forked from M-Labs/nac3
core: irrt ndarray setup
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#pragma once
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#include <irrt/int_defs.hpp>
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#include <irrt/ndarray/ndarray_util.hpp>
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namespace {
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// The NDArray object. `SizeT` is the *signed* size type of this ndarray.
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//
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// NOTE: The order of fields is IMPORTANT. DON'T TOUCH IT
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//
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// Some resources you might find helpful:
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// - The official numpy implementations:
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// - https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
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// - On strides (about reshaping, slicing, C-contagiousness, etc)
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// - https://ajcr.net/stride-guide-part-1/.
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// - https://ajcr.net/stride-guide-part-2/.
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// - https://ajcr.net/stride-guide-part-3/.
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template <typename SizeT>
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struct NDArray {
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// The underlying data this `ndarray` is pointing to.
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//
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// NOTE: Formally this should be of type `void *`, but clang
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// translates `void *` to `i8 *` when run with `-S -emit-llvm`,
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// so we will put `uint8_t *` here for clarity.
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//
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// This pointer should point to the first element of the ndarray directly
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uint8_t *data;
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// The number of bytes of a single element in `data`.
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//
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// The `SizeT` is treated as `unsigned`.
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SizeT itemsize;
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// The number of dimensions of this shape.
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//
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// The `SizeT` is treated as `unsigned`.
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SizeT ndims;
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// Array shape, with length equal to `ndims`.
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//
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// The `SizeT` is treated as `unsigned`.
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//
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// NOTE: `shape` can contain 0.
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// (those appear when the user makes an out of bounds slice into an ndarray, e.g., `np.zeros((3, 3))[400:].shape == (0, 3)`)
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SizeT *shape;
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// Array strides (stride value is in number of bytes, NOT number of elements), with length equal to `ndims`.
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//
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// The `SizeT` is treated as `signed`.
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//
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// NOTE: `strides` can have negative numbers.
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// (those appear when there is a slice with a negative step, e.g., `my_array[::-1]`)
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SizeT *strides;
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// Calculate the size/# of elements of an `ndarray`.
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// This function corresponds to `np.size(<ndarray>)` or `ndarray.size`
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SizeT size() {
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return ndarray_util::calc_size_from_shape(ndims, shape);
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}
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// Calculate the number of bytes of its content of an `ndarray` *in its view*.
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// This function corresponds to `ndarray.nbytes`
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SizeT nbytes() {
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return this->size() * itemsize;
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}
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// Set the strides of the ndarray with `ndarray_util::set_strides_by_shape`
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void set_strides_by_shape() {
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ndarray_util::set_strides_by_shape(itemsize, ndims, strides, shape);
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}
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uint8_t* get_pelement_by_indices(const SizeT *indices) {
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uint8_t* element = data;
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for (SizeT dim_i = 0; dim_i < ndims; dim_i++)
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element += indices[dim_i] * strides[dim_i];
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return element;
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}
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uint8_t* get_nth_pelement(SizeT nth) {
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SizeT* indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * this->ndims);
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ndarray_util::set_indices_by_nth(this->ndims, this->shape, indices, nth);
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return get_pelement_by_indices(indices);
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}
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// Get the pointer to the nth element of the ndarray as if it were flattened.
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uint8_t* checked_get_nth_pelement(ErrorContext* errctx, SizeT nth) {
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SizeT arr_size = this->size();
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if (!(0 <= nth && nth < arr_size)) {
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errctx->set_error(
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errctx->error_ids->index_error,
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"index {0} is out of bounds, valid range is {1} <= index < {2}",
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nth, 0, arr_size
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);
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return 0;
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}
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return get_nth_pelement(nth);
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}
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};
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}
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extern "C" {
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uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
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return ndarray->size();
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}
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uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
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return ndarray->size();
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}
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uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
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return ndarray->nbytes();
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}
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uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
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return ndarray->nbytes();
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}
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void __nac3_ndarray_util_assert_shape_no_negative(ErrorContext* errctx, int32_t ndims, int32_t* shape) {
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ndarray_util::assert_shape_no_negative(errctx, ndims, shape);
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}
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void __nac3_ndarray_util_assert_shape_no_negative64(ErrorContext* errctx, int64_t ndims, int64_t* shape) {
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ndarray_util::assert_shape_no_negative(errctx, ndims, shape);
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}
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void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t>* ndarray) {
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ndarray->set_strides_by_shape();
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}
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void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
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ndarray->set_strides_by_shape();
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}
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}
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#pragma once
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#include <irrt/int_defs.hpp>
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namespace {
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namespace ndarray_util {
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// Throw an error if there is an axis with negative dimension
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template <typename SizeT>
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void assert_shape_no_negative(ErrorContext* errctx, SizeT ndims, const SizeT* shape) {
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for (SizeT axis = 0; axis < ndims; axis++) {
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if (shape[axis] < 0) {
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errctx->set_error(
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errctx->error_ids->value_error,
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"negative dimensions are not allowed; axis {0} has dimension {1}",
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axis, shape[axis]
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);
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return;
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}
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}
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}
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// Compute the size/# of elements of an ndarray given its shape
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template <typename SizeT>
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SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
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SizeT size = 1;
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for (SizeT axis = 0; axis < ndims; axis++) size *= shape[axis];
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return size;
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}
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// Compute the strides of an ndarray given an ndarray `shape`
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// and assuming that the ndarray is *fully C-contagious*.
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//
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// You might want to read up on https://ajcr.net/stride-guide-part-1/.
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template <typename SizeT>
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void set_strides_by_shape(SizeT itemsize, SizeT ndims, SizeT* dst_strides, const SizeT* shape) {
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SizeT stride_product = 1;
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for (SizeT i = 0; i < ndims; i++) {
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int axis = ndims - i - 1;
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dst_strides[axis] = stride_product * itemsize;
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stride_product *= shape[axis];
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}
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}
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template <typename SizeT>
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void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices, SizeT nth) {
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for (int32_t i = 0; i < ndims; i++) {
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int32_t axis = ndims - i - 1;
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int32_t dim = shape[axis];
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indices[axis] = nth % dim;
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nth /= dim;
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}
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}
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template <typename SizeT>
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bool can_broadcast_shape_to(
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const SizeT target_ndims,
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const SizeT *target_shape,
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const SizeT src_ndims,
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const SizeT *src_shape
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) {
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/*
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// See https://numpy.org/doc/stable/user/basics.broadcasting.html
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This function handles this example:
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```
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Image (3d array): 256 x 256 x 3
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Scale (1d array): 3
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Result (3d array): 256 x 256 x 3
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```
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Other interesting examples to consider:
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- `can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) == true`
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- `can_broadcast_shape_to([3], [3, 1]) == false`
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- `can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) == true`
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In cases when the shapes contain zero(es):
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- `can_broadcast_shape_to([0], [1]) == true`
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- `can_broadcast_shape_to([0], [2]) == false`
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- `can_broadcast_shape_to([0, 4, 0, 0], [1]) == true`
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- `can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) == true`
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- `can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) == true`
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- `can_broadcast_shape_to([4, 3], [0, 3]) == false`
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- `can_broadcast_shape_to([4, 3], [0, 0]) == false`
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*/
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// This is essentially doing the following in Python:
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// `for target_dim, src_dim in itertools.zip_longest(target_shape[::-1], src_shape[::-1], fillvalue=1)`
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for (SizeT i = 0; i < max(target_ndims, src_ndims); i++) {
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SizeT target_axis = target_ndims - i - 1;
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SizeT src_axis = src_ndims - i - 1;
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bool target_dim_exists = target_axis >= 0;
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bool src_dim_exists = src_axis >= 0;
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SizeT target_dim = target_dim_exists ? target_shape[target_axis] : 1;
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SizeT src_dim = src_dim_exists ? src_shape[src_axis] : 1;
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bool ok = src_dim == 1 || target_dim == src_dim;
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if (!ok) return false;
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}
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return true;
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}
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}
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}
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#include <irrt/core.hpp>
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#include <irrt/error_context.hpp>
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#include <irrt/int_defs.hpp>
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#include <irrt/utils.hpp>
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#include <irrt/utils.hpp>
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#include <irrt/ndarray/ndarray.hpp>
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@ -1,6 +1,11 @@
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use inkwell::types::{BasicTypeEnum, IntType};
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use inkwell::types::IntType;
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use crate::codegen::optics::{AddressLens, FieldBuilder, GepGetter, IntLens, StructureOptic};
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use crate::codegen::{
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optics::{
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Address, AddressLens, ArraySlice, FieldBuilder, GepGetter, IntLens, Optic, StructureOptic,
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},
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CodeGenContext,
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};
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#[derive(Debug, Clone)]
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pub struct StrLens<'ctx> {
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@ -36,13 +41,18 @@ pub struct NpArrayFields<'ctx> {
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pub strides: GepGetter<AddressLens<IntLens<'ctx>>>,
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}
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// Note: NpArrayLens's ElementOptic is purely for type-safety and type-guidances
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// The underlying LLVM ndarray doesn't care, it only holds an opaque (uint8_t*) pointer to the elements.
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#[derive(Debug, Clone, Copy)]
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pub struct NpArrayLens<'ctx> {
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pub struct NpArrayLens<'ctx, ElementOptic> {
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pub size_type: IntType<'ctx>,
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pub elem_type: BasicTypeEnum<'ctx>,
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pub element_optic: ElementOptic,
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}
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impl<'ctx> StructureOptic<'ctx> for NpArrayLens<'ctx> {
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// NDArray is *frequently* used, so here is a type alias
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pub type NpArray<'ctx, ElementOptic> = Address<'ctx, NpArrayLens<'ctx, ElementOptic>>;
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impl<'ctx, ElementOptic: Optic<'ctx>> StructureOptic<'ctx> for NpArrayLens<'ctx, ElementOptic> {
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type Fields = NpArrayFields<'ctx>;
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fn struct_name(&self) -> &'static str {
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@ -63,6 +73,21 @@ impl<'ctx> StructureOptic<'ctx> for NpArrayLens<'ctx> {
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}
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}
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// Other convenient utilities for NpArray
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impl<'ctx, ElementOptic: Optic<'ctx>> NpArray<'ctx, ElementOptic> {
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pub fn shape_array(&self, ctx: &CodeGenContext<'ctx, '_>) -> ArraySlice<'ctx, IntLens<'ctx>> {
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let ndims = self.focus(ctx, |fields| &fields.ndims).load(ctx, "ndims");
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let shape_base_ptr = self.focus(ctx, |fields| &fields.shape).load(ctx, "shape");
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ArraySlice { num_elements: ndims, base: shape_base_ptr }
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}
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pub fn strides_array(&self, ctx: &CodeGenContext<'ctx, '_>) -> ArraySlice<'ctx, IntLens<'ctx>> {
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let ndims = self.focus(ctx, |fields| &fields.ndims).load(ctx, "ndims");
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let strides_base_ptr = self.focus(ctx, |fields| &fields.strides).load(ctx, "strides");
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ArraySlice { num_elements: ndims, base: strides_base_ptr }
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}
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}
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pub struct IrrtStringFields<'ctx> {
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pub buffer: GepGetter<AddressLens<IntLens<'ctx>>>,
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pub capacity: GepGetter<IntLens<'ctx>>,
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@ -1,5 +1,6 @@
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use crate::typecheck::typedef::Type;
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pub mod numpy;
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mod test;
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use super::{
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@ -19,7 +19,8 @@ fn get_size_variant(ty: IntType) -> SizeVariant {
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}
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}
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fn get_sized_dependent_function_name(ty: IntType, fn_name: &str) -> String {
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#[must_use]
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pub fn get_sized_dependent_function_name(ty: IntType, fn_name: &str) -> String {
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let mut fn_name = fn_name.to_owned();
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match get_size_variant(ty) {
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SizeVariant::Bits32 => {
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@ -0,0 +1,354 @@
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use std::marker::PhantomData;
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use inkwell::{
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types::BasicType,
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values::{BasicValueEnum, IntValue},
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};
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use crate::{
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codegen::{
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classes::{ListValue, UntypedArrayLikeAccessor},
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optics::{Address, AddressLens, ArraySlice, IntLens, Ixed, Optic},
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stmt::gen_for_callback_incrementing,
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CodeGenContext, CodeGenerator,
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},
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typecheck::typedef::{Type, TypeEnum},
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};
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use super::{
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classes::{ErrorContextLens, NpArray, NpArrayLens},
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new::{
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check_error_context, get_sized_dependent_function_name, prepare_error_context,
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FunctionBuilder,
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},
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};
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type ProducerWriteToArray<'ctx, G, ElementOptic> = Box<
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dyn Fn(
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&mut G,
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&mut CodeGenContext<'ctx, '_>,
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&ArraySlice<'ctx, ElementOptic>,
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) -> Result<(), String>
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+ 'ctx,
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>;
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struct Producer<'ctx, G: CodeGenerator + ?Sized, ElementOptic> {
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pub count: IntValue<'ctx>,
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pub write_to_array: ProducerWriteToArray<'ctx, G, ElementOptic>,
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}
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/// TODO: UPDATE DOCUMENTATION
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/// LLVM-typed implementation for generating a [`Producer`] that sets a list of ints.
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///
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/// * `elem_ty` - The element type of the `NDArray`.
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/// * `shape` - The `shape` parameter used to construct the `NDArray`.
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///
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/// ### Notes on `shape`
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///
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/// Just like numpy, the `shape` argument can be:
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/// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
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/// 2. A tuple of `int32`; e.g., `np.empty((600, 800, 3))`
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/// 3. A scalar `int32`; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
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///
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/// See also [`typecheck::type_inferencer::fold_numpy_function_call_shape_argument`] to
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/// learn how `shape` gets from being a Python user expression to here.
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fn parse_input_shape_arg<'ctx, G>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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shape: BasicValueEnum<'ctx>,
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shape_ty: Type,
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) -> Producer<'ctx, G, IntLens<'ctx>>
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where
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G: CodeGenerator + ?Sized,
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{
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let size_type = generator.get_size_type(ctx.ctx);
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match &*ctx.unifier.get_ty(shape_ty) {
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TypeEnum::TObj { obj_id, .. }
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if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
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{
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// 1. A list of ints; e.g., `np.empty([600, 800, 3])`
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// A list has to be a PointerValue
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let shape_list = ListValue::from_ptr_val(shape.into_pointer_value(), size_type, None);
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// Create `Producer`
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let ndims = shape_list.load_size(ctx, Some("count"));
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Producer {
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count: ndims,
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write_to_array: Box::new(move |ctx, generator, dst_array| {
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// Basically iterate through the list and write to `dst_slice` accordingly
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let init_val = size_type.const_zero();
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let max_val = (ndims, false);
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let incr_val = size_type.const_int(1, false);
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gen_for_callback_incrementing(
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ctx,
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generator,
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init_val,
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max_val,
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|generator, ctx, _hooks, axis| {
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// Get the dimension at `axis`
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let dim =
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shape_list.data().get(ctx, generator, &axis, None).into_int_value();
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// Cast `dim` to SizeT
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let dim = ctx
|
||||
.builder
|
||||
.build_int_s_extend_or_bit_cast(dim, size_type, "dim_casted")
|
||||
.unwrap();
|
||||
|
||||
// Write
|
||||
dst_array.ix(ctx, axis, "dim").store(ctx, &dim);
|
||||
Ok(())
|
||||
},
|
||||
incr_val,
|
||||
)
|
||||
}),
|
||||
}
|
||||
}
|
||||
TypeEnum::TTuple { ty: tuple_types } => {
|
||||
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
|
||||
|
||||
// Get the length/size of the tuple, which also happens to be the value of `ndims`.
|
||||
let ndims = tuple_types.len();
|
||||
|
||||
// A tuple has to be a StructValue
|
||||
// Read [`codegen::expr::gen_expr`] to see how `nac3core` translates a Python tuple into LLVM.
|
||||
let shape_tuple = shape.into_struct_value();
|
||||
|
||||
Producer {
|
||||
count: size_type.const_int(ndims as u64, false),
|
||||
write_to_array: Box::new(move |_generator, ctx, dst_array| {
|
||||
for axis in 0..ndims {
|
||||
// Get the dimension at `axis`
|
||||
let dim = ctx
|
||||
.builder
|
||||
.build_extract_value(
|
||||
shape_tuple,
|
||||
axis as u32,
|
||||
format!("dim{axis}").as_str(),
|
||||
)
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
|
||||
// Cast `dim` to SizeT
|
||||
let dim = ctx
|
||||
.builder
|
||||
.build_int_s_extend_or_bit_cast(dim, size_type, "dim_casted")
|
||||
.unwrap();
|
||||
|
||||
// Write
|
||||
dst_array
|
||||
.ix(ctx, size_type.const_int(axis as u64, false), "dim")
|
||||
.store(ctx, &dim);
|
||||
}
|
||||
Ok(())
|
||||
}),
|
||||
}
|
||||
}
|
||||
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])`
|
||||
|
||||
// The value has to be an integer
|
||||
let shape_int = shape.into_int_value();
|
||||
|
||||
Producer {
|
||||
count: size_type.const_int(1, false),
|
||||
write_to_array: Box::new(move |_generator, ctx, dst_array| {
|
||||
// Cast `shape_int` to SizeT
|
||||
let dim = ctx
|
||||
.builder
|
||||
.build_int_s_extend_or_bit_cast(shape_int, size_type, "dim_casted")
|
||||
.unwrap();
|
||||
|
||||
// Write
|
||||
dst_array
|
||||
.ix(ctx, size_type.const_zero() /* Only index 0 is set */, "dim")
|
||||
.store(ctx, &dim);
|
||||
|
||||
Ok(())
|
||||
}),
|
||||
}
|
||||
}
|
||||
_ => panic!("parse_input_shape_arg encountered unknown type"),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn alloca_ndarray<'ctx, G, ElementOptic: Optic<'ctx>>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
element_optic: ElementOptic,
|
||||
ndims: IntValue<'ctx>,
|
||||
name: &str,
|
||||
) -> Result<NpArray<'ctx, ElementOptic>, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
let size_type = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let itemsize = element_optic.get_llvm_type(ctx.ctx).size_of().unwrap();
|
||||
let itemsize =
|
||||
ctx.builder.build_int_s_extend_or_bit_cast(itemsize, size_type, "itemsize").unwrap();
|
||||
|
||||
let shape = ctx.builder.build_array_alloca(size_type, ndims, "shape").unwrap();
|
||||
let strides = ctx.builder.build_array_alloca(size_type, ndims, "strides").unwrap();
|
||||
|
||||
let ndarray = NpArrayLens { size_type, element_optic }.alloca(ctx, name);
|
||||
|
||||
// Set ndims, itemsize; and allocate shape and store on the stack
|
||||
ndarray.focus(ctx, |fields| &fields.ndims).store(ctx, &ndims);
|
||||
ndarray.focus(ctx, |fields| &fields.itemsize).store(ctx, &itemsize);
|
||||
ndarray
|
||||
.focus(ctx, |fields| &fields.shape)
|
||||
.store(ctx, &Address { addressee_optic: IntLens(size_type), address: shape });
|
||||
ndarray
|
||||
.focus(ctx, |fields| &fields.strides)
|
||||
.store(ctx, &Address { addressee_optic: IntLens(size_type), address: strides });
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
enum NDArrayInitMode<'ctx, G: CodeGenerator + ?Sized> {
|
||||
NDim { ndim: IntValue<'ctx>, _phantom: PhantomData<&'ctx G> },
|
||||
Shape { shape: Producer<'ctx, G, IntLens<'ctx>> },
|
||||
ShapeAndAllocaData { shape: Producer<'ctx, G, IntLens<'ctx>> },
|
||||
}
|
||||
|
||||
/// TODO: DOCUMENT ME
|
||||
fn alloca_ndarray_and_init<'ctx, G, ElementOptic: Optic<'ctx>>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
element_optic: ElementOptic,
|
||||
init_mode: NDArrayInitMode<'ctx, G>,
|
||||
name: &str,
|
||||
) -> Result<NpArray<'ctx, ElementOptic>, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
{
|
||||
// It is implemented verbosely in order to make the initialization modes super clear in their intent.
|
||||
match init_mode {
|
||||
NDArrayInitMode::NDim { ndim: ndims, _phantom } => {
|
||||
let ndarray = alloca_ndarray(generator, ctx, element_optic, ndims, name)?;
|
||||
Ok(ndarray)
|
||||
}
|
||||
NDArrayInitMode::Shape { shape } => {
|
||||
let ndims = shape.count;
|
||||
let ndarray = alloca_ndarray(generator, ctx, element_optic, ndims, name)?;
|
||||
|
||||
// Fill `ndarray.shape`
|
||||
(shape.write_to_array)(generator, ctx, &ndarray.shape_array(ctx))?;
|
||||
|
||||
// Check if `shape` has bad inputs
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(
|
||||
generator,
|
||||
ctx,
|
||||
ndims,
|
||||
&ndarray.focus(ctx, |fields| &fields.shape).load(ctx, "shape"),
|
||||
);
|
||||
|
||||
// NOTE: DO NOT DO `set_strides_by_shape` HERE.
|
||||
// Simply this is because we specified that `SetShape` wouldn't do `set_strides_by_shape`
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
NDArrayInitMode::ShapeAndAllocaData { shape } => {
|
||||
let ndims = shape.count;
|
||||
let ndarray = alloca_ndarray(generator, ctx, element_optic, ndims, name)?;
|
||||
|
||||
// Fill `ndarray.shape`
|
||||
(shape.write_to_array)(generator, ctx, &ndarray.shape_array(ctx))?;
|
||||
|
||||
// Check if `shape` has bad inputs
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(
|
||||
generator,
|
||||
ctx,
|
||||
ndims,
|
||||
&ndarray.focus(ctx, |fields| &fields.shape).load(ctx, "shape"),
|
||||
);
|
||||
|
||||
// Now we populate `ndarray.data` by alloca-ing.
|
||||
// But first, we need to know the size of the ndarray to know how many elements to alloca,
|
||||
// since calculating nbytes of an ndarray requires `ndarray.shape` to be set.
|
||||
let ndarray_nbytes = call_nac3_ndarray_nbytes(generator, ctx, &ndarray);
|
||||
|
||||
// Alloca `data` and assign it to `ndarray.data`
|
||||
let data_ptr =
|
||||
ctx.builder.build_array_alloca(ctx.ctx.i8_type(), ndarray_nbytes, "data").unwrap();
|
||||
ndarray.focus(ctx, |fields| &fields.data).store(
|
||||
ctx,
|
||||
&Address { addressee_optic: IntLens::int8(ctx.ctx), address: data_ptr },
|
||||
);
|
||||
|
||||
// Finally, do `set_strides_by_shape`
|
||||
// Check out https://ajcr.net/stride-guide-part-1/ to see what numpy "strides" are.
|
||||
call_nac3_ndarray_set_strides_by_shape(generator, ctx, &ndarray);
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndims: IntValue<'ctx>,
|
||||
shape: &Address<'ctx, IntLens<'ctx>>,
|
||||
) {
|
||||
let size_type = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let errctx = prepare_error_context(ctx);
|
||||
FunctionBuilder::begin(
|
||||
ctx,
|
||||
&get_sized_dependent_function_name(
|
||||
size_type,
|
||||
"__nac3_ndarray_util_assert_shape_no_negative",
|
||||
),
|
||||
)
|
||||
.arg("errctx", &AddressLens(ErrorContextLens), &errctx)
|
||||
.arg("ndims", &IntLens(size_type), &ndims)
|
||||
.arg("shape", &AddressLens(IntLens(size_type)), shape)
|
||||
.returning_void();
|
||||
check_error_context(generator, ctx, &errctx);
|
||||
}
|
||||
|
||||
fn call_nac3_ndarray_set_strides_by_shape<
|
||||
'ctx,
|
||||
G: CodeGenerator + ?Sized,
|
||||
ElementOptic: Optic<'ctx>,
|
||||
>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: &NpArray<'ctx, ElementOptic>,
|
||||
) {
|
||||
let size_type = generator.get_size_type(ctx.ctx);
|
||||
|
||||
FunctionBuilder::begin(
|
||||
ctx,
|
||||
&get_sized_dependent_function_name(
|
||||
size_type,
|
||||
"__nac3_ndarray_util_assert_shape_no_negative",
|
||||
),
|
||||
)
|
||||
.arg("ndarray", &AddressLens(ndarray.addressee_optic.clone()), ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized, ElementOptic: Optic<'ctx>>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: &NpArray<'ctx, ElementOptic>,
|
||||
) -> IntValue<'ctx> {
|
||||
let size_type = generator.get_size_type(ctx.ctx);
|
||||
|
||||
FunctionBuilder::begin(
|
||||
ctx,
|
||||
&get_sized_dependent_function_name(
|
||||
size_type,
|
||||
"__nac3_ndarray_util_assert_shape_no_negative",
|
||||
),
|
||||
)
|
||||
.arg("ndarray", &AddressLens(ndarray.addressee_optic.clone()), ndarray)
|
||||
.returning("nbytes", &IntLens(size_type))
|
||||
}
|
||||
|
|
@ -58,6 +58,23 @@ pub trait SizedIntLens<'ctx>: Optic<'ctx, Value = IntValue<'ctx>> {}
|
|||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct IntLens<'ctx>(pub IntType<'ctx>);
|
||||
|
||||
impl<'ctx> IntLens<'ctx> {
|
||||
#[must_use]
|
||||
pub fn int8(ctx: &'ctx Context) -> IntLens<'ctx> {
|
||||
IntLens(ctx.i8_type())
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn int32(ctx: &'ctx Context) -> IntLens<'ctx> {
|
||||
IntLens(ctx.i32_type())
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn int64(ctx: &'ctx Context) -> IntLens<'ctx> {
|
||||
IntLens(ctx.i64_type())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> Optic<'ctx> for IntLens<'ctx> {
|
||||
type Value = IntValue<'ctx>;
|
||||
|
||||
|
|
@ -111,7 +128,7 @@ impl<'ctx, AddresseeOptic> Address<'ctx, AddresseeOptic> {
|
|||
}
|
||||
|
||||
pub fn cast_to_opaque(&self, ctx: &CodeGenContext<'ctx, '_>) -> Address<'ctx, IntLens<'ctx>> {
|
||||
self.cast_to(ctx, IntLens(ctx.ctx.i8_type()))
|
||||
self.cast_to(ctx, IntLens::int8(ctx.ctx))
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -126,7 +143,7 @@ pub struct AddressLens<AddresseeOptic>(pub AddresseeOptic);
|
|||
|
||||
impl<AddresseeOptic> AddressLens<AddresseeOptic> {
|
||||
pub fn new_opaque<'ctx>(&self, ctx: &CodeGenContext<'ctx, '_>) -> AddressLens<IntLens<'ctx>> {
|
||||
AddressLens(IntLens(ctx.ctx.i8_type()))
|
||||
AddressLens(IntLens::int8(ctx.ctx))
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue