forked from M-Labs/nac3
core/ndstrides: checkpoint
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@ -4,7 +4,7 @@
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#include <irrt/util.hpp>
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// NDArray indices are always `uint32_t`.
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using NDIndex = uint32_t;
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using NDIndexInt = 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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@ -43,7 +43,7 @@ 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 +55,7 @@ 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 +104,8 @@ 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 +293,24 @@ 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 +333,16 @@ 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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@ -50,9 +50,9 @@ SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
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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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for (SizeT i = 0; i < ndims; i++) {
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SizeT axis = ndims - i - 1;
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SizeT dim = shape[axis];
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indices[axis] = nth % dim;
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nth /= dim;
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@ -93,9 +93,10 @@ SizeT len(const NDArray<SizeT>* ndarray) {
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if (ndarray->ndims == 0) {
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raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object",
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NO_PARAM, NO_PARAM, NO_PARAM);
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}
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} else {
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return ndarray->shape[0];
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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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@ -156,6 +157,8 @@ uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
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return element;
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}
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int counter = 0;
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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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@ -163,9 +166,14 @@ uint8_t* get_pelement_by_indices(const NDArray<SizeT>* 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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uint8_t* element = ndarray->data;
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for (SizeT i = 0; i < ndarray->ndims; i++) {
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SizeT axis = ndarray->ndims - i - 1;
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SizeT dim = ndarray->shape[axis];
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element += ndarray->strides[axis] * (nth % dim);
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nth /= dim;
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}
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return element;
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}
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/**
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@ -259,7 +267,8 @@ bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
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return is_c_contiguous(ndarray);
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}
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uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
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uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray,
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int32_t nth) {
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return get_nth_pelement(ndarray, nth);
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}
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@ -0,0 +1,157 @@
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#pragma once
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#include <irrt/int_defs.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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template <typename SizeT>
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struct ShapeEntry {
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SizeT ndims;
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SizeT* shape;
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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 broadcast {
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namespace util {
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/**
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* @brief Return true if `src_shape` can broadcast to `dst_shape`.
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*
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* See https://numpy.org/doc/stable/user/basics.broadcasting.html
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*/
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template <typename SizeT>
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bool can_broadcast_shape_to(SizeT target_ndims, const SizeT* target_shape,
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SizeT src_ndims, const SizeT* src_shape) {
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if (src_ndims > target_ndims) {
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return false;
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}
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for (SizeT i = 0; i < src_ndims; i++) {
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SizeT target_dim = target_shape[target_ndims - i - 1];
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SizeT src_dim = src_shape[src_ndims - i - 1];
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if (!(src_dim == 1 || target_dim == src_dim)) {
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return false;
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}
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}
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return true;
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}
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/**
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* @brief Performs `np.broadcast_shapes`
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*/
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template <typename SizeT>
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void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes,
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SizeT dst_ndims, SizeT* dst_shape) {
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// `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it
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// for this function since they should already know in order to allocate `dst_shape` in the first place.
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// `dst_shape` must be pre-allocated.
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// `dst_shape` does not have to be initialized
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for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
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dst_shape[dst_axis] = 1;
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}
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for (SizeT i = 0; i < num_shapes; i++) {
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ShapeEntry<SizeT> entry = shapes[i];
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for (SizeT j = 0; j < entry.ndims; j++) {
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SizeT entry_axis = entry.ndims - j - 1;
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SizeT dst_axis = dst_ndims - j - 1;
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SizeT entry_dim = entry.shape[entry_axis];
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SizeT dst_dim = dst_shape[dst_axis];
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if (dst_dim == 1) {
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dst_shape[dst_axis] = entry_dim;
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} else if (entry_dim == 1 || entry_dim == dst_dim) {
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// Do nothing
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} else {
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raise_exception(SizeT, EXN_VALUE_ERROR,
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"shape mismatch: objects cannot be broadcast "
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"to a single shape.",
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NO_PARAM, NO_PARAM, NO_PARAM);
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}
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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 `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
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*
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* This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
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* and return the result by modifying `dst_ndarray`.
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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, determining the length of `dst_ndarray->shape`
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* - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
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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 unchanged.
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* - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
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*/
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template <typename SizeT>
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void broadcast_to(const NDArray<SizeT>* src_ndarray,
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NDArray<SizeT>* dst_ndarray) {
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if (!ndarray::broadcast::util::can_broadcast_shape_to(
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dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
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src_ndarray->shape)) {
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raise_exception(SizeT, EXN_VALUE_ERROR,
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"operands could not be broadcast together",
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dst_ndarray->shape[0], src_ndarray->shape[0], NO_PARAM);
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}
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dst_ndarray->data = src_ndarray->data;
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dst_ndarray->itemsize = src_ndarray->itemsize;
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for (SizeT i = 0; i < dst_ndarray->ndims; i++) {
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SizeT src_axis = src_ndarray->ndims - i - 1;
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SizeT dst_axis = dst_ndarray->ndims - i - 1;
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if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 &&
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dst_ndarray->shape[dst_axis] != 1)) {
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// Freeze the steps in-place
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dst_ndarray->strides[dst_axis] = 0;
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} else {
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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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}
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} // namespace broadcast
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} // namespace ndarray
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} // namespace
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extern "C" {
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using namespace ndarray::broadcast;
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void __nac3_ndarray_broadcast_to(NDArray<int32_t>* src_ndarray,
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NDArray<int32_t>* dst_ndarray) {
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broadcast_to(src_ndarray, dst_ndarray);
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}
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void __nac3_ndarray_broadcast_to64(NDArray<int64_t>* src_ndarray,
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NDArray<int64_t>* dst_ndarray) {
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broadcast_to(src_ndarray, dst_ndarray);
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}
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void __nac3_ndarray_broadcast_shapes(int32_t num_shapes,
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const ShapeEntry<int32_t>* shapes,
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int32_t dst_ndims, int32_t* dst_shape) {
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ndarray::broadcast::util::broadcast_shapes(num_shapes, shapes, dst_ndims,
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dst_shape);
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}
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void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes,
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const ShapeEntry<int64_t>* shapes,
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int64_t dst_ndims, int64_t* dst_shape) {
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ndarray::broadcast::util::broadcast_shapes(num_shapes, shapes, dst_ndims,
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dst_shape);
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}
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}
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@ -0,0 +1,182 @@
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#pragma once
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#include <irrt/exception.hpp>
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#include <irrt/int_defs.hpp>
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#include <irrt/ndarray/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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SizeT deduce_ndims_after_indexing(SizeT ndims, SizeT num_indexes,
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const NDIndex* indexes) {
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if (num_indexes > ndims) {
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raise_exception(SizeT, EXN_INDEX_ERROR,
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"too many indices for array: array is {0}-dimensional, "
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"but {1} were indexed",
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ndims, num_indexes, NO_PARAM);
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}
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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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ndims--;
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}
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}
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return ndims;
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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.
|
||||
*
|
||||
* @param indexes Indexes to index `src_ndarray`, ordered in the same way you would write them in Python.
|
||||
* @param src_ndarray The NDArray to be indexed.
|
||||
* @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void index(SizeT num_indexes, const NDIndex* indexes,
|
||||
const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
|
||||
// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
|
||||
|
||||
SizeT expected_dst_ndarray_ndims = util::deduce_ndims_after_indexing(
|
||||
src_ndarray->ndims, num_indexes, indexes);
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
SizeT src_axis = 0;
|
||||
SizeT dst_axis = 0;
|
||||
|
||||
for (SliceIndex i = 0; i < num_indexes; i++) {
|
||||
const NDIndex* index = &indexes[i];
|
||||
if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
|
||||
SliceIndex input = *((SliceIndex*)index->data);
|
||||
SliceIndex k = slice::resolve_index_in_length(
|
||||
src_ndarray->shape[src_axis], input);
|
||||
|
||||
if (k == slice::OUT_OF_BOUNDS) {
|
||||
raise_exception(SizeT, EXN_INDEX_ERROR,
|
||||
"index {0} is out of bounds for axis {1} "
|
||||
"with size {2}",
|
||||
input, src_axis, src_ndarray->shape[src_axis]);
|
||||
}
|
||||
|
||||
dst_ndarray->data += k * src_ndarray->strides[src_axis];
|
||||
|
||||
src_axis++;
|
||||
} else if (index->type == ND_INDEX_TYPE_SLICE) {
|
||||
UserSlice* input = (UserSlice*)index->data;
|
||||
|
||||
Slice slice;
|
||||
input->indices_checked<SizeT>(src_ndarray->shape[src_axis], &slice);
|
||||
|
||||
dst_ndarray->data +=
|
||||
(SizeT)slice.start * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->strides[dst_axis] =
|
||||
((SizeT)slice.step) * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->shape[dst_axis] = (SizeT)slice.len();
|
||||
|
||||
dst_axis++;
|
||||
src_axis++;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
|
||||
for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
|
||||
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
}
|
||||
}
|
||||
} // namespace indexing
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::indexing;
|
||||
|
||||
void __nac3_ndarray_index(int32_t num_indexes, NDIndex* indexes,
|
||||
NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray) {
|
||||
index(num_indexes, indexes, src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_index64(int64_t num_indexes, NDIndex* indexes,
|
||||
NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray) {
|
||||
index(num_indexes, indexes, src_ndarray, dst_ndarray);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,111 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace reshape {
|
||||
namespace util {
|
||||
|
||||
/**
|
||||
* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
|
||||
*
|
||||
* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
|
||||
* modified to contain the resolved dimension.
|
||||
*
|
||||
* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
|
||||
* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
|
||||
*
|
||||
* @param size The `.size` of `<ndarray>`
|
||||
* @param new_ndims Number of elements in `new_shape`
|
||||
* @param new_shape Target shape to reshape to
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void resolve_and_check_new_shape(SizeT size, SizeT new_ndims,
|
||||
SizeT* new_shape) {
|
||||
// Is there a -1 in `new_shape`?
|
||||
bool neg1_exists = false;
|
||||
// Location of -1, only initialized if `neg1_exists` is true
|
||||
SizeT neg1_axis_i;
|
||||
// The computed ndarray size of `new_shape`
|
||||
SizeT new_size = 1;
|
||||
|
||||
for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
|
||||
SizeT dim = new_shape[axis_i];
|
||||
if (dim < 0) {
|
||||
if (dim == -1) {
|
||||
if (neg1_exists) {
|
||||
// Multiple `-1` found. Throw an error.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"can only specify one unknown dimension",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
} else {
|
||||
neg1_exists = true;
|
||||
neg1_axis_i = axis_i;
|
||||
}
|
||||
} else {
|
||||
// TODO: What? In `np.reshape` any negative dimensions is
|
||||
// treated like its `-1`.
|
||||
//
|
||||
// Try running `np.zeros((3, 4)).reshape((-999, 2))`
|
||||
//
|
||||
// It is not documented by numpy.
|
||||
// Throw an error for now...
|
||||
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"Found non -1 negative dimension {0} on axis {1}", dim,
|
||||
axis_i, NO_PARAM);
|
||||
}
|
||||
} else {
|
||||
new_size *= dim;
|
||||
}
|
||||
}
|
||||
|
||||
bool can_reshape;
|
||||
if (neg1_exists) {
|
||||
// Let `x` be the unknown dimension
|
||||
// solve `x * <new_size> = <size>`
|
||||
if (new_size == 0 && size == 0) {
|
||||
// `x` has infinitely many solutions
|
||||
can_reshape = false;
|
||||
} else if (new_size == 0 && size != 0) {
|
||||
// `x` has no solutions
|
||||
can_reshape = false;
|
||||
} else if (size % new_size != 0) {
|
||||
// `x` has no integer solutions
|
||||
can_reshape = false;
|
||||
} else {
|
||||
can_reshape = true;
|
||||
new_shape[neg1_axis_i] = size / new_size; // Resolve dimension
|
||||
}
|
||||
} else {
|
||||
can_reshape = (new_size == size);
|
||||
}
|
||||
|
||||
if (!can_reshape) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"cannot reshape array of size {0} into given shape",
|
||||
size, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
}
|
||||
} // namespace util
|
||||
} // namespace reshape
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
void __nac3_ndarray_resolve_and_check_new_shape(int32_t size, int32_t new_ndims,
|
||||
int32_t* new_shape) {
|
||||
ndarray::reshape::util::resolve_and_check_new_shape(size, new_ndims,
|
||||
new_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_resolve_and_check_new_shape64(int64_t size,
|
||||
int64_t new_ndims,
|
||||
int64_t* new_shape) {
|
||||
ndarray::reshape::util::resolve_and_check_new_shape(size, new_ndims,
|
||||
new_shape);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,148 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
/*
|
||||
* Notes on `np.transpose(<array>, <axes>)`
|
||||
*
|
||||
* TODO: `axes`, if specified, can actually contain negative indices,
|
||||
* but it is not documented in numpy.
|
||||
*
|
||||
* Supporting it for now.
|
||||
*/
|
||||
|
||||
namespace {
|
||||
namespace ndarray {
|
||||
namespace transpose {
|
||||
namespace util {
|
||||
|
||||
/**
|
||||
* @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
|
||||
*
|
||||
* Note that `np.transpose`'s `<axe>` argument is optional. If the argument
|
||||
* is specified but the user, use this function to do assertions on it.
|
||||
*
|
||||
* @param ndims The number of dimensions of `<array>`
|
||||
* @param num_axes Number of elements in `<axes>` as specified by the user.
|
||||
* This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
|
||||
* @param axes The user specified `<axes>`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
|
||||
if (ndims != num_axes) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
// TODO: Optimize this
|
||||
bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
|
||||
for (SizeT i = 0; i < ndims; i++) axe_specified[i] = false;
|
||||
|
||||
for (SizeT i = 0; i < ndims; i++) {
|
||||
SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
|
||||
if (axis == slice::OUT_OF_BOUNDS) {
|
||||
// TODO: numpy actually throws a `numpy.exceptions.AxisError`
|
||||
raise_exception(
|
||||
SizeT, EXN_VALUE_ERROR,
|
||||
"axis {0} is out of bounds for array of dimension {1}", axis,
|
||||
ndims, NO_PARAM);
|
||||
}
|
||||
|
||||
if (axe_specified[axis]) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"repeated axis in transpose", NO_PARAM, NO_PARAM,
|
||||
NO_PARAM);
|
||||
}
|
||||
|
||||
axe_specified[axis] = true;
|
||||
}
|
||||
}
|
||||
} // namespace util
|
||||
|
||||
/**
|
||||
* @brief Create a transpose view of `src_ndarray` and perform proper assertions.
|
||||
*
|
||||
* This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
|
||||
* If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
|
||||
*
|
||||
* The transpose view created is returned by modifying `dst_ndarray`.
|
||||
*
|
||||
* The caller is responsible for setting up `dst_ndarray` before calling this function.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
|
||||
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
|
||||
* - `dst_ndarray->ndims` is unchanged
|
||||
* - `dst_ndarray->shape` is updated according to how `np.transpose` works
|
||||
* - `dst_ndarray->strides` is updated according to how `np.transpose` works
|
||||
*
|
||||
* @param src_ndarray The NDArray to build a transpose view on
|
||||
* @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
|
||||
* @param num_axes Number of elements in axes, can be undefined if `axes` is nullptr.
|
||||
* @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray,
|
||||
SizeT num_axes, const SizeT* axes) {
|
||||
__builtin_assume(src_ndarray->ndims == dst_ndarray->ndims);
|
||||
const auto ndims = src_ndarray->ndims;
|
||||
|
||||
if (axes != nullptr) util::assert_transpose_axes(ndims, num_axes, axes);
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
// Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
|
||||
if (axes == nullptr) {
|
||||
// `np.transpose(<array>, axes=None)`
|
||||
|
||||
/*
|
||||
* Minor note: `np.transpose(<array>, axes=None)` is equivalent to
|
||||
* `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
|
||||
* is reversing the order of strides and shape.
|
||||
*
|
||||
* This is a fast implementation to handle this special (but very common) case.
|
||||
*/
|
||||
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
|
||||
dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
|
||||
}
|
||||
} else {
|
||||
// `np.transpose(<array>, <axes>)`
|
||||
|
||||
// Permute strides and shape according to `axes`, while resolving negative indices in `axes`
|
||||
for (SizeT axis = 0; axis < ndims; axis++) {
|
||||
// `i` cannot be OUT_OF_BOUNDS because of assertions
|
||||
SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
|
||||
|
||||
dst_ndarray->shape[axis] = src_ndarray->shape[i];
|
||||
dst_ndarray->strides[axis] = src_ndarray->strides[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace transpose
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::transpose;
|
||||
void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray, int32_t num_axes,
|
||||
const int32_t* axes) {
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray, int64_t num_axes,
|
||||
const int64_t* axes) {
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,165 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
||||
#include "exception.hpp"
|
||||
|
||||
// The type of an index or a value describing the length of a
|
||||
// range/slice is always `int32_t`.
|
||||
using SliceIndex = int32_t;
|
||||
|
||||
namespace {
|
||||
|
||||
/**
|
||||
* @brief A Python-like slice with resolved indices.
|
||||
*
|
||||
* "Resolved indices" means that `start` and `stop` must be positive and are
|
||||
* bound to a known length.
|
||||
*/
|
||||
struct Slice {
|
||||
SliceIndex start;
|
||||
SliceIndex stop;
|
||||
SliceIndex step;
|
||||
|
||||
/**
|
||||
* @brief Calculate and return the length / the number of the slice.
|
||||
*
|
||||
* If this were a Python range, this function would be `len(range(start, stop, step))`.
|
||||
*/
|
||||
SliceIndex len() {
|
||||
SliceIndex diff = stop - start;
|
||||
if (diff > 0 && step > 0) {
|
||||
return ((diff - 1) / step) + 1;
|
||||
} else if (diff < 0 && step < 0) {
|
||||
return ((diff + 1) / step) + 1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
namespace slice {
|
||||
/**
|
||||
* @brief Resolve a slice index under a given length like Python indexing.
|
||||
*
|
||||
* In Python, if you have a `list` of length 100, `list[-1]` resolves to
|
||||
* `list[99]`, so `resolve_index_in_length_clamped(100, -1)` returns `99`.
|
||||
*
|
||||
* If `length` is 0, 0 is returned for any value of `index`.
|
||||
*
|
||||
* If `index` is out of bounds, clamps the returned value between `0` and
|
||||
* `length - 1` (inclusive).
|
||||
*
|
||||
*/
|
||||
SliceIndex resolve_index_in_length_clamped(SliceIndex length,
|
||||
SliceIndex index) {
|
||||
if (index < 0) {
|
||||
return max<SliceIndex>(length + index, 0);
|
||||
} else {
|
||||
return min<SliceIndex>(length, index);
|
||||
}
|
||||
}
|
||||
|
||||
const SliceIndex OUT_OF_BOUNDS = -1;
|
||||
|
||||
/**
|
||||
* @brief Like `resolve_index_in_length_clamped`, but returns `OUT_OF_BOUNDS`
|
||||
* if `index` is out of bounds.
|
||||
*/
|
||||
SliceIndex resolve_index_in_length(SliceIndex length, SliceIndex index) {
|
||||
SliceIndex resolved = index < 0 ? length + index : index;
|
||||
if (0 <= resolved && resolved < length) {
|
||||
return resolved;
|
||||
} else {
|
||||
return OUT_OF_BOUNDS;
|
||||
}
|
||||
}
|
||||
} // namespace slice
|
||||
|
||||
/**
|
||||
* @brief A Python-like slice with **unresolved** indices.
|
||||
*/
|
||||
struct UserSlice {
|
||||
bool start_defined;
|
||||
SliceIndex start;
|
||||
|
||||
bool stop_defined;
|
||||
SliceIndex stop;
|
||||
|
||||
bool step_defined;
|
||||
SliceIndex step;
|
||||
|
||||
UserSlice() { this->reset(); }
|
||||
|
||||
void reset() {
|
||||
this->start_defined = false;
|
||||
this->stop_defined = false;
|
||||
this->step_defined = false;
|
||||
}
|
||||
|
||||
void set_start(SliceIndex start) {
|
||||
this->start_defined = true;
|
||||
this->start = start;
|
||||
}
|
||||
|
||||
void set_stop(SliceIndex stop) {
|
||||
this->stop_defined = true;
|
||||
this->stop = stop;
|
||||
}
|
||||
|
||||
void set_step(SliceIndex step) {
|
||||
this->step_defined = true;
|
||||
this->step = step;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Resolve this slice.
|
||||
*
|
||||
* In Python, this would be `slice(start, stop, step).indices(length)`.
|
||||
*
|
||||
* @return A `Slice` with the resolved indices.
|
||||
*/
|
||||
Slice indices(SliceIndex length) {
|
||||
Slice result;
|
||||
|
||||
result.step = step_defined ? step : 1;
|
||||
bool step_is_negative = result.step < 0;
|
||||
|
||||
if (start_defined) {
|
||||
result.start =
|
||||
slice::resolve_index_in_length_clamped(length, start);
|
||||
} else {
|
||||
result.start = step_is_negative ? length - 1 : 0;
|
||||
}
|
||||
|
||||
if (stop_defined) {
|
||||
result.stop = slice::resolve_index_in_length_clamped(length, stop);
|
||||
} else {
|
||||
result.stop = step_is_negative ? -1 : length;
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Like `.indices()` but with assertions.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void indices_checked(SliceIndex length, Slice* result) {
|
||||
if (length < 0) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"length should not be negative, got {0}", length,
|
||||
NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
if (this->step_defined && this->step == 0) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
*result = this->indices(length);
|
||||
}
|
||||
};
|
||||
} // namespace
|
|
@ -4,5 +4,9 @@
|
|||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_defs.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/broadcast.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/ndarray/indexing.hpp>
|
||||
#include <irrt/ndarray/reshape.hpp>
|
||||
#include <irrt/ndarray/transpose.hpp>
|
||||
#include <irrt/util.hpp>
|
||||
|
|
|
@ -6,15 +6,20 @@
|
|||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
|
||||
// Special macro to inform `#include <irrt/*>` that we
|
||||
// are testing.
|
||||
// Special macro to inform `#include <irrt/*>` that we are testing.
|
||||
#define IRRT_TESTING
|
||||
|
||||
// Note that failure unit tests are not supported.
|
||||
|
||||
#include <test/test_core.hpp>
|
||||
#include <test/test_ndarray_basic.hpp>
|
||||
#include <test/test_ndarray_broadcast.hpp>
|
||||
#include <test/test_ndarray_indexing.hpp>
|
||||
|
||||
int main() {
|
||||
test::core::run();
|
||||
test::ndarray_basic::run();
|
||||
test::ndarray_indexing::run();
|
||||
test::ndarray_broadcast::run();
|
||||
return 0;
|
||||
}
|
|
@ -7,8 +7,8 @@ namespace core {
|
|||
void test_int_exp() {
|
||||
BEGIN_TEST();
|
||||
|
||||
assert_values_match(125, __nac3_int_exp_impl<int32_t>(5, 3));
|
||||
assert_values_match(3125, __nac3_int_exp_impl<int32_t>(5, 5));
|
||||
assert_values_match(125L, __nac3_int_exp_impl<int64_t>(5, 3));
|
||||
assert_values_match(3125L, __nac3_int_exp_impl<int64_t>(5, 5));
|
||||
}
|
||||
|
||||
void run() { test_int_exp(); }
|
||||
|
|
|
@ -8,18 +8,18 @@ void test_calc_size_from_shape_normal() {
|
|||
// Test shapes with normal values
|
||||
BEGIN_TEST();
|
||||
|
||||
int32_t shape[4] = {2, 3, 5, 7};
|
||||
int64_t shape[4] = {2, 3, 5, 7};
|
||||
assert_values_match(
|
||||
210, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
|
||||
210L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
|
||||
}
|
||||
|
||||
void test_calc_size_from_shape_has_zero() {
|
||||
// Test shapes with 0 in them
|
||||
BEGIN_TEST();
|
||||
|
||||
int32_t shape[4] = {2, 0, 5, 7};
|
||||
int64_t shape[4] = {2, 0, 5, 7};
|
||||
assert_values_match(
|
||||
0, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
|
||||
0L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
|
||||
}
|
||||
|
||||
void run() {
|
||||
|
|
|
@ -0,0 +1,127 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace ndarray_broadcast {
|
||||
void test_can_broadcast_shape() {
|
||||
BEGIN_TEST();
|
||||
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){3}, 5, (int32_t[]){1, 1, 1, 1, 3}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){3}, 2, (int32_t[]){3, 1}));
|
||||
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){3}, 1, (int32_t[]){3}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){1}, 1, (int32_t[]){3}));
|
||||
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){1}, 1, (int32_t[]){1}));
|
||||
assert_values_match(
|
||||
true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 3, (int32_t[]){256, 1, 3}));
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){3}));
|
||||
assert_values_match(false,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){2}));
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){1}));
|
||||
|
||||
// In cases when the shapes contain zero(es)
|
||||
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){0}, 1, (int32_t[]){1}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
1, (int32_t[]){0}, 1, (int32_t[]){2}));
|
||||
assert_values_match(true,
|
||||
ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
4, (int32_t[]){0, 4, 0, 0}, 1, (int32_t[]){1}));
|
||||
assert_values_match(
|
||||
true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 1, 1, 1}));
|
||||
assert_values_match(
|
||||
true, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 4, 1, 1}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 3}));
|
||||
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
|
||||
2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 0}));
|
||||
}
|
||||
|
||||
void test_ndarray_broadcast() {
|
||||
/*
|
||||
# array = np.array([[19.9, 29.9, 39.9, 49.9]], dtype=np.float64)
|
||||
# >>> [[19.9 29.9 39.9 49.9]]
|
||||
#
|
||||
# array = np.broadcast_to(array, (2, 3, 4))
|
||||
# >>> [[[19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]]
|
||||
# >>> [[19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]
|
||||
# >>> [19.9 29.9 39.9 49.9]]]
|
||||
#
|
||||
# assery array.strides == (0, 0, 8)
|
||||
|
||||
*/
|
||||
BEGIN_TEST();
|
||||
|
||||
double in_data[4] = {19.9, 29.9, 39.9, 49.9};
|
||||
const int32_t in_ndims = 2;
|
||||
int32_t in_shape[in_ndims] = {1, 4};
|
||||
int32_t in_strides[in_ndims] = {};
|
||||
NDArray<int32_t> ndarray = {.data = (uint8_t*)in_data,
|
||||
.itemsize = sizeof(double),
|
||||
.ndims = in_ndims,
|
||||
.shape = in_shape,
|
||||
.strides = in_strides};
|
||||
ndarray::basic::set_strides_by_shape(&ndarray);
|
||||
|
||||
const int32_t dst_ndims = 3;
|
||||
int32_t dst_shape[dst_ndims] = {2, 3, 4};
|
||||
int32_t dst_strides[dst_ndims] = {};
|
||||
NDArray<int32_t> dst_ndarray = {
|
||||
.ndims = dst_ndims, .shape = dst_shape, .strides = dst_strides};
|
||||
|
||||
ndarray::broadcast::broadcast_to(&ndarray, &dst_ndarray);
|
||||
|
||||
assert_arrays_match(dst_ndims, ((int32_t[]){0, 0, 8}), dst_ndarray.strides);
|
||||
|
||||
assert_values_match(19.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 0}))));
|
||||
assert_values_match(29.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 1}))));
|
||||
assert_values_match(39.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 2}))));
|
||||
assert_values_match(49.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 0, 3}))));
|
||||
assert_values_match(19.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 0}))));
|
||||
assert_values_match(29.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 1}))));
|
||||
assert_values_match(39.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 2}))));
|
||||
assert_values_match(49.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){0, 1, 3}))));
|
||||
assert_values_match(49.9,
|
||||
*((double*)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, ((int32_t[]){1, 2, 3}))));
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_can_broadcast_shape();
|
||||
test_ndarray_broadcast();
|
||||
}
|
||||
} // namespace ndarray_broadcast
|
||||
} // namespace test
|
|
@ -0,0 +1,165 @@
|
|||
#pragma once
|
||||
|
||||
#include <test/includes.hpp>
|
||||
|
||||
namespace test {
|
||||
namespace ndarray_indexing {
|
||||
void test_normal_1() {
|
||||
/*
|
||||
Reference Python code:
|
||||
```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:, 1::2]
|
||||
# array([[ 5., 7.],
|
||||
# [ 9., 11.]])
|
||||
|
||||
assert dst_ndarray.shape == (2, 2)
|
||||
assert dst_ndarray.strides == (32, 16)
|
||||
assert dst_ndarray[0, 0] == 5.0
|
||||
assert dst_ndarray[0, 1] == 7.0
|
||||
assert dst_ndarray[1, 0] == 9.0
|
||||
assert dst_ndarray[1, 1] == 11.0
|
||||
```
|
||||
*/
|
||||
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};
|
||||
int64_t src_itemsize = sizeof(double);
|
||||
const int64_t src_ndims = 2;
|
||||
int64_t src_shape[src_ndims] = {3, 4};
|
||||
int64_t src_strides[src_ndims] = {};
|
||||
NDArray<int64_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 int64_t dst_ndims = 2;
|
||||
int64_t dst_shape[dst_ndims] = {999, 999}; // Empty values
|
||||
int64_t dst_strides[dst_ndims] = {999, 999}; // Empty values
|
||||
NDArray<int64_t> dst_ndarray = {.data = nullptr,
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides};
|
||||
|
||||
// Create the subscripts in `ndarray[-2::, 1::2]`
|
||||
UserSlice subscript_1;
|
||||
subscript_1.set_start(-2);
|
||||
|
||||
UserSlice subscript_2;
|
||||
subscript_2.set_start(1);
|
||||
subscript_2.set_step(2);
|
||||
|
||||
const int64_t num_indexes = 2;
|
||||
NDIndex indexes[num_indexes] = {
|
||||
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_1},
|
||||
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
|
||||
|
||||
ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
|
||||
|
||||
int64_t expected_shape[dst_ndims] = {2, 2};
|
||||
int64_t expected_strides[dst_ndims] = {32, 16};
|
||||
|
||||
assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
|
||||
assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
|
||||
|
||||
// dst_ndarray[0, 0]
|
||||
assert_values_match(5.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){0, 0})));
|
||||
// dst_ndarray[0, 1]
|
||||
assert_values_match(7.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){0, 1})));
|
||||
// dst_ndarray[1, 0]
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){1, 0})));
|
||||
// dst_ndarray[1, 1]
|
||||
assert_values_match(11.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_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};
|
||||
int64_t src_itemsize = sizeof(double);
|
||||
const int64_t src_ndims = 2;
|
||||
int64_t src_shape[src_ndims] = {3, 4};
|
||||
int64_t src_strides[src_ndims] = {};
|
||||
NDArray<int64_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 int64_t dst_ndims = 1;
|
||||
int64_t dst_shape[dst_ndims] = {999}; // Empty values
|
||||
int64_t dst_strides[dst_ndims] = {999}; // Empty values
|
||||
NDArray<int64_t> dst_ndarray = {.data = nullptr,
|
||||
.ndims = dst_ndims,
|
||||
.shape = dst_shape,
|
||||
.strides = dst_strides};
|
||||
|
||||
// Create the subscripts in `ndarray[2, ::-2]`
|
||||
int64_t subscript_1 = 2;
|
||||
|
||||
UserSlice subscript_2;
|
||||
subscript_2.set_step(-2);
|
||||
|
||||
const int64_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}};
|
||||
|
||||
ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
|
||||
|
||||
int64_t expected_shape[dst_ndims] = {2};
|
||||
int64_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, (int64_t[dst_ndims]){0})));
|
||||
assert_values_match(9.0,
|
||||
*((double *)ndarray::basic::get_pelement_by_indices(
|
||||
&dst_ndarray, (int64_t[dst_ndims]){1})));
|
||||
}
|
||||
|
||||
void run() {
|
||||
test_normal_1();
|
||||
test_normal_2();
|
||||
}
|
||||
} // namespace ndarray_indexing
|
||||
} // namespace test
|
|
@ -6,6 +6,11 @@
|
|||
template <class T>
|
||||
void print_value(const T& value);
|
||||
|
||||
template <>
|
||||
void print_value(const bool& value) {
|
||||
printf("%s", value ? "true" : "false");
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const int8_t& value) {
|
||||
printf("%d", value);
|
||||
|
@ -16,6 +21,11 @@ void print_value(const int32_t& value) {
|
|||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const int64_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const uint8_t& value) {
|
||||
printf("%u", value);
|
||||
|
@ -26,6 +36,11 @@ void print_value(const uint32_t& value) {
|
|||
printf("%u", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const uint64_t& value) {
|
||||
printf("%d", value);
|
||||
}
|
||||
|
||||
template <>
|
||||
void print_value(const float& value) {
|
||||
printf("%f", value);
|
||||
|
|
|
@ -11,7 +11,9 @@ use crate::codegen::stmt::gen_for_callback_incrementing;
|
|||
use crate::codegen::{extern_fns, irrt, llvm_intrinsics, numpy, CodeGenContext, CodeGenerator};
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
use crate::toplevel::numpy::unpack_ndarray_var_tys;
|
||||
use crate::typecheck::typedef::Type;
|
||||
use crate::typecheck::typedef::{Type, TypeEnum};
|
||||
|
||||
use super::structure::ndarray::scalar::{split_scalar_or_ndarray, ScalarOrNDArray};
|
||||
|
||||
/// Shorthand for [`unreachable!()`] when a type of argument is not supported.
|
||||
///
|
||||
|
@ -27,62 +29,56 @@ fn unsupported_type(ctx: &CodeGenContext<'_, '_>, fn_name: &str, tys: &[Type]) -
|
|||
pub fn call_int32<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
n: (Type, BasicValueEnum<'ctx>),
|
||||
(n_ty, n): (Type, BasicValueEnum<'ctx>),
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let (n_ty, n) = n;
|
||||
Ok(match n {
|
||||
BasicValueEnum::IntValue(n) if matches!(n.get_type().get_bit_width(), 1 | 8) => {
|
||||
debug_assert!(ctx.unifier.unioned(n_ty, ctx.primitives.bool));
|
||||
|
||||
ctx.builder.build_int_z_extend(n, llvm_i32, "zext").map(Into::into).unwrap()
|
||||
}
|
||||
|
||||
BasicValueEnum::IntValue(n) if n.get_type().get_bit_width() == 32 => {
|
||||
debug_assert!([ctx.primitives.int32, ctx.primitives.uint32,]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(n_ty, *ty)));
|
||||
|
||||
n.into()
|
||||
}
|
||||
|
||||
BasicValueEnum::IntValue(n) if n.get_type().get_bit_width() == 64 => {
|
||||
debug_assert!([ctx.primitives.int64, ctx.primitives.uint64,]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(n_ty, *ty)));
|
||||
|
||||
ctx.builder.build_int_truncate(n, llvm_i32, "trunc").map(Into::into).unwrap()
|
||||
}
|
||||
|
||||
BasicValueEnum::FloatValue(n) => {
|
||||
debug_assert!(ctx.unifier.unioned(n_ty, ctx.primitives.float));
|
||||
|
||||
let to_int64 =
|
||||
ctx.builder.build_float_to_signed_int(n, ctx.ctx.i64_type(), "").unwrap();
|
||||
ctx.builder.build_int_truncate(to_int64, llvm_i32, "conv").map(Into::into).unwrap()
|
||||
}
|
||||
|
||||
BasicValueEnum::PointerValue(n)
|
||||
if n_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
|
||||
split_scalar_or_ndarray(generator, ctx, n, n_ty)
|
||||
.map(generator, ctx, ctx.primitives.int32, |_generator, ctx, _i, scalar| {
|
||||
match &*ctx.unifier.get_ty(scalar.dtype) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.bool.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, n_ty);
|
||||
|
||||
let ndarray = ndarray_elementwise_unaryop_impl(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.int32,
|
||||
None,
|
||||
NDArrayValue::from_ptr_val(n, llvm_usize, None),
|
||||
|generator, ctx, val| call_int32(generator, ctx, (elem_ty, val)),
|
||||
)?;
|
||||
|
||||
ndarray.as_base_value().into()
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_z_extend(scalar.value.into_int_value(), llvm_i32, "zext")
|
||||
.unwrap()
|
||||
.as_basic_value_enum())
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
Ok(scalar.value)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.int64.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_truncate(scalar.value.into_int_value(), llvm_i32, "trunc")
|
||||
.unwrap()
|
||||
.as_basic_value_enum())
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.float.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let to_int64 = ctx
|
||||
.builder
|
||||
.build_float_to_signed_int(
|
||||
scalar.value.into_float_value(),
|
||||
ctx.ctx.i64_type(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_truncate(to_int64, llvm_i32, "conv")
|
||||
.unwrap()
|
||||
.as_basic_value_enum())
|
||||
}
|
||||
_ => unsupported_type(ctx, "int32", &[scalar.dtype]),
|
||||
}
|
||||
|
||||
_ => unsupported_type(ctx, "int32", &[n_ty]),
|
||||
})
|
||||
.map(ScalarOrNDArray::to_basic_value_enum)
|
||||
}
|
||||
|
||||
/// Invokes the `int64` builtin function.
|
||||
|
|
|
@ -4,7 +4,7 @@ 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,
|
||||
|
@ -18,14 +18,11 @@ use crate::{
|
|||
gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
|
||||
gen_var,
|
||||
},
|
||||
structure::ndarray::NDArrayObject,
|
||||
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},
|
||||
|
@ -43,7 +40,7 @@ use nac3parser::ast::{
|
|||
Unaryop,
|
||||
};
|
||||
|
||||
use super::structure::cslice::CSlice;
|
||||
use super::structure::{cslice::CSlice, ndarray::indexing::util::gen_ndarray_subscript_ndindexes};
|
||||
use super::{
|
||||
model::*,
|
||||
structure::exception::{Exception, ExceptionId},
|
||||
|
@ -554,7 +551,6 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
|||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
self.const_strings.get(string).copied().unwrap_or_else(|| {
|
||||
let type_context = generator.type_context(self.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let pbyte_model = PtrModel(IntModel(Byte));
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
@ -562,9 +558,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
|||
let base = self.builder.build_global_string_ptr(string, "constant_string").unwrap();
|
||||
let base = pbyte_model.believe_value(base.as_pointer_value());
|
||||
|
||||
let len = sizet_model.constant(type_context, self.ctx, string.len() as u64);
|
||||
let len = sizet_model.constant(generator, self.ctx, string.len() as u64);
|
||||
|
||||
let cslice = cslice_model.create_const(type_context, self, base, len);
|
||||
let cslice = cslice_model.create_const(generator, self.ctx, base, len);
|
||||
|
||||
self.const_strings.insert(string.to_owned(), cslice);
|
||||
|
||||
|
@ -580,12 +576,11 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
|||
params: [Option<Int<'ctx, Int64>>; 3],
|
||||
loc: Location,
|
||||
) {
|
||||
let type_context = generator.type_context(self.ctx);
|
||||
let exn_model = StructModel(Exception);
|
||||
let exn_id_model = IntModel(ExceptionId::default());
|
||||
|
||||
let exn_id =
|
||||
exn_id_model.constant(type_context, self.ctx, self.resolver.get_string_id(name) as u64);
|
||||
exn_id_model.constant(generator, self.ctx, self.resolver.get_string_id(name) as u64);
|
||||
let exn = self.exception_val.unwrap_or_else(|| {
|
||||
let exn = exn_model.var_alloca(generator, self, Some("exn")).unwrap();
|
||||
*self.exception_val.insert(exn)
|
||||
|
@ -611,15 +606,11 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
|||
params: [Option<IntValue<'ctx>>; 3],
|
||||
loc: Location,
|
||||
) {
|
||||
let type_context = generator.type_context(self.ctx);
|
||||
let param_model = IntModel(Int64);
|
||||
let params =
|
||||
params.map(|p| p.map(|p| param_model.check_value(generator, self.ctx, p).unwrap()));
|
||||
|
||||
let err_msg = self.gen_string(generator, err_msg);
|
||||
|
||||
let ctx = self.ctx;
|
||||
let params =
|
||||
params.map(|p| p.map(|p| param_model.check_value(type_context, ctx, p).unwrap()));
|
||||
|
||||
self.make_assert_impl(generator, cond, err_name, err_msg, params, loc);
|
||||
}
|
||||
|
||||
|
@ -632,7 +623,6 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
|||
params: [Option<Int<'ctx, Int64>>; 3],
|
||||
loc: Location,
|
||||
) {
|
||||
let type_context = generator.type_context(self.ctx);
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
// We assume that the condition is most probably true, so the normal path is the most
|
||||
|
@ -640,7 +630,7 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
|||
let cond = call_expect(
|
||||
self,
|
||||
generator.bool_to_i1(self, cond),
|
||||
bool_model.const_true(type_context, self.ctx).value,
|
||||
bool_model.const_true(generator, self.ctx).value,
|
||||
Some("expect"),
|
||||
);
|
||||
|
||||
|
@ -2138,338 +2128,6 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
|
|||
)
|
||||
}
|
||||
|
||||
/// Generates code for a subscript expression on an `ndarray`.
|
||||
///
|
||||
/// * `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`.
|
||||
fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ty: Type,
|
||||
ndims: Type,
|
||||
v: NDArrayValue<'ctx>,
|
||||
slice: &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);
|
||||
|
||||
let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
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(
|
||||
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();
|
||||
|
||||
let len = unsafe {
|
||||
v.dim_sizes().get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(dim, true),
|
||||
None,
|
||||
)
|
||||
};
|
||||
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_int_add(
|
||||
len,
|
||||
ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
|
||||
},
|
||||
)
|
||||
.map(|v| v.map(BasicValueEnum::into_int_value))
|
||||
};
|
||||
|
||||
// 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);
|
||||
};
|
||||
|
||||
Ok(Some((index, index, llvm_i32.const_int(1, true))))
|
||||
}
|
||||
|
||||
ExprKind::Slice { lower, upper, step } => {
|
||||
let dim_sz = unsafe {
|
||||
v.dim_sizes().get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(dim, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
|
||||
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()
|
||||
} 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()
|
||||
}
|
||||
}
|
||||
}))
|
||||
}
|
||||
|
||||
/// See [`CodeGenerator::gen_expr`].
|
||||
pub fn gen_expr<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
|
@ -3109,18 +2767,20 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
|
|||
v.data().get(ctx, generator, &index, None).into()
|
||||
}
|
||||
}
|
||||
TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let (ty, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap();
|
||||
|
||||
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 {
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
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_ty = value.custom.unwrap();
|
||||
let ndarray = ndarray.to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
let ndarray =
|
||||
NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
|
||||
let indexes = gen_ndarray_subscript_ndindexes(generator, ctx, slice)?;
|
||||
let result = ndarray.index_or_scalar(generator, ctx, &indexes, "index_result");
|
||||
let result = result.to_basic_value_enum();
|
||||
return Ok(Some(ValueEnum::Dynamic(result)));
|
||||
}
|
||||
TypeEnum::TTuple { .. } => {
|
||||
let index: u32 =
|
||||
|
|
|
@ -5,6 +5,8 @@ mod test;
|
|||
pub mod util;
|
||||
|
||||
use super::model::*;
|
||||
use super::structure::ndarray::broadcast::ShapeEntry;
|
||||
use super::structure::ndarray::indexing::NDIndex;
|
||||
use super::structure::ndarray::NpArray;
|
||||
use super::{
|
||||
classes::{
|
||||
|
@ -427,15 +429,13 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
|
|||
// TODO: Temporary fix. Rewrite `list_slice_assignment` later
|
||||
// Exception params should have been i64
|
||||
{
|
||||
let type_context = generator.type_context(ctx.ctx);
|
||||
let param_model = IntModel(Int64);
|
||||
|
||||
let src_slice_len =
|
||||
param_model.s_extend_or_bit_cast(type_context, ctx, src_slice_len, "src_slice_len");
|
||||
param_model.s_extend_or_bit_cast(generator, ctx, src_slice_len, "src_slice_len");
|
||||
let dest_slice_len =
|
||||
param_model.s_extend_or_bit_cast(type_context, ctx, dest_slice_len, "dest_slice_len");
|
||||
let dest_idx_2 =
|
||||
param_model.s_extend_or_bit_cast(type_context, ctx, dest_idx.2, "dest_idx_2");
|
||||
param_model.s_extend_or_bit_cast(generator, ctx, dest_slice_len, "dest_slice_len");
|
||||
let dest_idx_2 = param_model.s_extend_or_bit_cast(generator, ctx, dest_idx.2, "dest_idx_2");
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
|
@ -897,7 +897,7 @@ pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
|
|||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
|
||||
/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
|
||||
/// containing the indices used for accessing `array` corresponding to the index of the broadcast
|
||||
/// array `broadcast_idx`.
|
||||
pub fn call_ndarray_calc_broadcast_index<
|
||||
'ctx,
|
||||
|
@ -953,13 +953,12 @@ pub fn call_ndarray_calc_broadcast_index<
|
|||
)
|
||||
}
|
||||
|
||||
pub fn call_nac3_throw_dummy_error<'ctx>(tyctx: TypeContext<'ctx>, ctx: &CodeGenContext<'ctx, '_>) {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_throw_dummy_error"),
|
||||
)
|
||||
.returning_void();
|
||||
pub fn call_nac3_throw_dummy_error<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_throw_dummy_error");
|
||||
CallFunction::begin(generator, ctx, &name).returning_void();
|
||||
}
|
||||
|
||||
/// Initialize all global `EXN_*` exception IDs in IRRT with the [`SymbolResolver`].
|
||||
|
@ -989,116 +988,176 @@ pub fn setup_irrt_exceptions<'ctx>(
|
|||
}
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
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>>,
|
||||
) {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_util_assert_shape_no_negative"),
|
||||
)
|
||||
"__nac3_ndarray_util_assert_shape_no_negative",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("ndims", ndims)
|
||||
.arg("shape", shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_size<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_size"),
|
||||
)
|
||||
.arg("ndarray", pndarray)
|
||||
.returning_auto("size")
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
|
||||
CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("size")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_nbytes<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_nbytes"),
|
||||
)
|
||||
.arg("ndarray", pndarray)
|
||||
.returning_auto("nbytes")
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
|
||||
CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("nbytes")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_len<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
CallFunction::begin(tyctx, ctx, &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_len"))
|
||||
.arg("ndarray", pndarray)
|
||||
.returning_auto("len")
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
|
||||
CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("len")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_is_c_contiguous<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
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> {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_is_c_contiguous"),
|
||||
)
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("ndarray", ndarray_ptr)
|
||||
.returning_auto("is_c_contiguous")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_get_nth_pelement<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
index: Int<'ctx, SizeT>,
|
||||
) -> Ptr<'ctx, IntModel<Byte>> {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_get_nth_pelement"),
|
||||
)
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("ndarray", pndarray)
|
||||
.arg("index", index)
|
||||
.returning_auto("pelement")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
pdnarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_set_strides_by_shape"),
|
||||
)
|
||||
.arg("ndarray", pdnarray)
|
||||
.returning_void();
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
|
||||
CallFunction::begin(generator, ctx, &name).arg("ndarray", pdnarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_copy_data<'ctx>(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
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>>,
|
||||
) {
|
||||
CallFunction::begin(
|
||||
tyctx,
|
||||
ctx,
|
||||
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_copy_data"),
|
||||
)
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
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 name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("num_indexes", num_indexes)
|
||||
.arg("indexes", indexes)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_shape_entries: Int<'ctx, SizeT>,
|
||||
shape_entries: Ptr<'ctx, StructModel<ShapeEntry>>,
|
||||
dst_ndims: Int<'ctx, SizeT>,
|
||||
dst_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("num_shapes", num_shape_entries)
|
||||
.arg("shapes", shape_entries)
|
||||
.arg("dst_ndims", dst_ndims)
|
||||
.arg("dst_shape", dst_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
size: Int<'ctx, SizeT>,
|
||||
new_ndims: Int<'ctx, SizeT>,
|
||||
new_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_resolve_and_check_new_shape",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("size", size)
|
||||
.arg("new_ndims", new_ndims)
|
||||
.arg("new_shape", new_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
|
||||
num_axes: Int<'ctx, SizeT>,
|
||||
axes: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg("src_ndarray", src_ndarray)
|
||||
.arg("dst_ndarray", dst_ndarray)
|
||||
.arg("num_axes", num_axes)
|
||||
.arg("axes", axes)
|
||||
.returning_void();
|
||||
}
|
||||
|
|
|
@ -1,11 +1,15 @@
|
|||
use crate::codegen::model::*;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
// When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
|
||||
// When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
|
||||
#[must_use]
|
||||
pub fn get_sizet_dependent_function_name(tyctx: TypeContext<'_>, name: &str) -> String {
|
||||
pub fn get_sizet_dependent_function_name<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> String {
|
||||
let mut name = name.to_owned();
|
||||
match tyctx.size_type.get_bit_width() {
|
||||
match generator.get_size_type(ctx.ctx).get_bit_width() {
|
||||
32 => {}
|
||||
64 => name.push_str("64"),
|
||||
bit_width => {
|
||||
|
@ -16,7 +20,7 @@ pub fn get_sizet_dependent_function_name(tyctx: TypeContext<'_>, name: &str) ->
|
|||
}
|
||||
|
||||
pub mod function {
|
||||
use crate::codegen::{model::*, CodeGenContext};
|
||||
use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
|
||||
use inkwell::{
|
||||
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
|
||||
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
|
||||
|
@ -30,8 +34,8 @@ pub mod function {
|
|||
}
|
||||
|
||||
/// Helper structure to reduce IRRT Inkwell function call boilerplate
|
||||
pub struct CallFunction<'ctx, 'a, 'b, 'c> {
|
||||
tyctx: TypeContext<'ctx>,
|
||||
pub struct CallFunction<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> {
|
||||
generator: &'d mut G,
|
||||
ctx: &'b CodeGenContext<'ctx, 'a>,
|
||||
/// Function name
|
||||
name: &'c str,
|
||||
|
@ -39,13 +43,13 @@ pub mod function {
|
|||
args: Vec<Arg<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, 'b, 'c> CallFunction<'ctx, 'a, 'b, 'c> {
|
||||
impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> CallFunction<'ctx, 'a, 'b, 'c, 'd, G> {
|
||||
pub fn begin(
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &'d mut G,
|
||||
ctx: &'b CodeGenContext<'ctx, 'a>,
|
||||
name: &'c str,
|
||||
) -> Self {
|
||||
CallFunction { tyctx, ctx, name, args: Vec::new() }
|
||||
CallFunction { generator, ctx, name, args: Vec::new() }
|
||||
}
|
||||
|
||||
/// Push a call argument to the function call.
|
||||
|
@ -55,7 +59,7 @@ pub mod function {
|
|||
#[must_use]
|
||||
pub fn arg<M: Model<'ctx>>(mut self, _name: &str, arg: Instance<'ctx, M>) -> Self {
|
||||
let arg = Arg {
|
||||
ty: arg.model.get_type(self.tyctx, self.ctx.ctx).as_basic_type_enum().into(),
|
||||
ty: arg.model.get_type(self.generator, self.ctx.ctx).as_basic_type_enum().into(),
|
||||
val: arg.value.as_basic_value_enum().into(),
|
||||
};
|
||||
self.args.push(arg);
|
||||
|
@ -65,11 +69,11 @@ pub mod function {
|
|||
/// Call the function and expect the function to return a value of type of `return_model`.
|
||||
#[must_use]
|
||||
pub fn returning<M: Model<'ctx>>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
|
||||
let ret_ty = return_model.get_type(self.tyctx, self.ctx.ctx);
|
||||
let ret_ty = return_model.get_type(self.generator, self.ctx.ctx);
|
||||
|
||||
let ret = self.get_function(|tys| ret_ty.fn_type(tys, false), name);
|
||||
let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
|
||||
let ret = return_model.check_value(self.tyctx, self.ctx.ctx, ret).unwrap(); // Must work
|
||||
let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
|
||||
ret
|
||||
}
|
||||
|
||||
|
|
|
@ -1,7 +1,7 @@
|
|||
use crate::{
|
||||
codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
|
||||
codegen::classes::{ListType, ProxyType, RangeType},
|
||||
symbol_resolver::{StaticValue, SymbolResolver},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
|
||||
toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
|
||||
typecheck::{
|
||||
type_inferencer::{CodeLocation, PrimitiveStore},
|
||||
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
|
||||
|
@ -494,10 +494,8 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
|||
}
|
||||
|
||||
TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let tyctx = generator.type_context(ctx);
|
||||
|
||||
let pndarray_model = PtrModel(StructModel(NpArray));
|
||||
pndarray_model.get_type(tyctx, ctx).as_basic_type_enum()
|
||||
pndarray_model.get_type(generator, ctx).as_basic_type_enum()
|
||||
}
|
||||
|
||||
_ => unreachable!(
|
||||
|
@ -667,7 +665,6 @@ pub fn gen_func_impl<
|
|||
..primitives
|
||||
};
|
||||
|
||||
let type_context = generator.type_context(context);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
let pexn_model = PtrModel(StructModel(Exception));
|
||||
|
||||
|
@ -678,9 +675,9 @@ pub fn gen_func_impl<
|
|||
(primitives.uint64, context.i64_type().into()),
|
||||
(primitives.float, context.f64_type().into()),
|
||||
(primitives.bool, context.i8_type().into()),
|
||||
(primitives.str, cslice_model.get_type(type_context, context).into()),
|
||||
(primitives.str, cslice_model.get_type(generator, context).into()),
|
||||
(primitives.range, RangeType::new(context).as_base_type().into()),
|
||||
(primitives.exception, pexn_model.get_type(type_context, context).into()),
|
||||
(primitives.exception, pexn_model.get_type(generator, context).into()),
|
||||
]
|
||||
.iter()
|
||||
.copied()
|
||||
|
|
|
@ -4,6 +4,8 @@ use inkwell::{
|
|||
values::BasicValueEnum,
|
||||
};
|
||||
|
||||
use crate::codegen::CodeGenerator;
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
|
@ -14,13 +16,17 @@ impl<'ctx> Model<'ctx> for AnyModel<'ctx> {
|
|||
type Value = BasicValueEnum<'ctx>;
|
||||
type Type = BasicTypeEnum<'ctx>;
|
||||
|
||||
fn get_type(&self, _tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> Self::Type {
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> Self::Type {
|
||||
self.0
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>>(
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_tyctx: TypeContext<'ctx>,
|
||||
_generator: &mut G,
|
||||
_ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
|
|
|
@ -5,21 +5,6 @@ use inkwell::{context::Context, types::*, values::*};
|
|||
use super::*;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
#[derive(Clone, Copy)]
|
||||
pub struct TypeContext<'ctx> {
|
||||
pub size_type: IntType<'ctx>,
|
||||
}
|
||||
|
||||
pub trait HasTypeContext {
|
||||
fn type_context<'ctx>(&self, ctx: &'ctx Context) -> TypeContext<'ctx>;
|
||||
}
|
||||
|
||||
impl<T: CodeGenerator + ?Sized> HasTypeContext for T {
|
||||
fn type_context<'ctx>(&self, ctx: &'ctx Context) -> TypeContext<'ctx> {
|
||||
TypeContext { size_type: self.get_size_type(ctx) }
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ModelError(pub String);
|
||||
|
||||
|
@ -36,12 +21,16 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
|
|||
type Type: BasicType<'ctx>;
|
||||
|
||||
/// Return the [`BasicType`] of this model.
|
||||
fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type;
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Self::Type;
|
||||
|
||||
/// Check if a [`BasicType`] is the same type of this model.
|
||||
fn check_type<T: BasicType<'ctx>>(
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError>;
|
||||
|
@ -55,15 +44,15 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
|
|||
|
||||
/// Check if a [`BasicValue`]'s type is equivalent to the type of this model.
|
||||
/// Wrap it into an [`Instance`] if it is.
|
||||
fn check_value<V: BasicValue<'ctx>>(
|
||||
fn check_value<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
value: V,
|
||||
) -> Result<Instance<'ctx, Self>, ModelError> {
|
||||
let value = value.as_basic_value_enum();
|
||||
self.check_type(tyctx, ctx, value.get_type())
|
||||
.map_err(|err| err.under_context("the value {value:?}"))?;
|
||||
self.check_type(generator, ctx, value.get_type())
|
||||
.map_err(|err| err.under_context(format!("the value {value:?}").as_str()))?;
|
||||
|
||||
let Ok(value) = Self::Value::try_from(value) else {
|
||||
unreachable!("check_type() has bad implementation")
|
||||
|
@ -72,27 +61,28 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
|
|||
}
|
||||
|
||||
// Allocate a value on the stack and return its pointer.
|
||||
fn alloca(
|
||||
fn alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Self> {
|
||||
let pmodel = PtrModel(*self);
|
||||
let p = ctx.builder.build_alloca(self.get_type(tyctx, ctx.ctx), name).unwrap();
|
||||
let p = ctx.builder.build_alloca(self.get_type(generator, ctx.ctx), name).unwrap();
|
||||
pmodel.believe_value(p)
|
||||
}
|
||||
|
||||
// Allocate an array on the stack and return its pointer.
|
||||
fn array_alloca(
|
||||
fn array_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
len: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Self> {
|
||||
let pmodel = PtrModel(*self);
|
||||
let p = ctx.builder.build_array_alloca(self.get_type(tyctx, ctx.ctx), len, name).unwrap();
|
||||
let p =
|
||||
ctx.builder.build_array_alloca(self.get_type(generator, ctx.ctx), len, name).unwrap();
|
||||
pmodel.believe_value(p)
|
||||
}
|
||||
|
||||
|
@ -102,14 +92,9 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
|
|||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&str>,
|
||||
) -> Result<Ptr<'ctx, Self>, String> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
let pmodel = PtrModel(*self);
|
||||
let p = generator.gen_var_alloc(
|
||||
ctx,
|
||||
self.get_type(tyctx, ctx.ctx).as_basic_type_enum(),
|
||||
name,
|
||||
)?;
|
||||
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
|
||||
let p = generator.gen_var_alloc(ctx, ty, name)?;
|
||||
Ok(pmodel.believe_value(p))
|
||||
}
|
||||
|
||||
|
@ -120,16 +105,10 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
|
|||
len: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Result<Ptr<'ctx, Self>, String> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
|
||||
// TODO: Remove ArraySliceValue
|
||||
let pmodel = PtrModel(*self);
|
||||
let p = generator.gen_array_var_alloc(
|
||||
ctx,
|
||||
self.get_type(tyctx, ctx.ctx).as_basic_type_enum(),
|
||||
len,
|
||||
name,
|
||||
)?;
|
||||
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
|
||||
let p = generator.gen_array_var_alloc(ctx, ty, len, name)?;
|
||||
Ok(pmodel.believe_value(PointerValue::from(p)))
|
||||
}
|
||||
}
|
||||
|
|
|
@ -7,7 +7,11 @@ use crate::codegen::{CodeGenContext, CodeGenerator};
|
|||
use super::*;
|
||||
|
||||
pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
|
||||
fn get_int_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx>;
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx>;
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
|
@ -22,32 +26,52 @@ pub struct Int64;
|
|||
pub struct SizeT;
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Bool {
|
||||
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.bool_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Byte {
|
||||
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i8_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int32 {
|
||||
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i32_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int64 {
|
||||
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i64_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for SizeT {
|
||||
fn get_int_type(&self, tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
tyctx.size_type
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
generator.get_size_type(ctx)
|
||||
}
|
||||
}
|
||||
|
||||
|
@ -55,7 +79,11 @@ impl<'ctx> IntKind<'ctx> for SizeT {
|
|||
pub struct AnyInt<'ctx>(pub IntType<'ctx>);
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
|
||||
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> IntType<'ctx> {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &mut G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
@ -69,13 +97,17 @@ impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
|
|||
type Type = IntType<'ctx>;
|
||||
|
||||
#[must_use]
|
||||
fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_int_type(tyctx, ctx)
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Self::Type {
|
||||
self.0.get_int_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: inkwell::types::BasicType<'ctx>>(
|
||||
fn check_type<T: inkwell::types::BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
|
@ -84,7 +116,7 @@ impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
|
|||
return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
let exp_ty = self.0.get_int_type(tyctx, ctx);
|
||||
let exp_ty = self.0.get_int_type(generator, ctx);
|
||||
if ty.get_bit_width() != exp_ty.get_bit_width() {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting IntType to have {} bit(s), but got {} bit(s)",
|
||||
|
@ -98,90 +130,98 @@ impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
|
|||
}
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> IntModel<N> {
|
||||
pub fn constant(
|
||||
pub fn constant<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
value: u64,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = self.get_type(tyctx, ctx).const_int(value, false);
|
||||
let value = self.get_type(generator, ctx).const_int(value, false);
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn const_0(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Int<'ctx, N> {
|
||||
self.constant(tyctx, ctx, 0)
|
||||
}
|
||||
|
||||
pub fn const_1(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Int<'ctx, N> {
|
||||
self.constant(tyctx, ctx, 1)
|
||||
}
|
||||
|
||||
pub fn s_extend_or_bit_cast(
|
||||
pub fn const_0<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, N> {
|
||||
self.constant(generator, ctx, 0)
|
||||
}
|
||||
|
||||
pub fn const_1<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, N> {
|
||||
self.constant(generator, ctx, 1)
|
||||
}
|
||||
|
||||
pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value = ctx
|
||||
.builder
|
||||
.build_int_s_extend_or_bit_cast(value, self.get_type(tyctx, ctx.ctx), name)
|
||||
.build_int_s_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), name)
|
||||
.unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn truncate(
|
||||
pub fn truncate<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Int<'ctx, N> {
|
||||
let value =
|
||||
ctx.builder.build_int_truncate(value, self.get_type(tyctx, ctx.ctx), name).unwrap();
|
||||
ctx.builder.build_int_truncate(value, self.get_type(generator, ctx.ctx), name).unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
}
|
||||
|
||||
impl IntModel<Bool> {
|
||||
#[must_use]
|
||||
pub fn const_false<'ctx>(
|
||||
pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, Bool> {
|
||||
self.constant(tyctx, ctx, 0)
|
||||
self.constant(generator, ctx, 0)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn const_true<'ctx>(
|
||||
pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, Bool> {
|
||||
self.constant(tyctx, ctx, 1)
|
||||
self.constant(generator, ctx, 1)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> Int<'ctx, N> {
|
||||
pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>>(
|
||||
pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
name: &str,
|
||||
) -> Int<'ctx, NewN> {
|
||||
IntModel(to_int_kind).s_extend_or_bit_cast(tyctx, ctx, self.value, name)
|
||||
IntModel(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value, name)
|
||||
}
|
||||
|
||||
pub fn truncate<NewN: IntKind<'ctx>>(
|
||||
pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
name: &str,
|
||||
) -> Int<'ctx, NewN> {
|
||||
IntModel(to_int_kind).truncate(tyctx, ctx, self.value, name)
|
||||
IntModel(to_int_kind).truncate(generator, ctx, self.value, name)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
|
|
|
@ -3,6 +3,7 @@ mod core;
|
|||
mod int;
|
||||
mod ptr;
|
||||
mod structure;
|
||||
pub mod util;
|
||||
|
||||
pub use any::*;
|
||||
pub use core::*;
|
||||
|
|
|
@ -5,7 +5,7 @@ use inkwell::{
|
|||
AddressSpace,
|
||||
};
|
||||
|
||||
use crate::codegen::CodeGenContext;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
|
@ -17,13 +17,17 @@ impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
|
|||
type Value = PointerValue<'ctx>;
|
||||
type Type = PointerType<'ctx>;
|
||||
|
||||
fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_type(tyctx, ctx).ptr_type(AddressSpace::default())
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Self::Type {
|
||||
self.0.get_type(generator, ctx).ptr_type(AddressSpace::default())
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>>(
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
|
@ -41,7 +45,9 @@ impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
|
|||
|
||||
// TODO: inkwell `get_element_type()` will be deprecated.
|
||||
// Remove the check for `get_element_type()` when the time comes.
|
||||
self.0.check_type(tyctx, ctx, elem_ty).map_err(|err| err.under_context("a PointerType"))?;
|
||||
self.0
|
||||
.check_type(generator, ctx, elem_ty)
|
||||
.map_err(|err| err.under_context("a PointerType"))?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
@ -49,20 +55,25 @@ impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
|
|||
|
||||
impl<'ctx, Element: Model<'ctx>> PtrModel<Element> {
|
||||
/// Return a ***constant*** nullptr.
|
||||
pub fn nullptr(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Ptr<'ctx, Element> {
|
||||
let ptr = self.get_type(tyctx, ctx).const_null();
|
||||
pub fn nullptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let ptr = self.get_type(generator, ctx).const_null();
|
||||
self.believe_value(ptr)
|
||||
}
|
||||
|
||||
/// Cast a pointer into this model with [`inkwell::builder::Builder::build_pointer_cast`]
|
||||
pub fn pointer_cast(
|
||||
pub fn pointer_cast<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ptr: PointerValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let ptr = ctx.builder.build_pointer_cast(ptr, self.get_type(tyctx, ctx.ctx), name).unwrap();
|
||||
let ptr =
|
||||
ctx.builder.build_pointer_cast(ptr, self.get_type(generator, ctx.ctx), name).unwrap();
|
||||
self.believe_value(ptr)
|
||||
}
|
||||
}
|
||||
|
@ -70,38 +81,38 @@ impl<'ctx, Element: Model<'ctx>> PtrModel<Element> {
|
|||
impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, Element> {
|
||||
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
|
||||
#[must_use]
|
||||
pub fn offset(
|
||||
pub fn offset<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
offset: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, Element> {
|
||||
let new_ptr =
|
||||
unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], name).unwrap() };
|
||||
self.model.check_value(tyctx, ctx.ctx, new_ptr).unwrap()
|
||||
self.model.check_value(generator, ctx.ctx, new_ptr).unwrap()
|
||||
}
|
||||
|
||||
// Load the `i`-th element (0-based) on the array with [`inkwell::builder::Builder::build_in_bounds_gep`].
|
||||
pub fn ix(
|
||||
pub fn ix<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Instance<'ctx, Element> {
|
||||
self.offset(tyctx, ctx, i, name).load(tyctx, ctx, name)
|
||||
self.offset(generator, ctx, i, name).load(generator, ctx, name)
|
||||
}
|
||||
|
||||
/// Load the value with [`inkwell::builder::Builder::build_load`].
|
||||
pub fn load(
|
||||
pub fn load<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: &str,
|
||||
) -> Instance<'ctx, Element> {
|
||||
let value = ctx.builder.build_load(self.value, name).unwrap();
|
||||
self.model.0.check_value(tyctx, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
|
||||
self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
|
||||
}
|
||||
|
||||
/// Store a value with [`inkwell::builder::Builder::build_store`].
|
||||
|
@ -110,14 +121,14 @@ impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, Element> {
|
|||
}
|
||||
|
||||
/// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
|
||||
pub fn transmute<NewElement: Model<'ctx>>(
|
||||
pub fn transmute<NewElement: Model<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
new_model: NewElement,
|
||||
name: &str,
|
||||
) -> Ptr<'ctx, NewElement> {
|
||||
PtrModel(new_model).pointer_cast(tyctx, ctx, self.value, name)
|
||||
PtrModel(new_model).pointer_cast(generator, ctx, self.value, name)
|
||||
}
|
||||
|
||||
/// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
|
||||
|
|
|
@ -6,7 +6,7 @@ use inkwell::{
|
|||
values::StructValue,
|
||||
};
|
||||
|
||||
use crate::codegen::CodeGenContext;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
|
@ -42,30 +42,34 @@ impl<'ctx> FieldTraversal<'ctx> for GepFieldTraversal {
|
|||
}
|
||||
}
|
||||
|
||||
struct TypeFieldTraversal<'ctx> {
|
||||
tyctx: TypeContext<'ctx>,
|
||||
struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
|
||||
generator: &'a mut G,
|
||||
ctx: &'ctx Context,
|
||||
field_types: Vec<BasicTypeEnum<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx> {
|
||||
impl<'ctx, 'a, 'b, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
|
||||
for TypeFieldTraversal<'ctx, 'a, G>
|
||||
{
|
||||
type Out<M> = ();
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Out<M> {
|
||||
let t = model.get_type(self.tyctx, self.ctx).as_basic_type_enum();
|
||||
let t = model.get_type(self.generator, self.ctx).as_basic_type_enum();
|
||||
self.field_types.push(t);
|
||||
}
|
||||
}
|
||||
|
||||
struct CheckTypeFieldTraversal<'ctx> {
|
||||
tyctx: TypeContext<'ctx>,
|
||||
struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
|
||||
generator: &'a mut G,
|
||||
ctx: &'ctx Context,
|
||||
index: u32,
|
||||
scrutinee: StructType<'ctx>,
|
||||
errors: Vec<ModelError>,
|
||||
}
|
||||
|
||||
impl<'ctx> FieldTraversal<'ctx> for CheckTypeFieldTraversal<'ctx> {
|
||||
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
|
||||
for CheckTypeFieldTraversal<'ctx, 'a, G>
|
||||
{
|
||||
type Out<M> = ();
|
||||
|
||||
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
|
||||
|
@ -73,8 +77,8 @@ impl<'ctx> FieldTraversal<'ctx> for CheckTypeFieldTraversal<'ctx> {
|
|||
self.index += 1;
|
||||
|
||||
if let Some(t) = self.scrutinee.get_field_type_at_index(i) {
|
||||
if let Err(err) = model.check_type(self.tyctx, self.ctx, t) {
|
||||
self.errors.push(err.under_context(format!("At field #{i} '{name}'").as_str()));
|
||||
if let Err(err) = model.check_type(self.generator, self.ctx, t) {
|
||||
self.errors.push(err.under_context(format!("field #{i} '{name}'").as_str()));
|
||||
}
|
||||
} // Otherwise, it will be caught
|
||||
}
|
||||
|
@ -89,8 +93,12 @@ pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
|
|||
self.traverse_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
|
||||
}
|
||||
|
||||
fn get_struct_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> StructType<'ctx> {
|
||||
let mut traversal = TypeFieldTraversal { tyctx, ctx, field_types: Vec::new() };
|
||||
fn get_struct_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> StructType<'ctx> {
|
||||
let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
|
||||
self.traverse_fields(&mut traversal);
|
||||
|
||||
ctx.struct_type(&traversal.field_types, false)
|
||||
|
@ -105,13 +113,17 @@ impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
|
|||
type Value = StructValue<'ctx>;
|
||||
type Type = StructType<'ctx>;
|
||||
|
||||
fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_struct_type(tyctx, ctx)
|
||||
fn get_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Self::Type {
|
||||
self.0.get_struct_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>>(
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
|
@ -121,7 +133,7 @@ impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
|
|||
};
|
||||
|
||||
let mut traversal =
|
||||
CheckTypeFieldTraversal { tyctx, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
|
||||
CheckTypeFieldTraversal { generator, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
|
||||
self.0.traverse_fields(&mut traversal);
|
||||
|
||||
let exp_num_fields = traversal.index;
|
||||
|
@ -168,9 +180,9 @@ impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
|
|||
}
|
||||
|
||||
/// Convenience function equivalent to `.gep(...).load(...)`.
|
||||
pub fn get<M, GetField>(
|
||||
pub fn get<M, GetField, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
get_field: GetField,
|
||||
name: &str,
|
||||
|
@ -179,7 +191,7 @@ impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
|
|||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
self.gep(ctx, get_field).load(tyctx, ctx, name)
|
||||
self.gep(ctx, get_field).load(generator, ctx, name)
|
||||
}
|
||||
|
||||
/// Convenience function equivalent to `.gep(...).store(...)`.
|
||||
|
|
|
@ -0,0 +1,62 @@
|
|||
use inkwell::{types::BasicType, values::IntValue};
|
||||
|
||||
/// `llvm.memcpy` but under the [`Model`] abstraction
|
||||
use crate::codegen::{
|
||||
llvm_intrinsics::call_memcpy_generic,
|
||||
stmt::{gen_for_callback_incrementing, BreakContinueHooks},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// Convenience function.
|
||||
///
|
||||
/// Like [`call_memcpy_generic`] but with model abstractions and `is_volatile` set to `false`.
|
||||
pub fn call_memcpy_model<'ctx, Item: Model<'ctx> + Default, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_array: Ptr<'ctx, Item>,
|
||||
src_array: Ptr<'ctx, Item>,
|
||||
num_items: IntValue<'ctx>,
|
||||
) {
|
||||
let itemsize = Item::default().get_type(generator, ctx.ctx).size_of().unwrap();
|
||||
let totalsize = ctx.builder.build_int_mul(itemsize, num_items, "totalsize").unwrap(); // TODO: Int types may not match.
|
||||
let is_volatile = ctx.ctx.bool_type().const_zero();
|
||||
call_memcpy_generic(ctx, dst_array.value, src_array.value, totalsize, is_volatile);
|
||||
}
|
||||
|
||||
/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
|
||||
/// The [`IntKind`] is automatically inferred.
|
||||
pub fn gen_for_model_auto<'ctx, 'a, G, F, I>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
start: Int<'ctx, I>,
|
||||
stop: Int<'ctx, I>,
|
||||
step: Int<'ctx, I>,
|
||||
body: F,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
Int<'ctx, I>,
|
||||
) -> Result<(), String>,
|
||||
I: IntKind<'ctx> + Default,
|
||||
{
|
||||
let int_model = IntModel(I::default());
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
start.value,
|
||||
(stop.value, false),
|
||||
|g, ctx, hooks, i| {
|
||||
let i = int_model.believe_value(i);
|
||||
body(g, ctx, hooks, i)
|
||||
},
|
||||
step.value,
|
||||
)
|
||||
}
|
|
@ -0,0 +1,527 @@
|
|||
// TODO: Replace numpy.rs
|
||||
|
||||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
use nac3parser::ast::StrRef;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{call_nac3_ndarray_resolve_and_check_new_shape, call_nac3_ndarray_transpose},
|
||||
structure::{
|
||||
ndarray::{
|
||||
scalar::split_scalar_or_ndarray, shape_util::parse_numpy_int_sequence,
|
||||
NDArrayObject,
|
||||
},
|
||||
tuple::TupleObject,
|
||||
},
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{numpy::unpack_ndarray_var_tys, DefinitionId},
|
||||
typecheck::{
|
||||
numpy::extract_ndims,
|
||||
typedef::{FunSignature, Type},
|
||||
},
|
||||
};
|
||||
|
||||
use super::{
|
||||
irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Get the zero value in `np.zeros()` of a `dtype`.
|
||||
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i32_type().const_zero().into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "").value.into()
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the one value in `np.ones()` of a `dtype`.
|
||||
fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(elem_ty, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
|
||||
ctx.ctx.i32_type().const_int(1, is_signed).into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(elem_ty, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
|
||||
ctx.ctx.i64_type().const_int(1, is_signed).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_float(1.0).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_int(1, false).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "1").value.into()
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper function to create an ndarray with uninitialized values.
|
||||
///
|
||||
/// * `ndarray_ty` - The [`Type`] of the ndarray
|
||||
/// * `shape` - The user input shape argument
|
||||
/// * `shape_ty` - The [`Type`] of the shape argument
|
||||
///
|
||||
/// This function does data validation the `shape` input.
|
||||
fn create_empty_ndarray<'ctx, G>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ty: Type,
|
||||
shape: BasicValueEnum<'ctx>,
|
||||
shape_ty: Type,
|
||||
) -> NDArrayObject<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
let shape = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
|
||||
let shape = shape.value.get(generator, ctx, |f| f.items, "shape");
|
||||
|
||||
let ndarray =
|
||||
NDArrayObject::alloca_uninitialized_of_type(generator, ctx, ndarray_ty, "ndarray");
|
||||
|
||||
// Validate `shape`
|
||||
let ndims = ndarray.get_ndims(generator, ctx.ctx);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims, shape);
|
||||
|
||||
// Setup `ndarray` with `shape`
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.create_data(generator, ctx); // `shape` has to be set
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.empty`.
|
||||
pub fn gen_ndarray_empty<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse arguments
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ty = fun.0.ret;
|
||||
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.zero`.
|
||||
pub fn gen_ndarray_zeros<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse arguments
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ty = fun.0.ret;
|
||||
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
|
||||
|
||||
let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.ones`.
|
||||
pub fn gen_ndarray_ones<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse arguments
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ty = fun.0.ret;
|
||||
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
|
||||
|
||||
let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.full`.
|
||||
pub fn gen_ndarray_full<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
// Parse argument #1 shape
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Parse argument #2 fill_value
|
||||
let fill_value_ty = fun.0.args[1].ty;
|
||||
let fill_value = args[1].1.clone().to_basic_value_enum(ctx, generator, fill_value_ty)?;
|
||||
|
||||
// Implementation
|
||||
let ndarray_ty = fun.0.ret;
|
||||
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
|
||||
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.broadcast_to`.
|
||||
pub fn gen_ndarray_broadcast_to<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
// Parse argument #1 input
|
||||
let input_ty = fun.0.args[0].ty;
|
||||
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
|
||||
|
||||
// Parse argument #2 shape
|
||||
let shape_ty = fun.0.args[1].ty;
|
||||
let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Extract broadcast_ndims, this is the only way to get the
|
||||
// ndims of the ndarray result statically.
|
||||
let (_, broadcast_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
|
||||
let broadcast_ndims = extract_ndims(&ctx.unifier, broadcast_ndims_ty);
|
||||
|
||||
// Process `input`
|
||||
let in_ndarray =
|
||||
split_scalar_or_ndarray(generator, ctx, input, input_ty).as_ndarray(generator, ctx);
|
||||
|
||||
// Process `shape`
|
||||
let broadcast_shape = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
|
||||
let broadcast_shape = broadcast_shape.value.get(generator, ctx, |f| f.items, "shape");
|
||||
// NOTE: shape.size should equal to `broadcasted_ndims`.
|
||||
let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(
|
||||
generator,
|
||||
ctx,
|
||||
broadcast_ndims_llvm,
|
||||
broadcast_shape,
|
||||
);
|
||||
|
||||
// Create broadcast view
|
||||
let broadcast_ndarray =
|
||||
in_ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape);
|
||||
|
||||
Ok(broadcast_ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.reshape`.
|
||||
pub fn gen_ndarray_reshape<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
// Parse argument #1 input
|
||||
let input_ty = fun.0.args[0].ty;
|
||||
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
|
||||
|
||||
// Parse argument #2 shape
|
||||
let shape_ty = fun.0.args[1].ty;
|
||||
let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Extract reshaped_ndims
|
||||
let (_, reshaped_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
|
||||
let reshaped_ndims = extract_ndims(&ctx.unifier, reshaped_ndims_ty);
|
||||
|
||||
// Process `input`
|
||||
let in_ndarray =
|
||||
split_scalar_or_ndarray(generator, ctx, input, input_ty).as_ndarray(generator, ctx);
|
||||
let in_size = in_ndarray.size(generator, ctx);
|
||||
|
||||
// Process the shape input from user and resolve negative indices.
|
||||
// The resulting `new_shape`'s size should be equal to reshaped_ndims.
|
||||
// This is ensured by the typechecker.
|
||||
let new_shape = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
|
||||
let new_shape = new_shape.value.get(generator, ctx, |f| f.items, "new_shape");
|
||||
|
||||
// Resolve unknown dimensions & validate `new_shape`.
|
||||
let new_ndims = sizet_model.constant(generator, ctx.ctx, reshaped_ndims);
|
||||
call_nac3_ndarray_resolve_and_check_new_shape(generator, ctx, in_size, new_ndims, new_shape);
|
||||
|
||||
// Reshape or copy
|
||||
let reshaped_ndarray = in_ndarray.reshape_or_copy(generator, ctx, reshaped_ndims, new_shape);
|
||||
|
||||
Ok(reshaped_ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.arange`.
|
||||
pub fn gen_ndarray_arange<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 len
|
||||
let input_ty = fun.0.args[0].ty;
|
||||
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?.into_int_value();
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Process input
|
||||
let input = sizet_model.s_extend_or_bit_cast(generator, ctx, input, "input_dim");
|
||||
|
||||
// Allocate the resulting ndarray
|
||||
let ndarray = NDArrayObject::alloca_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
ctx.primitives.float,
|
||||
1, // ndims = 1
|
||||
"arange_ndarray",
|
||||
);
|
||||
|
||||
// `ndarray.shape[0] = input`
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
ndarray
|
||||
.value
|
||||
.get(generator, ctx, |f| f.shape, "shape")
|
||||
.offset(generator, ctx, zero.value, "dim")
|
||||
.store(ctx, input);
|
||||
|
||||
// Create data and set elements
|
||||
ndarray.create_data(generator, ctx);
|
||||
ndarray.foreach(generator, ctx, |_generator, ctx, _hooks, i, pelement| {
|
||||
let val =
|
||||
ctx.builder.build_unsigned_int_to_float(i.value, ctx.ctx.f64_type(), "val").unwrap();
|
||||
ctx.builder.build_store(pelement, val).unwrap();
|
||||
Ok(())
|
||||
})?;
|
||||
|
||||
Ok(ndarray.value.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.size`.
|
||||
pub fn gen_ndarray_size<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
|
||||
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
|
||||
let size = ndarray.size(generator, ctx).truncate(generator, ctx, Int32, "size");
|
||||
Ok(size.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.shape`.
|
||||
pub fn gen_ndarray_shape<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 ndarray
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Process ndarray
|
||||
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
|
||||
let mut items = Vec::with_capacity(ndarray.ndims as usize);
|
||||
|
||||
for i in 0..ndarray.ndims {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i);
|
||||
let dim =
|
||||
ndarray.value.get(generator, ctx, |f| f.shape, "").ix(generator, ctx, i.value, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
|
||||
}
|
||||
|
||||
let shape = TupleObject::create(generator, ctx, items, "shape");
|
||||
Ok(shape.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `<ndarray>.strides`.
|
||||
pub fn gen_ndarray_strides<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
// TODO: This function looks exactly like `gen_ndarray_shapes`, code duplication?
|
||||
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 ndarray
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Process ndarray
|
||||
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
|
||||
let mut items = Vec::with_capacity(ndarray.ndims as usize);
|
||||
|
||||
for i in 0..ndarray.ndims {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i);
|
||||
let dim =
|
||||
ndarray.value.get(generator, ctx, |f| f.strides, "").ix(generator, ctx, i.value, "dim");
|
||||
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
|
||||
|
||||
items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
|
||||
}
|
||||
|
||||
let strides = TupleObject::create(generator, ctx, items, "strides");
|
||||
Ok(strides.value.as_basic_value_enum())
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `np.transpose`.
|
||||
pub fn gen_ndarray_transpose<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<BasicValueEnum<'ctx>, String> {
|
||||
// TODO: The implementation will be changed once default values start working again.
|
||||
// Read the comment on this function in BuiltinBuilder.
|
||||
|
||||
// TODO: Change axes values to `SizeT`
|
||||
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
// Parse argument #1 ndarray
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
|
||||
// Define models
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Implementation
|
||||
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
|
||||
let transposed_ndarray = NDArrayObject::alloca_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.dtype,
|
||||
ndarray.ndims,
|
||||
"transposed_ndarray",
|
||||
);
|
||||
|
||||
let has_axes = args.len() >= 2;
|
||||
if has_axes {
|
||||
// Parse argument #2 axes
|
||||
let in_axes_ty = fun.0.args[1].ty;
|
||||
let in_axes = args[1].1.clone().to_basic_value_enum(ctx, generator, in_axes_ty)?;
|
||||
let in_axes = parse_numpy_int_sequence(generator, ctx, in_axes, in_axes_ty);
|
||||
|
||||
let num_axes = ndarray.get_ndims(generator, ctx.ctx);
|
||||
let axes = in_axes.value.get(generator, ctx, |f| f.items, "axes");
|
||||
|
||||
call_nac3_ndarray_transpose(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.value,
|
||||
transposed_ndarray.value,
|
||||
num_axes,
|
||||
axes,
|
||||
);
|
||||
} else {
|
||||
let num_axes = sizet_model.const_0(generator, ctx.ctx); // Placeholder value
|
||||
let axes = PtrModel(sizet_model).nullptr(generator, ctx.ctx);
|
||||
|
||||
// See IRRT implementation for argument requirements when axes is None
|
||||
call_nac3_ndarray_transpose(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.value,
|
||||
transposed_ndarray.value,
|
||||
num_axes,
|
||||
axes,
|
||||
);
|
||||
}
|
||||
|
||||
Ok(transposed_ndarray.value.value.as_basic_value_enum())
|
||||
}
|
|
@ -1,5 +1,6 @@
|
|||
use super::model::*;
|
||||
use super::structure::cslice::CSlice;
|
||||
use super::structure::ndarray::broadcast::broadcast_all_ndarrays;
|
||||
use super::{
|
||||
super::symbol_resolver::ValueEnum,
|
||||
expr::destructure_range,
|
||||
|
@ -7,6 +8,9 @@ use super::{
|
|||
structure::exception::Exception,
|
||||
CodeGenContext, CodeGenerator, Int32, IntModel, Ptr, StructModel,
|
||||
};
|
||||
use crate::codegen::structure::ndarray::indexing::util::gen_ndarray_subscript_ndindexes;
|
||||
use crate::codegen::structure::ndarray::scalar::split_scalar_or_ndarray;
|
||||
use crate::codegen::structure::ndarray::NDArrayObject;
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
|
||||
|
@ -403,7 +407,43 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
|
|||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// Handle NDArray item assignment
|
||||
todo!("ndarray subscript assignment is not yet implemented");
|
||||
// Process target
|
||||
let target = generator
|
||||
.gen_expr(ctx, target)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, target_ty)?;
|
||||
let target = NDArrayObject::from_value_and_type(generator, ctx, target, target_ty);
|
||||
|
||||
// Process key
|
||||
let key = gen_ndarray_subscript_ndindexes(generator, ctx, key)?;
|
||||
|
||||
// Process value
|
||||
let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
|
||||
|
||||
/*
|
||||
Reference code:
|
||||
```python
|
||||
target = target[key]
|
||||
value = np.asarray(value)
|
||||
|
||||
shape = np.broadcast_shape((target, value))
|
||||
|
||||
target = np.broadcast_to(target, shape)
|
||||
value = np.broadcast_to(value, shape)
|
||||
|
||||
...and finally copy 1-1 from value to target.
|
||||
```
|
||||
*/
|
||||
let target = target.index(generator, ctx, &key, "assign_target_ndarray");
|
||||
let value =
|
||||
split_scalar_or_ndarray(generator, ctx, value, value_ty).as_ndarray(generator, ctx);
|
||||
|
||||
let broadcast_result = broadcast_all_ndarrays(generator, ctx, &vec![target, value]);
|
||||
|
||||
let target = broadcast_result.ndarrays[0];
|
||||
let value = broadcast_result.ndarrays[1];
|
||||
|
||||
target.copy_data_from(generator, ctx, value);
|
||||
}
|
||||
_ => {
|
||||
panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
|
||||
|
@ -640,8 +680,12 @@ where
|
|||
I: Clone,
|
||||
InitFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
|
||||
CondFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
|
||||
BodyFn:
|
||||
FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, BreakContinueHooks, I) -> Result<(), String>,
|
||||
BodyFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
I,
|
||||
) -> Result<(), String>,
|
||||
UpdateFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
|
||||
{
|
||||
let label = label.unwrap_or("for");
|
||||
|
@ -721,7 +765,7 @@ where
|
|||
BodyFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks,
|
||||
BreakContinueHooks<'ctx>,
|
||||
IntValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
|
@ -1265,20 +1309,19 @@ pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
|
|||
loc: Location,
|
||||
) {
|
||||
if let Some(pexn) = exception {
|
||||
let type_context = generator.type_context(ctx.ctx);
|
||||
let i32_model = IntModel(Int32);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
||||
// Get and store filename
|
||||
let filename = loc.file.0;
|
||||
let filename = ctx.gen_string(generator, &String::from(filename)).value;
|
||||
let filename = cslice_model.check_value(type_context, ctx.ctx, filename).unwrap();
|
||||
let filename = cslice_model.check_value(generator, ctx.ctx, filename).unwrap();
|
||||
pexn.set(ctx, |f| f.filename, filename);
|
||||
|
||||
let row = i32_model.constant(type_context, ctx.ctx, loc.row as u64);
|
||||
let row = i32_model.constant(generator, ctx.ctx, loc.row as u64);
|
||||
pexn.set(ctx, |f| f.line, row);
|
||||
|
||||
let column = i32_model.constant(type_context, ctx.ctx, loc.column as u64);
|
||||
let column = i32_model.constant(generator, ctx.ctx, loc.column as u64);
|
||||
pexn.set(ctx, |f| f.column, column);
|
||||
|
||||
let current_fn = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
|
||||
|
@ -1754,9 +1797,8 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
return Ok(());
|
||||
};
|
||||
|
||||
let type_context = generator.type_context(ctx.ctx);
|
||||
let pexn_model = PtrModel(StructModel(Exception));
|
||||
let exn = pexn_model.check_value(type_context, ctx.ctx, exc).unwrap();
|
||||
let exn = pexn_model.check_value(generator, ctx.ctx, exc).unwrap();
|
||||
|
||||
gen_raise(generator, ctx, Some(exn), stmt.location);
|
||||
} else {
|
||||
|
@ -1764,7 +1806,6 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
}
|
||||
}
|
||||
StmtKind::Assert { test, msg, .. } => {
|
||||
let type_context = generator.type_context(ctx.ctx);
|
||||
let byte_model = IntModel(Byte);
|
||||
let cslice_model = StructModel(CSlice);
|
||||
|
||||
|
@ -1772,7 +1813,7 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
return Ok(());
|
||||
};
|
||||
let test = test.to_basic_value_enum(ctx, generator, ctx.primitives.bool)?;
|
||||
let test = byte_model.check_value(type_context, ctx.ctx, test).unwrap(); // Python `bool` is represented as `i8` in nac3core
|
||||
let test = byte_model.check_value(generator, ctx.ctx, test).unwrap(); // Python `bool` is represented as `i8` in nac3core
|
||||
|
||||
// Check `msg`
|
||||
let err_msg = match msg {
|
||||
|
@ -1782,7 +1823,7 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
};
|
||||
|
||||
let msg = msg.to_basic_value_enum(ctx, generator, ctx.primitives.str)?;
|
||||
cslice_model.check_value(type_context, ctx.ctx, msg).unwrap()
|
||||
cslice_model.check_value(generator, ctx.ctx, msg).unwrap()
|
||||
}
|
||||
None => ctx.gen_string(generator, ""),
|
||||
};
|
||||
|
|
|
@ -1,4 +1,6 @@
|
|||
use crate::codegen::{model::*, CodeGenContext};
|
||||
use inkwell::context::Context;
|
||||
|
||||
use crate::codegen::{model::*, CodeGenerator};
|
||||
|
||||
/// Fields of [`CSlice<'ctx>`].
|
||||
pub struct CSliceFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
|
@ -27,16 +29,16 @@ impl StructModel<CSlice> {
|
|||
/// Create a [`CSlice`].
|
||||
///
|
||||
/// `base` and `len` must be LLVM global constants.
|
||||
pub fn create_const<'ctx>(
|
||||
pub fn create_const<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
type_context: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
base: Ptr<'ctx, IntModel<Byte>>,
|
||||
len: Int<'ctx, SizeT>,
|
||||
) -> Struct<'ctx, CSlice> {
|
||||
let value = self
|
||||
.0
|
||||
.get_struct_type(type_context, ctx.ctx)
|
||||
.get_struct_type(generator, ctx)
|
||||
.const_named_struct(&[base.value.into(), len.value.into()]);
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
|
|
@ -0,0 +1,76 @@
|
|||
use inkwell::values::BasicValue;
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{iter_type_vars, Type, TypeEnum},
|
||||
};
|
||||
|
||||
/// Fields of [`List`]
|
||||
pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>, Size: IntKind<'ctx>> {
|
||||
/// Array pointer to content
|
||||
pub items: F::Out<PtrModel<Item>>,
|
||||
/// Number of items in the array
|
||||
pub len: F::Out<IntModel<Size>>,
|
||||
}
|
||||
|
||||
/// A list in NAC3.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct List<Item, Len> {
|
||||
/// Model of the list items
|
||||
pub item: Item,
|
||||
/// Model of type of integer storing the number of items on the list
|
||||
pub len: Len,
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>, Size: IntKind<'ctx>> StructKind<'ctx> for List<Item, Size> {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item, Size>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
items: traversal.add("data", PtrModel(self.item)),
|
||||
len: traversal.add("len", IntModel(self.len)),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub struct ListObject<'ctx, Item: Model<'ctx>, Size: IntKind<'ctx>> {
|
||||
/// Typechecker type of the list items
|
||||
pub item_type: Type,
|
||||
pub value: Ptr<'ctx, StructModel<List<Item, Size>>>,
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>, Len: IntKind<'ctx>> ListObject<'ctx, Item, Len> {
|
||||
/// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
|
||||
///
|
||||
/// - The `Item` model has to be manually provided, and should match the
|
||||
/// `get_llvm_type()` of `ty` and the `get_type()`. You may want to use
|
||||
/// [`AnyModel`] if `ty`'s type is not knowable statically.
|
||||
pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
val: V,
|
||||
ty: Type,
|
||||
item_model: Item,
|
||||
len_model: Len,
|
||||
) -> Self {
|
||||
let plist_model = PtrModel(StructModel(List { item: item_model, len: len_model }));
|
||||
|
||||
// Check typechecker type and extract `item_type`
|
||||
let item_type = match &*ctx.unifier.get_ty(ty) {
|
||||
TypeEnum::TObj { obj_id, params, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
iter_type_vars(params).next().unwrap().ty // Extract `item_type`
|
||||
}
|
||||
_ => panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(ty)),
|
||||
};
|
||||
|
||||
// LLVM types of `item_model` and `ty` should match
|
||||
let llvm_ty = ctx.get_llvm_type(generator, ty);
|
||||
item_model.check_type(generator, ctx.ctx, llvm_ty).unwrap();
|
||||
|
||||
// Create object
|
||||
let val = plist_model.check_value(generator, ctx.ctx, val).unwrap();
|
||||
ListObject { item_type: item_type, value: val }
|
||||
}
|
||||
}
|
|
@ -1,3 +1,5 @@
|
|||
pub mod cslice;
|
||||
pub mod exception;
|
||||
pub mod list;
|
||||
pub mod ndarray;
|
||||
pub mod tuple;
|
||||
|
|
|
@ -1,224 +0,0 @@
|
|||
use irrt::{
|
||||
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
|
||||
call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
|
||||
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
|
||||
};
|
||||
|
||||
use crate::{codegen::*, symbol_resolver::SymbolValue};
|
||||
|
||||
pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub data: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
pub itemsize: F::Out<IntModel<SizeT>>,
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
}
|
||||
|
||||
// TODO: Rename to `NDArray` when the old NDArray is removed.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NpArray;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NpArray {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NpArrayFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
data: traversal.add_auto("data"),
|
||||
itemsize: traversal.add_auto("itemsize"),
|
||||
ndims: traversal.add_auto("ndims"),
|
||||
shape: traversal.add_auto("shape"),
|
||||
strides: traversal.add_auto("strides"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
|
||||
/// The `ndims` must only contain 1 value.
|
||||
#[must_use]
|
||||
pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
|
||||
let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
|
||||
let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
|
||||
panic!("ndims_ty should be a TLiteral");
|
||||
};
|
||||
|
||||
assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
|
||||
|
||||
let ndims = values[0].clone();
|
||||
u64::try_from(ndims).unwrap()
|
||||
}
|
||||
|
||||
/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
|
||||
pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
|
||||
unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDArrayObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub ndims: Type,
|
||||
pub value: Ptr<'ctx, StructModel<NpArray>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
|
||||
///
|
||||
/// `shape` and `strides` will be automatically allocated on the stack.
|
||||
///
|
||||
/// The returned ndarray's content will be:
|
||||
/// - `data`: set to `nullptr`.
|
||||
/// - `itemsize`: set to the `sizeof()` of `dtype`.
|
||||
/// - `ndims`: set to the value of `ndims`.
|
||||
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
|
||||
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
|
||||
pub fn alloca_uninitialized<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: Type,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let ndarray_model = StructModel(NpArray);
|
||||
let ndarray_data_model = PtrModel(IntModel(Byte));
|
||||
|
||||
let pndarray = ndarray_model.alloca(tyctx, ctx, name);
|
||||
|
||||
let data = ndarray_data_model.nullptr(tyctx, ctx.ctx);
|
||||
|
||||
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
|
||||
let itemsize = sizet_model.s_extend_or_bit_cast(tyctx, ctx, itemsize, "itemsize");
|
||||
|
||||
let ndims_val = extract_ndims(&ctx.unifier, ndims);
|
||||
let ndims_val = sizet_model.constant(tyctx, ctx.ctx, ndims_val);
|
||||
|
||||
let shape = sizet_model.array_alloca(tyctx, ctx, ndims_val.value, "shape");
|
||||
let strides = sizet_model.array_alloca(tyctx, ctx, ndims_val.value, "strides");
|
||||
|
||||
pndarray.set(ctx, |f| f.data, data);
|
||||
pndarray.set(ctx, |f| f.itemsize, itemsize);
|
||||
pndarray.set(ctx, |f| f.ndims, ndims_val);
|
||||
pndarray.set(ctx, |f| f.shape, shape);
|
||||
pndarray.set(ctx, |f| f.strides, strides);
|
||||
|
||||
NDArrayObject { dtype, ndims, value: pndarray }
|
||||
}
|
||||
|
||||
/// Get this ndarray's `ndims` as an LLVM constant.
|
||||
pub fn get_ndims(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let ndims_val = extract_ndims(&ctx.unifier, self.ndims);
|
||||
sizet_model.constant(tyctx, ctx.ctx, ndims_val)
|
||||
}
|
||||
|
||||
/// Return true if this ndarray is unsized.
|
||||
#[must_use]
|
||||
pub fn is_unsized(&self, unifier: &Unifier) -> bool {
|
||||
extract_ndims(unifier, self.ndims) == 0
|
||||
}
|
||||
|
||||
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
|
||||
/// The allocated data buffer is considered to be *owned* by the ndarray.
|
||||
///
|
||||
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
|
||||
///
|
||||
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
|
||||
pub fn create_data<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
let byte_model = IntModel(Byte);
|
||||
|
||||
let data = byte_model.array_alloca(tyctx, ctx, self.get_ndims(tyctx, ctx).value, "data");
|
||||
self.value.set(ctx, |f| f.data, data);
|
||||
|
||||
self.update_strides_by_shape(tyctx, ctx);
|
||||
}
|
||||
|
||||
/// Get the `np.size()` of this ndarray.
|
||||
pub fn size(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_size(tyctx, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the `ndarray.nbytes` of this ndarray.
|
||||
pub fn nbytes(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_nbytes(tyctx, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the `len()` of this ndarray.
|
||||
pub fn len(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_len(tyctx, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Check if this ndarray is C-contiguous.
|
||||
///
|
||||
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
|
||||
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
call_nac3_ndarray_is_c_contiguous(tyctx, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the pointer to the n-th (0-based) element.
|
||||
///
|
||||
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
|
||||
pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
name: &str,
|
||||
) -> PointerValue<'ctx> {
|
||||
let tyctx = generator.type_context(ctx.ctx);
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
|
||||
|
||||
let p = call_nac3_ndarray_get_nth_pelement(tyctx, ctx, self.value, nth);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
|
||||
///
|
||||
/// Please refer to the IRRT implementation to see its purpose.
|
||||
pub fn update_strides_by_shape(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
call_nac3_ndarray_set_strides_by_shape(tyctx, ctx, self.value);
|
||||
}
|
||||
|
||||
/// Copy data from another ndarray.
|
||||
///
|
||||
/// Panics if the `dtype`s of ndarrays are different.
|
||||
pub fn copy_data_from(
|
||||
&self,
|
||||
tyctx: TypeContext<'ctx>,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
|
||||
call_nac3_ndarray_copy_data(tyctx, ctx, src.value, self.value);
|
||||
}
|
||||
}
|
|
@ -0,0 +1,132 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::numpy::get_broadcast_all_ndims,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
/// Fields of [`ShapeEntry`]
|
||||
pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
}
|
||||
|
||||
/// An IRRT structure used in broadcasting.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct ShapeEntry;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for ShapeEntry {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { ndims: traversal.add_auto("ndims"), shape: traversal.add_auto("shape") }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create a broadcast view on this ndarray with a target shape.
|
||||
///
|
||||
/// * `target_ndims` - The ndims type after broadcasting to the given shape.
|
||||
/// The caller has to figure this out for this function.
|
||||
/// * `target_shape` - An array pointer pointing to the target shape.
|
||||
#[must_use]
|
||||
pub fn broadcast_to<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
target_ndims: u64,
|
||||
target_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
let broadcast_ndarray = NDArrayObject::alloca_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
self.dtype,
|
||||
target_ndims,
|
||||
"broadcast_ndarray_to_dst",
|
||||
);
|
||||
broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
|
||||
|
||||
call_nac3_ndarray_broadcast_to(generator, ctx, self.value, broadcast_ndarray.value);
|
||||
broadcast_ndarray
|
||||
}
|
||||
}
|
||||
/// A result produced by [`broadcast_all_ndarrays`]
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct BroadcastAllResult<'ctx> {
|
||||
/// The statically known `ndims` of the broadcast result.
|
||||
pub ndims: u64,
|
||||
/// The broadcasting shape.
|
||||
pub shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
/// Broadcasted views on the inputs.
|
||||
///
|
||||
/// All of them will have `shape` [`BroadcastAllResult::shape`] and
|
||||
/// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
|
||||
/// is the same as the input.
|
||||
pub ndarrays: Vec<NDArrayObject<'ctx>>,
|
||||
}
|
||||
|
||||
// TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
|
||||
pub fn broadcast_all_ndarrays<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarrays: &Vec<NDArrayObject<'ctx>>,
|
||||
) -> BroadcastAllResult<'ctx> {
|
||||
assert!(!ndarrays.is_empty());
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let shape_model = StructModel(ShapeEntry);
|
||||
|
||||
let broadcast_ndims = get_broadcast_all_ndims(ndarrays.iter().map(|ndarray| ndarray.ndims));
|
||||
|
||||
// Prepare input shape entries
|
||||
let num_shape_entries =
|
||||
sizet_model.constant(generator, ctx.ctx, u64::try_from(ndarrays.len()).unwrap());
|
||||
let shape_entries =
|
||||
shape_model.array_alloca(generator, ctx, num_shape_entries.value, "shape_entries");
|
||||
for (i, ndarray) in ndarrays.iter().enumerate() {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i as u64).value;
|
||||
|
||||
let shape_entry = shape_entries.offset(generator, ctx, i, "shape_entry");
|
||||
|
||||
let this_ndims = ndarray.value.get(generator, ctx, |f| f.ndims, "this_ndims");
|
||||
shape_entry.set(ctx, |f| f.ndims, this_ndims);
|
||||
|
||||
let this_shape = ndarray.value.get(generator, ctx, |f| f.shape, "this_shape");
|
||||
shape_entry.set(ctx, |f| f.shape, this_shape);
|
||||
}
|
||||
|
||||
// Prepare destination
|
||||
let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
|
||||
let broadcast_shape =
|
||||
sizet_model.array_alloca(generator, ctx, broadcast_ndims_llvm.value, "dst_shape");
|
||||
|
||||
// Compute the target broadcast shape `dst_shape` for all ndarrays.
|
||||
call_nac3_ndarray_broadcast_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
num_shape_entries,
|
||||
shape_entries,
|
||||
broadcast_ndims_llvm,
|
||||
broadcast_shape,
|
||||
);
|
||||
|
||||
// Now that we know about the broadcasting shape, broadcast all the inputs.
|
||||
|
||||
// Broadcast all the inputs to shape `dst_shape`.
|
||||
let broadcast_ndarrays: Vec<_> = ndarrays
|
||||
.into_iter()
|
||||
.map(|ndarray| ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape))
|
||||
.collect_vec();
|
||||
|
||||
BroadcastAllResult {
|
||||
ndims: broadcast_ndims,
|
||||
shape: broadcast_shape,
|
||||
ndarrays: broadcast_ndarrays,
|
||||
}
|
||||
}
|
|
@ -0,0 +1,343 @@
|
|||
use crate::codegen::{
|
||||
irrt::call_nac3_ndarray_index, model::*, structure::ndarray::scalar::ScalarObject,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::{scalar::ScalarOrNDArray, NDArrayObject};
|
||||
|
||||
pub type NDIndexType = Byte;
|
||||
|
||||
/// Fields of [`NDIndex`]
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub type_: F::Out<IntModel<NDIndexType>>, // Defined to be uint8_t in IRRT
|
||||
pub data: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation fo an ndarray subscript index.
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct NDIndex;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NDIndex {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NDIndexFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
|
||||
}
|
||||
}
|
||||
|
||||
/// Fields of [`UserSlice`]
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct UserSliceFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub start_defined: F::Out<IntModel<Bool>>,
|
||||
pub start: F::Out<IntModel<Int32>>,
|
||||
pub stop_defined: F::Out<IntModel<Bool>>,
|
||||
pub stop: F::Out<IntModel<Int32>>,
|
||||
pub step_defined: F::Out<IntModel<Bool>>,
|
||||
pub step: F::Out<IntModel<Int32>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation of a user slice.
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct UserSlice;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for UserSlice {
|
||||
type Fields<F: FieldTraversal<'ctx>> = UserSliceFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
start_defined: traversal.add_auto("start_defined"),
|
||||
start: traversal.add_auto("start"),
|
||||
stop_defined: traversal.add_auto("stop_defined"),
|
||||
stop: traversal.add_auto("stop"),
|
||||
step_defined: traversal.add_auto("step_defined"),
|
||||
step: traversal.add_auto("step"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A convenience structure to prepare a [`UserSlice`].
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RustUserSlice<'ctx> {
|
||||
pub start: Option<Int<'ctx, Int32>>,
|
||||
pub stop: Option<Int<'ctx, Int32>>,
|
||||
pub step: Option<Int<'ctx, Int32>>,
|
||||
}
|
||||
|
||||
impl<'ctx> RustUserSlice<'ctx> {
|
||||
/// Write the contents to an LLVM [`UserSlice`].
|
||||
pub fn write_to_user_slice<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_slice_ptr: Ptr<'ctx, StructModel<UserSlice>>,
|
||||
) {
|
||||
let bool_model = IntModel(Bool);
|
||||
|
||||
let false_ = bool_model.constant(generator, ctx.ctx, 0);
|
||||
let true_ = bool_model.constant(generator, 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_),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// A convenience enum variant to store the content and type of an NDIndex in high level.
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum RustNDIndex<'ctx> {
|
||||
SingleElement(Int<'ctx, Int32>), // TODO: To be SizeT
|
||||
Slice(RustUserSlice<'ctx>),
|
||||
}
|
||||
|
||||
impl<'ctx> RustNDIndex<'ctx> {
|
||||
/// Get the value to set `NDIndex::type` for this variant.
|
||||
fn get_type_id(&self) -> u64 {
|
||||
// Defined in IRRT, must be in sync
|
||||
match self {
|
||||
RustNDIndex::SingleElement(_) => 0,
|
||||
RustNDIndex::Slice(_) => 1,
|
||||
}
|
||||
}
|
||||
|
||||
/// Write the contents to an LLVM [`NDIndex`].
|
||||
fn write_to_ndindex<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_ndindex_ptr: Ptr<'ctx, StructModel<NDIndex>>,
|
||||
) {
|
||||
let ndindex_type_model = IntModel(NDIndexType::default());
|
||||
let i32_model = IntModel(Int32::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(generator, ctx.ctx, self.get_type_id()));
|
||||
|
||||
// Set `dst_ndindex_ptr->data`
|
||||
let data = match self {
|
||||
RustNDIndex::SingleElement(in_index) => {
|
||||
let index_ptr = i32_model.alloca(generator, ctx, "index");
|
||||
index_ptr.store(ctx, *in_index);
|
||||
index_ptr.transmute(generator, ctx, IntModel(Byte), "")
|
||||
}
|
||||
RustNDIndex::Slice(in_rust_slice) => {
|
||||
let user_slice_ptr = user_slice_model.alloca(generator, ctx, "user_slice");
|
||||
in_rust_slice.write_to_user_slice(generator, ctx, user_slice_ptr);
|
||||
user_slice_ptr.transmute(generator, ctx, IntModel(Byte), "")
|
||||
}
|
||||
};
|
||||
dst_ndindex_ptr.gep(ctx, |f| f.data).store(ctx, data);
|
||||
}
|
||||
|
||||
/// Allocate an array of `NDIndex`es on the stack and return its stack pointer.
|
||||
pub fn alloca_ndindexes<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
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(generator, ctx.ctx, in_ndindexes.len() as u64);
|
||||
let ndindexes =
|
||||
ndindex_model.array_alloca(generator, ctx, num_ndindexes.value, "ndindexes");
|
||||
for (i, in_ndindex) in in_ndindexes.iter().enumerate() {
|
||||
let i = sizet_model.constant(generator, ctx.ctx, i as u64);
|
||||
let pndindex = ndindexes.offset(generator, ctx, i.value, "");
|
||||
in_ndindex.write_to_ndindex(generator, ctx, pndindex);
|
||||
}
|
||||
|
||||
(num_ndindexes, ndindexes)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Get the ndims [`Type`] after indexing with a given slice.
|
||||
pub fn deduce_ndims_after_indexing_with(&self, indexes: &[RustNDIndex<'ctx>]) -> u64 {
|
||||
let mut ndims = self.ndims;
|
||||
for index in indexes {
|
||||
match index {
|
||||
RustNDIndex::SingleElement(_) => {
|
||||
ndims -= 1; // Single elements decrements ndims
|
||||
}
|
||||
RustNDIndex::Slice(_) => {}
|
||||
}
|
||||
}
|
||||
ndims
|
||||
}
|
||||
|
||||
/// Index into the ndarray, and return a newly-allocated view on this ndarray.
|
||||
///
|
||||
/// This function behaves like NumPy's ndarray indexing, but if the indexes index
|
||||
/// into a single element, an unsized ndarray is returned.
|
||||
#[must_use]
|
||||
pub fn index<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
indexes: &[RustNDIndex<'ctx>],
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let dst_ndims = self.deduce_ndims_after_indexing_with(indexes);
|
||||
let dst_ndarray =
|
||||
NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, dst_ndims, name);
|
||||
|
||||
let (num_indexes, indexes) = RustNDIndex::alloca_ndindexes(generator, ctx, indexes);
|
||||
call_nac3_ndarray_index(
|
||||
generator,
|
||||
ctx,
|
||||
num_indexes,
|
||||
indexes,
|
||||
self.value,
|
||||
dst_ndarray.value,
|
||||
);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
|
||||
/// Like [`NDArrayObject::index`] but returns a scalar if the indexes index
|
||||
/// into a single element.
|
||||
#[must_use]
|
||||
pub fn index_or_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
indexes: &[RustNDIndex<'ctx>],
|
||||
name: &str,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
|
||||
let subndarray = self.index(generator, ctx, indexes, name);
|
||||
if subndarray.is_unsized() {
|
||||
// NOTE: `np.size(self) == 0` is impossible.
|
||||
let pfirst = subndarray.get_nth_pelement(generator, ctx, zero, "pfirst");
|
||||
let first = ctx.builder.build_load(pfirst, "first").unwrap();
|
||||
|
||||
ScalarOrNDArray::Scalar(ScalarObject { dtype: self.dtype, value: first })
|
||||
} else {
|
||||
ScalarOrNDArray::NDArray(subndarray)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub mod util {
|
||||
use itertools::Itertools;
|
||||
use nac3parser::ast::{Expr, ExprKind};
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::{RustNDIndex, RustUserSlice};
|
||||
|
||||
/// Generate LLVM code to transform an ndarray subscript expression to
|
||||
/// its list of [`RustNDIndex`]
|
||||
///
|
||||
/// i.e.,
|
||||
/// ```python
|
||||
/// my_ndarray[::3, 1, :2:]
|
||||
/// ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
|
||||
/// ```
|
||||
pub fn gen_ndarray_subscript_ndindexes<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
subscript: &Expr<Option<Type>>,
|
||||
) -> Result<Vec<RustNDIndex<'ctx>>, String> {
|
||||
// TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
|
||||
let i32_model = IntModel(Int32);
|
||||
|
||||
// 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<Expr<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 =
|
||||
i32_model.check_value(generator, 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 = i32_model.check_value(generator, ctx.ctx, index).unwrap();
|
||||
|
||||
RustNDIndex::SingleElement(index)
|
||||
};
|
||||
rust_ndindexes.push(ndindex);
|
||||
}
|
||||
Ok(rust_ndindexes)
|
||||
}
|
||||
}
|
|
@ -0,0 +1,191 @@
|
|||
use inkwell::values::BasicValueEnum;
|
||||
use itertools::Itertools;
|
||||
use util::gen_for_model_auto;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
model::*,
|
||||
structure::ndarray::{
|
||||
broadcast::broadcast_all_ndarrays, scalar::ScalarObject, NDArrayObject,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::scalar::ScalarOrNDArray;
|
||||
|
||||
pub fn starmap_scalars_array_like<'ctx, 'a, F, G>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
inputs: &Vec<ScalarOrNDArray<'ctx>>,
|
||||
ret_dtype: Type,
|
||||
mapping: F,
|
||||
) -> Result<ScalarOrNDArray<'ctx>, String>
|
||||
where
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
Int<'ctx, SizeT>,
|
||||
&Vec<ScalarObject<'ctx>>,
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
assert!(!inputs.is_empty());
|
||||
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
// Check if all inputs are ScalarObjects
|
||||
let scalars: Option<Vec<_>> =
|
||||
inputs.iter().map(|input| ScalarObject::try_from(input)).try_collect().ok();
|
||||
|
||||
match scalars {
|
||||
Some(scalars) => {
|
||||
// When inputs are all scalars, return a ScalarObject back
|
||||
|
||||
let i = sizet_model.const_0(generator, ctx.ctx);
|
||||
let ret = mapping(generator, ctx, i, &scalars)?;
|
||||
|
||||
Ok(ScalarOrNDArray::Scalar(ScalarObject { value: ret, dtype: ret_dtype }))
|
||||
}
|
||||
None => {
|
||||
// When not all inputs are scalars, promote all non-ndarray inputs
|
||||
// to ndarrays, do broadcast_shapes on them, and map.
|
||||
|
||||
let ndarrays =
|
||||
inputs.into_iter().map(|input| input.as_ndarray(generator, ctx)).collect_vec();
|
||||
|
||||
let broadcast_result = broadcast_all_ndarrays(generator, ctx, &ndarrays);
|
||||
|
||||
let mapped_ndarray = NDArrayObject::alloca_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
ret_dtype,
|
||||
broadcast_result.ndims,
|
||||
"mapped_ndarray",
|
||||
);
|
||||
mapped_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
|
||||
mapped_ndarray.create_data(generator, ctx);
|
||||
|
||||
let start = sizet_model.const_0(generator, ctx.ctx);
|
||||
let stop = mapped_ndarray.size(generator, ctx);
|
||||
let step = sizet_model.const_1(generator, ctx.ctx);
|
||||
|
||||
// Map element-wise and store results into `mapped_ndarray`.
|
||||
gen_for_model_auto(
|
||||
generator,
|
||||
ctx,
|
||||
start,
|
||||
stop,
|
||||
step,
|
||||
move |generator, ctx, _hooks, i| {
|
||||
let elements = ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| {
|
||||
let pelement = ndarray.get_nth_pelement(generator, ctx, i, "pelement");
|
||||
let element = ctx.builder.build_load(pelement, "element").unwrap();
|
||||
ScalarObject { value: element, dtype: ndarray.dtype }
|
||||
})
|
||||
.collect_vec();
|
||||
|
||||
let ret = mapping(generator, ctx, i, &elements)?;
|
||||
let pret = mapped_ndarray.get_nth_pelement(generator, ctx, i, "pret");
|
||||
ctx.builder.build_store(pret, ret).unwrap();
|
||||
Ok(())
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(ScalarOrNDArray::NDArray(mapped_ndarray))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarObject<'ctx> {
|
||||
pub fn map<'a, F, G>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ret_dtype: Type,
|
||||
mapping: F,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
Int<'ctx, SizeT>,
|
||||
ScalarObject<'ctx>,
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
let ScalarOrNDArray::Scalar(ret) = starmap_scalars_array_like(
|
||||
generator,
|
||||
ctx,
|
||||
&vec![ScalarOrNDArray::Scalar(*self)],
|
||||
ret_dtype,
|
||||
|generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
|
||||
)?
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
Ok(ret)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
pub fn map<'a, F, G>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ret_dtype: Type,
|
||||
mapping: F,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
Int<'ctx, SizeT>,
|
||||
ScalarObject<'ctx>,
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
let ScalarOrNDArray::NDArray(ret) = starmap_scalars_array_like(
|
||||
generator,
|
||||
ctx,
|
||||
&vec![ScalarOrNDArray::NDArray(*self)],
|
||||
ret_dtype,
|
||||
|generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
|
||||
)?
|
||||
else {
|
||||
unreachable!()
|
||||
};
|
||||
Ok(ret)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
pub fn map<'a, F, G>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ret_dtype: Type,
|
||||
mapping: F,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
Int<'ctx, SizeT>,
|
||||
ScalarObject<'ctx>,
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
match self {
|
||||
ScalarOrNDArray::Scalar(scalar) => {
|
||||
scalar.map(generator, ctx, ret_dtype, mapping).map(ScalarOrNDArray::Scalar)
|
||||
}
|
||||
ScalarOrNDArray::NDArray(ndarray) => {
|
||||
ndarray.map(generator, ctx, ret_dtype, mapping).map(ScalarOrNDArray::NDArray)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
|
@ -0,0 +1,404 @@
|
|||
pub mod broadcast;
|
||||
pub mod indexing;
|
||||
pub mod mapping;
|
||||
pub mod scalar;
|
||||
pub mod shape_util;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{
|
||||
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
|
||||
call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
|
||||
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
|
||||
},
|
||||
model::*,
|
||||
stmt::BreakContinueHooks,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::numpy::unpack_ndarray_var_tys,
|
||||
typecheck::{numpy::extract_ndims, typedef::Type},
|
||||
};
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::BasicType,
|
||||
values::{BasicValue, BasicValueEnum, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use util::{call_memcpy_model, gen_for_model_auto};
|
||||
|
||||
pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub data: F::Out<PtrModel<IntModel<Byte>>>,
|
||||
pub itemsize: F::Out<IntModel<SizeT>>,
|
||||
pub ndims: F::Out<IntModel<SizeT>>,
|
||||
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
|
||||
}
|
||||
|
||||
// TODO: Rename to `NDArray` when the old NDArray is removed.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NpArray;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NpArray {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NpArrayFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
data: traversal.add_auto("data"),
|
||||
itemsize: traversal.add_auto("itemsize"),
|
||||
ndims: traversal.add_auto("ndims"),
|
||||
shape: traversal.add_auto("shape"),
|
||||
strides: traversal.add_auto("strides"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDArrayObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub ndims: u64,
|
||||
pub value: Ptr<'ctx, StructModel<NpArray>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create an [`NDArrayObject`] from an LLVM value and its typechecker [`Type`].
|
||||
pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: V,
|
||||
ty: Type,
|
||||
) -> Self {
|
||||
let pndarray_model = PtrModel(StructModel(NpArray));
|
||||
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
let value = pndarray_model.check_value(generator, ctx.ctx, value).unwrap();
|
||||
NDArrayObject { dtype, ndims, value }
|
||||
}
|
||||
|
||||
/// Get the `np.size()` of this ndarray.
|
||||
pub fn size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_size(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the `ndarray.nbytes` of this ndarray.
|
||||
pub fn nbytes<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_nbytes(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the `len()` of this ndarray.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
call_nac3_ndarray_len(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Check if this ndarray is C-contiguous.
|
||||
///
|
||||
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
|
||||
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Int<'ctx, Bool> {
|
||||
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Get the pointer to the n-th (0-based) element.
|
||||
///
|
||||
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
|
||||
pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Int<'ctx, SizeT>,
|
||||
name: &str,
|
||||
) -> PointerValue<'ctx> {
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
|
||||
|
||||
let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.value, nth);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
|
||||
///
|
||||
/// Please refer to the IRRT implementation to see its purpose.
|
||||
pub fn update_strides_by_shape<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.value);
|
||||
}
|
||||
|
||||
/// Copy data from another ndarray.
|
||||
///
|
||||
/// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
|
||||
/// do not matter. The copying order is determined by how their flattened views look.
|
||||
///
|
||||
/// Panics if the `dtype`s of ndarrays are different.
|
||||
pub fn copy_data_from<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
|
||||
call_nac3_ndarray_copy_data(generator, ctx, src.value, self.value);
|
||||
}
|
||||
|
||||
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
|
||||
///
|
||||
/// `shape` and `strides` will be automatically allocated on the stack.
|
||||
///
|
||||
/// The returned ndarray's content will be:
|
||||
/// - `data`: set to `nullptr`.
|
||||
/// - `itemsize`: set to the `sizeof()` of `dtype`.
|
||||
/// - `ndims`: set to the value of `ndims`.
|
||||
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
|
||||
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
|
||||
pub fn alloca_uninitialized<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let ndarray_model = StructModel(NpArray);
|
||||
let ndarray_data_model = PtrModel(IntModel(Byte));
|
||||
|
||||
let pndarray = ndarray_model.alloca(generator, ctx, name);
|
||||
|
||||
let data = ndarray_data_model.nullptr(generator, ctx.ctx);
|
||||
pndarray.set(ctx, |f| f.data, data);
|
||||
|
||||
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
|
||||
let itemsize =
|
||||
sizet_model.s_extend_or_bit_cast(generator, ctx, itemsize, "alloca_itemsize");
|
||||
pndarray.set(ctx, |f| f.itemsize, itemsize);
|
||||
|
||||
let ndims_val = sizet_model.constant(generator, ctx.ctx, ndims);
|
||||
pndarray.set(ctx, |f| f.ndims, ndims_val);
|
||||
|
||||
let shape = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_shape");
|
||||
pndarray.set(ctx, |f| f.shape, shape);
|
||||
|
||||
let strides = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_strides");
|
||||
pndarray.set(ctx, |f| f.strides, strides);
|
||||
|
||||
NDArrayObject { dtype, ndims, value: pndarray }
|
||||
}
|
||||
|
||||
/// Convenience function.
|
||||
/// Like [`NDArrayObject::alloca_uninitialized`] but directly takes the typechecker type of the ndarray.
|
||||
pub fn alloca_uninitialized_of_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray_ty: Type,
|
||||
name: &str,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
|
||||
let ndims = extract_ndims(&mut ctx.unifier, ndims);
|
||||
Self::alloca_uninitialized(generator, ctx, dtype, ndims, name)
|
||||
}
|
||||
|
||||
/// Get this ndarray's `ndims` as an LLVM constant.
|
||||
pub fn get_ndims<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Int<'ctx, SizeT> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
sizet_model.constant(generator, ctx, self.ndims)
|
||||
}
|
||||
|
||||
/// Return true if this ndarray is unsized.
|
||||
#[must_use]
|
||||
pub fn is_unsized(&self) -> bool {
|
||||
self.ndims == 0
|
||||
}
|
||||
|
||||
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
|
||||
/// The allocated data buffer is considered to be *owned* by the ndarray.
|
||||
///
|
||||
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
|
||||
///
|
||||
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
|
||||
pub fn create_data<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
let byte_model = IntModel(Byte);
|
||||
|
||||
let nbytes = self.nbytes(generator, ctx);
|
||||
|
||||
let data = byte_model.array_alloca(generator, ctx, nbytes.value, "data");
|
||||
self.value.set(ctx, |f| f.data, data);
|
||||
|
||||
self.update_strides_by_shape(generator, ctx);
|
||||
}
|
||||
|
||||
/// Copy shape dimensions from an array.
|
||||
pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let dst_shape = self.value.get(generator, ctx, |f| f.shape, "dst_shape");
|
||||
let num_items = self.get_ndims(generator, ctx.ctx).value;
|
||||
call_memcpy_model(generator, ctx, dst_shape, src_shape, num_items);
|
||||
}
|
||||
|
||||
/// Copy shape dimensions from an ndarray.
|
||||
/// Panics if `ndims` mismatches.
|
||||
pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert_eq!(self.ndims, src_ndarray.ndims);
|
||||
let src_shape = src_ndarray.value.get(generator, ctx, |f| f.shape, "src_shape");
|
||||
self.copy_shape_from_array(generator, ctx, src_shape)
|
||||
}
|
||||
|
||||
/// Copy strides dimensions from an array.
|
||||
pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_strides: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) {
|
||||
let dst_strides = self.value.get(generator, ctx, |f| f.strides, "dst_strides");
|
||||
let num_items = self.get_ndims(generator, ctx.ctx).value;
|
||||
call_memcpy_model(generator, ctx, dst_strides, src_strides, num_items);
|
||||
}
|
||||
|
||||
/// Copy strides dimensions from an ndarray.
|
||||
/// Panics if `ndims` mismatches.
|
||||
pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert_eq!(self.ndims, src_ndarray.ndims);
|
||||
let src_strides = src_ndarray.value.get(generator, ctx, |f| f.strides, "src_strides");
|
||||
self.copy_strides_from_array(generator, ctx, src_strides)
|
||||
}
|
||||
|
||||
/// Loop through every element pointer in the ndarray in its flatten view.
|
||||
///
|
||||
/// `body` also access to [`BreakContinueHooks`] to short-circuit and an element's
|
||||
/// index. The given element pointer also has been casted to the LLVM type of this ndarray's `dtype`.
|
||||
pub fn foreach<'a, G, F>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
body: F,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
Int<'ctx, SizeT>,
|
||||
PointerValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
let sizet_model = IntModel(SizeT);
|
||||
|
||||
let start = sizet_model.const_0(generator, ctx.ctx);
|
||||
let stop = self.size(generator, ctx);
|
||||
let step = sizet_model.const_1(generator, ctx.ctx);
|
||||
|
||||
gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, hooks, i| {
|
||||
let pelement = self.get_nth_pelement(generator, ctx, i, "element");
|
||||
body(generator, ctx, hooks, i, pelement)
|
||||
})
|
||||
}
|
||||
|
||||
/// Fill the NDArray with a value.
|
||||
///
|
||||
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
|
||||
pub fn fill<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
fill_value: BasicValueEnum<'ctx>,
|
||||
) {
|
||||
self.foreach(generator, ctx, |_generator, ctx, _hooks, _i, pelement| {
|
||||
ctx.builder.build_store(pelement, fill_value).unwrap();
|
||||
Ok(())
|
||||
})
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Create a reshaped view on this ndarray like `np.reshape()`.
|
||||
///
|
||||
/// If reshape without copying is impossible, this function will allocate a new ndarray
|
||||
/// and copy contents.
|
||||
///
|
||||
/// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
|
||||
/// * `new_shape` - The target shape to do `np.reshape()`.
|
||||
#[must_use]
|
||||
pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
new_ndims: u64,
|
||||
new_shape: Ptr<'ctx, IntModel<SizeT>>,
|
||||
) -> Self {
|
||||
// TODO: The current criterion for whether to do a full copy or not is by checking `is_c_contiguous`,
|
||||
// but this is not optimal. Look into how numpy does it.
|
||||
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
|
||||
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
|
||||
|
||||
let dst_ndarray = NDArrayObject::alloca_uninitialized(
|
||||
generator,
|
||||
ctx,
|
||||
self.dtype,
|
||||
new_ndims,
|
||||
"reshaped_ndarray",
|
||||
);
|
||||
dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
|
||||
|
||||
let is_c_contiguous = self.is_c_contiguous(generator, ctx);
|
||||
ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
|
||||
|
||||
// Inserting into then_bb: reshape is possible without copying
|
||||
ctx.builder.position_at_end(then_bb);
|
||||
dst_ndarray.update_strides_by_shape(generator, ctx);
|
||||
dst_ndarray.value.set(ctx, |f| f.data, self.value.get(generator, ctx, |f| f.data, "data"));
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Inserting into else_bb: reshape is impossible without copying
|
||||
ctx.builder.position_at_end(else_bb);
|
||||
dst_ndarray.create_data(generator, ctx);
|
||||
dst_ndarray.copy_data_from(generator, ctx, *self);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Reposition for continuation
|
||||
ctx.builder.position_at_end(end_bb);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
}
|
|
@ -0,0 +1,104 @@
|
|||
use inkwell::values::{BasicValue, BasicValueEnum};
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
/// An LLVM numpy scalar with its [`Type`].
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct ScalarObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub value: BasicValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarObject<'ctx> {
|
||||
/// Promote this scalar to an unsized ndarray (like doing `np.asarray`).
|
||||
///
|
||||
/// The scalar value is allocated onto the stack, and the ndarray's `data` will point to that
|
||||
/// allocated value.
|
||||
pub fn as_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
let pbyte_model = PtrModel(IntModel(Byte));
|
||||
|
||||
// We have to put the value on the stack to get a data pointer.
|
||||
let data = ctx.builder.build_alloca(self.value.get_type(), "as_ndarray_scalar").unwrap();
|
||||
ctx.builder.build_store(data, self.value).unwrap();
|
||||
let data = pbyte_model.pointer_cast(generator, ctx, data, "data");
|
||||
|
||||
let ndarray =
|
||||
NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, 0, "scalar_ndarray");
|
||||
ndarray.value.set(ctx, |f| f.data, data);
|
||||
ndarray
|
||||
}
|
||||
}
|
||||
|
||||
/// A convenience enum for implementing scalar/ndarray agnostic utilities.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum ScalarOrNDArray<'ctx> {
|
||||
Scalar(ScalarObject<'ctx>),
|
||||
NDArray(NDArrayObject<'ctx>),
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
|
||||
#[must_use]
|
||||
pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.value,
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.value.value.as_basic_value_enum(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
|
||||
/// - If this is an ndarray, the ndarray is returned.
|
||||
/// - If this is a scalar, an unsized ndarray view is created on it.
|
||||
pub fn as_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.as_ndarray(generator, ctx),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for ScalarObject<'ctx> {
|
||||
type Error = ();
|
||||
|
||||
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
|
||||
match value {
|
||||
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
|
||||
ScalarOrNDArray::NDArray(_) => Err(()),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Split an [`BasicValueEnum<'ctx>`] into a [`ScalarOrNDArray`] depending
|
||||
/// on its [`Type`].
|
||||
pub fn split_scalar_or_ndarray<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
input: BasicValueEnum<'ctx>,
|
||||
input_ty: Type,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
match &*ctx.unifier.get_ty(input_ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, input, input_ty);
|
||||
ScalarOrNDArray::NDArray(ndarray)
|
||||
}
|
||||
_ => {
|
||||
let scalar = ScalarObject { dtype: input_ty, value: input };
|
||||
ScalarOrNDArray::Scalar(scalar)
|
||||
}
|
||||
}
|
||||
}
|
|
@ -0,0 +1,118 @@
|
|||
use inkwell::values::BasicValueEnum;
|
||||
use util::gen_for_model_auto;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
model::*,
|
||||
structure::list::{List, ListObject},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
/// Parse a NumPy-like "int sequence" input and return the int sequence as a [`ListObject`]
|
||||
///
|
||||
/// * `sequence` - The `sequence` parameter.
|
||||
/// * `sequence_ty` - The typechecker type of `sequence`
|
||||
///
|
||||
/// The `sequence` argument type may only be one of the following:
|
||||
/// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
|
||||
/// 2. A tuple of `int32`; e.g., `np.empty((600, 800, 3))`
|
||||
/// 3. A scalar `int32`; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
|
||||
///
|
||||
/// `int32` values will be sign-extended to `SizeT`
|
||||
pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
sequence: BasicValueEnum<'ctx>,
|
||||
sequence_ty: Type,
|
||||
) -> ListObject<'ctx, IntModel<SizeT>, SizeT> {
|
||||
let sizet_model = IntModel(SizeT);
|
||||
let list_model = StructModel(List { len: SizeT, item: IntModel(SizeT) });
|
||||
|
||||
let zero = sizet_model.const_0(generator, ctx.ctx);
|
||||
let one = sizet_model.const_1(generator, ctx.ctx);
|
||||
|
||||
// The result `list` to return.
|
||||
let result = list_model.alloca(generator, ctx, "result_sequence");
|
||||
match &*ctx.unifier.get_ty(sequence_ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
|
||||
let in_sequence_model =
|
||||
PtrModel(StructModel(List { item: IntModel(Int32), len: SizeT }));
|
||||
let in_sequence = in_sequence_model.check_value(generator, ctx.ctx, sequence).unwrap();
|
||||
|
||||
/*
|
||||
Reference code:
|
||||
```
|
||||
result.size = sequence.size;
|
||||
result.data = __builtin_alloca(sizeof(SizeT) * sequence.size);
|
||||
for (SizeT i = 0; i < sequence.size; i++) {
|
||||
result.data[i] = (SizeT) sequence.data[i];
|
||||
}
|
||||
return result
|
||||
```
|
||||
*/
|
||||
|
||||
let ndims = in_sequence.get(generator, ctx, |f| f.len, "size");
|
||||
result.set(ctx, |f| f.len, ndims);
|
||||
|
||||
let result_data = sizet_model.array_alloca(generator, ctx, ndims.value, "data");
|
||||
result.set(ctx, |f| f.items, result_data);
|
||||
|
||||
gen_for_model_auto(generator, ctx, zero, ndims, one, |generator, ctx, _hooks, i| {
|
||||
let in_dim = in_sequence
|
||||
.get(generator, ctx, |f| f.items, "in_dim")
|
||||
.ix(generator, ctx, i.value, "in_dim")
|
||||
.s_extend_or_bit_cast(generator, ctx, SizeT, "in_dim");
|
||||
result_data.offset(generator, ctx, i.value, "dim").store(ctx, in_dim);
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
}
|
||||
TypeEnum::TTuple { ty: tuple_types } => {
|
||||
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
|
||||
let ndims_int = tuple_types.len();
|
||||
let ndims = sizet_model.constant(generator, ctx.ctx, ndims_int as u64);
|
||||
result.set(ctx, |f| f.len, ndims);
|
||||
|
||||
// A tuple has to be a StructValue
|
||||
// Read [`codegen::expr::gen_expr`] to see how `nac3core` translates a Python tuple into LLVM.
|
||||
let tuple = sequence.into_struct_value();
|
||||
let data = sizet_model.array_alloca(generator, ctx, ndims.value, "sequence_data");
|
||||
result.set(ctx, |f| f.items, data);
|
||||
|
||||
for i in 0..ndims_int {
|
||||
// Get the i-th (0-based) element off of the tuple and load it
|
||||
// into `result`.
|
||||
let dim = ctx
|
||||
.builder
|
||||
.build_extract_value(tuple, i as u32, format!("dim").as_str())
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
let dim = sizet_model.s_extend_or_bit_cast(generator, ctx, dim, "dim");
|
||||
|
||||
let offset = sizet_model.constant(generator, ctx.ctx, i as u64);
|
||||
data.offset(generator, ctx, offset.value, "dim").store(ctx, dim);
|
||||
}
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
|
||||
let sequence_int = sizet_model.check_value(generator, ctx.ctx, sequence).unwrap();
|
||||
|
||||
// Size is 1
|
||||
result.set(ctx, |f| f.len, one);
|
||||
|
||||
// Alloca an array of length 1 and store the sole integer input into the array.
|
||||
let data = sizet_model.array_alloca(generator, ctx, one.value, "data");
|
||||
data.offset(generator, ctx, zero.value, "dim").store(ctx, sequence_int);
|
||||
}
|
||||
_ => panic!("encountered unknown sequence type: {}", ctx.unifier.stringify(sequence_ty)),
|
||||
}
|
||||
|
||||
ListObject { item_type: ctx.primitives.usize(), value: result }
|
||||
}
|
|
@ -0,0 +1,67 @@
|
|||
use inkwell::values::{BasicValueEnum, StructValue};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
pub struct TupleObject<'ctx> {
|
||||
pub tys: Vec<Type>,
|
||||
pub value: StructValue<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> TupleObject<'ctx> {
|
||||
pub fn create<I, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
items: I,
|
||||
name: &str,
|
||||
) -> Self
|
||||
where
|
||||
I: IntoIterator<Item = (BasicValueEnum<'ctx>, Type)>,
|
||||
{
|
||||
let (vals, tys): (Vec<_>, Vec<_>) = items.into_iter().unzip();
|
||||
|
||||
// let tuple_ty = ctx.unifier.add_ty(TypeEnum::TTuple { ty: tys });
|
||||
let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
|
||||
let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
|
||||
let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
|
||||
for (i, val) in vals.into_iter().enumerate() {
|
||||
// Store the dim value into the tuple
|
||||
let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
|
||||
ctx.builder.build_store(pval, val).unwrap();
|
||||
}
|
||||
|
||||
let value = ctx.builder.build_load(pllvm_tuple, name).unwrap().into_struct_value();
|
||||
TupleObject { tys, value }
|
||||
}
|
||||
|
||||
// pub fn create_from_array<G: CodeGenerator + ?Sized>(
|
||||
// generator: &mut G,
|
||||
// ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
// array: PointerValue<'ctx>,
|
||||
// elem_ty: Type,
|
||||
// count: u64,
|
||||
// name: &str,
|
||||
// ) -> Self {
|
||||
// let i32_type = ctx.ctx.i32_type();
|
||||
|
||||
// let mut items: Vec<(BasicValueEnum<'ctx>, Type)> = Vec::with_capacity(count as usize);
|
||||
|
||||
// for i in 0..count {
|
||||
// let pval = unsafe {
|
||||
// ctx.builder.build_in_bounds_gep(
|
||||
// array,
|
||||
// &[i32_type.const_zero(), i32_type.const_int(i as u64, false)],
|
||||
// name,
|
||||
// )
|
||||
// }
|
||||
// .unwrap();
|
||||
// let val = ctx.builder.build_load(pval, "value").unwrap();
|
||||
// items.push((val, elem_ty));
|
||||
// }
|
||||
|
||||
// Self::create(generator, ctx, items, name)
|
||||
// }
|
||||
}
|
|
@ -14,15 +14,21 @@ use strum::IntoEnumIterator;
|
|||
use crate::{
|
||||
codegen::{
|
||||
builtin_fns,
|
||||
classes::{ArrayLikeValue, NDArrayValue, ProxyValue, RangeValue, TypedArrayLikeAccessor},
|
||||
classes::{ProxyValue, RangeValue},
|
||||
expr::destructure_range,
|
||||
irrt::*,
|
||||
model::Int32,
|
||||
numpy::*,
|
||||
numpy_new::{self, gen_ndarray_transpose},
|
||||
stmt::exn_constructor,
|
||||
structure::ndarray::NDArrayObject,
|
||||
},
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::{helper::PrimDef, numpy::make_ndarray_ty},
|
||||
typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
|
||||
typecheck::{
|
||||
numpy::create_ndims,
|
||||
typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
|
||||
},
|
||||
};
|
||||
|
||||
use super::*;
|
||||
|
@ -490,7 +496,16 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
PrimDef::FunNpArray
|
||||
| PrimDef::FunNpFull
|
||||
| PrimDef::FunNpEye
|
||||
| PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
|
||||
| PrimDef::FunNpIdentity
|
||||
| PrimDef::FunNpArange => self.build_ndarray_other_factory_function(prim),
|
||||
|
||||
PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape | PrimDef::FunNpTranspose => {
|
||||
self.build_ndarray_view_function(prim)
|
||||
}
|
||||
|
||||
PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
|
||||
self.build_ndarray_property_getter_function(prim)
|
||||
}
|
||||
|
||||
PrimDef::FunStr => self.build_str_function(),
|
||||
|
||||
|
@ -557,10 +572,6 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
| PrimDef::FunNpHypot
|
||||
| PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
|
||||
|
||||
PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
|
||||
self.build_np_sp_ndarray_function(prim)
|
||||
}
|
||||
|
||||
PrimDef::FunNpDot
|
||||
| PrimDef::FunNpLinalgCholesky
|
||||
| PrimDef::FunNpLinalgQr
|
||||
|
@ -1218,9 +1229,9 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
&[(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
|
||||
Box::new(move |ctx, obj, fun, args, generator| {
|
||||
let func = match prim {
|
||||
PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty,
|
||||
PrimDef::FunNpZeros => gen_ndarray_zeros,
|
||||
PrimDef::FunNpOnes => gen_ndarray_ones,
|
||||
PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => numpy_new::gen_ndarray_empty,
|
||||
PrimDef::FunNpZeros => numpy_new::gen_ndarray_zeros,
|
||||
PrimDef::FunNpOnes => numpy_new::gen_ndarray_ones,
|
||||
_ => unreachable!(),
|
||||
};
|
||||
func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
|
||||
|
@ -1234,7 +1245,13 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
fn build_ndarray_other_factory_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(
|
||||
prim,
|
||||
&[PrimDef::FunNpArray, PrimDef::FunNpFull, PrimDef::FunNpEye, PrimDef::FunNpIdentity],
|
||||
&[
|
||||
PrimDef::FunNpArray,
|
||||
PrimDef::FunNpFull,
|
||||
PrimDef::FunNpEye,
|
||||
PrimDef::FunNpIdentity,
|
||||
PrimDef::FunNpArange,
|
||||
],
|
||||
);
|
||||
|
||||
let PrimitiveStore { int32, bool, ndarray, .. } = *self.primitives;
|
||||
|
@ -1288,7 +1305,7 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
// type variable
|
||||
&[(self.list_int32, "shape"), (tv.ty, "fill_value")],
|
||||
Box::new(move |ctx, obj, fun, args, generator| {
|
||||
gen_ndarray_full(ctx, &obj, fun, &args, generator)
|
||||
numpy_new::gen_ndarray_full(ctx, &obj, fun, &args, generator)
|
||||
.map(|val| Some(val.as_basic_value_enum()))
|
||||
}),
|
||||
)
|
||||
|
@ -1339,6 +1356,150 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
.map(|val| Some(val.as_basic_value_enum()))
|
||||
}),
|
||||
),
|
||||
PrimDef::FunNpArange => {
|
||||
// TODO: Support `np.arange(start, stop, step)`
|
||||
let ndims1 = create_ndims(self.unifier, 1);
|
||||
let ndarray_float_1d = make_ndarray_ty(
|
||||
self.unifier,
|
||||
&self.primitives,
|
||||
Some(self.primitives.float),
|
||||
Some(ndims1),
|
||||
);
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&VarMap::new(),
|
||||
prim.name(),
|
||||
ndarray_float_1d,
|
||||
&[(int32, "n")],
|
||||
Box::new(|ctx, obj, fun, args, generator| {
|
||||
numpy_new::gen_ndarray_arange(ctx, &obj, fun, &args, generator)
|
||||
.map(|val| Some(val.as_basic_value_enum()))
|
||||
}),
|
||||
)
|
||||
}
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(
|
||||
prim,
|
||||
&[PrimDef::FunNpBroadcastTo, PrimDef::FunNpReshape, PrimDef::FunNpTranspose],
|
||||
);
|
||||
|
||||
match prim {
|
||||
PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape => {
|
||||
// `array_ty` can be ndarrays and arbitrary scalars and objects.
|
||||
let array_tvar = self.unifier.get_dummy_var();
|
||||
|
||||
// The return type is handled by special folding in the type inferencer,
|
||||
// since the returned `ndims` depends on input shape.
|
||||
let return_tvar = self.unifier.get_dummy_var();
|
||||
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&into_var_map([array_tvar, return_tvar]),
|
||||
prim.name(),
|
||||
return_tvar.ty,
|
||||
&[
|
||||
(array_tvar.ty, "array"),
|
||||
(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"),
|
||||
],
|
||||
Box::new(move |ctx, obj, fun, args, generator| {
|
||||
let f = match prim {
|
||||
PrimDef::FunNpBroadcastTo => numpy_new::gen_ndarray_broadcast_to,
|
||||
PrimDef::FunNpReshape => numpy_new::gen_ndarray_reshape,
|
||||
_ => unreachable!(),
|
||||
};
|
||||
f(ctx, &obj, fun, &args, generator).map(Some)
|
||||
}),
|
||||
)
|
||||
}
|
||||
PrimDef::FunNpTranspose => {
|
||||
// TODO: Allow tuple inputs.
|
||||
// TODO: Support scalar inputs (difficult)
|
||||
|
||||
// TODO: Default values don't work for some reason.
|
||||
// `axes` should have been `Option[List[int32]]` with default `None`.
|
||||
// Workaround with some bogus types and values for now.
|
||||
|
||||
let axes_ty = self.list_int32;
|
||||
|
||||
TopLevelDef::Function {
|
||||
name: prim.name().into(),
|
||||
simple_name: prim.simple_name().into(),
|
||||
signature: self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
|
||||
args: vec![
|
||||
FuncArg {
|
||||
name: "a".into(),
|
||||
ty: self.primitives.ndarray,
|
||||
default_value: None,
|
||||
},
|
||||
FuncArg {
|
||||
name: "axes".into(),
|
||||
ty: axes_ty,
|
||||
default_value: Some(SymbolValue::OptionNone), // Bogus
|
||||
},
|
||||
],
|
||||
ret: self.primitives.ndarray,
|
||||
vars: VarMap::new(),
|
||||
})),
|
||||
var_id: Vec::default(),
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|ctx, obj, fun, args, generator| {
|
||||
gen_ndarray_transpose(ctx, &obj, fun, &args, generator).map(Some)
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
}
|
||||
}
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(
|
||||
prim,
|
||||
&[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
|
||||
);
|
||||
|
||||
match prim {
|
||||
PrimDef::FunNpSize => {
|
||||
// TODO: Make the return type usize
|
||||
create_fn_by_codegen(
|
||||
&mut self.unifier,
|
||||
&VarMap::new(),
|
||||
prim.name(),
|
||||
self.primitives.int32,
|
||||
&[(self.primitives.ndarray, "a")],
|
||||
Box::new(|ctx, obj, fun, args, generator| {
|
||||
numpy_new::gen_ndarray_size(ctx, &obj, fun, &args, generator).map(Some)
|
||||
}),
|
||||
)
|
||||
}
|
||||
PrimDef::FunNpShape | PrimDef::FunNpStrides => {
|
||||
// The return type is a tuple of variable length depending on the ndims
|
||||
// of the input ndarray.
|
||||
let ret_ty = self.unifier.get_dummy_var().ty;
|
||||
create_fn_by_codegen(
|
||||
&mut self.unifier,
|
||||
&VarMap::new(),
|
||||
prim.name(),
|
||||
ret_ty,
|
||||
&[(self.primitives.ndarray, "a")],
|
||||
Box::new(move |ctx, obj, fun, args, generator| {
|
||||
let f = match prim {
|
||||
PrimDef::FunNpShape => numpy_new::gen_ndarray_shape,
|
||||
PrimDef::FunNpStrides => numpy_new::gen_ndarray_strides,
|
||||
_ => unreachable!(),
|
||||
};
|
||||
f(ctx, &obj, fun, &args, generator).map(Some)
|
||||
}),
|
||||
)
|
||||
}
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
@ -1480,51 +1641,11 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
}
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let arg = NDArrayValue::from_ptr_val(
|
||||
arg.into_pointer_value(),
|
||||
llvm_usize,
|
||||
None,
|
||||
);
|
||||
|
||||
let ndims = arg.dim_sizes().size(ctx, generator);
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
ctx.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::NE,
|
||||
ndims,
|
||||
llvm_usize.const_zero(),
|
||||
"",
|
||||
)
|
||||
.unwrap(),
|
||||
"0:TypeError",
|
||||
&format!("{name}() of unsized object", name = prim.name()),
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
let len = unsafe {
|
||||
arg.dim_sizes().get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_zero(),
|
||||
None,
|
||||
)
|
||||
};
|
||||
|
||||
if len.get_type().get_bit_width() == 32 {
|
||||
Some(len.into())
|
||||
} else {
|
||||
Some(
|
||||
ctx.builder
|
||||
.build_int_truncate(len, llvm_i32, "len")
|
||||
.map(Into::into)
|
||||
.unwrap(),
|
||||
)
|
||||
}
|
||||
let ndarray =
|
||||
NDArrayObject::from_value_and_type(generator, ctx, arg, arg_ty);
|
||||
let len = ndarray.len(generator, ctx);
|
||||
let len = len.truncate(generator, ctx, Int32, "len"); // TODO: Currently `len()` returns an int32. It should have been SizeT
|
||||
Some(len.value.as_basic_value_enum())
|
||||
}
|
||||
_ => unreachable!(),
|
||||
}
|
||||
|
@ -1890,57 +2011,6 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
}
|
||||
}
|
||||
|
||||
/// Build np/sp functions that take as input `NDArray` only
|
||||
fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
|
||||
|
||||
match prim {
|
||||
PrimDef::FunNpTranspose => {
|
||||
let ndarray_ty = self.unifier.get_fresh_var_with_range(
|
||||
&[self.ndarray_num_ty],
|
||||
Some("T".into()),
|
||||
None,
|
||||
);
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&into_var_map([ndarray_ty]),
|
||||
prim.name(),
|
||||
ndarray_ty.ty,
|
||||
&[(ndarray_ty.ty, "x")],
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let arg_ty = fun.0.args[0].ty;
|
||||
let arg_val =
|
||||
args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
|
||||
Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
|
||||
// the `param_ty` for `create_fn_by_codegen`.
|
||||
//
|
||||
// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
|
||||
// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
|
||||
// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
|
||||
PrimDef::FunNpReshape => create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&VarMap::new(),
|
||||
prim.name(),
|
||||
self.ndarray_num_ty,
|
||||
&[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let x1_ty = fun.0.args[0].ty;
|
||||
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
|
||||
let x2_ty = fun.0.args[1].ty;
|
||||
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
|
||||
Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
|
||||
}),
|
||||
),
|
||||
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Build `np_linalg` and `sp_linalg` functions
|
||||
///
|
||||
/// The input to these functions must be floating point `NDArray`
|
||||
|
|
|
@ -51,6 +51,17 @@ pub enum PrimDef {
|
|||
FunNpArray,
|
||||
FunNpEye,
|
||||
FunNpIdentity,
|
||||
FunNpArange,
|
||||
|
||||
// NumPy view functions
|
||||
FunNpBroadcastTo,
|
||||
FunNpReshape,
|
||||
FunNpTranspose,
|
||||
|
||||
// NumPy NDArray property getters
|
||||
FunNpSize,
|
||||
FunNpShape,
|
||||
FunNpStrides,
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
FunNpRound,
|
||||
|
@ -99,8 +110,6 @@ pub enum PrimDef {
|
|||
FunNpLdExp,
|
||||
FunNpHypot,
|
||||
FunNpNextAfter,
|
||||
FunNpTranspose,
|
||||
FunNpReshape,
|
||||
|
||||
// Linalg functions
|
||||
FunNpDot,
|
||||
|
@ -237,6 +246,17 @@ impl PrimDef {
|
|||
PrimDef::FunNpArray => fun("np_array", None),
|
||||
PrimDef::FunNpEye => fun("np_eye", None),
|
||||
PrimDef::FunNpIdentity => fun("np_identity", None),
|
||||
PrimDef::FunNpArange => fun("np_arange", None),
|
||||
|
||||
// NumPy view functions
|
||||
PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
|
||||
PrimDef::FunNpReshape => fun("np_reshape", None),
|
||||
PrimDef::FunNpTranspose => fun("np_transpose", None),
|
||||
|
||||
// NumPy NDArray property getters
|
||||
PrimDef::FunNpSize => fun("np_size", None),
|
||||
PrimDef::FunNpShape => fun("np_shape", None),
|
||||
PrimDef::FunNpStrides => fun("np_strides", None),
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
PrimDef::FunNpRound => fun("np_round", None),
|
||||
|
@ -285,8 +305,6 @@ impl PrimDef {
|
|||
PrimDef::FunNpLdExp => fun("np_ldexp", None),
|
||||
PrimDef::FunNpHypot => fun("np_hypot", None),
|
||||
PrimDef::FunNpNextAfter => fun("np_nextafter", None),
|
||||
PrimDef::FunNpTranspose => fun("np_transpose", None),
|
||||
PrimDef::FunNpReshape => fun("np_reshape", None),
|
||||
|
||||
// Linalg functions
|
||||
PrimDef::FunNpDot => fun("np_dot", None),
|
||||
|
|
|
@ -1,5 +1,6 @@
|
|||
mod function_check;
|
||||
pub mod magic_methods;
|
||||
pub mod numpy;
|
||||
pub mod type_error;
|
||||
pub mod type_inferencer;
|
||||
pub mod typedef;
|
||||
|
|
|
@ -0,0 +1,33 @@
|
|||
use crate::{symbol_resolver::SymbolValue, typecheck::typedef::TypeEnum};
|
||||
|
||||
use super::typedef::{Type, Unifier};
|
||||
|
||||
/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
|
||||
/// The `ndims` must only contain 1 value.
|
||||
#[must_use]
|
||||
pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
|
||||
let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
|
||||
let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
|
||||
panic!("ndims_ty should be a TLiteral");
|
||||
};
|
||||
|
||||
assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
|
||||
|
||||
let ndims = values[0].clone();
|
||||
u64::try_from(ndims).unwrap()
|
||||
}
|
||||
|
||||
/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
|
||||
pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
|
||||
unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
|
||||
}
|
||||
|
||||
/// Return the ndims after broadcasting ndarrays of different ndims.
|
||||
///
|
||||
/// Panics if the input list is empty.
|
||||
pub fn get_broadcast_all_ndims<I>(ndims: I) -> u64
|
||||
where
|
||||
I: IntoIterator<Item = u64>,
|
||||
{
|
||||
ndims.into_iter().max().unwrap()
|
||||
}
|
|
@ -1,6 +1,6 @@
|
|||
use std::collections::{HashMap, HashSet};
|
||||
use std::convert::{From, TryInto};
|
||||
use std::iter::once;
|
||||
use std::iter::{self, once};
|
||||
use std::{cell::RefCell, sync::Arc};
|
||||
|
||||
use super::{
|
||||
|
@ -11,6 +11,7 @@ use super::{
|
|||
RecordField, RecordKey, Type, TypeEnum, TypeVar, Unifier, VarMap,
|
||||
},
|
||||
};
|
||||
use crate::typecheck::numpy::extract_ndims;
|
||||
use crate::{
|
||||
symbol_resolver::{SymbolResolver, SymbolValue},
|
||||
toplevel::{
|
||||
|
@ -1259,7 +1260,7 @@ impl<'a> Inferencer<'a> {
|
|||
arg_ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
|
||||
}) {
|
||||
// typeof_ndarray_broadcast requires both dtypes to be the same, but ldexp accepts
|
||||
// (float, int32), so convert it to align with the dtype of the first arg
|
||||
// (float, int32), so convert it to align with t#he dtype of the first arg
|
||||
let arg1_ty = if id == &"np_ldexp".into() {
|
||||
if arg1_ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) {
|
||||
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, arg1_ty);
|
||||
|
@ -1423,7 +1424,7 @@ impl<'a> Inferencer<'a> {
|
|||
},
|
||||
}));
|
||||
}
|
||||
// 2-argument ndarray n-dimensional factory functions
|
||||
|
||||
if id == &"np_reshape".into() && args.len() == 2 {
|
||||
let arg0 = self.fold_expr(args.remove(0))?;
|
||||
|
||||
|
@ -1462,6 +1463,49 @@ impl<'a> Inferencer<'a> {
|
|||
},
|
||||
}));
|
||||
}
|
||||
|
||||
if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
|
||||
// Output tuple size depends on input ndarray's ndims.
|
||||
|
||||
let ndarray = self.fold_expr(args.remove(0))?;
|
||||
|
||||
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, ndarray.custom.unwrap());
|
||||
let ndims = extract_ndims(self.unifier, ndims);
|
||||
|
||||
// Create a tuple of size `ndims` full of int32
|
||||
// TODO: Make it usize
|
||||
|
||||
let ret_ty = TypeEnum::TTuple {
|
||||
ty: iter::repeat(self.primitives.int32).take(ndims as usize).collect_vec(),
|
||||
};
|
||||
let ret_ty = self.unifier.add_ty(ret_ty);
|
||||
|
||||
let func_ty = TypeEnum::TFunc(FunSignature {
|
||||
args: vec![FuncArg {
|
||||
name: "a".into(),
|
||||
default_value: None,
|
||||
ty: ndarray.custom.unwrap(),
|
||||
}],
|
||||
ret: ret_ty,
|
||||
vars: VarMap::new(),
|
||||
});
|
||||
let func_ty = self.unifier.add_ty(func_ty);
|
||||
|
||||
return Ok(Some(Located {
|
||||
location,
|
||||
custom: Some(ret_ty),
|
||||
node: ExprKind::Call {
|
||||
func: Box::new(Located {
|
||||
custom: Some(func_ty),
|
||||
location: func.location,
|
||||
node: ExprKind::Name { id: *id, ctx: *ctx },
|
||||
}),
|
||||
args: vec![ndarray],
|
||||
keywords: vec![],
|
||||
},
|
||||
}));
|
||||
}
|
||||
|
||||
// 2-argument ndarray n-dimensional creation functions
|
||||
if id == &"np_full".into() && args.len() == 2 {
|
||||
let ExprKind::List { elts, .. } = &args[0].node else {
|
||||
|
|
|
@ -218,8 +218,16 @@ def patch(module):
|
|||
module.np_ldexp = np.ldexp
|
||||
module.np_hypot = np.hypot
|
||||
module.np_nextafter = np.nextafter
|
||||
module.np_transpose = np.transpose
|
||||
|
||||
# NumPy view functions
|
||||
module.np_broadcast_to = np.broadcast_to
|
||||
module.np_reshape = np.reshape
|
||||
module.np_transpose = np.transpose
|
||||
|
||||
# NumPy NDArray property getter functions
|
||||
module.np_size = np.size
|
||||
module.np_shape = np.shape
|
||||
module.np_strides = lambda ndarray: ndarray.strides
|
||||
|
||||
# SciPy Math functions
|
||||
module.sp_spec_erf = special.erf
|
||||
|
|
Loading…
Reference in New Issue