forked from M-Labs/nac3
artiq: reimplement get_obj_value to use ndarray with strides
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bb512b8f57
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1f0463463f
@ -1,14 +1,15 @@
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use crate::PrimitivePythonId;
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use inkwell::{
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module::Linkage,
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types::{BasicType, BasicTypeEnum},
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values::BasicValueEnum,
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types::BasicType,
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values::{BasicValue, BasicValueEnum},
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AddressSpace,
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};
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use itertools::Itertools;
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use nac3core::{
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codegen::{
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classes::{NDArrayType, ProxyType},
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model::*,
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object::ndarray::{make_contiguous_strides, NDArray},
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CodeGenContext, CodeGenerator,
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},
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symbol_resolver::{StaticValue, SymbolResolver, SymbolValue, ValueEnum},
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@ -26,7 +27,7 @@ use nac3parser::ast::{self, StrRef};
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use parking_lot::RwLock;
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use pyo3::{
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types::{PyDict, PyTuple},
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PyAny, PyObject, PyResult, Python,
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PyAny, PyErr, PyObject, PyResult, Python,
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};
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use std::{
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collections::{HashMap, HashSet},
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@ -1085,15 +1086,12 @@ impl InnerResolver {
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let (ndarray_dtype, ndarray_ndims) =
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unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
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let llvm_usize = generator.get_size_type(ctx.ctx);
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let ndarray_dtype_llvm_ty = ctx.get_llvm_type(generator, ndarray_dtype);
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let ndarray_llvm_ty = NDArrayType::new(generator, ctx.ctx, ndarray_dtype_llvm_ty);
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let dtype = Any(ctx.get_llvm_type(generator, ndarray_dtype));
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{
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if self.global_value_ids.read().contains_key(&id) {
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let global = ctx.module.get_global(&id_str).unwrap_or_else(|| {
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ctx.module.add_global(
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ndarray_llvm_ty.as_underlying_type(),
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Struct(NDArray).llvm_type(generator, ctx.ctx),
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Some(AddressSpace::default()),
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&id_str,
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)
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@ -1113,100 +1111,138 @@ impl InnerResolver {
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} else {
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todo!("Unpacking literal of more than one element unimplemented")
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};
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let Ok(ndarray_ndims) = u64::try_from(ndarray_ndims) else {
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let Ok(ndims) = u64::try_from(ndarray_ndims) else {
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unreachable!("Expected u64 value for ndarray_ndims")
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};
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// Obtain the shape of the ndarray
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let shape_tuple: &PyTuple = obj.getattr("shape")?.downcast()?;
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assert_eq!(shape_tuple.len(), ndarray_ndims as usize);
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let shape_values: Result<Option<Vec<_>>, _> = shape_tuple
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assert_eq!(shape_tuple.len(), ndims as usize);
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// The Rust type inferencer cannot figure this out
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let shape_values: Result<Vec<Instance<'ctx, Int<SizeT>>>, PyErr> = shape_tuple
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.iter()
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.enumerate()
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.map(|(i, elem)| {
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self.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize()).map_err(
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|e| super::CompileError::new_err(format!("Error getting element {i}: {e}")),
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)
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let value = self
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.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize())
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.map_err(|e| {
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super::CompileError::new_err(format!("Error getting element {i}: {e}"))
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})?
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.unwrap();
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let value = Int(SizeT).check_value(generator, ctx.ctx, value).unwrap();
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Ok(value)
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})
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.collect();
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let shape_values = shape_values?.unwrap();
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let shape_values = llvm_usize.const_array(
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&shape_values.into_iter().map(BasicValueEnum::into_int_value).collect_vec(),
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);
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let shape_values = shape_values?;
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// Also use this opportunity to get the constant values of `shape_values` for calculating strides.
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let shape_u64s = shape_values
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.iter()
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.map(|dim| {
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assert!(dim.value.is_const());
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dim.value.get_zero_extended_constant().unwrap()
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})
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.collect_vec();
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let shape_values = Int(SizeT).const_array(generator, ctx.ctx, &shape_values);
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// create a global for ndarray.shape and initialize it using the shape
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let shape_global = ctx.module.add_global(
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llvm_usize.array_type(ndarray_ndims as u32),
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Array { len: AnyLen(ndims as u32), item: Int(SizeT) }.llvm_type(generator, ctx.ctx),
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Some(AddressSpace::default()),
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&(id_str.clone() + ".shape"),
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);
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shape_global.set_initializer(&shape_values);
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shape_global.set_initializer(&shape_values.value);
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// Obtain the (flattened) elements of the ndarray
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let sz: usize = obj.getattr("size")?.extract()?;
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let data: Result<Option<Vec<_>>, _> = (0..sz)
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let data_values: Vec<Instance<'ctx, Any>> = (0..sz)
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.map(|i| {
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obj.getattr("flat")?.get_item(i).and_then(|elem| {
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self.get_obj_value(py, elem, ctx, generator, ndarray_dtype).map_err(|e| {
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super::CompileError::new_err(format!("Error getting element {i}: {e}"))
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})
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let value = self
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.get_obj_value(py, elem, ctx, generator, ndarray_dtype)
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.map_err(|e| {
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super::CompileError::new_err(format!(
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"Error getting element {i}: {e}"
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))
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})?
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.unwrap();
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let value = dtype.check_value(generator, ctx.ctx, value).unwrap();
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Ok(value)
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})
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})
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.collect();
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let data = data?.unwrap().into_iter();
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let data = match ndarray_dtype_llvm_ty {
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BasicTypeEnum::ArrayType(ty) => {
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ty.const_array(&data.map(BasicValueEnum::into_array_value).collect_vec())
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}
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BasicTypeEnum::FloatType(ty) => {
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ty.const_array(&data.map(BasicValueEnum::into_float_value).collect_vec())
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}
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BasicTypeEnum::IntType(ty) => {
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ty.const_array(&data.map(BasicValueEnum::into_int_value).collect_vec())
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}
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BasicTypeEnum::PointerType(ty) => {
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ty.const_array(&data.map(BasicValueEnum::into_pointer_value).collect_vec())
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}
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BasicTypeEnum::StructType(ty) => {
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ty.const_array(&data.map(BasicValueEnum::into_struct_value).collect_vec())
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}
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BasicTypeEnum::VectorType(_) => unreachable!(),
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};
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.try_collect()?;
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let data = dtype.const_array(generator, ctx.ctx, &data_values);
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// create a global for ndarray.data and initialize it using the elements
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//
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// NOTE: NDArray's `data` is `u8*`. Here, `data_global` is an array of `dtype`.
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// We will have to cast it to an `u8*` later.
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let data_global = ctx.module.add_global(
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ndarray_dtype_llvm_ty.array_type(sz as u32),
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Array { len: AnyLen(sz as u32), item: dtype }.llvm_type(generator, ctx.ctx),
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Some(AddressSpace::default()),
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&(id_str.clone() + ".data"),
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);
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data_global.set_initializer(&data);
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data_global.set_initializer(&data.value);
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// Get the constant itemsize.
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let itemsize = dtype.llvm_type(generator, ctx.ctx).size_of().unwrap();
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let itemsize = itemsize.get_zero_extended_constant().unwrap();
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// Create the strides needed for ndarray.strides
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let strides = make_contiguous_strides(itemsize, ndims, &shape_u64s);
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let strides = strides
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.into_iter()
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.map(|stride| Int(SizeT).const_int(generator, ctx.ctx, stride, false))
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.collect_vec();
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let strides = Int(SizeT).const_array(generator, ctx.ctx, &strides);
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// create a global for ndarray.strides and initialize it
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let strides_global = ctx.module.add_global(
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Array { len: AnyLen(ndims as u32), item: Int(Byte) }.llvm_type(generator, ctx.ctx),
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Some(AddressSpace::default()),
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&(id_str.clone() + ".strides"),
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);
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strides_global.set_initializer(&strides.value);
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// create a global for the ndarray object and initialize it
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let value = ndarray_llvm_ty.as_underlying_type().const_named_struct(&[
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llvm_usize.const_int(ndarray_ndims, false).into(),
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shape_global
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.as_pointer_value()
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.const_cast(llvm_usize.ptr_type(AddressSpace::default()))
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.into(),
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data_global
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.as_pointer_value()
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.const_cast(ndarray_dtype_llvm_ty.ptr_type(AddressSpace::default()))
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.into(),
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]);
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// We are also doing [`Model::check_value`] instead of [`Model::believe_value`] to catch bugs.
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let ndarray = ctx.module.add_global(
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ndarray_llvm_ty.as_underlying_type(),
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// NOTE: data_global is an array of dtype, we want a `u8*`.
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let ndarray_data = Ptr(dtype).check_value(generator, ctx.ctx, data_global).unwrap();
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let ndarray_data = Ptr(Int(Byte)).pointer_cast(generator, ctx, ndarray_data.value);
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let ndarray_itemsize = Int(SizeT).const_int(generator, ctx.ctx, itemsize, false);
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let ndarray_ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims, false);
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let ndarray_shape =
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Ptr(Int(SizeT)).check_value(generator, ctx.ctx, shape_global).unwrap();
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let ndarray_strides =
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Ptr(Int(SizeT)).check_value(generator, ctx.ctx, strides_global).unwrap();
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let ndarray = Struct(NDArray).const_struct(
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generator,
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ctx.ctx,
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&[
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ndarray_data.value.as_basic_value_enum(),
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ndarray_itemsize.value.as_basic_value_enum(),
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ndarray_ndims.value.as_basic_value_enum(),
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ndarray_shape.value.as_basic_value_enum(),
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ndarray_strides.value.as_basic_value_enum(),
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],
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);
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let ndarray_global = ctx.module.add_global(
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Struct(NDArray).llvm_type(generator, ctx.ctx),
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Some(AddressSpace::default()),
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&id_str,
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);
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ndarray.set_initializer(&value);
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ndarray_global.set_initializer(&ndarray.value);
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Ok(Some(ndarray.as_pointer_value().into()))
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Ok(Some(ndarray_global.as_pointer_value().into()))
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} else if ty_id == self.primitive_ids.tuple {
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let expected_ty_enum = ctx.unifier.get_ty_immutable(expected_ty);
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let TypeEnum::TTuple { ty, is_vararg_ctx: false } = expected_ty_enum.as_ref() else {
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@ -653,3 +653,18 @@ impl<'ctx> NDArrayOut<'ctx> {
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}
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}
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}
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/// A version of [`call_nac3_ndarray_set_strides_by_shape`] in Rust.
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///
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/// This function is used generating strides for globally defined contiguous ndarrays.
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#[must_use]
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pub fn make_contiguous_strides(itemsize: u64, ndims: u64, shape: &[u64]) -> Vec<u64> {
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let mut strides = Vec::with_capacity(ndims as usize);
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let mut stride_product = 1u64;
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for i in 0..ndims {
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let axis = ndims - i - 1;
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strides[axis as usize] = stride_product * itemsize;
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stride_product *= shape[axis as usize];
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}
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strides
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}
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