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WIP: core/ndstrides: checkpoint 6

This commit is contained in:
lyken 2024-08-09 15:50:55 +08:00
parent fbf861142b
commit 2546053013
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48 changed files with 4972 additions and 2439 deletions
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26
nac3core/irrt/irrt/core.hpp
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@ -4,7 +4,7 @@
#include <irrt/util.hpp>
// NDArray indices are always `uint32_t`.
using NDIndex = uint32_t;
using NDIndexInt = 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;
@ -43,7 +43,7 @@ SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len,
template <typename SizeT>
void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
SizeT num_dims, NDIndex* idxs) {
SizeT num_dims, NDIndexInt* idxs) {
SizeT stride = 1;
for (SizeT dim = 0; dim < num_dims; dim++) {
SizeT i = num_dims - dim - 1;
@ -55,7 +55,7 @@ void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
template <typename SizeT>
SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
const NDIndex* indices,
const NDIndexInt* indices,
SizeT num_indices) {
SizeT idx = 0;
SizeT stride = 1;
@ -104,8 +104,8 @@ void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims, SizeT lhs_ndims,
template <typename SizeT>
void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
SizeT src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx) {
const NDIndexInt* in_idx,
NDIndexInt* 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];
@ -293,24 +293,24 @@ uint64_t __nac3_ndarray_calc_size64(const uint64_t* list_data,
}
void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims,
uint32_t num_dims, NDIndex* idxs) {
uint32_t num_dims, NDIndexInt* 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) {
uint64_t num_dims, NDIndexInt* 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,
const NDIndexInt* 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,
const NDIndexInt* indices,
uint64_t num_indices) {
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
num_indices);
@ -333,16 +333,16 @@ void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
uint32_t src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx) {
const NDIndexInt* in_idx,
NDIndexInt* 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) {
const NDIndexInt* in_idx,
NDIndexInt* out_idx) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
out_idx);
}

27
nac3core/irrt/irrt/ndarray/basic.hpp
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@ -50,9 +50,9 @@ SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
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];
for (SizeT i = 0; i < ndims; i++) {
SizeT axis = ndims - i - 1;
SizeT dim = shape[axis];
indices[axis] = nth % dim;
nth /= dim;
@ -93,8 +93,9 @@ SizeT len(const NDArray<SizeT>* ndarray) {
if (ndarray->ndims == 0) {
raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object",
NO_PARAM, NO_PARAM, NO_PARAM);
} else {
return ndarray->shape[0];
}
return ndarray->shape[0];
}
/**
@ -156,6 +157,8 @@ uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
return element;
}
int counter = 0;
/**
* @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
*
@ -163,9 +166,14 @@ uint8_t* get_pelement_by_indices(const NDArray<SizeT>* 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);
uint8_t* element = ndarray->data;
for (SizeT i = 0; i < ndarray->ndims; i++) {
SizeT axis = ndarray->ndims - i - 1;
SizeT dim = ndarray->shape[axis];
element += ndarray->strides[axis] * (nth % dim);
nth /= dim;
}
return element;
}
/**
@ -259,12 +267,13 @@ bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
return is_c_contiguous(ndarray);
}
uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray,
int32_t nth) {
return get_nth_pelement(ndarray, nth);
}
uint8_t* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray,
int64_t nth) {
int64_t nth) {
return get_nth_pelement(ndarray, nth);
}

157
nac3core/irrt/irrt/ndarray/broadcast.hpp Normal file
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@ -0,0 +1,157 @@
#pragma once
#include <irrt/int_defs.hpp>
#include <irrt/ndarray/def.hpp>
#include <irrt/slice.hpp>
namespace {
template <typename SizeT>
struct ShapeEntry {
SizeT ndims;
SizeT* shape;
};
} // namespace
namespace {
namespace ndarray {
namespace broadcast {
namespace util {
/**
* @brief Return true if `src_shape` can broadcast to `dst_shape`.
*
* See https://numpy.org/doc/stable/user/basics.broadcasting.html
*/
template <typename SizeT>
bool can_broadcast_shape_to(SizeT target_ndims, const SizeT* target_shape,
SizeT src_ndims, const SizeT* src_shape) {
if (src_ndims > target_ndims) {
return false;
}
for (SizeT i = 0; i < src_ndims; i++) {
SizeT target_dim = target_shape[target_ndims - i - 1];
SizeT src_dim = src_shape[src_ndims - i - 1];
if (!(src_dim == 1 || target_dim == src_dim)) {
return false;
}
}
return true;
}
/**
* @brief Performs `np.broadcast_shapes`
*/
template <typename SizeT>
void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes,
SizeT dst_ndims, SizeT* dst_shape) {
// `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it
// for this function since they should already know in order to allocate `dst_shape` in the first place.
// `dst_shape` must be pre-allocated.
// `dst_shape` does not have to be initialized
for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
dst_shape[dst_axis] = 1;
}
for (SizeT i = 0; i < num_shapes; i++) {
ShapeEntry<SizeT> entry = shapes[i];
for (SizeT j = 0; j < entry.ndims; j++) {
SizeT entry_axis = entry.ndims - j - 1;
SizeT dst_axis = dst_ndims - j - 1;
SizeT entry_dim = entry.shape[entry_axis];
SizeT dst_dim = dst_shape[dst_axis];
if (dst_dim == 1) {
dst_shape[dst_axis] = entry_dim;
} else if (entry_dim == 1 || entry_dim == dst_dim) {
// Do nothing
} else {
raise_exception(SizeT, EXN_VALUE_ERROR,
"shape mismatch: objects cannot be broadcast "
"to a single shape.",
NO_PARAM, NO_PARAM, NO_PARAM);
}
}
}
}
} // namespace util
/**
* @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
*
* This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
* and return the result by modifying `dst_ndarray`.
*
* # Notes on `dst_ndarray`
* The caller is responsible for allocating space for the resulting ndarray.
* Here is what this function expects from `dst_ndarray` when called:
* - `dst_ndarray->data` does not have to be initialized.
* - `dst_ndarray->itemsize` does not have to be initialized.
* - `dst_ndarray->ndims` must be initialized, determining the length of `dst_ndarray->shape`
* - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
* When this function call ends:
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
* - `dst_ndarray->ndims` is unchanged.
* - `dst_ndarray->shape` is unchanged.
* - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
*/
template <typename SizeT>
void broadcast_to(const NDArray<SizeT>* src_ndarray,
NDArray<SizeT>* dst_ndarray) {
if (!ndarray::broadcast::util::can_broadcast_shape_to(
dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
src_ndarray->shape)) {
raise_exception(SizeT, EXN_VALUE_ERROR,
"operands could not be broadcast together",
dst_ndarray->shape[0], src_ndarray->shape[0], NO_PARAM);
}
dst_ndarray->data = src_ndarray->data;
dst_ndarray->itemsize = src_ndarray->itemsize;
for (SizeT i = 0; i < dst_ndarray->ndims; i++) {
SizeT src_axis = src_ndarray->ndims - i - 1;
SizeT dst_axis = dst_ndarray->ndims - i - 1;
if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 &&
dst_ndarray->shape[dst_axis] != 1)) {
// Freeze the steps in-place
dst_ndarray->strides[dst_axis] = 0;
} else {
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
}
}
}
} // namespace broadcast
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::broadcast;
void __nac3_ndarray_broadcast_to(NDArray<int32_t>* src_ndarray,
NDArray<int32_t>* dst_ndarray) {
broadcast_to(src_ndarray, dst_ndarray);
}
void __nac3_ndarray_broadcast_to64(NDArray<int64_t>* src_ndarray,
NDArray<int64_t>* dst_ndarray) {
broadcast_to(src_ndarray, dst_ndarray);
}
void __nac3_ndarray_broadcast_shapes(int32_t num_shapes,
const ShapeEntry<int32_t>* shapes,
int32_t dst_ndims, int32_t* dst_shape) {
ndarray::broadcast::util::broadcast_shapes(num_shapes, shapes, dst_ndims,
dst_shape);
}
void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes,
const ShapeEntry<int64_t>* shapes,
int64_t dst_ndims, int64_t* dst_shape) {
ndarray::broadcast::util::broadcast_shapes(num_shapes, shapes, dst_ndims,
dst_shape);
}
}

228
nac3core/irrt/irrt/ndarray/indexing.hpp Normal file
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@ -0,0 +1,228 @@
#pragma once
#include <irrt/exception.hpp>
#include <irrt/int_defs.hpp>
#include <irrt/ndarray/basic.hpp>
#include <irrt/ndarray/def.hpp>
#include <irrt/slice.hpp>
namespace {
typedef uint8_t NDIndexType;
/**
* @brief A single element index
*
* `data` points to a `SliceIndex`.
*/
const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
/**
* @brief A slice index
*
* `data` points to a `UserRange`.
*/
const NDIndexType ND_INDEX_TYPE_SLICE = 1;
/**
* @brief `np.newaxis` / `None`
*
* `data` is unused.
*/
const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
/**
* @brief `Ellipsis` / `...`
*
* `data` is unused.
*/
const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
/**
* @brief An index used in ndarray indexing
*/
struct NDIndex {
/**
* @brief Enum tag to specify the type of index.
*
* Please see comments of each enum constant.
*/
NDIndexType type;
/**
* @brief The accompanying data associated with `type`.
*
* Please see comments of each enum constant.
*/
uint8_t* data;
};
} // namespace
namespace {
namespace ndarray {
namespace indexing {
namespace util {
/**
* @brief Return the expected rank of the resulting ndarray
* created by indexing an ndarray of rank `ndims` using `indexes`.
*
* An IndexError is raised if the indexes are invalid.
*/
template <typename SizeT>
SizeT validate_and_deduce_ndims_after_indexing(SizeT ndims, SizeT num_indexes,
const NDIndex* indexes) {
if (num_indexes > ndims) {
raise_exception(SizeT, EXN_INDEX_ERROR,
"too many indices for array: array is {0}-dimensional, "
"but {1} were indexed",
ndims, num_indexes, NO_PARAM);
}
// There may be ellipsis `...` in `indexes`. There can only be 0 or 1 ellipsis.
SizeT num_ellipsis = 0;
for (SizeT i = 0; i < num_indexes; i++) {
if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
ndims--;
} else if (indexes[i].type == ND_INDEX_TYPE_SLICE) {
// Nothing
} else if (indexes[i].type == ND_INDEX_TYPE_NEWAXIS) {
ndims++;
} else if (indexes[i].type == ND_INDEX_TYPE_ELLIPSIS) {
// `...` doesn't do anything to `ndims`.
num_ellipsis++;
if (num_ellipsis > 1) {
raise_exception(
SizeT, EXN_INDEX_ERROR,
"an index can only have a single ellipsis ('...')",
NO_PARAM, NO_PARAM, NO_PARAM);
}
} else {
__builtin_unreachable();
}
}
return ndims;
}
} // namespace util
/**
* @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
*
* This is function very similar to performing `dst_ndarray = src_ndarray[indexes]` in Python (where the variables
* can all be found in the parameter of this function).
*
* In other words, this function takes in an ndarray (`src_ndarray`), index it with `indexes`, and return the
* indexed array (by writing the result to `dst_ndarray`).
*
* This function also does proper assertions on `indexes`.
*
* # Notes on `dst_ndarray`
* The caller is responsible for allocating space for the resulting ndarray.
* Here is what this function expects from `dst_ndarray` when called:
* - `dst_ndarray->data` does not have to be initialized.
* - `dst_ndarray->itemsize` does not have to be initialized.
* - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
* indexing `src_ndarray` with `indexes`.
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
* When this function call ends:
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
* - `dst_ndarray->ndims` is unchanged.
* - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
* - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
*
* @param indexes Indexes to index `src_ndarray`, ordered in the same way you would write them in Python.
* @param src_ndarray The NDArray to be indexed.
* @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
*/
template <typename SizeT>
void index(SizeT num_indexes, const NDIndex* indexes,
const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
SizeT expected_dst_ndarray_ndims =
util::validate_and_deduce_ndims_after_indexing(src_ndarray->ndims,
num_indexes, indexes);
dst_ndarray->data = src_ndarray->data;
dst_ndarray->itemsize = src_ndarray->itemsize;
// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
SizeT src_axis = 0;
SizeT dst_axis = 0;
for (SliceIndex i = 0; i < num_indexes; i++) {
const NDIndex* index = &indexes[i];
if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
SliceIndex input = *((SliceIndex*)index->data);
SliceIndex k = slice::resolve_index_in_length(
src_ndarray->shape[src_axis], input);
if (k == slice::OUT_OF_BOUNDS) {
raise_exception(SizeT, EXN_INDEX_ERROR,
"index {0} is out of bounds for axis {1} "
"with size {2}",
input, src_axis, src_ndarray->shape[src_axis]);
}
dst_ndarray->data += k * src_ndarray->strides[src_axis];
src_axis++;
} else if (index->type == ND_INDEX_TYPE_SLICE) {
UserSlice* input = (UserSlice*)index->data;
Slice slice;
input->indices_checked<SizeT>(src_ndarray->shape[src_axis], &slice);
dst_ndarray->data +=
(SizeT)slice.start * src_ndarray->strides[src_axis];
dst_ndarray->strides[dst_axis] =
((SizeT)slice.step) * src_ndarray->strides[src_axis];
dst_ndarray->shape[dst_axis] = (SizeT)slice.len();
dst_axis++;
src_axis++;
} else if (index->type == ND_INDEX_TYPE_NEWAXIS) {
dst_ndarray->strides[dst_axis] = 0;
dst_ndarray->shape[dst_axis] = 1;
dst_axis++;
} else if (index->type == ND_INDEX_TYPE_ELLIPSIS) {
// The number of ':' entries this '...' implies.
SizeT ellipsis_size = src_ndarray->ndims - (num_indexes - 1);
for (SizeT j = 0; j < ellipsis_size; j++) {
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
dst_axis++;
src_axis++;
}
} else {
__builtin_unreachable();
}
}
for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
}
}
} // namespace indexing
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::indexing;
void __nac3_ndarray_index(int32_t num_indexes, NDIndex* indexes,
NDArray<int32_t>* src_ndarray,
NDArray<int32_t>* dst_ndarray) {
index(num_indexes, indexes, src_ndarray, dst_ndarray);
}
void __nac3_ndarray_index64(int64_t num_indexes, NDIndex* indexes,
NDArray<int64_t>* src_ndarray,
NDArray<int64_t>* dst_ndarray) {
index(num_indexes, indexes, src_ndarray, dst_ndarray);
}
}

26
nac3core/irrt/irrt/ndarray/product.hpp Normal file
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@ -0,0 +1,26 @@
namespace {
namespace ndarray {
namespace matmul {
namespace util {
template <typename SizeT>
void broadcast_shape(SizeT a_ndims, SizeT* a_shape, SizeT b_ndims,
SizeT* b_shape, SizeT* dst_shape) {
__builtin_assume(!(a_ndims == 1 && b_ndims == 1));
__builtin_assume(a_ndims >= 1);
__builtin_assume(b_ndims >= 1);
}
} // namespace util
template <typename SizeT, typename T>
void matmul_at_least_2d(NDArray<SizeT>* a_ndarray, NDArray<SizeT>* b_ndarray,
NDArray<SizeT>* dst_ndarray) {
__builtin_assume(sizeof(T) == a_ndarray->itemsize);
__builtin_assume(sizeof(T) == b_ndarray->itemsize);
// See https://numpy.org/doc/stable/reference/generated/numpy.matmul.html#numpy-matmul
}
} // namespace matmul
} // namespace ndarray
} // namespace

111
nac3core/irrt/irrt/ndarray/reshape.hpp Normal file
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@ -0,0 +1,111 @@
#pragma once
#include <irrt/int_defs.hpp>
#include <irrt/ndarray/def.hpp>
namespace {
namespace ndarray {
namespace reshape {
namespace util {
/**
* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
*
* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
* modified to contain the resolved dimension.
*
* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
*
* @param size The `.size` of `<ndarray>`
* @param new_ndims Number of elements in `new_shape`
* @param new_shape Target shape to reshape to
*/
template <typename SizeT>
void resolve_and_check_new_shape(SizeT size, SizeT new_ndims,
SizeT* new_shape) {
// Is there a -1 in `new_shape`?
bool neg1_exists = false;
// Location of -1, only initialized if `neg1_exists` is true
SizeT neg1_axis_i;
// The computed ndarray size of `new_shape`
SizeT new_size = 1;
for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
SizeT dim = new_shape[axis_i];
if (dim < 0) {
if (dim == -1) {
if (neg1_exists) {
// Multiple `-1` found. Throw an error.
raise_exception(SizeT, EXN_VALUE_ERROR,
"can only specify one unknown dimension",
NO_PARAM, NO_PARAM, NO_PARAM);
} else {
neg1_exists = true;
neg1_axis_i = axis_i;
}
} else {
// TODO: What? In `np.reshape` any negative dimensions is
// treated like its `-1`.
//
// Try running `np.zeros((3, 4)).reshape((-999, 2))`
//
// It is not documented by numpy.
// Throw an error for now...
raise_exception(
SizeT, EXN_VALUE_ERROR,
"Found non -1 negative dimension {0} on axis {1}", dim,
axis_i, NO_PARAM);
}
} else {
new_size *= dim;
}
}
bool can_reshape;
if (neg1_exists) {
// Let `x` be the unknown dimension
// solve `x * <new_size> = <size>`
if (new_size == 0 && size == 0) {
// `x` has infinitely many solutions
can_reshape = false;
} else if (new_size == 0 && size != 0) {
// `x` has no solutions
can_reshape = false;
} else if (size % new_size != 0) {
// `x` has no integer solutions
can_reshape = false;
} else {
can_reshape = true;
new_shape[neg1_axis_i] = size / new_size; // Resolve dimension
}
} else {
can_reshape = (new_size == size);
}
if (!can_reshape) {
raise_exception(SizeT, EXN_VALUE_ERROR,
"cannot reshape array of size {0} into given shape",
size, NO_PARAM, NO_PARAM);
}
}
} // namespace util
} // namespace reshape
} // namespace ndarray
} // namespace
extern "C" {
void __nac3_ndarray_resolve_and_check_new_shape(int32_t size, int32_t new_ndims,
int32_t* new_shape) {
ndarray::reshape::util::resolve_and_check_new_shape(size, new_ndims,
new_shape);
}
void __nac3_ndarray_resolve_and_check_new_shape64(int64_t size,
int64_t new_ndims,
int64_t* new_shape) {
ndarray::reshape::util::resolve_and_check_new_shape(size, new_ndims,
new_shape);
}
}

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#pragma once
#include <irrt/int_defs.hpp>
#include <irrt/ndarray/def.hpp>
#include <irrt/slice.hpp>
/*
* Notes on `np.transpose(<array>, <axes>)`
*
* TODO: `axes`, if specified, can actually contain negative indices,
* but it is not documented in numpy.
*
* Supporting it for now.
*/
namespace {
namespace ndarray {
namespace transpose {
namespace util {
/**
* @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
*
* Note that `np.transpose`'s `<axe>` argument is optional. If the argument
* is specified but the user, use this function to do assertions on it.
*
* @param ndims The number of dimensions of `<array>`
* @param num_axes Number of elements in `<axes>` as specified by the user.
* This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
* @param axes The user specified `<axes>`.
*/
template <typename SizeT>
void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
if (ndims != num_axes) {
raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array",
NO_PARAM, NO_PARAM, NO_PARAM);
}
// TODO: Optimize this
bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
for (SizeT i = 0; i < ndims; i++) axe_specified[i] = false;
for (SizeT i = 0; i < ndims; i++) {
SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
if (axis == slice::OUT_OF_BOUNDS) {
// TODO: numpy actually throws a `numpy.exceptions.AxisError`
raise_exception(
SizeT, EXN_VALUE_ERROR,
"axis {0} is out of bounds for array of dimension {1}", axis,
ndims, NO_PARAM);
}
if (axe_specified[axis]) {
raise_exception(SizeT, EXN_VALUE_ERROR,
"repeated axis in transpose", NO_PARAM, NO_PARAM,
NO_PARAM);
}
axe_specified[axis] = true;
}
}
} // namespace util
/**
* @brief Create a transpose view of `src_ndarray` and perform proper assertions.
*
* This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
* If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
*
* The transpose view created is returned by modifying `dst_ndarray`.
*
* The caller is responsible for setting up `dst_ndarray` before calling this function.
* Here is what this function expects from `dst_ndarray` when called:
* - `dst_ndarray->data` does not have to be initialized.
* - `dst_ndarray->itemsize` does not have to be initialized.
* - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
* When this function call ends:
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
* - `dst_ndarray->ndims` is unchanged
* - `dst_ndarray->shape` is updated according to how `np.transpose` works
* - `dst_ndarray->strides` is updated according to how `np.transpose` works
*
* @param src_ndarray The NDArray to build a transpose view on
* @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
* @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
* @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
*/
template <typename SizeT>
void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray,
SizeT num_axes, const SizeT* axes) {
__builtin_assume(src_ndarray->ndims == dst_ndarray->ndims);
const auto ndims = src_ndarray->ndims;
if (axes != nullptr) util::assert_transpose_axes(ndims, num_axes, axes);
dst_ndarray->data = src_ndarray->data;
dst_ndarray->itemsize = src_ndarray->itemsize;
// Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
if (axes == nullptr) {
// `np.transpose(<array>, axes=None)`
/*
* Minor note: `np.transpose(<array>, axes=None)` is equivalent to
* `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
* is reversing the order of strides and shape.
*
* This is a fast implementation to handle this special (but very common) case.
*/
for (SizeT axis = 0; axis < ndims; axis++) {
dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
}
} else {
// `np.transpose(<array>, <axes>)`
// Permute strides and shape according to `axes`, while resolving negative indices in `axes`
for (SizeT axis = 0; axis < ndims; axis++) {
// `i` cannot be OUT_OF_BOUNDS because of assertions
SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
dst_ndarray->shape[axis] = src_ndarray->shape[i];
dst_ndarray->strides[axis] = src_ndarray->strides[i];
}
}
}
} // namespace transpose
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::transpose;
void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
NDArray<int32_t>* dst_ndarray, int32_t num_axes,
const int32_t* axes) {
transpose(src_ndarray, dst_ndarray, num_axes, axes);
}
void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
NDArray<int64_t>* dst_ndarray, int64_t num_axes,
const int64_t* axes) {
transpose(src_ndarray, dst_ndarray, num_axes, axes);
}
}

165
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#pragma once
#include <irrt/int_defs.hpp>
#include <irrt/slice.hpp>
#include <irrt/util.hpp>
#include "exception.hpp"
// The type of an index or a value describing the length of a
// range/slice is always `int32_t`.
using SliceIndex = int32_t;
namespace {
/**
* @brief A Python-like slice with resolved indices.
*
* "Resolved indices" means that `start` and `stop` must be positive and are
* bound to a known length.
*/
struct Slice {
SliceIndex start;
SliceIndex stop;
SliceIndex step;
/**
* @brief Calculate and return the length / the number of the slice.
*
* If this were a Python range, this function would be `len(range(start, stop, step))`.
*/
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;
}
}
};
namespace slice {
/**
* @brief Resolve a slice index under a given length like Python indexing.
*
* In Python, if you have a `list` of length 100, `list[-1]` resolves to
* `list[99]`, so `resolve_index_in_length_clamped(100, -1)` returns `99`.
*
* If `length` is 0, 0 is returned for any value of `index`.
*
* If `index` is out of bounds, clamps the returned value between `0` and
* `length - 1` (inclusive).
*
*/
SliceIndex resolve_index_in_length_clamped(SliceIndex length,
SliceIndex index) {
if (index < 0) {
return max<SliceIndex>(length + index, 0);
} else {
return min<SliceIndex>(length, index);
}
}
const SliceIndex OUT_OF_BOUNDS = -1;
/**
* @brief Like `resolve_index_in_length_clamped`, but returns `OUT_OF_BOUNDS`
* if `index` is out of bounds.
*/
SliceIndex resolve_index_in_length(SliceIndex length, SliceIndex index) {
SliceIndex resolved = index < 0 ? length + index : index;
if (0 <= resolved && resolved < length) {
return resolved;
} else {
return OUT_OF_BOUNDS;
}
}
} // namespace slice
/**
* @brief A Python-like slice with **unresolved** indices.
*/
struct UserSlice {
bool start_defined;
SliceIndex start;
bool stop_defined;
SliceIndex stop;
bool step_defined;
SliceIndex step;
UserSlice() { this->reset(); }
void reset() {
this->start_defined = false;
this->stop_defined = false;
this->step_defined = false;
}
void set_start(SliceIndex start) {
this->start_defined = true;
this->start = start;
}
void set_stop(SliceIndex stop) {
this->stop_defined = true;
this->stop = stop;
}
void set_step(SliceIndex step) {
this->step_defined = true;
this->step = step;
}
/**
* @brief Resolve this slice.
*
* In Python, this would be `slice(start, stop, step).indices(length)`.
*
* @return A `Slice` with the resolved indices.
*/
Slice indices(SliceIndex length) {
Slice result;
result.step = step_defined ? step : 1;
bool step_is_negative = result.step < 0;
if (start_defined) {
result.start =
slice::resolve_index_in_length_clamped(length, start);
} else {
result.start = step_is_negative ? length - 1 : 0;
}
if (stop_defined) {
result.stop = slice::resolve_index_in_length_clamped(length, stop);
} else {
result.stop = step_is_negative ? -1 : length;
}
return result;
}
/**
* @brief Like `.indices()` but with assertions.
*/
template <typename SizeT>
void indices_checked(SliceIndex length, Slice* result) {
if (length < 0) {
raise_exception(SizeT, EXN_VALUE_ERROR,
"length should not be negative, got {0}", length,
NO_PARAM, NO_PARAM);
}
if (this->step_defined && this->step == 0) {
raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero",
NO_PARAM, NO_PARAM, NO_PARAM);
}
*result = this->indices(length);
}
};
} // namespace

4
nac3core/irrt/irrt_everything.hpp
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@ -4,5 +4,9 @@
#include <irrt/exception.hpp>
#include <irrt/int_defs.hpp>
#include <irrt/ndarray/basic.hpp>
#include <irrt/ndarray/broadcast.hpp>
#include <irrt/ndarray/def.hpp>
#include <irrt/ndarray/indexing.hpp>
#include <irrt/ndarray/reshape.hpp>
#include <irrt/ndarray/transpose.hpp>
#include <irrt/util.hpp>

9
nac3core/irrt/irrt_test.cpp
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@ -6,15 +6,20 @@
#include <cstdio>
#include <cstdlib>
// Special macro to inform `#include <irrt/*>` that we
// are testing.
// Special macro to inform `#include <irrt/*>` that we are testing.
#define IRRT_TESTING
// Note that failure unit tests are not supported.
#include <test/test_core.hpp>
#include <test/test_ndarray_basic.hpp>
#include <test/test_ndarray_broadcast.hpp>
#include <test/test_ndarray_indexing.hpp>
int main() {
test::core::run();
test::ndarray_basic::run();
test::ndarray_indexing::run();
test::ndarray_broadcast::run();
return 0;
}

4
nac3core/irrt/test/test_core.hpp
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@ -7,8 +7,8 @@ namespace core {
void test_int_exp() {
BEGIN_TEST();
assert_values_match(125, __nac3_int_exp_impl<int32_t>(5, 3));
assert_values_match(3125, __nac3_int_exp_impl<int32_t>(5, 5));
assert_values_match(125L, __nac3_int_exp_impl<int64_t>(5, 3));
assert_values_match(3125L, __nac3_int_exp_impl<int64_t>(5, 5));
}
void run() { test_int_exp(); }

8
nac3core/irrt/test/test_ndarray_basic.hpp
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@ -8,18 +8,18 @@ void test_calc_size_from_shape_normal() {
// Test shapes with normal values
BEGIN_TEST();
int32_t shape[4] = {2, 3, 5, 7};
int64_t shape[4] = {2, 3, 5, 7};
assert_values_match(
210, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
210L, ndarray::basic::util::calc_size_from_shape<int64_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};
int64_t shape[4] = {2, 0, 5, 7};
assert_values_match(
0, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
0L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
}
void run() {

127
nac3core/irrt/test/test_ndarray_broadcast.hpp Normal file
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@ -0,0 +1,127 @@
#pragma once
#include <test/includes.hpp>
namespace test {
namespace ndarray_broadcast {
void test_can_broadcast_shape() {
BEGIN_TEST();
assert_values_match(true,
ndarray::broadcast::util::can_broadcast_shape_to(
1, (int32_t[]){3}, 5, (int32_t[]){1, 1, 1, 1, 3}));
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
1, (int32_t[]){3}, 2, (int32_t[]){3, 1}));
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
1, (int32_t[]){3}, 1, (int32_t[]){3}));
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
1, (int32_t[]){1}, 1, (int32_t[]){3}));
assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
1, (int32_t[]){1}, 1, (int32_t[]){1}));
assert_values_match(
true, ndarray::broadcast::util::can_broadcast_shape_to(
3, (int32_t[]){256, 256, 3}, 3, (int32_t[]){256, 1, 3}));
assert_values_match(true,
ndarray::broadcast::util::can_broadcast_shape_to(
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){3}));
assert_values_match(false,
ndarray::broadcast::util::can_broadcast_shape_to(
3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){2}));
assert_values_match(true,
ndarray::broadcast::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(true, ndarray::broadcast::util::can_broadcast_shape_to(
1, (int32_t[]){0}, 1, (int32_t[]){1}));
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
1, (int32_t[]){0}, 1, (int32_t[]){2}));
assert_values_match(true,
ndarray::broadcast::util::can_broadcast_shape_to(
4, (int32_t[]){0, 4, 0, 0}, 1, (int32_t[]){1}));
assert_values_match(
true, ndarray::broadcast::util::can_broadcast_shape_to(
4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 1, 1, 1}));
assert_values_match(
true, ndarray::broadcast::util::can_broadcast_shape_to(
4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 4, 1, 1}));
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 3}));
assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 0}));
}
void test_ndarray_broadcast() {
/*
# 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]]]
#
# assery array.strides == (0, 0, 8)
*/
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::basic::set_strides_by_shape(&ndarray);
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::broadcast_to(&ndarray, &dst_ndarray);
assert_arrays_match(dst_ndims, ((int32_t[]){0, 0, 8}), dst_ndarray.strides);
assert_values_match(19.9,
*((double*)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, ((int32_t[]){0, 0, 0}))));
assert_values_match(29.9,
*((double*)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, ((int32_t[]){0, 0, 1}))));
assert_values_match(39.9,
*((double*)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, ((int32_t[]){0, 0, 2}))));
assert_values_match(49.9,
*((double*)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, ((int32_t[]){0, 0, 3}))));
assert_values_match(19.9,
*((double*)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, ((int32_t[]){0, 1, 0}))));
assert_values_match(29.9,
*((double*)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, ((int32_t[]){0, 1, 1}))));
assert_values_match(39.9,
*((double*)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, ((int32_t[]){0, 1, 2}))));
assert_values_match(49.9,
*((double*)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, ((int32_t[]){0, 1, 3}))));
assert_values_match(49.9,
*((double*)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, ((int32_t[]){1, 2, 3}))));
}
void run() {
test_can_broadcast_shape();
test_ndarray_broadcast();
}
} // namespace ndarray_broadcast
} // namespace test

165
nac3core/irrt/test/test_ndarray_indexing.hpp Normal file
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#pragma once
#include <test/includes.hpp>
namespace test {
namespace ndarray_indexing {
void test_normal_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();
// Prepare src_ndarray
double src_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};
int64_t src_itemsize = sizeof(double);
const int64_t src_ndims = 2;
int64_t src_shape[src_ndims] = {3, 4};
int64_t src_strides[src_ndims] = {};
NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
.itemsize = src_itemsize,
.ndims = src_ndims,
.shape = src_shape,
.strides = src_strides};
ndarray::basic::set_strides_by_shape(&src_ndarray);
// Prepare dst_ndarray
const int64_t dst_ndims = 2;
int64_t dst_shape[dst_ndims] = {999, 999}; // Empty values
int64_t dst_strides[dst_ndims] = {999, 999}; // Empty values
NDArray<int64_t> dst_ndarray = {.data = nullptr,
.ndims = dst_ndims,
.shape = dst_shape,
.strides = dst_strides};
// Create the subscripts in `ndarray[-2::, 1::2]`
UserSlice subscript_1;
subscript_1.set_start(-2);
UserSlice subscript_2;
subscript_2.set_start(1);
subscript_2.set_step(2);
const int64_t num_indexes = 2;
NDIndex indexes[num_indexes] = {
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_1},
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
int64_t expected_shape[dst_ndims] = {2, 2};
int64_t expected_strides[dst_ndims] = {32, 16};
assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
// dst_ndarray[0, 0]
assert_values_match(5.0,
*((double *)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, (int64_t[dst_ndims]){0, 0})));
// dst_ndarray[0, 1]
assert_values_match(7.0,
*((double *)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, (int64_t[dst_ndims]){0, 1})));
// dst_ndarray[1, 0]
assert_values_match(9.0,
*((double *)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, (int64_t[dst_ndims]){1, 0})));
// dst_ndarray[1, 1]
assert_values_match(11.0,
*((double *)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, (int64_t[dst_ndims]){1, 1})));
}
void test_normal_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
```
*/
BEGIN_TEST();
// Prepare src_ndarray
double src_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};
int64_t src_itemsize = sizeof(double);
const int64_t src_ndims = 2;
int64_t src_shape[src_ndims] = {3, 4};
int64_t src_strides[src_ndims] = {};
NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
.itemsize = src_itemsize,
.ndims = src_ndims,
.shape = src_shape,
.strides = src_strides};
ndarray::basic::set_strides_by_shape(&src_ndarray);
// Prepare dst_ndarray
const int64_t dst_ndims = 1;
int64_t dst_shape[dst_ndims] = {999}; // Empty values
int64_t dst_strides[dst_ndims] = {999}; // Empty values
NDArray<int64_t> dst_ndarray = {.data = nullptr,
.ndims = dst_ndims,
.shape = dst_shape,
.strides = dst_strides};
// Create the subscripts in `ndarray[2, ::-2]`
int64_t subscript_1 = 2;
UserSlice subscript_2;
subscript_2.set_step(-2);
const int64_t num_indexes = 2;
NDIndex indexes[num_indexes] = {
{.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
{.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
int64_t expected_shape[dst_ndims] = {2};
int64_t expected_strides[dst_ndims] = {-16};
assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
assert_values_match(11.0,
*((double *)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, (int64_t[dst_ndims]){0})));
assert_values_match(9.0,
*((double *)ndarray::basic::get_pelement_by_indices(
&dst_ndarray, (int64_t[dst_ndims]){1})));
}
void run() {
test_normal_1();
test_normal_2();
}
} // namespace ndarray_indexing
} // namespace test

15
nac3core/irrt/test/util.hpp
View File

@ -6,6 +6,11 @@
template <class T>
void print_value(const T& value);
template <>
void print_value(const bool& value) {
printf("%s", value ? "true" : "false");
}
template <>
void print_value(const int8_t& value) {
printf("%d", value);
@ -16,6 +21,11 @@ void print_value(const int32_t& value) {
printf("%d", value);
}
template <>
void print_value(const int64_t& value) {
printf("%d", value);
}
template <>
void print_value(const uint8_t& value) {
printf("%u", value);
@ -26,6 +36,11 @@ void print_value(const uint32_t& value) {
printf("%u", value);
}
template <>
void print_value(const uint64_t& value) {
printf("%d", value);
}
template <>
void print_value(const float& value) {
printf("%f", value);

1402
nac3core/src/codegen/builtin_fns.rs
View File

File diff suppressed because it is too large Load Diff

382
nac3core/src/codegen/expr.rs
View File

@ -2,7 +2,7 @@ use crate::{
codegen::{
classes::{
ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayValue, ProxyType,
ProxyValue, RangeValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
ProxyValue, RangeValue, UntypedArrayLikeAccessor,
},
concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
gen_in_range_check, get_llvm_abi_type, get_llvm_type, get_va_count_arg_name,
@ -16,14 +16,11 @@ use crate::{
gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
gen_var,
},
structure::ndarray::NDArrayObject,
CodeGenContext, CodeGenTask, CodeGenerator,
},
symbol_resolver::{SymbolValue, ValueEnum},
toplevel::{
helper::PrimDef,
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
DefinitionId, TopLevelDef,
},
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
typecheck::{
magic_methods::{Binop, BinopVariant, HasOpInfo},
typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
@ -43,7 +40,7 @@ use nac3parser::ast::{
use std::iter::{repeat, repeat_with};
use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
use super::structure::cslice::CSlice;
use super::structure::{cslice::CSlice, ndarray::indexing::util::gen_ndarray_subscript_ndindexes};
use super::{
model::*,
structure::exception::{Exception, ExceptionId},
@ -562,7 +559,6 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
G: CodeGenerator + ?Sized,
{
self.const_strings.get(string).copied().unwrap_or_else(|| {
let type_context = generator.type_context(self.ctx);
let sizet_model = IntModel(SizeT);
let pbyte_model = PtrModel(IntModel(Byte));
let cslice_model = StructModel(CSlice);
@ -570,9 +566,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
let base = self.builder.build_global_string_ptr(string, "constant_string").unwrap();
let base = pbyte_model.believe_value(base.as_pointer_value());
let len = sizet_model.constant(type_context, self.ctx, string.len() as u64);
let len = sizet_model.constant(generator, self.ctx, string.len() as u64);
let cslice = cslice_model.create_const(type_context, self, base, len);
let cslice = cslice_model.create_const(generator, self.ctx, base, len);
self.const_strings.insert(string.to_owned(), cslice);
@ -588,12 +584,11 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
params: [Option<Int<'ctx, Int64>>; 3],
loc: Location,
) {
let type_context = generator.type_context(self.ctx);
let exn_model = StructModel(Exception);
let exn_id_model = IntModel(ExceptionId::default());
let exn_id =
exn_id_model.constant(type_context, self.ctx, self.resolver.get_string_id(name) as u64);
exn_id_model.constant(generator, self.ctx, self.resolver.get_string_id(name) as u64);
let exn = self.exception_val.unwrap_or_else(|| {
let exn = exn_model.var_alloca(generator, self, Some("exn")).unwrap();
*self.exception_val.insert(exn)
@ -619,15 +614,11 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
params: [Option<IntValue<'ctx>>; 3],
loc: Location,
) {
let type_context = generator.type_context(self.ctx);
let param_model = IntModel(Int64);
let params =
params.map(|p| p.map(|p| param_model.check_value(generator, self.ctx, p).unwrap()));
let err_msg = self.gen_string(generator, err_msg);
let ctx = self.ctx;
let params =
params.map(|p| p.map(|p| param_model.check_value(type_context, ctx, p).unwrap()));
self.make_assert_impl(generator, cond, err_name, err_msg, params, loc);
}
@ -640,7 +631,6 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
params: [Option<Int<'ctx, Int64>>; 3],
loc: Location,
) {
let type_context = generator.type_context(self.ctx);
let bool_model = IntModel(Bool);
// We assume that the condition is most probably true, so the normal path is the most
@ -648,7 +638,7 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
let cond = call_expect(
self,
generator.bool_to_i1(self, cond),
bool_model.const_true(type_context, self.ctx).value,
bool_model.const_true(generator, self.ctx).value,
Some("expect"),
);
@ -2257,338 +2247,6 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
)
}
/// Generates code for a subscript expression on an `ndarray`.
///
/// * `ty` - The `Type` of the `NDArray` elements.
/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
/// * `v` - The `NDArray` value.
/// * `slice` - The slice expression used to subscript into the `ndarray`.
fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ty: Type,
ndims: Type,
v: NDArrayValue<'ctx>,
slice: &Expr<Option<Type>>,
) -> Result<Option<ValueEnum<'ctx>>, String> {
let llvm_i1 = ctx.ctx.bool_type();
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
unreachable!()
};
let ndims = values
.iter()
.map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
.collect::<Result<Vec<_>, _>>()
.map_err(|val| {
format!(
"Expected non-negative literal for ndarray.ndims, got {}",
i128::try_from(val).unwrap()
)
})?;
assert!(!ndims.is_empty());
// The number of dimensions subscripted by the index expression.
// Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
// dimension will remove a dimension.
let subscripted_dims = match &slice.node {
ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
if let ExprKind::Slice { .. } = &value_subexpr.node {
acc
} else {
acc + 1
}
}),
ExprKind::Slice { .. } => 0,
_ => 1,
};
let ndarray_ndims_ty = ctx.unifier.get_fresh_literal(
ndims.iter().map(|v| SymbolValue::U64(v - subscripted_dims)).collect(),
None,
);
let ndarray_ty =
make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(ty), Some(ndarray_ndims_ty));
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
let sizeof_elem = llvm_ndarray_data_t.size_of().unwrap();
// Check that len is non-zero
let len = v.load_ndims(ctx);
ctx.make_assert(
generator,
ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), "").unwrap(),
"0:IndexError",
"too many indices for array: array is {0}-dimensional but 1 were indexed",
[Some(len), None, None],
slice.location,
);
// Normalizes a possibly-negative index to its corresponding positive index
let normalize_index = |generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
dim: u64| {
gen_if_else_expr_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(IntPredicate::SGE, index, index.get_type().const_zero(), "")
.unwrap())
},
|_, _| Ok(Some(index)),
|generator, ctx| {
let llvm_i32 = ctx.ctx.i32_type();
let len = unsafe {
v.dim_sizes().get_typed_unchecked(
ctx,
generator,
&llvm_usize.const_int(dim, true),
None,
)
};
let index = ctx
.builder
.build_int_add(
len,
ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
"",
)
.unwrap();
Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
},
)
.map(|v| v.map(BasicValueEnum::into_int_value))
};
// Converts a slice expression into a slice-range tuple
let expr_to_slice = |generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
node: &ExprKind<Option<Type>>,
dim: u64| {
match node {
ExprKind::Constant { value: Constant::Int(v), .. } => {
let Some(index) =
normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
else {
return Ok(None);
};
Ok(Some((index, index, llvm_i32.const_int(1, true))))
}
ExprKind::Slice { lower, upper, step } => {
let dim_sz = unsafe {
v.dim_sizes().get_typed_unchecked(
ctx,
generator,
&llvm_usize.const_int(dim, false),
None,
)
};
handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
}
_ => {
let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
let index = index
.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
.into_int_value();
let Some(index) = normalize_index(generator, ctx, index, dim)? else {
return Ok(None);
};
Ok(Some((index, index, llvm_i32.const_int(1, true))))
}
}
};
let make_indices_arr = |generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>|
-> Result<_, String> {
Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
let index_addr = generator.gen_array_var_alloc(
ctx,
llvm_int_ty,
llvm_usize.const_int(elts.len() as u64, false),
None,
)?;
for (i, elt) in elts.iter().enumerate() {
let Some(index) = generator.gen_expr(ctx, elt)? else {
return Ok(None);
};
let index = index
.to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
.into_int_value();
let Some(index) = normalize_index(generator, ctx, index, 0)? else {
return Ok(None);
};
let store_ptr = unsafe {
index_addr.ptr_offset_unchecked(
ctx,
generator,
&llvm_usize.const_int(i as u64, false),
None,
)
};
ctx.builder.build_store(store_ptr, index).unwrap();
}
Some(index_addr)
} else if let Some(index) = generator.gen_expr(ctx, slice)? {
let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
let index_addr = generator.gen_array_var_alloc(
ctx,
llvm_int_ty,
llvm_usize.const_int(1u64, false),
None,
)?;
let index =
index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
let store_ptr = unsafe {
index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
};
ctx.builder.build_store(store_ptr, index).unwrap();
Some(index_addr)
} else {
None
})
};
Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
v.data().get(ctx, generator, &index_addr, None).into()
} else {
match &slice.node {
ExprKind::Tuple { elts, .. } => {
let slices = elts
.iter()
.enumerate()
.map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
.take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
.collect::<Result<Vec<_>, _>>()?;
if slices.len() < elts.len() {
return Ok(None);
}
let slices = slices.into_iter().map(Option::unwrap).collect_vec();
numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
}
ExprKind::Slice { .. } => {
let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
return Ok(None);
};
numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
}
_ => {
// Accessing an element from a multi-dimensional `ndarray`
let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
// Create a new array, remove the top dimension from the dimension-size-list, and copy the
// elements over
let subscripted_ndarray =
generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
let ndarray = NDArrayValue::from_ptr_val(subscripted_ndarray, llvm_usize, None);
let num_dims = v.load_ndims(ctx);
ndarray.store_ndims(
ctx,
generator,
ctx.builder
.build_int_sub(num_dims, llvm_usize.const_int(1, false), "")
.unwrap(),
);
let ndarray_num_dims = ndarray.load_ndims(ctx);
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
let ndarray_num_dims = ctx
.builder
.build_int_z_extend_or_bit_cast(
ndarray.load_ndims(ctx),
llvm_usize.size_of().get_type(),
"",
)
.unwrap();
let v_dims_src_ptr = unsafe {
v.dim_sizes().ptr_offset_unchecked(
ctx,
generator,
&llvm_usize.const_int(1, false),
None,
)
};
call_memcpy_generic(
ctx,
ndarray.dim_sizes().base_ptr(ctx, generator),
v_dims_src_ptr,
ctx.builder
.build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
.map(Into::into)
.unwrap(),
llvm_i1.const_zero(),
);
let ndarray_num_elems = call_ndarray_calc_size(
generator,
ctx,
&ndarray.dim_sizes().as_slice_value(ctx, generator),
(None, None),
);
let ndarray_num_elems = ctx
.builder
.build_int_z_extend_or_bit_cast(ndarray_num_elems, sizeof_elem.get_type(), "")
.unwrap();
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
call_memcpy_generic(
ctx,
ndarray.data().base_ptr(ctx, generator),
v_data_src_ptr,
ctx.builder
.build_int_mul(
ndarray_num_elems,
llvm_ndarray_data_t.size_of().unwrap(),
"",
)
.map(Into::into)
.unwrap(),
llvm_i1.const_zero(),
);
ndarray.as_base_value().into()
}
}
}))
}
/// See [`CodeGenerator::gen_expr`].
pub fn gen_expr<'ctx, G: CodeGenerator>(
generator: &mut G,
@ -3228,18 +2886,20 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
v.data().get(ctx, generator, &index, None).into()
}
}
TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => {
let (ty, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap();
let v = if let Some(v) = generator.gen_expr(ctx, value)? {
v.to_basic_value_enum(ctx, generator, value.custom.unwrap())?
.into_pointer_value()
} else {
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
let Some(ndarray) = generator.gen_expr(ctx, value)? else {
return Ok(None);
};
let v = NDArrayValue::from_ptr_val(v, usize, None);
return gen_ndarray_subscript_expr(generator, ctx, *ty, *ndims, v, slice);
let ndarray_ty = value.custom.unwrap();
let ndarray = ndarray.to_basic_value_enum(ctx, generator, ndarray_ty)?;
let ndarray =
NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
let indexes = gen_ndarray_subscript_ndindexes(generator, ctx, slice)?;
let result = ndarray.index_or_scalar(generator, ctx, &indexes, "index_result");
let result = result.to_basic_value_enum();
return Ok(Some(ValueEnum::Dynamic(result)));
}
TypeEnum::TTuple { .. } => {
let index: u32 =

225
nac3core/src/codegen/irrt/mod.rs
View File

@ -5,6 +5,8 @@ mod test;
pub mod util;
use super::model::*;
use super::structure::ndarray::broadcast::ShapeEntry;
use super::structure::ndarray::indexing::NDIndex;
use super::structure::ndarray::NpArray;
use super::{
classes::{
@ -427,15 +429,13 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
// TODO: Temporary fix. Rewrite `list_slice_assignment` later
// Exception params should have been i64
{
let type_context = generator.type_context(ctx.ctx);
let param_model = IntModel(Int64);
let src_slice_len =
param_model.s_extend_or_bit_cast(type_context, ctx, src_slice_len, "src_slice_len");
param_model.s_extend_or_bit_cast(generator, ctx, src_slice_len, "src_slice_len");
let dest_slice_len =
param_model.s_extend_or_bit_cast(type_context, ctx, dest_slice_len, "dest_slice_len");
let dest_idx_2 =
param_model.s_extend_or_bit_cast(type_context, ctx, dest_idx.2, "dest_idx_2");
param_model.s_extend_or_bit_cast(generator, ctx, dest_slice_len, "dest_slice_len");
let dest_idx_2 = param_model.s_extend_or_bit_cast(generator, ctx, dest_idx.2, "dest_idx_2");
ctx.make_assert(
generator,
@ -897,7 +897,7 @@ pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
}
/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
/// containing the indices used for accessing `array` corresponding to the index of the broadcast
/// array `broadcast_idx`.
pub fn call_ndarray_calc_broadcast_index<
'ctx,
@ -953,13 +953,12 @@ pub fn call_ndarray_calc_broadcast_index<
)
}
pub fn call_nac3_throw_dummy_error<'ctx>(tyctx: TypeContext<'ctx>, ctx: &CodeGenContext<'ctx, '_>) {
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_throw_dummy_error"),
)
.returning_void();
pub fn call_nac3_throw_dummy_error<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'_, '_>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_throw_dummy_error");
CallFunction::begin(generator, ctx, &name).returning_void();
}
/// Initialize all global `EXN_*` exception IDs in IRRT with the [`SymbolResolver`].
@ -989,116 +988,176 @@ pub fn setup_irrt_exceptions<'ctx>(
}
}
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx>(
tyctx: TypeContext<'ctx>,
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>>,
) {
CallFunction::begin(
tyctx,
let name = get_sizet_dependent_function_name(
generator,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_util_assert_shape_no_negative"),
)
.arg("ndims", ndims)
.arg("shape", shape)
.returning_void();
"__nac3_ndarray_util_assert_shape_no_negative",
);
CallFunction::begin(generator, ctx, &name)
.arg("ndims", ndims)
.arg("shape", shape)
.returning_void();
}
pub fn call_nac3_ndarray_size<'ctx>(
tyctx: TypeContext<'ctx>,
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
) -> Int<'ctx, SizeT> {
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_size"),
)
.arg("ndarray", pndarray)
.returning_auto("size")
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("size")
}
pub fn call_nac3_ndarray_nbytes<'ctx>(
tyctx: TypeContext<'ctx>,
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
) -> Int<'ctx, SizeT> {
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_nbytes"),
)
.arg("ndarray", pndarray)
.returning_auto("nbytes")
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("nbytes")
}
pub fn call_nac3_ndarray_len<'ctx>(
tyctx: TypeContext<'ctx>,
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
) -> Int<'ctx, SizeT> {
CallFunction::begin(tyctx, ctx, &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_len"))
.arg("ndarray", pndarray)
.returning_auto("len")
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("len")
}
pub fn call_nac3_ndarray_is_c_contiguous<'ctx>(
tyctx: TypeContext<'ctx>,
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> {
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_is_c_contiguous"),
)
.arg("ndarray", ndarray_ptr)
.returning_auto("is_c_contiguous")
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
CallFunction::begin(generator, ctx, &name)
.arg("ndarray", ndarray_ptr)
.returning_auto("is_c_contiguous")
}
pub fn call_nac3_ndarray_get_nth_pelement<'ctx>(
tyctx: TypeContext<'ctx>,
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
index: Int<'ctx, SizeT>,
) -> Ptr<'ctx, IntModel<Byte>> {
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_get_nth_pelement"),
)
.arg("ndarray", pndarray)
.arg("index", index)
.returning_auto("pelement")
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
CallFunction::begin(generator, ctx, &name)
.arg("ndarray", pndarray)
.arg("index", index)
.returning_auto("pelement")
}
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx>(
tyctx: TypeContext<'ctx>,
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pdnarray: Ptr<'ctx, StructModel<NpArray>>,
) {
CallFunction::begin(
tyctx,
ctx,
&get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_set_strides_by_shape"),
)
.arg("ndarray", pdnarray)
.returning_void();
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
CallFunction::begin(generator, ctx, &name).arg("ndarray", pdnarray).returning_void();
}
pub fn call_nac3_ndarray_copy_data<'ctx>(
tyctx: TypeContext<'ctx>,
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>>,
) {
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_void();
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
CallFunction::begin(generator, ctx, &name)
.arg("src_ndarray", src_ndarray)
.arg("dst_ndarray", dst_ndarray)
.returning_void();
}
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
num_indexes: Int<'ctx, SizeT>,
indexes: Ptr<'ctx, StructModel<NDIndex>>,
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
CallFunction::begin(generator, ctx, &name)
.arg("num_indexes", num_indexes)
.arg("indexes", indexes)
.arg("src_ndarray", src_ndarray)
.arg("dst_ndarray", dst_ndarray)
.returning_void();
}
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
CallFunction::begin(generator, ctx, &name)
.arg("src_ndarray", src_ndarray)
.arg("dst_ndarray", dst_ndarray)
.returning_void();
}
pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
num_shape_entries: Int<'ctx, SizeT>,
shape_entries: Ptr<'ctx, StructModel<ShapeEntry>>,
dst_ndims: Int<'ctx, SizeT>,
dst_shape: Ptr<'ctx, IntModel<SizeT>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
CallFunction::begin(generator, ctx, &name)
.arg("num_shapes", num_shape_entries)
.arg("shapes", shape_entries)
.arg("dst_ndims", dst_ndims)
.arg("dst_shape", dst_shape)
.returning_void();
}
pub fn call_nac3_ndarray_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
size: Int<'ctx, SizeT>,
new_ndims: Int<'ctx, SizeT>,
new_shape: Ptr<'ctx, IntModel<SizeT>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_resolve_and_check_new_shape",
);
CallFunction::begin(generator, ctx, &name)
.arg("size", size)
.arg("new_ndims", new_ndims)
.arg("new_shape", new_shape)
.returning_void();
}
pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
num_axes: Int<'ctx, SizeT>,
axes: Ptr<'ctx, IntModel<SizeT>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
CallFunction::begin(generator, ctx, &name)
.arg("src_ndarray", src_ndarray)
.arg("dst_ndarray", dst_ndarray)
.arg("num_axes", num_axes)
.arg("axes", axes)
.returning_void();
}

28
nac3core/src/codegen/irrt/util.rs
View File

@ -1,11 +1,15 @@
use crate::codegen::model::*;
use crate::codegen::{CodeGenContext, CodeGenerator};
// When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
// When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
#[must_use]
pub fn get_sizet_dependent_function_name(tyctx: TypeContext<'_>, name: &str) -> String {
pub fn get_sizet_dependent_function_name<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'_, '_>,
name: &str,
) -> String {
let mut name = name.to_owned();
match tyctx.size_type.get_bit_width() {
match generator.get_size_type(ctx.ctx).get_bit_width() {
32 => {}
64 => name.push_str("64"),
bit_width => {
@ -16,7 +20,7 @@ pub fn get_sizet_dependent_function_name(tyctx: TypeContext<'_>, name: &str) ->
}
pub mod function {
use crate::codegen::{model::*, CodeGenContext};
use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
use inkwell::{
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
@ -30,8 +34,8 @@ pub mod function {
}
/// Helper structure to reduce IRRT Inkwell function call boilerplate
pub struct CallFunction<'ctx, 'a, 'b, 'c> {
tyctx: TypeContext<'ctx>,
pub struct CallFunction<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> {
generator: &'d mut G,
ctx: &'b CodeGenContext<'ctx, 'a>,
/// Function name
name: &'c str,
@ -39,13 +43,13 @@ pub mod function {
args: Vec<Arg<'ctx>>,
}
impl<'ctx, 'a, 'b, 'c> CallFunction<'ctx, 'a, 'b, 'c> {
impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> CallFunction<'ctx, 'a, 'b, 'c, 'd, G> {
pub fn begin(
tyctx: TypeContext<'ctx>,
generator: &'d mut G,
ctx: &'b CodeGenContext<'ctx, 'a>,
name: &'c str,
) -> Self {
CallFunction { tyctx, ctx, name, args: Vec::new() }
CallFunction { generator, ctx, name, args: Vec::new() }
}
/// Push a call argument to the function call.
@ -55,7 +59,7 @@ pub mod function {
#[must_use]
pub fn arg<M: Model<'ctx>>(mut self, _name: &str, arg: Instance<'ctx, M>) -> Self {
let arg = Arg {
ty: arg.model.get_type(self.tyctx, self.ctx.ctx).as_basic_type_enum().into(),
ty: arg.model.get_type(self.generator, self.ctx.ctx).as_basic_type_enum().into(),
val: arg.value.as_basic_value_enum().into(),
};
self.args.push(arg);
@ -65,11 +69,11 @@ pub mod function {
/// Call the function and expect the function to return a value of type of `return_model`.
#[must_use]
pub fn returning<M: Model<'ctx>>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
let ret_ty = return_model.get_type(self.tyctx, self.ctx.ctx);
let ret_ty = return_model.get_type(self.generator, self.ctx.ctx);
let ret = self.get_function(|tys| ret_ty.fn_type(tys, false), name);
let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
let ret = return_model.check_value(self.tyctx, self.ctx.ctx, ret).unwrap(); // Must work
let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
ret
}

13
nac3core/src/codegen/mod.rs
View File

@ -1,7 +1,7 @@
use crate::{
codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
codegen::classes::{ListType, ProxyType, RangeType},
symbol_resolver::{StaticValue, SymbolResolver},
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
typecheck::{
type_inferencer::{CodeLocation, PrimitiveStore},
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
@ -494,10 +494,8 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
}
TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
let tyctx = generator.type_context(ctx);
let pndarray_model = PtrModel(StructModel(NpArray));
pndarray_model.get_type(tyctx, ctx).as_basic_type_enum()
pndarray_model.get_type(generator, ctx).as_basic_type_enum()
}
_ => unreachable!(
@ -703,7 +701,6 @@ pub fn gen_func_impl<
..primitives
};
let type_context = generator.type_context(context);
let cslice_model = StructModel(CSlice);
let pexn_model = PtrModel(StructModel(Exception));
@ -714,9 +711,9 @@ pub fn gen_func_impl<
(primitives.uint64, context.i64_type().into()),
(primitives.float, context.f64_type().into()),
(primitives.bool, context.i8_type().into()),
(primitives.str, cslice_model.get_type(type_context, context).into()),
(primitives.str, cslice_model.get_type(generator, context).into()),
(primitives.range, RangeType::new(context).as_base_type().into()),
(primitives.exception, pexn_model.get_type(type_context, context).into()),
(primitives.exception, pexn_model.get_type(generator, context).into()),
]
.iter()
.copied()

12
nac3core/src/codegen/model/any.rs
View File

@ -4,6 +4,8 @@ use inkwell::{
values::BasicValueEnum,
};
use crate::codegen::CodeGenerator;
use super::*;
#[derive(Debug, Clone, Copy)]
@ -14,13 +16,17 @@ impl<'ctx> Model<'ctx> for AnyModel<'ctx> {
type Value = BasicValueEnum<'ctx>;
type Type = BasicTypeEnum<'ctx>;
fn get_type(&self, _tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> Self::Type {
fn get_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> Self::Type {
self.0
}
fn check_type<T: BasicType<'ctx>>(
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
_tyctx: TypeContext<'ctx>,
_generator: &mut G,
_ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {

65
nac3core/src/codegen/model/core.rs
View File

@ -5,21 +5,6 @@ use inkwell::{context::Context, types::*, values::*};
use super::*;
use crate::codegen::{CodeGenContext, CodeGenerator};
#[derive(Clone, Copy)]
pub struct TypeContext<'ctx> {
pub size_type: IntType<'ctx>,
}
pub trait HasTypeContext {
fn type_context<'ctx>(&self, ctx: &'ctx Context) -> TypeContext<'ctx>;
}
impl<T: CodeGenerator + ?Sized> HasTypeContext for T {
fn type_context<'ctx>(&self, ctx: &'ctx Context) -> TypeContext<'ctx> {
TypeContext { size_type: self.get_size_type(ctx) }
}
}
#[derive(Debug, Clone)]
pub struct ModelError(pub String);
@ -36,12 +21,16 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
type Type: BasicType<'ctx>;
/// Return the [`BasicType`] of this model.
fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type;
fn get_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type;
/// Check if a [`BasicType`] is the same type of this model.
fn check_type<T: BasicType<'ctx>>(
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError>;
@ -55,15 +44,15 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
/// Check if a [`BasicValue`]'s type is equivalent to the type of this model.
/// Wrap it into an [`Instance`] if it is.
fn check_value<V: BasicValue<'ctx>>(
fn check_value<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &'ctx Context,
value: V,
) -> Result<Instance<'ctx, Self>, ModelError> {
let value = value.as_basic_value_enum();
self.check_type(tyctx, ctx, value.get_type())
.map_err(|err| err.under_context("the value {value:?}"))?;
self.check_type(generator, ctx, value.get_type())
.map_err(|err| err.under_context(format!("the value {value:?}").as_str()))?;
let Ok(value) = Self::Value::try_from(value) else {
unreachable!("check_type() has bad implementation")
@ -72,27 +61,28 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
}
// Allocate a value on the stack and return its pointer.
fn alloca(
fn alloca<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
name: &str,
) -> Ptr<'ctx, Self> {
let pmodel = PtrModel(*self);
let p = ctx.builder.build_alloca(self.get_type(tyctx, ctx.ctx), name).unwrap();
let p = ctx.builder.build_alloca(self.get_type(generator, ctx.ctx), name).unwrap();
pmodel.believe_value(p)
}
// Allocate an array on the stack and return its pointer.
fn array_alloca(
fn array_alloca<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
len: IntValue<'ctx>,
name: &str,
) -> Ptr<'ctx, Self> {
let pmodel = PtrModel(*self);
let p = ctx.builder.build_array_alloca(self.get_type(tyctx, ctx.ctx), len, name).unwrap();
let p =
ctx.builder.build_array_alloca(self.get_type(generator, ctx.ctx), len, name).unwrap();
pmodel.believe_value(p)
}
@ -102,14 +92,9 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
ctx: &mut CodeGenContext<'ctx, '_>,
name: Option<&str>,
) -> Result<Ptr<'ctx, Self>, String> {
let tyctx = generator.type_context(ctx.ctx);
let pmodel = PtrModel(*self);
let p = generator.gen_var_alloc(
ctx,
self.get_type(tyctx, ctx.ctx).as_basic_type_enum(),
name,
)?;
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
let p = generator.gen_var_alloc(ctx, ty, name)?;
Ok(pmodel.believe_value(p))
}
@ -120,16 +105,10 @@ pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
len: IntValue<'ctx>,
name: Option<&'ctx str>,
) -> Result<Ptr<'ctx, Self>, String> {
let tyctx = generator.type_context(ctx.ctx);
// TODO: Remove ArraySliceValue
let pmodel = PtrModel(*self);
let p = generator.gen_array_var_alloc(
ctx,
self.get_type(tyctx, ctx.ctx).as_basic_type_enum(),
len,
name,
)?;
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
let p = generator.gen_array_var_alloc(ctx, ty, len, name)?;
Ok(pmodel.believe_value(PointerValue::from(p)))
}
}

133
nac3core/src/codegen/model/int.rs
View File

@ -7,7 +7,11 @@ use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
fn get_int_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx>;
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx>;
}
#[derive(Debug, Clone, Copy, Default)]
@ -22,32 +26,52 @@ pub struct Int64;
pub struct SizeT;
impl<'ctx> IntKind<'ctx> for Bool {
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.bool_type()
}
}
impl<'ctx> IntKind<'ctx> for Byte {
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i8_type()
}
}
impl<'ctx> IntKind<'ctx> for Int32 {
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i32_type()
}
}
impl<'ctx> IntKind<'ctx> for Int64 {
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> IntType<'ctx> {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i64_type()
}
}
impl<'ctx> IntKind<'ctx> for SizeT {
fn get_int_type(&self, tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> IntType<'ctx> {
tyctx.size_type
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
generator.get_size_type(ctx)
}
}
@ -55,7 +79,11 @@ impl<'ctx> IntKind<'ctx> for SizeT {
pub struct AnyInt<'ctx>(pub IntType<'ctx>);
impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
fn get_int_type(&self, _tyctx: TypeContext<'ctx>, _ctx: &'ctx Context) -> IntType<'ctx> {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> IntType<'ctx> {
self.0
}
}
@ -69,13 +97,17 @@ impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
type Type = IntType<'ctx>;
#[must_use]
fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type {
self.0.get_int_type(tyctx, ctx)
fn get_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
self.0.get_int_type(generator, ctx)
}
fn check_type<T: inkwell::types::BasicType<'ctx>>(
fn check_type<T: inkwell::types::BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
@ -84,7 +116,7 @@ impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
};
let exp_ty = self.0.get_int_type(tyctx, ctx);
let exp_ty = self.0.get_int_type(generator, ctx);
if ty.get_bit_width() != exp_ty.get_bit_width() {
return Err(ModelError(format!(
"Expecting IntType to have {} bit(s), but got {} bit(s)",
@ -98,90 +130,107 @@ impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
}
impl<'ctx, N: IntKind<'ctx>> IntModel<N> {
pub fn constant(
pub fn constant<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &'ctx Context,
value: u64,
) -> Int<'ctx, N> {
let value = self.get_type(tyctx, ctx).const_int(value, false);
let value = self.get_type(generator, ctx).const_int(value, false);
self.believe_value(value)
}
pub fn const_0(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Int<'ctx, N> {
self.constant(tyctx, ctx, 0)
}
pub fn const_1(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Int<'ctx, N> {
self.constant(tyctx, ctx, 1)
}
pub fn s_extend_or_bit_cast(
pub fn const_0<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &'ctx Context,
) -> Int<'ctx, N> {
self.constant(generator, ctx, 0)
}
pub fn const_1<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Int<'ctx, N> {
self.constant(generator, ctx, 1)
}
pub fn const_all_1s<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Int<'ctx, N> {
let value = self.get_type(generator, ctx).const_all_ones();
self.believe_value(value)
}
pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
name: &str,
) -> Int<'ctx, N> {
let value = ctx
.builder
.build_int_s_extend_or_bit_cast(value, self.get_type(tyctx, ctx.ctx), name)
.build_int_s_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), name)
.unwrap();
self.believe_value(value)
}
pub fn truncate(
pub fn truncate<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
name: &str,
) -> Int<'ctx, N> {
let value =
ctx.builder.build_int_truncate(value, self.get_type(tyctx, ctx.ctx), name).unwrap();
ctx.builder.build_int_truncate(value, self.get_type(generator, ctx.ctx), name).unwrap();
self.believe_value(value)
}
}
impl IntModel<Bool> {
#[must_use]
pub fn const_false<'ctx>(
pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &'ctx Context,
) -> Int<'ctx, Bool> {
self.constant(tyctx, ctx, 0)
self.constant(generator, ctx, 0)
}
#[must_use]
pub fn const_true<'ctx>(
pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &'ctx Context,
) -> Int<'ctx, Bool> {
self.constant(tyctx, ctx, 1)
self.constant(generator, ctx, 1)
}
}
impl<'ctx, N: IntKind<'ctx>> Int<'ctx, N> {
pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>>(
pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
name: &str,
) -> Int<'ctx, NewN> {
IntModel(to_int_kind).s_extend_or_bit_cast(tyctx, ctx, self.value, name)
IntModel(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value, name)
}
pub fn truncate<NewN: IntKind<'ctx>>(
pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
name: &str,
) -> Int<'ctx, NewN> {
IntModel(to_int_kind).truncate(tyctx, ctx, self.value, name)
IntModel(to_int_kind).truncate(generator, ctx, self.value, name)
}
#[must_use]

1
nac3core/src/codegen/model/mod.rs
View File

@ -3,6 +3,7 @@ mod core;
mod int;
mod ptr;
mod structure;
pub mod util;
pub use any::*;
pub use core::*;

57
nac3core/src/codegen/model/ptr.rs
View File

@ -5,7 +5,7 @@ use inkwell::{
AddressSpace,
};
use crate::codegen::CodeGenContext;
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
@ -17,13 +17,17 @@ impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
type Value = PointerValue<'ctx>;
type Type = PointerType<'ctx>;
fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type {
self.0.get_type(tyctx, ctx).ptr_type(AddressSpace::default())
fn get_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
self.0.get_type(generator, ctx).ptr_type(AddressSpace::default())
}
fn check_type<T: BasicType<'ctx>>(
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
@ -41,7 +45,9 @@ impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
// TODO: inkwell `get_element_type()` will be deprecated.
// Remove the check for `get_element_type()` when the time comes.
self.0.check_type(tyctx, ctx, elem_ty).map_err(|err| err.under_context("a PointerType"))?;
self.0
.check_type(generator, ctx, elem_ty)
.map_err(|err| err.under_context("a PointerType"))?;
Ok(())
}
@ -49,20 +55,25 @@ impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
impl<'ctx, Element: Model<'ctx>> PtrModel<Element> {
/// Return a ***constant*** nullptr.
pub fn nullptr(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Ptr<'ctx, Element> {
let ptr = self.get_type(tyctx, ctx).const_null();
pub fn nullptr<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Ptr<'ctx, Element> {
let ptr = self.get_type(generator, ctx).const_null();
self.believe_value(ptr)
}
/// Cast a pointer into this model with [`inkwell::builder::Builder::build_pointer_cast`]
pub fn pointer_cast(
pub fn pointer_cast<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
ptr: PointerValue<'ctx>,
name: &str,
) -> Ptr<'ctx, Element> {
let ptr = ctx.builder.build_pointer_cast(ptr, self.get_type(tyctx, ctx.ctx), name).unwrap();
let ptr =
ctx.builder.build_pointer_cast(ptr, self.get_type(generator, ctx.ctx), name).unwrap();
self.believe_value(ptr)
}
}
@ -70,38 +81,38 @@ impl<'ctx, Element: Model<'ctx>> PtrModel<Element> {
impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, Element> {
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
#[must_use]
pub fn offset(
pub fn offset<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
offset: IntValue<'ctx>,
name: &str,
) -> Ptr<'ctx, Element> {
let new_ptr =
unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], name).unwrap() };
self.model.check_value(tyctx, ctx.ctx, new_ptr).unwrap()
self.model.check_value(generator, ctx.ctx, new_ptr).unwrap()
}
// Load the `i`-th element (0-based) on the array with [`inkwell::builder::Builder::build_in_bounds_gep`].
pub fn ix(
pub fn ix<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
i: IntValue<'ctx>,
name: &str,
) -> Instance<'ctx, Element> {
self.offset(tyctx, ctx, i, name).load(tyctx, ctx, name)
self.offset(generator, ctx, i, name).load(generator, ctx, name)
}
/// Load the value with [`inkwell::builder::Builder::build_load`].
pub fn load(
pub fn load<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
name: &str,
) -> Instance<'ctx, Element> {
let value = ctx.builder.build_load(self.value, name).unwrap();
self.model.0.check_value(tyctx, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
}
/// Store a value with [`inkwell::builder::Builder::build_store`].
@ -110,14 +121,14 @@ impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, Element> {
}
/// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
pub fn transmute<NewElement: Model<'ctx>>(
pub fn transmute<NewElement: Model<'ctx>, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
new_model: NewElement,
name: &str,
) -> Ptr<'ctx, NewElement> {
PtrModel(new_model).pointer_cast(tyctx, ctx, self.value, name)
PtrModel(new_model).pointer_cast(generator, ctx, self.value, name)
}
/// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].

52
nac3core/src/codegen/model/structure.rs
View File

@ -6,7 +6,7 @@ use inkwell::{
values::StructValue,
};
use crate::codegen::CodeGenContext;
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
@ -42,30 +42,32 @@ impl<'ctx> FieldTraversal<'ctx> for GepFieldTraversal {
}
}
struct TypeFieldTraversal<'ctx> {
tyctx: TypeContext<'ctx>,
struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
generator: &'a G,
ctx: &'ctx Context,
field_types: Vec<BasicTypeEnum<'ctx>>,
}
impl<'ctx> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx> {
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
type Out<M> = ();
fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Out<M> {
let t = model.get_type(self.tyctx, self.ctx).as_basic_type_enum();
let t = model.get_type(self.generator, self.ctx).as_basic_type_enum();
self.field_types.push(t);
}
}
struct CheckTypeFieldTraversal<'ctx> {
tyctx: TypeContext<'ctx>,
struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
generator: &'a mut G,
ctx: &'ctx Context,
index: u32,
scrutinee: StructType<'ctx>,
errors: Vec<ModelError>,
}
impl<'ctx> FieldTraversal<'ctx> for CheckTypeFieldTraversal<'ctx> {
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
for CheckTypeFieldTraversal<'ctx, 'a, G>
{
type Out<M> = ();
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
@ -73,8 +75,8 @@ impl<'ctx> FieldTraversal<'ctx> for CheckTypeFieldTraversal<'ctx> {
self.index += 1;
if let Some(t) = self.scrutinee.get_field_type_at_index(i) {
if let Err(err) = model.check_type(self.tyctx, self.ctx, t) {
self.errors.push(err.under_context(format!("At field #{i} '{name}'").as_str()));
if let Err(err) = model.check_type(self.generator, self.ctx, t) {
self.errors.push(err.under_context(format!("field #{i} '{name}'").as_str()));
}
} // Otherwise, it will be caught
}
@ -89,8 +91,12 @@ pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
self.traverse_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
}
fn get_struct_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> StructType<'ctx> {
let mut traversal = TypeFieldTraversal { tyctx, ctx, field_types: Vec::new() };
fn get_struct_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> StructType<'ctx> {
let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
self.traverse_fields(&mut traversal);
ctx.struct_type(&traversal.field_types, false)
@ -105,13 +111,17 @@ impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
type Value = StructValue<'ctx>;
type Type = StructType<'ctx>;
fn get_type(&self, tyctx: TypeContext<'ctx>, ctx: &'ctx Context) -> Self::Type {
self.0.get_struct_type(tyctx, ctx)
fn get_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
self.0.get_struct_type(generator, ctx)
}
fn check_type<T: BasicType<'ctx>>(
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
@ -121,7 +131,7 @@ impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
};
let mut traversal =
CheckTypeFieldTraversal { tyctx, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
CheckTypeFieldTraversal { generator, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
self.0.traverse_fields(&mut traversal);
let exp_num_fields = traversal.index;
@ -168,9 +178,9 @@ impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
}
/// Convenience function equivalent to `.gep(...).load(...)`.
pub fn get<M, GetField>(
pub fn get<M, GetField, G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
get_field: GetField,
name: &str,
@ -179,7 +189,7 @@ impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
self.gep(ctx, get_field).load(tyctx, ctx, name)
self.gep(ctx, get_field).load(generator, ctx, name)
}
/// Convenience function equivalent to `.gep(...).store(...)`.
@ -192,6 +202,6 @@ impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
M: Model<'ctx>,
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
{
self.gep(ctx, get_field).store(ctx, value)
self.gep(ctx, get_field).store(ctx, value);
}
}

91
nac3core/src/codegen/model/util.rs Normal file
View File

@ -0,0 +1,91 @@
use inkwell::{types::BasicType, values::IntValue};
/// `llvm.memcpy` but under the [`Model`] abstraction
use crate::codegen::{
llvm_intrinsics::call_memcpy_generic,
stmt::{gen_for_callback_incrementing, BreakContinueHooks},
CodeGenContext, CodeGenerator,
};
use super::*;
/// Convenience function.
///
/// Like [`call_memcpy_generic`] but with model abstractions and `is_volatile` set to `false`.
pub fn call_memcpy_model<'ctx, Item: Model<'ctx> + Default, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
dst_array: Ptr<'ctx, Item>,
src_array: Ptr<'ctx, Item>,
num_items: IntValue<'ctx>,
) {
let itemsize = Item::default().get_type(generator, ctx.ctx).size_of().unwrap();
let totalsize = ctx.builder.build_int_mul(itemsize, num_items, "totalsize").unwrap(); // TODO: Int types may not match.
let is_volatile = ctx.ctx.bool_type().const_zero();
call_memcpy_generic(ctx, dst_array.value, src_array.value, totalsize, is_volatile);
}
/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
/// The [`IntKind`] is automatically inferred.
pub fn gen_for_model_auto<'ctx, 'a, G, F, I>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
start: Int<'ctx, I>,
stop: Int<'ctx, I>,
step: Int<'ctx, I>,
body: F,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
F: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BreakContinueHooks<'ctx>,
Int<'ctx, I>,
) -> Result<(), String>,
I: IntKind<'ctx> + Default,
{
let int_model = IntModel(I::default());
gen_for_callback_incrementing(
generator,
ctx,
None,
start.value,
(stop.value, false),
|g, ctx, hooks, i| {
let i = int_model.believe_value(i);
body(g, ctx, hooks, i)
},
step.value,
)
}
/// Like [`gen_if_callback`] with [`Model`] abstractions and without the `else` block.
pub fn gen_if_model<'ctx, 'a, G, ThenFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
cond: Int<'ctx, Bool>,
then: ThenFn,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
ThenFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<(), String>,
{
let current_bb = ctx.builder.get_insert_block().unwrap();
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "if.then");
let end_bb = ctx.ctx.insert_basic_block_after(then_bb, "if.end");
// Inserting into `current_bb`.
ctx.builder.build_conditional_branch(cond.value, then_bb, end_bb).unwrap();
// Inserting into `then_bb`
ctx.builder.position_at_end(then_bb);
then(generator, ctx)?;
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Reposition to `end_bb` for continuation.
ctx.builder.position_at_end(end_bb);
Ok(())
}

483
nac3core/src/codegen/numpy_new.rs Normal file
View File

@ -0,0 +1,483 @@
// TODO: Replace numpy.rs
use inkwell::values::{BasicValue, BasicValueEnum};
use nac3parser::ast::StrRef;
use crate::{
codegen::{
structure::{
ndarray::{
scalar::split_scalar_or_ndarray, shape_util::parse_numpy_int_sequence,
NDArrayObject,
},
tuple::TupleObject,
},
},
symbol_resolver::ValueEnum,
toplevel::{
numpy::{extract_ndims, unpack_ndarray_var_tys},
DefinitionId,
},
typecheck::typedef::{FunSignature, Type},
};
use super::{
irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext, CodeGenerator,
};
/// Get the zero value in `np.zeros()` of a `dtype`.
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
ctx.ctx.i32_type().const_zero().into()
} else if [ctx.primitives.int64, ctx.primitives.uint64]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
ctx.ctx.i64_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
ctx.ctx.f64_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
ctx.ctx.bool_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
ctx.gen_string(generator, "").value.into()
} else {
unreachable!()
}
}
/// Get the one value in `np.ones()` of a `dtype`.
fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32]
.iter()
.any(|ty| ctx.unifier.unioned(elem_ty, *ty))
{
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
ctx.ctx.i32_type().const_int(1, is_signed).into()
} else if [ctx.primitives.int64, ctx.primitives.uint64]
.iter()
.any(|ty| ctx.unifier.unioned(elem_ty, *ty))
{
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
ctx.ctx.i64_type().const_int(1, is_signed).into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
ctx.ctx.f64_type().const_float(1.0).into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
ctx.ctx.bool_type().const_int(1, false).into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
ctx.gen_string(generator, "1").value.into()
} else {
unreachable!()
}
}
/// Helper function to create an ndarray with uninitialized values.
///
/// * `ndarray_ty` - The [`Type`] of the ndarray
/// * `shape` - The user input shape argument
/// * `shape_ty` - The [`Type`] of the shape argument
///
/// This function does data validation the `shape` input.
fn create_empty_ndarray<'ctx, G>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ty: Type,
shape: BasicValueEnum<'ctx>,
shape_ty: Type,
) -> NDArrayObject<'ctx>
where
G: CodeGenerator + ?Sized,
{
let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
let ndarray =
NDArrayObject::alloca_uninitialized_of_type(generator, ctx, ndarray_ty, "ndarray");
// Validate `shape`
let ndims = ndarray.get_ndims(generator, ctx.ctx);
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims, shape);
// Setup `ndarray` with `shape`
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.create_data(generator, ctx); // `shape` has to be set
ndarray
}
/// Generates LLVM IR for `np.empty`.
pub fn gen_ndarray_empty<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
// Parse arguments
let shape_ty = fun.0.args[0].ty;
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
// Implementation
let ndarray_ty = fun.0.ret;
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
Ok(ndarray.value.value.as_basic_value_enum())
}
/// Generates LLVM IR for `np.zero`.
pub fn gen_ndarray_zeros<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
// Parse arguments
let shape_ty = fun.0.args[0].ty;
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
// Implementation
let ndarray_ty = fun.0.ret;
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
ndarray.fill(generator, ctx, fill_value);
Ok(ndarray.value.value.as_basic_value_enum())
}
/// Generates LLVM IR for `np.ones`.
pub fn gen_ndarray_ones<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
// Parse arguments
let shape_ty = fun.0.args[0].ty;
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
// Implementation
let ndarray_ty = fun.0.ret;
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
ndarray.fill(generator, ctx, fill_value);
Ok(ndarray.value.value.as_basic_value_enum())
}
/// Generates LLVM IR for `np.full`.
pub fn gen_ndarray_full<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 2);
// Parse argument #1 shape
let shape_ty = fun.0.args[0].ty;
let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
// Parse argument #2 fill_value
let fill_value_ty = fun.0.args[1].ty;
let fill_value = args[1].1.clone().to_basic_value_enum(ctx, generator, fill_value_ty)?;
// Implementation
let ndarray_ty = fun.0.ret;
let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
ndarray.fill(generator, ctx, fill_value);
Ok(ndarray.value.value.as_basic_value_enum())
}
/// Generates LLVM IR for `np.broadcast_to`.
pub fn gen_ndarray_broadcast_to<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 2);
// Parse argument #1 input
let input_ty = fun.0.args[0].ty;
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
// Parse argument #2 shape
let shape_ty = fun.0.args[1].ty;
let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
// Define models
let sizet_model = IntModel(SizeT);
// Extract broadcast_ndims, this is the only way to get the
// ndims of the ndarray result statically.
let (_, broadcast_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
let broadcast_ndims = extract_ndims(&ctx.unifier, broadcast_ndims_ty);
// Process `input`
let in_ndarray =
split_scalar_or_ndarray(generator, ctx, input, input_ty).as_ndarray(generator, ctx);
// Process `shape`
let (_, broadcast_shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
// NOTE: shape.size should equal to `broadcasted_ndims`.
let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
call_nac3_ndarray_util_assert_shape_no_negative(
generator,
ctx,
broadcast_ndims_llvm,
broadcast_shape,
);
// Create broadcast view
let broadcast_ndarray =
in_ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape);
Ok(broadcast_ndarray.value.value.as_basic_value_enum())
}
/// Generates LLVM IR for `np.reshape`.
pub fn gen_ndarray_reshape<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 2);
// Parse argument #1 input
let input_ty = fun.0.args[0].ty;
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
// Parse argument #2 shape
let shape_ty = fun.0.args[1].ty;
let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
// Extract reshaped_ndims
let (_, reshaped_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
let reshaped_ndims = extract_ndims(&ctx.unifier, reshaped_ndims_ty);
// Process `input`
let in_ndarray =
split_scalar_or_ndarray(generator, ctx, input, input_ty).as_ndarray(generator, ctx);
// Process the shape input from user and resolve negative indices.
// The resulting `new_shape`'s size should be equal to reshaped_ndims.
// This is ensured by the typechecker.
let (_, new_shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
let reshaped_ndarray = in_ndarray.reshape_or_copy(generator, ctx, reshaped_ndims, new_shape);
Ok(reshaped_ndarray.value.value.as_basic_value_enum())
}
/// Generates LLVM IR for `np.arange`.
pub fn gen_ndarray_arange<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
// Parse argument #1 len
let input_ty = fun.0.args[0].ty;
let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?.into_int_value();
// Define models
let sizet_model = IntModel(SizeT);
// Process input
let input = sizet_model.s_extend_or_bit_cast(generator, ctx, input, "input_dim");
// Allocate the resulting ndarray
let ndarray = NDArrayObject::alloca_uninitialized(
generator,
ctx,
ctx.primitives.float,
1, // ndims = 1
"arange_ndarray",
);
// `ndarray.shape[0] = input`
let zero = sizet_model.const_0(generator, ctx.ctx);
ndarray
.value
.get(generator, ctx, |f| f.shape, "shape")
.offset(generator, ctx, zero.value, "dim")
.store(ctx, input);
// Create data and set elements
ndarray.create_data(generator, ctx);
ndarray.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, i, pelement| {
let val =
ctx.builder.build_unsigned_int_to_float(i.value, ctx.ctx.f64_type(), "val").unwrap();
ctx.builder.build_store(pelement, val).unwrap();
Ok(())
})?;
Ok(ndarray.value.value.as_basic_value_enum())
}
/// Generates LLVM IR for `np.size`.
pub fn gen_ndarray_size<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let ndarray_ty = fun.0.args[0].ty;
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
let size = ndarray.size(generator, ctx).truncate(generator, ctx, Int32, "size");
Ok(size.value.as_basic_value_enum())
}
/// Generates LLVM IR for `np.shape`.
pub fn gen_ndarray_shape<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
// Parse argument #1 ndarray
let ndarray_ty = fun.0.args[0].ty;
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
// Define models
let sizet_model = IntModel(SizeT);
// Process ndarray
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
let mut items = Vec::with_capacity(ndarray.ndims as usize);
for i in 0..ndarray.ndims {
let i = sizet_model.constant(generator, ctx.ctx, i);
let dim =
ndarray.value.get(generator, ctx, |f| f.shape, "").ix(generator, ctx, i.value, "dim");
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
}
let shape = TupleObject::create(generator, ctx, items, "shape");
Ok(shape.value.as_basic_value_enum())
}
/// Generates LLVM IR for `<ndarray>.strides`.
pub fn gen_ndarray_strides<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
// TODO: This function looks exactly like `gen_ndarray_shapes`, code duplication?
assert!(obj.is_none());
assert_eq!(args.len(), 1);
// Parse argument #1 ndarray
let ndarray_ty = fun.0.args[0].ty;
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
// Define models
let sizet_model = IntModel(SizeT);
// Process ndarray
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
let mut items = Vec::with_capacity(ndarray.ndims as usize);
for i in 0..ndarray.ndims {
let i = sizet_model.constant(generator, ctx.ctx, i);
let dim =
ndarray.value.get(generator, ctx, |f| f.strides, "").ix(generator, ctx, i.value, "dim");
let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
}
let strides = TupleObject::create(generator, ctx, items, "strides");
Ok(strides.value.as_basic_value_enum())
}
/// Generates LLVM IR for `np.transpose`.
pub fn gen_ndarray_transpose<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<BasicValueEnum<'ctx>, String> {
// TODO: The implementation will be changed once default values start working again.
// Read the comment on this function in BuiltinBuilder.
// TODO: Change axes values to `SizeT`
assert!(obj.is_none());
assert_eq!(args.len(), 1);
// Parse argument #1 ndarray
let ndarray_ty = fun.0.args[0].ty;
let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
// Implementation
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
let has_axes = args.len() >= 2;
let transposed_ndarray = if has_axes {
// Parse argument #2 axes
let in_axes_ty = fun.0.args[1].ty;
let in_axes = args[1].1.clone().to_basic_value_enum(ctx, generator, in_axes_ty)?;
let (_, axes) = parse_numpy_int_sequence(generator, ctx, in_axes, in_axes_ty);
ndarray.transpose(generator, ctx, Some(axes))
} else {
ndarray.transpose(generator, ctx, None)
};
Ok(transposed_ndarray.value.value.as_basic_value_enum())
}

66
nac3core/src/codegen/stmt.rs
View File

@ -7,6 +7,9 @@ use super::{
structure::exception::Exception,
CodeGenContext, CodeGenerator, Int32, IntModel, Ptr, StructModel,
};
use crate::codegen::structure::ndarray::indexing::util::gen_ndarray_subscript_ndindexes;
use crate::codegen::structure::ndarray::scalar::split_scalar_or_ndarray;
use crate::codegen::structure::ndarray::NDArrayObject;
use crate::{
codegen::{
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
@ -404,7 +407,43 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
// Handle NDArray item assignment
todo!("ndarray subscript assignment is not yet implemented");
// Process target
let target = generator
.gen_expr(ctx, target)?
.unwrap()
.to_basic_value_enum(ctx, generator, target_ty)?;
let target = NDArrayObject::from_value_and_type(generator, ctx, target, target_ty);
// Process key
let key = gen_ndarray_subscript_ndindexes(generator, ctx, key)?;
// Process value
let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
/*
Reference code:
```python
target = target[key]
value = np.asarray(value)
shape = np.broadcast_shape((target, value))
target = np.broadcast_to(target, shape)
value = np.broadcast_to(value, shape)
...and finally copy 1-1 from value to target.
```
*/
let target = target.index(generator, ctx, &key, "assign_target_ndarray");
let value =
split_scalar_or_ndarray(generator, ctx, value, value_ty).as_ndarray(generator, ctx);
let broadcast_result = NDArrayObject::broadcast_all(generator, ctx, &[target, value]);
let target = broadcast_result.ndarrays[0];
let value = broadcast_result.ndarrays[1];
target.copy_data_from(generator, ctx, value);
}
_ => {
panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
@ -641,8 +680,12 @@ where
I: Clone,
InitFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
CondFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
BodyFn:
FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, BreakContinueHooks, I) -> Result<(), String>,
BodyFn: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BreakContinueHooks<'ctx>,
I,
) -> Result<(), String>,
UpdateFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
{
let label = label.unwrap_or("for");
@ -722,7 +765,7 @@ where
BodyFn: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BreakContinueHooks,
BreakContinueHooks<'ctx>,
IntValue<'ctx>,
) -> Result<(), String>,
{
@ -1266,20 +1309,19 @@ pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
loc: Location,
) {
if let Some(pexn) = exception {
let type_context = generator.type_context(ctx.ctx);
let i32_model = IntModel(Int32);
let cslice_model = StructModel(CSlice);
// Get and store filename
let filename = loc.file.0;
let filename = ctx.gen_string(generator, &String::from(filename)).value;
let filename = cslice_model.check_value(type_context, ctx.ctx, filename).unwrap();
let filename = cslice_model.check_value(generator, ctx.ctx, filename).unwrap();
pexn.set(ctx, |f| f.filename, filename);
let row = i32_model.constant(type_context, ctx.ctx, loc.row as u64);
let row = i32_model.constant(generator, ctx.ctx, loc.row as u64);
pexn.set(ctx, |f| f.line, row);
let column = i32_model.constant(type_context, ctx.ctx, loc.column as u64);
let column = i32_model.constant(generator, ctx.ctx, loc.column as u64);
pexn.set(ctx, |f| f.column, column);
let current_fn = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
@ -1755,9 +1797,8 @@ pub fn gen_stmt<G: CodeGenerator>(
return Ok(());
};
let type_context = generator.type_context(ctx.ctx);
let pexn_model = PtrModel(StructModel(Exception));
let exn = pexn_model.check_value(type_context, ctx.ctx, exc).unwrap();
let exn = pexn_model.check_value(generator, ctx.ctx, exc).unwrap();
gen_raise(generator, ctx, Some(exn), stmt.location);
} else {
@ -1765,7 +1806,6 @@ pub fn gen_stmt<G: CodeGenerator>(
}
}
StmtKind::Assert { test, msg, .. } => {
let type_context = generator.type_context(ctx.ctx);
let byte_model = IntModel(Byte);
let cslice_model = StructModel(CSlice);
@ -1773,7 +1813,7 @@ pub fn gen_stmt<G: CodeGenerator>(
return Ok(());
};
let test = test.to_basic_value_enum(ctx, generator, ctx.primitives.bool)?;
let test = byte_model.check_value(type_context, ctx.ctx, test).unwrap(); // Python `bool` is represented as `i8` in nac3core
let test = byte_model.check_value(generator, ctx.ctx, test).unwrap(); // Python `bool` is represented as `i8` in nac3core
// Check `msg`
let err_msg = match msg {
@ -1783,7 +1823,7 @@ pub fn gen_stmt<G: CodeGenerator>(
};
let msg = msg.to_basic_value_enum(ctx, generator, ctx.primitives.str)?;
cslice_model.check_value(type_context, ctx.ctx, msg).unwrap()
cslice_model.check_value(generator, ctx.ctx, msg).unwrap()
}
None => ctx.gen_string(generator, ""),
};

12
nac3core/src/codegen/structure/cslice.rs
View File

@ -1,4 +1,6 @@
use crate::codegen::{model::*, CodeGenContext};
use inkwell::context::Context;
use crate::codegen::{model::*, CodeGenerator};
/// Fields of [`CSlice<'ctx>`].
pub struct CSliceFields<'ctx, F: FieldTraversal<'ctx>> {
@ -27,16 +29,16 @@ impl StructModel<CSlice> {
/// Create a [`CSlice`].
///
/// `base` and `len` must be LLVM global constants.
pub fn create_const<'ctx>(
pub fn create_const<'ctx, G: CodeGenerator + ?Sized>(
&self,
type_context: TypeContext<'ctx>,
ctx: &CodeGenContext<'ctx, '_>,
generator: &mut G,
ctx: &'ctx Context,
base: Ptr<'ctx, IntModel<Byte>>,
len: Int<'ctx, SizeT>,
) -> Struct<'ctx, CSlice> {
let value = self
.0
.get_struct_type(type_context, ctx.ctx)
.get_struct_type(generator, ctx)
.const_named_struct(&[base.value.into(), len.value.into()]);
self.believe_value(value)
}

70
nac3core/src/codegen/structure/list.rs Normal file
View File

@ -0,0 +1,70 @@
use inkwell::values::BasicValue;
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::{iter_type_vars, Type, TypeEnum},
};
/// Fields of [`List`]
pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>, Size: IntKind<'ctx>> {
/// Array pointer to content
pub items: F::Out<PtrModel<Item>>,
/// Number of items in the array
pub len: F::Out<IntModel<Size>>,
}
/// A list in NAC3.
#[derive(Debug, Clone, Copy, Default)]
pub struct List<Item, Len> {
/// Model of the list items
pub item: Item,
/// Model of type of integer storing the number of items on the list
pub len: Len,
}
impl<'ctx, Item: Model<'ctx>, Size: IntKind<'ctx>> StructKind<'ctx> for List<Item, Size> {
type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item, Size>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
items: traversal.add("data", PtrModel(self.item)),
len: traversal.add("len", IntModel(self.len)),
}
}
}
/// A NAC3 Python List object.
pub struct ListObject<'ctx> {
/// Typechecker type of the list items
pub item_type: Type,
pub value: Ptr<'ctx, StructModel<List<AnyModel<'ctx>, SizeT>>>,
}
impl<'ctx> ListObject<'ctx> {
/// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list_val: V,
list_type: Type,
) -> Self {
// Check typechecker type and extract `item_type`
let item_type = match &*ctx.unifier.get_ty(list_type) {
TypeEnum::TObj { obj_id, params, .. }
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
{
iter_type_vars(params).next().unwrap().ty // Extract `item_type`
}
_ => {
panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(list_type))
}
};
let item_model = AnyModel(ctx.get_llvm_type(generator, item_type));
let plist_model = PtrModel(StructModel(List { item: item_model, len: SizeT }));
// Create object
let value = plist_model.check_value(generator, ctx.ctx, list_val).unwrap();
ListObject { item_type, value }
}
}

2
nac3core/src/codegen/structure/mod.rs
View File

@ -1,3 +1,5 @@
pub mod cslice;
pub mod exception;
pub mod list;
pub mod ndarray;
pub mod tuple;

224
nac3core/src/codegen/structure/ndarray.rs
View File

@ -1,224 +0,0 @@
use irrt::{
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
};
use crate::{codegen::*, symbol_resolver::SymbolValue};
pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
pub data: F::Out<PtrModel<IntModel<Byte>>>,
pub itemsize: F::Out<IntModel<SizeT>>,
pub ndims: F::Out<IntModel<SizeT>>,
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
}
// TODO: Rename to `NDArray` when the old NDArray is removed.
#[derive(Debug, Clone, Copy, Default)]
pub struct NpArray;
impl<'ctx> StructKind<'ctx> for NpArray {
type Fields<F: FieldTraversal<'ctx>> = NpArrayFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
data: traversal.add_auto("data"),
itemsize: traversal.add_auto("itemsize"),
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
strides: traversal.add_auto("strides"),
}
}
}
/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
/// The `ndims` must only contain 1 value.
#[must_use]
pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
panic!("ndims_ty should be a TLiteral");
};
assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
let ndims = values[0].clone();
u64::try_from(ndims).unwrap()
}
/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
}
#[derive(Debug, Clone, Copy)]
pub struct NDArrayObject<'ctx> {
pub dtype: Type,
pub ndims: Type,
pub value: Ptr<'ctx, StructModel<NpArray>>,
}
impl<'ctx> NDArrayObject<'ctx> {
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
///
/// `shape` and `strides` will be automatically allocated on the stack.
///
/// The returned ndarray's content will be:
/// - `data`: set to `nullptr`.
/// - `itemsize`: set to the `sizeof()` of `dtype`.
/// - `ndims`: set to the value of `ndims`.
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
pub fn alloca_uninitialized<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: Type,
name: &str,
) -> Self {
let tyctx = generator.type_context(ctx.ctx);
let sizet_model = IntModel(SizeT);
let ndarray_model = StructModel(NpArray);
let ndarray_data_model = PtrModel(IntModel(Byte));
let pndarray = ndarray_model.alloca(tyctx, ctx, name);
let data = ndarray_data_model.nullptr(tyctx, ctx.ctx);
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
let itemsize = sizet_model.s_extend_or_bit_cast(tyctx, ctx, itemsize, "itemsize");
let ndims_val = extract_ndims(&ctx.unifier, ndims);
let ndims_val = sizet_model.constant(tyctx, ctx.ctx, ndims_val);
let shape = sizet_model.array_alloca(tyctx, ctx, ndims_val.value, "shape");
let strides = sizet_model.array_alloca(tyctx, ctx, ndims_val.value, "strides");
pndarray.set(ctx, |f| f.data, data);
pndarray.set(ctx, |f| f.itemsize, itemsize);
pndarray.set(ctx, |f| f.ndims, ndims_val);
pndarray.set(ctx, |f| f.shape, shape);
pndarray.set(ctx, |f| f.strides, strides);
NDArrayObject { dtype, ndims, value: pndarray }
}
/// Get this ndarray's `ndims` as an LLVM constant.
pub fn get_ndims(
&self,
tyctx: TypeContext<'ctx>,
ctx: &CodeGenContext<'ctx, '_>,
) -> Int<'ctx, SizeT> {
let sizet_model = IntModel(SizeT);
let ndims_val = extract_ndims(&ctx.unifier, self.ndims);
sizet_model.constant(tyctx, ctx.ctx, ndims_val)
}
/// Return true if this ndarray is unsized.
#[must_use]
pub fn is_unsized(&self, unifier: &Unifier) -> bool {
extract_ndims(unifier, self.ndims) == 0
}
/// 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 create_data<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
let byte_model = IntModel(Byte);
let data = byte_model.array_alloca(tyctx, ctx, self.get_ndims(tyctx, ctx).value, "data");
self.value.set(ctx, |f| f.data, data);
self.update_strides_by_shape(tyctx, ctx);
}
/// Get the `np.size()` of this ndarray.
pub fn size(
&self,
tyctx: TypeContext<'ctx>,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Int<'ctx, SizeT> {
call_nac3_ndarray_size(tyctx, ctx, self.value)
}
/// Get the `ndarray.nbytes` of this ndarray.
pub fn nbytes(
&self,
tyctx: TypeContext<'ctx>,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Int<'ctx, SizeT> {
call_nac3_ndarray_nbytes(tyctx, ctx, self.value)
}
/// Get the `len()` of this ndarray.
pub fn len(
&self,
tyctx: TypeContext<'ctx>,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Int<'ctx, SizeT> {
call_nac3_ndarray_len(tyctx, ctx, self.value)
}
/// Check if this ndarray is C-contiguous.
///
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
&self,
tyctx: TypeContext<'ctx>,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Int<'ctx, Bool> {
call_nac3_ndarray_is_c_contiguous(tyctx, ctx, self.value)
}
/// Get the pointer to the n-th (0-based) element.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Int<'ctx, SizeT>,
name: &str,
) -> PointerValue<'ctx> {
let tyctx = generator.type_context(ctx.ctx);
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_nth_pelement(tyctx, ctx, self.value, nth);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
.unwrap()
}
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
///
/// Please refer to the IRRT implementation to see its purpose.
pub fn update_strides_by_shape(
&self,
tyctx: TypeContext<'ctx>,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
call_nac3_ndarray_set_strides_by_shape(tyctx, ctx, self.value);
}
/// Copy data from another ndarray.
///
/// Panics if the `dtype`s of ndarrays are different.
pub fn copy_data_from(
&self,
tyctx: TypeContext<'ctx>,
ctx: &mut CodeGenContext<'ctx, '_>,
src: NDArrayObject<'ctx>,
) {
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
call_nac3_ndarray_copy_data(tyctx, ctx, src.value, self.value);
}
}

134
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use itertools::Itertools;
use crate::{
codegen::{
irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
model::*,
CodeGenContext, CodeGenerator,
},
toplevel::numpy::get_broadcast_all_ndims,
};
use super::NDArrayObject;
/// Fields of [`ShapeEntry`]
pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
pub ndims: F::Out<IntModel<SizeT>>,
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
}
/// An IRRT structure used in broadcasting.
#[derive(Debug, Clone, Copy, Default)]
pub struct ShapeEntry;
impl<'ctx> StructKind<'ctx> for ShapeEntry {
type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields { ndims: traversal.add_auto("ndims"), shape: traversal.add_auto("shape") }
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create a broadcast view on this ndarray with a target shape.
///
/// * `target_ndims` - The ndims type after broadcasting to the given shape.
/// The caller has to figure this out for this function.
/// * `target_shape` - An array pointer pointing to the target shape.
#[must_use]
pub fn broadcast_to<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
target_ndims: u64,
target_shape: Ptr<'ctx, IntModel<SizeT>>,
) -> Self {
let broadcast_ndarray = NDArrayObject::alloca_uninitialized(
generator,
ctx,
self.dtype,
target_ndims,
"broadcast_ndarray_to_dst",
);
broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
call_nac3_ndarray_broadcast_to(generator, ctx, self.value, broadcast_ndarray.value);
broadcast_ndarray
}
}
/// A result produced by [`broadcast_all_ndarrays`]
#[derive(Debug, Clone)]
pub struct BroadcastAllResult<'ctx> {
/// The statically known `ndims` of the broadcast result.
pub ndims: u64,
/// The broadcasting shape.
pub shape: Ptr<'ctx, IntModel<SizeT>>,
/// Broadcasted views on the inputs.
///
/// All of them will have `shape` [`BroadcastAllResult::shape`] and
/// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
/// is the same as the input.
pub ndarrays: Vec<NDArrayObject<'ctx>>,
}
impl<'ctx> NDArrayObject<'ctx> {
// TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
pub fn broadcast_all<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarrays: &[Self],
) -> BroadcastAllResult<'ctx> {
assert!(!ndarrays.is_empty());
let sizet_model = IntModel(SizeT);
let shape_model = StructModel(ShapeEntry);
let broadcast_ndims = get_broadcast_all_ndims(ndarrays.iter().map(|ndarray| ndarray.ndims));
// Prepare input shape entries
let num_shape_entries =
sizet_model.constant(generator, ctx.ctx, u64::try_from(ndarrays.len()).unwrap());
let shape_entries =
shape_model.array_alloca(generator, ctx, num_shape_entries.value, "shape_entries");
for (i, ndarray) in ndarrays.iter().enumerate() {
let i = sizet_model.constant(generator, ctx.ctx, i as u64).value;
let shape_entry = shape_entries.offset(generator, ctx, i, "shape_entry");
let this_ndims = ndarray.value.get(generator, ctx, |f| f.ndims, "this_ndims");
shape_entry.set(ctx, |f| f.ndims, this_ndims);
let this_shape = ndarray.value.get(generator, ctx, |f| f.shape, "this_shape");
shape_entry.set(ctx, |f| f.shape, this_shape);
}
// Prepare destination
let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
let broadcast_shape =
sizet_model.array_alloca(generator, ctx, broadcast_ndims_llvm.value, "dst_shape");
// Compute the target broadcast shape `dst_shape` for all ndarrays.
call_nac3_ndarray_broadcast_shapes(
generator,
ctx,
num_shape_entries,
shape_entries,
broadcast_ndims_llvm,
broadcast_shape,
);
// Now that we know about the broadcasting shape, broadcast all the inputs.
// Broadcast all the inputs to shape `dst_shape`.
let broadcast_ndarrays: Vec<_> = ndarrays
.iter()
.map(|ndarray| ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape))
.collect_vec();
BroadcastAllResult {
ndims: broadcast_ndims,
shape: broadcast_shape,
ndarrays: broadcast_ndarrays,
}
}
}

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use inkwell::{
values::{BasicValue, FloatValue, IntValue},
FloatPredicate, IntPredicate,
};
use itertools::Itertools;
use crate::{
codegen::{
llvm_intrinsics,
model::{
util::{gen_for_model_auto, gen_if_model},
*,
},
CodeGenContext, CodeGenerator,
},
typecheck::typedef::Type,
};
use super::{scalar::ScalarObject, NDArrayObject};
/// Convenience function to crash the program when types of arguments are not supported.
/// Used to be debugged with a stacktrace.
fn unsupported_type<I>(ctx: &CodeGenContext<'_, '_>, tys: I) -> !
where
I: IntoIterator<Item = Type>,
{
unreachable!(
"unsupported types found '{}'",
tys.into_iter().map(|ty| format!("'{}'", ctx.unifier.stringify(ty))).join(", "),
)
}
#[derive(Debug, Clone, Copy)]
pub enum FloorOrCeil {
Floor,
Ceil,
}
#[derive(Debug, Clone, Copy)]
pub enum MinOrMax {
Min,
Max,
}
fn signed_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
vec![ctx.primitives.int32, ctx.primitives.int64]
}
fn unsigned_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
vec![ctx.primitives.uint32, ctx.primitives.uint64]
}
fn ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
vec![ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64]
}
fn int_like(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
vec![
ctx.primitives.bool,
ctx.primitives.int32,
ctx.primitives.int64,
ctx.primitives.uint32,
ctx.primitives.uint64,
]
}
fn cast_to_int_conversion<'ctx, 'a, G, HandleFloatFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
scalar: ScalarObject<'ctx>,
ret_int_dtype: Type,
handle_float: HandleFloatFn,
) -> ScalarObject<'ctx>
where
G: CodeGenerator + ?Sized,
HandleFloatFn:
FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, FloatValue<'ctx>) -> IntValue<'ctx>,
{
let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
let result = if ctx.unifier.unioned(scalar.dtype, ctx.primitives.float) {
// Special handling for floats
let n = scalar.value.into_float_value();
handle_float(generator, ctx, n)
} else if ctx.unifier.unioned_any(scalar.dtype, int_like(ctx)) {
let n = scalar.value.into_int_value();
if n.get_type().get_bit_width() <= ret_int_dtype_llvm.get_bit_width() {
ctx.builder.build_int_z_extend(n, ret_int_dtype_llvm, "zext").unwrap()
} else {
ctx.builder.build_int_truncate(n, ret_int_dtype_llvm, "trunc").unwrap()
}
} else {
unsupported_type(ctx, [scalar.dtype]);
};
assert_eq!(ret_int_dtype_llvm.get_bit_width(), result.get_type().get_bit_width()); // Sanity check
ScalarObject { value: result.into(), dtype: ret_int_dtype }
}
impl<'ctx> ScalarObject<'ctx> {
/// Convenience function. Assume this scalar has typechecker type float64, get its underlying LLVM value.
///
/// Panic if the type is wrong.
pub fn into_float64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> FloatValue<'ctx> {
if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
self.value.into_float_value() // self.value must be a FloatValue
} else {
panic!("not a float type")
}
}
/// Convenience function. Assume this scalar has typechecker type int32, get its underlying LLVM value.
///
/// Panic if the type is wrong.
pub fn into_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
if ctx.unifier.unioned(self.dtype, ctx.primitives.int32) {
let value = self.value.into_int_value();
debug_assert_eq!(value.get_type().get_bit_width(), 32); // Sanity check
value
} else {
panic!("not a float type")
}
}
/// Compare two scalars. Only int-to-int and float-to-float comparisons are allowed.
/// Panic otherwise.
pub fn compare<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
lhs: ScalarObject<'ctx>,
rhs: ScalarObject<'ctx>,
int_predicate: IntPredicate,
float_predicate: FloatPredicate,
name: &str,
) -> Int<'ctx, Bool> {
if !ctx.unifier.unioned(lhs.dtype, rhs.dtype) {
unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
}
let bool_model = IntModel(Bool);
let common_ty = lhs.dtype;
let result = if ctx.unifier.unioned(common_ty, ctx.primitives.float) {
let lhs = lhs.value.into_float_value();
let rhs = rhs.value.into_float_value();
ctx.builder.build_float_compare(float_predicate, lhs, rhs, name).unwrap()
} else if ctx.unifier.unioned_any(common_ty, int_like(ctx)) {
let lhs = lhs.value.into_int_value();
let rhs = rhs.value.into_int_value();
ctx.builder.build_int_compare(int_predicate, lhs, rhs, name).unwrap()
} else {
unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
};
bool_model.check_value(generator, ctx.ctx, result).unwrap()
}
/// Invoke NAC3's builtin `int32()`.
#[must_use]
pub fn cast_to_int32<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Self {
cast_to_int_conversion(
generator,
ctx,
*self,
ctx.primitives.int32,
|_generator, ctx, input| {
let n =
ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap();
ctx.builder.build_int_truncate(n, ctx.ctx.i32_type(), "conv").unwrap()
},
)
}
/// Invoke NAC3's builtin `int64()`.
#[must_use]
pub fn cast_to_int64<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Self {
cast_to_int_conversion(
generator,
ctx,
*self,
ctx.primitives.int64,
|_generator, ctx, input| {
ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap()
},
)
}
/// Invoke NAC3's builtin `uint32()`.
#[must_use]
pub fn cast_to_uint32<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Self {
cast_to_int_conversion(
generator,
ctx,
*self,
ctx.primitives.uint32,
|_generator, ctx, n| {
let n_gez = ctx
.builder
.build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
.unwrap();
let to_int32 =
ctx.builder.build_float_to_signed_int(n, ctx.ctx.i32_type(), "").unwrap();
let to_uint64 =
ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
ctx.builder
.build_select(
n_gez,
ctx.builder.build_int_truncate(to_uint64, ctx.ctx.i32_type(), "").unwrap(),
to_int32,
"conv",
)
.unwrap()
.into_int_value()
},
)
}
/// Invoke NAC3's builtin `uint64()`.
#[must_use]
pub fn cast_to_uint64<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Self {
cast_to_int_conversion(
generator,
ctx,
*self,
ctx.primitives.uint64,
|_generator, ctx, n| {
let val_gez = ctx
.builder
.build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
.unwrap();
let to_int64 =
ctx.builder.build_float_to_signed_int(n, ctx.ctx.i64_type(), "").unwrap();
let to_uint64 =
ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
ctx.builder
.build_select(val_gez, to_uint64, to_int64, "conv")
.unwrap()
.into_int_value()
},
)
}
/// Invoke NAC3's builtin `bool()`.
#[must_use]
pub fn cast_to_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
// TODO: Why is the original code being so lax about i1 and i8 for the returned int type?
let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.bool) {
self.value.into_int_value()
} else if ctx.unifier.unioned_any(self.dtype, ints(ctx)) {
let n = self.value.into_int_value();
ctx.builder
.build_int_compare(inkwell::IntPredicate::NE, n, n.get_type().const_zero(), "bool")
.unwrap()
} else if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
let n = self.value.into_float_value();
ctx.builder
.build_float_compare(FloatPredicate::UNE, n, n.get_type().const_zero(), "bool")
.unwrap()
} else {
unsupported_type(ctx, [self.dtype])
};
ScalarObject { dtype: ctx.primitives.bool, value: result.as_basic_value_enum() }
}
/// Invoke NAC3's builtin `float()`.
#[must_use]
pub fn cast_to_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
let llvm_f64 = ctx.ctx.f64_type();
let result: FloatValue<'_> = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
self.value.into_float_value()
} else if ctx
.unifier
.unioned_any(self.dtype, [signed_ints(ctx).as_slice(), &[ctx.primitives.bool]].concat())
{
let n = self.value.into_int_value();
ctx.builder.build_signed_int_to_float(n, llvm_f64, "sitofp").unwrap()
} else if ctx.unifier.unioned_any(self.dtype, unsigned_ints(ctx)) {
let n = self.value.into_int_value();
ctx.builder.build_unsigned_int_to_float(n, llvm_f64, "uitofp").unwrap()
} else {
unsupported_type(ctx, [self.dtype]);
};
ScalarObject { value: result.as_basic_value_enum(), dtype: ctx.primitives.float }
}
/// Invoke NAC3's builtin `round()`.
#[must_use]
pub fn round<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ret_int_dtype: Type,
) -> Self {
let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
let n = self.value.into_float_value();
let n = llvm_intrinsics::call_float_round(ctx, n, None);
ctx.builder.build_float_to_signed_int(n, ret_int_dtype_llvm, "round").unwrap()
} else {
unsupported_type(ctx, [self.dtype, ret_int_dtype])
};
ScalarObject { dtype: ret_int_dtype, value: result.as_basic_value_enum() }
}
/// Invoke NAC3's builtin `np_round()`.
///
/// NOTE: `np.round()` has different behaviors than `round()` in terms of their result
/// on "tie" cases and return type.
#[must_use]
pub fn np_round(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
let n = self.value.into_float_value();
llvm_intrinsics::call_float_rint(ctx, n, None)
} else {
unsupported_type(ctx, [self.dtype])
};
ScalarObject { dtype: ctx.primitives.float, value: result.as_basic_value_enum() }
}
/// Invoke NAC3's builtin `min()` or `max()`.
pub fn min_or_max(
ctx: &mut CodeGenContext<'ctx, '_>,
kind: MinOrMax,
a: Self,
b: Self,
) -> Self {
if !ctx.unifier.unioned(a.dtype, b.dtype) {
unsupported_type(ctx, [a.dtype, b.dtype])
}
let common_dtype = a.dtype;
if ctx.unifier.unioned(common_dtype, ctx.primitives.float) {
let function = match kind {
MinOrMax::Min => llvm_intrinsics::call_float_minnum,
MinOrMax::Max => llvm_intrinsics::call_float_maxnum,
};
let result =
function(ctx, a.value.into_float_value(), b.value.into_float_value(), None);
ScalarObject { value: result.as_basic_value_enum(), dtype: ctx.primitives.float }
} else if ctx.unifier.unioned_any(
common_dtype,
[unsigned_ints(ctx).as_slice(), &[ctx.primitives.bool]].concat(),
) {
// Treating bool has an unsigned int since that is convenient
let function = match kind {
MinOrMax::Min => llvm_intrinsics::call_int_umin,
MinOrMax::Max => llvm_intrinsics::call_int_umax,
};
let result = function(ctx, a.value.into_int_value(), b.value.into_int_value(), None);
ScalarObject { value: result.as_basic_value_enum(), dtype: common_dtype }
} else {
unsupported_type(ctx, [common_dtype])
}
}
/// Invoke NAC3's builtin `floor()` or `ceil()`.
///
/// * `ret_int_dtype` - The type of int to return.
///
/// Takes in a float/int and returns an int of type `ret_int_dtype`
#[must_use]
pub fn floor_or_ceil<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
kind: FloorOrCeil,
ret_int_dtype: Type,
) -> Self {
let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
let function = match kind {
FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
};
let n = self.value.into_float_value();
let n = function(ctx, n, None);
let n = ctx.builder.build_float_to_signed_int(n, ret_int_dtype_llvm, "").unwrap();
ScalarObject { dtype: ret_int_dtype, value: n.as_basic_value_enum() }
} else {
unsupported_type(ctx, [self.dtype])
}
}
/// Invoke NAC3's builtin `np_floor()`/ `np_ceil()`.
///
/// Takes in a float/int and returns a float64 result.
#[must_use]
pub fn np_floor_or_ceil(&self, ctx: &mut CodeGenContext<'ctx, '_>, kind: FloorOrCeil) -> Self {
if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
let function = match kind {
FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
};
let n = self.value.into_float_value();
let n = function(ctx, n, None);
ScalarObject { dtype: ctx.primitives.float, value: n.as_basic_value_enum() }
} else {
unsupported_type(ctx, [self.dtype])
}
}
/// Invoke NAC3's builtin `abs()`.
pub fn abs(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
let n = self.value.into_float_value();
let n = llvm_intrinsics::call_float_fabs(ctx, n, Some("abs"));
ScalarObject { value: n.into(), dtype: ctx.primitives.float }
} else if ctx.unifier.unioned_any(self.dtype, ints(ctx)) {
let n = self.value.into_int_value();
let is_poisoned = ctx.ctx.bool_type().const_zero(); // is_poisoned = false
let n = llvm_intrinsics::call_int_abs(ctx, n, is_poisoned, Some("abs"));
ScalarObject { value: n.into(), dtype: self.dtype }
} else {
unsupported_type(ctx, [self.dtype])
}
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Helper function to implement NAC3's builtin `np_min()`, `np_max()`, `np_argmin()`, and `np_argmax()`.
///
/// Generate LLVM IR to find the extremum and index of the **first** extremum value.
///
/// Care has also been taken to make the error messages match that of NumPy.
fn min_max_argmin_argmax_helper<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
kind: MinOrMax,
on_empty_err_msg: &str,
) -> (ScalarObject<'ctx>, Int<'ctx, SizeT>) {
let sizet_model = IntModel(SizeT);
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
// If the ndarray is empty, throw an error.
let is_empty = self.is_empty(generator, ctx);
ctx.make_assert(
generator,
is_empty.value,
"0:ValueError",
on_empty_err_msg,
[None, None, None],
ctx.current_loc,
);
// Setup and initialize the extremum to be the first element in the ndarray
let pextremum_index = sizet_model.alloca(generator, ctx, "extremum_index");
let pextremum = ctx.builder.build_alloca(dtype_llvm, "extremum").unwrap();
let zero = sizet_model.const_0(generator, ctx.ctx);
pextremum_index.store(ctx, zero);
let first_scalar = self.get_nth(generator, ctx, zero);
ctx.builder.build_store(pextremum, first_scalar.value).unwrap();
// Find extremum
let start = sizet_model.const_1(generator, ctx.ctx); // Start on 1
let stop = self.size(generator, ctx);
let step = sizet_model.const_1(generator, ctx.ctx);
gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, _hooks, i| {
// Worth reading on "Notes" in <https://numpy.org/doc/stable/reference/generated/numpy.min.html#numpy.min>
// on how `NaN` values have to be handled.
let scalar = self.get_nth(generator, ctx, i);
let old_extremum = ctx.builder.build_load(pextremum, "current_extremum").unwrap();
let old_extremum = ScalarObject { dtype: self.dtype, value: old_extremum };
let new_extremum = ScalarObject::min_or_max(ctx, kind, old_extremum, scalar);
// Check if new_extremum is more extreme than old_extremum.
let update_index = ScalarObject::compare(
generator,
ctx,
new_extremum,
old_extremum,
IntPredicate::NE,
FloatPredicate::ONE,
"",
);
gen_if_model(generator, ctx, update_index, |_generator, ctx| {
pextremum_index.store(ctx, i);
Ok(())
})
.unwrap();
Ok(())
})
.unwrap();
// Finally return the extremum and extremum index.
let extremum_index = pextremum_index.load(generator, ctx, "extremum_index");
let extremum = ctx.builder.build_load(pextremum, "extremum_value").unwrap();
let extremum = ScalarObject { dtype: self.dtype, value: extremum };
(extremum, extremum_index)
}
/// Invoke NAC3's builtin `np_min()` or `np_max()`.
pub fn min_or_max<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
kind: MinOrMax,
) -> ScalarObject<'ctx> {
let on_empty_err_msg = format!(
"zero-size array to reduction operation {} which has no identity",
match kind {
MinOrMax::Min => "minimum",
MinOrMax::Max => "maximum",
}
);
self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).0
}
/// Invoke NAC3's builtin `np_argmin()` or `np_argmax()`.
pub fn argmin_or_argmax<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
kind: MinOrMax,
) -> Int<'ctx, SizeT> {
let on_empty_err_msg = format!(
"attempt to get {} of an empty sequence",
match kind {
MinOrMax::Min => "argmin",
MinOrMax::Max => "argmax",
}
);
self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).1
}
}

353
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use crate::codegen::{irrt::call_nac3_ndarray_index, model::*, CodeGenContext, CodeGenerator};
use super::{scalar::ScalarOrNDArray, NDArrayObject};
pub type NDIndexType = Byte;
/// Fields of [`NDIndex`]
#[derive(Debug, Clone, Copy)]
pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
pub type_: F::Out<IntModel<NDIndexType>>, // Defined to be uint8_t in IRRT
pub data: F::Out<PtrModel<IntModel<Byte>>>,
}
/// An IRRT representation fo an ndarray subscript index.
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
pub struct NDIndex;
impl<'ctx> StructKind<'ctx> for NDIndex {
type Fields<F: FieldTraversal<'ctx>> = NDIndexFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
}
}
/// Fields of [`UserSlice`]
#[derive(Debug, Clone)]
pub struct UserSliceFields<'ctx, F: FieldTraversal<'ctx>> {
pub start_defined: F::Out<IntModel<Bool>>,
pub start: F::Out<IntModel<Int32>>,
pub stop_defined: F::Out<IntModel<Bool>>,
pub stop: F::Out<IntModel<Int32>>,
pub step_defined: F::Out<IntModel<Bool>>,
pub step: F::Out<IntModel<Int32>>,
}
/// An IRRT representation of a user slice.
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
pub struct UserSlice;
impl<'ctx> StructKind<'ctx> for UserSlice {
type Fields<F: FieldTraversal<'ctx>> = UserSliceFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
start_defined: traversal.add_auto("start_defined"),
start: traversal.add_auto("start"),
stop_defined: traversal.add_auto("stop_defined"),
stop: traversal.add_auto("stop"),
step_defined: traversal.add_auto("step_defined"),
step: traversal.add_auto("step"),
}
}
}
/// A convenience structure to prepare a [`UserSlice`].
#[derive(Debug, Clone)]
pub struct RustUserSlice<'ctx> {
pub start: Option<Int<'ctx, Int32>>,
pub stop: Option<Int<'ctx, Int32>>,
pub step: Option<Int<'ctx, Int32>>,
}
impl<'ctx> RustUserSlice<'ctx> {
/// Write the contents to an LLVM [`UserSlice`].
pub fn write_to_user_slice<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
dst_slice_ptr: Ptr<'ctx, StructModel<UserSlice>>,
) {
let bool_model = IntModel(Bool);
let false_ = bool_model.constant(generator, ctx.ctx, 0);
let true_ = bool_model.constant(generator, ctx.ctx, 1);
// TODO: Code duplication. Probably okay...?
match self.start {
Some(start) => {
dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, true_);
dst_slice_ptr.gep(ctx, |f| f.start).store(ctx, start);
}
None => dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, false_),
}
match self.stop {
Some(stop) => {
dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, true_);
dst_slice_ptr.gep(ctx, |f| f.stop).store(ctx, stop);
}
None => dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, false_),
}
match self.step {
Some(step) => {
dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, true_);
dst_slice_ptr.gep(ctx, |f| f.step).store(ctx, step);
}
None => dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, false_),
}
}
}
// A convenience enum variant to store the content and type of an NDIndex in high level.
#[derive(Debug, Clone)]
pub enum RustNDIndex<'ctx> {
SingleElement(Int<'ctx, Int32>), // TODO: To be SizeT
Slice(RustUserSlice<'ctx>),
NewAxis,
Ellipsis,
}
impl<'ctx> RustNDIndex<'ctx> {
/// Get the value to set `NDIndex::type` for this variant.
fn get_type_id(&self) -> u64 {
// Defined in IRRT, must be in sync
match self {
RustNDIndex::SingleElement(_) => 0,
RustNDIndex::Slice(_) => 1,
RustNDIndex::NewAxis => 2,
RustNDIndex::Ellipsis => 3,
}
}
/// Write the contents to an LLVM [`NDIndex`].
fn write_to_ndindex<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
dst_ndindex_ptr: Ptr<'ctx, StructModel<NDIndex>>,
) {
let ndindex_type_model = IntModel(NDIndexType::default());
let i32_model = IntModel(Int32);
let user_slice_model = StructModel(UserSlice);
// Set `dst_ndindex_ptr->type`
dst_ndindex_ptr
.gep(ctx, |f| f.type_)
.store(ctx, ndindex_type_model.constant(generator, ctx.ctx, self.get_type_id()));
// Set `dst_ndindex_ptr->data`
match self {
RustNDIndex::SingleElement(in_index) => {
let index_ptr = i32_model.alloca(generator, ctx, "index");
index_ptr.store(ctx, *in_index);
dst_ndindex_ptr
.gep(ctx, |f| f.data)
.store(ctx, index_ptr.transmute(generator, ctx, IntModel(Byte), ""));
}
RustNDIndex::Slice(in_rust_slice) => {
let user_slice_ptr = user_slice_model.alloca(generator, ctx, "user_slice");
in_rust_slice.write_to_user_slice(generator, ctx, user_slice_ptr);
dst_ndindex_ptr
.gep(ctx, |f| f.data)
.store(ctx, user_slice_ptr.transmute(generator, ctx, IntModel(Byte), ""));
}
RustNDIndex::NewAxis => {}
RustNDIndex::Ellipsis => {}
}
}
/// Allocate an array of `NDIndex`es on the stack and return its stack pointer.
pub fn alloca_ndindexes<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
in_ndindexes: &[RustNDIndex<'ctx>],
) -> (Int<'ctx, SizeT>, Ptr<'ctx, StructModel<NDIndex>>) {
let sizet_model = IntModel(SizeT);
let ndindex_model = StructModel(NDIndex);
let num_ndindexes = sizet_model.constant(generator, ctx.ctx, in_ndindexes.len() as u64);
let ndindexes =
ndindex_model.array_alloca(generator, ctx, num_ndindexes.value, "ndindexes");
for (i, in_ndindex) in in_ndindexes.iter().enumerate() {
let i = sizet_model.constant(generator, ctx.ctx, i as u64);
let pndindex = ndindexes.offset(generator, ctx, i.value, "");
in_ndindex.write_to_ndindex(generator, ctx, pndindex);
}
(num_ndindexes, ndindexes)
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Get the ndims [`Type`] after indexing with a given slice.
#[must_use]
pub fn deduce_ndims_after_indexing_with(&self, indexes: &[RustNDIndex<'ctx>]) -> u64 {
let mut ndims = self.ndims;
for index in indexes {
match index {
RustNDIndex::SingleElement(_) => {
ndims -= 1; // Single elements decrements ndims
}
RustNDIndex::Slice(_) => {}
RustNDIndex::NewAxis => {
ndims += 1; // `np.newaxis` / `none` adds a new axis
}
RustNDIndex::Ellipsis => {}
}
}
ndims
}
/// Index into the ndarray, and return a newly-allocated view on this ndarray.
///
/// This function behaves like NumPy's ndarray indexing, but if the indexes index
/// into a single element, an unsized ndarray is returned.
#[must_use]
pub fn index<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indexes: &[RustNDIndex<'ctx>],
name: &str,
) -> Self {
let dst_ndims = self.deduce_ndims_after_indexing_with(indexes);
let dst_ndarray =
NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, dst_ndims, name);
let (num_indexes, indexes) = RustNDIndex::alloca_ndindexes(generator, ctx, indexes);
call_nac3_ndarray_index(
generator,
ctx,
num_indexes,
indexes,
self.value,
dst_ndarray.value,
);
dst_ndarray
}
/// Like [`NDArrayObject::index`] but returns a scalar if the indexes index
/// into a single element.
#[must_use]
pub fn index_or_scalar<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indexes: &[RustNDIndex<'ctx>],
name: &str,
) -> ScalarOrNDArray<'ctx> {
let sizet_model = IntModel(SizeT);
let zero = sizet_model.const_0(generator, ctx.ctx);
let subndarray = self.index(generator, ctx, indexes, name);
if subndarray.is_unsized() {
// NOTE: `np.size(self) == 0` here is never possible.
ScalarOrNDArray::Scalar(subndarray.get_nth(generator, ctx, zero))
} else {
ScalarOrNDArray::NDArray(subndarray)
}
}
}
pub mod util {
use itertools::Itertools;
use nac3parser::ast::{Expr, ExprKind};
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
use super::{RustNDIndex, RustUserSlice};
/// Generate LLVM code to transform an ndarray subscript expression to
/// its list of [`RustNDIndex`]
///
/// i.e.,
/// ```python
/// my_ndarray[::3, 1, :2:]
/// ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
/// ```
pub fn gen_ndarray_subscript_ndindexes<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
subscript: &Expr<Option<Type>>,
) -> Result<Vec<RustNDIndex<'ctx>>, String> {
// TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
let i32_model = IntModel(Int32);
// Annoying notes about `slice`
// - `my_array[5]`
// - slice is a `Constant`
// - `my_array[:5]`
// - slice is a `Slice`
// - `my_array[:]`
// - slice is a `Slice`, but lower upper step would all be `Option::None`
// - `my_array[:, :]`
// - slice is now a `Tuple` of two `Slice`-s
//
// In summary:
// - when there is a comma "," within [], `slice` will be a `Tuple` of the entries.
// - when there is not comma "," within [] (i.e., just a single entry), `slice` will be that entry itself.
//
// So we first "flatten" out the slice expression
let index_exprs = match &subscript.node {
ExprKind::Tuple { elts, .. } => elts.iter().collect_vec(),
_ => vec![subscript],
};
// Process all index expressions
let mut rust_ndindexes: Vec<RustNDIndex> = Vec::with_capacity(index_exprs.len()); // Not using iterators here because `?` is used here.
for index_expr in index_exprs {
// NOTE: Currently nac3core's slices do not have an object representation,
// so the code/implementation looks awkward - we have to do pattern matching on the expression
let ndindex =
if let ExprKind::Slice { lower: start, upper: stop, step } = &index_expr.node {
// Helper function here to deduce code duplication
type ValueExpr = Option<Box<Expr<Option<Type>>>>;
let mut help = |value_expr: &ValueExpr| -> Result<_, String> {
Ok(match value_expr {
None => None,
Some(value_expr) => {
let value_expr = generator
.gen_expr(ctx, value_expr)?
.unwrap()
.to_basic_value_enum(ctx, generator, ctx.primitives.int32)?;
let value_expr =
i32_model.check_value(generator, ctx.ctx, value_expr).unwrap();
Some(value_expr)
}
})
};
let start = help(start)?;
let stop = help(stop)?;
let step = help(step)?;
RustNDIndex::Slice(RustUserSlice { start, stop, step })
} else {
// Anything else that is not a slice (might be illegal values),
// For nac3core, this should be e.g., an int32 constant, an int32 variable, otherwise its an error
let index = generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
ctx,
generator,
ctx.primitives.int32,
)?;
let index = i32_model.check_value(generator, ctx.ctx, index).unwrap();
RustNDIndex::SingleElement(index)
};
rust_ndindexes.push(ndindex);
}
Ok(rust_ndindexes)
}
}

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nac3core/src/codegen/structure/ndarray/mapping.rs Normal file
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use inkwell::values::BasicValueEnum;
use itertools::Itertools;
use util::gen_for_model_auto;
use crate::{
codegen::{
model::*,
structure::ndarray::{scalar::ScalarObject, NDArrayObject},
CodeGenContext, CodeGenerator,
},
typecheck::typedef::Type,
};
use super::scalar::ScalarOrNDArray;
impl<'ctx> NDArrayObject<'ctx> {
/// TODO: Document me. Has complex behavior.
/// and explain why `ret_dtype` has to be specified beforehand.
pub fn broadcasting_starmap<'a, G, MappingFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
ndarrays: &[Self],
ret_dtype: Type,
mapping: MappingFn,
) -> Result<Self, String>
where
G: CodeGenerator + ?Sized,
MappingFn: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
Int<'ctx, SizeT>,
&[ScalarObject<'ctx>],
) -> Result<BasicValueEnum<'ctx>, String>,
{
let sizet_model = IntModel(SizeT);
// Broadcast inputs
let broadcast_result = NDArrayObject::broadcast_all(generator, ctx, ndarrays);
// Allocate the resulting ndarray
let mapped_ndarray = NDArrayObject::alloca_uninitialized(
generator,
ctx,
ret_dtype,
broadcast_result.ndims,
"mapped_ndarray",
);
mapped_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
mapped_ndarray.create_data(generator, ctx);
// Map element-wise and store results into `mapped_ndarray`.
let start = sizet_model.const_0(generator, ctx.ctx);
let stop = broadcast_result.ndarrays[0].size(generator, ctx); // They all should have the same `np.size`.
let step = sizet_model.const_1(generator, ctx.ctx);
gen_for_model_auto(generator, ctx, start, stop, step, move |generator, ctx, _hooks, i| {
let elements =
ndarrays.iter().map(|ndarray| ndarray.get_nth(generator, ctx, i)).collect_vec();
let ret = mapping(generator, ctx, i, &elements)?;
let pret = mapped_ndarray.get_nth_pointer(generator, ctx, i, "pret");
ctx.builder.build_store(pret, ret).unwrap();
Ok(())
})?;
Ok(mapped_ndarray)
}
pub fn map<'a, G, Mapping>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
ret_dtype: Type,
mapping: Mapping,
) -> Result<Self, String>
where
G: CodeGenerator + ?Sized,
Mapping: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
Int<'ctx, SizeT>,
ScalarObject<'ctx>,
) -> Result<BasicValueEnum<'ctx>, String>,
{
NDArrayObject::broadcasting_starmap(
generator,
ctx,
&[*self],
ret_dtype,
|generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
)
}
}
impl<'ctx> ScalarOrNDArray<'ctx> {
/// TODO: Document me. Has complex behavior.
/// and explain why `ret_dtype` has to be specified beforehand.
pub fn broadcasting_starmap<'a, G, MappingFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
inputs: &[Self],
ret_dtype: Type,
mapping: MappingFn,
) -> Result<Self, String>
where
G: CodeGenerator + ?Sized,
MappingFn: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
Int<'ctx, SizeT>,
&[ScalarObject<'ctx>],
) -> Result<BasicValueEnum<'ctx>, String>,
{
let sizet_model = IntModel(SizeT);
// Check if all inputs are ScalarObjects
let all_scalars: Option<Vec<_>> =
inputs.iter().map(ScalarObject::try_from).try_collect().ok();
if let Some(scalars) = all_scalars {
let i = sizet_model.const_0(generator, ctx.ctx); // Pass 0 as the index
let scalar =
ScalarObject { value: mapping(generator, ctx, i, &scalars)?, dtype: ret_dtype };
Ok(ScalarOrNDArray::Scalar(scalar))
} else {
// Promote all input to ndarrays and map through them.
let inputs = inputs.iter().map(|input| input.as_ndarray(generator, ctx)).collect_vec();
let ndarray =
NDArrayObject::broadcasting_starmap(generator, ctx, &inputs, ret_dtype, mapping)?;
Ok(ScalarOrNDArray::NDArray(ndarray))
}
}
pub fn map<'a, G, Mapping>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
ret_dtype: Type,
mapping: Mapping,
) -> Result<Self, String>
where
G: CodeGenerator + ?Sized,
Mapping: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
Int<'ctx, SizeT>,
ScalarObject<'ctx>,
) -> Result<BasicValueEnum<'ctx>, String>,
{
ScalarOrNDArray::broadcasting_starmap(
generator,
ctx,
&[*self],
ret_dtype,
|generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
)
}
}

543
nac3core/src/codegen/structure/ndarray/mod.rs Normal file
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@ -0,0 +1,543 @@
pub mod broadcast;
pub mod functions;
pub mod indexing;
pub mod mapping;
pub mod product;
pub mod scalar;
pub mod shape_util;
use crate::{
codegen::{
irrt::{
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
call_nac3_ndarray_resolve_and_check_new_shape, call_nac3_ndarray_set_strides_by_shape,
call_nac3_ndarray_size, call_nac3_ndarray_transpose,
},
model::*,
stmt::BreakContinueHooks,
CodeGenContext, CodeGenerator,
},
toplevel::numpy::{extract_ndims, unpack_ndarray_var_tys},
typecheck::typedef::Type,
};
use inkwell::{
context::Context,
types::BasicType,
values::{BasicValue, BasicValueEnum, PointerValue},
AddressSpace, IntPredicate,
};
use scalar::ScalarObject;
use util::{call_memcpy_model, gen_for_model_auto};
pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
pub data: F::Out<PtrModel<IntModel<Byte>>>,
pub itemsize: F::Out<IntModel<SizeT>>,
pub ndims: F::Out<IntModel<SizeT>>,
pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
}
// TODO: Rename to `NDArray` when the old NDArray is removed.
#[derive(Debug, Clone, Copy, Default)]
pub struct NpArray;
impl<'ctx> StructKind<'ctx> for NpArray {
type Fields<F: FieldTraversal<'ctx>> = NpArrayFields<'ctx, F>;
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
data: traversal.add_auto("data"),
itemsize: traversal.add_auto("itemsize"),
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
strides: traversal.add_auto("strides"),
}
}
}
/// A NAC3 Python ndarray object.
#[derive(Debug, Clone, Copy)]
pub struct NDArrayObject<'ctx> {
pub dtype: Type,
pub ndims: u64,
pub value: Ptr<'ctx, StructModel<NpArray>>,
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create an [`NDArrayObject`] from an LLVM value and its typechecker [`Type`].
pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
value: V,
ty: Type,
) -> Self {
let pndarray_model = PtrModel(StructModel(NpArray));
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
let ndims = extract_ndims(&ctx.unifier, ndims);
let value = pndarray_model.check_value(generator, ctx.ctx, value).unwrap();
NDArrayObject { dtype, ndims, value }
}
/// Get the `np.size()` of this ndarray.
pub fn size<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Int<'ctx, SizeT> {
call_nac3_ndarray_size(generator, ctx, self.value)
}
/// Get the `ndarray.nbytes` of this ndarray.
pub fn nbytes<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Int<'ctx, SizeT> {
call_nac3_ndarray_nbytes(generator, ctx, self.value)
}
/// Get the `len()` of this ndarray.
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Int<'ctx, SizeT> {
call_nac3_ndarray_len(generator, ctx, self.value)
}
/// Check if this ndarray is C-contiguous.
///
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Int<'ctx, Bool> {
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.value)
}
/// Get the pointer to the n-th (0-based) element.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_nth_pointer<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Int<'ctx, SizeT>,
name: &str,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.value, nth);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
.unwrap()
}
/// Get the n-th (0-based) scalar.
pub fn get_nth<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Int<'ctx, SizeT>,
) -> ScalarObject<'ctx> {
let p = self.get_nth_pointer(generator, ctx, nth, "value");
let value = ctx.builder.build_load(p, "value").unwrap();
ScalarObject { dtype: self.dtype, value }
}
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
///
/// Please refer to the IRRT implementation to see its purpose.
pub fn update_strides_by_shape<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.value);
}
/// Copy data from another ndarray.
///
/// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
/// do not matter. The copying order is determined by how their flattened views look.
///
/// Panics if the `dtype`s of ndarrays are different.
pub fn copy_data_from<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src: NDArrayObject<'ctx>,
) {
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
call_nac3_ndarray_copy_data(generator, ctx, src.value, self.value);
}
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
///
/// `shape` and `strides` will be automatically allocated on the stack.
///
/// The returned ndarray's content will be:
/// - `data`: set to `nullptr`.
/// - `itemsize`: set to the `sizeof()` of `dtype`.
/// - `ndims`: set to the value of `ndims`.
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
pub fn alloca_uninitialized<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
name: &str,
) -> Self {
let sizet_model = IntModel(SizeT);
let ndarray_model = StructModel(NpArray);
let ndarray_data_model = PtrModel(IntModel(Byte));
let pndarray = ndarray_model.alloca(generator, ctx, name);
let data = ndarray_data_model.nullptr(generator, ctx.ctx);
pndarray.set(ctx, |f| f.data, data);
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
let itemsize =
sizet_model.s_extend_or_bit_cast(generator, ctx, itemsize, "alloca_itemsize");
pndarray.set(ctx, |f| f.itemsize, itemsize);
let ndims_val = sizet_model.constant(generator, ctx.ctx, ndims);
pndarray.set(ctx, |f| f.ndims, ndims_val);
let shape = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_shape");
pndarray.set(ctx, |f| f.shape, shape);
let strides = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_strides");
pndarray.set(ctx, |f| f.strides, strides);
NDArrayObject { dtype, ndims, value: pndarray }
}
/// Convenience function.
/// Like [`NDArrayObject::alloca_uninitialized`] but directly takes the typechecker type of the ndarray.
pub fn alloca_uninitialized_of_type<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ty: Type,
name: &str,
) -> Self {
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
let ndims = extract_ndims(&ctx.unifier, ndims);
Self::alloca_uninitialized(generator, ctx, dtype, ndims, name)
}
/// Clone this ndaarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents
/// over.
///
/// The new ndarray will own its data and will be C-contiguous.
pub fn make_clone<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
name: &str,
) -> Self {
let clone =
NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, self.ndims, name);
let shape = self.value.gep(ctx, |f| f.shape).load(generator, ctx, "shape");
clone.copy_shape_from_array(generator, ctx, shape);
clone.create_data(generator, ctx);
clone.copy_data_from(generator, ctx, *self);
clone
}
/// Get this ndarray's `ndims` as an LLVM constant.
pub fn get_ndims<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Int<'ctx, SizeT> {
let sizet_model = IntModel(SizeT);
sizet_model.constant(generator, ctx, self.ndims)
}
/// Get if this ndarray's `np.size` is `0` - containing no content.
pub fn is_empty<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Int<'ctx, Bool> {
let sizet_model = IntModel(SizeT);
let size = self.size(generator, ctx);
size.compare(ctx, IntPredicate::EQ, sizet_model.const_0(generator, ctx.ctx), "is_empty")
}
/// Return true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
///
/// This is a staticially known property of ndarrays. This is why it is returning
/// a Rust boolean instead of a [`BasicValue`].
#[must_use]
pub fn is_unsized(&self) -> bool {
self.ndims == 0
}
/// 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 create_data<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
let byte_model = IntModel(Byte);
let nbytes = self.nbytes(generator, ctx);
let data = byte_model.array_alloca(generator, ctx, nbytes.value, "data");
self.value.set(ctx, |f| f.data, data);
self.update_strides_by_shape(generator, ctx);
}
/// Copy shape dimensions from an array.
pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_shape: Ptr<'ctx, IntModel<SizeT>>,
) {
let dst_shape = self.value.get(generator, ctx, |f| f.shape, "dst_shape");
let num_items = self.get_ndims(generator, ctx.ctx).value;
call_memcpy_model(generator, ctx, dst_shape, src_shape, num_items);
}
/// Copy shape dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_shape = src_ndarray.value.get(generator, ctx, |f| f.shape, "src_shape");
self.copy_shape_from_array(generator, ctx, src_shape);
}
/// Copy strides dimensions from an array.
pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_strides: Ptr<'ctx, IntModel<SizeT>>,
) {
let dst_strides = self.value.get(generator, ctx, |f| f.strides, "dst_strides");
let num_items = self.get_ndims(generator, ctx.ctx).value;
call_memcpy_model(generator, ctx, dst_strides, src_strides, num_items);
}
/// Copy strides dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_strides = src_ndarray.value.get(generator, ctx, |f| f.strides, "src_strides");
self.copy_strides_from_array(generator, ctx, src_strides);
}
/// Iterate through every element pointer in the ndarray in its flatten view.
///
/// `body` also access to [`BreakContinueHooks`] to short-circuit and an element's
/// index. The given element pointer also has been casted to the LLVM type of this ndarray's `dtype`.
pub fn foreach_pointer<'a, G, F>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
body: F,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
F: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BreakContinueHooks<'ctx>,
Int<'ctx, SizeT>,
PointerValue<'ctx>,
) -> Result<(), String>,
{
let sizet_model = IntModel(SizeT);
let start = sizet_model.const_0(generator, ctx.ctx);
let stop = self.size(generator, ctx);
let step = sizet_model.const_1(generator, ctx.ctx);
gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, hooks, i| {
let pelement = self.get_nth_pointer(generator, ctx, i, "element");
body(generator, ctx, hooks, i, pelement)
})
}
/// Iterate through every scalar in this ndarray.
pub fn foreach<'a, G, F>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, 'a>,
body: F,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
F: FnOnce(
&mut G,
&mut CodeGenContext<'ctx, 'a>,
BreakContinueHooks<'ctx>,
Int<'ctx, SizeT>,
ScalarObject<'ctx>,
) -> Result<(), String>,
{
self.foreach_pointer(generator, ctx, |generator, ctx, hooks, i, p| {
let value = ctx.builder.build_load(p, "value").unwrap();
let scalar = ScalarObject { dtype: self.dtype, value };
body(generator, ctx, hooks, i, scalar)
})
}
/// Fill the ndarray with a value.
///
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
pub fn fill<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
fill_value: BasicValueEnum<'ctx>,
) {
self.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, _i, pelement| {
ctx.builder.build_store(pelement, fill_value).unwrap();
Ok(())
})
.unwrap();
}
/// Create a reshaped view on this ndarray like `np.reshape()`.
///
/// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
///
/// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
///
/// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
/// * `new_shape` - The target shape to do `np.reshape()`.
#[must_use]
pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
new_ndims: u64,
new_shape: Ptr<'ctx, IntModel<SizeT>>,
) -> Self {
// TODO: The current criterion for whether to do a full copy or not is by checking `is_c_contiguous`,
// but this is not optimal. Look into how numpy does it.
let current_bb = ctx.builder.get_insert_block().unwrap();
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
let dst_ndarray = NDArrayObject::alloca_uninitialized(
generator,
ctx,
self.dtype,
new_ndims,
"reshaped_ndarray",
);
dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
let size = self.size(generator, ctx);
let new_ndims = dst_ndarray.get_ndims(generator, ctx.ctx);
call_nac3_ndarray_resolve_and_check_new_shape(generator, ctx, size, new_ndims, new_shape);
let is_c_contiguous = self.is_c_contiguous(generator, ctx);
ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
// Inserting into then_bb: reshape is possible without copying
ctx.builder.position_at_end(then_bb);
dst_ndarray.update_strides_by_shape(generator, ctx);
dst_ndarray.value.set(ctx, |f| f.data, self.value.get(generator, ctx, |f| f.data, "data"));
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Inserting into else_bb: reshape is impossible without copying
ctx.builder.position_at_end(else_bb);
dst_ndarray.create_data(generator, ctx);
dst_ndarray.copy_data_from(generator, ctx, *self);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Reposition for continuation
ctx.builder.position_at_end(end_bb);
dst_ndarray
}
/// Create a flattened view of this ndarray, like `np.ravel()`.
///
/// Uses [`NDArrayObject::reshape_or_copy`] under-the-hood so ndarray may or may not be copied.
pub fn ravel_or_copy<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Self {
// Define models
let sizet_model = IntModel(SizeT);
let num0 = sizet_model.const_0(generator, ctx.ctx);
let num1 = sizet_model.const_1(generator, ctx.ctx);
let num_neg1 = sizet_model.const_all_1s(generator, ctx.ctx);
// Create `[-1]` and pass to `reshape_or_copy`.
let new_shape = sizet_model.array_alloca(generator, ctx, num1.value, "new_shape");
new_shape.offset(generator, ctx, num0.value, "").store(ctx, num_neg1);
self.reshape_or_copy(generator, ctx, 1, new_shape)
}
/// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
/// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
pub fn transpose<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
axes: Option<Ptr<'ctx, IntModel<SizeT>>>,
) -> Self {
// Define models
let sizet_model = IntModel(SizeT);
let transposed_ndarray = NDArrayObject::alloca_uninitialized(
generator,
ctx,
self.dtype,
self.ndims,
"transposed_ndarray",
);
let num_axes = self.get_ndims(generator, ctx.ctx);
// `axes = nullptr` if `axes` is unspecified.
let axes = axes.unwrap_or_else(|| PtrModel(sizet_model).nullptr(generator, ctx.ctx));
call_nac3_ndarray_transpose(
generator,
ctx,
self.value,
transposed_ndarray.value,
num_axes,
axes,
);
transposed_ndarray
}
}

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use crate::codegen::{CodeGenContext, CodeGenerator};
use super::NDArrayObject;
impl<'ctx> NDArrayObject<'ctx> {
/// TODO: Document me
pub fn matmul_at_least_2d<G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &mut CodeGenContext<'ctx, '_>,
a: Self,
b: Self,
) -> Self {
assert!(a.ndims >= 2);
assert!(b.ndims >= 2);
todo!()
}
}

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use inkwell::values::{BasicValue, BasicValueEnum};
use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::{Type, TypeEnum},
};
use super::NDArrayObject;
/// An LLVM numpy scalar with its [`Type`].
///
/// Intended to be used with [`ScalarOrNDArray`].
///
/// A scalar does not have to be an actual number. It could be arbitrary objects.
#[derive(Debug, Clone, Copy)]
pub struct ScalarObject<'ctx> {
pub dtype: Type,
pub value: BasicValueEnum<'ctx>,
}
impl<'ctx> ScalarObject<'ctx> {
/// Promote this scalar to an unsized ndarray (like doing `np.asarray`).
///
/// The scalar value is allocated onto the stack, and the ndarray's `data` will point to that
/// allocated value.
pub fn as_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> NDArrayObject<'ctx> {
let pbyte_model = PtrModel(IntModel(Byte));
// We have to put the value on the stack to get a data pointer.
let data = ctx.builder.build_alloca(self.value.get_type(), "as_ndarray_scalar").unwrap();
ctx.builder.build_store(data, self.value).unwrap();
let data = pbyte_model.pointer_cast(generator, ctx, data, "data");
let ndarray =
NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, 0, "scalar_ndarray");
ndarray.value.set(ctx, |f| f.data, data);
ndarray
}
}
/// A convenience enum for implementing scalar/ndarray agnostic utilities.
#[derive(Debug, Clone, Copy)]
pub enum ScalarOrNDArray<'ctx> {
Scalar(ScalarObject<'ctx>),
NDArray(NDArrayObject<'ctx>),
}
impl<'ctx> ScalarOrNDArray<'ctx> {
/// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
#[must_use]
pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
match self {
ScalarOrNDArray::Scalar(scalar) => scalar.value,
ScalarOrNDArray::NDArray(ndarray) => ndarray.value.value.as_basic_value_enum(),
}
}
#[must_use]
pub fn into_scalar(&self) -> ScalarObject<'ctx> {
match self {
ScalarOrNDArray::NDArray(_ndarray) => panic!("Got NDArray"),
ScalarOrNDArray::Scalar(scalar) => *scalar,
}
}
#[must_use]
pub fn into_ndarray(&self) -> NDArrayObject<'ctx> {
match self {
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
ScalarOrNDArray::Scalar(_scalar) => panic!("Got Scalar"),
}
}
/// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
/// - If this is an ndarray, the ndarray is returned.
/// - If this is a scalar, an unsized ndarray view is created on it.
pub fn as_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> NDArrayObject<'ctx> {
match self {
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
ScalarOrNDArray::Scalar(scalar) => scalar.as_ndarray(generator, ctx),
}
}
#[must_use]
pub fn dtype(&self) -> Type {
match self {
ScalarOrNDArray::Scalar(scalar) => scalar.dtype,
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
}
}
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for ScalarObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
ScalarOrNDArray::NDArray(_ndarray) => Err(()),
}
}
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(_scalar) => Err(()),
ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
}
}
}
/// Split an [`BasicValueEnum<'ctx>`] into a [`ScalarOrNDArray`] depending
/// on its [`Type`].
pub fn split_scalar_or_ndarray<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
input: BasicValueEnum<'ctx>,
input_ty: Type,
) -> ScalarOrNDArray<'ctx> {
match &*ctx.unifier.get_ty(input_ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
let ndarray = NDArrayObject::from_value_and_type(generator, ctx, input, input_ty);
ScalarOrNDArray::NDArray(ndarray)
}
_ => {
let scalar = ScalarObject { dtype: input_ty, value: input };
ScalarOrNDArray::Scalar(scalar)
}
}
}

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use inkwell::values::BasicValueEnum;
use util::gen_for_model_auto;
use crate::{
codegen::{model::*, structure::list::ListObject, CodeGenContext, CodeGenerator},
typecheck::typedef::{Type, TypeEnum},
};
/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
///
/// * `sequence` - The `sequence` parameter.
/// * `sequence_ty` - The typechecker type of `sequence`
///
/// The `sequence` argument type may only be one of the following:
/// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
/// 2. A tuple of `int32`; e.g., `np.empty((600, 800, 3))`
/// 3. A scalar `int32`; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
///
/// All `int32` values will be sign-extended to `SizeT`.
pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
input_sequence: BasicValueEnum<'ctx>,
input_sequence_ty: Type,
) -> (Int<'ctx, SizeT>, Ptr<'ctx, IntModel<SizeT>>) {
let sizet_model = IntModel(SizeT);
let zero = sizet_model.const_0(generator, ctx.ctx);
let one = sizet_model.const_1(generator, ctx.ctx);
// The result `list` to return.
match &*ctx.unifier.get_ty(input_sequence_ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
{
// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
// Check `input_sequence`
let input_sequence =
ListObject::from_value_and_type(generator, ctx, input_sequence, input_sequence_ty);
let len = input_sequence.value.gep(ctx, |f| f.len).load(generator, ctx, "len");
let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
// Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
gen_for_model_auto(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
// Load the i-th int32 in the input sequence
let int = input_sequence
.value
.get(generator, ctx, |f| f.items, "int")
.ix(generator, ctx, i.value, "int")
.value
.into_int_value();
// Cast to SizeT
let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
// Store
result.offset(generator, ctx, i.value, "int").store(ctx, int);
Ok(())
})
.unwrap();
(len, result)
}
TypeEnum::TTuple { ty: tuple_types, .. } => {
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
let input_sequence = input_sequence.into_struct_value(); // A tuple is a struct
let len_int = tuple_types.len();
let len = sizet_model.constant(generator, ctx.ctx, len_int as u64);
let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
for i in 0..len_int {
// Get the i-th element off of the tuple and load it into `result`.
let int = ctx
.builder
.build_extract_value(input_sequence, i as u32, "int")
.unwrap()
.into_int_value();
let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
let offset = sizet_model.constant(generator, ctx.ctx, i as u64);
result.offset(generator, ctx, offset.value, "int").store(ctx, int);
}
(len, result)
}
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
{
// 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
let input_int = input_sequence.into_int_value();
let len = sizet_model.const_1(generator, ctx.ctx);
let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
let int = sizet_model.s_extend_or_bit_cast(generator, ctx, input_int, "int");
// Storing into result[0]
result.store(ctx, int);
(len, result)
}
_ => panic!(
"encountered unknown sequence type: {}",
ctx.unifier.stringify(input_sequence_ty)
),
}
}

39
nac3core/src/codegen/structure/tuple.rs Normal file
View File

@ -0,0 +1,39 @@
use inkwell::values::{BasicValueEnum, StructValue};
use itertools::Itertools;
use crate::{
codegen::{CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
pub struct TupleObject<'ctx> {
pub tys: Vec<Type>,
pub value: StructValue<'ctx>,
}
impl<'ctx> TupleObject<'ctx> {
pub fn create<I, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
items: I,
name: &str,
) -> Self
where
I: IntoIterator<Item = (BasicValueEnum<'ctx>, Type)>,
{
let (vals, tys): (Vec<_>, Vec<_>) = items.into_iter().unzip();
// let tuple_ty = ctx.unifier.add_ty(TypeEnum::TTuple { ty: tys });
let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
for (i, val) in vals.into_iter().enumerate() {
// Store the dim value into the tuple
let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
ctx.builder.build_store(pval, val).unwrap();
}
let value = ctx.builder.build_load(pllvm_tuple, name).unwrap().into_struct_value();
TupleObject { tys, value }
}
}

653
nac3core/src/toplevel/builtins.rs
View File

@ -13,15 +13,27 @@ use strum::IntoEnumIterator;
use crate::{
codegen::{
builtin_fns,
classes::{ArrayLikeValue, NDArrayValue, ProxyValue, RangeValue, TypedArrayLikeAccessor},
builtin_fns::{self},
classes::{ProxyValue, RangeValue},
expr::destructure_range,
irrt::*,
extern_fns,
irrt::{self, *},
llvm_intrinsics,
model::Int32,
numpy::*,
numpy_new::{self, gen_ndarray_transpose},
stmt::exn_constructor,
structure::ndarray::{
functions::{FloorOrCeil, MinOrMax},
scalar::{split_scalar_or_ndarray, ScalarObject, ScalarOrNDArray},
NDArrayObject,
},
},
symbol_resolver::SymbolValue,
toplevel::{helper::PrimDef, numpy::make_ndarray_ty},
toplevel::{
helper::PrimDef,
numpy::{create_ndims, make_ndarray_ty},
},
typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
};
@ -511,7 +523,16 @@ impl<'a> BuiltinBuilder<'a> {
PrimDef::FunNpArray
| PrimDef::FunNpFull
| PrimDef::FunNpEye
| PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
| PrimDef::FunNpIdentity
| PrimDef::FunNpArange => self.build_ndarray_other_factory_function(prim),
PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape | PrimDef::FunNpTranspose => {
self.build_ndarray_view_function(prim)
}
PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
self.build_ndarray_property_getter_function(prim)
}
PrimDef::FunStr => self.build_str_function(),
@ -578,10 +599,6 @@ impl<'a> BuiltinBuilder<'a> {
| PrimDef::FunNpHypot
| PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
self.build_np_sp_ndarray_function(prim)
}
PrimDef::FunNpDot
| PrimDef::FunNpLinalgCholesky
| PrimDef::FunNpLinalgQr
@ -1072,16 +1089,34 @@ 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 func = match prim {
PrimDef::FunInt32 => builtin_fns::call_int32,
PrimDef::FunInt64 => builtin_fns::call_int64,
PrimDef::FunUInt32 => builtin_fns::call_uint32,
PrimDef::FunUInt64 => builtin_fns::call_uint64,
PrimDef::FunFloat => builtin_fns::call_float,
PrimDef::FunBool => builtin_fns::call_bool,
let ret_dtype = match prim {
PrimDef::FunInt32 => ctx.primitives.int32,
PrimDef::FunInt64 => ctx.primitives.int64,
PrimDef::FunUInt32 => ctx.primitives.uint32,
PrimDef::FunUInt64 => ctx.primitives.uint64,
PrimDef::FunFloat => ctx.primitives.float,
PrimDef::FunBool => ctx.primitives.bool,
_ => unreachable!(),
};
Ok(Some(func(generator, ctx, (arg_ty, arg))?))
let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
generator,
ctx,
ret_dtype,
|generator, ctx, _i, scalar| {
let result = match prim {
PrimDef::FunInt32 => scalar.cast_to_int32(generator, ctx),
PrimDef::FunInt64 => scalar.cast_to_int64(generator, ctx),
PrimDef::FunUInt32 => scalar.cast_to_uint32(generator, ctx),
PrimDef::FunUInt64 => scalar.cast_to_uint64(generator, ctx),
PrimDef::FunFloat => scalar.cast_to_float(ctx),
PrimDef::FunBool => scalar.cast_to_bool(ctx),
_ => unreachable!(),
};
Ok(result.value)
},
)?;
Ok(Some(result.to_basic_value_enum()))
},
)))),
loc: None,
@ -1132,20 +1167,23 @@ 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);
Ok(Some(builtin_fns::call_round(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
let ret_int_dtype = size_variant.of_int(&ctx.primitives);
let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
generator,
ctx,
ret_int_dtype,
|generator, ctx, _i, scalar| {
Ok(scalar.round(generator, ctx, ret_int_dtype).value)
},
)?;
Ok(Some(result.to_basic_value_enum()))
}),
)
}
/// Build the functions `ceil()` and `floor()` and their 64 bit variants.
fn build_ceil_floor_function(&mut self, prim: PrimDef) -> TopLevelDef {
#[derive(Clone, Copy)]
enum Kind {
Floor,
Ceil,
}
debug_assert_prim_is_allowed(
prim,
&[PrimDef::FunFloor, PrimDef::FunFloor64, PrimDef::FunCeil, PrimDef::FunCeil64],
@ -1153,10 +1191,10 @@ impl<'a> BuiltinBuilder<'a> {
let (size_variant, kind) = {
match prim {
PrimDef::FunFloor => (SizeVariant::Bits32, Kind::Floor),
PrimDef::FunFloor64 => (SizeVariant::Bits64, Kind::Floor),
PrimDef::FunCeil => (SizeVariant::Bits32, Kind::Ceil),
PrimDef::FunCeil64 => (SizeVariant::Bits64, Kind::Ceil),
PrimDef::FunFloor => (SizeVariant::Bits32, FloorOrCeil::Floor),
PrimDef::FunFloor64 => (SizeVariant::Bits64, FloorOrCeil::Floor),
PrimDef::FunCeil => (SizeVariant::Bits32, FloorOrCeil::Ceil),
PrimDef::FunCeil64 => (SizeVariant::Bits64, FloorOrCeil::Ceil),
_ => unreachable!(),
}
};
@ -1196,12 +1234,15 @@ 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 func = match kind {
Kind::Ceil => builtin_fns::call_ceil,
Kind::Floor => builtin_fns::call_floor,
};
Ok(Some(func(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
generator,
ctx,
int_sized,
|generator, ctx, _i, scalar| {
Ok(scalar.floor_or_ceil(generator, ctx, kind, int_sized).value)
},
)?;
Ok(Some(result.to_basic_value_enum()))
}),
)
}
@ -1248,9 +1289,9 @@ impl<'a> BuiltinBuilder<'a> {
&[(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
Box::new(move |ctx, obj, fun, args, generator| {
let func = match prim {
PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty,
PrimDef::FunNpZeros => gen_ndarray_zeros,
PrimDef::FunNpOnes => gen_ndarray_ones,
PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => numpy_new::gen_ndarray_empty,
PrimDef::FunNpZeros => numpy_new::gen_ndarray_zeros,
PrimDef::FunNpOnes => numpy_new::gen_ndarray_ones,
_ => unreachable!(),
};
func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
@ -1264,7 +1305,13 @@ impl<'a> BuiltinBuilder<'a> {
fn build_ndarray_other_factory_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(
prim,
&[PrimDef::FunNpArray, PrimDef::FunNpFull, PrimDef::FunNpEye, PrimDef::FunNpIdentity],
&[
PrimDef::FunNpArray,
PrimDef::FunNpFull,
PrimDef::FunNpEye,
PrimDef::FunNpIdentity,
PrimDef::FunNpArange,
],
);
let PrimitiveStore { int32, bool, ndarray, .. } = *self.primitives;
@ -1325,7 +1372,7 @@ 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)
numpy_new::gen_ndarray_full(ctx, &obj, fun, &args, generator)
.map(|val| Some(val.as_basic_value_enum()))
}),
)
@ -1383,6 +1430,152 @@ impl<'a> BuiltinBuilder<'a> {
.map(|val| Some(val.as_basic_value_enum()))
}),
),
PrimDef::FunNpArange => {
// TODO: Support `np.arange(start, stop, step)`
let ndims1 = create_ndims(self.unifier, 1);
let ndarray_float_1d = make_ndarray_ty(
self.unifier,
self.primitives,
Some(self.primitives.float),
Some(ndims1),
);
create_fn_by_codegen(
self.unifier,
&VarMap::new(),
prim.name(),
ndarray_float_1d,
&[(int32, "n")],
Box::new(|ctx, obj, fun, args, generator| {
numpy_new::gen_ndarray_arange(ctx, &obj, fun, &args, generator)
.map(|val| Some(val.as_basic_value_enum()))
}),
)
}
_ => unreachable!(),
}
}
fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(
prim,
&[PrimDef::FunNpBroadcastTo, PrimDef::FunNpReshape, PrimDef::FunNpTranspose],
);
match prim {
PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape => {
// `array_ty` can be ndarrays and arbitrary scalars and objects.
let array_tvar = self.unifier.get_dummy_var();
// The return type is handled by special folding in the type inferencer,
// since the returned `ndims` depends on input shape.
let return_tvar = self.unifier.get_dummy_var();
create_fn_by_codegen(
self.unifier,
&into_var_map([array_tvar, return_tvar]),
prim.name(),
return_tvar.ty,
&[
(array_tvar.ty, "array"),
(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"),
],
Box::new(move |ctx, obj, fun, args, generator| {
let f = match prim {
PrimDef::FunNpBroadcastTo => numpy_new::gen_ndarray_broadcast_to,
PrimDef::FunNpReshape => numpy_new::gen_ndarray_reshape,
_ => unreachable!(),
};
f(ctx, &obj, fun, &args, generator).map(Some)
}),
)
}
PrimDef::FunNpTranspose => {
// TODO: Allow tuple inputs.
// TODO: Support scalar inputs (difficult)
// TODO: Default values don't work for some reason.
// `axes` should have been `Option[List[int32]]` with default `None`.
// Workaround with some bogus types and values for now.
let axes_ty = self.list_int32;
TopLevelDef::Function {
name: prim.name().into(),
simple_name: prim.simple_name().into(),
signature: self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
args: vec![
FuncArg {
name: "a".into(),
ty: self.primitives.ndarray,
default_value: None,
is_vararg: false,
},
FuncArg {
name: "axes".into(),
ty: axes_ty,
default_value: Some(SymbolValue::OptionNone), // Bogus
is_vararg: false,
},
],
ret: self.primitives.ndarray,
vars: VarMap::new(),
})),
var_id: Vec::default(),
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|ctx, obj, fun, args, generator| {
gen_ndarray_transpose(ctx, &obj, fun, &args, generator).map(Some)
},
)))),
loc: None,
}
}
_ => unreachable!(),
}
}
fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(
prim,
&[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
);
match prim {
PrimDef::FunNpSize => {
// TODO: Make the return type usize
create_fn_by_codegen(
self.unifier,
&VarMap::new(),
prim.name(),
self.primitives.int32,
&[(self.primitives.ndarray, "a")],
Box::new(|ctx, obj, fun, args, generator| {
numpy_new::gen_ndarray_size(ctx, &obj, fun, &args, generator).map(Some)
}),
)
}
PrimDef::FunNpShape | PrimDef::FunNpStrides => {
// The return type is a tuple of variable length depending on the ndims
// of the input ndarray.
let ret_ty = self.unifier.get_dummy_var().ty;
create_fn_by_codegen(
self.unifier,
&VarMap::new(),
prim.name(),
ret_ty,
&[(self.primitives.ndarray, "a")],
Box::new(move |ctx, obj, fun, args, generator| {
let f = match prim {
PrimDef::FunNpShape => numpy_new::gen_ndarray_shape,
PrimDef::FunNpStrides => numpy_new::gen_ndarray_strides,
_ => unreachable!(),
};
f(ctx, &obj, fun, &args, generator).map(Some)
}),
)
}
_ => unreachable!(),
}
}
@ -1434,12 +1627,22 @@ 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 func = match prim {
PrimDef::FunNpCeil => builtin_fns::call_ceil,
PrimDef::FunNpFloor => builtin_fns::call_floor,
let kind = match prim {
PrimDef::FunNpFloor => FloorOrCeil::Floor,
PrimDef::FunNpCeil => FloorOrCeil::Ceil,
_ => unreachable!(),
};
Ok(Some(func(generator, ctx, (arg_ty, arg), ctx.primitives.float)?))
let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
generator,
ctx,
ctx.primitives.float,
move |_generator, ctx, _i, scalar| {
let result = scalar.np_floor_or_ceil(ctx, kind);
Ok(result.value)
},
)?;
Ok(Some(result.to_basic_value_enum()))
}),
)
}
@ -1457,7 +1660,17 @@ impl<'a> BuiltinBuilder<'a> {
Box::new(|ctx, _, fun, args, generator| {
let arg_ty = fun.0.args[0].ty;
let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
Ok(Some(builtin_fns::call_numpy_round(generator, ctx, (arg_ty, arg))?))
let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
generator,
ctx,
ctx.primitives.float,
|_generator, ctx, _i, scalar| {
let result = scalar.np_round(ctx);
Ok(result.value)
},
)?;
Ok(Some(result.to_basic_value_enum()))
}),
)
}
@ -1534,51 +1747,11 @@ 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);
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(),
)
}
let ndarray =
NDArrayObject::from_value_and_type(generator, ctx, arg, arg_ty);
let len = ndarray.len(generator, ctx);
let len = len.truncate(generator, ctx, Int32, "len"); // TODO: Currently `len()` returns an int32. It should have been SizeT
Some(len.value.as_basic_value_enum())
}
_ => unreachable!(),
}
@ -1621,16 +1794,21 @@ impl<'a> BuiltinBuilder<'a> {
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
move |ctx, _, fun, args, generator| {
let m_ty = fun.0.args[0].ty;
let n_ty = fun.0.args[1].ty;
let m_val = args[0].1.clone().to_basic_value_enum(ctx, generator, m_ty)?;
let n_ty = fun.0.args[1].ty;
let n_val = args[1].1.clone().to_basic_value_enum(ctx, generator, n_ty)?;
let func = match prim {
PrimDef::FunMin => builtin_fns::call_min,
PrimDef::FunMax => builtin_fns::call_max,
let kind = match prim {
PrimDef::FunMin => MinOrMax::Min,
PrimDef::FunMax => MinOrMax::Max,
_ => unreachable!(),
};
Ok(Some(func(ctx, (m_ty, m_val), (n_ty, n_val))))
let m = ScalarObject { dtype: m_ty, value: m_val };
let n = ScalarObject { dtype: n_ty, value: n_val };
let result = ScalarObject::min_or_max(ctx, kind, m, n);
Ok(Some(result.value))
},
)))),
loc: None,
@ -1672,7 +1850,25 @@ impl<'a> BuiltinBuilder<'a> {
let a_ty = fun.0.args[0].ty;
let a = args[0].1.clone().to_basic_value_enum(ctx, generator, a_ty)?;
Ok(Some(builtin_fns::call_numpy_max_min(generator, ctx, (a_ty, a), prim.name())?))
let a = split_scalar_or_ndarray(generator, ctx, a, a_ty).as_ndarray(generator, ctx);
let result = match prim {
PrimDef::FunNpArgmin => a
.argmin_or_argmax(generator, ctx, MinOrMax::Min)
.value
.as_basic_value_enum(),
PrimDef::FunNpArgmax => a
.argmin_or_argmax(generator, ctx, MinOrMax::Max)
.value
.as_basic_value_enum(),
PrimDef::FunNpMin => {
a.min_or_max(generator, ctx, MinOrMax::Min).value.as_basic_value_enum()
}
PrimDef::FunNpMax => {
a.min_or_max(generator, ctx, MinOrMax::Max).value.as_basic_value_enum()
}
_ => unreachable!(),
};
Ok(Some(result))
}),
)
}
@ -1712,13 +1908,32 @@ impl<'a> BuiltinBuilder<'a> {
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
let func = match prim {
PrimDef::FunNpMinimum => builtin_fns::call_numpy_minimum,
PrimDef::FunNpMaximum => builtin_fns::call_numpy_maximum,
let kind = match prim {
PrimDef::FunNpMinimum => MinOrMax::Min,
PrimDef::FunNpMaximum => MinOrMax::Max,
_ => unreachable!(),
};
Ok(Some(func(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
let x1 = split_scalar_or_ndarray(generator, ctx, x1_val, x1_ty);
let x2 = split_scalar_or_ndarray(generator, ctx, x2_val, x2_ty);
// NOTE: x1.dtype() and x2.dtype() should be the same
let common_ty = x1.dtype();
let result = ScalarOrNDArray::broadcasting_starmap(
generator,
ctx,
&[x1, x2],
common_ty,
|_generator, ctx, _i, scalars| {
let x1 = scalars[0];
let x2 = scalars[1];
let result = ScalarObject::min_or_max(ctx, kind, x1, x2);
Ok(result.value)
},
)?;
Ok(Some(result.to_basic_value_enum()))
},
)))),
loc: None,
@ -1729,6 +1944,7 @@ impl<'a> BuiltinBuilder<'a> {
fn build_abs_function(&mut self) -> TopLevelDef {
let prim = PrimDef::FunAbs;
let num_ty = self.num_ty; // To move into codegen_callback
TopLevelDef::Function {
name: prim.name().into(),
simple_name: prim.simple_name().into(),
@ -1747,11 +1963,17 @@ impl<'a> BuiltinBuilder<'a> {
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|ctx, _, fun, args, generator| {
move |ctx, _, fun, args, generator| {
let n_ty = fun.0.args[0].ty;
let n_val = args[0].1.clone().to_basic_value_enum(ctx, generator, n_ty)?;
Ok(Some(builtin_fns::call_abs(generator, ctx, (n_ty, n_val))?))
let result = split_scalar_or_ndarray(generator, ctx, n_val, n_ty).map(
generator,
ctx,
num_ty.ty,
|_generator, ctx, _i, scalar| Ok(scalar.abs(ctx).value),
)?;
Ok(Some(result.to_basic_value_enum()))
},
)))),
loc: None,
@ -1774,13 +1996,23 @@ impl<'a> BuiltinBuilder<'a> {
let x_ty = fun.0.args[0].ty;
let x_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x_ty)?;
let func = match prim {
PrimDef::FunNpIsInf => builtin_fns::call_numpy_isinf,
PrimDef::FunNpIsNan => builtin_fns::call_numpy_isnan,
let function = match prim {
PrimDef::FunNpIsInf => irrt::call_isnan,
PrimDef::FunNpIsNan => irrt::call_isinf,
_ => unreachable!(),
};
Ok(Some(func(generator, ctx, (x_ty, x_val))?))
let result = split_scalar_or_ndarray(generator, ctx, x_val, x_ty).map(
generator,
ctx,
ctx.primitives.bool,
|generator, ctx, _i, scalar| {
let n = scalar.into_float64(ctx);
let n = function(generator, ctx, n);
Ok(n.as_basic_value_enum())
},
)?;
Ok(Some(result.to_basic_value_enum()))
}),
)
}
@ -1838,49 +2070,58 @@ impl<'a> BuiltinBuilder<'a> {
let arg_ty = fun.0.args[0].ty;
let arg_val = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
let func = match prim {
PrimDef::FunNpSin => builtin_fns::call_numpy_sin,
PrimDef::FunNpCos => builtin_fns::call_numpy_cos,
PrimDef::FunNpTan => builtin_fns::call_numpy_tan,
let result = split_scalar_or_ndarray(generator, ctx, arg_val, arg_ty).map(
generator,
ctx,
ctx.primitives.float,
|_generator, ctx, _i, scalar| {
let n = scalar.into_float64(ctx);
let n = match prim {
PrimDef::FunNpSin => llvm_intrinsics::call_float_sin(ctx, n, None),
PrimDef::FunNpCos => llvm_intrinsics::call_float_cos(ctx, n, None),
PrimDef::FunNpTan => extern_fns::call_tan(ctx, n, None),
PrimDef::FunNpArcsin => builtin_fns::call_numpy_arcsin,
PrimDef::FunNpArccos => builtin_fns::call_numpy_arccos,
PrimDef::FunNpArctan => builtin_fns::call_numpy_arctan,
PrimDef::FunNpArcsin => extern_fns::call_asin(ctx, n, None),
PrimDef::FunNpArccos => extern_fns::call_acos(ctx, n, None),
PrimDef::FunNpArctan => extern_fns::call_atan(ctx, n, None),
PrimDef::FunNpSinh => builtin_fns::call_numpy_sinh,
PrimDef::FunNpCosh => builtin_fns::call_numpy_cosh,
PrimDef::FunNpTanh => builtin_fns::call_numpy_tanh,
PrimDef::FunNpSinh => extern_fns::call_sinh(ctx, n, None),
PrimDef::FunNpCosh => extern_fns::call_cosh(ctx, n, None),
PrimDef::FunNpTanh => extern_fns::call_tanh(ctx, n, None),
PrimDef::FunNpArcsinh => builtin_fns::call_numpy_arcsinh,
PrimDef::FunNpArccosh => builtin_fns::call_numpy_arccosh,
PrimDef::FunNpArctanh => builtin_fns::call_numpy_arctanh,
PrimDef::FunNpArcsinh => extern_fns::call_asinh(ctx, n, None),
PrimDef::FunNpArccosh => extern_fns::call_acosh(ctx, n, None),
PrimDef::FunNpArctanh => extern_fns::call_atanh(ctx, n, None),
PrimDef::FunNpExp => builtin_fns::call_numpy_exp,
PrimDef::FunNpExp2 => builtin_fns::call_numpy_exp2,
PrimDef::FunNpExpm1 => builtin_fns::call_numpy_expm1,
PrimDef::FunNpExp => llvm_intrinsics::call_float_exp(ctx, n, None),
PrimDef::FunNpExp2 => llvm_intrinsics::call_float_exp2(ctx, n, None),
PrimDef::FunNpExpm1 => extern_fns::call_expm1(ctx, n, None),
PrimDef::FunNpLog => builtin_fns::call_numpy_log,
PrimDef::FunNpLog2 => builtin_fns::call_numpy_log2,
PrimDef::FunNpLog10 => builtin_fns::call_numpy_log10,
PrimDef::FunNpLog => llvm_intrinsics::call_float_log(ctx, n, None),
PrimDef::FunNpLog2 => llvm_intrinsics::call_float_log2(ctx, n, None),
PrimDef::FunNpLog10 => llvm_intrinsics::call_float_log10(ctx, n, None),
PrimDef::FunNpSqrt => builtin_fns::call_numpy_sqrt,
PrimDef::FunNpCbrt => builtin_fns::call_numpy_cbrt,
PrimDef::FunNpSqrt => llvm_intrinsics::call_float_sqrt(ctx, n, None),
PrimDef::FunNpCbrt => extern_fns::call_cbrt(ctx, n, None),
PrimDef::FunNpFabs => builtin_fns::call_numpy_fabs,
PrimDef::FunNpRint => builtin_fns::call_numpy_rint,
PrimDef::FunNpFabs => llvm_intrinsics::call_float_fabs(ctx, n, None),
PrimDef::FunNpRint => llvm_intrinsics::call_float_rint(ctx, n, None),
PrimDef::FunSpSpecErf => builtin_fns::call_scipy_special_erf,
PrimDef::FunSpSpecErfc => builtin_fns::call_scipy_special_erfc,
PrimDef::FunSpSpecErf => extern_fns::call_erf(ctx, n, None),
PrimDef::FunSpSpecErfc => extern_fns::call_erfc(ctx, n, None),
PrimDef::FunSpSpecGamma => builtin_fns::call_scipy_special_gamma,
PrimDef::FunSpSpecGammaln => builtin_fns::call_scipy_special_gammaln,
PrimDef::FunSpSpecGamma => irrt::call_gamma(ctx, n),
PrimDef::FunSpSpecGammaln => irrt::call_gammaln(ctx, n),
PrimDef::FunSpSpecJ0 => builtin_fns::call_scipy_special_j0,
PrimDef::FunSpSpecJ1 => builtin_fns::call_scipy_special_j1,
PrimDef::FunSpSpecJ0 => irrt::call_j0(ctx, n),
PrimDef::FunSpSpecJ1 => extern_fns::call_j1(ctx, n, None),
_ => unreachable!(),
};
Ok(Some(func(generator, ctx, (arg_ty, arg_val))?))
_ => unreachable!(),
};
Ok(n.as_basic_value_enum())
},
)?;
Ok(Some(result.to_basic_value_enum()))
}),
)
}
@ -1902,20 +2143,20 @@ impl<'a> BuiltinBuilder<'a> {
let PrimitiveStore { float, int32, .. } = *self.primitives;
// The argument types of the two input arguments are controlled here.
let (x1_ty, x2_ty) = match prim {
// The argument types of the two input arguments + the return type
let (x1_dtype, x2_dtype, ret_dtype) = match prim {
PrimDef::FunNpArctan2
| PrimDef::FunNpCopysign
| PrimDef::FunNpFmax
| PrimDef::FunNpFmin
| PrimDef::FunNpHypot
| PrimDef::FunNpNextAfter => (float, float),
PrimDef::FunNpLdExp => (float, int32),
| PrimDef::FunNpNextAfter => (float, float, float),
PrimDef::FunNpLdExp => (float, int32, float),
_ => unreachable!(),
};
let x1_ty = self.new_type_or_ndarray_ty(x1_ty);
let x2_ty = self.new_type_or_ndarray_ty(x2_ty);
let x1_ty = self.new_type_or_ndarray_ty(x1_dtype);
let x2_ty = self.new_type_or_ndarray_ty(x2_dtype);
let param_ty = &[(x1_ty.ty, "x1"), (x2_ty.ty, "x2")];
let ret_ty = self.unifier.get_fresh_var(None, None);
@ -1944,78 +2185,78 @@ impl<'a> BuiltinBuilder<'a> {
move |ctx, _, fun, args, generator| {
let x1_ty = fun.0.args[0].ty;
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
let x2_ty = fun.0.args[1].ty;
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
let func = match prim {
PrimDef::FunNpArctan2 => builtin_fns::call_numpy_arctan2,
PrimDef::FunNpCopysign => builtin_fns::call_numpy_copysign,
PrimDef::FunNpFmax => builtin_fns::call_numpy_fmax,
PrimDef::FunNpFmin => builtin_fns::call_numpy_fmin,
PrimDef::FunNpLdExp => builtin_fns::call_numpy_ldexp,
PrimDef::FunNpHypot => builtin_fns::call_numpy_hypot,
PrimDef::FunNpNextAfter => builtin_fns::call_numpy_nextafter,
_ => unreachable!(),
};
let x1 = split_scalar_or_ndarray(generator, ctx, x1_val, x1_ty);
let x2 = split_scalar_or_ndarray(generator, ctx, x2_val, x2_ty);
Ok(Some(func(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
let result = ScalarOrNDArray::broadcasting_starmap(
generator,
ctx,
&[x1, x2],
ret_dtype,
|_generator, ctx, _i, scalars| {
let x1 = scalars[0];
let x2 = scalars[1];
// TODO: This looks ugly
let result = match prim {
PrimDef::FunNpArctan2 => {
let x1 = x1.into_float64(ctx);
let x2 = x2.into_float64(ctx);
extern_fns::call_atan2(ctx, x1, x2, None).as_basic_value_enum()
}
PrimDef::FunNpCopysign => {
let x1 = x1.into_float64(ctx);
let x2 = x2.into_float64(ctx);
llvm_intrinsics::call_float_copysign(ctx, x1, x2, None)
.as_basic_value_enum()
}
PrimDef::FunNpFmax => {
let x1 = x1.into_float64(ctx);
let x2 = x2.into_float64(ctx);
llvm_intrinsics::call_float_maxnum(ctx, x1, x2, None)
.as_basic_value_enum()
}
PrimDef::FunNpFmin => {
let x1 = x1.into_float64(ctx);
let x2 = x2.into_float64(ctx);
llvm_intrinsics::call_float_minnum(ctx, x1, x2, None)
.as_basic_value_enum()
}
PrimDef::FunNpHypot => {
let x1 = x1.into_float64(ctx);
let x2 = x2.into_float64(ctx);
llvm_intrinsics::call_float_minnum(ctx, x1, x2, None)
.as_basic_value_enum()
}
PrimDef::FunNpNextAfter => {
let x1 = x1.into_float64(ctx);
let x2 = x2.into_float64(ctx);
extern_fns::call_nextafter(ctx, x1, x2, None)
.as_basic_value_enum()
}
PrimDef::FunNpLdExp => {
let x1 = x1.into_float64(ctx);
let x2 = x2.into_int32(ctx);
extern_fns::call_ldexp(ctx, x1, x2, None).as_basic_value_enum()
}
_ => unreachable!(),
};
Ok(result)
},
)?;
Ok(Some(result.to_basic_value_enum()))
},
)))),
loc: None,
}
}
/// Build np/sp functions that take as input `NDArray` only
fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
match prim {
PrimDef::FunNpTranspose => {
let ndarray_ty = self.unifier.get_fresh_var_with_range(
&[self.ndarray_num_ty],
Some("T".into()),
None,
);
create_fn_by_codegen(
self.unifier,
&into_var_map([ndarray_ty]),
prim.name(),
ndarray_ty.ty,
&[(ndarray_ty.ty, "x")],
Box::new(move |ctx, _, fun, args, generator| {
let arg_ty = fun.0.args[0].ty;
let arg_val =
args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
}),
)
}
// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
// the `param_ty` for `create_fn_by_codegen`.
//
// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
PrimDef::FunNpReshape => create_fn_by_codegen(
self.unifier,
&VarMap::new(),
prim.name(),
self.ndarray_num_ty,
&[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
Box::new(move |ctx, _, fun, args, generator| {
let x1_ty = fun.0.args[0].ty;
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
let x2_ty = fun.0.args[1].ty;
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
}),
),
_ => unreachable!(),
}
}
/// Build `np_linalg` and `sp_linalg` functions
///
/// The input to these functions must be floating point `NDArray`

26
nac3core/src/toplevel/helper.rs
View File

@ -51,6 +51,17 @@ pub enum PrimDef {
FunNpArray,
FunNpEye,
FunNpIdentity,
FunNpArange,
// NumPy view functions
FunNpBroadcastTo,
FunNpReshape,
FunNpTranspose,
// NumPy NDArray property getters
FunNpSize,
FunNpShape,
FunNpStrides,
// Miscellaneous NumPy & SciPy functions
FunNpRound,
@ -99,8 +110,6 @@ pub enum PrimDef {
FunNpLdExp,
FunNpHypot,
FunNpNextAfter,
FunNpTranspose,
FunNpReshape,
// Linalg functions
FunNpDot,
@ -237,6 +246,17 @@ impl PrimDef {
PrimDef::FunNpArray => fun("np_array", None),
PrimDef::FunNpEye => fun("np_eye", None),
PrimDef::FunNpIdentity => fun("np_identity", None),
PrimDef::FunNpArange => fun("np_arange", None),
// NumPy view functions
PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
PrimDef::FunNpReshape => fun("np_reshape", None),
PrimDef::FunNpTranspose => fun("np_transpose", None),
// NumPy NDArray property getters
PrimDef::FunNpSize => fun("np_size", None),
PrimDef::FunNpShape => fun("np_shape", None),
PrimDef::FunNpStrides => fun("np_strides", None),
// Miscellaneous NumPy & SciPy functions
PrimDef::FunNpRound => fun("np_round", None),
@ -285,8 +305,6 @@ impl PrimDef {
PrimDef::FunNpLdExp => fun("np_ldexp", None),
PrimDef::FunNpHypot => fun("np_hypot", None),
PrimDef::FunNpNextAfter => fun("np_nextafter", None),
PrimDef::FunNpTranspose => fun("np_transpose", None),
PrimDef::FunNpReshape => fun("np_reshape", None),
// Linalg functions
PrimDef::FunNpDot => fun("np_dot", None),

33
nac3core/src/toplevel/numpy.rs
View File

@ -1,4 +1,7 @@
use std::sync::Arc;
use crate::{
symbol_resolver::SymbolValue,
toplevel::helper::PrimDef,
typecheck::{
type_inferencer::PrimitiveStore,
@ -83,3 +86,33 @@ pub fn unpack_ndarray_var_ids(unifier: &mut Unifier, ndarray: Type) -> (TypeVarI
pub fn unpack_ndarray_var_tys(unifier: &mut Unifier, ndarray: Type) -> (Type, Type) {
unpack_ndarray_tvars(unifier, ndarray).into_iter().map(|v| v.1).collect_tuple().unwrap()
}
/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
/// The `ndims` must only contain 1 value.
#[must_use]
pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
panic!("ndims_ty should be a TLiteral");
};
assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
let ndims = values[0].clone();
u64::try_from(ndims).unwrap()
}
/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
}
/// Return the ndims after broadcasting ndarrays of different ndims.
///
/// Panics if the input list is empty.
pub fn get_broadcast_all_ndims<I>(ndims: I) -> u64
where
I: IntoIterator<Item = u64>,
{
ndims.into_iter().max().unwrap()
}

79
nac3core/src/typecheck/type_inferencer/mod.rs
View File

@ -1,6 +1,6 @@
use std::collections::{HashMap, HashSet};
use std::convert::{From, TryInto};
use std::iter::once;
use std::iter::{self, once};
use std::{cell::RefCell, sync::Arc};
use super::{
@ -15,12 +15,13 @@ use crate::{
symbol_resolver::{SymbolResolver, SymbolValue},
toplevel::{
helper::{arraylike_flatten_element_type, arraylike_get_ndims, PrimDef},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
numpy::{extract_ndims, make_ndarray_ty, unpack_ndarray_var_tys},
TopLevelContext, TopLevelDef,
},
typecheck::typedef::Mapping,
};
use itertools::{izip, Itertools};
use nac3parser::ast::Constant;
use nac3parser::ast::{
self,
fold::{self, Fold},
@ -1272,7 +1273,7 @@ impl<'a> Inferencer<'a> {
arg_ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
}) {
// typeof_ndarray_broadcast requires both dtypes to be the same, but ldexp accepts
// (float, int32), so convert it to align with the dtype of the first arg
// (float, int32), so convert it to align with t#he dtype of the first arg
let arg1_ty = if id == &"np_ldexp".into() {
if arg1_ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) {
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, arg1_ty);
@ -1448,7 +1449,7 @@ impl<'a> Inferencer<'a> {
},
}));
}
// 2-argument ndarray n-dimensional factory functions
if id == &"np_reshape".into() && args.len() == 2 {
let arg0 = self.fold_expr(args.remove(0))?;
@ -1493,6 +1494,51 @@ impl<'a> Inferencer<'a> {
},
}));
}
if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
// Output tuple size depends on input ndarray's ndims.
let ndarray = self.fold_expr(args.remove(0))?;
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, ndarray.custom.unwrap());
let ndims = extract_ndims(self.unifier, ndims);
// Create a tuple of size `ndims` full of int32
// TODO: Make it usize
let ret_ty = TypeEnum::TTuple {
ty: iter::repeat(self.primitives.int32).take(ndims as usize).collect_vec(),
is_vararg_ctx: false,
};
let ret_ty = self.unifier.add_ty(ret_ty);
let func_ty = TypeEnum::TFunc(FunSignature {
args: vec![FuncArg {
name: "a".into(),
default_value: None,
ty: ndarray.custom.unwrap(),
is_vararg: false,
}],
ret: ret_ty,
vars: VarMap::new(),
});
let func_ty = self.unifier.add_ty(func_ty);
return Ok(Some(Located {
location,
custom: Some(ret_ty),
node: ExprKind::Call {
func: Box::new(Located {
custom: Some(func_ty),
location: func.location,
node: ExprKind::Name { id: *id, ctx: *ctx },
}),
args: vec![ndarray],
keywords: vec![],
},
}));
}
// 2-argument ndarray n-dimensional creation functions
if id == &"np_full".into() && args.len() == 2 {
let ExprKind::List { elts, .. } = &args[0].node else {
@ -2210,14 +2256,25 @@ impl<'a> Inferencer<'a> {
// We will also take the opportunity to deduce `dims_to_subtract` as well
let mut dims_to_subtract: i128 = 0;
for index in indices {
if let ExprKind::Slice { lower, upper, step } = &index.node {
for v in [lower.as_ref(), upper.as_ref(), step.as_ref()].iter().flatten() {
self.constrain(v.custom.unwrap(), self.primitives.int32, &v.location)?;
match &index.node {
ExprKind::Slice { lower, upper, step } => {
// Handle slices
for v in [lower.as_ref(), upper.as_ref(), step.as_ref()].iter().flatten() {
self.constrain(v.custom.unwrap(), self.primitives.int32, &v.location)?;
}
}
ExprKind::Constant { value: Constant::Ellipsis, .. } => {
// Handle `...`. Do nothing.
}
ExprKind::Name { id, .. } if id == &"none".into() => {
// Handle `np.newaxis` / `None`.
dims_to_subtract -= 1;
}
_ => {
// Treat anything else as an integer index, and force unify their type to int32.
self.unify(index.custom.unwrap(), self.primitives.int32, &index.location)?;
dims_to_subtract += 1;
}
} else {
// Treat anything else as an integer index, and force unify their type to int32.
self.unify(index.custom.unwrap(), self.primitives.int32, &index.location)?;
dims_to_subtract += 1;
}
}

8
nac3core/src/typecheck/typedef/mod.rs
View File

@ -342,6 +342,14 @@ impl Unifier {
self.unification_table.unioned(a, b)
}
/// Determine if a type unions with a type in `tys`.
pub fn unioned_any<I>(&mut self, a: Type, tys: I) -> bool
where
I: IntoIterator<Item = Type>,
{
tys.into_iter().any(|ty| self.unioned(a, ty))
}
pub fn from_shared_unifier(unifier: &SharedUnifier) -> Unifier {
let lock = unifier.lock().unwrap();
Unifier {

10
nac3standalone/demo/interpret_demo.py
View File

@ -218,8 +218,16 @@ def patch(module):
module.np_ldexp = np.ldexp
module.np_hypot = np.hypot
module.np_nextafter = np.nextafter
module.np_transpose = np.transpose
# NumPy view functions
module.np_broadcast_to = np.broadcast_to
module.np_reshape = np.reshape
module.np_transpose = np.transpose
# NumPy NDArray property getter functions
module.np_size = np.size
module.np_shape = np.shape
module.np_strides = lambda ndarray: ndarray.strides
# SciPy Math functions
module.sp_spec_erf = special.erf