forked from M-Labs/nac3
core: implement ndarray_iter_elem_impl and np.any() & np.all()
This commit is contained in:
parent
42c5f906fb
commit
b1e97aa2b0
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@ -7,12 +7,11 @@ use crate::{
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},
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},
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expr::gen_binop_expr_with_values,
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expr::gen_binop_expr_with_values,
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irrt::{
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irrt::{
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calculate_len_for_slice_range, call_ndarray_calc_broadcast,
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self, calculate_len_for_slice_range, call_ndarray_calc_broadcast,
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call_ndarray_calc_broadcast_index, call_ndarray_calc_nd_indices,
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call_ndarray_calc_broadcast_index, call_ndarray_calc_nd_indices,
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call_ndarray_calc_size,
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call_ndarray_calc_size,
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},
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},
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llvm_intrinsics,
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llvm_intrinsics::{self, call_memcpy_generic},
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llvm_intrinsics::call_memcpy_generic,
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stmt::{gen_for_callback_incrementing, gen_for_range_callback, gen_if_else_expr_callback},
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stmt::{gen_for_callback_incrementing, gen_for_range_callback, gen_if_else_expr_callback},
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CodeGenContext, CodeGenerator,
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CodeGenContext, CodeGenerator,
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},
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},
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@ -32,6 +31,8 @@ use inkwell::{
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};
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};
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use nac3parser::ast::{Operator, StrRef};
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use nac3parser::ast::{Operator, StrRef};
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use super::{builtin_fns::call_bool, stmt::BreakContinueHooks};
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/// Creates an uninitialized `NDArray` instance.
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/// Creates an uninitialized `NDArray` instance.
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fn create_ndarray_uninitialized<'ctx, G: CodeGenerator + ?Sized>(
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fn create_ndarray_uninitialized<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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generator: &mut G,
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@ -1366,6 +1367,46 @@ where
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Ok(ndarray)
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Ok(ndarray)
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}
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}
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/// LLVM-typed implementation for iterating through all elements within an `ndarray`.
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///
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/// * `ndarray`: The input [`NDArrayValue`] to iterate through.
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/// * `body`: A lambda containing IR statements that acts on every element within `ndarray`.
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/// It may also implement short-circuiting logic by branching with [`BreakContinueHooks`].
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pub fn ndarray_iter_elem_impl<'ctx, G, BodyFn>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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ndarray: NDArrayValue<'ctx>,
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body: BodyFn,
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) -> Result<(), String>
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where
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G: CodeGenerator + ?Sized,
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BodyFn: FnOnce(
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&mut G,
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&mut CodeGenContext<'ctx, '_>,
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BreakContinueHooks,
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BasicValueEnum<'ctx>,
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) -> Result<(), String>,
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{
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let llvm_usize = generator.get_size_type(ctx.ctx);
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let ndarray_size =
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irrt::call_ndarray_calc_size(generator, ctx, &ndarray.dim_sizes(), (None, None));
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gen_for_callback_incrementing(
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generator,
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ctx,
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llvm_usize.const_int(0, false),
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(ndarray_size, false),
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|generator, ctx, hooks, idx| {
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let scalar = unsafe { ndarray.data().get_unchecked(ctx, generator, &idx, None) };
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body(generator, ctx, hooks, scalar)
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},
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llvm_usize.const_int(1, false),
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)?;
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Ok(())
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}
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/// LLVM-typed implementation for computing matrix multiplication between two 2D `ndarray`s.
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/// LLVM-typed implementation for computing matrix multiplication between two 2D `ndarray`s.
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///
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///
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/// * `elem_ty` - The element type of the `NDArray`.
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/// * `elem_ty` - The element type of the `NDArray`.
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@ -1984,3 +2025,136 @@ pub fn gen_ndarray_fill<'ctx>(
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Ok(())
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Ok(())
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}
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}
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/// Used by [`call_ndarray_any_all_impl`]
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#[derive(Debug, Clone, Copy)]
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enum AnyOrAll {
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/// The numpy function `np.any()`
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IsAny,
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/// The numpy function `np.all()`
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IsAll,
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}
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/// Helper function to create `np.any()` and `np.all()`.
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///
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/// Returns a boolean result in the form of an `i8` [`IntValue`].
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///
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/// They are mixed together since they are extremely similar in terms of implementation.
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fn call_ndarray_any_all_impl<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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kind: AnyOrAll,
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elem_ty: Type,
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ndarray: NDArrayValue<'ctx>,
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) -> Result<IntValue<'ctx>, String> {
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/*
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NOTE: `np.any(<empty ndarray>)` returns false.
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NOTE: `np.all(<empty ndarray>)` returns true.
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Here is the reference C code of what the implemented LLVM of `np.any()` is essentially doing.
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```c
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// np.any(ndarray)
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const int8 neutral = 0;
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const int8 on_hit = 1;
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int8 has_true = neutral;
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for (size_t index = 0; index < ndarray.size; index++) {
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Element *elem = ndarray.get(index);
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bool elem_is_truthy = call_bool(*elem);
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if (elem_is_truthy) {
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has_true = on_hit;
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break; // Short-circuiting for performance
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}
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}
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```
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*/
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// Name of the function. Used here for creating LLVM labels and names.
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let fn_name = match kind {
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AnyOrAll::IsAny => "np_any",
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AnyOrAll::IsAll => "np_all",
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};
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let llvm_i8 = ctx.ctx.i8_type();
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let (neutral, on_hit) = match kind {
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AnyOrAll::IsAny => (0, 1),
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AnyOrAll::IsAll => (1, 0),
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};
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// The result of `np.any()`/`np.all()`
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let result_ptr =
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ctx.builder.build_alloca(llvm_i8, format!("{fn_name}.result").as_str()).unwrap();
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ctx.builder.build_store(result_ptr, llvm_i8.const_int(neutral, false)).unwrap();
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ndarray_iter_elem_impl(generator, ctx, ndarray, |generator, ctx, hooks, elem| {
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// The basic block to go to when...
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// - np.any() sees a `true`, then `result` is set from `false` to `true` and short-circuit.
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// - np.all() sees a `false`, then `result` is set from `true` to `false` and short-circuit.
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let on_hit_bb =
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ctx.ctx.insert_basic_block_after(hooks.break_bb, format!("{fn_name}.on_hit").as_str());
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let elem_is_truthy = call_bool(generator, ctx, (elem_ty, elem)).unwrap().into_int_value();
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let (on_true, on_false) = match kind {
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AnyOrAll::IsAny => (on_hit_bb, hooks.continue_bb),
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AnyOrAll::IsAll => (hooks.continue_bb, on_hit_bb),
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};
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ctx.builder.build_conditional_branch(elem_is_truthy, on_true, on_false).unwrap();
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// Begin inserting into `on_hit_bb`
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ctx.builder.position_at_end(on_hit_bb);
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ctx.builder.build_store(result_ptr, llvm_i8.const_int(on_hit, false)).unwrap();
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ctx.builder.build_unconditional_branch(hooks.break_bb).unwrap();
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Ok(())
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})?;
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// Load `result` and return it
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let result = ctx.builder.build_load(result_ptr, "result").unwrap();
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Ok(result.into_int_value())
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}
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/// Helper function to generate LLVM IR for `np.any()` and `np.all()`.
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fn gen_ndarray_any_all_helper<'ctx>(
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kind: AnyOrAll,
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context: &mut CodeGenContext<'ctx, '_>,
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obj: &Option<(Type, ValueEnum<'ctx>)>,
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fun: (&FunSignature, DefinitionId),
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args: &[(Option<StrRef>, ValueEnum<'ctx>)],
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generator: &mut dyn CodeGenerator,
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) -> Result<IntValue<'ctx>, String> {
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assert!(obj.is_none());
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assert_eq!(args.len(), 1);
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let llvm_usize = generator.get_size_type(context.ctx);
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let in_ty = fun.0.args[0].ty;
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let (elem_ty, _) = unpack_ndarray_var_tys(&mut context.unifier, in_ty);
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let in_arg = args[0].1.clone().to_basic_value_enum(context, generator, elem_ty)?;
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let ndarray = NDArrayValue::from_ptr_val(in_arg.into_pointer_value(), llvm_usize, None);
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call_ndarray_any_all_impl(generator, context, kind, elem_ty, ndarray)
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}
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/// Generates LLVM IR for `np.any()`.
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pub fn gen_ndarray_any<'ctx>(
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context: &mut CodeGenContext<'ctx, '_>,
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obj: &Option<(Type, ValueEnum<'ctx>)>,
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fun: (&FunSignature, DefinitionId),
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args: &[(Option<StrRef>, ValueEnum<'ctx>)],
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generator: &mut dyn CodeGenerator,
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) -> Result<IntValue<'ctx>, String> {
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gen_ndarray_any_all_helper(AnyOrAll::IsAny, context, obj, fun, args, generator)
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}
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/// Generates LLVM IR for `np.all()`.
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pub fn gen_ndarray_all<'ctx>(
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context: &mut CodeGenContext<'ctx, '_>,
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obj: &Option<(Type, ValueEnum<'ctx>)>,
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fun: (&FunSignature, DefinitionId),
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args: &[(Option<StrRef>, ValueEnum<'ctx>)],
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generator: &mut dyn CodeGenerator,
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) -> Result<IntValue<'ctx>, String> {
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gen_ndarray_any_all_helper(AnyOrAll::IsAll, context, obj, fun, args, generator)
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}
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@ -462,7 +462,7 @@ pub fn gen_for<G: CodeGenerator>(
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Ok(())
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Ok(())
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}
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}
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#[derive(Debug, Clone, Copy)]
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
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pub struct BreakContinueHooks<'ctx> {
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pub struct BreakContinueHooks<'ctx> {
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/// [`BasicBlock`] to branch to for `break`-ing out of the loop.
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/// [`BasicBlock`] to branch to for `break`-ing out of the loop.
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pub break_bb: BasicBlock<'ctx>,
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pub break_bb: BasicBlock<'ctx>,
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@ -497,6 +497,8 @@ impl<'a> BuiltinBuilder<'a> {
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PrimDef::FunNpIsNan | PrimDef::FunNpIsInf => self.build_np_float_to_bool_function(prim),
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PrimDef::FunNpIsNan | PrimDef::FunNpIsInf => self.build_np_float_to_bool_function(prim),
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PrimDef::FunNpAny | PrimDef::FunNpAll => self.build_np_any_all_function(prim),
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PrimDef::FunNpSin
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PrimDef::FunNpSin
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| PrimDef::FunNpCos
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| PrimDef::FunNpCos
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| PrimDef::FunNpTan
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| PrimDef::FunNpTan
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@ -1757,6 +1759,24 @@ impl<'a> BuiltinBuilder<'a> {
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}
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}
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}
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}
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fn build_np_any_all_function(&mut self, prim: PrimDef) -> TopLevelDef {
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debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpAny, PrimDef::FunNpAll]);
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let param_ty = &[(self.ndarray_num_ty, "a")];
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let ret_ty = self.primitives.bool;
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let var_map = &into_var_map([]);
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let codegen_callback: Box<GenCallCallback> =
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Box::new(move |ctx, obj, fun, args, generator| {
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let func = match prim {
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PrimDef::FunNpAny => gen_ndarray_any,
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PrimDef::FunNpAll => gen_ndarray_all,
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_ => unreachable!(),
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};
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func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
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});
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create_fn_by_codegen(self.unifier, var_map, prim.name(), ret_ty, param_ty, codegen_callback)
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}
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fn create_method(prim: PrimDef, method_ty: Type) -> (StrRef, Type, DefinitionId) {
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fn create_method(prim: PrimDef, method_ty: Type) -> (StrRef, Type, DefinitionId) {
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(prim.simple_name().into(), method_ty, prim.id())
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(prim.simple_name().into(), method_ty, prim.id())
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}
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}
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@ -100,6 +100,8 @@ pub enum PrimDef {
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FunNpHypot,
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FunNpHypot,
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FunNpNextAfter,
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FunNpNextAfter,
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FunSome,
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FunSome,
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FunNpAny,
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FunNpAll,
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}
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}
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/// Associated details of a [`PrimDef`]
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/// Associated details of a [`PrimDef`]
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@ -251,6 +253,8 @@ impl PrimDef {
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PrimDef::FunNpHypot => fun("np_hypot", None),
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PrimDef::FunNpHypot => fun("np_hypot", None),
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PrimDef::FunNpNextAfter => fun("np_nextafter", None),
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PrimDef::FunNpNextAfter => fun("np_nextafter", None),
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PrimDef::FunSome => fun("Some", None),
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PrimDef::FunSome => fun("Some", None),
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PrimDef::FunNpAny => fun("np_any", None),
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PrimDef::FunNpAll => fun("np_all", None),
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}
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}
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}
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}
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}
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}
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@ -226,6 +226,8 @@ def patch(module):
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module.np_full = np.full
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module.np_full = np.full
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module.np_eye = np.eye
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module.np_eye = np.eye
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module.np_identity = np.identity
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module.np_identity = np.identity
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module.np_any = np.any
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module.np_all = np.all
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def file_import(filename, prefix="file_import_"):
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def file_import(filename, prefix="file_import_"):
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filename = pathlib.Path(filename)
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filename = pathlib.Path(filename)
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@ -1388,6 +1388,48 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
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output_ndarray_float_2(nextafter_x_zeros)
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output_ndarray_float_2(nextafter_x_zeros)
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output_ndarray_float_2(nextafter_x_ones)
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output_ndarray_float_2(nextafter_x_ones)
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def test_ndarray_any():
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x1 = np_identity(5)
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y1 = np_any(x1)
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output_ndarray_float_2(x1)
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output_bool(y1)
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x2 = np_identity(1)
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y2 = np_any(x2)
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output_ndarray_float_2(x2)
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output_bool(y2)
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x3 = np_array([[1.0, 2.0], [3.0, 4.0]])
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y3 = np_any(x3)
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output_ndarray_float_2(x3)
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output_bool(y3)
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x4 = np_zeros([3, 5])
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y4 = np_any(x4)
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output_ndarray_float_2(x4)
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output_bool(y4)
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def test_ndarray_all():
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x1 = np_identity(5)
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y1 = np_all(x1)
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output_ndarray_float_2(x1)
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output_bool(y1)
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x2 = np_identity(1)
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y2 = np_all(x2)
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output_ndarray_float_2(x2)
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output_bool(y2)
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x3 = np_array([[1.0, 2.0], [3.0, 4.0]])
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y3 = np_all(x3)
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output_ndarray_float_2(x3)
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output_bool(y3)
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x4 = np_zeros([3, 5])
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y4 = np_all(x4)
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output_ndarray_float_2(x4)
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output_bool(y4)
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def run() -> int32:
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def run() -> int32:
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test_ndarray_ctor()
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test_ndarray_ctor()
|
||||||
test_ndarray_empty()
|
test_ndarray_empty()
|
||||||
|
@ -1565,4 +1607,7 @@ def run() -> int32:
|
||||||
test_ndarray_nextafter_broadcast_lhs_scalar()
|
test_ndarray_nextafter_broadcast_lhs_scalar()
|
||||||
test_ndarray_nextafter_broadcast_rhs_scalar()
|
test_ndarray_nextafter_broadcast_rhs_scalar()
|
||||||
|
|
||||||
|
test_ndarray_any()
|
||||||
|
test_ndarray_all()
|
||||||
|
|
||||||
return 0
|
return 0
|
||||||
|
|
Loading…
Reference in New Issue