forked from M-Labs/nac3
466 lines
19 KiB
C++
466 lines
19 KiB
C++
#pragma once
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#include "irrt_utils.hpp"
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#include "irrt_typedefs.hpp"
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#include "irrt_slice.hpp"
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/*
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NDArray-related implementations.
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`*/
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// NDArray indices are always `uint32_t`.
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using NDIndex = uint32_t;
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namespace {
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namespace ndarray_util {
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template <typename SizeT>
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static 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 dim_i = ndims - i - 1;
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int32_t dim = shape[dim_i];
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indices[dim_i] = nth % dim;
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nth /= dim;
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}
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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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static 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 dim_i = ndims - i - 1;
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dst_strides[dim_i] = stride_product * itemsize;
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stride_product *= shape[dim_i];
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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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static SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
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SizeT size = 1;
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for (SizeT dim_i = 0; dim_i < ndims; dim_i++) size *= shape[dim_i];
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return size;
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}
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template <typename SizeT>
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static 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_dim_i = target_ndims - i - 1;
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SizeT src_dim_i = src_ndims - i - 1;
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bool target_dim_exists = target_dim_i >= 0;
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bool src_dim_exists = src_dim_i >= 0;
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SizeT target_dim = target_dim_exists ? target_shape[target_dim_i] : 1;
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SizeT src_dim = src_dim_exists ? src_shape[src_dim_i] : 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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typedef uint8_t NDSliceType;
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extern "C" {
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const NDSliceType INPUT_SLICE_TYPE_INDEX = 0;
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const NDSliceType INPUT_SLICE_TYPE_SLICE = 1;
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}
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struct NDSlice {
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// A poor-man's `std::variant<int, UserRange>`
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NDSliceType type;
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/*
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if type == INPUT_SLICE_TYPE_INDEX => `slice` points to a single `SizeT`
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if type == INPUT_SLICE_TYPE_SLICE => `slice` points to a single `UserRange`
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*/
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uint8_t *slice;
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};
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namespace ndarray_util {
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template<typename SizeT>
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SizeT deduce_ndims_after_slicing(SizeT ndims, SizeT num_slices, const NDSlice *slices) {
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irrt_assert(num_slices <= ndims);
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SizeT final_ndims = ndims;
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for (SizeT i = 0; i < num_slices; i++) {
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if (slices[i].type == INPUT_SLICE_TYPE_INDEX) {
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final_ndims--; // An integer slice demotes the rank by 1
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}
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}
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return final_ndims;
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}
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}
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template <typename SizeT>
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struct NDArrayIndicesIter {
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SizeT ndims;
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const SizeT *shape;
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SizeT *indices;
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void set_indices_zero() {
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__builtin_memset(indices, 0, sizeof(SizeT) * ndims);
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}
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void next() {
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for (SizeT i = 0; i < ndims; i++) {
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SizeT dim_i = ndims - i - 1;
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indices[dim_i]++;
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if (indices[dim_i] < shape[dim_i]) {
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break;
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} else {
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indices[dim_i] = 0;
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}
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}
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}
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};
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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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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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void set_value_at_pelement(uint8_t* pelement, const uint8_t* pvalue) {
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__builtin_memcpy(pelement, pvalue, itemsize);
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}
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uint8_t* get_pelement(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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irrt_assert(0 <= nth);
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irrt_assert(nth < this->size());
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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(indices);
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}
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// Get pointer to the first element of this ndarray, assuming
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// `this->size() > 0`, i.e., not "degenerate" due to zeroes in `this->shape`)
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//
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// This is particularly useful for when the ndarray is just containing a single scalar.
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uint8_t* get_first_pelement() {
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irrt_assert(this->size() > 0);
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return this->data; // ...It is simply `this->data`
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}
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// Is the given `indices` valid/in-bounds?
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bool in_bounds(const SizeT *indices) {
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for (SizeT dim_i = 0; dim_i < ndims; dim_i++) {
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bool dim_ok = indices[dim_i] < shape[dim_i];
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if (!dim_ok) return false;
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}
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return true;
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}
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// Fill the ndarray with a value
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void fill_generic(const uint8_t* pvalue) {
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NDArrayIndicesIter<SizeT> iter;
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iter.ndims = this->ndims;
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iter.shape = this->shape;
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iter.indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * ndims);
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iter.set_indices_zero();
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for (SizeT i = 0; i < this->size(); i++, iter.next()) {
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uint8_t* pelement = get_pelement(iter.indices);
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set_value_at_pelement(pelement, pvalue);
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}
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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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// https://numpy.org/doc/stable/reference/generated/numpy.eye.html
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void set_to_eye(SizeT k, const uint8_t* zero_pvalue, const uint8_t* one_pvalue) {
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__builtin_assume(ndims == 2);
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// TODO: Better implementation
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fill_generic(zero_pvalue);
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for (SizeT i = 0; i < min(shape[0], shape[1]); i++) {
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SizeT row = i;
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SizeT col = i + k;
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SizeT indices[2] = { row, col };
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if (!in_bounds(indices)) continue;
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uint8_t* pelement = get_pelement(indices);
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set_value_at_pelement(pelement, one_pvalue);
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}
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}
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// To support numpy complex slices (e.g., `my_array[:50:2,4,:2:-1]`)
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//
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// Things assumed by this function:
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// - `dst_ndarray` is allocated by the caller
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// - `dst_ndarray.ndims` has the correct value (according to `ndarray_util::deduce_ndims_after_slicing`).
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// - ... and `dst_ndarray.shape` and `dst_ndarray.strides` have been allocated by the caller as well
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//
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// Other notes:
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// - `dst_ndarray->data` does not have to be set, it will be derived.
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// - `dst_ndarray->itemsize` does not have to be set, it will be set to `this->itemsize`
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// - `dst_ndarray->shape` and `dst_ndarray.strides` can contain empty values
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void slice(SizeT num_ndslices, NDSlice* ndslices, NDArray<SizeT>* dst_ndarray) {
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// REFERENCE CODE (check out `_index_helper` in `__getitem__`):
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// https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
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irrt_assert(dst_ndarray->ndims == ndarray_util::deduce_ndims_after_slicing(this->ndims, num_ndslices, ndslices));
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dst_ndarray->data = this->data;
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SizeT this_axis = 0;
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SizeT dst_axis = 0;
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for (SizeT i = 0; i < num_ndslices; i++) {
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NDSlice *ndslice = &ndslices[i];
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if (ndslice->type == INPUT_SLICE_TYPE_INDEX) {
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// Handle when the ndslice is just a single (possibly negative) integer
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// e.g., `my_array[::2, -5, ::-1]`
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// ^^------ like this
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SizeT index_user = *((SizeT*) ndslice->slice);
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SizeT index = resolve_index_in_length(this->shape[this_axis], index_user);
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dst_ndarray->data += index * this->strides[this_axis]; // Add offset
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// Next
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this_axis++;
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} else if (ndslice->type == INPUT_SLICE_TYPE_SLICE) {
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// Handle when the ndslice is a slice (represented by UserSlice in IRRT)
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// e.g., `my_array[::2, -5, ::-1]`
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// ^^^------^^^^----- like these
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UserSlice<SizeT>* user_slice = (UserSlice<SizeT>*) ndslice->slice;
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Slice<SizeT> slice = user_slice->indices(this->shape[this_axis]); // To resolve negative indices and other funny stuff written by the user
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// NOTE: There is no need to write special code to handle negative steps/strides.
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// This simple implementation meticulously handles both positive and negative steps/strides.
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// Check out the tinynumpy and IRRT's test cases if you are not convinced.
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dst_ndarray->data += slice.start * this->strides[this_axis]; // Add offset (NOTE: no need to `* itemsize`, strides count in # of bytes)
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dst_ndarray->strides[dst_axis] = slice.step * this->strides[this_axis]; // Determine stride
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dst_ndarray->shape[dst_axis] = slice.len(); // Determine shape dimension
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// Next
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dst_axis++;
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this_axis++;
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} else {
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__builtin_unreachable();
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}
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}
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irrt_assert(dst_axis == dst_ndarray->ndims); // Sanity check on the implementation
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}
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// Similar to `np.broadcast_to(<ndarray>, <target_shape>)`
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// Assumptions:
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// - `this` has to be fully initialized.
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// - `dst_ndarray->ndims` has to be set.
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// - `dst_ndarray->shape` has to be set, this determines the shape `this` broadcasts to.
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//
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// Other notes:
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// - `dst_ndarray->data` does not have to be set, it will be set to `this->data`.
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// - `dst_ndarray->itemsize` does not have to be set, it will be set to `this->data`.
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// - `dst_ndarray->strides` does not have to be set, it will be overwritten.
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//
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// Cautions:
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// ```
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// xs = np.zeros((4,))
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// ys = np.zero((4, 1))
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// ys[:] = xs # ok
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//
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// xs = np.zeros((1, 4))
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// ys = np.zero((4,))
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// ys[:] = xs # allowed
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// # However `np.broadcast_to(xs, (4,))` would fails, as per numpy's broadcasting rule.
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// # and apparently numpy will "deprecate" this? SEE https://github.com/numpy/numpy/issues/21744
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// # This implementation will NOT support this assignment.
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// ```
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void broadcast_to(NDArray<SizeT>* dst_ndarray) {
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dst_ndarray->data = this->data;
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dst_ndarray->itemsize = this->itemsize;
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irrt_assert(
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ndarray_util::can_broadcast_shape_to(
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dst_ndarray->ndims,
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dst_ndarray->shape,
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this->ndims,
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this->shape
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)
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);
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SizeT stride_product = 1;
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for (SizeT i = 0; i < max(this->ndims, dst_ndarray->ndims); i++) {
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SizeT this_dim_i = this->ndims - i - 1;
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SizeT dst_dim_i = dst_ndarray->ndims - i - 1;
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bool this_dim_exists = this_dim_i >= 0;
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bool dst_dim_exists = dst_dim_i >= 0;
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// TODO: Explain how this works
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bool c1 = this_dim_exists && this->shape[this_dim_i] == 1;
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bool c2 = dst_dim_exists && dst_ndarray->shape[dst_dim_i] != 1;
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if (!this_dim_exists || (c1 && c2)) {
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dst_ndarray->strides[dst_dim_i] = 0; // Freeze it in-place
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} else {
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dst_ndarray->strides[dst_dim_i] = stride_product * this->itemsize;
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stride_product *= this->shape[this_dim_i]; // NOTE: this_dim_exist must be true here.
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}
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}
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}
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// Simulates `this_ndarray[:] = src_ndarray`, with automatic broadcasting.
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// Caution on https://github.com/numpy/numpy/issues/21744
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// Also see `NDArray::broadcast_to`
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void assign_with(NDArray<SizeT>* src_ndarray) {
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irrt_assert(
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ndarray_util::can_broadcast_shape_to(
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this->ndims,
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this->shape,
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src_ndarray->ndims,
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src_ndarray->shape
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)
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);
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// Broadcast the `src_ndarray` to make the reading process *much* easier
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SizeT* broadcasted_src_ndarray_strides = __builtin_alloca(sizeof(SizeT) * this->ndims); // Remember to allocate strides beforehand
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NDArray<SizeT> broadcasted_src_ndarray = {
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.ndims = this->ndims,
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.shape = this->shape,
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.strides = broadcasted_src_ndarray_strides
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};
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src_ndarray->broadcast_to(&broadcasted_src_ndarray);
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// Using iter instead of `get_nth_pelement` because it is slightly faster
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SizeT* indices = __builtin_alloca(sizeof(SizeT) * this->ndims);
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auto iter = NDArrayIndicesIter<SizeT> {
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.ndims = this->ndims,
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.shape = this->shape,
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.indices = indices
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};
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const SizeT this_size = this->size();
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for (SizeT i = 0; i < this_size; i++, iter.next()) {
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uint8_t* src_pelement = broadcasted_src_ndarray_strides->get_pelement(indices);
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uint8_t* this_pelement = this->get_pelement(indices);
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this->set_value_at_pelement(src_pelement, src_pelement);
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|
}
|
|
}
|
|
};
|
|
}
|
|
|
|
extern "C" {
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|
uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
|
|
return ndarray->size();
|
|
}
|
|
|
|
uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
|
|
return ndarray->size();
|
|
}
|
|
|
|
void __nac3_ndarray_fill_generic(NDArray<int32_t>* ndarray, uint8_t* pvalue) {
|
|
ndarray->fill_generic(pvalue);
|
|
}
|
|
|
|
void __nac3_ndarray_fill_generic64(NDArray<int64_t>* ndarray, uint8_t* pvalue) {
|
|
ndarray->fill_generic(pvalue);
|
|
}
|
|
|
|
// void __nac3_ndarray_slice(NDArray<int32_t>* ndarray, int32_t num_slices, NDSlice<int32_t> *slices, NDArray<int32_t> *dst_ndarray) {
|
|
// // ndarray->slice(num_slices, slices, dst_ndarray);
|
|
// }
|
|
} |