core/ndstrides: implement np_array()
It also checks for inconsistent dimensions if the input is a list. e.g., rejecting `[[1.0, 2.0], [3.0]]`. However, currently only `np_array(<input>, copy=False)` and `np_array(<input>, copy=True)` are supported. In NumPy, copy could be false, true, or None. Right now, NAC3's `np_array(<input>, copy=False)` behaves like NumPy's `np.array(<input>, copy=None)`.
This commit is contained in:
parent
13715dbda9
commit
d222236492
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@ -8,4 +8,5 @@
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#include "irrt/ndarray/basic.hpp"
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#include "irrt/ndarray/def.hpp"
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#include "irrt/ndarray/iter.hpp"
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#include "irrt/ndarray/indexing.hpp"
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#include "irrt/ndarray/indexing.hpp"
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#include "irrt/ndarray/array.hpp"
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@ -0,0 +1,134 @@
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#pragma once
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#include "irrt/debug.hpp"
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#include "irrt/exception.hpp"
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#include "irrt/int_types.hpp"
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#include "irrt/list.hpp"
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#include "irrt/ndarray/basic.hpp"
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#include "irrt/ndarray/def.hpp"
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namespace {
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namespace ndarray {
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namespace array {
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/**
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* @brief In the context of `np.array(<list>)`, deduce the ndarray's shape produced by `<list>` and raise
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* an exception if there is anything wrong with `<shape>` (e.g., inconsistent dimensions `np.array([[1.0, 2.0],
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* [3.0]])`)
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*
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* If this function finds no issues with `<list>`, the deduced shape is written to `shape`. The caller has the
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* responsibility to allocate `[SizeT; ndims]` for `shape`. The caller must also initialize `shape` with `-1`s because
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* of implementation details.
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*/
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template<typename SizeT>
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void set_and_validate_list_shape_helper(SizeT axis, List<SizeT>* list, SizeT ndims, SizeT* shape) {
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if (shape[axis] == -1) {
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// Dimension is unspecified. Set it.
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shape[axis] = list->len;
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} else {
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// Dimension is specified. Check.
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if (shape[axis] != list->len) {
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// Mismatch, throw an error.
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// NOTE: NumPy's error message is more complex and needs more PARAMS to display.
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raise_exception(SizeT, EXN_VALUE_ERROR,
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"The requested array has an inhomogenous shape "
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"after {0} dimension(s).",
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axis, shape[axis], list->len);
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}
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}
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if (axis + 1 == ndims) {
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// `list` has type `list[ItemType]`
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// Do nothing
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} else {
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// `list` has type `list[list[...]]`
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List<SizeT>** lists = (List<SizeT>**)(list->items);
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for (SizeT i = 0; i < list->len; i++) {
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set_and_validate_list_shape_helper<SizeT>(axis + 1, lists[i], ndims, shape);
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}
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}
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}
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/**
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* @brief See `set_and_validate_list_shape_helper`.
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*/
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template<typename SizeT>
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void set_and_validate_list_shape(List<SizeT>* list, SizeT ndims, SizeT* shape) {
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for (SizeT axis = 0; axis < ndims; axis++) {
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shape[axis] = -1; // Sentinel to say this dimension is unspecified.
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}
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set_and_validate_list_shape_helper<SizeT>(0, list, ndims, shape);
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}
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/**
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* @brief In the context of `np.array(<list>)`, copied the contents stored in `list` to `ndarray`.
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*
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* `list` is assumed to be "legal". (i.e., no inconsistent dimensions)
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*
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* # Notes on `ndarray`
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* The caller is responsible for allocating space for `ndarray`.
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* Here is what this function expects from `ndarray` when called:
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* - `ndarray->data` has to be allocated, contiguous, and may contain uninitialized values.
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* - `ndarray->itemsize` has to be initialized.
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* - `ndarray->ndims` has to be initialized.
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* - `ndarray->shape` has to be initialized.
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* - `ndarray->strides` is ignored, but note that `ndarray->data` is contiguous.
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* When this function call ends:
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* - `ndarray->data` is written with contents from `<list>`.
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*/
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template<typename SizeT>
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void write_list_to_array_helper(SizeT axis, SizeT* index, List<SizeT>* list, NDArray<SizeT>* ndarray) {
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debug_assert_eq(SizeT, list->len, ndarray->shape[axis]);
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if (IRRT_DEBUG_ASSERT_BOOL) {
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if (!ndarray::basic::is_c_contiguous(ndarray)) {
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raise_debug_assert(SizeT, "ndarray is not C-contiguous", ndarray->strides[0], ndarray->strides[1],
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NO_PARAM);
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}
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}
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if (axis + 1 == ndarray->ndims) {
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// `list` has type `list[scalar]`
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// `ndarray` is contiguous, so we can do this, and this is fast.
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uint8_t* dst = ndarray->data + (ndarray->itemsize * (*index));
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__builtin_memcpy(dst, list->items, ndarray->itemsize * list->len);
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*index += list->len;
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} else {
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// `list` has type `list[list[...]]`
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List<SizeT>** lists = (List<SizeT>**)(list->items);
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for (SizeT i = 0; i < list->len; i++) {
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write_list_to_array_helper<SizeT>(axis + 1, index, lists[i], ndarray);
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}
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}
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}
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/**
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* @brief See `write_list_to_array_helper`.
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*/
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template<typename SizeT>
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void write_list_to_array(List<SizeT>* list, NDArray<SizeT>* ndarray) {
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SizeT index = 0;
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write_list_to_array_helper<SizeT>((SizeT)0, &index, list, ndarray);
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}
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} // namespace array
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} // namespace ndarray
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} // namespace
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extern "C" {
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using namespace ndarray::array;
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void __nac3_ndarray_array_set_and_validate_list_shape(List<int32_t>* list, int32_t ndims, int32_t* shape) {
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set_and_validate_list_shape(list, ndims, shape);
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}
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void __nac3_ndarray_array_set_and_validate_list_shape64(List<int64_t>* list, int64_t ndims, int64_t* shape) {
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set_and_validate_list_shape(list, ndims, shape);
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}
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void __nac3_ndarray_array_write_list_to_array(List<int32_t>* list, NDArray<int32_t>* ndarray) {
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write_list_to_array(list, ndarray);
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}
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void __nac3_ndarray_array_write_list_to_array64(List<int64_t>* list, NDArray<int64_t>* ndarray) {
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write_list_to_array(list, ndarray);
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}
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}
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@ -8,7 +8,10 @@ use super::{
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llvm_intrinsics,
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macros::codegen_unreachable,
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model::*,
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object::ndarray::{indexing::NDIndex, nditer::NDIter, NDArray},
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object::{
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list::List,
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ndarray::{indexing::NDIndex, nditer::NDIter, NDArray},
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},
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stmt::gen_for_callback_incrementing,
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CodeGenContext, CodeGenerator,
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};
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@ -1129,3 +1132,32 @@ pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
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.arg(dst_ndarray)
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.returning_void();
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}
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pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
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ndims: Instance<'ctx, Int<SizeT>>,
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shape: Instance<'ctx, Ptr<Int<SizeT>>>,
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) {
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let name = get_sizet_dependent_function_name(
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generator,
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ctx,
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"__nac3_ndarray_array_set_and_validate_list_shape",
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);
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FnCall::builder(generator, ctx, &name).arg(list).arg(ndims).arg(shape).returning_void();
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}
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pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
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ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
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) {
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let name = get_sizet_dependent_function_name(
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generator,
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ctx,
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"__nac3_ndarray_array_write_list_to_array",
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);
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FnCall::builder(generator, ctx, &name).arg(list).arg(ndarray).returning_void();
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}
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@ -182,6 +182,15 @@ impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Item>> {
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Ptr(new_item).pointer_cast(generator, ctx, self.value)
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}
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/// Cast this pointer to `uint8_t*`
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pub fn cast_to_pi8<G: CodeGenerator + ?Sized>(
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&self,
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generator: &mut G,
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ctx: &CodeGenContext<'ctx, '_>,
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) -> Instance<'ctx, Ptr<Int<Byte>>> {
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Ptr(Int(Byte)).pointer_cast(generator, ctx, self.value)
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}
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/// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
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pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
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let value = ctx.builder.build_is_null(self.value, "").unwrap();
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@ -13,6 +13,7 @@ use crate::{
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},
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llvm_intrinsics::{self, call_memcpy_generic},
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macros::codegen_unreachable,
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model::*,
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object::{
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any::AnyObject,
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ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
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@ -22,13 +23,13 @@ use crate::{
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},
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symbol_resolver::ValueEnum,
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toplevel::{
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helper::{extract_ndims, PrimDef},
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helper::extract_ndims,
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numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
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DefinitionId,
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},
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typecheck::{
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magic_methods::Binop,
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typedef::{FunSignature, Type, TypeEnum},
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typedef::{FunSignature, Type},
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},
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};
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use inkwell::{
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@ -1840,26 +1841,6 @@ pub fn gen_ndarray_array<'ctx>(
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assert!(matches!(args.len(), 1..=3));
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let obj_ty = fun.0.args[0].ty;
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let obj_elem_ty = match &*context.unifier.get_ty(obj_ty) {
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TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
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unpack_ndarray_var_tys(&mut context.unifier, obj_ty).0
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}
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TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::List.id() => {
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let mut ty = *params.iter().next().unwrap().1;
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while let TypeEnum::TObj { obj_id, params, .. } = &*context.unifier.get_ty_immutable(ty)
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{
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if *obj_id != PrimDef::List.id() {
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break;
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}
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ty = *params.iter().next().unwrap().1;
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}
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ty
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}
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_ => obj_ty,
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};
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let obj_arg = args[0].1.clone().to_basic_value_enum(context, generator, obj_ty)?;
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let copy_arg = if let Some(arg) =
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@ -1875,28 +1856,18 @@ pub fn gen_ndarray_array<'ctx>(
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)
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};
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let ndmin_arg = if let Some(arg) =
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args.iter().find(|arg| arg.0.is_some_and(|name| name == fun.0.args[2].name))
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{
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let ndmin_ty = fun.0.args[2].ty;
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arg.1.clone().to_basic_value_enum(context, generator, ndmin_ty)?
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} else {
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context.gen_symbol_val(
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generator,
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fun.0.args[2].default_value.as_ref().unwrap(),
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fun.0.args[2].ty,
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)
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};
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// The ndmin argument is ignored. We can simply force the ndarray's number of dimensions to be
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// the `ndims` of the function return type.
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let (_, ndims) = unpack_ndarray_var_tys(&mut context.unifier, fun.0.ret);
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let ndims = extract_ndims(&context.unifier, ndims);
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call_ndarray_array_impl(
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generator,
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context,
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obj_elem_ty,
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obj_arg,
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copy_arg.into_int_value(),
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ndmin_arg.into_int_value(),
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)
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.map(NDArrayValue::into)
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let object = AnyObject { value: obj_arg, ty: obj_ty };
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// NAC3 booleans are i8.
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let copy = Int(Bool).truncate(generator, context, copy_arg.into_int_value());
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let ndarray = NDArrayObject::make_np_array(generator, context, object, copy)
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.atleast_nd(generator, context, ndims);
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Ok(ndarray.instance.value)
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}
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/// Generates LLVM IR for `ndarray.eye`.
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|
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@ -31,6 +31,17 @@ impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for List<Item> {
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}
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}
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impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Struct<List<Item>>>> {
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/// Cast the items pointer to `uint8_t*`.
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pub fn with_pi8_items<G: CodeGenerator + ?Sized>(
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&self,
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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) -> Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>> {
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self.pointer_cast(generator, ctx, Struct(List { item: Int(Byte) }))
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}
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}
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/// A NAC3 Python List object.
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#[derive(Debug, Clone, Copy)]
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pub struct ListObject<'ctx> {
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|
|
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|
|
@ -0,0 +1,184 @@
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use super::NDArrayObject;
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use crate::{
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codegen::{
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irrt::{
|
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call_nac3_ndarray_array_set_and_validate_list_shape,
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call_nac3_ndarray_array_write_list_to_array,
|
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},
|
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model::*,
|
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object::{any::AnyObject, list::ListObject},
|
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stmt::gen_if_else_expr_callback,
|
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CodeGenContext, CodeGenerator,
|
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},
|
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toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims},
|
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typecheck::typedef::{Type, TypeEnum},
|
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};
|
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|
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/// Get the expected `dtype` and `ndims` of the ndarray returned by `np_array(list)`.
|
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fn get_list_object_dtype_and_ndims<'ctx>(
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ctx: &mut CodeGenContext<'ctx, '_>,
|
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list: ListObject<'ctx>,
|
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) -> (Type, u64) {
|
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let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list.item_type);
|
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let ndims = arraylike_get_ndims(&mut ctx.unifier, list.item_type);
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let ndims = ndims + 1; // To count `list` itself.
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(dtype, ndims)
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}
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impl<'ctx> NDArrayObject<'ctx> {
|
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/// Implementation of `np_array(<list>, copy=True)`
|
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fn make_np_array_list_copy_true_impl<G: CodeGenerator + ?Sized>(
|
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generator: &mut G,
|
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ctx: &mut CodeGenContext<'ctx, '_>,
|
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list: ListObject<'ctx>,
|
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) -> Self {
|
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let (dtype, ndims_int) = get_list_object_dtype_and_ndims(ctx, list);
|
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let list_value = list.instance.with_pi8_items(generator, ctx);
|
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|
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// Validate `list` has a consistent shape.
|
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// Raise an exception if `list` is something abnormal like `[[1, 2], [3]]`.
|
||||
// If `list` has a consistent shape, deduce the shape and write it to `shape`.
|
||||
let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int, false);
|
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let shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
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call_nac3_ndarray_array_set_and_validate_list_shape(
|
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generator, ctx, list_value, ndims, shape,
|
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);
|
||||
|
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let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims_int);
|
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ndarray.copy_shape_from_array(generator, ctx, shape);
|
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ndarray.create_data(generator, ctx);
|
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|
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// Copy all contents from the list.
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call_nac3_ndarray_array_write_list_to_array(generator, ctx, list_value, ndarray.instance);
|
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|
||||
ndarray
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||||
}
|
||||
|
||||
/// Implementation of `np_array(<list>, copy=None)`
|
||||
fn make_np_array_list_copy_none_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> Self {
|
||||
// np_array without copying is only possible `list` is not nested.
|
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//
|
||||
// If `list` is `list[T]`, we can create an ndarray with `data` set
|
||||
// to the array pointer of `list`.
|
||||
//
|
||||
// If `list` is `list[list[T]]` or worse, copy.
|
||||
|
||||
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
if ndims == 1 {
|
||||
// `list` is not nested
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, 1);
|
||||
|
||||
// Set data
|
||||
let data = list.instance.get(generator, ctx, |f| f.items).cast_to_pi8(generator, ctx);
|
||||
ndarray.instance.set(ctx, |f| f.data, data);
|
||||
|
||||
// ndarray->shape[0] = list->len;
|
||||
let shape = ndarray.instance.get(generator, ctx, |f| f.shape);
|
||||
let list_len = list.instance.get(generator, ctx, |f| f.len);
|
||||
shape.set_index_const(ctx, 0, list_len);
|
||||
|
||||
// Set strides, the `data` is contiguous
|
||||
ndarray.set_strides_contiguous(generator, ctx);
|
||||
|
||||
ndarray
|
||||
} else {
|
||||
// `list` is nested, copy
|
||||
NDArrayObject::make_np_array_list_copy_true_impl(generator, ctx, list)
|
||||
}
|
||||
}
|
||||
|
||||
/// Implementation of `np_array(<list>, copy=copy)`
|
||||
fn make_np_array_list_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
copy: Instance<'ctx, Int<Bool>>,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
|
||||
let ndarray = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy.value),
|
||||
|generator, ctx| {
|
||||
let ndarray =
|
||||
NDArrayObject::make_np_array_list_copy_true_impl(generator, ctx, list);
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
|generator, ctx| {
|
||||
let ndarray =
|
||||
NDArrayObject::make_np_array_list_copy_none_impl(generator, ctx, list);
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.unwrap();
|
||||
|
||||
NDArrayObject::from_value_and_unpacked_types(generator, ctx, ndarray, dtype, ndims)
|
||||
}
|
||||
|
||||
/// Implementation of `np_array(<ndarray>, copy=copy)`.
|
||||
pub fn make_np_array_ndarray_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
copy: Instance<'ctx, Int<Bool>>,
|
||||
) -> Self {
|
||||
let ndarray_val = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy.value),
|
||||
|generator, ctx| {
|
||||
let ndarray = ndarray.make_copy(generator, ctx); // Force copy
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
|_generator, _ctx| {
|
||||
// No need to copy. Return `ndarray` itself.
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.unwrap();
|
||||
|
||||
NDArrayObject::from_value_and_unpacked_types(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray_val,
|
||||
ndarray.dtype,
|
||||
ndarray.ndims,
|
||||
)
|
||||
}
|
||||
|
||||
/// Create a new ndarray like `np.array()`.
|
||||
///
|
||||
/// NOTE: The `ndmin` argument is not here. You may want to
|
||||
/// do [`NDArrayObject::atleast_nd`] to achieve that.
|
||||
pub fn make_np_array<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
copy: Instance<'ctx, Int<Bool>>,
|
||||
) -> Self {
|
||||
match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let list = ListObject::from_object(generator, ctx, object);
|
||||
NDArrayObject::make_np_array_list_impl(generator, ctx, list, copy)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, object);
|
||||
NDArrayObject::make_np_array_ndarray_impl(generator, ctx, ndarray, copy)
|
||||
}
|
||||
_ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object.ty)), // Typechecker ensures this
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
pub mod array;
|
||||
pub mod factory;
|
||||
pub mod indexing;
|
||||
pub mod nditer;
|
||||
|
|
@ -74,8 +75,19 @@ impl<'ctx> NDArrayObject<'ctx> {
|
|||
) -> NDArrayObject<'ctx> {
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
|
||||
}
|
||||
|
||||
let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, object.value).unwrap();
|
||||
/// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
|
||||
/// `dtype` and `ndims`.
|
||||
pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: V,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, value).unwrap();
|
||||
NDArrayObject { dtype, ndims, instance: value }
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue