artiq: reimplement get_obj_value to use ndarray with strides

nac3artiq/src/symbol_resolver.rs

@@ -1,14 +1,15 @@
 use crate::PrimitivePythonId;
 use inkwell::{
     module::Linkage,
-    types::{BasicType, BasicTypeEnum},
+    types::BasicType,
-    values::BasicValueEnum,
+    values::{BasicValue, BasicValueEnum},
     AddressSpace,
 };
 use itertools::Itertools;
 use nac3core::{
     codegen::{
-        classes::{NDArrayType, ProxyType},
+        model::*,
+        object::ndarray::{make_contiguous_strides, NDArray},
         CodeGenContext, CodeGenerator,
     },
     symbol_resolver::{StaticValue, SymbolResolver, SymbolValue, ValueEnum},
@@ -26,7 +27,7 @@ use nac3parser::ast::{self, StrRef};
 use parking_lot::{Mutex, RwLock};
 use pyo3::{
     types::{PyDict, PyTuple},
-    PyAny, PyObject, PyResult, Python,
+    PyAny, PyErr, PyObject, PyResult, Python,
 };
 use std::{
     collections::{HashMap, HashSet},
@@ -1086,15 +1087,12 @@ impl InnerResolver {
             let (ndarray_dtype, ndarray_ndims) =
                 unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
 
-            let llvm_usize = generator.get_size_type(ctx.ctx);
+            let dtype = Any(ctx.get_llvm_type(generator, ndarray_dtype));
-            let ndarray_dtype_llvm_ty = ctx.get_llvm_type(generator, ndarray_dtype);
-            let ndarray_llvm_ty = NDArrayType::new(generator, ctx.ctx, ndarray_dtype_llvm_ty);
-
             {
                 if self.global_value_ids.read().contains_key(&id) {
                     let global = ctx.module.get_global(&id_str).unwrap_or_else(|| {
                         ctx.module.add_global(
-                            ndarray_llvm_ty.as_underlying_type(),
+                            Struct(NDArray).get_type(generator, ctx.ctx),
                             Some(AddressSpace::default()),
                             &id_str,
                         )
@@ -1114,100 +1112,138 @@ impl InnerResolver {
             } else {
                 todo!("Unpacking literal of more than one element unimplemented")
             };
-            let Ok(ndarray_ndims) = u64::try_from(ndarray_ndims) else {
+            let Ok(ndims) = u64::try_from(ndarray_ndims) else {
                 unreachable!("Expected u64 value for ndarray_ndims")
             };
 
             // Obtain the shape of the ndarray
             let shape_tuple: &PyTuple = obj.getattr("shape")?.downcast()?;
-            assert_eq!(shape_tuple.len(), ndarray_ndims as usize);
+            assert_eq!(shape_tuple.len(), ndims as usize);
-            let shape_values: Result<Option<Vec<_>>, _> = shape_tuple
+
+            // The Rust type inferencer cannot figure this out
+            let shape_values: Result<Vec<Instance<'ctx, Int<SizeT>>>, PyErr> = shape_tuple
                 .iter()
                 .enumerate()
                 .map(|(i, elem)| {
-                    self.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize()).map_err(
+                    let value = self
-                        |e| super::CompileError::new_err(format!("Error getting element {i}: {e}")),
+                        .get_obj_value(py, elem, ctx, generator, ctx.primitives.usize())
-                    )
+                        .map_err(|e| {
+                            super::CompileError::new_err(format!("Error getting element {i}: {e}"))
+                        })?
+                        .unwrap();
+                    let value = Int(SizeT).check_value(generator, ctx.ctx, value).unwrap();
+                    Ok(value)
                 })
                 .collect();
-            let shape_values = shape_values?.unwrap();
+            let shape_values = shape_values?;
-            let shape_values = llvm_usize.const_array(
+
-                &shape_values.into_iter().map(BasicValueEnum::into_int_value).collect_vec(),
+            // Also use this opportunity to get the constant values of `shape_values` for calculating strides.
-            );
+            let shape_u64s = shape_values
+                .iter()
+                .map(|dim| {
+                    assert!(dim.value.is_const());
+                    dim.value.get_zero_extended_constant().unwrap()
+                })
+                .collect_vec();
+            let shape_values = Int(SizeT).const_array(generator, ctx.ctx, &shape_values);
 
             // create a global for ndarray.shape and initialize it using the shape
             let shape_global = ctx.module.add_global(
-                llvm_usize.array_type(ndarray_ndims as u32),
+                Array { len: ndims as u32, item: Int(SizeT) }.get_type(generator, ctx.ctx),
                 Some(AddressSpace::default()),
                 &(id_str.clone() + ".shape"),
             );
-            shape_global.set_initializer(&shape_values);
+            shape_global.set_initializer(&shape_values.value);
 
             // Obtain the (flattened) elements of the ndarray
             let sz: usize = obj.getattr("size")?.extract()?;
-            let data: Result<Option<Vec<_>>, _> = (0..sz)
+            let data_values: Vec<Instance<'ctx, Any>> = (0..sz)
                 .map(|i| {
                     obj.getattr("flat")?.get_item(i).and_then(|elem| {
-                        self.get_obj_value(py, elem, ctx, generator, ndarray_dtype).map_err(|e| {
+                        let value = self
-                            super::CompileError::new_err(format!("Error getting element {i}: {e}"))
+                            .get_obj_value(py, elem, ctx, generator, ndarray_dtype)
-                        })
+                            .map_err(|e| {
+                                super::CompileError::new_err(format!(
+                                    "Error getting element {i}: {e}"
+                                ))
+                            })?
+                            .unwrap();
+
+                        let value = dtype.check_value(generator, ctx.ctx, value).unwrap();
+                        Ok(value)
                     })
                 })
-                .collect();
+                .try_collect()?;
-            let data = data?.unwrap().into_iter();
+            let data = dtype.const_array(generator, ctx.ctx, &data_values);
-            let data = match ndarray_dtype_llvm_ty {
-                BasicTypeEnum::ArrayType(ty) => {
-                    ty.const_array(&data.map(BasicValueEnum::into_array_value).collect_vec())
-                }
-
-                BasicTypeEnum::FloatType(ty) => {
-                    ty.const_array(&data.map(BasicValueEnum::into_float_value).collect_vec())
-                }
-
-                BasicTypeEnum::IntType(ty) => {
-                    ty.const_array(&data.map(BasicValueEnum::into_int_value).collect_vec())
-                }
-
-                BasicTypeEnum::PointerType(ty) => {
-                    ty.const_array(&data.map(BasicValueEnum::into_pointer_value).collect_vec())
-                }
-
-                BasicTypeEnum::StructType(ty) => {
-                    ty.const_array(&data.map(BasicValueEnum::into_struct_value).collect_vec())
-                }
-
-                BasicTypeEnum::VectorType(_) => unreachable!(),
-            };
 
             // create a global for ndarray.data and initialize it using the elements
+            //
+            // NOTE: NDArray's `data` is `u8*`. Here, `data_global` is an array of `dtype`.
+            // We will have to cast it to an `u8*` later.
             let data_global = ctx.module.add_global(
-                ndarray_dtype_llvm_ty.array_type(sz as u32),
+                Array { len: sz as u32, item: dtype }.get_type(generator, ctx.ctx),
                 Some(AddressSpace::default()),
                 &(id_str.clone() + ".data"),
             );
-            data_global.set_initializer(&data);
+            data_global.set_initializer(&data.value);
+
+            // Get the constant itemsize.
+            let itemsize = dtype.get_type(generator, ctx.ctx).size_of().unwrap();
+            let itemsize = itemsize.get_zero_extended_constant().unwrap();
+
+            // Create the strides needed for ndarray.strides
+            let strides = make_contiguous_strides(itemsize, ndims, &shape_u64s);
+            let strides = strides
+                .into_iter()
+                .map(|stride| Int(SizeT).const_int(generator, ctx.ctx, stride))
+                .collect_vec();
+            let strides = Int(SizeT).const_array(generator, ctx.ctx, &strides);
+
+            // create a global for ndarray.strides and initialize it
+            let strides_global = ctx.module.add_global(
+                Array { len: ndims as u32, item: Int(Byte) }.get_type(generator, ctx.ctx),
+                Some(AddressSpace::default()),
+                &(id_str.clone() + ".strides"),
+            );
+            strides_global.set_initializer(&strides.value);
 
             // create a global for the ndarray object and initialize it
-            let value = ndarray_llvm_ty.as_underlying_type().const_named_struct(&[
+            // We are also doing [`Model::check_value`] instead of [`Model::believe_value`] to catch bugs.
-                llvm_usize.const_int(ndarray_ndims, false).into(),
-                shape_global
-                    .as_pointer_value()
-                    .const_cast(llvm_usize.ptr_type(AddressSpace::default()))
-                    .into(),
-                data_global
-                    .as_pointer_value()
-                    .const_cast(ndarray_dtype_llvm_ty.ptr_type(AddressSpace::default()))
-                    .into(),
-            ]);
 
-            let ndarray = ctx.module.add_global(
+            // NOTE: data_global is an array of dtype, we want a `u8*`.
-                ndarray_llvm_ty.as_underlying_type(),
+            let ndarray_data = Ptr(dtype).check_value(generator, ctx.ctx, data_global).unwrap();
+            let ndarray_data = Ptr(Int(Byte)).pointer_cast(generator, ctx, ndarray_data.value);
+
+            let ndarray_itemsize = Int(SizeT).const_int(generator, ctx.ctx, itemsize);
+
+            let ndarray_ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims as u64);
+
+            let ndarray_shape =
+                Ptr(Int(SizeT)).check_value(generator, ctx.ctx, shape_global).unwrap();
+
+            let ndarray_strides =
+                Ptr(Int(SizeT)).check_value(generator, ctx.ctx, strides_global).unwrap();
+
+            let ndarray = Struct(NDArray).const_struct(
+                generator,
+                ctx.ctx,
+                &[
+                    ndarray_data.value.as_basic_value_enum(),
+                    ndarray_itemsize.value.as_basic_value_enum(),
+                    ndarray_ndims.value.as_basic_value_enum(),
+                    ndarray_shape.value.as_basic_value_enum(),
+                    ndarray_strides.value.as_basic_value_enum(),
+                ],
+            );
+
+            let ndarray_global = ctx.module.add_global(
+                Struct(NDArray).get_type(generator, ctx.ctx),
                 Some(AddressSpace::default()),
                 &id_str,
             );
-            ndarray.set_initializer(&value);
+            ndarray_global.set_initializer(&ndarray.value);
 
-            Ok(Some(ndarray.as_pointer_value().into()))
+            Ok(Some(ndarray_global.as_pointer_value().into()))
         } else if ty_id == self.primitive_ids.tuple {
             let expected_ty_enum = ctx.unifier.get_ty_immutable(expected_ty);
             let TypeEnum::TTuple { ty, is_vararg_ctx: false } = expected_ty_enum.as_ref() else {

nac3core/src/codegen/object/ndarray/mod.rs

@@ -656,3 +656,18 @@ impl<'ctx> NDArrayOut<'ctx> {
         }
     }
 }
+
+/// A version of [`call_nac3_ndarray_set_strides_by_shape`] in Rust.
+///
+/// This function is used generating strides for globally defined contiguous ndarrays.
+#[must_use]
+pub fn make_contiguous_strides(itemsize: u64, ndims: u64, shape: &[u64]) -> Vec<u64> {
+    let mut strides = Vec::with_capacity(ndims as usize);
+    let mut stride_product = 1u64;
+    for i in 0..ndims {
+        let axis = ndims - i - 1;
+        strides[axis as usize] = stride_product * itemsize;
+        stride_product *= shape[axis as usize];
+    }
+    strides
+}