forked from M-Labs/nac3
core: Implement numpy.matmul for 2D-2D ndarrays
This commit is contained in:
parent
5dfcc63978
commit
847615fc2f
|
@ -384,7 +384,7 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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rhs: BasicValueEnum<'ctx>,
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) -> BasicValueEnum<'ctx> {
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let (BasicValueEnum::FloatValue(lhs), BasicValueEnum::FloatValue(rhs)) = (lhs, rhs) else {
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unreachable!()
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unreachable!("Expected (FloatValue, FloatValue), got ({}, {})", lhs.get_type(), rhs.get_type())
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};
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match op {
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Operator::Add => self.builder.build_float_add(lhs, rhs, "fadd").map(Into::into).unwrap(),
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@ -589,8 +589,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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// even if this assumption is violated, it does not matter as exception unwinding is
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// slow anyway...
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let cond = call_expect(self, cond, i1_true, Some("expect"));
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let current_fun = self.builder.get_insert_block().unwrap().get_parent().unwrap();
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let then_block = self.ctx.append_basic_block(current_fun, "succ");
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let current_bb = self.builder.get_insert_block().unwrap();
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let current_fun = current_bb.get_parent().unwrap();
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let then_block = self.ctx.insert_basic_block_after(current_bb, "succ");
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let exn_block = self.ctx.append_basic_block(current_fun, "fail");
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self.builder.build_conditional_branch(cond, then_block, exn_block).unwrap();
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self.builder.position_at_end(exn_block);
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@ -1148,27 +1149,45 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
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let left_val = NDArrayValue::from_ptr_val(
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left_val.into_pointer_value(),
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llvm_usize,
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None
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None,
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);
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let res = numpy::ndarray_elementwise_binop_impl(
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generator,
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ctx,
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ndarray_dtype1,
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if is_aug_assign { Some(left_val) } else { None },
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(left_val.as_ptr_value().into(), false),
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(right_val, false),
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|generator, ctx, (lhs, rhs)| {
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gen_binop_expr_with_values(
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generator,
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ctx,
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(&Some(ndarray_dtype1), lhs),
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op,
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(&Some(ndarray_dtype2), rhs),
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ctx.current_loc,
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is_aug_assign,
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)?.unwrap().to_basic_value_enum(ctx, generator, ndarray_dtype1)
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},
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)?;
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let right_val = NDArrayValue::from_ptr_val(
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right_val.into_pointer_value(),
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llvm_usize,
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None,
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);
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let res = if *op == Operator::MatMult {
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// MatMult is the only binop which is not an elementwise op
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numpy::ndarray_matmul_2d(
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generator,
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ctx,
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ndarray_dtype1,
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if is_aug_assign { Some(left_val) } else { None },
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left_val,
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right_val,
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)?
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} else {
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numpy::ndarray_elementwise_binop_impl(
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generator,
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ctx,
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ndarray_dtype1,
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if is_aug_assign { Some(left_val) } else { None },
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(left_val.as_ptr_value().into(), false),
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(right_val.as_ptr_value().into(), false),
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|generator, ctx, (lhs, rhs)| {
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gen_binop_expr_with_values(
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generator,
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ctx,
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(&Some(ndarray_dtype1), lhs),
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op,
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(&Some(ndarray_dtype2), rhs),
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ctx.current_loc,
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is_aug_assign,
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)?.unwrap().to_basic_value_enum(ctx, generator, ndarray_dtype1)
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},
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)?
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};
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Ok(Some(res.as_ptr_value().into()))
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} else {
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@ -1,9 +1,5 @@
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use inkwell::{
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IntPredicate,
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types::BasicType,
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values::{BasicValueEnum, IntValue, PointerValue}
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};
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use nac3parser::ast::StrRef;
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use inkwell::{IntPredicate, OptimizationLevel, types::BasicType, values::{BasicValueEnum, IntValue, PointerValue}};
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use nac3parser::ast::{Operator, StrRef};
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use crate::{
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codegen::{
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classes::{
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@ -14,17 +10,20 @@ use crate::{
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TypedArrayLikeAccessor,
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TypedArrayLikeAdapter,
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UntypedArrayLikeAccessor,
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UntypedArrayLikeMutator,
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},
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CodeGenContext,
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CodeGenerator,
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expr::gen_binop_expr_with_values,
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irrt::{
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call_ndarray_calc_broadcast,
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call_ndarray_calc_broadcast_index,
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call_ndarray_calc_nd_indices,
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call_ndarray_calc_size,
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},
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llvm_intrinsics::call_memcpy_generic,
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stmt::gen_for_callback_incrementing,
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llvm_intrinsics,
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llvm_intrinsics::{call_memcpy_generic},
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stmt::{gen_for_callback_incrementing, gen_if_else_expr_callback},
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},
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symbol_resolver::ValueEnum,
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toplevel::{
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@ -85,6 +84,8 @@ fn create_ndarray_dyn_shape<'ctx, 'a, G, V, LenFn, DataFn>(
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[None, None, None],
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ctx.current_loc,
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);
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// TODO: Disallow dim_sz > u32_MAX
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Ok(())
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},
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@ -171,6 +172,8 @@ fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
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[None, None, None],
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ctx.current_loc,
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);
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// TODO: Disallow dim_sz > u32_MAX
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}
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let ndarray = generator.gen_var_alloc(
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@ -824,6 +827,319 @@ pub fn ndarray_elementwise_binop_impl<'ctx, G, ValueFn>(
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Ok(ndarray)
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}
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/// LLVM-typed implementation for computing matrix multiplication between two 2D `ndarray`s.
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///
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/// * `elem_ty` - The element type of the `NDArray`.
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/// * `res` - The `ndarray` instance to write results into, or [`None`] if the result should be
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/// written to a new `ndarray`.
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pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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elem_ty: Type,
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res: Option<NDArrayValue<'ctx>>,
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lhs: NDArrayValue<'ctx>,
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rhs: NDArrayValue<'ctx>,
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) -> Result<NDArrayValue<'ctx>, String> {
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let llvm_i32 = ctx.ctx.i32_type();
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let llvm_usize = generator.get_size_type(ctx.ctx);
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if cfg!(debug_assertions) {
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let lhs_ndims = lhs.load_ndims(ctx);
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let rhs_ndims = rhs.load_ndims(ctx);
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// lhs.ndims == 2
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ctx.make_assert(
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generator,
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ctx.builder.build_int_compare(
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IntPredicate::EQ,
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lhs_ndims,
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llvm_usize.const_int(2, false),
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"",
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).unwrap(),
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"0:ValueError",
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"",
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[None, None, None],
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ctx.current_loc,
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);
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// rhs.ndims == 2
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ctx.make_assert(
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generator,
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ctx.builder.build_int_compare(
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IntPredicate::EQ,
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rhs_ndims,
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llvm_usize.const_int(2, false),
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"",
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).unwrap(),
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"0:ValueError",
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"",
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[None, None, None],
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ctx.current_loc,
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);
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if let Some(res) = res {
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let res_ndims = res.load_ndims(ctx);
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let res_dim0 = unsafe {
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res.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
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};
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let res_dim1 = unsafe {
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res.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
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};
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let lhs_dim0 = unsafe {
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lhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
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};
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let rhs_dim1 = unsafe {
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rhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
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};
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// res.ndims == 2
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ctx.make_assert(
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generator,
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ctx.builder.build_int_compare(
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IntPredicate::EQ,
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res_ndims,
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llvm_usize.const_int(2, false),
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"",
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).unwrap(),
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"0:ValueError",
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"",
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[None, None, None],
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ctx.current_loc,
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);
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// res.dims[0] == lhs.dims[0]
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ctx.make_assert(
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generator,
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ctx.builder.build_int_compare(
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IntPredicate::EQ,
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lhs_dim0,
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res_dim0,
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"",
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).unwrap(),
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"0:ValueError",
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"",
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[None, None, None],
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ctx.current_loc,
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);
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// res.dims[1] == rhs.dims[0]
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ctx.make_assert(
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generator,
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ctx.builder.build_int_compare(
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IntPredicate::EQ,
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rhs_dim1,
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res_dim1,
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"",
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).unwrap(),
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"0:ValueError",
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"",
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[None, None, None],
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ctx.current_loc,
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);
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}
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}
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if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
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let lhs_dim1 = unsafe {
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lhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
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};
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let rhs_dim0 = unsafe {
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rhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
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};
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// lhs.dims[1] == rhs.dims[0]
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ctx.make_assert(
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generator,
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ctx.builder.build_int_compare(
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IntPredicate::EQ,
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lhs_dim1,
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rhs_dim0,
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"",
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).unwrap(),
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"0:ValueError",
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"",
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[None, None, None],
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ctx.current_loc,
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);
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}
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let lhs = if res.is_some_and(|res| res.as_ptr_value() == lhs.as_ptr_value()) {
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ndarray_copy_impl(generator, ctx, elem_ty, lhs)?
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} else {
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lhs
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};
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let ndarray = res.unwrap_or_else(|| {
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create_ndarray_dyn_shape(
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generator,
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ctx,
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elem_ty,
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&(lhs, rhs),
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|_, _, _| {
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Ok(llvm_usize.const_int(2, false))
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},
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|generator, ctx, (lhs, rhs), idx| {
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gen_if_else_expr_callback(
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generator,
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ctx,
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|_, ctx| {
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Ok(ctx.builder.build_int_compare(
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IntPredicate::EQ,
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idx,
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llvm_usize.const_zero(),
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"",
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).unwrap())
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},
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|generator, ctx| {
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Ok(Some(unsafe {
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lhs.dim_sizes().get_typed_unchecked(
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ctx,
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generator,
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&llvm_usize.const_zero(),
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None,
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)
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}))
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},
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|generator, ctx| {
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Ok(Some(unsafe {
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rhs.dim_sizes().get_typed_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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},
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).map(|v| v.map(BasicValueEnum::into_int_value).unwrap())
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},
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).unwrap()
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});
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let llvm_ndarray_ty = ctx.get_llvm_type(generator, elem_ty);
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ndarray_fill_indexed(
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generator,
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ctx,
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ndarray,
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|generator, ctx, idx| {
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llvm_intrinsics::call_expect(
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ctx,
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idx.size(ctx, generator).get_type().const_int(2, false),
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idx.size(ctx, generator),
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None,
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);
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let common_dim = {
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let lhs_idx1 = unsafe {
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lhs.dim_sizes().get_typed_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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let rhs_idx0 = unsafe {
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rhs.dim_sizes().get_typed_unchecked(
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ctx,
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generator,
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&llvm_usize.const_zero(),
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None,
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)
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};
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let idx = llvm_intrinsics::call_expect(ctx, rhs_idx0, lhs_idx1, None);
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ctx.builder.build_int_truncate(idx, llvm_i32, "").unwrap()
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};
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let idx0 = unsafe {
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let idx0 = idx.get_typed_unchecked(
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ctx,
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generator,
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&llvm_usize.const_zero(),
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None,
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);
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ctx.builder.build_int_truncate(idx0, llvm_i32, "").unwrap()
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};
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let idx1 = unsafe {
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let idx1 = idx.get_typed_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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ctx.builder.build_int_truncate(idx1, llvm_i32, "").unwrap()
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};
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let result_addr = generator.gen_var_alloc(ctx, llvm_ndarray_ty, None)?;
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let result_identity = ndarray_zero_value(generator, ctx, elem_ty);
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ctx.builder.build_store(result_addr, result_identity).unwrap();
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gen_for_callback_incrementing(
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generator,
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ctx,
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llvm_i32.const_zero(),
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(common_dim, false),
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|generator, ctx, i| {
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let i = ctx.builder.build_int_truncate(i, llvm_i32, "").unwrap();
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let ab_idx = generator.gen_array_var_alloc(
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ctx,
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llvm_i32.into(),
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llvm_usize.const_int(2, false),
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None,
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)?;
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let a = unsafe {
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ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_zero(), idx0.into());
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ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_int(1, false), i.into());
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lhs.data().get_unchecked(ctx, generator, &ab_idx, None)
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};
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let b = unsafe {
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ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_zero(), i.into());
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ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_int(1, false), idx1.into());
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|
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rhs.data().get_unchecked(ctx, generator, &ab_idx, None)
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};
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|
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let a_mul_b = gen_binop_expr_with_values(
|
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generator,
|
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ctx,
|
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(&Some(elem_ty), a),
|
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&Operator::Mult,
|
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(&Some(elem_ty), b),
|
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ctx.current_loc,
|
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false,
|
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)?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)?;
|
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|
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let result = ctx.builder.build_load(result_addr, "").unwrap();
|
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let result = gen_binop_expr_with_values(
|
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generator,
|
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ctx,
|
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(&Some(elem_ty), result),
|
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&Operator::Add,
|
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(&Some(elem_ty), a_mul_b),
|
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ctx.current_loc,
|
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false,
|
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)?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)?;
|
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ctx.builder.build_store(result_addr, result).unwrap();
|
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|
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Ok(())
|
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},
|
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llvm_usize.const_int(1, false),
|
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)?;
|
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|
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let result = ctx.builder.build_load(result_addr, "").unwrap();
|
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Ok(result)
|
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}
|
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)?;
|
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|
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Ok(ndarray)
|
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}
|
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|
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/// Generates LLVM IR for `ndarray.empty`.
|
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pub fn gen_ndarray_empty<'ctx>(
|
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context: &mut CodeGenContext<'ctx, '_>,
|
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|
|
|
@ -495,14 +495,14 @@ pub fn gen_for_callback<'ctx, 'a, G, I, InitFn, CondFn, BodyFn, UpdateFn>(
|
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BodyFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
|
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UpdateFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
|
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{
|
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let current = ctx.builder.get_insert_block().and_then(BasicBlock::get_parent).unwrap();
|
||||
let init_bb = ctx.ctx.append_basic_block(current, "for.init");
|
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let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let init_bb = ctx.ctx.insert_basic_block_after(current_bb, "for.init");
|
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// The BB containing the loop condition check
|
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let cond_bb = ctx.ctx.append_basic_block(current, "for.cond");
|
||||
let body_bb = ctx.ctx.append_basic_block(current, "for.body");
|
||||
let cond_bb = ctx.ctx.insert_basic_block_after(init_bb, "for.cond");
|
||||
let body_bb = ctx.ctx.insert_basic_block_after(cond_bb, "for.body");
|
||||
// The BB containing the increment expression
|
||||
let update_bb = ctx.ctx.append_basic_block(current, "for.update");
|
||||
let cont_bb = ctx.ctx.append_basic_block(current, "for.end");
|
||||
let update_bb = ctx.ctx.insert_basic_block_after(body_bb, "for.update");
|
||||
let cont_bb = ctx.ctx.insert_basic_block_after(update_bb, "for.end");
|
||||
|
||||
// store loop bb information and restore it later
|
||||
let loop_bb = ctx.loop_target.replace((update_bb, cont_bb));
|
||||
|
@ -719,12 +719,10 @@ pub fn gen_if_else_expr_callback<'ctx, 'a, G, CondFn, ThenFn, ElseFn, R>(
|
|||
R: BasicValue<'ctx>,
|
||||
{
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let current_fn = current_bb.get_parent().unwrap();
|
||||
|
||||
let end_bb = ctx.ctx.append_basic_block(current_fn, "if.end");
|
||||
|
||||
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "if.then");
|
||||
let else_bb = ctx.ctx.insert_basic_block_after(current_bb, "if.else");
|
||||
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "if.else");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "if.end");
|
||||
|
||||
let cond = cond_fn(generator, ctx)?;
|
||||
assert_eq!(cond.get_type().get_bit_width(), ctx.ctx.bool_type().get_bit_width());
|
||||
|
@ -742,6 +740,7 @@ pub fn gen_if_else_expr_callback<'ctx, 'a, G, CondFn, ThenFn, ElseFn, R>(
|
|||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
}
|
||||
|
||||
ctx.builder.position_at_end(end_bb);
|
||||
let phi = match (then_val, else_val) {
|
||||
(Some(tv), Some(ev)) => {
|
||||
let tv_ty = tv.as_basic_value_enum().get_type();
|
||||
|
|
|
@ -291,6 +291,17 @@ pub fn impl_mod(
|
|||
impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Mod]);
|
||||
}
|
||||
|
||||
/// [Operator::MatMult]
|
||||
pub fn impl_matmul(
|
||||
unifier: &mut Unifier,
|
||||
store: &PrimitiveStore,
|
||||
ty: Type,
|
||||
other_ty: &[Type],
|
||||
ret_ty: Option<Type>,
|
||||
) {
|
||||
impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::MatMult])
|
||||
}
|
||||
|
||||
/// `UAdd`, `USub`
|
||||
pub fn impl_sign(unifier: &mut Unifier, _store: &PrimitiveStore, ty: Type, ret_ty: Option<Type>) {
|
||||
impl_unaryop(unifier, ty, ret_ty, &[Unaryop::UAdd, Unaryop::USub]);
|
||||
|
@ -431,7 +442,38 @@ pub fn typeof_binop(
|
|||
}
|
||||
}
|
||||
|
||||
Operator::MatMult => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
|
||||
Operator::MatMult => {
|
||||
let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
|
||||
let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
|
||||
TypeEnum::TLiteral { values, .. } => {
|
||||
assert_eq!(values.len(), 1);
|
||||
u64::try_from(values[0].clone()).unwrap()
|
||||
}
|
||||
_ => unreachable!(),
|
||||
};
|
||||
let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
|
||||
let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
|
||||
TypeEnum::TLiteral { values, .. } => {
|
||||
assert_eq!(values.len(), 1);
|
||||
u64::try_from(values[0].clone()).unwrap()
|
||||
}
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
||||
match (lhs_ndims, rhs_ndims) {
|
||||
(2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
|
||||
(lhs, rhs) if lhs == 0 || rhs == 0 => {
|
||||
return Err(format!(
|
||||
"Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
|
||||
(rhs == 0) as u8
|
||||
))
|
||||
}
|
||||
(lhs, rhs) => {
|
||||
return Err(format!("ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Operator::Div => {
|
||||
if is_left_ndarray || is_right_ndarray {
|
||||
typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?
|
||||
|
@ -610,6 +652,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
|||
impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
|
||||
impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
|
||||
impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
|
||||
impl_matmul(unifier, store, ndarray_t, &[ndarray_t], Some(ndarray_t));
|
||||
impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
|
||||
impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
|
||||
impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
|
||||
|
|
|
@ -429,6 +429,19 @@ def test_ndarray_ipow_broadcast_scalar():
|
|||
|
||||
output_ndarray_float_2(x)
|
||||
|
||||
def test_ndarray_matmul():
|
||||
x = np_identity(2)
|
||||
y = x @ np_ones([2, 2])
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
output_ndarray_float_2(y)
|
||||
|
||||
def test_ndarray_imatmul():
|
||||
x = np_identity(2)
|
||||
x @= np_ones([2, 2])
|
||||
|
||||
output_ndarray_float_2(x)
|
||||
|
||||
def test_ndarray_pos():
|
||||
x_int32 = np_full([2, 2], -2)
|
||||
y_int32 = +x_int32
|
||||
|
@ -696,6 +709,8 @@ def run() -> int32:
|
|||
test_ndarray_ipow()
|
||||
test_ndarray_ipow_broadcast()
|
||||
test_ndarray_ipow_broadcast_scalar()
|
||||
test_ndarray_matmul()
|
||||
test_ndarray_imatmul()
|
||||
test_ndarray_pos()
|
||||
test_ndarray_neg()
|
||||
test_ndarray_inv()
|
||||
|
|
Loading…
Reference in New Issue