]; }
+    int64_t stride(size_t i) const { return m_strides[i]; }
+    Scalar *data() const { return m_data; }
+
+private:
+    template <typename...> friend class ndarray;
+
+    template <size_t... I1, size_t... I2>
+    ndarray_view(Scalar *data, const int64_t *shape, const int64_t *strides,
+                 std::index_sequence<I1...>, nanobind::shape<I2...>)
+        : m_data(data) {
+
+        /* Initialize shape/strides with compile-time knowledge if
+           available (to permit vectorization, loop unrolling, etc.) */
+        ((m_shape[I1] = (I2 == any) ? shape[I1] : I2), ...);
+        ((m_strides[I1] = strides[I1]), ...);
+
+        if constexpr (Order == 'F') {
+            m_strides[0] = 1;
+            for (size_t i = 1; i < Dim; ++i)
+                m_strides[i] = m_strides[i - 1] * m_shape[i - 1];
+        } else if constexpr (Order == 'C') {
+            m_strides[Dim - 1] = 1;
+            for (Py_ssize_t i = (Py_ssize_t) Dim - 2; i >= 0; --i)
+                m_strides[i] = m_strides[i + 1] * m_shape[i + 1];
+        }
+    }
+
+    Scalar *m_data = nullptr;
+    int64_t m_shape[Dim] { };
+    int64_t m_strides[Dim] { };
+};
+
+
 template <typename... Args> class ndarray {
 public:
     template <typename...> friend class ndarray;
@@ -405,24 +472,59 @@ template <typename... Args> class ndarray {
                                   byte_offset(indices...));
     }
 
+    template <typename... Extra> NB_INLINE auto view() {
+        using Info2 = typename ndarray<Args..., Extra...>::Info;
+        using Scalar2 = typename Info2::scalar_type;
+        using Shape2 = typename Info2::shape_type;
+
+        constexpr bool has_scalar = !std::is_same_v<Scalar2, void>,
+                       has_shape  = !std::is_same_v<Shape2, void>;
+
+        static_assert(has_scalar,
+            "To use the ndarray::view<..>() method, you must add a scalar type "
+            "annotation (e.g. 'float') to the template parameters of the parent "
+            "ndarray, or to the call to .view<..>()");
+
+        static_assert(has_shape,
+            "To use the ndarray::view<..>() method, you must add a shape<..> "
+            "or ndim<..> annotation to the template parameters of the parent "
+            "ndarray, or to the call to .view<..>()");
+
+        if constexpr (has_scalar && has_shape) {
+            return ndarray_view<Scalar2, Shape2, Info2::order>(
+                (Scalar2 *) data(), shape_ptr(), stride_ptr(),
+                std::make_index_sequence<Shape2::size>(), Shape2());
+        } else {
+            return nullptr;
+        }
+    }
+
 private:
     template <typename... Ts>
     NB_INLINE int64_t byte_offset(Ts... indices) const {
-        static_assert(
-            !std::is_same_v<Scalar, void>,
-            "To use nb::ndarray::operator(), you must add a scalar type "
+        constexpr bool has_scalar = !std::is_same_v<Scalar, void>,
+                       has_shape = !std::is_same_v<typename Info::shape_type, void>;
+
+        static_assert(has_scalar,
+            "To use ndarray::operator(), you must add a scalar type "
             "annotation (e.g. 'float') to the ndarray template parameters.");
-        static_assert(
-            !std::is_same_v<Scalar, void>,
-            "To use nb::ndarray::operator(), you must add a nb::shape<> "
-            "annotation to the ndarray template parameters.");
-        static_assert(sizeof...(Ts) == Info::shape_type::size,
-                      "nb::ndarray::operator(): invalid number of arguments");
-        size_t counter = 0;
-        int64_t index = 0;
-        ((index += int64_t(indices) * m_dltensor.strides[counter++]), ...);
-
-        return (int64_t) m_dltensor.byte_offset + index * sizeof(typename Info::scalar_type);
+
+        static_assert(has_shape,
+            "To use ndarray::operator(), you must add a shape<> or "
+            "ndim<> annotation to the ndarray template parameters.");
+
+        if constexpr (has_scalar && has_shape) {
+            static_assert(sizeof...(Ts) == Info::shape_type::size,
+                          "ndarray::operator(): invalid number of arguments");
+
+            size_t counter = 0;
+            int64_t index = 0;
+            ((index += int64_t(indices) * m_dltensor.strides[counter++]), ...);
+
+            return (int64_t) m_dltensor.byte_offset + index * sizeof(typename Info::scalar_type);
+        } else {
+            return 0;
+        }
     }
 
     detail::ndarray_handle *m_handle = nullptr;