forked from M-Labs/nac3
core/ndstrides: add basic ndarray IRRT functions
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nac3core/irrt/irrt/ndarray/basic.hpp
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288
nac3core/irrt/irrt/ndarray/basic.hpp
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#pragma once
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#include <irrt/exception.hpp>
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#include <irrt/int_defs.hpp>
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#include <irrt/ndarray/def.hpp>
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namespace {
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namespace ndarray {
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namespace basic {
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namespace util {
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/**
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* @brief Asserts that `shape` does not contain negative dimensions.
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*
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* @param ndims Number of dimensions in `shape`
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* @param shape The shape to check on
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*/
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template <typename SizeT>
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void assert_shape_no_negative(SizeT ndims, const SizeT* shape) {
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for (SizeT axis = 0; axis < ndims; axis++) {
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if (shape[axis] < 0) {
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raise_exception(SizeT, EXN_VALUE_ERROR,
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"negative dimensions are not allowed; axis {0} "
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"has dimension {1}",
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axis, shape[axis], NO_PARAM);
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}
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}
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}
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/**
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* @brief Returns the number of elements of an ndarray given its shape.
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*
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* @param ndims Number of dimensions in `shape`
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* @param shape The shape of the ndarray
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*/
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template <typename SizeT>
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SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
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SizeT size = 1;
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for (SizeT axis = 0; axis < ndims; axis++) size *= shape[axis];
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return size;
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}
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/**
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* @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
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*
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* @param ndims Number of elements in `shape` and `indices`
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* @param shape The shape of the ndarray
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* @param indices The returned indices indexing the ndarray with shape `shape`.
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* @param nth The index of the element of interest.
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*/
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template <typename SizeT>
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void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices,
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SizeT nth) {
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for (int32_t i = 0; i < ndims; i++) {
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int32_t axis = ndims - i - 1;
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int32_t dim = shape[axis];
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indices[axis] = nth % dim;
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nth /= dim;
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}
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}
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} // namespace util
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/**
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* @brief Return the number of elements of an `ndarray`
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*
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* This function corresponds to `<an_ndarray>.size`
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*/
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template <typename SizeT>
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SizeT size(const NDArray<SizeT>* ndarray) {
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return util::calc_size_from_shape(ndarray->ndims, ndarray->shape);
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}
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/**
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* @brief Return of the number of its content of an `ndarray`.
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*
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* This function corresponds to `<an_ndarray>.nbytes`.
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*/
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template <typename SizeT>
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SizeT nbytes(const NDArray<SizeT>* ndarray) {
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return size(ndarray) * ndarray->itemsize;
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}
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/**
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* @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
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*
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* This function corresponds to `<an_ndarray>.__len__`.
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*
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* @param dst_length The returned result
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*/
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template <typename SizeT>
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SizeT len(const NDArray<SizeT>* ndarray) {
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// numpy prohibits `__len__` on unsized objects
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if (ndarray->ndims == 0) {
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raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object",
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NO_PARAM, NO_PARAM, NO_PARAM);
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}
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return ndarray->shape[0];
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}
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/**
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* @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
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*
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* You may want to see: ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
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*/
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template <typename SizeT>
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bool is_c_contiguous(const NDArray<SizeT>* ndarray) {
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// Other references:
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// - tinynumpy's implementation: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
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// - ndarray's flags["C_CONTIGUOUS"]: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
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// - ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
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// From https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
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//
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// The traditional rule is that for an array to be flagged as C contiguous,
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// the following must hold:
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//
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// strides[-1] == itemsize
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// strides[i] == shape[i+1] * strides[i + 1]
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// [...]
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// According to these rules, a 0- or 1-dimensional array is either both
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// C- and F-contiguous, or neither; and an array with 2+ dimensions
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// can be C- or F- contiguous, or neither, but not both. Though there
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// there are exceptions for arrays with zero or one item, in the first
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// case the check is relaxed up to and including the first dimension
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// with shape[i] == 0. In the second case `strides == itemsize` will
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// can be true for all dimensions and both flags are set.
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if (ndarray->ndims == 0) {
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return true;
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}
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if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize) {
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return false;
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}
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for (SizeT i = 1; i < ndarray->ndims; i++) {
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SizeT axis_i = ndarray->ndims - i - 1;
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if (ndarray->strides[axis_i] !=
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ndarray->shape[axis_i + 1] + ndarray->strides[axis_i + 1]) {
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return false;
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}
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}
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return true;
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}
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/**
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* @brief Return the pointer to the element indexed by `indices`.
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*/
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template <typename SizeT>
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uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
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const SizeT* indices) {
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uint8_t* element = ndarray->data;
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for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
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element += indices[dim_i] * ndarray->strides[dim_i];
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return element;
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}
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/**
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* @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
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*
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* This function does no bound check.
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*/
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template <typename SizeT>
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uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
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SizeT* indices = (SizeT*)__builtin_alloca(sizeof(SizeT) * ndarray->ndims);
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util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, nth);
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return get_pelement_by_indices(ndarray, indices);
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}
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/**
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* @brief Update the strides of an ndarray given an ndarray `shape`
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* and assuming that the ndarray is fully c-contagious.
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*
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* You might want to read https://ajcr.net/stride-guide-part-1/.
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*/
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template <typename SizeT>
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void set_strides_by_shape(NDArray<SizeT>* ndarray) {
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SizeT stride_product = 1;
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for (SizeT i = 0; i < ndarray->ndims; i++) {
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int axis = ndarray->ndims - i - 1;
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ndarray->strides[axis] = stride_product * ndarray->itemsize;
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stride_product *= ndarray->shape[axis];
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}
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}
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/**
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* @brief Set an element in `ndarray`.
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*
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* @param pelement Pointer to the element in `ndarray` to be set.
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* @param pvalue Pointer to the value `pelement` will be set to.
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*/
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template <typename SizeT>
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void set_pelement_value(NDArray<SizeT>* ndarray, uint8_t* pelement,
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const uint8_t* pvalue) {
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__builtin_memcpy(pelement, pvalue, ndarray->itemsize);
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}
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/**
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* @brief Copy data from one ndarray to another of the exact same size and itemsize.
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*
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* Both ndarrays will be viewed in their flatten views when copying the elements.
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*/
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template <typename SizeT>
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void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
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// TODO: Make this faster with memcpy
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__builtin_assume(src_ndarray->itemsize == dst_ndarray->itemsize);
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for (SizeT i = 0; i < size(src_ndarray); i++) {
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auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
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auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
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ndarray::basic::set_pelement_value(dst_ndarray, dst_element,
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src_element);
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}
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}
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} // namespace basic
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} // namespace ndarray
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} // namespace
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extern "C" {
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using namespace ndarray::basic;
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void __nac3_ndarray_util_assert_shape_no_negative(int32_t ndims,
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int32_t* shape) {
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util::assert_shape_no_negative(ndims, shape);
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}
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void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims,
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int64_t* shape) {
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util::assert_shape_no_negative(ndims, shape);
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}
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uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
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return size(ndarray);
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}
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uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
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return size(ndarray);
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}
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uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
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return nbytes(ndarray);
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}
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uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
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return nbytes(ndarray);
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}
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int32_t __nac3_ndarray_len(NDArray<int32_t>* ndarray) { return len(ndarray); }
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int64_t __nac3_ndarray_len64(NDArray<int64_t>* ndarray) { return len(ndarray); }
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bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
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return is_c_contiguous(ndarray);
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}
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bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
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return is_c_contiguous(ndarray);
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}
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uint8_t* __nac3_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
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return get_nth_pelement(ndarray, nth);
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}
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uint8_t* __nac3_get_nth_pelement64(const NDArray<int64_t>* ndarray,
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int64_t nth) {
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return get_nth_pelement(ndarray, nth);
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}
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void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t>* ndarray) {
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set_strides_by_shape(ndarray);
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}
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void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
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set_strides_by_shape(ndarray);
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}
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void __nac3_ndarray_copy_data(NDArray<int32_t>* src_ndarray,
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NDArray<int32_t>* dst_ndarray) {
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copy_data(src_ndarray, dst_ndarray);
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}
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void __nac3_ndarray_copy_data64(NDArray<int64_t>* src_ndarray,
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NDArray<int64_t>* dst_ndarray) {
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copy_data(src_ndarray, dst_ndarray);
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}
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}
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@ -3,5 +3,6 @@
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#include <irrt/core.hpp>
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#include <irrt/exception.hpp>
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#include <irrt/int_defs.hpp>
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#include <irrt/ndarray/basic.hpp>
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#include <irrt/ndarray/def.hpp>
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#include <irrt/util.hpp>
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#define IRRT_TESTING
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#include <test/test_core.hpp>
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#include <test/test_ndarray_basic.hpp>
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int main() {
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test::core::run();
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test::ndarray_basic::run();
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return 0;
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}
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30
nac3core/irrt/test/test_ndarray_basic.hpp
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30
nac3core/irrt/test/test_ndarray_basic.hpp
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#pragma once
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#include <test/includes.hpp>
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namespace test {
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namespace ndarray_basic {
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void test_calc_size_from_shape_normal() {
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// Test shapes with normal values
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BEGIN_TEST();
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int32_t shape[4] = {2, 3, 5, 7};
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assert_values_match(
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210, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
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}
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void test_calc_size_from_shape_has_zero() {
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// Test shapes with 0 in them
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BEGIN_TEST();
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int32_t shape[4] = {2, 0, 5, 7};
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assert_values_match(
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0, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
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}
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void run() {
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test_calc_size_from_shape_normal();
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test_calc_size_from_shape_has_zero();
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}
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} // namespace ndarray_basic
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} // namespace test
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@ -5,6 +5,7 @@ mod test;
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pub mod util;
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use super::model::*;
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use super::structure::ndarray::NpArray;
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use super::{
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classes::{
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ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
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@ -987,3 +988,117 @@ pub fn setup_irrt_exceptions<'ctx>(
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global.set_initializer(&exn_id);
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}
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}
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pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx>(
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tyctx: TypeContext<'ctx>,
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ctx: &mut CodeGenContext<'ctx, '_>,
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ndims: Int<'ctx, SizeT>,
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shape: Ptr<'ctx, IntModel<SizeT>>,
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) {
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CallFunction::begin(
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tyctx,
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ctx,
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&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_util_assert_shape_no_negative"),
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)
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.arg("ndims", ndims)
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.arg("shape", shape)
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.returning_void();
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}
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pub fn call_nac3_ndarray_size<'ctx>(
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tyctx: TypeContext<'ctx>,
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ctx: &mut CodeGenContext<'ctx, '_>,
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pndarray: Ptr<'ctx, StructModel<NpArray>>,
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) -> Int<'ctx, SizeT> {
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CallFunction::begin(
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tyctx,
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ctx,
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&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_size"),
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)
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.arg("ndarray", pndarray)
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.returning_auto("size")
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}
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pub fn call_nac3_ndarray_nbytes<'ctx>(
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tyctx: TypeContext<'ctx>,
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ctx: &mut CodeGenContext<'ctx, '_>,
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pndarray: Ptr<'ctx, StructModel<NpArray>>,
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) -> Int<'ctx, SizeT> {
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CallFunction::begin(
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tyctx,
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ctx,
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&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_nbytes"),
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)
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.arg("ndarray", pndarray)
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.returning_auto("nbytes")
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}
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pub fn call_nac3_ndarray_len<'ctx>(
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tyctx: TypeContext<'ctx>,
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ctx: &mut CodeGenContext<'ctx, '_>,
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pndarray: Ptr<'ctx, StructModel<NpArray>>,
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) -> Int<'ctx, SizeT> {
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CallFunction::begin(tyctx, ctx, &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_len"))
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.arg("ndarray", pndarray)
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.returning_auto("len")
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}
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pub fn call_nac3_ndarray_is_c_contiguous<'ctx>(
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tyctx: TypeContext<'ctx>,
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ctx: &mut CodeGenContext<'ctx, '_>,
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ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
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) -> Int<'ctx, Bool> {
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CallFunction::begin(
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tyctx,
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ctx,
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&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_is_c_contiguous"),
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)
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.arg("ndarray", ndarray_ptr)
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.returning_auto("is_c_contiguous")
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}
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pub fn call_nac3_ndarray_get_nth_pelement<'ctx>(
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tyctx: TypeContext<'ctx>,
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ctx: &mut CodeGenContext<'ctx, '_>,
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pndarray: Ptr<'ctx, StructModel<NpArray>>,
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index: Int<'ctx, SizeT>,
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) -> Ptr<'ctx, IntModel<Byte>> {
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CallFunction::begin(
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tyctx,
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ctx,
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&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_get_nth_pelement"),
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)
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.arg("ndarray", pndarray)
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.arg("index", index)
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.returning_auto("pelement")
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}
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||||
|
||||
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pdnarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_set_strides_by_shape"),
|
||||
)
|
||||
.arg("ndarray", pdnarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_copy_data<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_copy_data"),
|
||||
)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
@ -1,4 +1,10 @@
|
||||
use crate::codegen::*;
|
||||
use irrt::{
|
||||
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
|
||||
call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
|
||||
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
|
||||
};
|
||||
|
||||
use crate::{codegen::*, symbol_resolver::SymbolValue};
|
||||
|
||||
pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub data: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
@ -26,9 +32,193 @@ impl<'ctx> StructKind<'ctx> for NpArray {
|
||||
}
|
||||
}
|
||||
|
||||
/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
|
||||
/// The `ndims` must only contain 1 value.
|
||||
#[must_use]
|
||||
pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
|
||||
let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
|
||||
let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
|
||||
panic!("ndims_ty should be a TLiteral");
|
||||
};
|
||||
|
||||
assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
|
||||
|
||||
let ndims = values[0].clone();
|
||||
u64::try_from(ndims).unwrap()
|
||||
}
|
||||
|
||||
/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
|
||||
pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
|
||||
unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDArrayObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub ndims: Type,
|
||||
pub value: Ptr<'ctx, StructModel<NpArray>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
|
||||
///
|
||||
/// `shape` and `strides` will be automatically allocated on the stack.
|
||||
///
|
||||
/// The returned ndarray's content will be:
|
||||
/// - `data`: set to `nullptr`.
|
||||
/// - `itemsize`: set to the `sizeof()` of `dtype`.
|
||||
/// - `ndims`: set to the value of `ndims`.
|
||||
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
|
||||
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
|
||||
pub fn alloca_uninitialized<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: Type,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let ndarray_model = StructModel(NpArray);
|
||||
let ndarray_data_model = PtrModel(IntModel(Byte));
|
||||
|
||||
let pndarray = ndarray_model.alloca(tyctx, ctx, name);
|
||||
|
||||
let data = ndarray_data_model.nullptr(tyctx, ctx.ctx);
|
||||
|
||||
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
|
||||
let itemsize = sizet_model.s_extend_or_bit_cast(tyctx, ctx, itemsize, "itemsize");
|
||||
|
||||
let ndims_val = extract_ndims(&ctx.unifier, ndims);
|
||||
let ndims_val = sizet_model.constant(tyctx, ctx.ctx, ndims_val);
|
||||
|
||||
let shape = sizet_model.array_alloca(tyctx, ctx, ndims_val.value, "shape");
|
||||
let strides = sizet_model.array_alloca(tyctx, ctx, ndims_val.value, "strides");
|
||||
|
||||
pndarray.gep(ctx, |f| f.data).store(ctx, data);
|
||||
pndarray.gep(ctx, |f| f.itemsize).store(ctx, itemsize);
|
||||
pndarray.gep(ctx, |f| f.ndims).store(ctx, ndims_val);
|
||||
pndarray.gep(ctx, |f| f.shape).store(ctx, shape);
|
||||
pndarray.gep(ctx, |f| f.strides).store(ctx, strides);
|
||||
|
||||
NDArrayObject { dtype, ndims, value: pndarray }
|
||||
}
|
||||
|
||||
/// Get this ndarray's `ndims` as an LLVM constant.
|
||||
pub fn get_ndims(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let ndims_val = extract_ndims(&ctx.unifier, self.ndims);
|
||||
sizet_model.constant(tyctx, ctx.ctx, ndims_val)
|
||||
}
|
||||
|
||||
/// Return true if this ndarray is unsized.
|
||||
#[must_use]
|
||||
pub fn is_unsized(&self, unifier: &Unifier) -> bool {
|
||||
extract_ndims(unifier, self.ndims) == 0
|
||||
}
|
||||
|
||||
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
|
||||
/// The allocated data buffer is considered to be *owned* by the ndarray.
|
||||
///
|
||||
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
|
||||
///
|
||||
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
|
||||
pub fn create_data<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
let byte_model = IntModel(Byte);
|
||||
|
||||
let data = byte_model.array_alloca(tyctx, ctx, self.get_ndims(tyctx, ctx).value, "data");
|
||||
self.value.gep(ctx, |f| f.data).store(ctx, data);
|
||||
|
||||
self.update_strides_by_shape(tyctx, ctx);
|
||||
}
|
||||
|
||||
/// Get the `np.size()` of this ndarray.
|
||||
pub fn size(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_size(tyctx, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the `ndarray.nbytes` of this ndarray.
|
||||
pub fn nbytes(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_nbytes(tyctx, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the `len()` of this ndarray.
|
||||
pub fn len(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_len(tyctx, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Check if this ndarray is C-contiguous.
|
||||
///
|
||||
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
|
||||
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
call_nac3_ndarray_is_c_contiguous(tyctx, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the pointer to the n-th (0-based) element.
|
||||
///
|
||||
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
|
||||
pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
name: &str,
|
||||
) -> PointerValue<'ctx> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
|
||||
|
||||
let p = call_nac3_ndarray_get_nth_pelement(tyctx, ctx, self.value, nth);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
|
||||
///
|
||||
/// Please refer to the IRRT implementation to see its purpose.
|
||||
pub fn update_strides_by_shape(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
call_nac3_ndarray_set_strides_by_shape(tyctx, ctx, self.value);
|
||||
}
|
||||
|
||||
/// Copy data from another ndarray.
|
||||
///
|
||||
/// Panics if the `dtype`s of ndarrays are different.
|
||||
pub fn copy_data_from(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
|
||||
call_nac3_ndarray_copy_data(tyctx, ctx, src.value, self.value);
|
||||
}
|
||||
}
|
||||
|
Loading…
Reference in New Issue
Block a user