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core/ndstrides: add basic ndarray IRRT functions

This commit is contained in:
lyken 2024-07-28 16:08:37 +08:00
parent e5fe86cc93
commit 8ae9a4294b
10 changed files with 565 additions and 1 deletions

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#pragma once
#include <irrt/error_context.hpp>
#include <irrt/int_defs.hpp>
#include <irrt/ndarray/def.hpp>
namespace {
namespace ndarray {
namespace basic {
namespace util {
/**
* @brief Asserts that `shape` does not contain negative dimensions.
*
* @param ndims Number of dimensions in `shape`
* @param shape The shape to check on
*/
template <typename SizeT>
void assert_shape_no_negative(ErrorContext* errctx, SizeT ndims,
const SizeT* shape) {
for (SizeT axis = 0; axis < ndims; axis++) {
if (shape[axis] < 0) {
errctx->set_exception(
errctx->exceptions->value_error,
"negative dimensions are not allowed; axis {0} "
"has dimension {1}",
axis, shape[axis]);
return;
}
}
}
/**
* @brief Returns the number of elements of an ndarray given its shape.
*
* @param ndims Number of dimensions in `shape`
* @param shape The shape of the ndarray
*/
template <typename SizeT>
SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
SizeT size = 1;
for (SizeT axis = 0; axis < ndims; axis++) size *= shape[axis];
return size;
}
/**
* @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
*
* @param ndims Number of elements in `shape` and `indices`
* @param shape The shape of the ndarray
* @param indices The returned indices indexing the ndarray with shape `shape`.
* @param nth The index of the element of interest.
*/
template <typename SizeT>
void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices,
SizeT nth) {
for (int32_t i = 0; i < ndims; i++) {
int32_t axis = ndims - i - 1;
int32_t dim = shape[axis];
indices[axis] = nth % dim;
nth /= dim;
}
}
} // namespace util
/**
* @brief Return the number of elements of an `ndarray`
*
* This function corresponds to `<an_ndarray>.size`
*/
template <typename SizeT>
SizeT size(const NDArray<SizeT>* ndarray) {
return util::calc_size_from_shape(ndarray->ndims, ndarray->shape);
}
/**
* @brief Return of the number of its content of an `ndarray`.
*
* This function corresponds to `<an_ndarray>.nbytes`.
*/
template <typename SizeT>
SizeT nbytes(const NDArray<SizeT>* ndarray) {
return size(ndarray) * ndarray->itemsize;
}
/**
* @brief Update the strides of an ndarray given an ndarray `shape`
* and assuming that the ndarray is fully c-contagious.
*
* You might want to read https://ajcr.net/stride-guide-part-1/.
*/
template <typename SizeT>
void set_strides_by_shape(NDArray<SizeT>* ndarray) {
SizeT stride_product = 1;
for (SizeT i = 0; i < ndarray->ndims; i++) {
int axis = ndarray->ndims - i - 1;
ndarray->strides[axis] = stride_product * ndarray->itemsize;
stride_product *= ndarray->shape[axis];
}
}
/**
* @brief Return the pointer to the element indexed by `indices`.
*/
template <typename SizeT>
uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
const SizeT* indices) {
uint8_t* element = ndarray->data;
for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
element += indices[dim_i] * ndarray->strides[dim_i];
return element;
}
/**
* @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
*/
template <typename SizeT>
uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
SizeT* indices = (SizeT*)__builtin_alloca(sizeof(SizeT) * ndarray->ndims);
util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, nth);
return get_pelement_by_indices(ndarray, indices);
}
/**
* @brief Like `get_nth_pelement` but asserts that `nth` is in bounds.
*/
template <typename SizeT>
uint8_t* checked_get_nth_pelement(ErrorContext* errctx,
const NDArray<SizeT>* ndarray, SizeT nth) {
SizeT arr_size = ndarray->size();
if (!(0 <= nth && nth < arr_size)) {
errctx->set_exception(
errctx->exceptions->index_error,
"index {0} is out of bounds, valid range is {1} <= index < {2}",
nth, 0, arr_size);
return 0;
}
return get_nth_pelement(ndarray, nth);
}
/**
* @brief Set an element in `ndarray`.
*
* @param pelement Pointer to the element in `ndarray` to be set.
* @param pvalue Pointer to the value `pelement` will be set to.
*/
template <typename SizeT>
void set_pelement_value(NDArray<SizeT>* ndarray, uint8_t* pelement,
const uint8_t* pvalue) {
__builtin_memcpy(pelement, pvalue, ndarray->itemsize);
}
/**
* @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
*
* This function corresponds to `<an_ndarray>.__len__`.
*
* @param dst_length The returned result
*/
template <typename SizeT>
void len(ErrorContext* errctx, const NDArray<SizeT>* ndarray,
SliceIndex* dst_length) {
// numpy prohibits `__len__` on unsized objects
if (ndarray->ndims == 0) {
errctx->set_exception(errctx->exceptions->type_error,
"len() of unsized object");
return;
}
*dst_length = (SliceIndex)ndarray->shape[0];
}
/**
* @brief Copy data from one ndarray to another of the exact same size and itemsize.
*
* Both ndarrays will be viewed in their flatten views when copying the elements.
*/
template <typename SizeT>
void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
__builtin_assume(src_ndarray->itemsize == dst_ndarray->itemsize);
for (SizeT i = 0; i < size(src_ndarray); i++) {
auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
ndarray::basic::set_pelement_value(dst_ndarray, dst_element,
src_element);
}
}
/**
* @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
*
* You may want to see: ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
*/
template <typename SizeT>
bool is_c_contiguous(const NDArray<SizeT>* ndarray) {
// Other references:
// - tinynumpy's implementation: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
// - ndarray's flags["C_CONTIGUOUS"]: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
// - ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
// From https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
//
// The traditional rule is that for an array to be flagged as C contiguous,
// the following must hold:
//
// strides[-1] == itemsize
// strides[i] == shape[i+1] * strides[i + 1]
// [...]
// According to these rules, a 0- or 1-dimensional array is either both
// C- and F-contiguous, or neither; and an array with 2+ dimensions
// can be C- or F- contiguous, or neither, but not both. Though there
// there are exceptions for arrays with zero or one item, in the first
// case the check is relaxed up to and including the first dimension
// with shape[i] == 0. In the second case `strides == itemsize` will
// can be true for all dimensions and both flags are set.
if (ndarray->ndims == 0) {
return true;
}
if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize) {
return false;
}
for (SizeT i = 1; i < ndarray->ndims; i++) {
SizeT axis_i = ndarray->ndims - i - 1;
if (ndarray->strides[axis_i] !=
ndarray->shape[axis_i + 1] + ndarray->strides[axis_i + 1]) {
return false;
}
}
return true;
}
} // namespace basic
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::basic;
uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
return size(ndarray);
}
uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
return size(ndarray);
}
uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
return nbytes(ndarray);
}
uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
return nbytes(ndarray);
}
void __nac3_ndarray_len(ErrorContext* errctx, NDArray<int32_t>* ndarray,
SliceIndex* dst_len) {
return len(errctx, ndarray, dst_len);
}
void __nac3_ndarray_len64(ErrorContext* errctx, NDArray<int64_t>* ndarray,
SliceIndex* dst_len) {
return len(errctx, ndarray, dst_len);
}
void __nac3_ndarray_util_assert_shape_no_negative(ErrorContext* errctx,
int32_t ndims,
int32_t* shape) {
util::assert_shape_no_negative(errctx, ndims, shape);
}
void __nac3_ndarray_util_assert_shape_no_negative64(ErrorContext* errctx,
int64_t ndims,
int64_t* shape) {
util::assert_shape_no_negative(errctx, ndims, shape);
}
void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t>* ndarray) {
set_strides_by_shape(ndarray);
}
void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
set_strides_by_shape(ndarray);
}
bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
return is_c_contiguous(ndarray);
}
bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
return is_c_contiguous(ndarray);
}
void __nac3_ndarray_copy_data(NDArray<int32_t>* src_ndarray,
NDArray<int32_t>* dst_ndarray) {
copy_data(src_ndarray, dst_ndarray);
}
void __nac3_ndarray_copy_data64(NDArray<int64_t>* src_ndarray,
NDArray<int64_t>* dst_ndarray) {
copy_data(src_ndarray, dst_ndarray);
}
}

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#include <irrt/core.hpp>
#include <irrt/error_context.hpp>
#include <irrt/int_defs.hpp>
#include <irrt/ndarray/basic.hpp>
#include <irrt/ndarray/def.hpp>
#include <irrt/utils.hpp>

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#include <cstdio>
#include <cstdlib>
#include <test/test_core.hpp>
#include <test/test_ndarray_basic.hpp>
int main() {
test::core::run();
test::ndarray_basic::run();
return 0;
}

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#pragma once
#include <test/includes.hpp>
namespace test {
namespace ndarray_basic {
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(
210, ndarray::basic::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(
0, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
}
void run() {
test_calc_size_from_shape_normal();
test_calc_size_from_shape_has_zero();
}
} // namespace ndarray_basic
} // namespace test

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use crate::typecheck::typedef::Type;
pub mod error_context;
pub mod ndarray;
pub mod slice;
mod test;
mod util;

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use crate::codegen::model::*;
use crate::codegen::util::array_writer::ArrayWriter;
use crate::codegen::{structure::ndarray::NpArray, CodeGenContext, CodeGenerator};
use super::basic::{
call_nac3_ndarray_nbytes, call_nac3_ndarray_set_strides_by_shape,
call_nac3_ndarray_util_assert_shape_no_negative,
};
/**
Allocate an ndarray on the stack given its `ndims`.
`shape` and `strides` will be automatically allocated on the stack.
The returned ndarray's content will be:
- `data`: `nullptr`
- `itemsize`: **uninitialized** value
- `ndims`: initialized value, set to the input `ndims`
- `shape`: initialized pointer to an allocated stack with **uninitialized** values
- `strides`: initialized pointer to an allocated stack with **uninitialized** values
*/
pub fn alloca_ndarray<'ctx, G>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndims: Int<'ctx, SizeT>,
name: &str,
) -> Result<Ptr<'ctx, StructModel<NpArray>>, String>
where
G: CodeGenerator + ?Sized,
{
let tyctx = generator.type_context(ctx.ctx);
let sizet_model = IntModel(SizeT);
let ndarray_model = StructModel(NpArray);
let ndarray_data_model = PtrModel(IntModel(Byte));
// Setup ndarray
let ndarray_ptr = ndarray_model.alloca(tyctx, ctx, name);
let shape = sizet_model.array_alloca(tyctx, ctx, ndims.value, "shape");
let strides = sizet_model.array_alloca(tyctx, ctx, ndims.value, "strides");
ndarray_ptr.gep(ctx, |f| f.data).store(ctx, ndarray_data_model.nullptr(tyctx, ctx.ctx));
ndarray_ptr.gep(ctx, |f| f.ndims).store(ctx, ndims);
ndarray_ptr.gep(ctx, |f| f.shape).store(ctx, shape);
ndarray_ptr.gep(ctx, |f| f.strides).store(ctx, strides);
Ok(ndarray_ptr)
}
/// Initialize an ndarray's `shape` and asserts on.
/// `shape`'s values and prohibit illegal inputs like negative dimensions.
pub fn init_ndarray_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
shape_writer: &ArrayWriter<'ctx, G, SizeT, IntModel<SizeT>>,
) -> Result<(), String> {
let tyctx = generator.type_context(ctx.ctx);
let shape = pndarray.gep(ctx, |f| f.shape).load(tyctx, ctx, "shape");
(shape_writer.write)(generator, ctx, shape)?;
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, shape_writer.len, shape);
Ok(())
}
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
/// The allocated data buffer is considered to be *owned* by the ndarray.
///
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
///
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
pub fn init_ndarray_data_by_alloca<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
) {
let tyctx = generator.type_context(ctx.ctx);
let ndarray_data_model = IntModel(Byte);
let nbytes = call_nac3_ndarray_nbytes(generator, ctx, pndarray);
let data = ndarray_data_model.array_alloca(tyctx, ctx, nbytes.value, "data");
pndarray.gep(ctx, |f| f.data).store(ctx, data);
call_nac3_ndarray_set_strides_by_shape(generator, ctx, pndarray);
}

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use crate::codegen::irrt::error_context::{check_error_context, setup_error_context};
use crate::codegen::irrt::slice::SliceIndex;
use crate::codegen::irrt::util::function::CallFunction;
use crate::codegen::irrt::util::get_sizet_dependent_function_name;
use crate::codegen::model::*;
use crate::codegen::structure::ndarray::NpArray;
use crate::codegen::{CodeGenContext, CodeGenerator};
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
) -> Int<'ctx, SizeT> {
let tyctx = generator.type_context(ctx.ctx);
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_size"),
)
.arg("ndarray", ndarray_ptr)
.returning("size")
}
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
) -> Int<'ctx, SizeT> {
let tyctx = generator.type_context(ctx.ctx);
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_nbytes"),
)
.arg("ndarray", ndarray_ptr)
.returning("nbytes")
}
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
) -> Int<'ctx, SliceIndex> {
let tyctx = generator.type_context(ctx.ctx);
let slice_index_model = IntModel(SliceIndex::default());
let dst_len = slice_index_model.alloca(tyctx, ctx, "dst_len");
let errctx = setup_error_context(tyctx, ctx);
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_len"),
)
.arg("errctx", errctx)
.arg("ndarray", ndarray_ptr)
.arg("dst_len", dst_len)
.returning_void();
check_error_context(generator, ctx, errctx);
dst_len.load(tyctx, ctx, "len")
}
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndims: Int<'ctx, SizeT>,
shape: Ptr<'ctx, IntModel<SizeT>>,
) {
let tyctx = generator.type_context(ctx.ctx);
let errctx = setup_error_context(tyctx, ctx);
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_util_assert_shape_no_negative"),
)
.arg("errctx", errctx)
.arg("ndims", ndims)
.arg("shape", shape)
.returning_void();
check_error_context(generator, ctx, errctx);
}
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
) {
let tyctx = generator.type_context(ctx.ctx);
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_set_strides_by_shape"),
)
.arg("ndarray", ndarray_ptr)
.returning_void();
}
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
) -> Int<'ctx, Bool> {
let tyctx = generator.type_context(ctx.ctx);
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_is_c_contiguous"),
)
.arg("ndarray", ndarray_ptr)
.returning("is_c_contiguous")
}
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
) -> Int<'ctx, Bool> {
let tyctx = generator.type_context(ctx.ctx);
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_copy_data"),
)
.arg("src_ndarray", src_ndarray)
.arg("dst_ndarray", dst_ndarray)
.returning("is_c_contiguous")
}

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pub mod allocation;
pub mod basic;

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use crate::codegen::model::*;
pub type SliceIndex = Int32;