forked from M-Labs/nac3
core/ndstrides: add basic ndarray IRRT functions
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#pragma once
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#include <irrt/error_context.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(ErrorContext* errctx, SizeT ndims,
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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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errctx->set_exception(
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errctx->exceptions->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]);
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return;
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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 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 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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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 Like `get_nth_pelement` but asserts that `nth` is in bounds.
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*/
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template <typename SizeT>
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uint8_t* checked_get_nth_pelement(ErrorContext* errctx,
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const NDArray<SizeT>* ndarray, SizeT nth) {
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SizeT arr_size = ndarray->size();
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if (!(0 <= nth && nth < arr_size)) {
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errctx->set_exception(
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errctx->exceptions->index_error,
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"index {0} is out of bounds, valid range is {1} <= index < {2}",
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nth, 0, arr_size);
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return 0;
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}
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return get_nth_pelement(ndarray, nth);
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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 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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void len(ErrorContext* errctx, const NDArray<SizeT>* ndarray,
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SliceIndex* dst_length) {
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// numpy prohibits `__len__` on unsized objects
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if (ndarray->ndims == 0) {
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errctx->set_exception(errctx->exceptions->type_error,
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"len() of unsized object");
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return;
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}
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*dst_length = (SliceIndex)ndarray->shape[0];
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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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__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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/**
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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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} // 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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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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void __nac3_ndarray_len(ErrorContext* errctx, NDArray<int32_t>* ndarray,
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SliceIndex* dst_len) {
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return len(errctx, ndarray, dst_len);
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}
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void __nac3_ndarray_len64(ErrorContext* errctx, NDArray<int64_t>* ndarray,
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SliceIndex* dst_len) {
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return len(errctx, ndarray, dst_len);
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}
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void __nac3_ndarray_util_assert_shape_no_negative(ErrorContext* errctx,
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int32_t ndims,
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int32_t* shape) {
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util::assert_shape_no_negative(errctx, ndims, shape);
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}
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void __nac3_ndarray_util_assert_shape_no_negative64(ErrorContext* errctx,
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int64_t ndims,
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int64_t* shape) {
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util::assert_shape_no_negative(errctx, ndims, shape);
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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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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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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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@ -4,5 +4,6 @@
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#include <irrt/core.hpp>
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#include <irrt/error_context.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/utils.hpp>
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#include <cstdio>
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#include <cstdlib>
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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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#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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@ -1,6 +1,8 @@
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use crate::typecheck::typedef::Type;
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pub mod error_context;
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pub mod ndarray;
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pub mod slice;
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mod test;
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mod util;
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use crate::codegen::model::*;
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use crate::codegen::util::array_writer::ArrayWriter;
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use crate::codegen::{structure::ndarray::NpArray, CodeGenContext, CodeGenerator};
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use super::basic::{
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call_nac3_ndarray_nbytes, call_nac3_ndarray_set_strides_by_shape,
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call_nac3_ndarray_util_assert_shape_no_negative,
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};
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/**
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Allocate an ndarray on the stack given its `ndims`.
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`shape` and `strides` will be automatically allocated on the stack.
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The returned ndarray's content will be:
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- `data`: `nullptr`
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- `itemsize`: **uninitialized** value
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- `ndims`: initialized value, set to the input `ndims`
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- `shape`: initialized pointer to an allocated stack with **uninitialized** values
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- `strides`: initialized pointer to an allocated stack with **uninitialized** values
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*/
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pub fn alloca_ndarray<'ctx, G>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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ndims: Int<'ctx, SizeT>,
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name: &str,
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) -> Result<Ptr<'ctx, StructModel<NpArray>>, String>
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where
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G: CodeGenerator + ?Sized,
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{
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let tyctx = generator.type_context(ctx.ctx);
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let sizet_model = IntModel(SizeT);
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let ndarray_model = StructModel(NpArray);
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let ndarray_data_model = PtrModel(IntModel(Byte));
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// Setup ndarray
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let ndarray_ptr = ndarray_model.alloca(tyctx, ctx, name);
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let shape = sizet_model.array_alloca(tyctx, ctx, ndims.value, "shape");
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let strides = sizet_model.array_alloca(tyctx, ctx, ndims.value, "strides");
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ndarray_ptr.gep(ctx, |f| f.data).store(ctx, ndarray_data_model.nullptr(tyctx, ctx.ctx));
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ndarray_ptr.gep(ctx, |f| f.ndims).store(ctx, ndims);
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ndarray_ptr.gep(ctx, |f| f.shape).store(ctx, shape);
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ndarray_ptr.gep(ctx, |f| f.strides).store(ctx, strides);
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Ok(ndarray_ptr)
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}
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/// Initialize an ndarray's `shape` and asserts on.
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/// `shape`'s values and prohibit illegal inputs like negative dimensions.
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pub fn init_ndarray_shape<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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pndarray: Ptr<'ctx, StructModel<NpArray>>,
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shape_writer: &ArrayWriter<'ctx, G, SizeT, IntModel<SizeT>>,
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) -> Result<(), String> {
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let tyctx = generator.type_context(ctx.ctx);
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let shape = pndarray.gep(ctx, |f| f.shape).load(tyctx, ctx, "shape");
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(shape_writer.write)(generator, ctx, shape)?;
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call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, shape_writer.len, shape);
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Ok(())
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}
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/// Initialize an ndarray's `data` by allocating a buffer on the stack.
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/// The allocated data buffer is considered to be *owned* by the ndarray.
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///
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/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
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///
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||||
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
|
||||
pub fn init_ndarray_data_by_alloca<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let ndarray_data_model = IntModel(Byte);
|
||||
|
||||
let nbytes = call_nac3_ndarray_nbytes(generator, ctx, pndarray);
|
||||
let data = ndarray_data_model.array_alloca(tyctx, ctx, nbytes.value, "data");
|
||||
pndarray.gep(ctx, |f| f.data).store(ctx, data);
|
||||
call_nac3_ndarray_set_strides_by_shape(generator, ctx, pndarray);
|
||||
}
|
|
@ -0,0 +1,135 @@
|
|||
use crate::codegen::irrt::error_context::{check_error_context, setup_error_context};
|
||||
use crate::codegen::irrt::slice::SliceIndex;
|
||||
use crate::codegen::irrt::util::function::CallFunction;
|
||||
use crate::codegen::irrt::util::get_sizet_dependent_function_name;
|
||||
use crate::codegen::model::*;
|
||||
use crate::codegen::structure::ndarray::NpArray;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_size"),
|
||||
)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning("size")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_nbytes"),
|
||||
)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning("nbytes")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SliceIndex> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let slice_index_model = IntModel(SliceIndex::default());
|
||||
|
||||
let dst_len = slice_index_model.alloca(tyctx, ctx, "dst_len");
|
||||
|
||||
let errctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_len"),
|
||||
)
|
||||
.arg("errctx", errctx)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.arg("dst_len", dst_len)
|
||||
.returning_void();
|
||||
check_error_context(generator, ctx, errctx);
|
||||
|
||||
dst_len.load(tyctx, ctx, "len")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndims: Int<'ctx, SizeT>,
|
||||
shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let errctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_util_assert_shape_no_negative"),
|
||||
)
|
||||
.arg("errctx", errctx)
|
||||
.arg("ndims", ndims)
|
||||
.arg("shape", shape)
|
||||
.returning_void();
|
||||
check_error_context(generator, ctx, errctx);
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_set_strides_by_shape"),
|
||||
)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_is_c_contiguous"),
|
||||
)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning("is_c_contiguous")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
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("is_c_contiguous")
|
||||
}
|
|
@ -0,0 +1,2 @@
|
|||
pub mod allocation;
|
||||
pub mod basic;
|
|
@ -0,0 +1,3 @@
|
|||
use crate::codegen::model::*;
|
||||
|
||||
pub type SliceIndex = Int32;
|
Loading…
Reference in New Issue