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numpy-anya
Author | SHA1 | Date |
---|---|---|
lyken | b1e97aa2b0 | |
lyken | 42c5f906fb |
|
@ -725,7 +725,7 @@ pub fn call_numpy_min<'ctx, G: CodeGenerator + ?Sized>(
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ctx,
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llvm_usize.const_int(1, false),
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(n_sz, false),
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|generator, ctx, idx| {
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|generator, ctx, _, idx| {
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let elem = unsafe { n.data().get_unchecked(ctx, generator, &idx, None) };
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let accumulator = ctx.builder.build_load(accumulator_addr, "").unwrap();
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@ -941,7 +941,7 @@ pub fn call_numpy_max<'ctx, G: CodeGenerator + ?Sized>(
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ctx,
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llvm_usize.const_int(1, false),
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(n_sz, false),
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|generator, ctx, idx| {
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|generator, ctx, _, idx| {
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let elem = unsafe { n.data().get_unchecked(ctx, generator, &idx, None) };
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let accumulator = ctx.builder.build_load(accumulator_addr, "").unwrap();
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@ -1706,7 +1706,7 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
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ctx,
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llvm_usize.const_zero(),
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(len, false),
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|generator, ctx, i| {
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|generator, ctx, _, i| {
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let (dim_idx, dim_sz) = unsafe {
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(
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indices.get_unchecked(ctx, generator, &i, None).into_int_value(),
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@ -802,7 +802,7 @@ pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
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ctx,
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llvm_usize.const_zero(),
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(min_ndims, false),
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|generator, ctx, idx| {
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|generator, ctx, _, idx| {
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let idx = ctx.builder.build_int_sub(min_ndims, idx, "").unwrap();
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let (lhs_dim_sz, rhs_dim_sz) = unsafe {
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(
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@ -7,12 +7,11 @@ use crate::{
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},
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expr::gen_binop_expr_with_values,
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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_size,
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},
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llvm_intrinsics,
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llvm_intrinsics::call_memcpy_generic,
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llvm_intrinsics::{self, 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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CodeGenContext, CodeGenerator,
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},
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@ -32,6 +31,8 @@ use inkwell::{
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};
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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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fn create_ndarray_uninitialized<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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@ -86,7 +87,7 @@ where
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ctx,
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llvm_usize.const_zero(),
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(shape_len, false),
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|generator, ctx, i| {
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|generator, ctx, _, i| {
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let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
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debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
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@ -131,7 +132,7 @@ where
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ctx,
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llvm_usize.const_zero(),
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(shape_len, false),
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|generator, ctx, i| {
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|generator, ctx, _, i| {
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let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
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debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
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let shape_dim = ctx.builder.build_int_z_extend(shape_dim, llvm_usize, "").unwrap();
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@ -334,7 +335,7 @@ where
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ctx,
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llvm_usize.const_zero(),
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(ndarray_num_elems, false),
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|generator, ctx, i| {
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|generator, ctx, _, i| {
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let elem = unsafe { ndarray.data().ptr_offset_unchecked(ctx, generator, &i, None) };
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let value = value_fn(generator, ctx, i)?;
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@ -1193,7 +1194,7 @@ pub fn ndarray_sliced_copy<'ctx, G: CodeGenerator + ?Sized>(
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ctx,
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llvm_usize.const_int(slices.len() as u64, false),
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(this.load_ndims(ctx), false),
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|generator, ctx, idx| {
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|generator, ctx, _, idx| {
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unsafe {
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let dim_sz = this.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None);
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ndarray.dim_sizes().set_typed_unchecked(ctx, generator, &idx, dim_sz);
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@ -1366,6 +1367,46 @@ where
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Ok(ndarray)
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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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///
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/// * `elem_ty` - The element type of the `NDArray`.
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@ -1597,7 +1638,7 @@ pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
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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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|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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@ -1984,3 +2025,136 @@ pub fn gen_ndarray_fill<'ctx>(
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Ok(())
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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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|
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|
@ -462,6 +462,14 @@ pub fn gen_for<G: CodeGenerator>(
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Ok(())
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}
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
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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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pub break_bb: BasicBlock<'ctx>,
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/// [`BasicBlock`] to branch to for `continue`-ing to the next iteration in the loop.
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pub continue_bb: BasicBlock<'ctx>,
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}
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/// Generates a C-style `for` construct using lambdas, similar to the following C code:
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///
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/// ```c
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|
@ -489,7 +497,8 @@ where
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I: Clone,
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InitFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
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CondFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
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BodyFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
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BodyFn:
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FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, BreakContinueHooks, 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_bb = ctx.builder.get_insert_block().unwrap();
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|
@ -520,7 +529,8 @@ where
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}
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ctx.builder.position_at_end(body_bb);
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body(generator, ctx, loop_var.clone())?;
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let hooks = BreakContinueHooks { break_bb: cont_bb, continue_bb: update_bb };
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body(generator, ctx, hooks, loop_var.clone())?;
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if !ctx.is_terminated() {
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ctx.builder.build_unconditional_branch(update_bb).unwrap();
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}
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|
@ -562,7 +572,12 @@ pub fn gen_for_callback_incrementing<'ctx, 'a, G, 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(&mut G, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<(), String>,
|
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BodyFn: FnOnce(
|
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&mut G,
|
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&mut CodeGenContext<'ctx, 'a>,
|
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BreakContinueHooks,
|
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IntValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
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let init_val_t = init_val.get_type();
|
||||
|
||||
|
@ -584,10 +599,10 @@ where
|
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|
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Ok(ctx.builder.build_int_compare(cmp_op, i, max_val, "").unwrap())
|
||||
},
|
||||
|generator, ctx, i_addr| {
|
||||
|generator, ctx, hooks, i_addr| {
|
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let i = ctx.builder.build_load(i_addr, "").map(BasicValueEnum::into_int_value).unwrap();
|
||||
|
||||
body(generator, ctx, i)
|
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body(generator, ctx, hooks, i)
|
||||
},
|
||||
|_, ctx, i_addr| {
|
||||
let i = ctx.builder.build_load(i_addr, "").map(BasicValueEnum::into_int_value).unwrap();
|
||||
|
@ -698,7 +713,7 @@ where
|
|||
|
||||
Ok(cond)
|
||||
},
|
||||
|generator, ctx, (i_addr, _)| {
|
||||
|generator, ctx, _, (i_addr, _)| {
|
||||
let i = ctx.builder.build_load(i_addr, "").map(BasicValueEnum::into_int_value).unwrap();
|
||||
|
||||
body_fn(generator, ctx, i)
|
||||
|
|
|
@ -497,6 +497,8 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
|
||||
PrimDef::FunNpIsNan | PrimDef::FunNpIsInf => self.build_np_float_to_bool_function(prim),
|
||||
|
||||
PrimDef::FunNpAny | PrimDef::FunNpAll => self.build_np_any_all_function(prim),
|
||||
|
||||
PrimDef::FunNpSin
|
||||
| PrimDef::FunNpCos
|
||||
| PrimDef::FunNpTan
|
||||
|
@ -1757,6 +1759,24 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
}
|
||||
}
|
||||
|
||||
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")];
|
||||
let ret_ty = self.primitives.bool;
|
||||
let var_map = &into_var_map([]);
|
||||
let codegen_callback: Box<GenCallCallback> =
|
||||
Box::new(move |ctx, obj, fun, args, generator| {
|
||||
let func = match prim {
|
||||
PrimDef::FunNpAny => gen_ndarray_any,
|
||||
PrimDef::FunNpAll => gen_ndarray_all,
|
||||
_ => unreachable!(),
|
||||
};
|
||||
func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
|
||||
});
|
||||
|
||||
create_fn_by_codegen(self.unifier, var_map, prim.name(), ret_ty, param_ty, codegen_callback)
|
||||
}
|
||||
|
||||
fn create_method(prim: PrimDef, method_ty: Type) -> (StrRef, Type, DefinitionId) {
|
||||
(prim.simple_name().into(), method_ty, prim.id())
|
||||
}
|
||||
|
|
|
@ -100,6 +100,8 @@ pub enum PrimDef {
|
|||
FunNpHypot,
|
||||
FunNpNextAfter,
|
||||
FunSome,
|
||||
FunNpAny,
|
||||
FunNpAll,
|
||||
}
|
||||
|
||||
/// Associated details of a [`PrimDef`]
|
||||
|
@ -251,6 +253,8 @@ impl PrimDef {
|
|||
PrimDef::FunNpHypot => fun("np_hypot", None),
|
||||
PrimDef::FunNpNextAfter => fun("np_nextafter", None),
|
||||
PrimDef::FunSome => fun("Some", None),
|
||||
PrimDef::FunNpAny => fun("np_any", None),
|
||||
PrimDef::FunNpAll => fun("np_all", None),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
@ -226,6 +226,8 @@ def patch(module):
|
|||
module.np_full = np.full
|
||||
module.np_eye = np.eye
|
||||
module.np_identity = np.identity
|
||||
module.np_any = np.any
|
||||
module.np_all = np.all
|
||||
|
||||
def file_import(filename, prefix="file_import_"):
|
||||
filename = pathlib.Path(filename)
|
||||
|
|
|
@ -1388,6 +1388,48 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
|
|||
output_ndarray_float_2(nextafter_x_zeros)
|
||||
output_ndarray_float_2(nextafter_x_ones)
|
||||
|
||||
def test_ndarray_any():
|
||||
x1 = np_identity(5)
|
||||
y1 = np_any(x1)
|
||||
output_ndarray_float_2(x1)
|
||||
output_bool(y1)
|
||||
|
||||
x2 = np_identity(1)
|
||||
y2 = np_any(x2)
|
||||
output_ndarray_float_2(x2)
|
||||
output_bool(y2)
|
||||
|
||||
x3 = np_array([[1.0, 2.0], [3.0, 4.0]])
|
||||
y3 = np_any(x3)
|
||||
output_ndarray_float_2(x3)
|
||||
output_bool(y3)
|
||||
|
||||
x4 = np_zeros([3, 5])
|
||||
y4 = np_any(x4)
|
||||
output_ndarray_float_2(x4)
|
||||
output_bool(y4)
|
||||
|
||||
def test_ndarray_all():
|
||||
x1 = np_identity(5)
|
||||
y1 = np_all(x1)
|
||||
output_ndarray_float_2(x1)
|
||||
output_bool(y1)
|
||||
|
||||
x2 = np_identity(1)
|
||||
y2 = np_all(x2)
|
||||
output_ndarray_float_2(x2)
|
||||
output_bool(y2)
|
||||
|
||||
x3 = np_array([[1.0, 2.0], [3.0, 4.0]])
|
||||
y3 = np_all(x3)
|
||||
output_ndarray_float_2(x3)
|
||||
output_bool(y3)
|
||||
|
||||
x4 = np_zeros([3, 5])
|
||||
y4 = np_all(x4)
|
||||
output_ndarray_float_2(x4)
|
||||
output_bool(y4)
|
||||
|
||||
def run() -> int32:
|
||||
test_ndarray_ctor()
|
||||
test_ndarray_empty()
|
||||
|
@ -1565,4 +1607,7 @@ def run() -> int32:
|
|||
test_ndarray_nextafter_broadcast_lhs_scalar()
|
||||
test_ndarray_nextafter_broadcast_rhs_scalar()
|
||||
|
||||
test_ndarray_any()
|
||||
test_ndarray_all()
|
||||
|
||||
return 0
|
||||
|
|
Loading…
Reference in New Issue