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32 changed files with 7468 additions and 3706 deletions

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@ -13,6 +13,7 @@
''
mkdir -p $out/bin
ln -s ${pkgs.llvmPackages_14.clang-unwrapped}/bin/clang $out/bin/clang-irrt
ln -s ${pkgs.llvmPackages_14.clang}/bin/clang $out/bin/clang-irrt-test
ln -s ${pkgs.llvmPackages_14.llvm.out}/bin/llvm-as $out/bin/llvm-as-irrt
'';
nac3artiq = pkgs.python3Packages.toPythonModule (
@ -23,6 +24,7 @@
cargoLock = {
lockFile = ./Cargo.lock;
};
cargoTestFlags = [ "--features" "test" ];
passthru.cargoLock = cargoLock;
nativeBuildInputs = [ pkgs.python3 pkgs.llvmPackages_14.clang llvm-tools-irrt pkgs.llvmPackages_14.llvm.out llvm-nac3 ];
buildInputs = [ pkgs.python3 llvm-nac3 ];
@ -161,7 +163,10 @@
clippy
pre-commit
rustfmt
rust-analyzer
];
# https://nixos.wiki/wiki/Rust#Shell.nix_example
RUST_SRC_PATH = "${pkgs.rust.packages.stable.rustPlatform.rustLibSrc}";
};
devShells.x86_64-linux.msys2 = pkgs.mkShell {
name = "nac3-dev-shell-msys2";

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@ -1,3 +1,6 @@
[features]
test = []
[package]
name = "nac3core"
version = "0.1.0"

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@ -7,8 +7,8 @@ use std::{
process::{Command, Stdio},
};
fn main() {
const FILE: &str = "src/codegen/irrt/irrt.cpp";
fn compile_irrt(irrt_dir: &Path, out_dir: &Path) {
let irrt_cpp_path = irrt_dir.join("irrt.cpp");
/*
* HACK: Sadly, clang doesn't let us emit generic LLVM bitcode.
@ -16,7 +16,7 @@ fn main() {
*/
let flags: &[&str] = &[
"--target=wasm32",
FILE,
irrt_cpp_path.to_str().unwrap(),
"-x",
"c++",
"-fno-discard-value-names",
@ -31,13 +31,14 @@ fn main() {
"-S",
"-Wall",
"-Wextra",
"-Werror=return-type",
"-I",
irrt_dir.to_str().unwrap(),
"-o",
"-",
];
println!("cargo:rerun-if-changed={FILE}");
let out_dir = env::var("OUT_DIR").unwrap();
let out_path = Path::new(&out_dir);
println!("cargo:rerun-if-changed={}", out_dir.to_str().unwrap());
let output = Command::new("clang-irrt")
.args(flags)
@ -52,7 +53,11 @@ fn main() {
let output = std::str::from_utf8(&output.stdout).unwrap().replace("\r\n", "\n");
let mut filtered_output = String::with_capacity(output.len());
let regex_filter = Regex::new(r"(?ms:^define.*?\}$)|(?m:^declare.*?$)").unwrap();
// (?ms:^define.*?\}$) to capture `define` blocks
// (?m:^declare.*?$) to capture `declare` blocks
// (?m:^%.+?=\s*type\s*\{.+?\}$) to capture `type` declarations
let regex_filter =
Regex::new(r"(?ms:^define.*?\}$)|(?m:^declare.*?$)|(?m:^%.+?=\s*type\s*\{.+?\}$)").unwrap();
for f in regex_filter.captures_iter(&output) {
assert_eq!(f.len(), 1);
filtered_output.push_str(&f[0]);
@ -65,18 +70,65 @@ fn main() {
println!("cargo:rerun-if-env-changed=DEBUG_DUMP_IRRT");
if env::var("DEBUG_DUMP_IRRT").is_ok() {
let mut file = File::create(out_path.join("irrt.ll")).unwrap();
let mut file = File::create(out_dir.join("irrt.ll")).unwrap();
file.write_all(output.as_bytes()).unwrap();
let mut file = File::create(out_path.join("irrt-filtered.ll")).unwrap();
let mut file = File::create(out_dir.join("irrt-filtered.ll")).unwrap();
file.write_all(filtered_output.as_bytes()).unwrap();
}
let mut llvm_as = Command::new("llvm-as-irrt")
.stdin(Stdio::piped())
.arg("-o")
.arg(out_path.join("irrt.bc"))
.arg(out_dir.join("irrt.bc"))
.spawn()
.unwrap();
llvm_as.stdin.as_mut().unwrap().write_all(filtered_output.as_bytes()).unwrap();
assert!(llvm_as.wait().unwrap().success());
}
fn compile_irrt_test(irrt_dir: &Path, out_dir: &Path) {
let irrt_test_cpp_path = irrt_dir.join("irrt_test.cpp");
let exe_path = out_dir.join("irrt_test.out");
let flags: &[&str] = &[
irrt_test_cpp_path.to_str().unwrap(),
"-x",
"c++",
"-I",
irrt_dir.to_str().unwrap(),
"-g",
"-fno-discard-value-names",
"-O0",
"-Wall",
"-Wextra",
"-Werror=return-type",
"-lm", // for `tgamma()`, `lgamma()`
"-o",
exe_path.to_str().unwrap(),
];
Command::new("clang-irrt-test")
.args(flags)
.output()
.map(|o| {
assert!(o.status.success(), "{}", std::str::from_utf8(&o.stderr).unwrap());
o
})
.unwrap();
println!("cargo:rerun-if-changed={}", out_dir.to_str().unwrap());
}
fn main() {
let out_dir = env::var("OUT_DIR").unwrap();
let out_dir = Path::new(&out_dir);
let irrt_dir = Path::new("./irrt");
compile_irrt(irrt_dir, out_dir);
// https://github.com/rust-lang/cargo/issues/2549
// `cargo test -F test` to also build `irrt_test.cpp
if cfg!(feature = "test") {
compile_irrt_test(irrt_dir, out_dir);
}
}

5
nac3core/irrt/irrt.cpp Normal file
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@ -0,0 +1,5 @@
#include "irrt_everything.hpp"
/*
This file will be read by `clang-irrt` to conveniently produce LLVM IR for `nac3core/codegen`.
*/

437
nac3core/irrt/irrt.hpp Normal file
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@ -0,0 +1,437 @@
#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
// NDArray indices are always `uint32_t`.
typedef uint32_t NDIndex;
// The type of an index or a value describing the length of a range/slice is
// always `int32_t`.
typedef int32_t SliceIndex;
template <typename T>
static T max(T a, T b) {
return a > b ? a : b;
}
template <typename T>
static T min(T a, T b) {
return a > b ? b : a;
}
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
// need to make sure `exp >= 0` before calling this function
template <typename T>
static T __nac3_int_exp_impl(T base, T exp) {
T res = 1;
/* repeated squaring method */
do {
if (exp & 1) {
res *= base; /* for n odd */
}
exp >>= 1;
base *= base;
} while (exp);
return res;
}
template <typename SizeT>
static SizeT __nac3_ndarray_calc_size_impl(
const SizeT *list_data,
SizeT list_len,
SizeT begin_idx,
SizeT end_idx
) {
__builtin_assume(end_idx <= list_len);
SizeT num_elems = 1;
for (SizeT i = begin_idx; i < end_idx; ++i) {
SizeT val = list_data[i];
__builtin_assume(val > 0);
num_elems *= val;
}
return num_elems;
}
template <typename SizeT>
static void __nac3_ndarray_calc_nd_indices_impl(
SizeT index,
const SizeT *dims,
SizeT num_dims,
NDIndex *idxs
) {
SizeT stride = 1;
for (SizeT dim = 0; dim < num_dims; dim++) {
SizeT i = num_dims - dim - 1;
__builtin_assume(dims[i] > 0);
idxs[i] = (index / stride) % dims[i];
stride *= dims[i];
}
}
template <typename SizeT>
static SizeT __nac3_ndarray_flatten_index_impl(
const SizeT *dims,
SizeT num_dims,
const NDIndex *indices,
SizeT num_indices
) {
SizeT idx = 0;
SizeT stride = 1;
for (SizeT i = 0; i < num_dims; ++i) {
SizeT ri = num_dims - i - 1;
if (ri < num_indices) {
idx += stride * indices[ri];
}
__builtin_assume(dims[i] > 0);
stride *= dims[ri];
}
return idx;
}
template <typename SizeT>
static void __nac3_ndarray_calc_broadcast_impl(
const SizeT *lhs_dims,
SizeT lhs_ndims,
const SizeT *rhs_dims,
SizeT rhs_ndims,
SizeT *out_dims
) {
SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
for (SizeT i = 0; i < max_ndims; ++i) {
const SizeT *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
const SizeT *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
SizeT *out_dim = &out_dims[max_ndims - i - 1];
if (lhs_dim_sz == nullptr) {
*out_dim = *rhs_dim_sz;
} else if (rhs_dim_sz == nullptr) {
*out_dim = *lhs_dim_sz;
} else if (*lhs_dim_sz == 1) {
*out_dim = *rhs_dim_sz;
} else if (*rhs_dim_sz == 1) {
*out_dim = *lhs_dim_sz;
} else if (*lhs_dim_sz == *rhs_dim_sz) {
*out_dim = *lhs_dim_sz;
} else {
__builtin_unreachable();
}
}
}
template <typename SizeT>
static void __nac3_ndarray_calc_broadcast_idx_impl(
const SizeT *src_dims,
SizeT src_ndims,
const NDIndex *in_idx,
NDIndex *out_idx
) {
for (SizeT i = 0; i < src_ndims; ++i) {
SizeT src_i = src_ndims - i - 1;
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
}
}
template<typename SizeT>
static void __nac3_ndarray_strides_from_shape_impl(
SizeT ndims,
SizeT *shape,
SizeT *dst_strides
) {
SizeT stride_product = 1;
for (SizeT i = 0; i < ndims; i++) {
int dim_i = ndims - i - 1;
dst_strides[dim_i] = stride_product;
stride_product *= shape[dim_i];
}
}
extern "C" {
#define DEF_nac3_int_exp_(T) \
T __nac3_int_exp_##T(T base, T exp) {\
return __nac3_int_exp_impl(base, exp);\
}
DEF_nac3_int_exp_(int32_t)
DEF_nac3_int_exp_(int64_t)
DEF_nac3_int_exp_(uint32_t)
DEF_nac3_int_exp_(uint64_t)
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
if (i < 0) {
i = len + i;
}
if (i < 0) {
return 0;
} else if (i > len) {
return len;
}
return i;
}
SliceIndex __nac3_range_slice_len(
const SliceIndex start,
const SliceIndex end,
const SliceIndex step
) {
SliceIndex diff = end - start;
if (diff > 0 && step > 0) {
return ((diff - 1) / step) + 1;
} else if (diff < 0 && step < 0) {
return ((diff + 1) / step) + 1;
} else {
return 0;
}
}
// Handle list assignment and dropping part of the list when
// both dest_step and src_step are +1.
// - All the index must *not* be out-of-bound or negative,
// - The end index is *inclusive*,
// - The length of src and dest slice size should already
// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
SliceIndex __nac3_list_slice_assign_var_size(
SliceIndex dest_start,
SliceIndex dest_end,
SliceIndex dest_step,
uint8_t *dest_arr,
SliceIndex dest_arr_len,
SliceIndex src_start,
SliceIndex src_end,
SliceIndex src_step,
uint8_t *src_arr,
SliceIndex src_arr_len,
const SliceIndex size
) {
/* if dest_arr_len == 0, do nothing since we do not support extending list */
if (dest_arr_len == 0) return dest_arr_len;
/* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
if (src_step == dest_step && dest_step == 1) {
const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
if (src_len > 0) {
__builtin_memmove(
dest_arr + dest_start * size,
src_arr + src_start * size,
src_len * size
);
}
if (dest_len > 0) {
/* dropping */
__builtin_memmove(
dest_arr + (dest_start + src_len) * size,
dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size
);
}
/* shrink size */
return dest_arr_len - (dest_len - src_len);
}
/* if two range overlaps, need alloca */
uint8_t need_alloca =
(dest_arr == src_arr)
&& !(
max(dest_start, dest_end) < min(src_start, src_end)
|| max(src_start, src_end) < min(dest_start, dest_end)
);
if (need_alloca) {
uint8_t *tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
src_arr = tmp;
}
SliceIndex src_ind = src_start;
SliceIndex dest_ind = dest_start;
for (;
(src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
src_ind += src_step, dest_ind += dest_step
) {
/* for constant optimization */
if (size == 1) {
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
} else if (size == 4) {
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
} else if (size == 8) {
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
} else {
/* memcpy for var size, cannot overlap after previous alloca */
__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
}
}
/* only dest_step == 1 can we shrink the dest list. */
/* size should be ensured prior to calling this function */
if (dest_step == 1 && dest_end >= dest_start) {
__builtin_memmove(
dest_arr + dest_ind * size,
dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size
);
return dest_arr_len - (dest_end - dest_ind) - 1;
}
return dest_arr_len;
}
int32_t __nac3_isinf(double x) {
return __builtin_isinf(x);
}
int32_t __nac3_isnan(double x) {
return __builtin_isnan(x);
}
double tgamma(double arg);
double __nac3_gamma(double z) {
// Handling for denormals
// | x | Python gamma(x) | C tgamma(x) |
// --- | ----------------- | --------------- | ----------- |
// (1) | nan | nan | nan |
// (2) | -inf | -inf | inf |
// (3) | inf | inf | inf |
// (4) | 0.0 | inf | inf |
// (5) | {-1.0, -2.0, ...} | inf | nan |
// (1)-(3)
if (__builtin_isinf(z) || __builtin_isnan(z)) {
return z;
}
double v = tgamma(z);
// (4)-(5)
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
}
double lgamma(double arg);
double __nac3_gammaln(double x) {
// libm's handling of value overflows differs from scipy:
// - scipy: gammaln(-inf) -> -inf
// - libm : lgamma(-inf) -> inf
if (__builtin_isinf(x)) {
return x;
}
return lgamma(x);
}
double j0(double x);
double __nac3_j0(double x) {
// libm's handling of value overflows differs from scipy:
// - scipy: j0(inf) -> nan
// - libm : j0(inf) -> 0.0
if (__builtin_isinf(x)) {
return __builtin_nan("");
}
return j0(x);
}
uint32_t __nac3_ndarray_calc_size(
const uint32_t *list_data,
uint32_t list_len,
uint32_t begin_idx,
uint32_t end_idx
) {
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
}
uint64_t __nac3_ndarray_calc_size64(
const uint64_t *list_data,
uint64_t list_len,
uint64_t begin_idx,
uint64_t end_idx
) {
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
}
void __nac3_ndarray_calc_nd_indices(
uint32_t index,
const uint32_t* dims,
uint32_t num_dims,
NDIndex* idxs
) {
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
}
void __nac3_ndarray_calc_nd_indices64(
uint64_t index,
const uint64_t* dims,
uint64_t num_dims,
NDIndex* idxs
) {
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
}
uint32_t __nac3_ndarray_flatten_index(
const uint32_t* dims,
uint32_t num_dims,
const NDIndex* indices,
uint32_t num_indices
) {
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
}
uint64_t __nac3_ndarray_flatten_index64(
const uint64_t* dims,
uint64_t num_dims,
const NDIndex* indices,
uint64_t num_indices
) {
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
}
void __nac3_ndarray_calc_broadcast(
const uint32_t *lhs_dims,
uint32_t lhs_ndims,
const uint32_t *rhs_dims,
uint32_t rhs_ndims,
uint32_t *out_dims
) {
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
}
void __nac3_ndarray_calc_broadcast64(
const uint64_t *lhs_dims,
uint64_t lhs_ndims,
const uint64_t *rhs_dims,
uint64_t rhs_ndims,
uint64_t *out_dims
) {
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
}
void __nac3_ndarray_calc_broadcast_idx(
const uint32_t *src_dims,
uint32_t src_ndims,
const NDIndex *in_idx,
NDIndex *out_idx
) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
}
void __nac3_ndarray_calc_broadcast_idx64(
const uint64_t *src_dims,
uint64_t src_ndims,
const NDIndex *in_idx,
NDIndex *out_idx
) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
}
void __nac3_ndarray_strides_from_shape(uint32_t ndims, uint32_t* shape, uint32_t* dst_strides) {
__nac3_ndarray_strides_from_shape_impl(ndims, shape, dst_strides);
}
void __nac3_ndarray_strides_from_shape64(uint64_t ndims, uint64_t* shape, uint64_t* dst_strides) {
__nac3_ndarray_strides_from_shape_impl(ndims, shape, dst_strides);
}
}

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@ -0,0 +1,216 @@
#pragma once
#include "irrt_utils.hpp"
#include "irrt_typedefs.hpp"
/*
This header contains IRRT implementations
that do not deserved to be categorized (e.g., into numpy, etc.)
Check out other *.hpp files before including them here!!
*/
// The type of an index or a value describing the length of a range/slice is
// always `int32_t`.
namespace {
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
// need to make sure `exp >= 0` before calling this function
template <typename T>
T __nac3_int_exp_impl(T base, T exp) {
T res = 1;
/* repeated squaring method */
do {
if (exp & 1) {
res *= base; /* for n odd */
}
exp >>= 1;
base *= base;
} while (exp);
return res;
}
}
extern "C" {
#define DEF_nac3_int_exp_(T) \
T __nac3_int_exp_##T(T base, T exp) {\
return __nac3_int_exp_impl(base, exp);\
}
DEF_nac3_int_exp_(int32_t)
DEF_nac3_int_exp_(int64_t)
DEF_nac3_int_exp_(uint32_t)
DEF_nac3_int_exp_(uint64_t)
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
if (i < 0) {
i = len + i;
}
if (i < 0) {
return 0;
} else if (i > len) {
return len;
}
return i;
}
SliceIndex __nac3_range_slice_len(
const SliceIndex start,
const SliceIndex end,
const SliceIndex step
) {
SliceIndex diff = end - start;
if (diff > 0 && step > 0) {
return ((diff - 1) / step) + 1;
} else if (diff < 0 && step < 0) {
return ((diff + 1) / step) + 1;
} else {
return 0;
}
}
// Handle list assignment and dropping part of the list when
// both dest_step and src_step are +1.
// - All the index must *not* be out-of-bound or negative,
// - The end index is *inclusive*,
// - The length of src and dest slice size should already
// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
SliceIndex __nac3_list_slice_assign_var_size(
SliceIndex dest_start,
SliceIndex dest_end,
SliceIndex dest_step,
uint8_t *dest_arr,
SliceIndex dest_arr_len,
SliceIndex src_start,
SliceIndex src_end,
SliceIndex src_step,
uint8_t *src_arr,
SliceIndex src_arr_len,
const SliceIndex size
) {
/* if dest_arr_len == 0, do nothing since we do not support extending list */
if (dest_arr_len == 0) return dest_arr_len;
/* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
if (src_step == dest_step && dest_step == 1) {
const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
if (src_len > 0) {
__builtin_memmove(
dest_arr + dest_start * size,
src_arr + src_start * size,
src_len * size
);
}
if (dest_len > 0) {
/* dropping */
__builtin_memmove(
dest_arr + (dest_start + src_len) * size,
dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size
);
}
/* shrink size */
return dest_arr_len - (dest_len - src_len);
}
/* if two range overlaps, need alloca */
uint8_t need_alloca =
(dest_arr == src_arr)
&& !(
max(dest_start, dest_end) < min(src_start, src_end)
|| max(src_start, src_end) < min(dest_start, dest_end)
);
if (need_alloca) {
uint8_t *tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
src_arr = tmp;
}
SliceIndex src_ind = src_start;
SliceIndex dest_ind = dest_start;
for (;
(src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
src_ind += src_step, dest_ind += dest_step
) {
/* for constant optimization */
if (size == 1) {
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
} else if (size == 4) {
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
} else if (size == 8) {
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
} else {
/* memcpy for var size, cannot overlap after previous alloca */
__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
}
}
/* only dest_step == 1 can we shrink the dest list. */
/* size should be ensured prior to calling this function */
if (dest_step == 1 && dest_end >= dest_start) {
__builtin_memmove(
dest_arr + dest_ind * size,
dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size
);
return dest_arr_len - (dest_end - dest_ind) - 1;
}
return dest_arr_len;
}
int32_t __nac3_isinf(double x) {
return __builtin_isinf(x);
}
int32_t __nac3_isnan(double x) {
return __builtin_isnan(x);
}
double tgamma(double arg);
double __nac3_gamma(double z) {
// Handling for denormals
// | x | Python gamma(x) | C tgamma(x) |
// --- | ----------------- | --------------- | ----------- |
// (1) | nan | nan | nan |
// (2) | -inf | -inf | inf |
// (3) | inf | inf | inf |
// (4) | 0.0 | inf | inf |
// (5) | {-1.0, -2.0, ...} | inf | nan |
// (1)-(3)
if (__builtin_isinf(z) || __builtin_isnan(z)) {
return z;
}
double v = tgamma(z);
// (4)-(5)
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
}
double lgamma(double arg);
double __nac3_gammaln(double x) {
// libm's handling of value overflows differs from scipy:
// - scipy: gammaln(-inf) -> -inf
// - libm : lgamma(-inf) -> inf
if (__builtin_isinf(x)) {
return x;
}
return lgamma(x);
}
double j0(double x);
double __nac3_j0(double x) {
// libm's handling of value overflows differs from scipy:
// - scipy: j0(inf) -> nan
// - libm : j0(inf) -> 0.0
if (__builtin_isinf(x)) {
return __builtin_nan("");
}
return j0(x);
}
}

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#pragma once
#include "irrt_printer.hpp"
namespace {
#define MAX_ERROR_NAME_LEN 32
// TODO: right now just to report some messages for now
struct ErrorContext {
Printer error;
// TODO: add error_class_name??
void initialize(char* string_base_ptr, uint32_t max_length) {
error.initialize(string_base_ptr, max_length);
}
bool has_error() {
return error.length > 0;
}
};
}
extern "C" {
void __nac3_error_context_init(ErrorContext* ctx, char* string_base_ptr, uint32_t max_length) {
ctx->initialize(string_base_ptr, max_length);
}
uint8_t __nac3_error_context_has_error(ErrorContext* ctx) {
return (uint8_t) ctx->has_error();
}
}

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#pragma once
#include "irrt_basic.hpp"
#include "irrt_error_context.hpp"
#include "irrt_numpy_ndarray.hpp"
#include "irrt_printer.hpp"
#include "irrt_slice.hpp"
#include "irrt_typedefs.hpp"
#include "irrt_utils.hpp"
/*
All IRRT implementations.
We don't have any pre-compiled objects, so we are writing all implementations in headers and
concatenate them with `#include` into one massive source file that contains all the IRRT stuff.
*/

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#pragma once
#include "irrt_utils.hpp"
#include "irrt_typedefs.hpp"
#include "irrt_slice.hpp"
/*
NDArray-related implementations.
`*/
namespace {
namespace ndarray_util {
template <typename SizeT>
static void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices, SizeT nth) {
for (int32_t i = 0; i < ndims; i++) {
int32_t dim_i = ndims - i - 1;
int32_t dim = shape[dim_i];
indices[dim_i] = nth % dim;
nth /= dim;
}
}
// Compute the strides of an ndarray given an ndarray `shape`
// and assuming that the ndarray is *fully C-contagious*.
//
// You might want to read up on https://ajcr.net/stride-guide-part-1/.
template <typename SizeT>
static void set_strides_by_shape(SizeT itemsize, SizeT ndims, SizeT* dst_strides, const SizeT* shape) {
SizeT stride_product = 1;
for (SizeT i = 0; i < ndims; i++) {
int dim_i = ndims - i - 1;
dst_strides[dim_i] = stride_product * itemsize;
stride_product *= shape[dim_i];
}
}
// Compute the size/# of elements of an ndarray given its shape
template <typename SizeT>
static SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
SizeT size = 1;
for (SizeT dim_i = 0; dim_i < ndims; dim_i++) size *= shape[dim_i];
return size;
}
template <typename SizeT>
static bool can_broadcast_shape_to(
const SizeT target_ndims,
const SizeT *target_shape,
const SizeT src_ndims,
const SizeT *src_shape
) {
/*
// See https://numpy.org/doc/stable/user/basics.broadcasting.html
This function handles this example:
```
Image (3d array): 256 x 256 x 3
Scale (1d array): 3
Result (3d array): 256 x 256 x 3
```
Other interesting examples to consider:
- `can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) == true`
- `can_broadcast_shape_to([3], [3, 1]) == false`
- `can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) == true`
In cases when the shapes contain zero(es):
- `can_broadcast_shape_to([0], [1]) == true`
- `can_broadcast_shape_to([0], [2]) == false`
- `can_broadcast_shape_to([0, 4, 0, 0], [1]) == true`
- `can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) == true`
- `can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) == true`
- `can_broadcast_shape_to([4, 3], [0, 3]) == false`
- `can_broadcast_shape_to([4, 3], [0, 0]) == false`
*/
// This is essentially doing the following in Python:
// `for target_dim, src_dim in itertools.zip_longest(target_shape[::-1], src_shape[::-1], fillvalue=1)`
for (SizeT i = 0; i < max(target_ndims, src_ndims); i++) {
SizeT target_dim_i = target_ndims - i - 1;
SizeT src_dim_i = src_ndims - i - 1;
bool target_dim_exists = target_dim_i >= 0;
bool src_dim_exists = src_dim_i >= 0;
SizeT target_dim = target_dim_exists ? target_shape[target_dim_i] : 1;
SizeT src_dim = src_dim_exists ? src_shape[src_dim_i] : 1;
bool ok = src_dim == 1 || target_dim == src_dim;
if (!ok) return false;
}
return true;
}
}
typedef uint8_t NDSliceType;
extern "C" {
const NDSliceType INPUT_SLICE_TYPE_INDEX = 0;
const NDSliceType INPUT_SLICE_TYPE_SLICE = 1;
}
struct NDSlice {
// A poor-man's enum variant type
NDSliceType type;
/*
if type == INPUT_SLICE_TYPE_INDEX => `slice` points to a single `SizeT`
if type == INPUT_SLICE_TYPE_SLICE => `slice` points to a single `UserRange<SizeT>`
`SizeT` is controlled by the caller: `NDSlice` only cares about where that
slice is (the pointer), `NDSlice` does not care/know about the actual `sizeof()`
of the slice value.
*/
uint8_t* slice;
};
namespace ndarray_util {
template<typename SizeT>
SizeT deduce_ndims_after_slicing(SizeT ndims, SizeT num_slices, const NDSlice *slices) {
irrt_assert(num_slices <= ndims);
SizeT final_ndims = ndims;
for (SizeT i = 0; i < num_slices; i++) {
if (slices[i].type == INPUT_SLICE_TYPE_INDEX) {
final_ndims--; // An index demotes the rank by 1
}
}
return final_ndims;
}
}
// template <typename SizeT>
// struct NDArrayIndicesIter {
// SizeT ndims;
// const SizeT *shape;
// SizeT *indices;
// void set_indices_zero() {
// __builtin_memset(indices, 0, sizeof(SizeT) * ndims);
// }
// void next() {
// for (SizeT i = 0; i < ndims; i++) {
// SizeT dim_i = ndims - i - 1;
// indices[dim_i]++;
// if (indices[dim_i] < shape[dim_i]) {
// break;
// } else {
// indices[dim_i] = 0;
// }
// }
// }
// };
// The NDArray object. `SizeT` is the *signed* size type of this ndarray.
//
// NOTE: The order of fields is IMPORTANT. DON'T TOUCH IT
//
// Some resources you might find helpful:
// - The official numpy implementations:
// - https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
// - On strides (about reshaping, slicing, C-contagiousness, etc)
// - https://ajcr.net/stride-guide-part-1/.
// - https://ajcr.net/stride-guide-part-2/.
// - https://ajcr.net/stride-guide-part-3/.
template <typename SizeT>
struct NDArray {
// The underlying data this `ndarray` is pointing to.
//
// NOTE: Formally this should be of type `void *`, but clang
// translates `void *` to `i8 *` when run with `-S -emit-llvm`,
// so we will put `uint8_t *` here for clarity.
//
// This pointer should point to the first element of the ndarray directly
uint8_t *data;
// The number of bytes of a single element in `data`.
//
// The `SizeT` is treated as `unsigned`.
SizeT itemsize;
// The number of dimensions of this shape.
//
// The `SizeT` is treated as `unsigned`.
SizeT ndims;
// Array shape, with length equal to `ndims`.
//
// The `SizeT` is treated as `unsigned`.
//
// NOTE: `shape` can contain 0.
// (those appear when the user makes an out of bounds slice into an ndarray, e.g., `np.zeros((3, 3))[400:].shape == (0, 3)`)
SizeT *shape;
// Array strides (stride value is in number of bytes, NOT number of elements), with length equal to `ndims`.
//
// The `SizeT` is treated as `signed`.
//
// NOTE: `strides` can have negative numbers.
// (those appear when there is a slice with a negative step, e.g., `my_array[::-1]`)
SizeT *strides;
// Calculate the size/# of elements of an `ndarray`.
// This function corresponds to `np.size(<ndarray>)` or `ndarray.size`
SizeT size() {
return ndarray_util::calc_size_from_shape(ndims, shape);
}
// Calculate the number of bytes of its content of an `ndarray` *in its view*.
// This function corresponds to `ndarray.nbytes`
SizeT nbytes() {
return this->size() * itemsize;
}
void set_pelement_value(uint8_t* pelement, const uint8_t* pvalue) {
__builtin_memcpy(pelement, pvalue, itemsize);
}
uint8_t* get_pelement_by_indices(const SizeT *indices) {
uint8_t* element = data;
for (SizeT dim_i = 0; dim_i < ndims; dim_i++)
element += indices[dim_i] * strides[dim_i];
return element;
}
uint8_t* get_nth_pelement(SizeT nth) {
irrt_assert(0 <= nth);
irrt_assert(nth < this->size());
SizeT* indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * this->ndims);
ndarray_util::set_indices_by_nth(this->ndims, this->shape, indices, nth);
return get_pelement_by_indices(indices);
}
// Get pointer to the first element of this ndarray, assuming
// `this->size() > 0`, i.e., not "degenerate" due to zeroes in `this->shape`)
//
// This is particularly useful for when the ndarray is just containing a single scalar.
uint8_t* get_first_pelement() {
irrt_assert(this->size() > 0);
return this->data; // ...It is simply `this->data`
}
// Is the given `indices` valid/in-bounds?
bool in_bounds(const SizeT *indices) {
for (SizeT dim_i = 0; dim_i < ndims; dim_i++) {
bool dim_ok = indices[dim_i] < shape[dim_i];
if (!dim_ok) return false;
}
return true;
}
// Fill the ndarray with a value
void fill_generic(const uint8_t* pvalue) {
const SizeT size = this->size();
for (SizeT i = 0; i < size; i++) {
uint8_t* pelement = get_nth_pelement(i);
set_pelement_value(pelement, pvalue);
}
}
// Set the strides of the ndarray with `ndarray_util::set_strides_by_shape`
void set_strides_by_shape() {
ndarray_util::set_strides_by_shape(itemsize, ndims, strides, shape);
}
// https://numpy.org/doc/stable/reference/generated/numpy.eye.html
void set_to_eye(SizeT k, const uint8_t* zero_pvalue, const uint8_t* one_pvalue) {
__builtin_assume(ndims == 2);
// TODO: Better implementation
fill_generic(zero_pvalue);
for (SizeT i = 0; i < min(shape[0], shape[1]); i++) {
SizeT row = i;
SizeT col = i + k;
SizeT indices[2] = { row, col };
if (!in_bounds(indices)) continue;
uint8_t* pelement = get_pelement_by_indices(indices);
set_pelement_value(pelement, one_pvalue);
}
}
// To support numpy "basic indexing" https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing
// "Advanced indexing" https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing is not supported
//
// This function supports:
// - "scalar indexing",
// - "slicing and strides",
// - and "dimensional indexing tools" (TODO, but this is really easy to implement).
//
// Things assumed by this function:
// - `dst_ndarray` is allocated by the caller
// - `dst_ndarray.ndims` has the correct value (according to `ndarray_util::deduce_ndims_after_slicing`).
// - ... and `dst_ndarray.shape` and `dst_ndarray.strides` have been allocated by the caller as well
//
// Other notes:
// - `dst_ndarray->data` does not have to be set, it will be derived.
// - `dst_ndarray->itemsize` does not have to be set, it will be set to `this->itemsize`
// - `dst_ndarray->shape` and `dst_ndarray.strides` can contain empty values
void subscript(SizeT num_ndslices, NDSlice* ndslices, NDArray<SizeT>* dst_ndarray) {
// REFERENCE CODE (check out `_index_helper` in `__getitem__`):
// https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
irrt_assert(dst_ndarray->ndims == ndarray_util::deduce_ndims_after_slicing(this->ndims, num_ndslices, ndslices));
dst_ndarray->data = this->data;
dst_ndarray->itemsize = this->itemsize;
SizeT this_axis = 0;
SizeT dst_axis = 0;
for (SizeT i = 0; i < num_ndslices; i++) {
NDSlice *ndslice = &ndslices[i];
if (ndslice->type == INPUT_SLICE_TYPE_INDEX) {
// Handle when the ndslice is just a single (possibly negative) integer
// e.g., `my_array[::2, -5, ::-1]`
// ^^------ like this
SizeT index_user = *((SizeT*) ndslice->slice);
SizeT index = resolve_index_in_length(this->shape[this_axis], index_user);
dst_ndarray->data += index * this->strides[this_axis]; // Add offset
// Next
this_axis++;
} else if (ndslice->type == INPUT_SLICE_TYPE_SLICE) {
// Handle when the ndslice is a slice (represented by UserSlice in IRRT)
// e.g., `my_array[::2, -5, ::-1]`
// ^^^------^^^^----- like these
UserSlice* user_slice = (UserSlice*) ndslice->slice;
Slice slice = user_slice->indices(this->shape[this_axis]); // To resolve negative indices and other funny stuff written by the user
// NOTE: There is no need to write special code to handle negative steps/strides.
// This simple implementation meticulously handles both positive and negative steps/strides.
// Check out the tinynumpy and IRRT's test cases if you are not convinced.
dst_ndarray->data += (SizeT) slice.start * this->strides[this_axis]; // Add offset (NOTE: no need to `* itemsize`, strides count in # of bytes)
dst_ndarray->strides[dst_axis] = ((SizeT) slice.step) * this->strides[this_axis]; // Determine stride
dst_ndarray->shape[dst_axis] = (SizeT) slice.len(); // Determine shape dimension
// Next
dst_axis++;
this_axis++;
} else {
__builtin_unreachable();
}
}
/*
Reference python code:
```python
dst_ndarray.shape.extend(this.shape[this_axis:])
dst_ndarray.strides.extend(this.strides[this_axis:])
```
*/
for (; dst_axis < dst_ndarray->ndims; dst_axis++, this_axis++) {
dst_ndarray->shape[dst_axis] = this->shape[this_axis];
dst_ndarray->strides[dst_axis] = this->strides[this_axis];
}
}
// Similar to `np.broadcast_to(<ndarray>, <target_shape>)`
// Assumptions:
// - `this` has to be fully initialized.
// - `dst_ndarray->ndims` has to be set.
// - `dst_ndarray->shape` has to be set, this determines the shape `this` broadcasts to.
//
// Other notes:
// - `dst_ndarray->data` does not have to be set, it will be set to `this->data`.
// - `dst_ndarray->itemsize` does not have to be set, it will be set to `this->data`.
// - `dst_ndarray->strides` does not have to be set, it will be overwritten.
//
// Cautions:
// ```
// xs = np.zeros((4,))
// ys = np.zero((4, 1))
// ys[:] = xs # ok
//
// xs = np.zeros((1, 4))
// ys = np.zero((4,))
// ys[:] = xs # allowed
// # However `np.broadcast_to(xs, (4,))` would fails, as per numpy's broadcasting rule.
// # and apparently numpy will "deprecate" this? SEE https://github.com/numpy/numpy/issues/21744
// # This implementation will NOT support this assignment.
// ```
void broadcast_to(NDArray<SizeT>* dst_ndarray) {
dst_ndarray->data = this->data;
dst_ndarray->itemsize = this->itemsize;
irrt_assert(
ndarray_util::can_broadcast_shape_to(
dst_ndarray->ndims,
dst_ndarray->shape,
this->ndims,
this->shape
)
);
SizeT stride_product = 1;
for (SizeT i = 0; i < max(this->ndims, dst_ndarray->ndims); i++) {
SizeT this_dim_i = this->ndims - i - 1;
SizeT dst_dim_i = dst_ndarray->ndims - i - 1;
bool this_dim_exists = this_dim_i >= 0;
bool dst_dim_exists = dst_dim_i >= 0;
// TODO: Explain how this works
bool c1 = this_dim_exists && this->shape[this_dim_i] == 1;
bool c2 = dst_dim_exists && dst_ndarray->shape[dst_dim_i] != 1;
if (!this_dim_exists || (c1 && c2)) {
dst_ndarray->strides[dst_dim_i] = 0; // Freeze it in-place
} else {
dst_ndarray->strides[dst_dim_i] = stride_product * this->itemsize;
stride_product *= this->shape[this_dim_i]; // NOTE: this_dim_exist must be true here.
}
}
}
// Simulates `this_ndarray[:] = src_ndarray`, with automatic broadcasting.
// Caution on https://github.com/numpy/numpy/issues/21744
// Also see `NDArray::broadcast_to`
void assign_with(NDArray<SizeT>* src_ndarray) {
irrt_assert(
ndarray_util::can_broadcast_shape_to(
this->ndims,
this->shape,
src_ndarray->ndims,
src_ndarray->shape
)
);
// Broadcast the `src_ndarray` to make the reading process *much* easier
SizeT* broadcasted_src_ndarray_strides = __builtin_alloca(sizeof(SizeT) * this->ndims); // Remember to allocate strides beforehand
NDArray<SizeT> broadcasted_src_ndarray = {
.ndims = this->ndims,
.shape = this->shape,
.strides = broadcasted_src_ndarray_strides
};
src_ndarray->broadcast_to(&broadcasted_src_ndarray);
const SizeT size = this->size();
for (SizeT i = 0; i < size; i++) {
uint8_t* src_pelement = broadcasted_src_ndarray_strides->get_nth_pelement(i);
uint8_t* this_pelement = this->get_nth_pelement(i);
this->set_pelement_value(this_pelement, src_pelement);
}
}
// TODO: DOCUMENT ME
bool is_unsized() {
return this->ndims == 0;
}
// Simulate `len(<ndarray>)`
// See (it doesn't help): https://numpy.org/doc/stable/reference/generated/numpy.ndarray.__len__.html#numpy.ndarray.__len__
SliceIndex len() {
// If you do `len(np.asarray(42))` (note that its `.shape` is just `()` - an empty tuple),
// numpy throws a `TypeError: len() of unsized object`
irrt_assert(!this->is_unsized());
// Apparently `len(<ndarray>)` is defined to be the first dimension
// REFERENCE: https://stackoverflow.com/questions/43081809/len-of-a-numpy-array-in-python
return (SliceIndex) this->shape[0];
}
};
}
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_set_strides_by_shape(NDArray<int32_t>* ndarray) {
ndarray->set_strides_by_shape();
}
void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
ndarray->set_strides_by_shape();
}
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);
}
int32_t __nac3_ndarray_deduce_ndims_after_slicing(int32_t ndims, int32_t num_slices, const NDSlice* slices) {
return ndarray_util::deduce_ndims_after_slicing(ndims, num_slices, slices);
}
int64_t __nac3_ndarray_deduce_ndims_after_slicing64(int64_t ndims, int64_t num_slices, const NDSlice* slices) {
return ndarray_util::deduce_ndims_after_slicing(ndims, num_slices, slices);
}
void __nac3_ndarray_subscript(NDArray<int32_t>* ndarray, int32_t num_slices, NDSlice* slices, NDArray<int32_t> *dst_ndarray) {
ndarray->subscript(num_slices, slices, dst_ndarray);
}
void __nac3_ndarray_subscript64(NDArray<int64_t>* ndarray, int32_t num_slices, NDSlice* slices, NDArray<int64_t> *dst_ndarray) {
ndarray->subscript(num_slices, slices, dst_ndarray);
}
SliceIndex __nac3_ndarray_len(NDArray<int32_t>* ndarray) {
return ndarray->len();
}
SliceIndex __nac3_ndarray_len64(NDArray<int64_t>* ndarray) {
return ndarray->len();
}
}

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#pragma once
#include "irrt_typedefs.hpp"
// TODO: obviously implementing printf from scratch is bad,
// is there a header only, no-cstdlib library for this?
namespace {
struct Printer {
char* string_base_ptr;
uint32_t max_length;
uint32_t length; // NOTE: this could be incremented past max_length, which indicates
void initialize(char *string_base_ptr, uint32_t max_length) {
this->string_base_ptr = string_base_ptr;
this->max_length = max_length;
this->length = 0;
}
void put_space() {
put_char(' ');
}
void put_char(char ch) {
push_char(ch);
}
void put_string(const char* string) {
// TODO: optimize?
while (*string != '\0') {
push_char(*string);
string++; // Move to next char
}
}
template<typename T>
void put_int(T value) {
// NOTE: Try not to use recursion to print the digits
// value == 0 is a special case
if (value == 0) {
push_char('0');
} else {
// Add a '-' if the value is negative
if (value < 0) {
push_char('-');
value = -value; // Negate then continue to print the digits
}
// TODO: Recursion is a bad idea on embedded systems?
uint32_t num_digits = int_log_floor(value, 10) + 1;
put_int_helper(num_digits, value);
}
}
// TODO: implement put_float() and more would be useful
private:
void push_char(char ch) {
if (length < max_length) {
string_base_ptr[length] = ch;
}
// NOTE: this could increment past max_length,
// to indicate the true length of the message even if it gets cut off
length++;
}
template <typename T>
void put_int_helper(uint32_t num_digits, T value) {
// Print the digits recursively
__builtin_assume(0 <= value);
if (num_digits > 0) {
put_int_helper(num_digits - 1, value / 10);
uint32_t digit = value % 10;
char digit_char = '0' + (char) digit;
put_char(digit_char);
}
}
};
}

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#pragma once
#include "irrt_utils.hpp"
#include "irrt_typedefs.hpp"
namespace {
struct Slice {
SliceIndex start;
SliceIndex stop;
SliceIndex step;
// The length/The number of elements of the slice if it were a range,
// i.e., the value of `len(range(this->start, this->stop, this->end))`
SliceIndex len() {
SliceIndex diff = stop - start;
if (diff > 0 && step > 0) {
return ((diff - 1) / step) + 1;
} else if (diff < 0 && step < 0) {
return ((diff + 1) / step) + 1;
} else {
return 0;
}
}
};
SliceIndex resolve_index_in_length(SliceIndex length, SliceIndex index) {
irrt_assert(length >= 0);
if (index < 0) {
// Remember that index is negative, so do a plus here
return max<SliceIndex>(length + index, 0);
} else {
return min<SliceIndex>(length, index);
}
}
// A user-written Python-like slice.
//
// i.e., this slice is a triple of either an int or nothing. (e.g., `my_array[:10:2]`, `start` is None)
//
// You can "resolve" a `UserSlice` by using `UserSlice::indices(<length>)`
//
// NOTE: using a bitfield for the `*_defined` is better, at the
// cost of a more annoying implementation in nac3core inkwell
struct UserSlice {
// Did the user specify `start`? If 0, `start` is undefined (and contains an empty value)
uint8_t start_defined;
SliceIndex start;
// Similar to `start_defined`
uint8_t stop_defined;
SliceIndex stop;
// Similar to `start_defined`
uint8_t step_defined;
SliceIndex step;
// Like Python's `slice(start, stop, step).indices(length)`
Slice indices(SliceIndex length) {
// NOTE: This function implements Python's `slice.indices` *FAITHFULLY*.
// SEE: https://github.com/python/cpython/blob/f62161837e68c1c77961435f1b954412dd5c2b65/Objects/sliceobject.c#L546
irrt_assert(length >= 0);
irrt_assert(!step_defined || step != 0); // step_defined -> step != 0; step cannot be zero if specified by user
Slice 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;
}
};
}

707
nac3core/irrt/irrt_test.cpp Normal file
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// This file will be compiled like a real C++ program,
// and we do have the luxury to use the standard libraries.
// That is if the nix flakes do not have issues... especially on msys2...
#include <cstdint>
#include <cstdio>
#include <cstdlib>
// Set `IRRT_DONT_TYPEDEF_INTS` because `cstdint` defines them
#define IRRT_DONT_TYPEDEF_INTS
#include "irrt_everything.hpp"
void test_fail() {
printf("[!] Test failed\n");
exit(1);
}
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>
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("]");
}
template <typename T>
void assert_arrays_match(const char* label, const char* format, int len, T* expected, T* got) {
if (!arrays_match(len, expected, got)) {
printf(">>>>>>> %s\n", label);
printf(" Expecting = ");
debug_print_array(format, len, expected);
printf("\n");
printf(" Got = ");
debug_print_array(format, len, got);
printf("\n");
test_fail();
}
}
template <typename T>
void assert_values_match(const char* label, const char* format, T expected, T got) {
if (expected != got) {
printf(">>>>>>> %s\n", label);
printf(" Expecting = ");
printf(format, expected);
printf("\n");
printf(" Got = ");
printf(format, got);
printf("\n");
test_fail();
}
}
void print_repeated(const char *str, int count) {
for (int i = 0; i < count; i++) {
printf("%s", str);
}
}
template<typename SizeT, typename ElementT>
void __print_ndarray_aux(const char *format, bool first, bool last, SizeT* cursor, SizeT depth, NDArray<SizeT>* ndarray) {
// A really lazy recursive implementation
// Add left padding unless its the first entry (since there would be "[[[" before it)
if (!first) {
print_repeated(" ", depth);
}
const SizeT dim = ndarray->shape[depth];
if (depth + 1 == ndarray->ndims) {
// Recursed down to last dimension, print the values in a nice list
printf("[");
SizeT* indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * ndarray->ndims);
for (SizeT i = 0; i < dim; i++) {
ndarray_util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, *cursor);
ElementT* pelement = (ElementT*) ndarray->get_pelement_by_indices(indices);
ElementT element = *pelement;
if (i != 0) printf(", "); // List delimiter
printf(format, element);
printf("(@");
debug_print_array("%d", ndarray->ndims, indices);
printf(")");
(*cursor)++;
}
printf("]");
} else {
printf("[");
for (SizeT i = 0; i < ndarray->shape[depth]; i++) {
__print_ndarray_aux<SizeT, ElementT>(
format,
i == 0, // first?
i + 1 == dim, // last?
cursor,
depth + 1,
ndarray
);
}
printf("]");
}
// Add newline unless its the last entry (since there will be "]]]" after it)
if (!last) {
print_repeated("\n", depth);
}
}
template<typename SizeT, typename ElementT>
void print_ndarray(const char *format, NDArray<SizeT>* ndarray) {
if (ndarray->ndims == 0) {
printf("<empty ndarray>");
} else {
SizeT cursor = 0;
__print_ndarray_aux<SizeT, ElementT>(format, true, true, &cursor, 0, ndarray);
}
printf("\n");
}
void test_calc_size_from_shape_normal() {
// Test shapes with normal values
BEGIN_TEST();
int32_t shape[4] = { 2, 3, 5, 7 };
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((int32_t) sizeof(int32_t), 4, strides, shape);
int32_t expected_strides[4] = {
105 * sizeof(int32_t),
35 * sizeof(int32_t),
7 * sizeof(int32_t),
1 * sizeof(int32_t)
};
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 = 3,
// .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 `subscript(5, None, None).indices(100) == subscript(5, 100, 1)`
BEGIN_TEST();
UserSlice 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 `subscript(400, 999, None).indices(100) == subscript(100, 100, 1)`
BEGIN_TEST();
UserSlice 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 `subscript(-10, -5, None).indices(100) == subscript(90, 95, 1)`
BEGIN_TEST();
UserSlice 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 `subscript(None, None, -5).indices(100) == (99, -1, -5)`
BEGIN_TEST();
UserSlice 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);
}
void test_ndslice_1() {
/*
Reference Python code:
```python
ndarray = np.arange(12, dtype=np.float64).reshape((3, 4));
# array([[ 0., 1., 2., 3.],
# [ 4., 5., 6., 7.],
# [ 8., 9., 10., 11.]])
dst_ndarray = ndarray[-2:, 1::2]
# array([[ 5., 7.],
# [ 9., 11.]])
assert dst_ndarray.shape == (2, 2)
assert dst_ndarray.strides == (32, 16)
assert dst_ndarray[0, 0] == 5.0
assert dst_ndarray[0, 1] == 7.0
assert dst_ndarray[1, 0] == 9.0
assert dst_ndarray[1, 1] == 11.0
```
*/
BEGIN_TEST();
double in_data[12] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
int32_t in_itemsize = sizeof(double);
const int32_t in_ndims = 2;
int32_t in_shape[in_ndims] = { 3, 4 };
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();
// Destination ndarray
// As documented, ndims and shape & strides must be allocated and determined by the caller.
const int32_t dst_ndims = 2;
int32_t dst_shape[dst_ndims] = {999, 999}; // Empty values
int32_t dst_strides[dst_ndims] = {999, 999}; // Empty values
NDArray<int32_t> dst_ndarray = {
.data = nullptr,
.ndims = dst_ndims,
.shape = dst_shape,
.strides = dst_strides
};
// Create the slice in `ndarray[-2::, 1::2]`
UserSlice user_slice_1 = {
.start_defined = 1,
.start = -2,
.stop_defined = 0,
.step_defined = 0
};
UserSlice user_slice_2 = {
.start_defined = 1,
.start = 1,
.stop_defined = 0,
.step_defined = 1,
.step = 2
};
const int32_t num_ndslices = 2;
NDSlice ndslices[num_ndslices] = {
{ .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_1 },
{ .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_2 }
};
ndarray.subscript(num_ndslices, ndslices, &dst_ndarray);
int32_t expected_shape[dst_ndims] = { 2, 2 };
int32_t expected_strides[dst_ndims] = { 32, 16 };
assert_arrays_match("shape", "%d", dst_ndims, expected_shape, dst_ndarray.shape);
assert_arrays_match("strides", "%d", dst_ndims, expected_strides, dst_ndarray.strides);
assert_values_match("dst_ndarray[0, 0]", "%f", 5.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 0, 0 })));
assert_values_match("dst_ndarray[0, 1]", "%f", 7.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 0, 1 })));
assert_values_match("dst_ndarray[1, 0]", "%f", 9.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 1, 0 })));
assert_values_match("dst_ndarray[1, 1]", "%f", 11.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 1, 1 })));
}
void test_ndslice_2() {
/*
```python
ndarray = np.arange(12, dtype=np.float64).reshape((3, 4))
# array([[ 0., 1., 2., 3.],
# [ 4., 5., 6., 7.],
# [ 8., 9., 10., 11.]])
dst_ndarray = ndarray[2, ::-2]
# array([11., 9.])
assert dst_ndarray.shape == (2,)
assert dst_ndarray.strides == (-16,)
assert dst_ndarray[0] == 11.0
assert dst_ndarray[1] == 9.0
dst_ndarray[1, 0] == 99 # If you write to `dst_ndarray`
assert ndarray[1, 3] == 99 # `ndarray` also updates!!
```
*/
BEGIN_TEST();
double in_data[12] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
int32_t in_itemsize = sizeof(double);
const int32_t in_ndims = 2;
int32_t in_shape[in_ndims] = { 3, 4 };
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();
// Destination ndarray
// As documented, ndims and shape & strides must be allocated and determined by the caller.
const int32_t dst_ndims = 1;
int32_t dst_shape[dst_ndims] = {999}; // Empty values
int32_t dst_strides[dst_ndims] = {999}; // Empty values
NDArray<int32_t> dst_ndarray = {
.data = nullptr,
.ndims = dst_ndims,
.shape = dst_shape,
.strides = dst_strides
};
// Create the slice in `ndarray[2, ::-2]`
int32_t user_slice_1 = 2;
UserSlice user_slice_2 = {
.start_defined = 0,
.stop_defined = 0,
.step_defined = 1,
.step = -2
};
const int32_t num_ndslices = 2;
NDSlice ndslices[num_ndslices] = {
{ .type = INPUT_SLICE_TYPE_INDEX, .slice = (uint8_t*) &user_slice_1 },
{ .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_2 }
};
ndarray.subscript(num_ndslices, ndslices, &dst_ndarray);
int32_t expected_shape[dst_ndims] = { 2 };
int32_t expected_strides[dst_ndims] = { -16 };
assert_arrays_match("shape", "%d", dst_ndims, expected_shape, dst_ndarray.shape);
assert_arrays_match("strides", "%d", dst_ndims, expected_strides, dst_ndarray.strides);
// [5.0, 3.0]
assert_values_match("dst_ndarray[0]", "%f", 11.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 0 })));
assert_values_match("dst_ndarray[1]", "%f", 9.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 1 })));
}
void test_ndslice_3() {
BEGIN_TEST();
double in_data[12] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
const int32_t in_itemsize = sizeof(double);
const int32_t in_ndims = 2;
int32_t in_shape[in_ndims] = { 3, 4 };
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();
const int32_t dst_ndims = 2;
int32_t dst_shape[dst_ndims] = {999, 999}; // Empty values
int32_t dst_strides[dst_ndims] = {999, 999}; // Empty values
NDArray<int32_t> dst_ndarray = {
.data = nullptr,
.ndims = dst_ndims,
.shape = dst_shape,
.strides = dst_strides
};
// Create the slice in `ndarray[2:3]`
UserSlice user_slice_1 = {
.start_defined = 1,
.start = 2,
.stop_defined = 1,
.stop = 3,
.step_defined = 0,
};
const int32_t num_ndslices = 1;
NDSlice ndslices[num_ndslices] = {
{ .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_1 },
};
ndarray.subscript(num_ndslices, ndslices, &dst_ndarray);
}
void test_can_broadcast_shape() {
BEGIN_TEST();
assert_values_match(
"can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 5, (int32_t[]) { 1, 1, 1, 1, 3 })
);
assert_values_match(
"can_broadcast_shape_to([3], [3, 1]) == false",
"%d",
false,
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 2, (int32_t[]) { 3, 1 }));
assert_values_match(
"can_broadcast_shape_to([3], [3]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 1, (int32_t[]) { 3 }));
assert_values_match(
"can_broadcast_shape_to([1], [3]) == false",
"%d",
false,
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 1 }, 1, (int32_t[]) { 3 }));
assert_values_match(
"can_broadcast_shape_to([1], [1]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 1 }, 1, (int32_t[]) { 1 }));
assert_values_match(
"can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 3, (int32_t[]) { 256, 1, 3 })
);
assert_values_match(
"can_broadcast_shape_to([256, 256, 3], [3]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 3 })
);
assert_values_match(
"can_broadcast_shape_to([256, 256, 3], [2]) == false",
"%d",
false,
ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 2 })
);
assert_values_match(
"can_broadcast_shape_to([256, 256, 3], [1]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 1 })
);
// In cases when the shapes contain zero(es)
assert_values_match(
"can_broadcast_shape_to([0], [1]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 0 }, 1, (int32_t[]) { 1 })
);
assert_values_match(
"can_broadcast_shape_to([0], [2]) == false",
"%d",
false,
ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 0 }, 1, (int32_t[]) { 2 })
);
assert_values_match(
"can_broadcast_shape_to([0, 4, 0, 0], [1]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 1, (int32_t[]) { 1 })
);
assert_values_match(
"can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 4, (int32_t[]) { 1, 1, 1, 1 })
);
assert_values_match(
"can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) == true",
"%d",
true,
ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 4, (int32_t[]) { 1, 4, 1, 1 })
);
assert_values_match(
"can_broadcast_shape_to([4, 3], [0, 3]) == false",
"%d",
false,
ndarray_util::can_broadcast_shape_to(2, (int32_t[]) { 4, 3 }, 2, (int32_t[]) { 0, 3 })
);
assert_values_match(
"can_broadcast_shape_to([4, 3], [0, 0]) == false",
"%d",
false,
ndarray_util::can_broadcast_shape_to(2, (int32_t[]) { 4, 3 }, 2, (int32_t[]) { 0, 0 })
);
}
void test_ndarray_broadcast_1() {
/*
```python
array = np.array([[19.9, 29.9, 39.9, 49.9]], dtype=np.float64)
>>> [[19.9 29.9 39.9 49.9]]
array = np.broadcast_to(array, (2, 3, 4))
>>> [[[19.9 29.9 39.9 49.9]
>>> [19.9 29.9 39.9 49.9]
>>> [19.9 29.9 39.9 49.9]]
>>> [[19.9 29.9 39.9 49.9]
>>> [19.9 29.9 39.9 49.9]
>>> [19.9 29.9 39.9 49.9]]]
assert array.strides == (0, 0, 8)
# and then pick some values in `array` and check them...
```
*/
BEGIN_TEST();
double in_data[4] = { 19.9, 29.9, 39.9, 49.9 };
const int32_t in_ndims = 2;
int32_t in_shape[in_ndims] = {1, 4};
int32_t in_strides[in_ndims] = {};
NDArray<int32_t> ndarray = {
.data = (uint8_t*) in_data,
.itemsize = sizeof(double),
.ndims = in_ndims,
.shape = in_shape,
.strides = in_strides
};
ndarray.set_strides_by_shape();
const int32_t dst_ndims = 3;
int32_t dst_shape[dst_ndims] = {2, 3, 4};
int32_t dst_strides[dst_ndims] = {};
NDArray<int32_t> dst_ndarray = {
.ndims = dst_ndims,
.shape = dst_shape,
.strides = dst_strides
};
ndarray.broadcast_to(&dst_ndarray);
assert_arrays_match("dst_ndarray->strides", "%d", dst_ndims, (int32_t[]) { 0, 0, 8 }, dst_ndarray.strides);
assert_values_match("dst_ndarray[0, 0, 0]", "%f", 19.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 0, 0})));
assert_values_match("dst_ndarray[0, 0, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 0, 1})));
assert_values_match("dst_ndarray[0, 0, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 0, 2})));
assert_values_match("dst_ndarray[0, 0, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 0, 3})));
assert_values_match("dst_ndarray[0, 1, 0]", "%f", 19.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 1, 0})));
assert_values_match("dst_ndarray[0, 1, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 1, 1})));
assert_values_match("dst_ndarray[0, 1, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 1, 2})));
assert_values_match("dst_ndarray[0, 1, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 1, 3})));
assert_values_match("dst_ndarray[1, 2, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {1, 2, 3})));
}
void test_printer() {
const uint32_t buffer_len = 256;
char buffer[buffer_len];
Printer printer = {
.string_base_ptr = buffer,
.max_length = buffer_len,
.length = 0
};
}
int main() {
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();
test_ndslice_1();
test_ndslice_2();
test_ndslice_3();
test_can_broadcast_shape();
test_ndarray_broadcast_1();
test_printer();
return 0;
}

View File

@ -0,0 +1,14 @@
#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
typedef int32_t SliceIndex;

View File

@ -0,0 +1,74 @@
#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;
}
template <typename T>
bool arrays_match(int len, T *as, T *bs) {
for (int i = 0; i < len; i++) {
if (as[i] != bs[i]) return false;
}
return true;
}
template<typename T>
uint32_t int_log_floor(T value, T base) {
uint32_t result = 0;
while (value < base) {
result++;
value /= base;
}
return result;
}
bool string_is_empty(const char *str) {
return str[0] == '\0';
}
// TODO: DOCUMENT ME!!!!!
// returns false if `src_str` could not be fully copied over to `dst_str`
bool string_copy(uint32_t dst_max_size, char* dst_str, const char* src_str) {
// This function guarantess that `dst_str` will be null-terminated,
for (uint32_t i = 0; i < dst_max_size; i++) {
bool is_last = i + 1 == dst_max_size;
if (is_last && src_str[i] != '\0') {
dst_str[i] = '\0';
return false;
}
if (src_str[i] == '\0') {
dst_str[i] = '\0';
return true;
}
dst_str[i] = src_str[i];
}
__builtin_unreachable();
}
void irrt_panic() {
// Crash the program for now.
// TODO: Don't crash the program
// ... or at least produce a good message when doing testing IRRT
uint8_t* death = nullptr;
*death = 0; // TODO: address 0 on hardware might be writable?
}
// TODO: Make this a macro and allow it to be toggled on/off (e.g., debug vs release)
void irrt_assert(bool condition) {
if (!condition) irrt_panic();
}
}

File diff suppressed because it is too large Load Diff

View File

@ -1,8 +1,9 @@
use crate::codegen::{
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
// irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
llvm_intrinsics::call_int_umin,
stmt::gen_for_callback_incrementing,
CodeGenContext, CodeGenerator,
CodeGenContext,
CodeGenerator,
};
use inkwell::context::Context;
use inkwell::types::{ArrayType, BasicType, StructType};
@ -12,6 +13,7 @@ use inkwell::{
values::{BasicValueEnum, IntValue, PointerValue},
AddressSpace, IntPredicate,
};
use itertools::Itertools;
/// A LLVM type that is used to represent a non-primitive type in NAC3.
pub trait ProxyType<'ctx>: Into<Self::Base> {
@ -1208,25 +1210,27 @@ impl<'ctx> NDArrayType<'ctx> {
ctx: &'ctx Context,
dtype: BasicTypeEnum<'ctx>,
) -> Self {
let llvm_usize = generator.get_size_type(ctx);
todo!()
// struct NDArray { num_dims: size_t, dims: size_t*, data: T* }
//
// * num_dims: Number of dimensions in the array
// * dims: Pointer to an array containing the size of each dimension
// * data: Pointer to an array containing the array data
let llvm_ndarray = ctx
.struct_type(
&[
llvm_usize.into(),
llvm_usize.ptr_type(AddressSpace::default()).into(),
dtype.ptr_type(AddressSpace::default()).into(),
],
false,
)
.ptr_type(AddressSpace::default());
// let llvm_usize = generator.get_size_type(ctx);
NDArrayType::from_type(llvm_ndarray, llvm_usize)
// // struct NDArray { num_dims: size_t, dims: size_t*, data: T* }
// //
// // * num_dims: Number of dimensions in the array
// // * dims: Pointer to an array containing the size of each dimension
// // * data: Pointer to an array containing the array data
// let llvm_ndarray = ctx
// .struct_type(
// &[
// llvm_usize.into(),
// llvm_usize.ptr_type(AddressSpace::default()).into(),
// dtype.ptr_type(AddressSpace::default()).into(),
// ],
// false,
// )
// .ptr_type(AddressSpace::default());
// NDArrayType::from_type(llvm_ndarray, llvm_usize)
}
/// Creates an [`NDArrayType`] from a [`PointerType`].
@ -1601,7 +1605,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> IntValue<'ctx> {
call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
todo!()
// call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
}
}
@ -1659,33 +1664,34 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
indices: &Index,
name: Option<&str>,
) -> PointerValue<'ctx> {
let llvm_usize = generator.get_size_type(ctx.ctx);
todo!()
// let llvm_usize = generator.get_size_type(ctx.ctx);
let indices_elem_ty = indices
.ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
.get_type()
.get_element_type();
let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
panic!("Expected list[int32] but got {indices_elem_ty}")
};
assert_eq!(
indices_elem_ty.get_bit_width(),
32,
"Expected list[int32] but got list[int{}]",
indices_elem_ty.get_bit_width()
);
// let indices_elem_ty = indices
// .ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
// .get_type()
// .get_element_type();
// let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
// panic!("Expected list[int32] but got {indices_elem_ty}")
// };
// assert_eq!(
// indices_elem_ty.get_bit_width(),
// 32,
// "Expected list[int32] but got list[int{}]",
// indices_elem_ty.get_bit_width()
// );
let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
// let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
unsafe {
ctx.builder
.build_in_bounds_gep(
self.base_ptr(ctx, generator),
&[index],
name.unwrap_or_default(),
)
.unwrap()
}
// unsafe {
// ctx.builder
// .build_in_bounds_gep(
// self.base_ptr(ctx, generator),
// &[index],
// name.unwrap_or_default(),
// )
// .unwrap()
// }
}
fn ptr_offset<G: CodeGenerator + ?Sized>(
@ -1761,3 +1767,164 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx,
for NDArrayDataProxy<'ctx, '_>
{
}
// #[derive(Debug, Clone, Copy)]
// pub struct StructField<'ctx> {
// /// The GEP index of this struct field.
// pub gep_index: u32,
// /// Name of this struct field.
// ///
// /// Used for generating names.
// pub name: &'static str,
// /// The type of this struct field.
// pub ty: BasicTypeEnum<'ctx>,
// }
//
// pub struct StructFields<'ctx> {
// /// Name of the struct.
// ///
// /// Used for generating names.
// pub name: &'static str,
//
// /// All the [`StructField`]s of this struct.
// ///
// /// **NOTE:** The index position of a [`StructField`]
// /// matches the element's [`StructField::index`].
// pub fields: Vec<StructField<'ctx>>,
// }
//
// pub struct StructFieldsBuilder<'ctx> {
// gep_index_counter: u32,
// /// Name of the struct to be built.
// name: &'static str,
// fields: Vec<StructField<'ctx>>,
// }
//
// impl<'ctx> StructField<'ctx> {
// /// TODO: DOCUMENT ME
// pub fn gep(
// &self,
// ctx: &CodeGenContext<'ctx, '_>,
// struct_ptr: PointerValue<'ctx>,
// ) -> PointerValue<'ctx> {
// let index_type = ctx.ctx.i32_type(); // TODO: I think I'm not supposed to use i32 for GEP like that
// unsafe {
// ctx.builder
// .build_in_bounds_gep(
// struct_ptr,
// &[index_type.const_zero(), index_type.const_int(self.gep_index as u64, false)],
// self.name,
// )
// .unwrap()
// }
// }
//
// /// TODO: DOCUMENT ME
// pub fn load(
// &self,
// ctx: &CodeGenContext<'ctx, '_>,
// struct_ptr: PointerValue<'ctx>,
// ) -> BasicValueEnum<'ctx> {
// ctx.builder.build_load(self.gep(ctx, struct_ptr), self.name).unwrap()
// }
//
// /// TODO: DOCUMENT ME
// pub fn store<V>(&self, ctx: &CodeGenContext<'ctx, '_>, struct_ptr: PointerValue<'ctx>, value: V)
// where
// V: BasicValue<'ctx>,
// {
// ctx.builder.build_store(self.gep(ctx, struct_ptr), value).unwrap();
// }
// }
// type IsInstanceError = String;
// type IsInstanceResult = Result<(), IsInstanceError>;
// pub fn check_basic_types_match<'ctx, A, B>(expected: A, got: B) -> IsInstanceResult
// where
// A: BasicType<'ctx>,
// B: BasicType<'ctx>,
// {
// let expected = expected.as_basic_type_enum();
// let got = got.as_basic_type_enum();
// // Put those logic into here,
// // otherwise there is always a fallback reporting on any kind of mismatch
// match (expected, got) {
// (BasicTypeEnum::IntType(expected), BasicTypeEnum::IntType(got)) => {
// if expected.get_bit_width() != got.get_bit_width() {
// return Err(format!(
// "Expected IntType ({expected}-bit(s)), got IntType ({got}-bit(s))"
// ));
// }
// }
// (expected, got) => {
// if expected != got {
// return Err(format!("Expected {expected}, got {got}"));
// }
// }
// }
// Ok(())
// }
// impl<'ctx> StructFields<'ctx> {
// pub fn num_fields(&self) -> u32 {
// self.fields.len() as u32
// }
//
// pub fn get_struct_type(&self, ctx: &'ctx Context) -> StructType<'ctx> {
// let llvm_fields = self.fields.iter().map(|field| field.ty).collect_vec();
// ctx.struct_type(llvm_fields.as_slice(), false)
// }
//
// pub fn is_type(&self, scrutinee: StructType<'ctx>) -> IsInstanceResult {
// // Check scrutinee's number of struct fields
// if scrutinee.count_fields() != self.num_fields() {
// return Err(format!(
// "Expected {expected_count} field(s) in `{struct_name}` type, got {got_count}",
// struct_name = self.name,
// expected_count = self.num_fields(),
// got_count = scrutinee.count_fields(),
// ));
// }
//
// // Check the scrutinee's field types
// for field in self.fields.iter() {
// let expected_field_ty = field.ty;
// let got_field_ty = scrutinee.get_field_type_at_index(field.gep_index).unwrap();
//
// if let Err(field_err) = check_basic_types_match(expected_field_ty, got_field_ty) {
// return Err(format!(
// "Field GEP index {gep_index} does not match the expected type of ({struct_name}::{field_name}): {field_err}",
// gep_index = field.gep_index,
// struct_name = self.name,
// field_name = field.name,
// ));
// }
// }
//
// // Done
// Ok(())
// }
// }
//
// impl<'ctx> StructFieldsBuilder<'ctx> {
// pub fn start(name: &'static str) -> Self {
// StructFieldsBuilder { gep_index_counter: 0, name, fields: Vec::new() }
// }
//
// pub fn add_field(&mut self, name: &'static str, ty: BasicTypeEnum<'ctx>) -> StructField<'ctx> {
// let index = self.gep_index_counter;
// self.gep_index_counter += 1;
//
// let field = StructField { gep_index: index, name, ty };
// self.fields.push(field); // Register into self.fields
//
// field // Return to the caller to conveniently let them do whatever they want
// }
//
// pub fn end(self) -> StructFields<'ctx> {
// StructFields { name: self.name, fields: self.fields }
// }
// }
//

File diff suppressed because it is too large Load Diff

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@ -0,0 +1,87 @@
// TODO: Use derppening's abstraction
use std::marker::PhantomData;
use inkwell::{
context::Context,
types::{BasicType, BasicTypeEnum, IntType},
values::BasicValueEnum,
AddressSpace,
};
use crate::codegen::structure::{
CustomStructType, CustomType, Field, FieldCreator, IntType2, Object, PointerType2,
PointingArrayType,
};
#[derive(Debug, Clone, Copy)]
pub struct NpArrayType<'ctx> {
pub size_type: IntType<'ctx>,
pub elem_type: BasicTypeEnum<'ctx>,
}
pub struct NpArrayFields<'ctx> {
pub data: Field<'ctx, PointerType2<'ctx>>,
pub itemsize: Field<'ctx, IntType2<'ctx>>,
pub ndims: Field<'ctx, IntType2<'ctx>>,
pub shape: Field<'ctx, PointingArrayType<'ctx, IntType2<'ctx>>>,
pub strides: Field<'ctx, PointingArrayType<'ctx, IntType2<'ctx>>>,
}
pub type NpArrayValue<'ctx> = Object<'ctx, NpArrayType<'ctx>>;
// impl<'ctx> CustomType<'ctx> for NpArrayType<'ctx> {
// type Value = NpArrayValue<'ctx>;
//
// fn llvm_basic_type_enum(
// &self,
// ctx: &'ctx inkwell::context::Context,
// ) -> inkwell::types::BasicTypeEnum<'ctx> {
// self.llvm_struct_type(ctx).as_basic_type_enum()
// }
//
// fn llvm_field_load(
// &self,
// ctx: &crate::codegen::CodeGenContext<'ctx, '_>,
// field: crate::codegen::structure::FieldInfo,
// struct_ptr: inkwell::values::PointerValue<'ctx>,
// ) -> Self::Value {
// let ok = field.llvm_load(ctx, struct_ptr);
// todo!()
// }
//
// fn llvm_field_store(
// &self,
// ctx: &crate::codegen::CodeGenContext<'ctx, '_>,
// field: crate::codegen::structure::FieldInfo,
// struct_ptr: inkwell::values::PointerValue<'ctx>,
// value: &Self::Value,
// ) {
// todo!()
// }
// }
impl<'ctx> CustomStructType<'ctx> for NpArrayType<'ctx> {
type Fields = NpArrayFields<'ctx>;
fn llvm_struct_name() -> &'static str {
"NDArray"
}
fn add_fields_to(&self, creator: &mut FieldCreator<'ctx>) -> Self::Fields {
let pi8 = creator.ctx.i8_type().ptr_type(AddressSpace::default());
NpArrayFields {
data: creator.add_field("data", PointerType2(pi8)),
itemsize: creator.add_field("itemsize", IntType2(self.size_type)),
ndims: creator.add_field("ndims", IntType2(self.size_type)),
shape: creator.add_field("shape", PointingArrayType::new(IntType2(self.size_type))),
strides: creator.add_field("strides", PointingArrayType::new(IntType2(self.size_type))),
}
}
}
impl<'ctx> NpArrayType<'ctx> {
pub fn new_opaque_elem(ctx: &'ctx Context, size_type: IntType<'ctx>) -> Self {
NpArrayType { elem_type: ctx.i8_type().into(), size_type }
}
}

View File

@ -1,414 +0,0 @@
using int8_t = _BitInt(8);
using uint8_t = unsigned _BitInt(8);
using int32_t = _BitInt(32);
using uint32_t = unsigned _BitInt(32);
using int64_t = _BitInt(64);
using uint64_t = unsigned _BitInt(64);
// NDArray indices are always `uint32_t`.
using NDIndex = uint32_t;
// The type of an index or a value describing the length of a range/slice is always `int32_t`.
using SliceIndex = int32_t;
namespace {
template <typename T>
const T& max(const T& a, const T& b) {
return a > b ? a : b;
}
template <typename T>
const T& min(const T& a, const T& b) {
return a > b ? b : a;
}
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
// need to make sure `exp >= 0` before calling this function
template <typename T>
T __nac3_int_exp_impl(T base, T exp) {
T res = 1;
/* repeated squaring method */
do {
if (exp & 1) {
res *= base; /* for n odd */
}
exp >>= 1;
base *= base;
} while (exp);
return res;
}
template <typename SizeT>
SizeT __nac3_ndarray_calc_size_impl(
const SizeT* list_data,
SizeT list_len,
SizeT begin_idx,
SizeT end_idx
) {
__builtin_assume(end_idx <= list_len);
SizeT num_elems = 1;
for (SizeT i = begin_idx; i < end_idx; ++i) {
SizeT val = list_data[i];
__builtin_assume(val > 0);
num_elems *= val;
}
return num_elems;
}
template <typename SizeT>
void __nac3_ndarray_calc_nd_indices_impl(
SizeT index,
const SizeT* dims,
SizeT num_dims,
NDIndex* idxs
) {
SizeT stride = 1;
for (SizeT dim = 0; dim < num_dims; dim++) {
SizeT i = num_dims - dim - 1;
__builtin_assume(dims[i] > 0);
idxs[i] = (index / stride) % dims[i];
stride *= dims[i];
}
}
template <typename SizeT>
SizeT __nac3_ndarray_flatten_index_impl(
const SizeT* dims,
SizeT num_dims,
const NDIndex* indices,
SizeT num_indices
) {
SizeT idx = 0;
SizeT stride = 1;
for (SizeT i = 0; i < num_dims; ++i) {
SizeT ri = num_dims - i - 1;
if (ri < num_indices) {
idx += stride * indices[ri];
}
__builtin_assume(dims[i] > 0);
stride *= dims[ri];
}
return idx;
}
template <typename SizeT>
void __nac3_ndarray_calc_broadcast_impl(
const SizeT* lhs_dims,
SizeT lhs_ndims,
const SizeT* rhs_dims,
SizeT rhs_ndims,
SizeT* out_dims
) {
SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
for (SizeT i = 0; i < max_ndims; ++i) {
const SizeT* lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
const SizeT* rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
SizeT* out_dim = &out_dims[max_ndims - i - 1];
if (lhs_dim_sz == nullptr) {
*out_dim = *rhs_dim_sz;
} else if (rhs_dim_sz == nullptr) {
*out_dim = *lhs_dim_sz;
} else if (*lhs_dim_sz == 1) {
*out_dim = *rhs_dim_sz;
} else if (*rhs_dim_sz == 1) {
*out_dim = *lhs_dim_sz;
} else if (*lhs_dim_sz == *rhs_dim_sz) {
*out_dim = *lhs_dim_sz;
} else {
__builtin_unreachable();
}
}
}
template <typename SizeT>
void __nac3_ndarray_calc_broadcast_idx_impl(
const SizeT* src_dims,
SizeT src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx
) {
for (SizeT i = 0; i < src_ndims; ++i) {
SizeT src_i = src_ndims - i - 1;
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
}
}
} // namespace
extern "C" {
#define DEF_nac3_int_exp_(T) \
T __nac3_int_exp_##T(T base, T exp) {\
return __nac3_int_exp_impl(base, exp);\
}
DEF_nac3_int_exp_(int32_t)
DEF_nac3_int_exp_(int64_t)
DEF_nac3_int_exp_(uint32_t)
DEF_nac3_int_exp_(uint64_t)
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
if (i < 0) {
i = len + i;
}
if (i < 0) {
return 0;
} else if (i > len) {
return len;
}
return i;
}
SliceIndex __nac3_range_slice_len(
const SliceIndex start,
const SliceIndex end,
const SliceIndex step
) {
SliceIndex diff = end - start;
if (diff > 0 && step > 0) {
return ((diff - 1) / step) + 1;
} else if (diff < 0 && step < 0) {
return ((diff + 1) / step) + 1;
} else {
return 0;
}
}
// Handle list assignment and dropping part of the list when
// both dest_step and src_step are +1.
// - All the index must *not* be out-of-bound or negative,
// - The end index is *inclusive*,
// - The length of src and dest slice size should already
// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
SliceIndex __nac3_list_slice_assign_var_size(
SliceIndex dest_start,
SliceIndex dest_end,
SliceIndex dest_step,
uint8_t* dest_arr,
SliceIndex dest_arr_len,
SliceIndex src_start,
SliceIndex src_end,
SliceIndex src_step,
uint8_t* src_arr,
SliceIndex src_arr_len,
const SliceIndex size
) {
/* if dest_arr_len == 0, do nothing since we do not support extending list */
if (dest_arr_len == 0) return dest_arr_len;
/* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
if (src_step == dest_step && dest_step == 1) {
const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
if (src_len > 0) {
__builtin_memmove(
dest_arr + dest_start * size,
src_arr + src_start * size,
src_len * size
);
}
if (dest_len > 0) {
/* dropping */
__builtin_memmove(
dest_arr + (dest_start + src_len) * size,
dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size
);
}
/* shrink size */
return dest_arr_len - (dest_len - src_len);
}
/* if two range overlaps, need alloca */
uint8_t need_alloca =
(dest_arr == src_arr)
&& !(
max(dest_start, dest_end) < min(src_start, src_end)
|| max(src_start, src_end) < min(dest_start, dest_end)
);
if (need_alloca) {
uint8_t* tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
src_arr = tmp;
}
SliceIndex src_ind = src_start;
SliceIndex dest_ind = dest_start;
for (;
(src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
src_ind += src_step, dest_ind += dest_step
) {
/* for constant optimization */
if (size == 1) {
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
} else if (size == 4) {
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
} else if (size == 8) {
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
} else {
/* memcpy for var size, cannot overlap after previous alloca */
__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
}
}
/* only dest_step == 1 can we shrink the dest list. */
/* size should be ensured prior to calling this function */
if (dest_step == 1 && dest_end >= dest_start) {
__builtin_memmove(
dest_arr + dest_ind * size,
dest_arr + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size
);
return dest_arr_len - (dest_end - dest_ind) - 1;
}
return dest_arr_len;
}
int32_t __nac3_isinf(double x) {
return __builtin_isinf(x);
}
int32_t __nac3_isnan(double x) {
return __builtin_isnan(x);
}
double tgamma(double arg);
double __nac3_gamma(double z) {
// Handling for denormals
// | x | Python gamma(x) | C tgamma(x) |
// --- | ----------------- | --------------- | ----------- |
// (1) | nan | nan | nan |
// (2) | -inf | -inf | inf |
// (3) | inf | inf | inf |
// (4) | 0.0 | inf | inf |
// (5) | {-1.0, -2.0, ...} | inf | nan |
// (1)-(3)
if (__builtin_isinf(z) || __builtin_isnan(z)) {
return z;
}
double v = tgamma(z);
// (4)-(5)
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
}
double lgamma(double arg);
double __nac3_gammaln(double x) {
// libm's handling of value overflows differs from scipy:
// - scipy: gammaln(-inf) -> -inf
// - libm : lgamma(-inf) -> inf
if (__builtin_isinf(x)) {
return x;
}
return lgamma(x);
}
double j0(double x);
double __nac3_j0(double x) {
// libm's handling of value overflows differs from scipy:
// - scipy: j0(inf) -> nan
// - libm : j0(inf) -> 0.0
if (__builtin_isinf(x)) {
return __builtin_nan("");
}
return j0(x);
}
uint32_t __nac3_ndarray_calc_size(
const uint32_t* list_data,
uint32_t list_len,
uint32_t begin_idx,
uint32_t end_idx
) {
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
}
uint64_t __nac3_ndarray_calc_size64(
const uint64_t* list_data,
uint64_t list_len,
uint64_t begin_idx,
uint64_t end_idx
) {
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
}
void __nac3_ndarray_calc_nd_indices(
uint32_t index,
const uint32_t* dims,
uint32_t num_dims,
NDIndex* idxs
) {
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
}
void __nac3_ndarray_calc_nd_indices64(
uint64_t index,
const uint64_t* dims,
uint64_t num_dims,
NDIndex* idxs
) {
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
}
uint32_t __nac3_ndarray_flatten_index(
const uint32_t* dims,
uint32_t num_dims,
const NDIndex* indices,
uint32_t num_indices
) {
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
}
uint64_t __nac3_ndarray_flatten_index64(
const uint64_t* dims,
uint64_t num_dims,
const NDIndex* indices,
uint64_t num_indices
) {
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
}
void __nac3_ndarray_calc_broadcast(
const uint32_t* lhs_dims,
uint32_t lhs_ndims,
const uint32_t* rhs_dims,
uint32_t rhs_ndims,
uint32_t* out_dims
) {
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
}
void __nac3_ndarray_calc_broadcast64(
const uint64_t* lhs_dims,
uint64_t lhs_ndims,
const uint64_t* rhs_dims,
uint64_t rhs_ndims,
uint64_t* out_dims
) {
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
}
void __nac3_ndarray_calc_broadcast_idx(
const uint32_t* src_dims,
uint32_t src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx
) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
}
void __nac3_ndarray_calc_broadcast_idx64(
const uint64_t* src_dims,
uint64_t src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx
) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
}
} // extern "C"

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@ -0,0 +1,26 @@
#[cfg(test)]
mod tests {
use std::{path::Path, process::Command};
#[test]
fn run_irrt_test() {
assert!(
cfg!(feature = "test"),
"Please do `cargo test -F test` to compile `irrt_test.out` and run test"
);
let irrt_test_out_path = Path::new(concat!(env!("OUT_DIR"), "/irrt_test.out"));
let output = Command::new(irrt_test_out_path.to_str().unwrap()).output().unwrap();
if !output.status.success() {
eprintln!("irrt_test failed with status {}:", output.status);
eprintln!("====== stdout ======");
eprintln!("{}", String::from_utf8(output.stdout).unwrap());
eprintln!("====== stderr ======");
eprintln!("{}", String::from_utf8(output.stderr).unwrap());
eprintln!("====================");
panic!("irrt_test failed");
}
}
}

View File

@ -35,6 +35,54 @@ fn get_float_intrinsic_repr(ctx: &Context, ft: FloatType) -> &'static str {
unreachable!()
}
/// Invokes the [`llvm.lifetime.start`](https://releases.llvm.org/14.0.0/docs/LangRef.html#llvm-lifetime-start-intrinsic)
/// intrinsic.
pub fn call_lifetime_start<'ctx>(
ctx: &CodeGenContext<'ctx, '_>,
size: IntValue<'ctx>,
ptr: PointerValue<'ctx>,
) {
const FN_NAME: &str = "llvm.lifetime.start";
// NOTE: inkwell temporary workaround, see [`call_stackrestore`] for details
let intrinsic_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
let llvm_void = ctx.ctx.void_type();
let llvm_i64 = ctx.ctx.i64_type();
let llvm_p0i8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
let fn_type = llvm_void.fn_type(&[llvm_i64.into(), llvm_p0i8.into()], false);
ctx.module.add_function(FN_NAME, fn_type, None)
});
ctx.builder
.build_call(intrinsic_fn, &[size.into(), ptr.into()], "")
.map(CallSiteValue::try_as_basic_value)
.unwrap();
}
/// Invokes the [`llvm.lifetime.end`](https://releases.llvm.org/14.0.0/docs/LangRef.html#llvm-lifetime-end-intrinsic)
/// intrinsic.
pub fn call_lifetime_end<'ctx>(
ctx: &CodeGenContext<'ctx, '_>,
size: IntValue<'ctx>,
ptr: PointerValue<'ctx>,
) {
const FN_NAME: &str = "llvm.lifetime.end";
// NOTE: inkwell temporary workaround, see [`call_stackrestore`] for details
let intrinsic_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
let llvm_void = ctx.ctx.void_type();
let llvm_i64 = ctx.ctx.i64_type();
let llvm_p0i8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
let fn_type = llvm_void.fn_type(&[llvm_i64.into(), llvm_p0i8.into()], false);
ctx.module.add_function(FN_NAME, fn_type, None)
});
ctx.builder
.build_call(intrinsic_fn, &[size.into(), ptr.into()], "")
.map(CallSiteValue::try_as_basic_value)
.unwrap();
}
/// Invokes the [`llvm.stacksave`](https://llvm.org/docs/LangRef.html#llvm-stacksave-intrinsic)
/// intrinsic.
pub fn call_stacksave<'ctx>(

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@ -7,6 +7,7 @@ use crate::{
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
},
};
use classes::NpArrayType;
use crossbeam::channel::{unbounded, Receiver, Sender};
use inkwell::{
attributes::{Attribute, AttributeLoc},
@ -43,6 +44,7 @@ pub mod irrt;
pub mod llvm_intrinsics;
pub mod numpy;
pub mod stmt;
pub mod structure;
#[cfg(test)]
mod test;
@ -476,7 +478,14 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
ctx, module, generator, unifier, top_level, type_cache, dtype,
);
NDArrayType::new(generator, ctx, element_type).as_base_type().into()
let ndarray_ty = NpArrayType {
size_type: generator.get_size_type(ctx),
elem_type: element_type,
};
ndarray_ty
.get_struct_type(ctx)
.ptr_type(AddressSpace::default())
.as_basic_type_enum()
}
_ => unreachable!(

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@ -0,0 +1,318 @@
use std::marker::PhantomData;
use inkwell::{
context::Context,
types::{BasicType, BasicTypeEnum, IntType, PointerType, StructType},
values::{BasicValue, BasicValueEnum, IntValue, PointerValue},
AddressSpace,
};
use super::CodeGenContext;
#[derive(Debug, Clone, Copy)]
pub struct FieldInfo {
gep_index: u32,
name: &'static str,
}
impl FieldInfo {
pub fn llvm_gep<'ctx>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
struct_ptr: PointerValue<'ctx>,
) -> PointerValue<'ctx> {
let index_type = ctx.ctx.i32_type(); // TODO: I think I'm not supposed to *just* use i32 for GEP like that
unsafe {
ctx.builder
.build_in_bounds_gep(
struct_ptr,
&[index_type.const_zero(), index_type.const_int(self.gep_index as u64, false)],
self.name,
)
.unwrap()
}
}
pub fn llvm_load<'ctx>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
struct_ptr: PointerValue<'ctx>,
) -> BasicValueEnum<'ctx> {
// We will use `self.name` as the LLVM label for debugging purposes
ctx.builder.build_load(self.llvm_gep(ctx, struct_ptr), self.name).unwrap()
}
pub fn llvm_store<'ctx>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
struct_ptr: PointerValue<'ctx>,
value: BasicValueEnum<'ctx>,
) {
ctx.builder.build_store(self.llvm_gep(ctx, struct_ptr), value).unwrap();
}
}
pub struct Object<'ctx, T> {
pub ty: T,
pub ptr: PointerValue<'ctx>,
}
pub struct Field<'ctx, T: CustomType<'ctx>> {
pub info: FieldInfo,
pub ty: T,
_phantom: PhantomData<&'ctx ()>,
}
pub struct FieldCreator<'ctx> {
pub ctx: &'ctx Context,
struct_name: &'ctx str,
gep_index_counter: u32,
fields: Vec<(FieldInfo, BasicTypeEnum<'ctx>)>,
}
impl<'ctx> FieldCreator<'ctx> {
pub fn new(ctx: &'ctx Context, struct_name: &'ctx str) -> Self {
FieldCreator { ctx, struct_name, gep_index_counter: 0, fields: Vec::new() }
}
fn next_gep_index(&mut self) -> u32 {
let index = self.gep_index_counter;
self.gep_index_counter += 1;
index
}
fn get_struct_field_types(&self) -> Vec<BasicTypeEnum<'ctx>> {
self.fields.iter().map(|x| x.1.clone()).collect()
}
pub fn add_field<T: CustomType<'ctx>>(&mut self, name: &'static str, ty: T) -> Field<'ctx, T> {
let gep_index = self.next_gep_index();
let field_type = ty.llvm_basic_type_enum(self.ctx);
let field_info = FieldInfo { gep_index, name };
let field = Field { info: field_info, ty, _phantom: PhantomData };
self.fields.push((field_info.clone(), field_type));
field
}
fn num_fields(&self) -> u32 {
self.fields.len() as u32 // casted to u32 because that is what inkwell returns
}
}
pub trait CustomType<'ctx>: Clone {
type Value;
fn llvm_basic_type_enum(&self, ctx: &'ctx Context) -> BasicTypeEnum<'ctx>;
fn llvm_field_load(
&self,
ctx: &CodeGenContext<'ctx, '_>,
field: FieldInfo,
struct_ptr: PointerValue<'ctx>,
) -> Self::Value;
fn llvm_field_store(
&self,
ctx: &CodeGenContext<'ctx, '_>,
field: FieldInfo,
struct_ptr: PointerValue<'ctx>,
value: &Self::Value,
);
}
#[derive(Debug, Clone, Copy)]
pub struct IntType2<'ctx>(pub IntType<'ctx>);
impl<'ctx> CustomType<'ctx> for IntType2<'ctx> {
type Value = IntValue<'ctx>;
fn llvm_basic_type_enum(&self, ctx: &'ctx Context) -> BasicTypeEnum<'ctx> {
self.0.as_basic_type_enum()
}
fn llvm_field_load(
&self,
ctx: &CodeGenContext<'ctx, '_>,
field: FieldInfo,
struct_ptr: PointerValue<'ctx>,
) -> Self::Value {
let int_value = field.llvm_load(ctx, struct_ptr).into_int_value();
assert_eq!(int_value.get_type().get_bit_width(), self.0.get_bit_width());
int_value
}
fn llvm_field_store(
&self,
ctx: &CodeGenContext<'ctx, '_>,
field: FieldInfo,
struct_ptr: PointerValue<'ctx>,
int_value: &Self::Value,
) {
assert_eq!(int_value.get_type().get_bit_width(), self.0.get_bit_width());
field.llvm_store(ctx, struct_ptr, int_value.as_basic_value_enum());
}
}
#[derive(Debug, Clone, Copy)]
pub struct PointerType2<'ctx>(pub PointerType<'ctx>);
impl<'ctx> CustomType<'ctx> for PointerType2<'ctx> {
type Value = PointerValue<'ctx>;
fn llvm_basic_type_enum(&self, ctx: &'ctx Context) -> BasicTypeEnum<'ctx> {
self.0.as_basic_type_enum()
}
fn llvm_field_load(
&self,
ctx: &CodeGenContext<'ctx, '_>,
field: FieldInfo,
struct_ptr: PointerValue<'ctx>,
) -> Self::Value {
field.llvm_load(ctx, struct_ptr).into_pointer_value()
}
fn llvm_field_store(
&self,
ctx: &CodeGenContext<'ctx, '_>,
field: FieldInfo,
struct_ptr: PointerValue<'ctx>,
pointer_value: &Self::Value,
) {
field.llvm_store(ctx, struct_ptr, pointer_value.as_basic_value_enum());
}
}
#[derive(Debug, Clone, Copy)]
pub struct PointingArrayType<'ctx, ElementType: CustomType<'ctx>> {
pub element_type: ElementType,
_phantom: PhantomData<&'ctx ()>,
}
impl<'ctx, ElementType: CustomType<'ctx>> PointingArrayType<'ctx, ElementType> {
pub fn new(element_type: ElementType) -> Self {
PointingArrayType { element_type, _phantom: PhantomData }
}
}
impl<'ctx, Element: CustomType<'ctx>> CustomType<'ctx> for PointingArrayType<'ctx, Element> {
type Value = Object<'ctx, Self>;
fn llvm_basic_type_enum(&self, ctx: &'ctx Context) -> BasicTypeEnum<'ctx> {
// Element*
self.element_type
.llvm_basic_type_enum(ctx)
.ptr_type(AddressSpace::default())
.as_basic_type_enum()
}
fn llvm_field_load(
&self,
ctx: &CodeGenContext<'ctx, '_>,
field: FieldInfo,
struct_ptr: PointerValue<'ctx>,
) -> Self::Value {
// Remember that it is just a pointer
Object { ty: self.clone(), ptr: field.llvm_load(ctx, struct_ptr).into_pointer_value() }
}
fn llvm_field_store(
&self,
ctx: &CodeGenContext<'ctx, '_>,
field: FieldInfo,
struct_ptr: PointerValue<'ctx>,
value: &Self::Value,
) {
// Remember that it is just a pointer
todo!()
}
}
pub fn check_basic_types_match<'ctx, A, B>(expected: A, got: B) -> Result<(), String>
where
A: BasicType<'ctx>,
B: BasicType<'ctx>,
{
let expected = expected.as_basic_type_enum();
let got = got.as_basic_type_enum();
// Put those logic into here,
// otherwise there is always a fallback reporting on any kind of mismatch
match (expected, got) {
(BasicTypeEnum::IntType(expected), BasicTypeEnum::IntType(got)) => {
if expected.get_bit_width() != got.get_bit_width() {
return Err(format!(
"Expected IntType ({expected}-bit(s)), got IntType ({got}-bit(s))"
));
}
}
(expected, got) => {
if expected != got {
return Err(format!("Expected {expected}, got {got}"));
}
}
}
Ok(())
}
pub trait CustomStructType<'ctx> {
type Fields;
fn llvm_struct_name() -> &'static str;
fn add_fields_to(&self, creator: &mut FieldCreator<'ctx>) -> Self::Fields;
fn fields(&self, ctx: &'ctx Context) -> Self::Fields {
let mut creator = FieldCreator::new(ctx, Self::llvm_struct_name());
let fields = self.add_fields_to(&mut creator);
fields
}
fn llvm_struct_type(&self, ctx: &'ctx Context) -> StructType<'ctx> {
let mut creator = FieldCreator::new(ctx, Self::llvm_struct_name());
self.add_fields_to(&mut creator);
ctx.struct_type(&creator.get_struct_field_types(), false)
}
fn check_struct_type(
&self,
ctx: &'ctx Context,
scrutinee: StructType<'ctx>,
) -> Result<(), String> {
let mut creator = FieldCreator::new(ctx, Self::llvm_struct_name());
self.add_fields_to(&mut creator);
// Check scrutinee's number of struct fields
let expected_field_count = creator.num_fields();
let got_field_count = scrutinee.count_fields();
if got_field_count != expected_field_count {
return Err(format!(
"Expected {expected_count} field(s) in `{struct_name}` type, got {got_count}",
struct_name = Self::llvm_struct_name(),
expected_count = expected_field_count,
got_count = got_field_count,
));
}
// Check the scrutinee's field types
for (field_info, expected_field_ty) in creator.fields {
let got_field_ty = scrutinee.get_field_type_at_index(field_info.gep_index).unwrap();
if let Err(field_err) = check_basic_types_match(expected_field_ty, got_field_ty) {
return Err(format!(
"Field GEP index {gep_index} does not match the expected type of ({struct_name}::{field_name}): {field_err}",
gep_index = field_info.gep_index,
struct_name = Self::llvm_struct_name(),
field_name = field_info.name,
));
}
}
// Done
Ok(())
}
}

View File

@ -438,14 +438,15 @@ fn test_classes_range_type_new() {
assert!(RangeType::is_type(llvm_range.as_base_type()).is_ok());
}
#[test]
fn test_classes_ndarray_type_new() {
let ctx = inkwell::context::Context::create();
let generator = DefaultCodeGenerator::new(String::new(), 64);
let llvm_i32 = ctx.i32_type();
let llvm_usize = generator.get_size_type(&ctx);
let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
assert!(NDArrayType::is_type(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
}
// #[test]
// fn test_classes_ndarray_type_new() {
// let ctx = inkwell::context::Context::create();
// let generator = DefaultCodeGenerator::new(String::new(), 64);
//
// let llvm_i32 = ctx.i32_type();
// let llvm_usize = generator.get_size_type(&ctx);
//
// let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
// assert!(NDArrayType::is_type(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
// }
//

View File

@ -7,6 +7,7 @@
)]
#![warn(clippy::pedantic)]
#![allow(
unused,
dead_code,
clippy::cast_possible_truncation,
clippy::cast_sign_loss,
@ -23,3 +24,4 @@ pub mod codegen;
pub mod symbol_resolver;
pub mod toplevel;
pub mod typecheck;
pub mod util;

View File

@ -1,5 +1,7 @@
use std::iter::once;
use crate::util::SizeVariant;
use classes::NpArrayType;
use helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails};
use indexmap::IndexMap;
use inkwell::{
@ -278,20 +280,11 @@ pub fn get_builtins(unifier: &mut Unifier, primitives: &PrimitiveStore) -> Built
.collect()
}
/// A helper enum used by [`BuiltinBuilder`]
#[derive(Clone, Copy)]
enum SizeVariant {
Bits32,
Bits64,
}
impl SizeVariant {
fn of_int(self, primitives: &PrimitiveStore) -> Type {
match self {
fn size_variant_to_int_type(variant: SizeVariant, primitives: &PrimitiveStore) -> Type {
match variant {
SizeVariant::Bits32 => primitives.int32,
SizeVariant::Bits64 => primitives.int64,
}
}
}
struct BuiltinBuilder<'a> {
@ -961,8 +954,9 @@ impl<'a> BuiltinBuilder<'a> {
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|ctx, obj, fun, args, generator| {
gen_ndarray_copy(ctx, &obj, fun, &args, generator)
.map(|val| Some(val.as_basic_value_enum()))
todo!()
// gen_ndarray_copy(ctx, &obj, fun, &args, generator)
// .map(|val| Some(val.as_basic_value_enum()))
},
)))),
loc: None,
@ -978,8 +972,9 @@ impl<'a> BuiltinBuilder<'a> {
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|ctx, obj, fun, args, generator| {
gen_ndarray_fill(ctx, &obj, fun, &args, generator)?;
Ok(None)
todo!()
// gen_ndarray_fill(ctx, &obj, fun, &args, generator)?;
// Ok(None)
},
)))),
loc: None,
@ -1059,7 +1054,7 @@ impl<'a> BuiltinBuilder<'a> {
);
// The size variant of the function determines the size of the returned int.
let int_sized = size_variant.of_int(self.primitives);
let int_sized = size_variant_to_int_type(size_variant, self.primitives);
let ndarray_int_sized =
make_ndarray_ty(self.unifier, self.primitives, Some(int_sized), Some(common_ndim.ty));
@ -1084,7 +1079,7 @@ impl<'a> BuiltinBuilder<'a> {
let arg_ty = fun.0.args[0].ty;
let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
let ret_elem_ty = size_variant.of_int(&ctx.primitives);
let ret_elem_ty = size_variant_to_int_type(size_variant, &ctx.primitives);
Ok(Some(builtin_fns::call_round(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
}),
)
@ -1125,7 +1120,7 @@ impl<'a> BuiltinBuilder<'a> {
make_ndarray_ty(self.unifier, self.primitives, Some(float), Some(common_ndim.ty));
// The size variant of the function determines the type of int returned
let int_sized = size_variant.of_int(self.primitives);
let int_sized = size_variant_to_int_type(size_variant, self.primitives);
let ndarray_int_sized =
make_ndarray_ty(self.unifier, self.primitives, Some(int_sized), Some(common_ndim.ty));
@ -1148,7 +1143,7 @@ impl<'a> BuiltinBuilder<'a> {
let arg_ty = fun.0.args[0].ty;
let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
let ret_elem_ty = size_variant.of_int(&ctx.primitives);
let ret_elem_ty = size_variant_to_int_type(size_variant, &ctx.primitives);
let func = match kind {
Kind::Ceil => builtin_fns::call_ceil,
Kind::Floor => builtin_fns::call_floor,
@ -1202,7 +1197,7 @@ impl<'a> BuiltinBuilder<'a> {
let func = match prim {
PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty,
PrimDef::FunNpZeros => gen_ndarray_zeros,
PrimDef::FunNpOnes => gen_ndarray_ones,
PrimDef::FunNpOnes => gen_ndarray_ones, // gen_ndarray_ones,
_ => unreachable!(),
};
func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
@ -1251,8 +1246,9 @@ impl<'a> BuiltinBuilder<'a> {
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|ctx, obj, fun, args, generator| {
gen_ndarray_array(ctx, &obj, fun, &args, generator)
.map(|val| Some(val.as_basic_value_enum()))
todo!()
// gen_ndarray_array(ctx, &obj, fun, &args, generator)
// .map(|val| Some(val.as_basic_value_enum()))
},
)))),
loc: None,
@ -1270,8 +1266,9 @@ impl<'a> BuiltinBuilder<'a> {
// type variable
&[(self.list_int32, "shape"), (tv.ty, "fill_value")],
Box::new(move |ctx, obj, fun, args, generator| {
gen_ndarray_full(ctx, &obj, fun, &args, generator)
.map(|val| Some(val.as_basic_value_enum()))
todo!()
// gen_ndarray_full(ctx, &obj, fun, &args, generator)
// .map(|val| Some(val.as_basic_value_enum()))
}),
)
}
@ -1303,8 +1300,9 @@ impl<'a> BuiltinBuilder<'a> {
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|ctx, obj, fun, args, generator| {
gen_ndarray_eye(ctx, &obj, fun, &args, generator)
.map(|val| Some(val.as_basic_value_enum()))
todo!()
// gen_ndarray_eye(ctx, &obj, fun, &args, generator)
// .map(|val| Some(val.as_basic_value_enum()))
},
)))),
loc: None,
@ -1317,8 +1315,9 @@ impl<'a> BuiltinBuilder<'a> {
self.ndarray_float_2d,
&[(int32, "n")],
Box::new(|ctx, obj, fun, args, generator| {
gen_ndarray_identity(ctx, &obj, fun, &args, generator)
.map(|val| Some(val.as_basic_value_enum()))
todo!()
// gen_ndarray_identity(ctx, &obj, fun, &args, generator)
// .map(|val| Some(val.as_basic_value_enum()))
}),
),
_ => unreachable!(),
@ -1462,51 +1461,65 @@ impl<'a> BuiltinBuilder<'a> {
}
}
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
// TODO: Check is unsized and throw error if so
let arg = NDArrayValue::from_ptr_val(
arg.into_pointer_value(),
llvm_usize,
None,
);
// Parse `arg`
let ndarray_ptr = arg.into_pointer_value(); // It has to be an ndarray
let ndims = arg.dim_sizes().size(ctx, generator);
ctx.make_assert(
generator,
ctx.builder
.build_int_compare(
IntPredicate::NE,
ndims,
llvm_usize.const_zero(),
"",
)
.unwrap(),
"0:TypeError",
&format!("{name}() of unsized object", name = prim.name()),
[None, None, None],
ctx.current_loc,
);
let size_type = generator.get_size_type(ctx.ctx);
let ndarray_ty = NpArrayType::new_opaque_elem(ctx.ctx, size_type); // We don't need to care about the element type - we only want the shape
let ndarray = ndarray_ty.value_from_ptr(ctx.ctx, ndarray_ptr);
let len = unsafe {
arg.dim_sizes().get_typed_unchecked(
ctx,
generator,
&llvm_usize.const_zero(),
None,
)
};
let result = call_nac3_len(ctx, ndarray).as_basic_value_enum();
Some(result)
if len.get_type().get_bit_width() == 32 {
Some(len.into())
} else {
Some(
ctx.builder
.build_int_truncate(len, llvm_i32, "len")
.map(Into::into)
.unwrap(),
)
}
// Some(.as_basic_value_enum())
// let llvm_i32 = ctx.ctx.i32_type();
// let llvm_usize = generator.get_size_type(ctx.ctx);
// let arg = NDArrayValue::from_ptr_val(
// arg.into_pointer_value(),
// llvm_usize,
// None,
// );
// let ndims = arg.dim_sizes().size(ctx, generator);
// ctx.make_assert(
// generator,
// ctx.builder
// .build_int_compare(
// IntPredicate::NE,
// ndims,
// llvm_usize.const_zero(),
// "",
// )
// .unwrap(),
// "0:TypeError",
// &format!("{name}() of unsized object", name = prim.name()),
// [None, None, None],
// ctx.current_loc,
// );
// let len = unsafe {
// arg.dim_sizes().get_typed_unchecked(
// ctx,
// generator,
// &llvm_usize.const_zero(),
// None,
// )
// };
// if len.get_type().get_bit_width() == 32 {
// Some(len.into())
// } else {
// Some(
// ctx.builder
// .build_int_truncate(len, llvm_i32, "len")
// .map(Into::into)
// .unwrap(),
// )
// }
}
_ => unreachable!(),
}

5
nac3core/src/util.rs Normal file
View File

@ -0,0 +1,5 @@
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub(crate) enum SizeVariant {
Bits32,
Bits64,
}

View File

@ -0,0 +1,18 @@
@extern
def output_float64(x: float):
...
def output_ndarray_float_1(n: ndarray[float, Literal[1]]):
for i in range(len(n)):
output_float64(n[i])
def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
for r in range(len(n)):
for c in range(len(n[r])):
output_float64(n[r][c])
def run() -> int32:
hello = np_ones((3, 4))
# output_float64(hello[2, 3])
output_ndarray_float_1(hello[::-2, 2])
return 0

View File

@ -449,6 +449,11 @@ fn main() {
.create_target_machine(llvm_options.opt_level)
.expect("couldn't create target machine");
// Debug print if DEBUG_STANDALONE_DUMP_IR is defined
if std::env::var("DEBUG_STANDALONE_DUMP_IR").is_ok() {
main.print_to_file("standalone.ll").unwrap();
}
let pass_options = PassBuilderOptions::create();
pass_options.set_merge_functions(true);
let passes = format!("default<O{}>", opt_level as u32);

View File

@ -81,6 +81,7 @@ in rec {
''
mkdir -p $out/bin
ln -s ${llvm-nac3}/bin/clang.exe $out/bin/clang-irrt.exe
ln -s ${llvm-nac3}/bin/clang.exe $out/bin/clang-irrt-test.exe
ln -s ${llvm-nac3}/bin/llvm-as.exe $out/bin/llvm-as-irrt.exe
'';
nac3artiq = pkgs.rustPlatform.buildRustPackage {