First draft of Eigen::Tensor support (#4201)

* First draft of Eigen::Tensor support

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Fix build errors

* Weird allocator stuff?

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Remove unused + additional allocator junk

* Disable warning

* Use constexpr

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* clang tidy fixes

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* Resolve comments

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Remove auto constexpr function

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* Try again for older C++

* Oops forgot constexpr

* Move...

.gitignore

@@ -43,3 +43,4 @@ pybind11Targets.cmake
 /pybind11/share/*
 /docs/_build/*
 .ipynb_checkpoints/
+tests/main.cpp

CMakeLists.txt

@@ -120,6 +120,8 @@ set(PYBIND11_HEADERS
     include/pybind11/complex.h
     include/pybind11/options.h
     include/pybind11/eigen.h
+    include/pybind11/eigen/matrix.h
+    include/pybind11/eigen/tensor.h
     include/pybind11/embed.h
     include/pybind11/eval.h
     include/pybind11/gil.h

include/pybind11/eigen/tensor.h

+/*
+    pybind11/eigen/tensor.h: Transparent conversion for Eigen tensors
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "../numpy.h"
+
+#if defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER)
+static_assert(__GNUC__ > 5, "Eigen Tensor support in pybind11 requires GCC > 5.0");
+#endif
+
+#if defined(_MSC_VER)
+#    pragma warning(push)
+#    pragma warning(disable : 4554) // Tensor.h warning
+#    pragma warning(disable : 4127) // Tensor.h warning
+#elif defined(__MINGW32__)
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#endif
+
+#include <unsupported/Eigen/CXX11/Tensor>
+
+#if defined(_MSC_VER)
+#    pragma warning(pop)
+#elif defined(__MINGW32__)
+#    pragma GCC diagnostic pop
+#endif
+
+static_assert(EIGEN_VERSION_AT_LEAST(3, 3, 0),
+              "Eigen Tensor support in pybind11 requires Eigen >= 3.3.0");
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline bool is_tensor_aligned(const void *data) {
+    return (reinterpret_cast<std::size_t>(data) % EIGEN_DEFAULT_ALIGN_BYTES) == 0;
+}
+
+template <typename T>
+constexpr int compute_array_flag_from_tensor() {
+    static_assert((static_cast<int>(T::Layout) == static_cast<int>(Eigen::RowMajor))
+                      || (static_cast<int>(T::Layout) == static_cast<int>(Eigen::ColMajor)),
+                  "Layout must be row or column major");
+    return (static_cast<int>(T::Layout) == static_cast<int>(Eigen::RowMajor)) ? array::c_style
+                                                                              : array::f_style;
+}
+
+template <typename T>
+struct eigen_tensor_helper {};
+
+template <typename Scalar_, int NumIndices_, int Options_, typename IndexType>
+struct eigen_tensor_helper<Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType>> {
+    using Type = Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType>;
+    using ValidType = void;
+
+    static Eigen::DSizes<typename Type::Index, Type::NumIndices> get_shape(const Type &f) {
+        return f.dimensions();
+    }
+
+    static constexpr bool
+    is_correct_shape(const Eigen::DSizes<typename Type::Index, Type::NumIndices> & /*shape*/) {
+        return true;
+    }
+
+    template <typename T>
+    struct helper {};
+
+    template <size_t... Is>
+    struct helper<index_sequence<Is...>> {
+        static constexpr auto value = concat(const_name(((void) Is, "?"))...);
+    };
+
+    static constexpr auto dimensions_descriptor
+        = helper<decltype(make_index_sequence<Type::NumIndices>())>::value;
+
+    template <typename... Args>
+    static Type *alloc(Args &&...args) {
+        return new Type(std::forward<Args>(args)...);
+    }
+
+    static void free(Type *tensor) { delete tensor; }
+};
+
+template <typename Scalar_, typename std::ptrdiff_t... Indices, int Options_, typename IndexType>
+struct eigen_tensor_helper<
+    Eigen::TensorFixedSize<Scalar_, Eigen::Sizes<Indices...>, Options_, IndexType>> {
+    using Type = Eigen::TensorFixedSize<Scalar_, Eigen::Sizes<Indices...>, Options_, IndexType>;
+    using ValidType = void;
+
+    static constexpr Eigen::DSizes<typename Type::Index, Type::NumIndices>
+    get_shape(const Type & /*f*/) {
+        return get_shape();
+    }
+
+    static constexpr Eigen::DSizes<typename Type::Index, Type::NumIndices> get_shape() {
+        return Eigen::DSizes<typename Type::Index, Type::NumIndices>(Indices...);
+    }
+
+    static bool
+    is_correct_shape(const Eigen::DSizes<typename Type::Index, Type::NumIndices> &shape) {
+        return get_shape() == shape;
+    }
+
+    static constexpr auto dimensions_descriptor = concat(const_name<Indices>()...);
+
+    template <typename... Args>
+    static Type *alloc(Args &&...args) {
+        Eigen::aligned_allocator<Type> allocator;
+        return ::new (allocator.allocate(1)) Type(std::forward<Args>(args)...);
+    }
+
+    static void free(Type *tensor) {
+        Eigen::aligned_allocator<Type> allocator;
+        tensor->~Type();
+        allocator.deallocate(tensor, 1);
+    }
+};
+
+template <typename Type, bool ShowDetails, bool NeedsWriteable = false>
+struct get_tensor_descriptor {
+    static constexpr auto details
+        = const_name<NeedsWriteable>(", flags.writeable", "")
+          + const_name<static_cast<int>(Type::Layout) == static_cast<int>(Eigen::RowMajor)>(
+              ", flags.c_contiguous", ", flags.f_contiguous");
+    static constexpr auto value
+        = const_name("numpy.ndarray[") + npy_format_descriptor<typename Type::Scalar>::name
+          + const_name("[") + eigen_tensor_helper<remove_cv_t<Type>>::dimensions_descriptor
+          + const_name("]") + const_name<ShowDetails>(details, const_name("")) + const_name("]");
+};
+
+// When EIGEN_AVOID_STL_ARRAY is defined, Eigen::DSizes<T, 0> does not have the begin() member
+// function. Falling back to a simple loop works around this issue.
+//
+// We need to disable the type-limits warning for the inner loop when size = 0.
+
+#if defined(__GNUC__)
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wtype-limits"
+#endif
+
+template <typename T, int size>
+std::vector<T> convert_dsizes_to_vector(const Eigen::DSizes<T, size> &arr) {
+    std::vector<T> result(size);
+
+    for (size_t i = 0; i < size; i++) {
+        result[i] = arr[i];
+    }
+
+    return result;
+}
+
+template <typename T, int size>
+Eigen::DSizes<T, size> get_shape_for_array(const array &arr) {
+    Eigen::DSizes<T, size> result;
+    const T *shape = arr.shape();
+    for (size_t i = 0; i < size; i++) {
+        result[i] = shape[i];
+    }
+
+    return result;
+}
+
+#if defined(__GNUC__)
+#    pragma GCC diagnostic pop
+#endif
+
+template <typename Type>
+struct type_caster<Type, typename eigen_tensor_helper<Type>::ValidType> {
+    using Helper = eigen_tensor_helper<Type>;
+    static constexpr auto temp_name = get_tensor_descriptor<Type, false>::value;
+    PYBIND11_TYPE_CASTER(Type, temp_name);
+
+    bool load(handle src, bool convert) {
+        if (!convert) {
+            if (!isinstance<array>(src)) {
+                return false;
+            }
+            array temp = array::ensure(src);
+            if (!temp) {
+                return false;
+            }
+
+            if (!convert && !temp.dtype().is(dtype::of<typename Type::Scalar>())) {
+                return false;
+            }
+        }
+
+        array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()> arr(
+            reinterpret_borrow<object>(src));
+
+        if (arr.ndim() != Type::NumIndices) {
+            return false;
+        }
+        auto shape = get_shape_for_array<typename Type::Index, Type::NumIndices>(arr);
+
+        if (!Helper::is_correct_shape(shape)) {
+            return false;
+        }
+
+#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
+        auto data_pointer = arr.data();
+#else
+        // Handle Eigen bug
+        auto data_pointer = const_cast<typename Type::Scalar *>(arr.data());
+#endif
+
+        if (is_tensor_aligned(arr.data())) {
+            value = Eigen::TensorMap<const Type, Eigen::Aligned>(data_pointer, shape);
+        } else {
+            value = Eigen::TensorMap<const Type>(data_pointer, shape);
+        }
+
+        return true;
+    }
+
+    static handle cast(Type &&src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::reference
+            || policy == return_value_policy::reference_internal) {
+            pybind11_fail("Cannot use a reference return value policy for an rvalue");
+        }
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+
+    static handle cast(const Type &&src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::reference
+            || policy == return_value_policy::reference_internal) {
+            pybind11_fail("Cannot use a reference return value policy for an rvalue");
+        }
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+
+    static handle cast(Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(const Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+
+    static handle cast(Type *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    template <typename C>
+    static handle cast_impl(C *src, return_value_policy policy, handle parent) {
+        object parent_object;
+        bool writeable = false;
+        switch (policy) {
+            case return_value_policy::move:
+                if (std::is_const<C>::value) {
+                    pybind11_fail("Cannot move from a constant reference");
+                }
+
+                src = Helper::alloc(std::move(*src));
+
+                parent_object
+                    = capsule(src, [](void *ptr) { Helper::free(reinterpret_cast<Type *>(ptr)); });
+                writeable = true;
+                break;
+
+            case return_value_policy::take_ownership:
+                if (std::is_const<C>::value) {
+                    // This cast is ugly, and might be UB in some cases, but we don't have an
+                    // alterantive here as we must free that memory
+                    Helper::free(const_cast<Type *>(src));
+                    pybind11_fail("Cannot take ownership of a const reference");
+                }
+
+                parent_object
+                    = capsule(src, [](void *ptr) { Helper::free(reinterpret_cast<Type *>(ptr)); });
+                writeable = true;
+                break;
+
+            case return_value_policy::copy:
+                writeable = true;
+                break;
+
+            case return_value_policy::reference:
+                parent_object = none();
+                writeable = !std::is_const<C>::value;
+                break;
+
+            case return_value_policy::reference_internal:
+                // Default should do the right thing
+                if (!parent) {
+                    pybind11_fail("Cannot use reference internal when there is no parent");
+                }
+                parent_object = reinterpret_borrow<object>(parent);
+                writeable = !std::is_const<C>::value;
+                break;
+
+            default:
+                pybind11_fail("pybind11 bug in eigen.h, please file a bug report");
+        }
+
+        auto result = array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()>(
+            convert_dsizes_to_vector(Helper::get_shape(*src)), src->data(), parent_object);
+
+        if (!writeable) {
+            array_proxy(result.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+        }
+
+        return result.release();
+    }
+};
+
+template <typename StoragePointerType,
+          bool needs_writeable,
+          enable_if_t<!needs_writeable, bool> = true>
+StoragePointerType get_array_data_for_type(array &arr) {
+#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
+    return reinterpret_cast<StoragePointerType>(arr.data());
+#else
+    // Handle Eigen bug
+    return reinterpret_cast<StoragePointerType>(const_cast<void *>(arr.data()));
+#endif
+}
+
+template <typename StoragePointerType,
+          bool needs_writeable,
+          enable_if_t<needs_writeable, bool> = true>
+StoragePointerType get_array_data_for_type(array &arr) {
+    return reinterpret_cast<StoragePointerType>(arr.mutable_data());
+}
+
+template <typename T, typename = void>
+struct get_storage_pointer_type;
+
+template <typename MapType>
+struct get_storage_pointer_type<MapType, void_t<typename MapType::StoragePointerType>> {
+    using SPT = typename MapType::StoragePointerType;
+};
+
+template <typename MapType>
+struct get_storage_pointer_type<MapType, void_t<typename MapType::PointerArgType>> {
+    using SPT = typename MapType::PointerArgType;
+};
+
+template <typename Type, int Options>
+struct type_caster<Eigen::TensorMap<Type, Options>,
+                   typename eigen_tensor_helper<remove_cv_t<Type>>::ValidType> {
+    using MapType = Eigen::TensorMap<Type, Options>;
+    using Helper = eigen_tensor_helper<remove_cv_t<Type>>;
+
+    bool load(handle src, bool /*convert*/) {
+        // Note that we have a lot more checks here as we want to make sure to avoid copies
+        if (!isinstance<array>(src)) {
+            return false;
+        }
+        auto arr = reinterpret_borrow<array>(src);
+        if ((arr.flags() & compute_array_flag_from_tensor<Type>()) == 0) {
+            return false;
+        }
+
+        if (!arr.dtype().is(dtype::of<typename Type::Scalar>())) {
+            return false;
+        }
+
+        if (arr.ndim() != Type::NumIndices) {
+            return false;
+        }
+
+        constexpr bool is_aligned = (Options & Eigen::Aligned) != 0;
+
+        if (PYBIND11_SILENCE_MSVC_C4127(is_aligned) && !is_tensor_aligned(arr.data())) {
+            return false;
+        }
+
+        auto shape = get_shape_for_array<typename Type::Index, Type::NumIndices>(arr);
+
+        if (!Helper::is_correct_shape(shape)) {
+            return false;
+        }
+
+        if (PYBIND11_SILENCE_MSVC_C4127(needs_writeable) && !arr.writeable()) {
+            return false;
+        }
+
+        auto result = get_array_data_for_type<typename get_storage_pointer_type<MapType>::SPT,
+                                              needs_writeable>(arr);
+
+        value.reset(new MapType(std::move(result), std::move(shape)));
+
+        return true;
+    }
+
+    static handle cast(MapType &&src, return_value_policy policy, handle parent) {
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(const MapType &&src, return_value_policy policy, handle parent) {
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(MapType &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(const MapType &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+
+    static handle cast(MapType *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    static handle cast(const MapType *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    template <typename C>
+    static handle cast_impl(C *src, return_value_policy policy, handle parent) {
+        object parent_object;
+        constexpr bool writeable = !std::is_const<C>::value;
+        switch (policy) {
+            case return_value_policy::reference:
+                parent_object = none();
+                break;
+
+            case return_value_policy::reference_internal:
+                // Default should do the right thing
+                if (!parent) {
+                    pybind11_fail("Cannot use reference internal when there is no parent");
+                }
+                parent_object = reinterpret_borrow<object>(parent);
+                break;
+
+            case return_value_policy::take_ownership:
+                delete src;
+                // fallthrough
+            default:
+                // move, take_ownership don't make any sense for a ref/map:
+                pybind11_fail("Invalid return_value_policy for Eigen Map type, must be either "
+                              "reference or reference_internal");
+        }
+
+        auto result = array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()>(
+            convert_dsizes_to_vector(Helper::get_shape(*src)),
+            src->data(),
+            std::move(parent_object));
+
+        if (!writeable) {
+            array_proxy(result.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+        }
+
+        return result.release();
+    }
+
+#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
+
+    static constexpr bool needs_writeable = !std::is_const<typename std::remove_pointer<
+        typename get_storage_pointer_type<MapType>::SPT>::type>::value;
+#else
+    // Handle Eigen bug
+    static constexpr bool needs_writeable = !std::is_const<Type>::value;
+#endif
+
+protected:
+    // TODO: Move to std::optional once std::optional has more support
+    std::unique_ptr<MapType> value;
+
+public:
+    static constexpr auto name = get_tensor_descriptor<Type, true, needs_writeable>::value;
+    explicit operator MapType *() { return value.get(); }
+    explicit operator MapType &() { return *value; }
+    explicit operator MapType &&() && { return std::move(*value); }
+
+    template <typename T_>
+    using cast_op_type = ::pybind11::detail::movable_cast_op_type<T_>;
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)

tests/CMakeLists.txt

@@ -128,7 +128,9 @@ set(PYBIND11_TEST_FILES
     test_custom_type_casters
     test_custom_type_setup
     test_docstring_options
-    test_eigen
+    test_eigen_matrix
+    test_eigen_tensor
+    test_eigen_tensor_avoid_stl_array.cpp
     test_enum
     test_eval
     test_exceptions
@@ -233,7 +235,10 @@ list(GET PYBIND11_EIGEN_VERSION_AND_HASH 1 PYBIND11_EIGEN_VERSION_HASH)
 # Check if Eigen is available; if not, remove from PYBIND11_TEST_FILES (but
 # keep it in PYBIND11_PYTEST_FILES, so that we get the "eigen is not installed"
 # skip message).
-list(FIND PYBIND11_TEST_FILES test_eigen.cpp PYBIND11_TEST_FILES_EIGEN_I)
+list(FIND PYBIND11_TEST_FILES test_eigen_matrix.cpp PYBIND11_TEST_FILES_EIGEN_I)
+if(PYBIND11_TEST_FILES_EIGEN_I EQUAL -1)
+  list(FIND PYBIND11_TEST_FILES test_eigen_tensor.cpp PYBIND11_TEST_FILES_EIGEN_I)
+endif()
 if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
   # Try loading via newer Eigen's Eigen3Config first (bypassing tools/FindEigen3.cmake).
   # Eigen 3.3.1+ exports a cmake 3.0+ target for handling dependency requirements, but also
@@ -289,12 +294,37 @@ if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
     endif()
     message(STATUS "Building tests with Eigen v${EIGEN3_VERSION}")
   else()
-    list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
+    list(FIND PYBIND11_TEST_FILES test_eigen_matrix.cpp PYBIND11_TEST_FILES_EIGEN_I)
+    if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
+      list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
+    endif()
+
+    list(FIND PYBIND11_TEST_FILES test_eigen_tensor.cpp PYBIND11_TEST_FILES_EIGEN_I)
+    if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
+      list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
+    endif()
+    list(FIND PYBIND11_TEST_FILES test_eigen_tensor_avoid_stl_array.cpp
+         PYBIND11_TEST_FILES_EIGEN_I)
+    if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
+      list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
+    endif()
     message(
       STATUS "Building tests WITHOUT Eigen, use -DDOWNLOAD_EIGEN=ON on CMake 3.11+ to download")
   endif()
 endif()
 
+# Some code doesn't support gcc 4
+if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 5.0)
+  list(FIND PYBIND11_TEST_FILES test_eigen_tensor.cpp PYBIND11_TEST_FILES_EIGEN_I)
+  if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
+    list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
+  endif()
+  list(FIND PYBIND11_TEST_FILES test_eigen_tensor_avoid_stl_array.cpp PYBIND11_TEST_FILES_EIGEN_I)
+  if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
+    list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
+  endif()
+endif()
+
 # Optional dependency for some tests (boost::variant is only supported with version >= 1.56)
 find_package(Boost 1.56)
 

tests/extra_python_package/test_files.py

@@ -55,6 +55,11 @@ detail_headers = {
     "include/pybind11/detail/typeid.h",
 }
 
+eigen_headers = {
+    "include/pybind11/eigen/matrix.h",
+    "include/pybind11/eigen/tensor.h",
+}
+
 stl_headers = {
     "include/pybind11/stl/filesystem.h",
 }
@@ -82,7 +87,7 @@ py_files = {
     "setup_helpers.py",
 }
 
-headers = main_headers | detail_headers | stl_headers
+headers = main_headers | detail_headers | eigen_headers | stl_headers
 src_files = headers | cmake_files | pkgconfig_files
 all_files = src_files | py_files
 
@@ -92,6 +97,7 @@ sdist_files = {
     "pybind11/include",
     "pybind11/include/pybind11",
     "pybind11/include/pybind11/detail",
+    "pybind11/include/pybind11/eigen",
     "pybind11/include/pybind11/stl",
     "pybind11/share",
     "pybind11/share/cmake",

tests/test_eigen.cpp → tests/test_eigen_matrix.cpp

@@ -7,7 +7,7 @@
     BSD-style license that can be found in the LICENSE file.
 */
 
-#include <pybind11/eigen.h>
+#include <pybind11/eigen/matrix.h>
 #include <pybind11/stl.h>
 
 #include "constructor_stats.h"
@@ -81,7 +81,7 @@ struct CustomOperatorNew {
     EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
 };
 
-TEST_SUBMODULE(eigen, m) {
+TEST_SUBMODULE(eigen_matrix, m) {
     using FixedMatrixR = Eigen::Matrix<float, 5, 6, Eigen::RowMajor>;
     using FixedMatrixC = Eigen::Matrix<float, 5, 6>;
     using DenseMatrixR = Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;

tests/test_eigen.py → tests/test_eigen_matrix.py

@@ -3,7 +3,7 @@ import pytest
 from pybind11_tests import ConstructorStats
 
 np = pytest.importorskip("numpy")
-m = pytest.importorskip("pybind11_tests.eigen")
+m = pytest.importorskip("pybind11_tests.eigen_matrix")
 
 
 ref = np.array(

tests/test_eigen_tensor.cpp

+/*
+    tests/eigen_tensor.cpp -- automatic conversion of Eigen Tensor
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+constexpr const char *test_eigen_tensor_module_name = "eigen_tensor";
+
+#define PYBIND11_TEST_EIGEN_TENSOR_NAMESPACE eigen_tensor
+
+#ifdef EIGEN_AVOID_STL_ARRAY
+#    undef EIGEN_AVOID_STL_ARRAY
+#endif
+
+#include "test_eigen_tensor.inl"

tests/test_eigen_tensor.inl

+/*
+    tests/eigen_tensor.cpp -- automatic conversion of Eigen Tensor
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#include <pybind11/eigen/tensor.h>
+
+#include "pybind11_tests.h"
+
+namespace PYBIND11_TEST_EIGEN_TENSOR_NAMESPACE {
+
+template <typename M>
+void reset_tensor(M &x) {
+    for (int i = 0; i < x.dimension(0); i++) {
+        for (int j = 0; j < x.dimension(1); j++) {
+            for (int k = 0; k < x.dimension(2); k++) {
+                x(i, j, k) = i * (5 * 2) + j * 2 + k;
+            }
+        }
+    }
+}
+
+template <typename M>
+bool check_tensor(M &x) {
+    for (int i = 0; i < x.dimension(0); i++) {
+        for (int j = 0; j < x.dimension(1); j++) {
+            for (int k = 0; k < x.dimension(2); k++) {
+                if (x(i, j, k) != (i * (5 * 2) + j * 2 + k)) {
+                    return false;
+                }
+            }
+        }
+    }
+    return true;
+}
+
+template <int Options>
+Eigen::Tensor<double, 3, Options> &get_tensor() {
+    static Eigen::Tensor<double, 3, Options> *x;
+
+    if (!x) {
+        x = new Eigen::Tensor<double, 3, Options>(3, 5, 2);
+        reset_tensor(*x);
+    }
+
+    return *x;
+}
+
+template <int Options>
+Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> &get_tensor_map() {
+    static Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> *x;
+
+    if (!x) {
+        x = new Eigen::TensorMap<Eigen::Tensor<double, 3, Options>>(get_tensor<Options>());
+    }
+
+    return *x;
+}
+
+template <int Options>
+Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options> &get_fixed_tensor() {
+    static Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options> *x;
+
+    if (!x) {
+        Eigen::aligned_allocator<Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>>
+            allocator;
+        x = new (allocator.allocate(1))
+            Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>();
+        reset_tensor(*x);
+    }
+
+    return *x;
+}
+
+template <int Options>
+const Eigen::Tensor<double, 3, Options> &get_const_tensor() {
+    return get_tensor<Options>();
+}
+
+template <int Options>
+struct CustomExample {
+    CustomExample() : member(get_tensor<Options>()), view_member(member) {}
+
+    Eigen::Tensor<double, 3, Options> member;
+    Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> view_member;
+};
+
+template <int Options>
+void init_tensor_module(pybind11::module &m) {
+    const char *needed_options = "";
+    if (PYBIND11_SILENCE_MSVC_C4127(Options == Eigen::ColMajor)) {
+        needed_options = "F";
+    } else {
+        needed_options = "C";
+    }
+    m.attr("needed_options") = needed_options;
+
+    m.def("setup", []() {
+        reset_tensor(get_tensor<Options>());
+        reset_tensor(get_fixed_tensor<Options>());
+    });
+
+    m.def("is_ok", []() {
+        return check_tensor(get_tensor<Options>()) && check_tensor(get_fixed_tensor<Options>());
+    });
+
+    py::class_<CustomExample<Options>>(m, "CustomExample")
+        .def(py::init<>())
+        .def_readonly(
+            "member", &CustomExample<Options>::member, py::return_value_policy::reference_internal)
+        .def_readonly("member_view",
+                      &CustomExample<Options>::view_member,
+                      py::return_value_policy::reference_internal);
+
+    m.def(
+        "copy_fixed_tensor",
+        []() { return &get_fixed_tensor<Options>(); },
+        py::return_value_policy::copy);
+
+    m.def(
+        "copy_tensor", []() { return &get_tensor<Options>(); }, py::return_value_policy::copy);
+
+    m.def(
+        "copy_const_tensor",
+        []() { return &get_const_tensor<Options>(); },
+        py::return_value_policy::copy);
+
+    m.def(
+        "move_fixed_tensor_copy",
+        []() -> Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options> {
+            return get_fixed_tensor<Options>();
+        },
+        py::return_value_policy::move);
+
+    m.def(
+        "move_tensor_copy",
+        []() -> Eigen::Tensor<double, 3, Options> { return get_tensor<Options>(); },
+        py::return_value_policy::move);
+
+    m.def(
+        "move_const_tensor",
+        []() -> const Eigen::Tensor<double, 3, Options> & { return get_const_tensor<Options>(); },
+        py::return_value_policy::move);
+
+    m.def(
+        "take_fixed_tensor",
+
+        []() {
+            Eigen::aligned_allocator<
+                Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>>
+                allocator;
+            return new (allocator.allocate(1))
+                Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>(
+                    get_fixed_tensor<Options>());
+        },
+        py::return_value_policy::take_ownership);
+
+    m.def(
+        "take_tensor",
+        []() { return new Eigen::Tensor<double, 3, Options>(get_tensor<Options>()); },
+        py::return_value_policy::take_ownership);
+
+    m.def(
+        "take_const_tensor",
+        []() -> const Eigen::Tensor<double, 3, Options> * {
+            return new Eigen::Tensor<double, 3, Options>(get_tensor<Options>());
+        },
+        py::return_value_policy::take_ownership);
+
+    m.def(
+        "take_view_tensor",
+        []() -> const Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> * {
+            return new Eigen::TensorMap<Eigen::Tensor<double, 3, Options>>(get_tensor<Options>());
+        },
+        py::return_value_policy::take_ownership);
+
+    m.def(
+        "reference_tensor",
+        []() { return &get_tensor<Options>(); },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_tensor_v2",
+        []() -> Eigen::Tensor<double, 3, Options> & { return get_tensor<Options>(); },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_tensor_internal",
+        []() { return &get_tensor<Options>(); },
+        py::return_value_policy::reference_internal);
+
+    m.def(
+        "reference_fixed_tensor",
+        []() { return &get_tensor<Options>(); },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_const_tensor",
+        []() { return &get_const_tensor<Options>(); },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_const_tensor_v2",
+        []() -> const Eigen::Tensor<double, 3, Options> & { return get_const_tensor<Options>(); },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_view_of_tensor",
+        []() -> Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> {
+            return get_tensor_map<Options>();
+        },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_view_of_tensor_v2",
+        // NOLINTNEXTLINE(readability-const-return-type)
+        []() -> const Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> {
+            return get_tensor_map<Options>(); // NOLINT(readability-const-return-type)
+        },                                    // NOLINT(readability-const-return-type)
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_view_of_tensor_v3",
+        []() -> Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> * {
+            return &get_tensor_map<Options>();
+        },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_view_of_tensor_v4",
+        []() -> const Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> * {
+            return &get_tensor_map<Options>();
+        },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_view_of_tensor_v5",
+        []() -> Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> & {
+            return get_tensor_map<Options>();
+        },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_view_of_tensor_v6",
+        []() -> const Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> & {
+            return get_tensor_map<Options>();
+        },
+        py::return_value_policy::reference);
+
+    m.def(
+        "reference_view_of_fixed_tensor",
+        []() {
+            return Eigen::TensorMap<
+                Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>>(
+                get_fixed_tensor<Options>());
+        },
+        py::return_value_policy::reference);
+
+    m.def("round_trip_tensor",
+          [](const Eigen::Tensor<double, 3, Options> &tensor) { return tensor; });
+
+    m.def(
+        "round_trip_tensor_noconvert",
+        [](const Eigen::Tensor<double, 3, Options> &tensor) { return tensor; },
+        py::arg("tensor").noconvert());
+
+    m.def("round_trip_tensor2",
+          [](const Eigen::Tensor<int32_t, 3, Options> &tensor) { return tensor; });
+
+    m.def("round_trip_fixed_tensor",
+          [](const Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options> &tensor) {
+              return tensor;
+          });
+
+    m.def(
+        "round_trip_view_tensor",
+        [](Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> view) { return view; },
+        py::return_value_policy::reference);
+
+    m.def(
+        "round_trip_view_tensor_ref",
+        [](Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> &view) { return view; },
+        py::return_value_policy::reference);
+
+    m.def(
+        "round_trip_view_tensor_ptr",
+        [](Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> *view) { return view; },
+        py::return_value_policy::reference);
+
+    m.def(
+        "round_trip_aligned_view_tensor",
+        [](Eigen::TensorMap<Eigen::Tensor<double, 3, Options>, Eigen::Aligned> view) {
+            return view;
+        },
+        py::return_value_policy::reference);
+
+    m.def(
+        "round_trip_const_view_tensor",
+        [](Eigen::TensorMap<const Eigen::Tensor<double, 3, Options>> view) {
+            return Eigen::Tensor<double, 3, Options>(view);
+        },
+        py::return_value_policy::move);
+
+    m.def(
+        "round_trip_rank_0",
+        [](const Eigen::Tensor<double, 0, Options> &tensor) { return tensor; },
+        py::return_value_policy::move);
+
+    m.def(
+        "round_trip_rank_0_noconvert",
+        [](const Eigen::Tensor<double, 0, Options> &tensor) { return tensor; },
+        py::arg("tensor").noconvert(),
+        py::return_value_policy::move);
+
+    m.def(
+        "round_trip_rank_0_view",
+        [](Eigen::TensorMap<Eigen::Tensor<double, 0, Options>> &tensor) { return tensor; },
+        py::return_value_policy::reference);
+}
+
+void test_module(py::module_ &);
+test_initializer name(test_eigen_tensor_module_name, test_module);
+void test_module(py::module_ &m) {
+    auto f_style = m.def_submodule("f_style");
+    auto c_style = m.def_submodule("c_style");
+
+    init_tensor_module<Eigen::ColMajor>(f_style);
+    init_tensor_module<Eigen::RowMajor>(c_style);
+}
+
+} // namespace PYBIND11_TEST_EIGEN_TENSOR_NAMESPACE

tests/test_eigen_tensor.py

+import sys
+
+import pytest
+
+np = pytest.importorskip("numpy")
+eigen_tensor = pytest.importorskip("pybind11_tests.eigen_tensor")
+submodules = [eigen_tensor.c_style, eigen_tensor.f_style]
+try:
+    from pybind11_tests import eigen_tensor_avoid_stl_array as avoid
+
+    submodules += [avoid.c_style, avoid.f_style]
+except ImportError:
+    pass
+
+tensor_ref = np.empty((3, 5, 2), dtype=np.int64)
+
+for i in range(tensor_ref.shape[0]):
+    for j in range(tensor_ref.shape[1]):
+        for k in range(tensor_ref.shape[2]):
+            tensor_ref[i, j, k] = i * (5 * 2) + j * 2 + k
+
+indices = (2, 3, 1)
+
+
+@pytest.fixture(autouse=True)
+def cleanup():
+    for module in submodules:
+        module.setup()
+
+    yield
+
+    for module in submodules:
+        assert module.is_ok()
+
+
+def test_import_avoid_stl_array():
+    pytest.importorskip("pybind11_tests.eigen_tensor_avoid_stl_array")
+    assert len(submodules) == 4
+
+
+def assert_equal_tensor_ref(mat, writeable=True, modified=None):
+    assert mat.flags.writeable == writeable
+
+    copy = np.array(tensor_ref)
+    if modified is not None:
+        copy[indices] = modified
+
+    np.testing.assert_array_equal(mat, copy)
+
+
+@pytest.mark.parametrize("m", submodules)
+@pytest.mark.parametrize("member_name", ["member", "member_view"])
+def test_reference_internal(m, member_name):
+
+    if not hasattr(sys, "getrefcount"):
+        pytest.skip("No reference counting")
+    foo = m.CustomExample()
+    counts = sys.getrefcount(foo)
+    mem = getattr(foo, member_name)
+    assert_equal_tensor_ref(mem, writeable=False)
+    new_counts = sys.getrefcount(foo)
+    assert new_counts == counts + 1
+    assert_equal_tensor_ref(mem, writeable=False)
+    del mem
+    assert sys.getrefcount(foo) == counts
+
+
+assert_equal_funcs = [
+    "copy_tensor",
+    "copy_fixed_tensor",
+    "copy_const_tensor",
+    "move_tensor_copy",
+    "move_fixed_tensor_copy",
+    "take_tensor",
+    "take_fixed_tensor",
+    "reference_tensor",
+    "reference_tensor_v2",
+    "reference_fixed_tensor",
+    "reference_view_of_tensor",
+    "reference_view_of_tensor_v3",
+    "reference_view_of_tensor_v5",
+    "reference_view_of_fixed_tensor",
+]
+
+assert_equal_const_funcs = [
+    "reference_view_of_tensor_v2",
+    "reference_view_of_tensor_v4",
+    "reference_view_of_tensor_v6",
+    "reference_const_tensor",
+    "reference_const_tensor_v2",
+]
+
+
+@pytest.mark.parametrize("m", submodules)
+@pytest.mark.parametrize("func_name", assert_equal_funcs + assert_equal_const_funcs)
+def test_convert_tensor_to_py(m, func_name):
+    writeable = func_name in assert_equal_funcs
+    assert_equal_tensor_ref(getattr(m, func_name)(), writeable=writeable)
+
+
+@pytest.mark.parametrize("m", submodules)
+def test_bad_cpp_to_python_casts(m):
+
+    with pytest.raises(
+        RuntimeError, match="Cannot use reference internal when there is no parent"
+    ):
+        m.reference_tensor_internal()
+
+    with pytest.raises(RuntimeError, match="Cannot move from a constant reference"):
+        m.move_const_tensor()
+
+    with pytest.raises(
+        RuntimeError, match="Cannot take ownership of a const reference"
+    ):
+        m.take_const_tensor()
+
+    with pytest.raises(
+        RuntimeError,
+        match="Invalid return_value_policy for Eigen Map type, must be either reference or reference_internal",
+    ):
+        m.take_view_tensor()
+
+
+@pytest.mark.parametrize("m", submodules)
+def test_bad_python_to_cpp_casts(m):
+
+    with pytest.raises(
+        TypeError, match=r"^round_trip_tensor\(\): incompatible function arguments"
+    ):
+        m.round_trip_tensor(np.zeros((2, 3)))
+
+    with pytest.raises(TypeError, match=r"^Cannot cast array data from dtype"):
+        m.round_trip_tensor(np.zeros(dtype=np.str_, shape=(2, 3, 1)))
+
+    with pytest.raises(
+        TypeError,
+        match=r"^round_trip_tensor_noconvert\(\): incompatible function arguments",
+    ):
+        m.round_trip_tensor_noconvert(tensor_ref)
+
+    assert_equal_tensor_ref(
+        m.round_trip_tensor_noconvert(tensor_ref.astype(np.float64))
+    )
+
+    if m.needed_options == "F":
+        bad_options = "C"
+    else:
+        bad_options = "F"
+    # Shape, dtype and the order need to be correct for a TensorMap cast
+    with pytest.raises(
+        TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
+    ):
+        m.round_trip_view_tensor(
+            np.zeros((3, 5, 2), dtype=np.float64, order=bad_options)
+        )
+
+    with pytest.raises(
+        TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
+    ):
+        m.round_trip_view_tensor(
+            np.zeros((3, 5, 2), dtype=np.float32, order=m.needed_options)
+        )
+
+    with pytest.raises(
+        TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
+    ):
+        m.round_trip_view_tensor(
+            np.zeros((3, 5), dtype=np.float64, order=m.needed_options)
+        )
+
+    with pytest.raises(
+        TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
+    ):
+        temp = np.zeros((3, 5, 2), dtype=np.float64, order=m.needed_options)
+        m.round_trip_view_tensor(
+            temp[:, ::-1, :],
+        )
+
+    with pytest.raises(
+        TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
+    ):
+        temp = np.zeros((3, 5, 2), dtype=np.float64, order=m.needed_options)
+        temp.setflags(write=False)
+        m.round_trip_view_tensor(temp)
+
+
+@pytest.mark.parametrize("m", submodules)
+def test_references_actually_refer(m):
+
+    a = m.reference_tensor()
+    temp = a[indices]
+    a[indices] = 100
+    assert_equal_tensor_ref(m.copy_const_tensor(), modified=100)
+    a[indices] = temp
+    assert_equal_tensor_ref(m.copy_const_tensor())
+
+    a = m.reference_view_of_tensor()
+    a[indices] = 100
+    assert_equal_tensor_ref(m.copy_const_tensor(), modified=100)
+    a[indices] = temp
+    assert_equal_tensor_ref(m.copy_const_tensor())
+
+
+@pytest.mark.parametrize("m", submodules)
+def test_round_trip(m):
+
+    assert_equal_tensor_ref(m.round_trip_tensor(tensor_ref))
+
+    with pytest.raises(TypeError, match="^Cannot cast array data from"):
+        assert_equal_tensor_ref(m.round_trip_tensor2(tensor_ref))
+
+    assert_equal_tensor_ref(m.round_trip_tensor2(np.array(tensor_ref, dtype=np.int32)))
+    assert_equal_tensor_ref(m.round_trip_fixed_tensor(tensor_ref))
+    assert_equal_tensor_ref(m.round_trip_aligned_view_tensor(m.reference_tensor()))
+
+    copy = np.array(tensor_ref, dtype=np.float64, order=m.needed_options)
+    assert_equal_tensor_ref(m.round_trip_view_tensor(copy))
+    assert_equal_tensor_ref(m.round_trip_view_tensor_ref(copy))
+    assert_equal_tensor_ref(m.round_trip_view_tensor_ptr(copy))
+    copy.setflags(write=False)
+    assert_equal_tensor_ref(m.round_trip_const_view_tensor(copy))
+
+    np.testing.assert_array_equal(
+        tensor_ref[:, ::-1, :], m.round_trip_tensor(tensor_ref[:, ::-1, :])
+    )
+
+    assert m.round_trip_rank_0(np.float64(3.5)) == 3.5
+    assert m.round_trip_rank_0(3.5) == 3.5
+
+    with pytest.raises(
+        TypeError,
+        match=r"^round_trip_rank_0_noconvert\(\): incompatible function arguments",
+    ):
+        m.round_trip_rank_0_noconvert(np.float64(3.5))
+
+    with pytest.raises(
+        TypeError,
+        match=r"^round_trip_rank_0_noconvert\(\): incompatible function arguments",
+    ):
+        m.round_trip_rank_0_noconvert(3.5)
+
+    with pytest.raises(
+        TypeError, match=r"^round_trip_rank_0_view\(\): incompatible function arguments"
+    ):
+        m.round_trip_rank_0_view(np.float64(3.5))
+
+    with pytest.raises(
+        TypeError, match=r"^round_trip_rank_0_view\(\): incompatible function arguments"
+    ):
+        m.round_trip_rank_0_view(3.5)
+
+
+@pytest.mark.parametrize("m", submodules)
+def test_round_trip_references_actually_refer(m):
+
+    # Need to create a copy that matches the type on the C side
+    copy = np.array(tensor_ref, dtype=np.float64, order=m.needed_options)
+    a = m.round_trip_view_tensor(copy)
+    temp = a[indices]
+    a[indices] = 100
+    assert_equal_tensor_ref(copy, modified=100)
+    a[indices] = temp
+    assert_equal_tensor_ref(copy)
+
+
+@pytest.mark.parametrize("m", submodules)
+def test_doc_string(m, doc):
+    assert (
+        doc(m.copy_tensor) == "copy_tensor() -> numpy.ndarray[numpy.float64[?, ?, ?]]"
+    )
+    assert (
+        doc(m.copy_fixed_tensor)
+        == "copy_fixed_tensor() -> numpy.ndarray[numpy.float64[3, 5, 2]]"
+    )
+    assert (
+        doc(m.reference_const_tensor)
+        == "reference_const_tensor() -> numpy.ndarray[numpy.float64[?, ?, ?]]"
+    )
+
+    order_flag = f"flags.{m.needed_options.lower()}_contiguous"
+    assert doc(m.round_trip_view_tensor) == (
+        f"round_trip_view_tensor(arg0: numpy.ndarray[numpy.float64[?, ?, ?], flags.writeable, {order_flag}])"
+        + f" -> numpy.ndarray[numpy.float64[?, ?, ?], flags.writeable, {order_flag}]"
+    )
+    assert doc(m.round_trip_const_view_tensor) == (
+        f"round_trip_const_view_tensor(arg0: numpy.ndarray[numpy.float64[?, ?, ?], {order_flag}])"
+        + " -> numpy.ndarray[numpy.float64[?, ?, ?]]"
+    )

tests/test_eigen_tensor_avoid_stl_array.cpp

+/*
+    tests/eigen_tensor.cpp -- automatic conversion of Eigen Tensor
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+constexpr const char *test_eigen_tensor_module_name = "eigen_tensor_avoid_stl_array";
+
+#ifndef EIGEN_AVOID_STL_ARRAY
+#    define EIGEN_AVOID_STL_ARRAY
+#endif
+
+#define PYBIND11_TEST_EIGEN_TENSOR_NAMESPACE eigen_tensor_avoid_stl_array
+
+#include "test_eigen_tensor.inl"

tests/test_numpy_array.cpp

@@ -521,4 +521,6 @@ TEST_SUBMODULE(numpy_array, sm) {
     sm.def("test_fmt_desc_double", [](const py::array_t<double> &) {});
     sm.def("test_fmt_desc_const_float", [](const py::array_t<const float> &) {});
     sm.def("test_fmt_desc_const_double", [](const py::array_t<const double> &) {});
+
+    sm.def("round_trip_float", [](double d) { return d; });
 }

tests/test_numpy_array.py

@@ -585,3 +585,9 @@ def test_dtype_refcount_leak():
     m.ndim(a)
     after = getrefcount(dtype)
     assert after == before
+
+
+def test_round_trip_float():
+    arr = np.zeros((), np.float64)
+    arr[()] = 37.2
+    assert m.round_trip_float(arr) == 37.2

tools/setup_global.py.in

@@ -27,10 +27,11 @@ class InstallHeadersNested(install_headers):
 
 main_headers = glob.glob("pybind11/include/pybind11/*.h")
 detail_headers = glob.glob("pybind11/include/pybind11/detail/*.h")
+eigen_headers = glob.glob("pybind11/include/pybind11/eigen/*.h")
 stl_headers = glob.glob("pybind11/include/pybind11/stl/*.h")
 cmake_files = glob.glob("pybind11/share/cmake/pybind11/*.cmake")
 pkgconfig_files = glob.glob("pybind11/share/pkgconfig/*.pc")
-headers = main_headers + detail_headers + stl_headers
+headers = main_headers + detail_headers + stl_headers + eigen_headers
 
 cmdclass = {"install_headers": InstallHeadersNested}
 $extra_cmd
@@ -55,6 +56,7 @@ setup(
         (base + "share/pkgconfig", pkgconfig_files),
         (base + "include/pybind11", main_headers),
         (base + "include/pybind11/detail", detail_headers),
+        (base + "include/pybind11/eigen", eigen_headers),
         (base + "include/pybind11/stl", stl_headers),
     ],
     cmdclass=cmdclass,

tools/setup_main.py.in

@@ -15,6 +15,7 @@ setup(
         "pybind11",
         "pybind11.include.pybind11",
         "pybind11.include.pybind11.detail",
+        "pybind11.include.pybind11.eigen",
         "pybind11.include.pybind11.stl",
         "pybind11.share.cmake.pybind11",
         "pybind11.share.pkgconfig",
@@ -23,6 +24,7 @@ setup(
         "pybind11": ["py.typed"],
         "pybind11.include.pybind11": ["*.h"],
         "pybind11.include.pybind11.detail": ["*.h"],
+        "pybind11.include.pybind11.eigen": ["*.h"],
         "pybind11.include.pybind11.stl": ["*.h"],
         "pybind11.share.cmake.pybind11": ["*.cmake"],
         "pybind11.share.pkgconfig": ["*.pc"],