forked from M-Labs/nac3
core: Fix codegen for tuple-index into ndarray
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parent
635c944c90
commit
0452e6de78
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@ -3,8 +3,8 @@ use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
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use crate::{
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codegen::{
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classes::{
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ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue, ProxyValue,
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RangeValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
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ArrayLikeIndexer, ArrayLikeValue, ListValue, NDArrayValue, ProxyValue, RangeValue,
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TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
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},
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concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
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gen_in_range_check, get_llvm_abi_type, get_llvm_type,
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@ -1741,22 +1741,37 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
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let ndims = values
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.iter()
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.map(|ndim| match *ndim {
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SymbolValue::U64(v) => Ok(v),
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SymbolValue::U32(v) => Ok(u64::from(v)),
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SymbolValue::I32(v) => u64::try_from(v)
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.map_err(|_| format!("Expected non-negative literal for ndarray.ndims, got {v}")),
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SymbolValue::I64(v) => u64::try_from(v)
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.map_err(|_| format!("Expected non-negative literal for ndarray.ndims, got {v}")),
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_ => unreachable!(),
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})
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.collect::<Result<Vec<_>, _>>()?;
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.map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
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.collect::<Result<Vec<_>, _>>()
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.map_err(|val| {
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format!(
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"Expected non-negative literal for ndarray.ndims, got {}",
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i128::try_from(val).unwrap()
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)
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})?;
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assert!(!ndims.is_empty());
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let ndarray_ndims_ty = ctx
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.unifier
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.get_fresh_literal(ndims.iter().map(|v| SymbolValue::U64(v - 1)).collect(), None);
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// The number of dimensions subscripted by the index expression.
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// Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
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// dimension will remove a dimension.
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let subscripted_dims = match &slice.node {
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ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
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if let ExprKind::Slice { .. } = &value_subexpr.node {
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acc
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} else {
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acc + 1
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}
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}),
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ExprKind::Slice { .. } => 0,
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_ => 1,
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};
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let ndarray_ndims_ty = ctx.unifier.get_fresh_literal(
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ndims.iter().map(|v| SymbolValue::U64(v - subscripted_dims)).collect(),
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None,
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);
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let ndarray_ty =
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make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(ty), Some(ndarray_ndims_ty));
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let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
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@ -1859,123 +1874,165 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
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}
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};
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Ok(Some(match &slice.node {
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ExprKind::Tuple { elts, .. } => {
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let slices = elts
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.iter()
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.enumerate()
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.map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
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.take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
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.collect::<Result<Vec<_>, _>>()?;
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if slices.len() < elts.len() {
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return Ok(None);
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}
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let slices = slices.into_iter().map(Option::unwrap).collect_vec();
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numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
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}
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ExprKind::Slice { .. } => {
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let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
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return Ok(None);
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};
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numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
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}
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_ => {
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let index = if let Some(index) = generator.gen_expr(ctx, slice)? {
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index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value()
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} else {
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return Ok(None);
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};
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let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
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let index_addr = generator.gen_var_alloc(ctx, index.get_type().into(), None)?;
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ctx.builder.build_store(index_addr, index).unwrap();
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if ndims.len() == 1 && ndims[0] == 1 {
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// Accessing an element from a 1-dimensional `ndarray`
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return Ok(Some(
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v.data()
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.get(
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ctx,
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generator,
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&ArraySliceValue::from_ptr_val(
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index_addr,
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llvm_usize.const_int(1, false),
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None,
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),
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None,
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)
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.into(),
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));
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}
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// Accessing an element from a multi-dimensional `ndarray`
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// Create a new array, remove the top dimension from the dimension-size-list, and copy the
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// elements over
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let subscripted_ndarray = generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
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let ndarray = NDArrayValue::from_ptr_val(subscripted_ndarray, llvm_usize, None);
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let num_dims = v.load_ndims(ctx);
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ndarray.store_ndims(
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let make_indices_arr = |generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>|
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-> Result<_, String> {
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Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
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let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
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let index_addr = generator.gen_array_var_alloc(
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ctx,
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generator,
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ctx.builder.build_int_sub(num_dims, llvm_usize.const_int(1, false), "").unwrap(),
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);
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llvm_int_ty,
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llvm_usize.const_int(elts.len() as u64, false),
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None,
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)?;
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let ndarray_num_dims = ndarray.load_ndims(ctx);
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ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
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for (i, elt) in elts.iter().enumerate() {
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let Some(index) = generator.gen_expr(ctx, elt)? else {
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return Ok(None);
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};
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let ndarray_num_dims = ndarray.load_ndims(ctx);
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let v_dims_src_ptr = unsafe {
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v.dim_sizes().ptr_offset_unchecked(
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let index = index
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.to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
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.into_int_value();
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let Some(index) = normalize_index(generator, ctx, index, 0)? else {
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return Ok(None);
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};
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let store_ptr = unsafe {
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index_addr.ptr_offset_unchecked(
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ctx,
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generator,
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&llvm_usize.const_int(i as u64, false),
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None,
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)
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};
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ctx.builder.build_store(store_ptr, index).unwrap();
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}
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Some(index_addr)
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} else if let Some(index) = generator.gen_expr(ctx, slice)? {
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let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
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let index_addr = generator.gen_array_var_alloc(
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ctx,
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llvm_int_ty,
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llvm_usize.const_int(1u64, false),
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None,
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)?;
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let index =
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index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
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let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
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let store_ptr = unsafe {
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index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
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};
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ctx.builder.build_store(store_ptr, index).unwrap();
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Some(index_addr)
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} else {
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None
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})
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};
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Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
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let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
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v.data().get(ctx, generator, &index_addr, None).into()
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} else {
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match &slice.node {
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ExprKind::Tuple { elts, .. } => {
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let slices = elts
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.iter()
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.enumerate()
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.map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
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.take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
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.collect::<Result<Vec<_>, _>>()?;
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if slices.len() < elts.len() {
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return Ok(None);
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}
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let slices = slices.into_iter().map(Option::unwrap).collect_vec();
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numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
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}
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ExprKind::Slice { .. } => {
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let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
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return Ok(None);
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};
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numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
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}
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_ => {
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// Accessing an element from a multi-dimensional `ndarray`
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let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
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// Create a new array, remove the top dimension from the dimension-size-list, and copy the
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// elements over
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let subscripted_ndarray =
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generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
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let ndarray = NDArrayValue::from_ptr_val(subscripted_ndarray, llvm_usize, None);
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let num_dims = v.load_ndims(ctx);
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ndarray.store_ndims(
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ctx,
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generator,
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&llvm_usize.const_int(1, false),
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None,
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)
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};
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call_memcpy_generic(
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ctx,
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ndarray.dim_sizes().base_ptr(ctx, generator),
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v_dims_src_ptr,
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ctx.builder
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.build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
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.map(Into::into)
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.unwrap(),
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llvm_i1.const_zero(),
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);
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ctx.builder
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.build_int_sub(num_dims, llvm_usize.const_int(1, false), "")
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.unwrap(),
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);
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let ndarray_num_elems = call_ndarray_calc_size(
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generator,
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ctx,
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&ndarray.dim_sizes().as_slice_value(ctx, generator),
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(None, None),
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);
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ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
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let ndarray_num_dims = ndarray.load_ndims(ctx);
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ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
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let v_data_src_ptr = v.data().ptr_offset(
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ctx,
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generator,
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&ArraySliceValue::from_ptr_val(index_addr, llvm_usize.const_int(1, false), None),
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None,
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);
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call_memcpy_generic(
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ctx,
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ndarray.data().base_ptr(ctx, generator),
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v_data_src_ptr,
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ctx.builder
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.build_int_mul(ndarray_num_elems, llvm_ndarray_data_t.size_of().unwrap(), "")
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.map(Into::into)
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.unwrap(),
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llvm_i1.const_zero(),
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);
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let ndarray_num_dims = ndarray.load_ndims(ctx);
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let v_dims_src_ptr = unsafe {
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v.dim_sizes().ptr_offset_unchecked(
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ctx,
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generator,
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&llvm_usize.const_int(1, false),
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None,
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)
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};
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call_memcpy_generic(
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ctx,
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ndarray.dim_sizes().base_ptr(ctx, generator),
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v_dims_src_ptr,
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ctx.builder
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.build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
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.map(Into::into)
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.unwrap(),
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llvm_i1.const_zero(),
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);
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ndarray.as_base_value().into()
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let ndarray_num_elems = call_ndarray_calc_size(
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generator,
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ctx,
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&ndarray.dim_sizes().as_slice_value(ctx, generator),
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(None, None),
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);
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ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
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let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
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call_memcpy_generic(
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ctx,
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ndarray.data().base_ptr(ctx, generator),
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v_data_src_ptr,
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ctx.builder
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.build_int_mul(
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ndarray_num_elems,
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llvm_ndarray_data_t.size_of().unwrap(),
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"",
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)
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.map(Into::into)
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.unwrap(),
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llvm_i1.const_zero(),
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);
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ndarray.as_base_value().into()
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}
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}
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}))
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}
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