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artiq: reimplement get_obj_value to use ndarray with strides

This commit is contained in:
lyken 2024-08-22 16:19:09 +08:00
parent 1dc18cfbd3
commit c9c9dae91b
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GPG Key ID: 3BD5FC6AC8325DD8
2 changed files with 118 additions and 67 deletions
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170
nac3artiq/src/symbol_resolver.rs
View File

@ -1,14 +1,15 @@
use crate::PrimitivePythonId;
use inkwell::{
module::Linkage,
types::{BasicType, BasicTypeEnum},
values::BasicValueEnum,
types::BasicType,
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 ndarray_dtype_llvm_ty = ctx.get_llvm_type(generator, ndarray_dtype);
let ndarray_llvm_ty = NDArrayType::new(generator, ctx.ctx, ndarray_dtype_llvm_ty);
let dtype = Any(ctx.get_llvm_type(generator, ndarray_dtype));
{
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);
let shape_values: Result<Option<Vec<_>>, _> = shape_tuple
assert_eq!(shape_tuple.len(), ndims as usize);
// 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(
|e| super::CompileError::new_err(format!("Error getting element {i}: {e}")),
)
let value = self
.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 = llvm_usize.const_array(
&shape_values.into_iter().map(BasicValueEnum::into_int_value).collect_vec(),
);
let shape_values = shape_values?;
// 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: AnyLen(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| {
super::CompileError::new_err(format!("Error getting element {i}: {e}"))
})
let value = self
.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();
let data = data?.unwrap().into_iter();
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!(),
};
.try_collect()?;
let data = dtype.const_array(generator, ctx.ctx, &data_values);
// 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: AnyLen(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: AnyLen(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(&[
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(),
]);
// We are also doing [`Model::check_value`] instead of [`Model::believe_value`] to catch bugs.
let ndarray = ctx.module.add_global(
ndarray_llvm_ty.as_underlying_type(),
// NOTE: data_global is an array of dtype, we want a `u8*`.
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);
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 {

15
nac3core/src/codegen/object/ndarray/mod.rs
View File

@ -651,3 +651,18 @@ 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
}