forked from M-Labs/nac3
core/ndstrides: implement general ndarray reshaping
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nac3core/irrt/irrt/ndarray/reshape.hpp
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116
nac3core/irrt/irrt/ndarray/reshape.hpp
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@ -0,0 +1,116 @@
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#pragma once
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#include <irrt/error_context.hpp>
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#include <irrt/int_defs.hpp>
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#include <irrt/ndarray/def.hpp>
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namespace {
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namespace ndarray {
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namespace reshape {
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namespace util {
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/**
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* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
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*
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* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
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* modified to contain the resolved dimension.
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*
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* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
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* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
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*
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* @param size The `.size` of `<ndarray>`
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* @param new_ndims Number of elements in `new_shape`
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* @param new_shape Target shape to reshape to
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*/
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template <typename SizeT>
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void resolve_and_check_new_shape(ErrorContext* errctx, SizeT size,
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SizeT new_ndims, SizeT* new_shape) {
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// Is there a -1 in `new_shape`?
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bool neg1_exists = false;
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// Location of -1, only initialized if `neg1_exists` is true
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SizeT neg1_axis_i;
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// The computed ndarray size of `new_shape`
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SizeT new_size = 1;
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for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
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SizeT dim = new_shape[axis_i];
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if (dim < 0) {
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if (dim == -1) {
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if (neg1_exists) {
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// Multiple `-1` found. Throw an error.
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errctx->set_error(errctx->error_ids->value_error,
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"can only specify one unknown dimension");
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return;
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} else {
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neg1_exists = true;
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neg1_axis_i = axis_i;
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}
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} else {
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// TODO: What? In `np.reshape` any negative dimensions is
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// treated like its `-1`.
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//
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// Try running `np.zeros((3, 4)).reshape((-999, 2))`
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//
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// It is not documented by numpy.
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// Throw an error for now...
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errctx->set_error(errctx->error_ids->value_error,
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"Found negative dimension {0} on axis {1}",
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dim, axis_i);
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return;
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}
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} else {
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new_size *= dim;
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}
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}
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bool can_reshape;
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if (neg1_exists) {
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// Let `x` be the unknown dimension
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// solve `x * <new_size> = <size>`
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if (new_size == 0 && size == 0) {
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// `x` has infinitely many solutions
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can_reshape = false;
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} else if (new_size == 0 && size != 0) {
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// `x` has no solutions
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can_reshape = false;
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} else if (size % new_size != 0) {
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// `x` has no integer solutions
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can_reshape = false;
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} else {
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can_reshape = true;
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new_shape[neg1_axis_i] = size / new_size; // Resolve dimension
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}
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} else {
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can_reshape = (new_size == size);
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}
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if (!can_reshape) {
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errctx->set_error(errctx->error_ids->value_error,
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"cannot reshape array of size {0} into given shape",
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size);
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return;
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}
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}
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} // namespace util
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} // namespace reshape
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} // namespace ndarray
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} // namespace
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extern "C" {
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void __nac3_ndarray_resolve_and_check_new_shape(ErrorContext* errctx,
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int32_t size, int32_t new_ndims,
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int32_t* new_shape) {
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ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
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new_shape);
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}
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void __nac3_ndarray_resolve_and_check_new_shape64(ErrorContext* errctx,
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int64_t size,
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int64_t new_ndims,
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int64_t* new_shape) {
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ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
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new_shape);
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}
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}
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@ -8,5 +8,6 @@
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#include <irrt/ndarray/def.hpp>
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#include <irrt/ndarray/fill.hpp>
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#include <irrt/ndarray/indexing.hpp>
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#include <irrt/ndarray/reshape.hpp>
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#include <irrt/slice.hpp>
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#include <irrt/utils.hpp>
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@ -2,3 +2,4 @@ pub mod allocation;
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pub mod basic;
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pub mod fill;
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pub mod indexing;
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pub mod reshape;
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30
nac3core/src/codegen/irrt/ndarray/reshape.rs
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30
nac3core/src/codegen/irrt/ndarray/reshape.rs
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@ -0,0 +1,30 @@
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use crate::codegen::{
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irrt::{
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error_context::{check_error_context, setup_error_context},
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util::get_sized_dependent_function_name,
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},
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model::*,
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CodeGenContext, CodeGenerator,
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};
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pub fn call_nac3_ndarray_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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size: SizeT<'ctx>,
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new_ndims: SizeT<'ctx>,
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new_shape: Pointer<'ctx, SizeTModel<'ctx>>,
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) {
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let sizet = generator.get_sizet(ctx.ctx);
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let perrctx = setup_error_context(ctx);
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FunctionBuilder::begin(
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ctx,
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&get_sized_dependent_function_name(sizet, "__nac3_ndarray_resolve_and_check_new_shape"),
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)
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.arg("errctx", perrctx)
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.arg("size", size)
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.arg("new_ndims", new_ndims)
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.arg("new_shape", new_shape)
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.returning_void();
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check_error_context(generator, ctx, perrctx);
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}
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@ -1 +1,2 @@
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pub mod factory;
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pub mod view;
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131
nac3core/src/codegen/numpy_new/view.rs
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131
nac3core/src/codegen/numpy_new/view.rs
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@ -0,0 +1,131 @@
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use inkwell::values::PointerValue;
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use nac3parser::ast::StrRef;
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use crate::{
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codegen::{
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irrt::ndarray::{
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allocation::{alloca_ndarray, init_ndarray_shape},
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basic::{
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call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_nbytes,
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call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
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},
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reshape::call_nac3_ndarray_resolve_and_check_new_shape,
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},
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model::*,
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structs::ndarray::NpArray,
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util::{array_writer::ArrayWriter, shape::parse_input_shape_arg},
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CodeGenContext, CodeGenerator,
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},
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symbol_resolver::ValueEnum,
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toplevel::DefinitionId,
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typecheck::typedef::{FunSignature, Type},
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};
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fn reshape_ndarray_or_copy<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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src_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
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new_shape: &ArrayWriter<'ctx, G, SizeTModel<'ctx>, SizeTModel<'ctx>>,
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) -> Result<Pointer<'ctx, StructModel<NpArray<'ctx>>>, String> {
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let byte_model = NIntModel(Byte);
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/*
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Reference pseudo-code:
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```c
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NDArray<SizeT>* src_ndarray;
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NDArray<SizeT>* dst_ndarray = __builtin_alloca(...);
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dst_ndarray->ndims = ...
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dst_ndarray->strides = __builtin_alloca(...);
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dst_ndarray->shape = ... // Directly set by user, may contain -1, or even illegal values.
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dst_ndarray->itemsize = src_ndarray->itemsize;
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set_strides_by_shape(dst_ndarray);
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// Do assertions on `dst_ndarray->shape` and resolve -1
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resolve_and_check_new_shape(ndarray_size(src_ndarray), dst_ndarray->shape);
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if (is_c_contiguous(src_ndarray)) {
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dst_ndarray->data = src_ndarray->data;
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} else {
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dst_ndarray->data = __builtin_alloca( ndarray_nbytes(dst_ndarray) );
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copy_data(src_ndarray, dst_ndarray);
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}
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return dst_ndarray;
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```
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*/
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let current_bb = ctx.builder.get_insert_block().unwrap();
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let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then");
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let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
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let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
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// current_bb
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let dst_ndarray = alloca_ndarray(generator, ctx, new_shape.count, "ndarray").unwrap();
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init_ndarray_shape(generator, ctx, dst_ndarray, new_shape)?;
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dst_ndarray
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.gep(ctx, |f| f.itemsize)
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.store(ctx, src_ndarray.gep(ctx, |f| f.itemsize).load(ctx, "itemsize"));
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call_nac3_ndarray_set_strides_by_shape(generator, ctx, dst_ndarray);
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let src_ndarray_size = call_nac3_ndarray_size(generator, ctx, src_ndarray);
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call_nac3_ndarray_resolve_and_check_new_shape(
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generator,
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ctx,
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src_ndarray_size,
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dst_ndarray.gep(ctx, |f| f.ndims).load(ctx, "ndims"),
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dst_ndarray.gep(ctx, |f| f.shape).load(ctx, "shape"),
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);
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let is_c_contiguous = call_nac3_ndarray_is_c_contiguous(generator, ctx, src_ndarray);
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ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
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// then_bb: reshape is possible without copying
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ctx.builder.position_at_end(then_bb);
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dst_ndarray.gep(ctx, |f| f.data).store(ctx, src_ndarray.gep(ctx, |f| f.data).load(ctx, "data"));
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ctx.builder.build_unconditional_branch(end_bb).unwrap();
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// else_bb: reshape is impossible without copying
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ctx.builder.position_at_end(else_bb);
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let dst_ndarray_nbytes = call_nac3_ndarray_nbytes(generator, ctx, dst_ndarray);
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let data = byte_model.array_alloca(ctx, dst_ndarray_nbytes, "new_data").pointer;
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dst_ndarray.gep(ctx, |f| f.data).store(ctx, data);
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ctx.builder.build_unconditional_branch(end_bb).unwrap();
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// Reposition for continuation
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ctx.builder.position_at_end(end_bb);
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Ok(dst_ndarray)
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}
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/// Generates LLVM IR for `np.reshape`.
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pub fn gen_ndarray_reshape<'ctx>(
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context: &mut CodeGenContext<'ctx, '_>,
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obj: &Option<(Type, ValueEnum<'ctx>)>,
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fun: (&FunSignature, DefinitionId),
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args: &[(Option<StrRef>, ValueEnum<'ctx>)],
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generator: &mut dyn CodeGenerator,
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) -> Result<PointerValue<'ctx>, String> {
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assert!(obj.is_none());
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assert_eq!(args.len(), 2);
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// Parse argument #1 ndarray
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let ndarray_ty = fun.0.args[0].ty;
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let ndarray_arg = args[0].1.clone().to_basic_value_enum(context, generator, ndarray_ty)?;
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// Parse argument #2 shape
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let shape_ty = fun.0.args[1].ty;
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let shape_arg = args[1].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
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let sizet = generator.get_sizet(context.ctx);
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let pndarray_model = PointerModel(StructModel(NpArray { sizet }));
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let src_ndarray = pndarray_model.review_value(context.ctx, ndarray_arg).unwrap();
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let new_shape = parse_input_shape_arg(generator, context, shape_arg, shape_ty);
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let reshaped_ndarray = reshape_ndarray_or_copy(generator, context, src_ndarray, &new_shape)?;
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Ok(reshaped_ndarray.value)
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}
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@ -496,6 +496,8 @@ impl<'a> BuiltinBuilder<'a> {
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| PrimDef::FunNpEye
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| PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
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PrimDef::FunNpReshape => self.build_ndarray_view_functions(prim),
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PrimDef::FunStr => self.build_str_function(),
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PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
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@ -1333,6 +1335,39 @@ impl<'a> BuiltinBuilder<'a> {
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}
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}
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// Build functions related to NDArray views
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fn build_ndarray_view_functions(&mut self, prim: PrimDef) -> TopLevelDef {
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debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpReshape]);
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match prim {
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PrimDef::FunNpReshape => {
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let new_ndim_ty = self.unifier.get_fresh_var(Some("NewNDim".into()), None);
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let returned_ndarray_ty = make_ndarray_ty(
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self.unifier,
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self.primitives,
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Some(self.ndarray_dtype_tvar.ty),
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Some(new_ndim_ty.ty),
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);
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create_fn_by_codegen(
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self.unifier,
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&into_var_map([self.ndarray_dtype_tvar, self.ndarray_ndims_tvar, new_ndim_ty]),
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prim.name(),
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returned_ndarray_ty,
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&[
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(self.primitives.ndarray, "array"),
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(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"),
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],
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Box::new(|ctx, obj, fun, args, generator| {
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numpy_new::view::gen_ndarray_reshape(ctx, &obj, fun, &args, generator)
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.map(|val| Some(val.as_basic_value_enum()))
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}),
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)
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}
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_ => unreachable!(),
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}
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}
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/// Build the `str()` function.
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fn build_str_function(&mut self) -> TopLevelDef {
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let prim = PrimDef::FunStr;
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@ -46,6 +46,7 @@ pub enum PrimDef {
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FunNpArray,
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FunNpEye,
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FunNpIdentity,
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FunNpReshape,
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FunRound,
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FunRound64,
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FunNpRound,
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@ -204,6 +205,7 @@ impl PrimDef {
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PrimDef::FunNpArray => fun("np_array", None),
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PrimDef::FunNpEye => fun("np_eye", None),
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PrimDef::FunNpIdentity => fun("np_identity", None),
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PrimDef::FunNpReshape => fun("np_reshape", None),
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PrimDef::FunRound => fun("round", None),
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PrimDef::FunRound64 => fun("round64", None),
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PrimDef::FunNpRound => fun("np_round", None),
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@ -5,7 +5,7 @@ expression: res_vec
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[
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"Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
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"Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
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"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(245)]\n}\n",
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"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
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"Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
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"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
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"Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",
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@ -7,7 +7,7 @@ expression: res_vec
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"Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
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"Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
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"Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
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"Class {\nname: \"B\",\nancestors: [\"B[typevar234]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar234\"]\n}\n",
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"Class {\nname: \"B\",\nancestors: [\"B[typevar237]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar237\"]\n}\n",
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"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
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"Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
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"Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",
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||||
|
@ -5,8 +5,8 @@ expression: res_vec
|
||||
[
|
||||
"Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(247)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(252)]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(250)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(255)]\n}\n",
|
||||
"Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
|
@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
|
||||
expression: res_vec
|
||||
---
|
||||
[
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar233, typevar234]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar233\", \"typevar234\"]\n}\n",
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar236, typevar237]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar236\", \"typevar237\"]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",
|
||||
|
@ -6,12 +6,12 @@ expression: res_vec
|
||||
"Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(253)]\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(256)]\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(261)]\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(264)]\n}\n",
|
||||
]
|
||||
|
@ -1390,6 +1390,55 @@ impl<'a> Inferencer<'a> {
|
||||
}));
|
||||
}
|
||||
|
||||
// Handle `np.reshape(<array>, <shape>)`
|
||||
if ["np_reshape".into()].contains(id) && args.len() == 2 {
|
||||
// Extract arguments
|
||||
let array_expr = args.remove(0);
|
||||
let shape_expr = args.remove(0);
|
||||
|
||||
// Fold `<array>`
|
||||
let array = self.fold_expr(array_expr)?;
|
||||
let array_ty = array.custom.unwrap();
|
||||
let (array_dtype, _) = unpack_ndarray_var_tys(self.unifier, array_ty);
|
||||
|
||||
// Fold `<shape>`
|
||||
let (target_ndims, target_shape) =
|
||||
self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?;
|
||||
let target_shape_ty = target_shape.custom.unwrap();
|
||||
// ... and deduce the return type of the call
|
||||
let target_ndims_ty =
|
||||
self.unifier.get_fresh_literal(vec![SymbolValue::U64(target_ndims)], None);
|
||||
let ret = make_ndarray_ty(
|
||||
self.unifier,
|
||||
self.primitives,
|
||||
Some(array_dtype),
|
||||
Some(target_ndims_ty),
|
||||
);
|
||||
|
||||
let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
|
||||
args: vec![
|
||||
FuncArg { name: "array".into(), ty: array_ty, default_value: None },
|
||||
FuncArg { name: "shape".into(), ty: target_shape_ty, default_value: None },
|
||||
],
|
||||
ret,
|
||||
vars: VarMap::new(),
|
||||
}));
|
||||
|
||||
return Ok(Some(Located {
|
||||
location,
|
||||
custom: Some(ret),
|
||||
node: ExprKind::Call {
|
||||
func: Box::new(Located {
|
||||
custom: Some(custom),
|
||||
location: func.location,
|
||||
node: ExprKind::Name { id: *id, ctx: *ctx },
|
||||
}),
|
||||
args: vec![array, target_shape],
|
||||
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 {
|
||||
|
@ -178,6 +178,9 @@ def patch(module):
|
||||
module.np_identity = np.identity
|
||||
module.np_array = np.array
|
||||
|
||||
# NumPy view functions
|
||||
module.np_reshape = np.reshape
|
||||
|
||||
# NumPy Math functions
|
||||
module.np_isnan = np.isnan
|
||||
module.np_isinf = np.isinf
|
||||
|
Loading…
Reference in New Issue
Block a user