core/ndstrides: implement general ndarray basic indexing
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@ -1,13 +1,11 @@
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#pragma once
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#include <irrt/int_defs.hpp>
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#include <irrt/slice.hpp>
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#include <irrt/utils.hpp>
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// NDArray indices are always `uint32_t`.
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using NDIndex = uint32_t;
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// The type of an index or a value describing the length of a
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// range/slice is always `int32_t`.
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using SliceIndex = int32_t;
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using NDIndexInt = uint32_t;
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namespace {
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// adapted from GNU Scientific Library:
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@ -43,7 +41,7 @@ SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len,
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template <typename SizeT>
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void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
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SizeT num_dims, NDIndex* idxs) {
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SizeT num_dims, NDIndexInt* idxs) {
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SizeT stride = 1;
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for (SizeT dim = 0; dim < num_dims; dim++) {
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SizeT i = num_dims - dim - 1;
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@ -55,7 +53,7 @@ void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
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template <typename SizeT>
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SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
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const NDIndex* indices,
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const NDIndexInt* indices,
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SizeT num_indices) {
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SizeT idx = 0;
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SizeT stride = 1;
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@ -104,8 +102,8 @@ void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims, SizeT lhs_ndims,
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template <typename SizeT>
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void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
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SizeT src_ndims,
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const NDIndex* in_idx,
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NDIndex* out_idx) {
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const NDIndexInt* in_idx,
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NDIndexInt* out_idx) {
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for (SizeT i = 0; i < src_ndims; ++i) {
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SizeT src_i = src_ndims - i - 1;
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out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
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@ -293,24 +291,24 @@ uint64_t __nac3_ndarray_calc_size64(const uint64_t* list_data,
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}
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void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims,
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uint32_t num_dims, NDIndex* idxs) {
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uint32_t num_dims, NDIndexInt* idxs) {
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__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
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}
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void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims,
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uint64_t num_dims, NDIndex* idxs) {
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uint64_t num_dims, NDIndexInt* idxs) {
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__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
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}
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uint32_t __nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims,
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const NDIndex* indices,
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const NDIndexInt* indices,
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uint32_t num_indices) {
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return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
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num_indices);
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}
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uint64_t __nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims,
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const NDIndex* indices,
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const NDIndexInt* indices,
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uint64_t num_indices) {
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return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
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num_indices);
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@ -333,16 +331,16 @@ void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
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void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
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uint32_t src_ndims,
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const NDIndex* in_idx,
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NDIndex* out_idx) {
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const NDIndexInt* in_idx,
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NDIndexInt* out_idx) {
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__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
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out_idx);
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}
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void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
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uint64_t src_ndims,
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const NDIndex* in_idx,
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NDIndex* out_idx) {
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const NDIndexInt* in_idx,
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NDIndexInt* out_idx) {
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__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
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out_idx);
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}
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200
nac3core/irrt/irrt/ndarray/indexing.hpp
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200
nac3core/irrt/irrt/ndarray/indexing.hpp
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#pragma once
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#include <irrt/error_context.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/slice.hpp>
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namespace {
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typedef uint8_t NDIndexType;
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/**
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* @brief A single element index
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*
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* See https://numpy.org/doc/stable/user/basics.indexing.html#single-element-indexing
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*
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* `data` points to a `SliceIndex`.
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*/
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const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
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/**
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* @brief A slice index
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*
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* See https://numpy.org/doc/stable/user/basics.indexing.html#slicing-and-striding
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*
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* `data` points to a `UserRange`.
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*/
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const NDIndexType ND_INDEX_TYPE_SLICE = 1;
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/**
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* @brief An index used in ndarray indexing
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*/
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struct NDIndex {
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/**
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* @brief Enum tag to specify the type of index.
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*
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* Please see comments of each enum constant.
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*/
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NDIndexType type;
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/**
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* @brief The accompanying data associated with `type`.
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*
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* Please see comments of each enum constant.
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*/
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uint8_t* data;
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};
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} // namespace
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namespace {
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namespace ndarray {
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namespace indexing {
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namespace util {
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/**
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* @brief Return the expected rank of the resulting ndarray
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* created by indexing an ndarray of rank `ndims` using `indexes`.
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*/
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template <typename SizeT>
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void deduce_ndims_after_indexing(ErrorContext* errctx, SizeT* final_ndims,
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SizeT ndims, SizeT num_indexes,
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const NDIndex* indexes) {
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if (num_indexes > ndims) {
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errctx->set_exception(errctx->exceptions->index_error,
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"too many indices for array: array is "
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"{0}-dimensional, but {1} were indexed",
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ndims, num_indexes);
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return;
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}
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*final_ndims = ndims;
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for (SizeT i = 0; i < num_indexes; i++) {
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if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
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// An index demotes the rank by 1
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(*final_ndims)--;
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}
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}
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}
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} // namespace util
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/**
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* @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
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*
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* This is function very similar to performing `dst_ndarray = src_ndarray[indexes]` in Python (where the variables
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* can all be found in the parameter of this function).
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*
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* In other words, this function takes in an ndarray (`src_ndarray`), index it with `indexes`, and return the
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* indexed array (by writing the result to `dst_ndarray`).
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*
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* This function also does proper assertions on `indexes`.
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*
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* # Notes on `dst_ndarray`
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* The caller is responsible for allocating space for the resulting ndarray.
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* Here is what this function expects from `dst_ndarray` when called:
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* - `dst_ndarray->data` does not have to be initialized.
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* - `dst_ndarray->itemsize` does not have to be initialized.
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* - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
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* indexing `src_ndarray` with `indexes`.
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* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
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* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
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* When this function call ends:
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* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
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* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
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* - `dst_ndarray->ndims` is unchanged.
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* - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
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* - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
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*
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* @param indexes Indexes to index `src_ndarray`, ordered in the same way you would write them in Python.
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* @param src_ndarray The NDArray to be indexed.
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* @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
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*/
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template <typename SizeT>
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void index(ErrorContext* errctx, SizeT num_indexes, const NDIndex* indexes,
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const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
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// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
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dst_ndarray->data = src_ndarray->data;
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dst_ndarray->itemsize = src_ndarray->itemsize;
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SizeT src_axis = 0;
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SizeT dst_axis = 0;
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for (SliceIndex i = 0; i < num_indexes; i++) {
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const NDIndex* index = &indexes[i];
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if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
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SliceIndex input = *((SliceIndex*)index->data);
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SliceIndex k = slice::resolve_index_in_length(
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src_ndarray->shape[src_axis], input);
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if (k == slice::OUT_OF_BOUNDS) {
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errctx->set_exception(errctx->exceptions->index_error,
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"index {0} is out of bounds for axis {1} "
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"with size {2}",
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input, src_axis,
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src_ndarray->shape[src_axis]);
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return;
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}
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dst_ndarray->data += k * src_ndarray->strides[src_axis];
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src_axis++;
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} else if (index->type == ND_INDEX_TYPE_SLICE) {
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UserSlice* input = (UserSlice*)index->data;
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Slice slice;
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input->indices_checked(errctx, src_ndarray->shape[src_axis],
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&slice);
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if (errctx->has_exception()) {
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return;
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}
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dst_ndarray->data +=
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(SizeT)slice.start * src_ndarray->strides[src_axis];
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dst_ndarray->strides[dst_axis] =
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((SizeT)slice.step) * src_ndarray->strides[src_axis];
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dst_ndarray->shape[dst_axis] = (SizeT)slice.len();
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dst_axis++;
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src_axis++;
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} else {
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__builtin_unreachable();
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}
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}
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for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
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dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
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dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
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}
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}
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} // namespace indexing
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} // namespace ndarray
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} // namespace
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extern "C" {
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using namespace ndarray::indexing;
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void __nac3_ndarray_indexing_deduce_ndims_after_indexing(
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ErrorContext* errctx, int32_t* result, int32_t ndims, int32_t num_indexes,
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const NDIndex* indexes) {
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ndarray::indexing::util::deduce_ndims_after_indexing(errctx, result, ndims,
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num_indexes, indexes);
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}
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void __nac3_ndarray_indexing_deduce_ndims_after_indexing64(
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ErrorContext* errctx, int64_t* result, int64_t ndims, int64_t num_indexes,
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const NDIndex* indexes) {
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ndarray::indexing::util::deduce_ndims_after_indexing(errctx, result, ndims,
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num_indexes, indexes);
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}
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void __nac3_ndarray_index(ErrorContext* errctx, int32_t num_indexes,
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NDIndex* indexes, NDArray<int32_t>* src_ndarray,
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NDArray<int32_t>* dst_ndarray) {
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index(errctx, num_indexes, indexes, src_ndarray, dst_ndarray);
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}
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void __nac3_ndarray_index64(ErrorContext* errctx, int64_t num_indexes,
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NDIndex* indexes, NDArray<int64_t>* src_ndarray,
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NDArray<int64_t>* dst_ndarray) {
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index(errctx, num_indexes, indexes, src_ndarray, dst_ndarray);
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}
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}
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@ -1,7 +1,13 @@
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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/slice.hpp>
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#include <irrt/utils.hpp>
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// The type of an index or a value describing the length of a
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// range/slice is always `int32_t`.
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using SliceIndex = int32_t;
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namespace {
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@ -6,5 +6,6 @@
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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/ndarray/indexing.hpp>
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#include <irrt/slice.hpp>
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#include <irrt/utils.hpp>
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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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#include <test/test_ndarray_indexing.hpp>
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#include <test/test_slice.hpp>
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int main() {
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test::core::run();
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test::slice::run();
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test::ndarray_basic::run();
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test::ndarray_indexing::run();
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return 0;
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}
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220
nac3core/irrt/test/test_ndarray_indexing.hpp
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220
nac3core/irrt/test/test_ndarray_indexing.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_indexing {
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void test_normal_1() {
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/*
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Reference Python code:
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```python
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ndarray = np.arange(12, dtype=np.float64).reshape((3, 4));
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# array([[ 0., 1., 2., 3.],
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# [ 4., 5., 6., 7.],
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# [ 8., 9., 10., 11.]])
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dst_ndarray = ndarray[-2:, 1::2]
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# array([[ 5., 7.],
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# [ 9., 11.]])
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assert dst_ndarray.shape == (2, 2)
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assert dst_ndarray.strides == (32, 16)
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assert dst_ndarray[0, 0] == 5.0
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assert dst_ndarray[0, 1] == 7.0
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assert dst_ndarray[1, 0] == 9.0
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assert dst_ndarray[1, 1] == 11.0
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```
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*/
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BEGIN_TEST();
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// Prepare src_ndarray
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double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0,
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6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
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int32_t src_itemsize = sizeof(double);
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const int32_t src_ndims = 2;
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int32_t src_shape[src_ndims] = {3, 4};
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int32_t src_strides[src_ndims] = {};
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NDArray<int32_t> src_ndarray = {.data = (uint8_t *)src_data,
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.itemsize = src_itemsize,
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.ndims = src_ndims,
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.shape = src_shape,
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.strides = src_strides};
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ndarray::basic::set_strides_by_shape(&src_ndarray);
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// Prepare dst_ndarray
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const int32_t dst_ndims = 2;
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int32_t dst_shape[dst_ndims] = {999, 999}; // Empty values
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int32_t dst_strides[dst_ndims] = {999, 999}; // Empty values
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NDArray<int32_t> dst_ndarray = {.data = nullptr,
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.ndims = dst_ndims,
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.shape = dst_shape,
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.strides = dst_strides};
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// Create the subscripts in `ndarray[-2::, 1::2]`
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UserSlice subscript_1;
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subscript_1.set_start(-2);
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UserSlice subscript_2;
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subscript_2.set_start(1);
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subscript_2.set_step(2);
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const int32_t num_indexes = 2;
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NDIndex indexes[num_indexes] = {
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{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_1},
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{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
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ErrorContext errctx = create_testing_errctx();
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ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
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&dst_ndarray);
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assert_errctx_no_exception(&errctx);
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int32_t expected_shape[dst_ndims] = {2, 2};
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int32_t expected_strides[dst_ndims] = {32, 16};
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assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
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assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
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// dst_ndarray[0, 0]
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assert_values_match(5.0,
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*((double *)ndarray::basic::get_pelement_by_indices(
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&dst_ndarray, (int32_t[dst_ndims]){0, 0})));
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// dst_ndarray[0, 1]
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assert_values_match(7.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int32_t[dst_ndims]){0, 1})));
|
||||
// dst_ndarray[1, 0]
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int32_t[dst_ndims]){1, 0})));
|
||||
// dst_ndarray[1, 1]
|
||||
assert_values_match(11.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int32_t[dst_ndims]){1, 1})));
|
||||
}
|
||||
|
||||
void test_normal_2() {
|
||||
/*
|
||||
```python
|
||||
ndarray = np.arange(12, dtype=np.float64).reshape((3, 4))
|
||||
# array([[ 0., 1., 2., 3.],
|
||||
# [ 4., 5., 6., 7.],
|
||||
# [ 8., 9., 10., 11.]])
|
||||
|
||||
dst_ndarray = ndarray[2, ::-2]
|
||||
# array([11., 9.])
|
||||
|
||||
assert dst_ndarray.shape == (2,)
|
||||
assert dst_ndarray.strides == (-16,)
|
||||
assert dst_ndarray[0] == 11.0
|
||||
assert dst_ndarray[1] == 9.0
|
||||
```
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
// Prepare src_ndarray
|
||||
double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0,
|
||||
6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
|
||||
int32_t src_itemsize = sizeof(double);
|
||||
const int32_t src_ndims = 2;
|
||||
int32_t src_shape[src_ndims] = {3, 4};
|
||||
int32_t src_strides[src_ndims] = {};
|
||||
NDArray<int32_t> src_ndarray = {.data = (uint8_t *)src_data,
|
||||
.itemsize = src_itemsize,
|
||||
.ndims = src_ndims,
|
||||
.shape = src_shape,
|
||||
.strides = src_strides};
|
||||
ndarray::basic::set_strides_by_shape(&src_ndarray);
|
||||
|
||||
// Prepare dst_ndarray
|
||||
const int32_t dst_ndims = 1;
|
||||
int32_t dst_shape[dst_ndims] = {999}; // Empty values
|
||||
int32_t dst_strides[dst_ndims] = {999}; // Empty values
|
||||
NDArray<int32_t> dst_ndarray = {.data = nullptr,
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides};
|
||||
|
||||
// Create the subscripts in `ndarray[2, ::-2]`
|
||||
int32_t subscript_1 = 2;
|
||||
|
||||
UserSlice subscript_2;
|
||||
subscript_2.set_step(-2);
|
||||
|
||||
const int32_t num_indexes = 2;
|
||||
NDIndex indexes[num_indexes] = {
|
||||
{.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
|
||||
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
|
||||
|
||||
ErrorContext errctx = create_testing_errctx();
|
||||
ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
|
||||
&dst_ndarray);
|
||||
assert_errctx_no_exception(&errctx);
|
||||
|
||||
int32_t expected_shape[dst_ndims] = {2};
|
||||
int32_t expected_strides[dst_ndims] = {-16};
|
||||
assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
|
||||
assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
|
||||
|
||||
assert_values_match(11.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int32_t[dst_ndims]){0})));
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int32_t[dst_ndims]){1})));
|
||||
}
|
||||
|
||||
void test_index_subscript_out_of_bounds() {
|
||||
/*
|
||||
# Consider `my_array`
|
||||
|
||||
print(my_array.shape)
|
||||
# (4, 5, 6)
|
||||
|
||||
my_array[2, 100] # error, index subscript at axis 1 is out of bounds
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
// Prepare src_ndarray
|
||||
const int32_t src_ndims = 2;
|
||||
int32_t src_shape[src_ndims] = {3, 4};
|
||||
int32_t src_strides[src_ndims] = {};
|
||||
NDArray<int32_t> src_ndarray = {
|
||||
.data = (uint8_t *)nullptr, // placeholder, we wouldn't access it
|
||||
.itemsize = sizeof(double), // placeholder
|
||||
.ndims = src_ndims,
|
||||
.shape = src_shape,
|
||||
.strides = src_strides};
|
||||
ndarray::basic::set_strides_by_shape(&src_ndarray);
|
||||
|
||||
// Create the subscripts in `my_array[2, 100]`
|
||||
int32_t subscript_1 = 2;
|
||||
int32_t subscript_2 = 100;
|
||||
|
||||
const int32_t num_indexes = 2;
|
||||
NDIndex indexes[num_indexes] = {
|
||||
{.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
|
||||
{.type = ND_INDEX_TYPE_SINGLE_ELEMENT,
|
||||
.data = (uint8_t *)&subscript_2}};
|
||||
|
||||
// Prepare dst_ndarray
|
||||
const int32_t dst_ndims = 0;
|
||||
int32_t dst_shape[dst_ndims] = {};
|
||||
int32_t dst_strides[dst_ndims] = {};
|
||||
NDArray<int32_t> dst_ndarray = {.data = nullptr, // placehloder
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides};
|
||||
|
||||
ErrorContext errctx = create_testing_errctx();
|
||||
ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
|
||||
&dst_ndarray);
|
||||
assert_errctx_has_exception(&errctx, errctx.exceptions->index_error);
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_normal_1();
|
||||
test_normal_2();
|
||||
test_index_subscript_out_of_bounds();
|
||||
}
|
||||
} // namespace ndarray_indexing
|
||||
} // namespace test
|
||||
@ -1,10 +1,14 @@
|
||||
use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
|
||||
|
||||
use super::{
|
||||
irrt::slice::{RustUserSlice, SliceIndex},
|
||||
structure::ndarray::NpArray,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayValue, ProxyType,
|
||||
ProxyValue, RangeValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
|
||||
ProxyValue, RangeValue, UntypedArrayLikeAccessor,
|
||||
},
|
||||
concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
|
||||
gen_in_range_check, get_llvm_abi_type, get_llvm_type,
|
||||
@ -21,11 +25,7 @@ use crate::{
|
||||
CodeGenContext, CodeGenTask, CodeGenerator,
|
||||
},
|
||||
symbol_resolver::{SymbolValue, ValueEnum},
|
||||
toplevel::{
|
||||
helper::PrimDef,
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
DefinitionId, TopLevelDef,
|
||||
},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
|
||||
typecheck::{
|
||||
magic_methods::{Binop, BinopVariant, HasOpInfo},
|
||||
typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
|
||||
@ -34,19 +34,25 @@ use crate::{
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
types::{AnyType, BasicType, BasicTypeEnum},
|
||||
values::{BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue},
|
||||
values::{BasicValue, BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue},
|
||||
AddressSpace, IntPredicate, OptimizationLevel,
|
||||
};
|
||||
use itertools::{chain, izip, Either, Itertools};
|
||||
use nac3parser::ast::{
|
||||
self, Boolop, Cmpop, Comprehension, Constant, Expr, ExprKind, Location, Operator, StrRef,
|
||||
Unaryop,
|
||||
self, Boolop, Cmpop, Comprehension, Constant, Expr, ExprKind, Located, Location, Operator,
|
||||
StrRef, Unaryop,
|
||||
};
|
||||
use ndarray::{
|
||||
allocation::alloca_ndarray,
|
||||
indexing::{call_nac3_ndarray_index, RustNDIndex},
|
||||
};
|
||||
|
||||
use super::structure::cslice::CSlice;
|
||||
use super::{
|
||||
model::*,
|
||||
structure::exception::{Exception, ExceptionId},
|
||||
structure::{
|
||||
cslice::CSlice,
|
||||
exception::{Exception, ExceptionId},
|
||||
},
|
||||
};
|
||||
|
||||
pub fn get_subst_key(
|
||||
@ -2127,334 +2133,149 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
|
||||
|
||||
/// Generates code for a subscript expression on an `ndarray`.
|
||||
///
|
||||
/// * `ty` - The `Type` of the `NDArray` elements.
|
||||
/// * `elem_ty` - The `Type` of the `NDArray` elements.
|
||||
/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
|
||||
/// * `v` - The `NDArray` value.
|
||||
/// * `slice` - The slice expression used to subscript into the `ndarray`.
|
||||
/// * `src_ndarray` - The `NDArray` value.
|
||||
/// * `subscript` - The subscript expression used to index into the `ndarray`.
|
||||
fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ty: Type,
|
||||
elem_ty: Type,
|
||||
ndims: Type,
|
||||
v: NDArrayValue<'ctx>,
|
||||
slice: &Expr<Option<Type>>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
subscript: &Expr<Option<Type>>,
|
||||
) -> Result<Option<ValueEnum<'ctx>>, String> {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
// TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let slice_index_model = IntModel(SliceIndex::default());
|
||||
|
||||
let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
|
||||
// Annoying notes about `slice`
|
||||
// - `my_array[5]`
|
||||
// - slice is a `Constant`
|
||||
// - `my_array[:5]`
|
||||
// - slice is a `Slice`
|
||||
// - `my_array[:]`
|
||||
// - slice is a `Slice`, but lower upper step would all be `Option::None`
|
||||
// - `my_array[:, :]`
|
||||
// - slice is now a `Tuple` of two `Slice`-s
|
||||
//
|
||||
// In summary:
|
||||
// - when there is a comma "," within [], `slice` will be a `Tuple` of the entries.
|
||||
// - when there is not comma "," within [] (i.e., just a single entry), `slice` will be that entry itself.
|
||||
//
|
||||
// So we first "flatten" out the slice expression
|
||||
let index_exprs = match &subscript.node {
|
||||
ExprKind::Tuple { elts, .. } => elts.iter().collect_vec(),
|
||||
_ => vec![subscript],
|
||||
};
|
||||
|
||||
// Process all index expressions
|
||||
let mut rust_ndindexes: Vec<RustNDIndex> = Vec::with_capacity(index_exprs.len()); // Not using iterators here because `?` is used here.
|
||||
for index_expr in index_exprs {
|
||||
// NOTE: Currently nac3core's slices do not have an object representation,
|
||||
// so the code/implementation looks awkward - we have to do pattern matching on the expression
|
||||
let ndindex = if let ExprKind::Slice { lower: start, upper: stop, step } = &index_expr.node
|
||||
{
|
||||
// Helper function here to deduce code duplication
|
||||
type ValueExpr = Option<Box<Located<ExprKind<Option<Type>>, Option<Type>>>>;
|
||||
let mut help = |value_expr: &ValueExpr| -> Result<_, String> {
|
||||
Ok(match value_expr {
|
||||
None => None,
|
||||
Some(value_expr) => {
|
||||
let value_expr = generator
|
||||
.gen_expr(ctx, value_expr)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, ctx.primitives.int32)?;
|
||||
|
||||
let value_expr =
|
||||
slice_index_model.check_value(tyctx, ctx.ctx, value_expr).unwrap();
|
||||
|
||||
Some(value_expr)
|
||||
}
|
||||
})
|
||||
};
|
||||
|
||||
let start = help(start)?;
|
||||
let stop = help(stop)?;
|
||||
let step = help(step)?;
|
||||
|
||||
RustNDIndex::Slice(RustUserSlice { start, stop, step })
|
||||
} else {
|
||||
// Anything else that is not a slice (might be illegal values),
|
||||
// For nac3core, this should be e.g., an int32 constant, an int32 variable, otherwise its an error
|
||||
|
||||
let index = generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.primitives.int32,
|
||||
)?;
|
||||
let index = slice_index_model.check_value(tyctx, ctx.ctx, index).unwrap();
|
||||
|
||||
RustNDIndex::SingleElement(index)
|
||||
};
|
||||
rust_ndindexes.push(ndindex);
|
||||
}
|
||||
|
||||
// Extract the `ndims` from a `Type` to `i128`
|
||||
// We *HAVE* to know this statically, this is used to determine
|
||||
// whether this subscript expression returns a scalar or an ndarray
|
||||
let TypeEnum::TLiteral { values: ndims_values, .. } = &*ctx.unifier.get_ty_immutable(ndims)
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
assert_eq!(ndims_values.len(), 1);
|
||||
let src_ndims = i128::try_from(ndims_values[0].clone()).unwrap();
|
||||
|
||||
let ndims = values
|
||||
.iter()
|
||||
.map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
|
||||
.collect::<Result<Vec<_>, _>>()
|
||||
.map_err(|val| {
|
||||
format!(
|
||||
"Expected non-negative literal for ndarray.ndims, got {}",
|
||||
i128::try_from(val).unwrap()
|
||||
)
|
||||
})?;
|
||||
|
||||
assert!(!ndims.is_empty());
|
||||
|
||||
// The number of dimensions subscripted by the index expression.
|
||||
// Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
|
||||
// dimension will remove a dimension.
|
||||
let subscripted_dims = match &slice.node {
|
||||
ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
|
||||
if let ExprKind::Slice { .. } = &value_subexpr.node {
|
||||
acc
|
||||
} else {
|
||||
acc + 1
|
||||
}
|
||||
}),
|
||||
|
||||
ExprKind::Slice { .. } => 0,
|
||||
_ => 1,
|
||||
};
|
||||
|
||||
let ndarray_ndims_ty = ctx.unifier.get_fresh_literal(
|
||||
ndims.iter().map(|v| SymbolValue::U64(v - subscripted_dims)).collect(),
|
||||
None,
|
||||
);
|
||||
let ndarray_ty =
|
||||
make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(ty), Some(ndarray_ndims_ty));
|
||||
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
|
||||
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
|
||||
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
|
||||
let sizeof_elem = llvm_ndarray_data_t.size_of().unwrap();
|
||||
|
||||
// Check that len is non-zero
|
||||
let len = v.load_ndims(ctx);
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), "").unwrap(),
|
||||
"0:IndexError",
|
||||
"too many indices for array: array is {0}-dimensional but 1 were indexed",
|
||||
[Some(len), None, None],
|
||||
slice.location,
|
||||
);
|
||||
|
||||
// Normalizes a possibly-negative index to its corresponding positive index
|
||||
let normalize_index = |generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
index: IntValue<'ctx>,
|
||||
dim: u64| {
|
||||
gen_if_else_expr_callback(
|
||||
// Check for "too many indices for array: array is ..." error
|
||||
if src_ndims < rust_ndindexes.len() as i128 {
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::SGE, index, index.get_type().const_zero(), "")
|
||||
.unwrap())
|
||||
},
|
||||
|_, _| Ok(Some(index)),
|
||||
|generator, ctx| {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
ctx.ctx.bool_type().const_int(1, false),
|
||||
"0:IndexError",
|
||||
"too many indices for array: array is {0}-dimensional, but {1} were indexed",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
let len = unsafe {
|
||||
v.dim_sizes().get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(dim, true),
|
||||
None,
|
||||
)
|
||||
};
|
||||
let dst_ndims = RustNDIndex::deduce_ndims_after_slicing(&rust_ndindexes, src_ndims as i32);
|
||||
let dst_ndarray = alloca_ndarray(
|
||||
generator,
|
||||
ctx,
|
||||
sizet_model.constant(tyctx, ctx.ctx, dst_ndims as u64),
|
||||
"subndarray",
|
||||
)?;
|
||||
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_int_add(
|
||||
len,
|
||||
ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
// Prepare the subscripts
|
||||
let (num_ndindexes, ndindexes) = RustNDIndex::alloca_ndindexes(tyctx, ctx, &rust_ndindexes);
|
||||
|
||||
Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
|
||||
},
|
||||
)
|
||||
.map(|v| v.map(BasicValueEnum::into_int_value))
|
||||
};
|
||||
// NOTE: IRRT does check for indexing errors
|
||||
call_nac3_ndarray_index(generator, ctx, num_ndindexes, ndindexes, src_ndarray, dst_ndarray);
|
||||
|
||||
// Converts a slice expression into a slice-range tuple
|
||||
let expr_to_slice = |generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
node: &ExprKind<Option<Type>>,
|
||||
dim: u64| {
|
||||
match node {
|
||||
ExprKind::Constant { value: Constant::Int(v), .. } => {
|
||||
let Some(index) =
|
||||
normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
|
||||
else {
|
||||
return Ok(None);
|
||||
};
|
||||
// ...and return the result, with two cases
|
||||
let result_llvm_value: BasicValueEnum<'_> = if dst_ndims == 0 {
|
||||
// 1) ndims == 0 (this happens when you do `np.zerps((3, 4))[1, 1]`), return the element
|
||||
|
||||
Ok(Some((index, index, llvm_i32.const_int(1, true))))
|
||||
}
|
||||
let pelement = dst_ndarray.gep(ctx, |f| f.data).load(tyctx, ctx, "pelement"); // `*data` points to the first element by definition
|
||||
|
||||
ExprKind::Slice { lower, upper, step } => {
|
||||
let dim_sz = unsafe {
|
||||
v.dim_sizes().get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(dim, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
// Cast the opaque `pelement` ptr to `elem_ty`
|
||||
let elem_ty = ctx.get_llvm_type(generator, elem_ty);
|
||||
let pelement = ctx
|
||||
.builder
|
||||
.build_pointer_cast(
|
||||
pelement.value,
|
||||
elem_ty.ptr_type(AddressSpace::default()),
|
||||
"pelement_casted",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
|
||||
}
|
||||
|
||||
_ => {
|
||||
let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
|
||||
let index = index
|
||||
.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
|
||||
.into_int_value();
|
||||
let Some(index) = normalize_index(generator, ctx, index, dim)? else {
|
||||
return Ok(None);
|
||||
};
|
||||
|
||||
Ok(Some((index, index, llvm_i32.const_int(1, true))))
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
let make_indices_arr = |generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>|
|
||||
-> Result<_, String> {
|
||||
Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
|
||||
let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
|
||||
let index_addr = generator.gen_array_var_alloc(
|
||||
ctx,
|
||||
llvm_int_ty,
|
||||
llvm_usize.const_int(elts.len() as u64, false),
|
||||
None,
|
||||
)?;
|
||||
|
||||
for (i, elt) in elts.iter().enumerate() {
|
||||
let Some(index) = generator.gen_expr(ctx, elt)? else {
|
||||
return Ok(None);
|
||||
};
|
||||
|
||||
let index = index
|
||||
.to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
|
||||
.into_int_value();
|
||||
let Some(index) = normalize_index(generator, ctx, index, 0)? else {
|
||||
return Ok(None);
|
||||
};
|
||||
|
||||
let store_ptr = unsafe {
|
||||
index_addr.ptr_offset_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(i as u64, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
ctx.builder.build_store(store_ptr, index).unwrap();
|
||||
}
|
||||
|
||||
Some(index_addr)
|
||||
} else if let Some(index) = generator.gen_expr(ctx, slice)? {
|
||||
let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
|
||||
let index_addr = generator.gen_array_var_alloc(
|
||||
ctx,
|
||||
llvm_int_ty,
|
||||
llvm_usize.const_int(1u64, false),
|
||||
None,
|
||||
)?;
|
||||
|
||||
let index =
|
||||
index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
|
||||
let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
|
||||
|
||||
let store_ptr = unsafe {
|
||||
index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
|
||||
};
|
||||
ctx.builder.build_store(store_ptr, index).unwrap();
|
||||
|
||||
Some(index_addr)
|
||||
} else {
|
||||
None
|
||||
})
|
||||
};
|
||||
|
||||
Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
|
||||
let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
|
||||
|
||||
v.data().get(ctx, generator, &index_addr, None).into()
|
||||
ctx.builder.build_load(pelement, "element").unwrap().as_basic_value_enum()
|
||||
} else {
|
||||
match &slice.node {
|
||||
ExprKind::Tuple { elts, .. } => {
|
||||
let slices = elts
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
|
||||
.take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
|
||||
.collect::<Result<Vec<_>, _>>()?;
|
||||
if slices.len() < elts.len() {
|
||||
return Ok(None);
|
||||
}
|
||||
|
||||
let slices = slices.into_iter().map(Option::unwrap).collect_vec();
|
||||
|
||||
numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
|
||||
}
|
||||
|
||||
ExprKind::Slice { .. } => {
|
||||
let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
|
||||
return Ok(None);
|
||||
};
|
||||
|
||||
numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
|
||||
}
|
||||
|
||||
_ => {
|
||||
// Accessing an element from a multi-dimensional `ndarray`
|
||||
|
||||
let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
|
||||
|
||||
// Create a new array, remove the top dimension from the dimension-size-list, and copy the
|
||||
// elements over
|
||||
let subscripted_ndarray =
|
||||
generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
|
||||
let ndarray = NDArrayValue::from_ptr_val(subscripted_ndarray, llvm_usize, None);
|
||||
|
||||
let num_dims = v.load_ndims(ctx);
|
||||
ndarray.store_ndims(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.builder
|
||||
.build_int_sub(num_dims, llvm_usize.const_int(1, false), "")
|
||||
.unwrap(),
|
||||
);
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
|
||||
|
||||
let ndarray_num_dims = ctx
|
||||
.builder
|
||||
.build_int_z_extend_or_bit_cast(
|
||||
ndarray.load_ndims(ctx),
|
||||
llvm_usize.size_of().get_type(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let v_dims_src_ptr = unsafe {
|
||||
v.dim_sizes().ptr_offset_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(1, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
ndarray.dim_sizes().base_ptr(ctx, generator),
|
||||
v_dims_src_ptr,
|
||||
ctx.builder
|
||||
.build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
|
||||
.map(Into::into)
|
||||
.unwrap(),
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
let ndarray_num_elems = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
&ndarray.dim_sizes().as_slice_value(ctx, generator),
|
||||
(None, None),
|
||||
);
|
||||
let ndarray_num_elems = ctx
|
||||
.builder
|
||||
.build_int_z_extend_or_bit_cast(ndarray_num_elems, sizeof_elem.get_type(), "")
|
||||
.unwrap();
|
||||
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
|
||||
|
||||
let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
ndarray.data().base_ptr(ctx, generator),
|
||||
v_data_src_ptr,
|
||||
ctx.builder
|
||||
.build_int_mul(
|
||||
ndarray_num_elems,
|
||||
llvm_ndarray_data_t.size_of().unwrap(),
|
||||
"",
|
||||
)
|
||||
.map(Into::into)
|
||||
.unwrap(),
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
ndarray.as_base_value().into()
|
||||
}
|
||||
}
|
||||
}))
|
||||
// 2) ndims > 0 (other cases), return subndarray
|
||||
dst_ndarray.value.as_basic_value_enum()
|
||||
};
|
||||
Ok(Some(ValueEnum::Dynamic(result_llvm_value)))
|
||||
}
|
||||
|
||||
/// See [`CodeGenerator::gen_expr`].
|
||||
@ -3097,17 +2918,22 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
|
||||
}
|
||||
}
|
||||
TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let (ty, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap();
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let pndarray_model = PtrModel(StructModel(NpArray));
|
||||
|
||||
let v = if let Some(v) = generator.gen_expr(ctx, value)? {
|
||||
v.to_basic_value_enum(ctx, generator, value.custom.unwrap())?
|
||||
.into_pointer_value()
|
||||
} else {
|
||||
let (dtype, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap();
|
||||
|
||||
let Some(ndarray) = generator.gen_expr(ctx, value)? else {
|
||||
return Ok(None);
|
||||
};
|
||||
let v = NDArrayValue::from_ptr_val(v, usize, None);
|
||||
|
||||
return gen_ndarray_subscript_expr(generator, ctx, *ty, *ndims, v, slice);
|
||||
let ndarray =
|
||||
ndarray.to_basic_value_enum(ctx, generator, value.custom.unwrap())?;
|
||||
let ndarray = pndarray_model.check_value(tyctx, ctx.ctx, ndarray).unwrap();
|
||||
|
||||
return gen_ndarray_subscript_expr(
|
||||
generator, ctx, *dtype, *ndims, ndarray, slice,
|
||||
);
|
||||
}
|
||||
TypeEnum::TTuple { .. } => {
|
||||
let index: u32 =
|
||||
|
||||
170
nac3core/src/codegen/irrt/ndarray/indexing.rs
Normal file
170
nac3core/src/codegen/irrt/ndarray/indexing.rs
Normal file
@ -0,0 +1,170 @@
|
||||
use crate::codegen::{
|
||||
irrt::{
|
||||
error_context::{check_error_context, setup_error_context},
|
||||
slice::{RustUserSlice, SliceIndex, UserSlice},
|
||||
util::{function::CallFunction, get_sizet_dependent_function_name},
|
||||
},
|
||||
model::*,
|
||||
structure::ndarray::NpArray,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
pub type NDIndexType = Byte;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDIndexFields<F: FieldVisitor> {
|
||||
pub type_: F::Field<IntModel<NDIndexType>>, // Defined to be uint8_t in IRRT
|
||||
pub data: F::Field<PtrModel<IntModel<Byte>>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct NDIndex;
|
||||
|
||||
impl StructKind for NDIndex {
|
||||
type Fields<F: FieldVisitor> = NDIndexFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { type_: visitor.add("type"), data: visitor.add("data") }
|
||||
}
|
||||
}
|
||||
|
||||
// An enum variant to store the content
|
||||
// and type of an NDIndex in high level.
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum RustNDIndex<'ctx> {
|
||||
SingleElement(Int<'ctx, SliceIndex>),
|
||||
Slice(RustUserSlice<'ctx>),
|
||||
}
|
||||
|
||||
impl<'ctx> RustNDIndex<'ctx> {
|
||||
fn get_type_id(&self) -> u64 {
|
||||
// Defined in IRRT, must be in sync
|
||||
match self {
|
||||
RustNDIndex::SingleElement(_) => 0,
|
||||
RustNDIndex::Slice(_) => 1,
|
||||
}
|
||||
}
|
||||
|
||||
fn write_to_ndindex(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_ndindex_ptr: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
) {
|
||||
let ndindex_type_model = IntModel(NDIndexType::default());
|
||||
let slice_index_model = IntModel(SliceIndex::default());
|
||||
let user_slice_model = StructModel(UserSlice);
|
||||
|
||||
// Set `dst_ndindex_ptr->type`
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.type_)
|
||||
.store(ctx, ndindex_type_model.constant(tyctx, ctx.ctx, self.get_type_id()));
|
||||
|
||||
// Set `dst_ndindex_ptr->data`
|
||||
let data = match self {
|
||||
RustNDIndex::SingleElement(in_index) => {
|
||||
let index_ptr = slice_index_model.alloca(tyctx, ctx, "index");
|
||||
index_ptr.store(ctx, *in_index);
|
||||
index_ptr.transmute(tyctx, ctx, IntModel(Byte), "")
|
||||
}
|
||||
RustNDIndex::Slice(in_rust_slice) => {
|
||||
let user_slice_ptr = user_slice_model.alloca(tyctx, ctx, "user_slice");
|
||||
in_rust_slice.write_to_user_slice(tyctx, ctx, user_slice_ptr);
|
||||
user_slice_ptr.transmute(tyctx, ctx, IntModel(Byte), "")
|
||||
}
|
||||
};
|
||||
dst_ndindex_ptr.gep(ctx, |f| f.data).store(ctx, data);
|
||||
}
|
||||
|
||||
/// Allocate an array of `NDIndex`es onto the stack and return its stack pointer.
|
||||
pub fn alloca_ndindexes(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
in_ndindexes: &[RustNDIndex<'ctx>],
|
||||
) -> (Int<'ctx, SizeT>, Ptr<'ctx, StructModel<NDIndex>>) {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let ndindex_model = StructModel(NDIndex);
|
||||
|
||||
let num_ndindexes = sizet_model.constant(tyctx, ctx.ctx, in_ndindexes.len() as u64);
|
||||
let ndindexes = ndindex_model.array_alloca(tyctx, ctx, num_ndindexes.value, "ndindexes");
|
||||
for (i, in_ndindex) in in_ndindexes.iter().enumerate() {
|
||||
let i = sizet_model.constant(tyctx, ctx.ctx, i as u64);
|
||||
let pndindex = ndindexes.offset(tyctx, ctx, i.value, "");
|
||||
in_ndindex.write_to_ndindex(tyctx, ctx, pndindex);
|
||||
}
|
||||
|
||||
(num_ndindexes, ndindexes)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn deduce_ndims_after_slicing(slices: &[RustNDIndex], original_ndims: i32) -> i32 {
|
||||
let mut final_ndims: i32 = original_ndims;
|
||||
for slice in slices {
|
||||
match slice {
|
||||
RustNDIndex::SingleElement(_) => {
|
||||
final_ndims -= 1;
|
||||
}
|
||||
RustNDIndex::Slice(_) => {}
|
||||
}
|
||||
}
|
||||
final_ndims
|
||||
}
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_indexing_deduce_ndims_after_indexing<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndims: Int<'ctx, SizeT>,
|
||||
num_ndindexes: Int<'ctx, SizeT>,
|
||||
ndindexs: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let pfinal_ndims = sizet_model.alloca(tyctx, ctx, "pfinal_ndims");
|
||||
|
||||
let errctx_ptr = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(
|
||||
tyctx,
|
||||
"__nac3_ndarray_indexing_deduce_ndims_after_indexing",
|
||||
),
|
||||
)
|
||||
.arg("errctx", errctx_ptr)
|
||||
.arg("result", pfinal_ndims)
|
||||
.arg("ndims", ndims)
|
||||
.arg("num_ndindexs", num_ndindexes)
|
||||
.arg("ndindexs", ndindexs)
|
||||
.returning_void();
|
||||
check_error_context(generator, ctx, errctx_ptr);
|
||||
|
||||
pfinal_ndims.load(tyctx, ctx, "final_ndims")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_indexes: Int<'ctx, SizeT>,
|
||||
indexes: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let perrctx = setup_error_context(tyctx, ctx);
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_index"),
|
||||
)
|
||||
.arg("errctx", perrctx)
|
||||
.arg("num_indexes", num_indexes)
|
||||
.arg("indexes", indexes)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
|
||||
check_error_context(generator, ctx, perrctx);
|
||||
}
|
||||
@ -1,2 +1,3 @@
|
||||
pub mod allocation;
|
||||
pub mod basic;
|
||||
pub mod indexing;
|
||||
|
||||
@ -1,3 +1,81 @@
|
||||
use crate::codegen::model::*;
|
||||
use crate::codegen::{model::*, CodeGenContext};
|
||||
|
||||
// nac3core's slicing index/length values are always int32_t
|
||||
pub type SliceIndex = Int32;
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct UserSliceFields<F: FieldVisitor> {
|
||||
pub start_defined: F::Field<IntModel<Bool>>,
|
||||
pub start: F::Field<IntModel<SliceIndex>>,
|
||||
pub stop_defined: F::Field<IntModel<Bool>>,
|
||||
pub stop: F::Field<IntModel<SliceIndex>>,
|
||||
pub step_defined: F::Field<IntModel<Bool>>,
|
||||
pub step: F::Field<IntModel<SliceIndex>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct UserSlice;
|
||||
|
||||
impl StructKind for UserSlice {
|
||||
type Fields<F: FieldVisitor> = UserSliceFields<F>;
|
||||
|
||||
fn visit_fields<F: FieldVisitor>(&self, visitor: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
start_defined: visitor.add("start_defined"),
|
||||
start: visitor.add("start"),
|
||||
stop_defined: visitor.add("stop_defined"),
|
||||
stop: visitor.add("stop"),
|
||||
step_defined: visitor.add("step_defined"),
|
||||
step: visitor.add("step"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RustUserSlice<'ctx> {
|
||||
pub start: Option<Int<'ctx, SliceIndex>>,
|
||||
pub stop: Option<Int<'ctx, SliceIndex>>,
|
||||
pub step: Option<Int<'ctx, SliceIndex>>,
|
||||
}
|
||||
|
||||
impl<'ctx> RustUserSlice<'ctx> {
|
||||
// Set the values of an LLVM UserSlice
|
||||
// in the format of Python's `slice()`
|
||||
pub fn write_to_user_slice(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_slice_ptr: Ptr<'ctx, StructModel<UserSlice>>,
|
||||
) {
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
let false_ = bool_model.constant(tyctx, ctx.ctx, 0);
|
||||
let true_ = bool_model.constant(tyctx, ctx.ctx, 1);
|
||||
|
||||
// TODO: Code duplication. Probably okay...?
|
||||
|
||||
match self.start {
|
||||
Some(start) => {
|
||||
dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, true_);
|
||||
dst_slice_ptr.gep(ctx, |f| f.start).store(ctx, start);
|
||||
}
|
||||
None => dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, false_),
|
||||
}
|
||||
|
||||
match self.stop {
|
||||
Some(stop) => {
|
||||
dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, true_);
|
||||
dst_slice_ptr.gep(ctx, |f| f.stop).store(ctx, stop);
|
||||
}
|
||||
None => dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, false_),
|
||||
}
|
||||
|
||||
match self.step {
|
||||
Some(step) => {
|
||||
dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, true_);
|
||||
dst_slice_ptr.gep(ctx, |f| f.step).store(ctx, step);
|
||||
}
|
||||
None => dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, false_),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user