core: more irrt
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#include "irrt.hpp"
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#include "irrt_everything.hpp"
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// All the implementations are from `irrt.hpp`
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/*
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This file will be read by `clang-irrt` to conveniently produce LLVM IR for `nac3core/codegen`.
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*/
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@ -0,0 +1,217 @@
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#pragma once
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#include "irrt_utils.hpp"
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#include "irrt_typedefs.hpp"
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/*
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This header contains IRRT implementations
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that do not deserved to be categorized (e.g., into numpy, etc.)
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Check out other *.hpp files before including them here!!
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*/
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// The type of an index or a value describing the length of a range/slice is
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// always `int32_t`.
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typedef int32_t SliceIndex;
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namespace {
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// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
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// need to make sure `exp >= 0` before calling this function
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template <typename T>
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T __nac3_int_exp_impl(T base, T exp) {
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T res = 1;
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/* repeated squaring method */
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do {
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if (exp & 1) {
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res *= base; /* for n odd */
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}
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exp >>= 1;
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base *= base;
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} while (exp);
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return res;
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}
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}
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extern "C" {
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#define DEF_nac3_int_exp_(T) \
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T __nac3_int_exp_##T(T base, T exp) {\
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return __nac3_int_exp_impl(base, exp);\
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}
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DEF_nac3_int_exp_(int32_t)
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DEF_nac3_int_exp_(int64_t)
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DEF_nac3_int_exp_(uint32_t)
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DEF_nac3_int_exp_(uint64_t)
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SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
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if (i < 0) {
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i = len + i;
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}
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if (i < 0) {
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return 0;
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} else if (i > len) {
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return len;
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}
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return i;
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}
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SliceIndex __nac3_range_slice_len(
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const SliceIndex start,
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const SliceIndex end,
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const SliceIndex step
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) {
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SliceIndex diff = end - start;
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if (diff > 0 && step > 0) {
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return ((diff - 1) / step) + 1;
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} else if (diff < 0 && step < 0) {
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return ((diff + 1) / step) + 1;
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} else {
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return 0;
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}
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}
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// Handle list assignment and dropping part of the list when
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// both dest_step and src_step are +1.
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// - All the index must *not* be out-of-bound or negative,
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// - The end index is *inclusive*,
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// - The length of src and dest slice size should already
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// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
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SliceIndex __nac3_list_slice_assign_var_size(
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SliceIndex dest_start,
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SliceIndex dest_end,
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SliceIndex dest_step,
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uint8_t *dest_arr,
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SliceIndex dest_arr_len,
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SliceIndex src_start,
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SliceIndex src_end,
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SliceIndex src_step,
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uint8_t *src_arr,
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SliceIndex src_arr_len,
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const SliceIndex size
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) {
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/* if dest_arr_len == 0, do nothing since we do not support extending list */
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if (dest_arr_len == 0) return dest_arr_len;
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/* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
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if (src_step == dest_step && dest_step == 1) {
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const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
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const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
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if (src_len > 0) {
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__builtin_memmove(
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dest_arr + dest_start * size,
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src_arr + src_start * size,
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src_len * size
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);
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}
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if (dest_len > 0) {
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/* dropping */
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__builtin_memmove(
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dest_arr + (dest_start + src_len) * size,
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dest_arr + (dest_end + 1) * size,
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(dest_arr_len - dest_end - 1) * size
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);
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}
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/* shrink size */
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return dest_arr_len - (dest_len - src_len);
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}
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/* if two range overlaps, need alloca */
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uint8_t need_alloca =
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(dest_arr == src_arr)
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&& !(
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max(dest_start, dest_end) < min(src_start, src_end)
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|| max(src_start, src_end) < min(dest_start, dest_end)
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);
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if (need_alloca) {
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uint8_t *tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
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__builtin_memcpy(tmp, src_arr, src_arr_len * size);
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src_arr = tmp;
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}
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SliceIndex src_ind = src_start;
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SliceIndex dest_ind = dest_start;
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for (;
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(src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
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src_ind += src_step, dest_ind += dest_step
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) {
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/* for constant optimization */
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if (size == 1) {
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__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
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} else if (size == 4) {
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__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
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} else if (size == 8) {
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__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
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} else {
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/* memcpy for var size, cannot overlap after previous alloca */
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__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
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}
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}
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/* only dest_step == 1 can we shrink the dest list. */
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/* size should be ensured prior to calling this function */
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if (dest_step == 1 && dest_end >= dest_start) {
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__builtin_memmove(
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dest_arr + dest_ind * size,
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dest_arr + (dest_end + 1) * size,
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(dest_arr_len - dest_end - 1) * size
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);
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return dest_arr_len - (dest_end - dest_ind) - 1;
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}
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return dest_arr_len;
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}
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int32_t __nac3_isinf(double x) {
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return __builtin_isinf(x);
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}
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int32_t __nac3_isnan(double x) {
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return __builtin_isnan(x);
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}
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double tgamma(double arg);
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double __nac3_gamma(double z) {
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// Handling for denormals
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// | x | Python gamma(x) | C tgamma(x) |
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// --- | ----------------- | --------------- | ----------- |
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// (1) | nan | nan | nan |
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// (2) | -inf | -inf | inf |
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// (3) | inf | inf | inf |
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// (4) | 0.0 | inf | inf |
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// (5) | {-1.0, -2.0, ...} | inf | nan |
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// (1)-(3)
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if (__builtin_isinf(z) || __builtin_isnan(z)) {
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return z;
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}
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double v = tgamma(z);
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// (4)-(5)
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return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
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}
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double lgamma(double arg);
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double __nac3_gammaln(double x) {
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// libm's handling of value overflows differs from scipy:
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// - scipy: gammaln(-inf) -> -inf
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// - libm : lgamma(-inf) -> inf
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if (__builtin_isinf(x)) {
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return x;
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}
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return lgamma(x);
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}
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double j0(double x);
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double __nac3_j0(double x) {
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// libm's handling of value overflows differs from scipy:
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// - scipy: j0(inf) -> nan
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// - libm : j0(inf) -> 0.0
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if (__builtin_isinf(x)) {
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return __builtin_nan("");
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}
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return j0(x);
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}
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}
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#pragma once
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#include "irrt_utils.hpp"
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#include "irrt_typedefs.hpp"
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#include "irrt_basic.hpp"
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#include "irrt_slice.hpp"
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#include "irrt_numpy_ndarray.hpp"
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/*
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All IRRT implementations.
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We don't have any pre-compiled objects, so we are writing all implementations in headers and
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concatenate them with `#include` into one massive source file that contains all the IRRT stuff.
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*/
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#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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// 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 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;
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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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}
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typedef uint8_t NDSliceType;
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extern "C" {
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const NDSliceType INPUT_SLICE_TYPE_INTEGER = 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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NDSliceType type;
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/*
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type = INPUT_SLICE_TYPE_INTEGER => `slice` points to a single `SizeT`
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type = INPUT_SLICE_TYPE_SLICE => `slice` points to a single `NDSliceRange`
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*/
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uint8_t *slice;
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};
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template<typename SizeT>
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SizeT deduce_ndims_after_slicing(SizeT ndims, const SizeT num_slices, const NDSlice *slices) {
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nac3_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_INTEGER) {
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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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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, uint8_t* pvalue) {
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__builtin_memcpy(pelement, pvalue, itemsize);
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}
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uint8_t* get_pelement(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] * itemsize;
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return element;
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}
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// Is the given `indices` valid/in-bounds?
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bool in_bounds(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(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(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, uint8_t* zero_pvalue, 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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void slice(SizeT num_slices, NDSlice* slices, NDArray<SizeT>*dst_ndarray) {
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// It is assumed that `dst_ndarray` is allocated by the caller and
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// has the correct `ndims`.
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nac3_assert(dst_ndarray->ndims == deduce_ndims_after_slicing(this->ndims, num_slices, slices));
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SizeT this_axis = 0;
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SizeT guest_axis = 0;
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// for () {
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// }
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||||
}
|
||||
};
|
||||
}
|
||||
|
||||
extern "C" {
|
||||
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);
|
||||
// }
|
||||
}
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt_utils.hpp"
|
||||
#include "irrt_typedefs.hpp"
|
||||
|
||||
namespace {
|
||||
// A proper slice in IRRT, all negative indices have be resolved to absolute values.
|
||||
template <typename T>
|
||||
struct Slice {
|
||||
T start;
|
||||
T stop;
|
||||
T step;
|
||||
};
|
||||
|
||||
template<typename T>
|
||||
T resolve_index_in_length(T length, T index) {
|
||||
nac3_assert(length >= 0);
|
||||
if (index < 0) {
|
||||
// Remember that index is negative, so do a plus here
|
||||
return max(length + index, 0);
|
||||
} else {
|
||||
return min(length, index);
|
||||
}
|
||||
}
|
||||
|
||||
// NOTE: using a bitfield for the `*_defined` is better, at the
|
||||
// cost of a more annoying implementation in nac3core inkwell
|
||||
template <typename T>
|
||||
struct UserSlice {
|
||||
uint8_t start_defined;
|
||||
T start;
|
||||
|
||||
uint8_t stop_defined;
|
||||
T stop;
|
||||
|
||||
uint8_t step_defined;
|
||||
T step;
|
||||
|
||||
// Like Python's `slice(start, stop, step).indices(length)`
|
||||
Slice<T> indices(T length) {
|
||||
// NOTE: This function implements Python's `slice.indices` *FAITHFULLY*.
|
||||
// SEE: https://github.com/python/cpython/blob/f62161837e68c1c77961435f1b954412dd5c2b65/Objects/sliceobject.c#L546
|
||||
nac3_assert(length >= 0);
|
||||
nac3_assert(!step_defined || step != 0); // step_defined -> step != 0; step cannot be zero if specified by user
|
||||
|
||||
Slice<T> result;
|
||||
result.step = step_defined ? step : 1;
|
||||
bool step_is_negative = result.step < 0;
|
||||
|
||||
if (start_defined) {
|
||||
result.start = resolve_index_in_length(length, start);
|
||||
} else {
|
||||
result.start = step_is_negative ? length - 1 : 0;
|
||||
}
|
||||
|
||||
if (stop_defined) {
|
||||
result.stop = resolve_index_in_length(length, stop);
|
||||
} else {
|
||||
result.stop = step_is_negative ? -1 : length;
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
};
|
||||
}
|
||||
|
|
@ -2,17 +2,20 @@
|
|||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
|
||||
// set `IRRT_DONT_TYPEDEF_INTS` because `cstdint` has it all
|
||||
#define IRRT_DONT_TYPEDEF_INTS
|
||||
#include "irrt.hpp"
|
||||
#include "irrt_everything.hpp"
|
||||
|
||||
static void __test_fail(const char *file, int line) {
|
||||
// NOTE: Try to make the location info follow a format that
|
||||
// VSCode/other IDEs would recognize as a clickable URL.
|
||||
printf("[!] test_fail() invoked at %s:%d", file, line);
|
||||
void test_fail() {
|
||||
printf("[!] Test failed\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
#define test_fail() __test_fail(__FILE__, __LINE__);
|
||||
void __begin_test(const char* function_name, const char* file, int line) {
|
||||
printf("######### Running %s @ %s:%d\n", function_name, file, line);
|
||||
}
|
||||
|
||||
#define BEGIN_TEST() __begin_test(__FUNCTION__, __FILE__, __LINE__)
|
||||
|
||||
template <typename T>
|
||||
bool arrays_match(int len, T *as, T *bs) {
|
||||
|
|
@ -23,40 +26,238 @@ bool arrays_match(int len, T *as, T *bs) {
|
|||
}
|
||||
|
||||
template <typename T>
|
||||
void debug_print_array(const char* format, int len, T *as) {
|
||||
void debug_print_array(const char* format, int len, T* as) {
|
||||
printf("[");
|
||||
for (int i = 0; i < len; i++) {
|
||||
if (i != 0) printf(", ");
|
||||
printf(format, as[i]);
|
||||
}
|
||||
printf("]\n");
|
||||
printf("]");
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
bool assert_arrays_match(const char *label, const char *format, int len, T *expected, T *got) {
|
||||
auto match = arrays_match(len, expected, got);
|
||||
|
||||
if (!match) {
|
||||
void assert_arrays_match(const char* label, const char* format, int len, T* expected, T* got) {
|
||||
if (!arrays_match(len, expected, got)) {
|
||||
printf("expected %s: ", label);
|
||||
debug_print_array(format, len, expected);
|
||||
printf("\n");
|
||||
printf("got %s: ", label);
|
||||
debug_print_array(format, len, got);
|
||||
printf("\n");
|
||||
test_fail();
|
||||
}
|
||||
|
||||
return match;
|
||||
}
|
||||
|
||||
static void test_strides_from_shape() {
|
||||
const uint64_t ndims = 4;
|
||||
uint64_t shape[ndims] = { 999, 3, 5, 7 };
|
||||
uint64_t strides[ndims] = { 0 };
|
||||
__nac3_ndarray_strides_from_shape64(ndims, shape, strides);
|
||||
template <typename T>
|
||||
void assert_values_match(const char* label, const char* format, T expected, T got) {
|
||||
if (expected != got) {
|
||||
printf("expected %s: ", label);
|
||||
printf(format, expected);
|
||||
printf("\n");
|
||||
printf("got %s: ", label);
|
||||
printf(format, got);
|
||||
printf("\n");
|
||||
test_fail();
|
||||
}
|
||||
}
|
||||
|
||||
uint64_t expected_strides[ndims] = { 3*5*7, 5*7, 7, 1 };
|
||||
if (!assert_arrays_match("strides", "%u", ndims, expected_strides, strides)) test_fail();
|
||||
void test_calc_size_from_shape_normal() {
|
||||
// Test shapes with normal values
|
||||
BEGIN_TEST();
|
||||
|
||||
int32_t shape[4] = { 2, 3, 5, 7 };
|
||||
debug_print_array("%d", 4, shape);
|
||||
assert_values_match("size", "%d", 210, ndarray_util::calc_size_from_shape<int32_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 };
|
||||
assert_values_match("size", "%d", 0, ndarray_util::calc_size_from_shape<int32_t>(4, shape));
|
||||
}
|
||||
|
||||
void test_set_strides_by_shape() {
|
||||
// Test `set_strides_by_shape()`
|
||||
BEGIN_TEST();
|
||||
|
||||
int32_t shape[4] = { 99, 3, 5, 7 };
|
||||
int32_t strides[4] = { 0 };
|
||||
ndarray_util::set_strides_by_shape(4, strides, shape);
|
||||
|
||||
int32_t expected_strides[4] = { 105, 35, 7, 1 };
|
||||
assert_arrays_match("strides", "%u", 4u, expected_strides, strides);
|
||||
}
|
||||
|
||||
void test_ndarray_indices_iter_normal() {
|
||||
// Test NDArrayIndicesIter normal behavior
|
||||
BEGIN_TEST();
|
||||
|
||||
int32_t shape[3] = { 1, 2, 3 };
|
||||
int32_t indices[3] = { 0, 0, 0 };
|
||||
auto iter = NDArrayIndicesIter<int32_t> {
|
||||
.ndims = 3u,
|
||||
.shape = shape,
|
||||
.indices = indices
|
||||
};
|
||||
|
||||
assert_arrays_match("indices #0", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 0 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #1", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 1 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #2", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 2 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #3", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 0 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #4", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 1 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #5", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 2 });
|
||||
iter.next();
|
||||
assert_arrays_match("indices #6", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 0 }); // Loops back
|
||||
iter.next();
|
||||
assert_arrays_match("indices #7", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 1 });
|
||||
}
|
||||
|
||||
void test_ndarray_fill_generic() {
|
||||
// Test ndarray fill_generic
|
||||
BEGIN_TEST();
|
||||
|
||||
// Choose a type that's neither int32_t nor uint64_t (candidates of SizeT) to spice it up
|
||||
// Also make all the octets non-zero, to see if `memcpy` in `fill_generic` is working perfectly.
|
||||
uint16_t fill_value = 0xFACE;
|
||||
|
||||
uint16_t in_data[6] = { 100, 101, 102, 103, 104, 105 }; // Fill `data` with values that != `999`
|
||||
int32_t in_itemsize = sizeof(uint16_t);
|
||||
const int32_t in_ndims = 2;
|
||||
int32_t in_shape[in_ndims] = { 2, 3 };
|
||||
int32_t in_strides[in_ndims] = {};
|
||||
NDArray<int32_t> ndarray = {
|
||||
.data = (uint8_t*) in_data,
|
||||
.itemsize = in_itemsize,
|
||||
.ndims = in_ndims,
|
||||
.shape = in_shape,
|
||||
.strides = in_strides,
|
||||
};
|
||||
ndarray.set_strides_by_shape();
|
||||
ndarray.fill_generic((uint8_t*) &fill_value); // `fill_generic` here
|
||||
|
||||
uint16_t expected_data[6] = { fill_value, fill_value, fill_value, fill_value, fill_value, fill_value };
|
||||
assert_arrays_match("data", "0x%hX", 6, expected_data, in_data);
|
||||
}
|
||||
|
||||
void test_ndarray_set_to_eye() {
|
||||
// Test `set_to_eye` behavior (helper function to implement `np.eye()`)
|
||||
BEGIN_TEST();
|
||||
|
||||
double in_data[9] = { 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0 };
|
||||
int32_t in_itemsize = sizeof(double);
|
||||
const int32_t in_ndims = 2;
|
||||
int32_t in_shape[in_ndims] = { 3, 3 };
|
||||
int32_t in_strides[in_ndims] = {};
|
||||
NDArray<int32_t> ndarray = {
|
||||
.data = (uint8_t*) in_data,
|
||||
.itemsize = in_itemsize,
|
||||
.ndims = in_ndims,
|
||||
.shape = in_shape,
|
||||
.strides = in_strides,
|
||||
};
|
||||
ndarray.set_strides_by_shape();
|
||||
|
||||
double zero = 0.0;
|
||||
double one = 1.0;
|
||||
ndarray.set_to_eye(1, (uint8_t*) &zero, (uint8_t*) &one);
|
||||
|
||||
assert_values_match("in_data[0]", "%f", 0.0, in_data[0]);
|
||||
assert_values_match("in_data[1]", "%f", 1.0, in_data[1]);
|
||||
assert_values_match("in_data[2]", "%f", 0.0, in_data[2]);
|
||||
assert_values_match("in_data[3]", "%f", 0.0, in_data[3]);
|
||||
assert_values_match("in_data[4]", "%f", 0.0, in_data[4]);
|
||||
assert_values_match("in_data[5]", "%f", 1.0, in_data[5]);
|
||||
assert_values_match("in_data[6]", "%f", 0.0, in_data[6]);
|
||||
assert_values_match("in_data[7]", "%f", 0.0, in_data[7]);
|
||||
assert_values_match("in_data[8]", "%f", 0.0, in_data[8]);
|
||||
}
|
||||
|
||||
void test_slice_1() {
|
||||
// Test `slice(5, None, None).indices(100) == slice(5, 100, 1)`
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice<int> user_slice = {
|
||||
.start_defined = 1,
|
||||
.start = 5,
|
||||
.stop_defined = 0,
|
||||
.step_defined = 0,
|
||||
};
|
||||
|
||||
auto slice = user_slice.indices(100);
|
||||
assert_values_match("start", "%d", 5, slice.start);
|
||||
assert_values_match("stop", "%d", 100, slice.stop);
|
||||
assert_values_match("step", "%d", 1, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_2() {
|
||||
// Test `slice(400, 999, None).indices(100) == slice(100, 100, 1)`
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice<int> user_slice = {
|
||||
.start_defined = 1,
|
||||
.start = 400,
|
||||
.stop_defined = 0,
|
||||
.step_defined = 0,
|
||||
};
|
||||
|
||||
auto slice = user_slice.indices(100);
|
||||
assert_values_match("start", "%d", 100, slice.start);
|
||||
assert_values_match("stop", "%d", 100, slice.stop);
|
||||
assert_values_match("step", "%d", 1, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_3() {
|
||||
// Test `slice(-10, -5, None).indices(100) == slice(90, 95, 1)`
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice<int> user_slice = {
|
||||
.start_defined = 1,
|
||||
.start = -10,
|
||||
.stop_defined = 1,
|
||||
.stop = -5,
|
||||
.step_defined = 0,
|
||||
};
|
||||
|
||||
auto slice = user_slice.indices(100);
|
||||
assert_values_match("start", "%d", 90, slice.start);
|
||||
assert_values_match("stop", "%d", 95, slice.stop);
|
||||
assert_values_match("step", "%d", 1, slice.step);
|
||||
}
|
||||
|
||||
void test_slice_4() {
|
||||
// Test `slice(None, None, -5).indices(100) == (99, -1, -5)`
|
||||
BEGIN_TEST();
|
||||
|
||||
UserSlice<int> user_slice = {
|
||||
.start_defined = 0,
|
||||
.stop_defined = 0,
|
||||
.step_defined = 1,
|
||||
.step = -5
|
||||
};
|
||||
|
||||
auto slice = user_slice.indices(100);
|
||||
assert_values_match("start", "%d", 99, slice.start);
|
||||
assert_values_match("stop", "%d", -1, slice.stop);
|
||||
assert_values_match("step", "%d", -5, slice.step);
|
||||
}
|
||||
|
||||
int main() {
|
||||
test_strides_from_shape();
|
||||
test_calc_size_from_shape_normal();
|
||||
test_calc_size_from_shape_has_zero();
|
||||
test_set_strides_by_shape();
|
||||
test_ndarray_indices_iter_normal();
|
||||
test_ndarray_fill_generic();
|
||||
test_ndarray_set_to_eye();
|
||||
test_slice_1();
|
||||
test_slice_2();
|
||||
test_slice_3();
|
||||
test_slice_4();
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
#pragma once
|
||||
|
||||
// This is made toggleable since `irrt_test.cpp` itself would include
|
||||
// headers that define the `int_t` family.
|
||||
#ifndef IRRT_DONT_TYPEDEF_INTS
|
||||
typedef _BitInt(8) int8_t;
|
||||
typedef unsigned _BitInt(8) uint8_t;
|
||||
typedef _BitInt(32) int32_t;
|
||||
typedef unsigned _BitInt(32) uint32_t;
|
||||
typedef _BitInt(64) int64_t;
|
||||
typedef unsigned _BitInt(64) uint64_t;
|
||||
#endif
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt_typedefs.hpp"
|
||||
|
||||
namespace {
|
||||
template <typename T>
|
||||
T max(T a, T b) {
|
||||
return a > b ? a : b;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
T min(T a, T b) {
|
||||
return a > b ? b : a;
|
||||
}
|
||||
|
||||
void nac3_assert(bool condition) {
|
||||
// Doesn't do anything (for now (?))
|
||||
// Helps to make code self-documenting
|
||||
|
||||
if (!condition) {
|
||||
// TODO: don't crash the program
|
||||
// TODO: address 0 on hardware might be writable?
|
||||
uint8_t* death = nullptr;
|
||||
*death = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue