diff --git a/nac3core/irrt/irrt_numpy_ndarray.hpp b/nac3core/irrt/irrt_numpy_ndarray.hpp index 396093ea..04599e2c 100644 --- a/nac3core/irrt/irrt_numpy_ndarray.hpp +++ b/nac3core/irrt/irrt_numpy_ndarray.hpp @@ -173,6 +173,8 @@ namespace { // NOTE: Formally this should be of type `void *`, but clang // translates `void *` to `i8 *` when run with `-S -emit-llvm`, // so we will put `uint8_t *` here for clarity. + // + // This pointer should point to the first element of the ndarray directly uint8_t *data; // The number of bytes of a single element in `data`. @@ -308,6 +310,7 @@ namespace { irrt_assert(dst_ndarray->ndims == ndarray_util::deduce_ndims_after_slicing(this->ndims, num_ndslices, ndslices)); dst_ndarray->data = this->data; + dst_ndarray->itemsize = this->itemsize; SizeT this_axis = 0; SizeT dst_axis = 0; @@ -346,7 +349,18 @@ namespace { } } - irrt_assert(dst_axis == dst_ndarray->ndims); // Sanity check on the implementation + /* + Reference python code: + ```python + dst_ndarray.shape.extend(this.shape[this_axis:]) + dst_ndarray.strides.extend(this.strides[this_axis:]) + ``` + */ + + for (; dst_axis < dst_ndarray->ndims; dst_axis++, this_axis++) { + dst_ndarray->shape[dst_axis] = this->shape[this_axis]; + dst_ndarray->strides[dst_axis] = this->strides[this_axis]; + } } // Similar to `np.broadcast_to(, )` @@ -435,6 +449,23 @@ namespace { this->set_pelement_value(this_pelement, src_pelement); } } + + // TODO: DOCUMENT ME + bool is_unsized() { + return this->ndims == 0; + } + + // Simulate `len()` + // See (it doesn't help): https://numpy.org/doc/stable/reference/generated/numpy.ndarray.__len__.html#numpy.ndarray.__len__ + SliceIndex len() { + // If you do `len(np.asarray(42))` (note that its `.shape` is just `()` - an empty tuple), + // numpy throws a `TypeError: len() of unsized object` + irrt_assert(!this->is_unsized()); + + // Apparently `len()` is defined to be the first dimension + // REFERENCE: https://stackoverflow.com/questions/43081809/len-of-a-numpy-array-in-python + return (SliceIndex) this->shape[0]; + } }; } @@ -478,4 +509,12 @@ extern "C" { void __nac3_ndarray_subscript64(NDArray* ndarray, int32_t num_slices, NDSlice* slices, NDArray *dst_ndarray) { ndarray->subscript(num_slices, slices, dst_ndarray); } + + SliceIndex __nac3_ndarray_len(NDArray* ndarray) { + return ndarray->len(); + } + + SliceIndex __nac3_ndarray_len64(NDArray* ndarray) { + return ndarray->len(); + } } \ No newline at end of file diff --git a/nac3core/irrt/irrt_test.cpp b/nac3core/irrt/irrt_test.cpp index c900ee8e..b05e0ac6 100644 --- a/nac3core/irrt/irrt_test.cpp +++ b/nac3core/irrt/irrt_test.cpp @@ -475,6 +475,50 @@ void test_ndslice_2() { assert_values_match("dst_ndarray[1]", "%f", 9.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 1 }))); } +void test_ndslice_3() { + BEGIN_TEST(); + + double in_data[12] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }; + const int32_t in_itemsize = sizeof(double); + const int32_t in_ndims = 2; + int32_t in_shape[in_ndims] = { 3, 4 }; + int32_t in_strides[in_ndims] = {}; + NDArray ndarray = { + .data = (uint8_t*) in_data, + .itemsize = in_itemsize, + .ndims = in_ndims, + .shape = in_shape, + .strides = in_strides + }; + ndarray.set_strides_by_shape(); + + const int32_t dst_ndims = 2; + int32_t dst_shape[dst_ndims] = {999, 999}; // Empty values + int32_t dst_strides[dst_ndims] = {999, 999}; // Empty values + NDArray dst_ndarray = { + .data = nullptr, + .ndims = dst_ndims, + .shape = dst_shape, + .strides = dst_strides + }; + + // Create the slice in `ndarray[2:3]` + UserSlice user_slice_1 = { + .start_defined = 1, + .start = 2, + .stop_defined = 1, + .stop = 3, + .step_defined = 0, + }; + + const int32_t num_ndslices = 1; + NDSlice ndslices[num_ndslices] = { + { .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_1 }, + }; + + ndarray.subscript(num_ndslices, ndslices, &dst_ndarray); +} + void test_can_broadcast_shape() { BEGIN_TEST(); @@ -644,6 +688,7 @@ int main() { test_slice_4(); test_ndslice_1(); test_ndslice_2(); + test_ndslice_3(); test_can_broadcast_shape(); test_ndarray_broadcast_1(); return 0; diff --git a/nac3core/src/codegen/classes.rs b/nac3core/src/codegen/classes.rs index de1fdbf9..04f26a5a 100644 --- a/nac3core/src/codegen/classes.rs +++ b/nac3core/src/codegen/classes.rs @@ -1979,43 +1979,32 @@ impl<'ctx> NpArrayType<'ctx> { /// - `ndarray.itemsize` will be initialized to the size of `self.elem_type.size_of()`. /// - `ndarray.shape` and `ndarray.strides` will be allocated on the stack with number of elements being `in_ndims`, /// all with empty/uninitialized values. - pub fn var_alloc( + pub fn alloca( &self, - generator: &mut G, ctx: &mut CodeGenContext<'ctx, '_>, in_ndims: IntValue<'ctx>, - name: Option<&str>, - ) -> NpArrayValue<'ctx> - where - G: CodeGenerator + ?Sized, - { - let ptr = generator - .gen_var_alloc(ctx, self.get_struct_type(ctx.ctx).as_basic_type_enum(), name) + name: &str, + ) -> NpArrayValue<'ctx> { + let ptr = ctx + .builder + .build_alloca(self.get_struct_type(ctx.ctx).as_basic_type_enum(), name) .unwrap(); // Allocate `in_dims` number of `size_type` on the stack for `shape` and `strides` - let allocated_shape = generator - .gen_array_var_alloc( - ctx, - self.size_type.as_basic_type_enum(), - in_ndims, - Some("allocated_shape"), - ) + let allocated_shape = ctx + .builder + .build_array_alloca(self.size_type.as_basic_type_enum(), in_ndims, "allocated_shape") .unwrap(); - let allocated_strides = generator - .gen_array_var_alloc( - ctx, - self.size_type.as_basic_type_enum(), - in_ndims, - Some("allocated_strides"), - ) + let allocated_strides = ctx + .builder + .build_array_alloca(self.size_type.as_basic_type_enum(), in_ndims, "allocated_strides") .unwrap(); let value = NpArrayValue { ty: *self, ptr }; value.store_ndims(ctx, in_ndims); value.store_itemsize(ctx, self.elem_type.size_of().unwrap()); - value.store_shape(ctx, allocated_shape.base_ptr(ctx, generator)); - value.store_strides(ctx, allocated_strides.base_ptr(ctx, generator)); + value.store_shape(ctx, allocated_shape); + value.store_strides(ctx, allocated_strides); return value; } @@ -2045,6 +2034,11 @@ pub struct NpArrayValue<'ctx> { } impl<'ctx> NpArrayValue<'ctx> { + pub fn load_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> { + let field = self.ty.fields(ctx.ctx).data; + field.load(ctx, self.ptr).into_pointer_value() + } + pub fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, new_data_ptr: PointerValue<'ctx>) { let field = self.ty.fields(ctx.ctx).data; field.store(ctx, self.ptr, new_data_ptr); diff --git a/nac3core/src/codegen/expr.rs b/nac3core/src/codegen/expr.rs index c91d8bba..9a28d42f 100644 --- a/nac3core/src/codegen/expr.rs +++ b/nac3core/src/codegen/expr.rs @@ -17,7 +17,8 @@ use crate::{ call_expect, call_float_floor, call_float_pow, call_float_powi, call_int_smax, call_memcpy_generic, }, - need_sret, numpy::{self, call_ndarray_subscript_impl}, + need_sret, + numpy::{self, call_ndarray_subscript_impl, get_ndarray_first_element}, stmt::{ gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise, gen_var, @@ -38,7 +39,7 @@ use crate::{ use inkwell::{ attributes::{Attribute, AttributeLoc}, types::{AnyType, BasicType, BasicTypeEnum}, - values::{BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue}, + values::{BasicValue, BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue}, AddressSpace, IntPredicate, OptimizationLevel, }; use itertools::{chain, izip, Either, Itertools}; @@ -2100,12 +2101,14 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>( /// Generates code for a subscript expression on an `ndarray`. /// /// * `ty` - The `Type` of the `NDArray` elements. +/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`. /// * `ndarray` - The `NDArray` value. /// * `slice` - The slice expression used to subscript into the `ndarray`. fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>( generator: &mut G, ctx: &mut CodeGenContext<'ctx, '_>, ty: Type, + ndims: Type, ndarray: NpArrayValue<'ctx>, slice: &Expr>, ) -> Result>, String> { @@ -2165,8 +2168,8 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>( let stop = help(stop)?; let step = help(step)?; - // NOTE: Now start stop step should all be 32-bit ints after typechecking - // ...and `IrrtUserSlice` expects `int32`s + // start stop step should all be 32-bit ints after typechecking, + // and `IrrtUserSlice` expects `int32`s NDSlice::Slice(UserSlice { start, stop, step }) } _ => { @@ -2185,12 +2188,36 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>( ndslices.push(ndslice); } - // Finally, perform the actual subscript logic - let subndarray = call_ndarray_subscript_impl(generator, ctx, ndarray, &ndslices.iter().collect_vec())?; + // TODO: what is going on? why the original implementation doesn't assert `ndims_values.len() == 1` + // Extract the `ndims` from a `Type` to `i128` + let TypeEnum::TLiteral { values: ndims_values, .. } = &*ctx.unifier.get_ty_immutable(ndims) + else { + unreachable!() + }; + assert_eq!(ndims_values.len(), 1); + let ndims = i128::try_from(ndims_values[0].clone()).unwrap() as u64; + assert!(ndims > 0); - // ...and return the result - let result = ValueEnum::Dynamic(subndarray.ptr.into()); - Ok(Some(result)) + // Deduce the subndarray's ndims + let dst_ndims = deduce_ndims_after_slicing(ndims, ndslices.iter()); + + // Finally, perform the actual subscript logic + let subndarray = call_ndarray_subscript_impl( + generator, + ctx, + ndarray, + &ndslices.iter().collect_vec(), + )?; + + // ...and return the result, with two cases + let result = if dst_ndims == 0 { + // 1) ndims == 0 (this happens when you do `np.zerps((3, 4))[1, 1]`), return *THE ELEMENT* + get_ndarray_first_element(ctx, subndarray, "element") + } else { + // 2) ndims > 0 (other cases), return subndarray + subndarray.ptr.as_basic_value_enum() + }; + Ok(Some(ValueEnum::Dynamic(result))) // let llvm_i1 = ctx.ctx.bool_type(); // let llvm_i32 = ctx.ctx.i32_type(); @@ -3116,7 +3143,7 @@ pub fn gen_expr<'ctx, G: CodeGenerator>( } } TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => { - let (elem_ty, _) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap(); + let (elem_ty, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap(); // Get the pointer to the ndarray described by `value` let ndarray_ptr = if let Some(v) = generator.gen_expr(ctx, value)? { @@ -3136,7 +3163,9 @@ pub fn gen_expr<'ctx, G: CodeGenerator>( let ndarray = ndarray_ty.value_from_ptr(ctx.ctx, ndarray_ptr); // Implementation - return gen_ndarray_subscript_expr(generator, ctx, *elem_ty, ndarray, slice); + return gen_ndarray_subscript_expr( + generator, ctx, *elem_ty, *ndims, ndarray, slice, + ); } TypeEnum::TTuple { .. } => { let index: u32 = diff --git a/nac3core/src/codegen/irrt/mod.rs b/nac3core/src/codegen/irrt/mod.rs index 2f8f6ae7..1fa10e7d 100644 --- a/nac3core/src/codegen/irrt/mod.rs +++ b/nac3core/src/codegen/irrt/mod.rs @@ -18,6 +18,7 @@ use super::{ }; use crate::codegen::classes::TypedArrayLikeAccessor; use crate::codegen::stmt::gen_for_callback_incrementing; +use crossbeam::channel::IntoIter; use inkwell::{ attributes::{Attribute, AttributeLoc}, context::Context, @@ -25,7 +26,7 @@ use inkwell::{ module::Module, types::{BasicType, BasicTypeEnum, FunctionType, IntType, PointerType, StructType}, values::{ - BasicValue, BasicValueEnum, CallSiteValue, FloatValue, FunctionValue, IntValue, + AnyValue, BasicValue, BasicValueEnum, CallSiteValue, FloatValue, FunctionValue, IntValue, PointerValue, }, AddressSpace, IntPredicate, @@ -1049,6 +1050,25 @@ pub enum NDSlice<'ctx> { // TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools; *should* be very easy to implement } +pub fn deduce_ndims_after_slicing<'ctx, I>(ndims: u64, ndslices: I) -> u64 +where + I: Iterator>, +{ + let mut final_ndims = ndims; + for ndslice in ndslices { + match ndslice { + NDSlice::Index(_) => { + // Index demote the output rank by 1 + final_ndims -= 1; + } + NDSlice::Slice(_) => { + // Rank isn't changed + } + } + } + final_ndims +} + // TODO: Empty struct pub struct IrrtNDSlice {} @@ -1280,7 +1300,7 @@ pub fn call_nac3_ndarray_set_strides_by_shape<'ctx>( .unwrap(); } -pub fn call_nac3_ndarray_deduce_ndims_after_slicing_raw<'ctx>( +pub fn call_nac3_ndarray_deduce_ndims_after_slicing<'ctx>( ctx: &CodeGenContext<'ctx, '_>, size_type: IntType<'ctx>, ndims: IntValue<'ctx>, @@ -1365,3 +1385,39 @@ pub fn call_nac3_ndarray_subscript<'ctx>( ) .unwrap(); } + +pub fn call_nac3_len<'ctx>( + ctx: &CodeGenContext<'ctx, '_>, + ndarray: NpArrayValue<'ctx>, +) -> IntValue<'ctx> { + let size_type = ndarray.ty.size_type; + + // Get the IRRT function + let function = get_size_type_dependent_function( + ctx, + size_type, + "__nac3_ndarray_deduce_ndims_after_slicing", + || { + get_sliceindex_type(ctx.ctx).fn_type( + &[ + get_irrt_ndarray_ptr_type(ctx.ctx, size_type).into(), // NDArray *ndarray + ], + false, + ) + }, + ); + + // Call the IRRT function + ctx.builder + .build_call( + function, + &[ + ndarray.ptr.into(), // ndarray + ], + "len_of_ndarray", + ) + .unwrap() + .try_as_basic_value() + .unwrap_left() + .into_int_value() +} diff --git a/nac3core/src/codegen/numpy.rs b/nac3core/src/codegen/numpy.rs index 317cfcff..ba89966c 100644 --- a/nac3core/src/codegen/numpy.rs +++ b/nac3core/src/codegen/numpy.rs @@ -36,7 +36,7 @@ use nac3parser::ast::{Operator, StrRef}; use super::{ classes::NpArrayValue, irrt::{ - call_nac3_ndarray_deduce_ndims_after_slicing_raw, call_nac3_ndarray_set_strides_by_shape, + call_nac3_ndarray_deduce_ndims_after_slicing, call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size, call_nac3_ndarray_subscript, get_irrt_ndarray_ptr_type, get_opaque_uint8_ptr_type, IrrtNDSlice, NDSlice, }, @@ -2087,19 +2087,19 @@ fn copy_array_slice<'ctx, G, Src, Dst>( .unwrap(); } -fn var_alloc_ndarray<'ctx, G>( +fn alloca_ndarray<'ctx, G>( generator: &mut G, ctx: &mut CodeGenContext<'ctx, '_>, elem_type: BasicTypeEnum<'ctx>, ndims: IntValue<'ctx>, - name: Option<&str>, + name: &str, ) -> Result, String> where G: CodeGenerator + ?Sized, { let size_type = generator.get_size_type(ctx.ctx); let ndarray_ty = NpArrayType { size_type, elem_type }; - let ndarray = ndarray_ty.var_alloc(generator, ctx, ndims, name); + let ndarray = ndarray_ty.alloca(ctx, ndims, name); Ok(ndarray) } @@ -2270,7 +2270,7 @@ fn alloca_ndarray_and_init<'ctx, G>( ctx: &mut CodeGenContext<'ctx, '_>, elem_type: BasicTypeEnum<'ctx>, init_mode: NDArrayInitMode<'ctx, G>, - name: Option<&str>, + name: &str, ) -> Result, String> where G: CodeGenerator + ?Sized, @@ -2278,12 +2278,12 @@ where // It is implemented verbosely in order to make the initialization modes super clear in their intent. match init_mode { NDArrayInitMode::SetNDim { ndim: ndims } => { - let ndarray = var_alloc_ndarray(generator, ctx, elem_type, ndims, name)?; + let ndarray = alloca_ndarray(generator, ctx, elem_type, ndims, name)?; Ok(ndarray) } NDArrayInitMode::SetShape { shape } => { let ndims = shape.count; - let ndarray = var_alloc_ndarray(generator, ctx, elem_type, ndims, name)?; + let ndarray = alloca_ndarray(generator, ctx, elem_type, ndims, name)?; // Fill `ndarray.shape` with `shape_producer` (shape.write_to_slice)(generator, ctx, &ndarray.shape_slice(ctx)); @@ -2292,7 +2292,7 @@ where } NDArrayInitMode::SetShapeAndAllocaData { shape } => { let ndims = shape.count; - let ndarray = var_alloc_ndarray(generator, ctx, elem_type, ndims, name)?; + let ndarray = alloca_ndarray(generator, ctx, elem_type, ndims, name)?; // Fill `ndarray.shape` with `shape_producer` (shape.write_to_slice)(generator, ctx, &ndarray.shape_slice(ctx)); @@ -2320,6 +2320,28 @@ where } } +pub fn get_ndarray_first_element<'ctx>( + ctx: &CodeGenContext<'ctx, '_>, + ndarray: NpArrayValue<'ctx>, + name: &str, +) -> BasicValueEnum<'ctx> { + let data = ndarray.load_data(ctx); + + // Cast `data` to the actual element the `subndarray` holds + // otherwise `subndarray.data` is just a bunch of `uint8_t*` + let data = ctx + .builder + .build_pointer_cast( + data, + ndarray.ty.elem_type.ptr_type(AddressSpace::default()), + "data_casted", + ) + .unwrap(); + + // Load the element + ctx.builder.build_load(data, name).unwrap() +} + pub fn call_ndarray_subscript_impl<'ctx, G>( generator: &mut G, ctx: &mut CodeGenContext<'ctx, '_>, @@ -2344,10 +2366,10 @@ where // Prepare the argument `slices` let ndslices_ptr = IrrtNDSlice::alloca_ndslices(ctx, ndslices); - // Deduce the ndims - let dst_ndims = call_nac3_ndarray_deduce_ndims_after_slicing_raw( + // Get `dst_ndims` + let dst_ndims = call_nac3_ndarray_deduce_ndims_after_slicing( ctx, - ndarray.ty.size_type, + size_type, ndims, num_slices, ndslices_ptr, @@ -2359,7 +2381,7 @@ where ctx, ndarray.ty.elem_type, NDArrayInitMode::SetNDim { ndim: dst_ndims }, - Some("subndarray"), + "subndarray", )?; call_nac3_ndarray_subscript(ctx, ndarray, num_slices, ndslices_ptr, dst_ndarray); @@ -2374,7 +2396,7 @@ fn call_ndarray_empty_impl<'ctx, G>( elem_ty: Type, shape: BasicValueEnum<'ctx>, shape_ty: Type, - name: Option<&str>, + name: &str, ) -> Result, String> where G: CodeGenerator + ?Sized, @@ -2398,7 +2420,7 @@ fn call_ndarray_fill_impl<'ctx, G>( shape: BasicValueEnum<'ctx>, shape_ty: Type, fill_value: BasicValueEnum<'ctx>, - name: Option<&str>, + name: &str, ) -> Result, String> where G: CodeGenerator + ?Sized, @@ -2430,7 +2452,7 @@ pub fn gen_ndarray_empty<'ctx>( context.primitives.float, shape, shape_ty, - None, + "empty_ndarray", )?; Ok(ndarray.ptr) } @@ -2462,7 +2484,7 @@ pub fn gen_ndarray_zeros<'ctx>( shape, shape_ty, float64_llvm_type.const_zero().as_basic_value_enum(), - Some("np_zeros.result"), + "zeros_ndarray", )?; Ok(ndarray.ptr) } @@ -2494,7 +2516,7 @@ pub fn gen_ndarray_ones<'ctx>( shape, shape_ty, float64_llvm_type.const_float(1.0).as_basic_value_enum(), - Some("np_ones.result"), + "ones_ndarray", )?; Ok(ndarray.ptr) } diff --git a/nac3core/src/toplevel/builtins.rs b/nac3core/src/toplevel/builtins.rs index 84fa91f6..abaaae24 100644 --- a/nac3core/src/toplevel/builtins.rs +++ b/nac3core/src/toplevel/builtins.rs @@ -1,6 +1,6 @@ use std::iter::once; -use crate::util::SizeVariant; +use crate::{codegen::classes::NpArrayType, util::SizeVariant}; use helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails}; use indexmap::IndexMap; use inkwell::{ @@ -1196,7 +1196,7 @@ impl<'a> BuiltinBuilder<'a> { let func = match prim { PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty, PrimDef::FunNpZeros => gen_ndarray_zeros, - PrimDef::FunNpOnes => todo!(), // gen_ndarray_ones, + PrimDef::FunNpOnes => gen_ndarray_ones, // gen_ndarray_ones, _ => unreachable!(), }; func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum())) @@ -1460,51 +1460,62 @@ impl<'a> BuiltinBuilder<'a> { } } TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => { - let llvm_i32 = ctx.ctx.i32_type(); - let llvm_usize = generator.get_size_type(ctx.ctx); + // TODO: Check is unsized and throw error if so - let arg = NDArrayValue::from_ptr_val( - arg.into_pointer_value(), - llvm_usize, - None, - ); + // Parse `arg` + let ndarray_ptr = arg.into_pointer_value(); // It has to be an ndarray - let ndims = arg.dim_sizes().size(ctx, generator); - ctx.make_assert( - generator, - ctx.builder - .build_int_compare( - IntPredicate::NE, - ndims, - llvm_usize.const_zero(), - "", - ) - .unwrap(), - "0:TypeError", - &format!("{name}() of unsized object", name = prim.name()), - [None, None, None], - ctx.current_loc, - ); + let size_type = generator.get_size_type(ctx.ctx); + let ndarray_ty = NpArrayType::new_opaque_elem(ctx, size_type); // We don't need to care about the element type - we only want the shape + let ndarray = ndarray_ty.value_from_ptr(ctx.ctx, ndarray_ptr); - let len = unsafe { - arg.dim_sizes().get_typed_unchecked( - ctx, - generator, - &llvm_usize.const_zero(), - None, - ) - }; + Some(call_nac3_len(ctx, ndarray).as_basic_value_enum()) - if len.get_type().get_bit_width() == 32 { - Some(len.into()) - } else { - Some( - ctx.builder - .build_int_truncate(len, llvm_i32, "len") - .map(Into::into) - .unwrap(), - ) - } + // let llvm_i32 = ctx.ctx.i32_type(); + // let llvm_usize = generator.get_size_type(ctx.ctx); + + // let arg = NDArrayValue::from_ptr_val( + // arg.into_pointer_value(), + // llvm_usize, + // None, + // ); + + // let ndims = arg.dim_sizes().size(ctx, generator); + // ctx.make_assert( + // generator, + // ctx.builder + // .build_int_compare( + // IntPredicate::NE, + // ndims, + // llvm_usize.const_zero(), + // "", + // ) + // .unwrap(), + // "0:TypeError", + // &format!("{name}() of unsized object", name = prim.name()), + // [None, None, None], + // ctx.current_loc, + // ); + + // let len = unsafe { + // arg.dim_sizes().get_typed_unchecked( + // ctx, + // generator, + // &llvm_usize.const_zero(), + // None, + // ) + // }; + + // if len.get_type().get_bit_width() == 32 { + // Some(len.into()) + // } else { + // Some( + // ctx.builder + // .build_int_truncate(len, llvm_i32, "len") + // .map(Into::into) + // .unwrap(), + // ) + // } } _ => unreachable!(), } diff --git a/nac3standalone/demo/src/my_ndarray.py b/nac3standalone/demo/src/my_ndarray.py index 94693d26..feec0fa6 100644 --- a/nac3standalone/demo/src/my_ndarray.py +++ b/nac3standalone/demo/src/my_ndarray.py @@ -1,3 +1,18 @@ +@extern +def output_float64(x: float): + ... + +def output_ndarray_float_1(n: ndarray[float, Literal[1]]): + for i in range(len(n)): + output_float64(n[i]) + +def output_ndarray_float_2(n: ndarray[float, Literal[2]]): + for r in range(len(n)): + for c in range(len(n[r])): + output_float64(n[r][c]) + def run() -> int32: - hello = np_zeros((3, 4)) + hello = np_ones((3, 4)) + # output_float64(hello[2, 3]) + output_ndarray_float_1(hello[::-2, 2]) return 0 \ No newline at end of file