forked from M-Labs/nac3
core/ndstrides: implement ndarray indexing
The functionality for `...` and `np.newaxis` is there in IRRT, but there is no implementation of them for @kernel Python expressions because of M-Labs/nac3#486.
This commit is contained in:
parent
c74b1eb9d8
commit
0a874da5fc
@ -3,6 +3,7 @@
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#include <irrt/math_util.hpp>
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#include <irrt/ndarray/basic.hpp>
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#include <irrt/ndarray/def.hpp>
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#include <irrt/ndarray/indexing.hpp>
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#include <irrt/ndarray/iter.hpp>
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#include <irrt/original.hpp>
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#include <irrt/slice.hpp>
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246
nac3core/irrt/irrt/ndarray/indexing.hpp
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246
nac3core/irrt/irrt/ndarray/indexing.hpp
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@ -0,0 +1,246 @@
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#pragma once
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#include <irrt/exception.hpp>
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#include <irrt/int_types.hpp>
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#include <irrt/ndarray/basic.hpp>
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#include <irrt/ndarray/def.hpp>
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#include <irrt/slice.hpp>
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namespace
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{
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typedef uint8_t NDIndexType;
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/**
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* @brief A single element index
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*
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* `data` points to a `SliceIndex`.
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*/
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const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
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/**
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* @brief A slice index
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*
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* `data` points to a `Slice`.
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*/
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const NDIndexType ND_INDEX_TYPE_SLICE = 1;
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/**
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* @brief `np.newaxis` / `None`
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*
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* `data` is unused.
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*/
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const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
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/**
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* @brief `Ellipsis` / `...`
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*
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* `data` is unused.
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*/
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const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
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/**
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* @brief An index used in ndarray indexing
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*/
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struct NDIndex
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{
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/**
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* @brief Enum tag to specify the type of index.
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*
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* Please see comments of each enum constant.
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*/
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NDIndexType type;
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/**
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* @brief The accompanying data associated with `type`.
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*
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* Please see comments of each enum constant.
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*/
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uint8_t *data;
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};
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} // namespace
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namespace
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{
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namespace ndarray
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{
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namespace indexing
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{
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/**
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* @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
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*
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* This is function very similar to performing `dst_ndarray = src_ndarray[indices]` in Python (where the variables
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* can all be found in the parameter of this function).
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*
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* In other words, this function takes in an ndarray (`src_ndarray`), index it with `indices`, and return the
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* indexed array (by writing the result to `dst_ndarray`).
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*
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* This function also does proper assertions on `indices`.
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*
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* # Notes on `dst_ndarray`
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* The caller is responsible for allocating space for the resulting ndarray.
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* Here is what this function expects from `dst_ndarray` when called:
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* - `dst_ndarray->data` does not have to be initialized.
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* - `dst_ndarray->itemsize` does not have to be initialized.
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* - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
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* indexing `src_ndarray` with `indices`.
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* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
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* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
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* When this function call ends:
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* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
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* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
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* - `dst_ndarray->ndims` is unchanged.
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* - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
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* - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
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*
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* @param indices indices to index `src_ndarray`, ordered in the same way you would write them in Python.
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* @param src_ndarray The NDArray to be indexed.
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* @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
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*/
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template <typename SizeT>
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void index(SizeT num_indices, const NDIndex *indices, const NDArray<SizeT> *src_ndarray, NDArray<SizeT> *dst_ndarray)
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{
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// Validate `indices`.
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// Expected value of `dst_ndarray->ndims`.
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SizeT expected_dst_ndims = src_ndarray->ndims;
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// To check for "too many indices for array: array is ?-dimensional, but ? were indexed"
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SizeT num_indexed = 0;
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// There may be ellipsis `...` in `indices`. There can only be 0 or 1 ellipsis.
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SizeT num_ellipsis = 0;
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for (SizeT i = 0; i < num_indices; i++)
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{
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if (indices[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT)
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{
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expected_dst_ndims--;
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num_indexed++;
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}
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else if (indices[i].type == ND_INDEX_TYPE_SLICE)
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{
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num_indexed++;
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}
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else if (indices[i].type == ND_INDEX_TYPE_NEWAXIS)
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{
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expected_dst_ndims++;
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}
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else if (indices[i].type == ND_INDEX_TYPE_ELLIPSIS)
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{
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num_ellipsis++;
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if (num_ellipsis > 1)
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{
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raise_exception(SizeT, EXN_INDEX_ERROR, "an index can only have a single ellipsis ('...')", NO_PARAM,
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NO_PARAM, NO_PARAM);
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}
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}
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else
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{
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__builtin_unreachable();
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}
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}
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debug_assert_eq(SizeT, expected_dst_ndims, dst_ndarray->ndims);
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if (src_ndarray->ndims - num_indexed < 0)
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{
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raise_exception(SizeT, EXN_INDEX_ERROR,
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"too many indices for array: array is {0}-dimensional, "
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"but {1} were indexed",
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src_ndarray->ndims, num_indices, NO_PARAM);
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}
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dst_ndarray->data = src_ndarray->data;
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dst_ndarray->itemsize = src_ndarray->itemsize;
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// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
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SizeT src_axis = 0;
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SizeT dst_axis = 0;
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for (SliceIndex i = 0; i < num_indices; i++)
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{
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const NDIndex *index = &indices[i];
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if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT)
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{
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SliceIndex input = *((SliceIndex *)index->data);
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SliceIndex k = slice::resolve_index_in_length(src_ndarray->shape[src_axis], input);
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if (k == slice::OUT_OF_BOUNDS)
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{
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raise_exception(SizeT, EXN_INDEX_ERROR,
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"index {0} is out of bounds for axis {1} "
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"with size {2}",
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input, src_axis, src_ndarray->shape[src_axis]);
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}
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dst_ndarray->data += k * src_ndarray->strides[src_axis];
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src_axis++;
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}
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else if (index->type == ND_INDEX_TYPE_SLICE)
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{
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Slice *slice = (Slice *)index->data;
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Range range = slice->indices_checked<SizeT>(src_ndarray->shape[src_axis]);
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dst_ndarray->data += (SizeT)range.start * src_ndarray->strides[src_axis];
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dst_ndarray->strides[dst_axis] = ((SizeT)range.step) * src_ndarray->strides[src_axis];
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dst_ndarray->shape[dst_axis] = (SizeT)range.len<SizeT>();
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dst_axis++;
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src_axis++;
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}
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else if (index->type == ND_INDEX_TYPE_NEWAXIS)
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{
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dst_ndarray->strides[dst_axis] = 0;
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dst_ndarray->shape[dst_axis] = 1;
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dst_axis++;
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}
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else if (index->type == ND_INDEX_TYPE_ELLIPSIS)
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{
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// The number of ':' entries this '...' implies.
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SizeT ellipsis_size = src_ndarray->ndims - num_indexed;
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for (SizeT j = 0; j < ellipsis_size; j++)
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{
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dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
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dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
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dst_axis++;
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src_axis++;
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}
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}
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else
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{
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__builtin_unreachable();
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}
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}
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for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++)
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{
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dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
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dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
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}
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debug_assert_eq(SizeT, src_ndarray->ndims, src_axis);
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debug_assert_eq(SizeT, dst_ndarray->ndims, dst_axis);
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}
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} // namespace indexing
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} // namespace ndarray
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} // namespace
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extern "C"
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{
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using namespace ndarray::indexing;
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void __nac3_ndarray_index(int32_t num_indices, NDIndex *indices, NDArray<int32_t> *src_ndarray,
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NDArray<int32_t> *dst_ndarray)
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{
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index(num_indices, indices, src_ndarray, dst_ndarray);
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}
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void __nac3_ndarray_index64(int64_t num_indices, NDIndex *indices, NDArray<int64_t> *src_ndarray,
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NDArray<int64_t> *dst_ndarray)
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{
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index(num_indices, indices, src_ndarray, dst_ndarray);
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}
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}
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@ -2,7 +2,7 @@ use crate::{
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codegen::{
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classes::{
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ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayValue, ProxyType,
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ProxyValue, RangeValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
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ProxyValue, RangeValue, UntypedArrayLikeAccessor,
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},
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concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
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gen_in_range_check, get_llvm_abi_type, get_llvm_type, get_va_count_arg_name,
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@ -19,11 +19,7 @@ use crate::{
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CodeGenContext, CodeGenTask, CodeGenerator,
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},
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symbol_resolver::{SymbolValue, ValueEnum},
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toplevel::{
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helper::PrimDef,
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numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
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DefinitionId, TopLevelDef,
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},
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toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
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typecheck::{
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magic_methods::{Binop, BinopVariant, HasOpInfo},
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typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
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@ -44,6 +40,14 @@ use std::cmp::min;
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use std::iter::{repeat, repeat_with};
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use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
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use super::{
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model::*,
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object::{
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any::AnyObject,
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ndarray::{indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject},
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},
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};
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pub fn get_subst_key(
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unifier: &mut Unifier,
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obj: Option<Type>,
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@ -2492,338 +2496,6 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
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)
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}
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/// Generates code for a subscript expression on an `ndarray`.
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///
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/// * `ty` - The `Type` of the `NDArray` elements.
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/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
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/// * `v` - The `NDArray` value.
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/// * `slice` - The slice expression used to subscript into the `ndarray`.
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fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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ty: Type,
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ndims: Type,
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v: NDArrayValue<'ctx>,
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slice: &Expr<Option<Type>>,
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) -> Result<Option<ValueEnum<'ctx>>, String> {
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let llvm_i1 = ctx.ctx.bool_type();
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let llvm_i32 = ctx.ctx.i32_type();
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let llvm_usize = generator.get_size_type(ctx.ctx);
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let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
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unreachable!()
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};
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let ndims = values
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.iter()
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.map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
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.collect::<Result<Vec<_>, _>>()
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.map_err(|val| {
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format!(
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"Expected non-negative literal for ndarray.ndims, got {}",
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i128::try_from(val).unwrap()
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)
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})?;
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assert!(!ndims.is_empty());
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// The number of dimensions subscripted by the index expression.
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// Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
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// dimension will remove a dimension.
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let subscripted_dims = match &slice.node {
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ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
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if let ExprKind::Slice { .. } = &value_subexpr.node {
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acc
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} else {
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acc + 1
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}
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}),
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ExprKind::Slice { .. } => 0,
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_ => 1,
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};
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let ndarray_ndims_ty = ctx.unifier.get_fresh_literal(
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ndims.iter().map(|v| SymbolValue::U64(v - subscripted_dims)).collect(),
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None,
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);
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let ndarray_ty =
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make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(ty), Some(ndarray_ndims_ty));
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let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
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let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
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let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
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let sizeof_elem = llvm_ndarray_data_t.size_of().unwrap();
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// Check that len is non-zero
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let len = v.load_ndims(ctx);
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ctx.make_assert(
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generator,
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ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), "").unwrap(),
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"0:IndexError",
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"too many indices for array: array is {0}-dimensional but 1 were indexed",
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[Some(len), None, None],
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slice.location,
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);
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// Normalizes a possibly-negative index to its corresponding positive index
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let normalize_index = |generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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index: IntValue<'ctx>,
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dim: u64| {
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gen_if_else_expr_callback(
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generator,
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ctx,
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|_, ctx| {
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Ok(ctx
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.builder
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.build_int_compare(IntPredicate::SGE, index, index.get_type().const_zero(), "")
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.unwrap())
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},
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|_, _| Ok(Some(index)),
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|generator, ctx| {
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let llvm_i32 = ctx.ctx.i32_type();
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let len = unsafe {
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v.dim_sizes().get_typed_unchecked(
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ctx,
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generator,
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&llvm_usize.const_int(dim, true),
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None,
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)
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};
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let index = ctx
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.builder
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.build_int_add(
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len,
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ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
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"",
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)
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.unwrap();
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Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
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},
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)
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.map(|v| v.map(BasicValueEnum::into_int_value))
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};
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// Converts a slice expression into a slice-range tuple
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let expr_to_slice = |generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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node: &ExprKind<Option<Type>>,
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dim: u64| {
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match node {
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ExprKind::Constant { value: Constant::Int(v), .. } => {
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let Some(index) =
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normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
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else {
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return Ok(None);
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};
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Ok(Some((index, index, llvm_i32.const_int(1, true))))
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}
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ExprKind::Slice { lower, upper, step } => {
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let dim_sz = unsafe {
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v.dim_sizes().get_typed_unchecked(
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ctx,
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generator,
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&llvm_usize.const_int(dim, false),
|
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None,
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)
|
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};
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handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
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}
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_ => {
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let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
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let index = index
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.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
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.into_int_value();
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let Some(index) = normalize_index(generator, ctx, index, dim)? else {
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return Ok(None);
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};
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|
||||
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,
|
||||
@ -3463,18 +3135,26 @@ 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_object(
|
||||
generator,
|
||||
ctx,
|
||||
AnyObject { ty: ndarray_ty, value: ndarray },
|
||||
);
|
||||
|
||||
let indices = gen_ndarray_subscript_ndindices(generator, ctx, slice)?;
|
||||
let result = ndarray
|
||||
.index(generator, ctx, &indices)
|
||||
.split_unsized(generator, ctx)
|
||||
.to_basic_value_enum();
|
||||
return Ok(Some(ValueEnum::Dynamic(result)));
|
||||
}
|
||||
TypeEnum::TTuple { .. } => {
|
||||
let index: u32 =
|
||||
@ -3517,3 +3197,42 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
|
||||
_ => unimplemented!(),
|
||||
}))
|
||||
}
|
||||
|
||||
/// Generate LLVM IR for an [`ExprKind::Slice`]
|
||||
#[allow(clippy::type_complexity)]
|
||||
pub fn gen_slice<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
lower: &Option<Box<Expr<Option<Type>>>>,
|
||||
upper: &Option<Box<Expr<Option<Type>>>>,
|
||||
step: &Option<Box<Expr<Option<Type>>>>,
|
||||
) -> Result<
|
||||
(
|
||||
Option<Instance<'ctx, Int<Int32>>>,
|
||||
Option<Instance<'ctx, Int<Int32>>>,
|
||||
Option<Instance<'ctx, Int<Int32>>>,
|
||||
),
|
||||
String,
|
||||
> {
|
||||
let mut help = |value_expr: &Option<Box<Expr<Option<Type>>>>| -> 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 = Int(Int32).check_value(generator, ctx.ctx, value_expr).unwrap();
|
||||
|
||||
Some(value_expr)
|
||||
}
|
||||
})
|
||||
};
|
||||
|
||||
let lower = help(lower)?;
|
||||
let upper = help(upper)?;
|
||||
let step = help(step)?;
|
||||
|
||||
Ok((lower, upper, step))
|
||||
}
|
||||
|
||||
@ -7,7 +7,7 @@ use super::{
|
||||
},
|
||||
llvm_intrinsics,
|
||||
model::*,
|
||||
object::ndarray::{nditer::NDIter, NDArray},
|
||||
object::ndarray::{indexing::NDIndex, nditer::NDIter, NDArray},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use crate::codegen::classes::TypedArrayLikeAccessor;
|
||||
@ -1119,3 +1119,20 @@ pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_next");
|
||||
CallFunction::begin(generator, ctx, &name).arg(iter).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_indices: Instance<'ctx, Int<SizeT>>,
|
||||
indices: Instance<'ctx, Ptr<Struct<NDIndex>>>,
|
||||
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(num_indices)
|
||||
.arg(indices)
|
||||
.arg(src_ndarray)
|
||||
.arg(dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
293
nac3core/src/codegen/object/ndarray/indexing.rs
Normal file
293
nac3core/src/codegen/object/ndarray/indexing.rs
Normal file
@ -0,0 +1,293 @@
|
||||
use crate::codegen::{irrt::call_nac3_ndarray_index, model::*, CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
pub type NDIndexType = Byte;
|
||||
|
||||
/// Fields of [`NDIndex`]
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub type_: F::Out<Int<NDIndexType>>, // Defined to be uint8_t in IRRT
|
||||
pub data: F::Out<Ptr<Int<Byte>>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation of 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 [`Slice`]
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct SliceFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub start_defined: F::Out<Int<Bool>>,
|
||||
pub start: F::Out<Int<Int32>>,
|
||||
pub stop_defined: F::Out<Int<Bool>>,
|
||||
pub stop: F::Out<Int<Int32>>,
|
||||
pub step_defined: F::Out<Int<Bool>>,
|
||||
pub step: F::Out<Int<Int32>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation of an (unresolved) slice.
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct Slice;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for Slice {
|
||||
type Fields<F: FieldTraversal<'ctx>> = SliceFields<'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 [`Slice`].
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RustSlice<'ctx> {
|
||||
pub start: Option<Instance<'ctx, Int<Int32>>>,
|
||||
pub stop: Option<Instance<'ctx, Int<Int32>>>,
|
||||
pub step: Option<Instance<'ctx, Int<Int32>>>,
|
||||
}
|
||||
|
||||
impl<'ctx> RustSlice<'ctx> {
|
||||
/// Write the contents to an LLVM [`Slice`].
|
||||
pub fn write_to_slice<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_slice_ptr: Instance<'ctx, Ptr<Struct<Slice>>>,
|
||||
) {
|
||||
let false_ = Int(Bool).const_false(generator, ctx.ctx);
|
||||
let true_ = Int(Bool).const_true(generator, ctx.ctx);
|
||||
|
||||
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 to prepare an [`NDIndex`].
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum RustNDIndex<'ctx> {
|
||||
SingleElement(Instance<'ctx, Int<Int32>>), // TODO: To be SizeT
|
||||
Slice(RustSlice<'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: Instance<'ctx, Ptr<Struct<NDIndex>>>,
|
||||
) {
|
||||
// Set `dst_ndindex_ptr->type`
|
||||
dst_ndindex_ptr.gep(ctx, |f| f.type_).store(
|
||||
ctx,
|
||||
Int(NDIndexType::default()).const_int(generator, ctx.ctx, self.get_type_id()),
|
||||
);
|
||||
|
||||
// Set `dst_ndindex_ptr->data`
|
||||
match self {
|
||||
RustNDIndex::SingleElement(in_index) => {
|
||||
let index_ptr = Int(Int32).alloca(generator, ctx);
|
||||
index_ptr.store(ctx, *in_index);
|
||||
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.data)
|
||||
.store(ctx, index_ptr.pointer_cast(generator, ctx, Int(Byte)));
|
||||
}
|
||||
RustNDIndex::Slice(in_rust_slice) => {
|
||||
let user_slice_ptr = Struct(Slice).alloca(generator, ctx);
|
||||
in_rust_slice.write_to_slice(generator, ctx, user_slice_ptr);
|
||||
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.data)
|
||||
.store(ctx, user_slice_ptr.pointer_cast(generator, ctx, Int(Byte)));
|
||||
}
|
||||
RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
|
||||
}
|
||||
}
|
||||
|
||||
/// Allocate an array of `NDIndex`es on the stack and return its stack pointer.
|
||||
pub fn alloca_ndindices<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
in_ndindices: &[RustNDIndex<'ctx>],
|
||||
) -> (Instance<'ctx, Int<SizeT>>, Instance<'ctx, Ptr<Struct<NDIndex>>>) {
|
||||
let ndindex_model = Struct(NDIndex);
|
||||
|
||||
let num_ndindices = Int(SizeT).const_int(generator, ctx.ctx, in_ndindices.len() as u64);
|
||||
let ndindices = ndindex_model.array_alloca(generator, ctx, num_ndindices.value);
|
||||
for (i, in_ndindex) in in_ndindices.iter().enumerate() {
|
||||
let pndindex = ndindices.offset_const(ctx, i as u64);
|
||||
in_ndindex.write_to_ndindex(generator, ctx, pndindex);
|
||||
}
|
||||
|
||||
(num_ndindices, ndindices)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Get the ndims [`Type`] after indexing with a given slice.
|
||||
#[must_use]
|
||||
pub fn deduce_ndims_after_indexing_with(&self, indices: &[RustNDIndex<'ctx>]) -> u64 {
|
||||
let mut ndims = self.ndims;
|
||||
for index in indices {
|
||||
match index {
|
||||
RustNDIndex::SingleElement(_) => {
|
||||
ndims -= 1; // Single elements decrements ndims
|
||||
}
|
||||
RustNDIndex::NewAxis => {
|
||||
ndims += 1; // `np.newaxis` / `none` adds a new axis
|
||||
}
|
||||
RustNDIndex::Ellipsis | RustNDIndex::Slice(_) => {}
|
||||
}
|
||||
}
|
||||
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 indices 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, '_>,
|
||||
indices: &[RustNDIndex<'ctx>],
|
||||
) -> Self {
|
||||
let dst_ndims = self.deduce_ndims_after_indexing_with(indices);
|
||||
let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, dst_ndims);
|
||||
|
||||
let (num_indices, indices) = RustNDIndex::alloca_ndindices(generator, ctx, indices);
|
||||
call_nac3_ndarray_index(
|
||||
generator,
|
||||
ctx,
|
||||
num_indices,
|
||||
indices,
|
||||
self.instance,
|
||||
dst_ndarray.instance,
|
||||
);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
}
|
||||
|
||||
pub mod util {
|
||||
use itertools::Itertools;
|
||||
use nac3parser::ast::{Expr, ExprKind};
|
||||
|
||||
use crate::{
|
||||
codegen::{expr::gen_slice, model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::{RustNDIndex, RustSlice};
|
||||
|
||||
/// 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_ndindices<'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
|
||||
|
||||
// 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_ndindices: 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, upper, step } = &index_expr.node {
|
||||
// Handle slices
|
||||
let (lower, upper, step) = gen_slice(generator, ctx, lower, upper, step)?;
|
||||
RustNDIndex::Slice(RustSlice { start: lower, stop: upper, step })
|
||||
} else {
|
||||
// Treat and handle everything else as a single element index.
|
||||
let index = generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.primitives.int32, // Must be int32, this checks for illegal values
|
||||
)?;
|
||||
let index = Int(Int32).check_value(generator, ctx.ctx, index).unwrap();
|
||||
|
||||
RustNDIndex::SingleElement(index)
|
||||
};
|
||||
rust_ndindices.push(ndindex);
|
||||
}
|
||||
Ok(rust_ndindices)
|
||||
}
|
||||
}
|
||||
@ -1,11 +1,12 @@
|
||||
pub mod factory;
|
||||
pub mod indexing;
|
||||
pub mod nditer;
|
||||
pub mod shape_util;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::BasicType,
|
||||
values::{BasicValueEnum, PointerValue},
|
||||
values::{BasicValue, BasicValueEnum, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
@ -361,6 +362,30 @@ impl<'ctx> NDArrayObject<'ctx> {
|
||||
call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
|
||||
}
|
||||
|
||||
/// Returns true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
|
||||
#[must_use]
|
||||
pub fn is_unsized(&self) -> bool {
|
||||
self.ndims == 0
|
||||
}
|
||||
|
||||
/// If this ndarray is unsized, return its sole value as an [`AnyObject`].
|
||||
/// Otherwise, do nothing and return the ndarray itself.
|
||||
pub fn split_unsized<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
if self.is_unsized() {
|
||||
// NOTE: `np.size(self) == 0` here is never possible.
|
||||
let zero = Int(SizeT).const_0(generator, ctx.ctx);
|
||||
let value = self.get_nth_scalar(generator, ctx, zero).value;
|
||||
|
||||
ScalarOrNDArray::Scalar(AnyObject { ty: self.dtype, value })
|
||||
} else {
|
||||
ScalarOrNDArray::NDArray(*self)
|
||||
}
|
||||
}
|
||||
|
||||
/// Fill the ndarray with a scalar.
|
||||
///
|
||||
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
|
||||
@ -378,3 +403,21 @@ impl<'ctx> NDArrayObject<'ctx> {
|
||||
.unwrap();
|
||||
}
|
||||
}
|
||||
|
||||
/// A convenience enum for implementing functions that acts on scalars or ndarrays or both.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum ScalarOrNDArray<'ctx> {
|
||||
Scalar(AnyObject<'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.instance.value.as_basic_value_enum(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user