[core] codegen/ndarray: Implement subscript assignment
Based on 5bed394e
: core/ndstrides: implement subscript assignment
Overlapping is not handled. Currently it has undefined behavior.
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@ -16,7 +16,11 @@ use super::{
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gen_in_range_check,
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irrt::{handle_slice_indices, list_slice_assignment},
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macros::codegen_unreachable,
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values::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
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types::ndarray::NDArrayType,
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values::{
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ndarray::{RustNDIndex, ScalarOrNDArray},
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ArrayLikeIndexer, ArraySliceValue, ListValue, ProxyValue, RangeValue,
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},
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CodeGenContext, CodeGenerator,
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};
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use crate::{
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@ -411,7 +415,56 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
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if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
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{
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// Handle NDArray item assignment
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todo!("ndarray subscript assignment is not yet implemented");
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// Process target
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let target = generator
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.gen_expr(ctx, target)?
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.unwrap()
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.to_basic_value_enum(ctx, generator, target_ty)?;
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// Process key
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let key = RustNDIndex::from_subscript_expr(generator, ctx, key)?;
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// Process value
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let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
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/*
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Reference code:
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```python
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target = target[key]
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value = np.asarray(value)
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shape = np.broadcast_shape((target, value))
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target = np.broadcast_to(target, shape)
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value = np.broadcast_to(value, shape)
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...and finally copy 1-1 from value to target.
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```
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*/
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let target = NDArrayType::from_unifier_type(generator, ctx, target_ty)
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.map_value(target.into_pointer_value(), None);
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let target = target.index(generator, ctx, &key);
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let value = ScalarOrNDArray::from_value(generator, ctx, (value_ty, value))
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.to_ndarray(generator, ctx);
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let broadcast_ndims = [target.get_type().ndims(), value.get_type().ndims()]
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.iter()
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.filter_map(|ndims| *ndims)
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.max();
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let broadcast_result = NDArrayType::new(
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generator,
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ctx.ctx,
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value.get_type().element_type(),
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broadcast_ndims,
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)
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.broadcast(generator, ctx, &[target, value]);
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let target = broadcast_result.ndarrays[0];
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let value = broadcast_result.ndarrays[1];
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target.copy_data_from(generator, ctx, value);
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}
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_ => {
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panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
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@ -276,6 +276,38 @@ def test_ndarray_broadcast_to():
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output_int32(np_shape(zs)[2])
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output_ndarray_float_3(zs)
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def test_ndarray_subscript_assignment():
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xs = np_array([[11.0, 22.0, 33.0, 44.0], [55.0, 66.0, 77.0, 88.0]])
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xs[0, 0] = 99.0
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output_ndarray_float_2(xs)
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xs[0] = 100.0
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output_ndarray_float_2(xs)
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xs[:, ::2] = 101.0
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output_ndarray_float_2(xs)
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xs[1:, 0] = 102.0
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output_ndarray_float_2(xs)
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xs[0] = np_array([-1.0, -2.0, -3.0, -4.0])
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output_ndarray_float_2(xs)
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xs[:] = np_array([-5.0, -6.0, -7.0, -8.0])
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output_ndarray_float_2(xs)
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# Test assignment with memory sharing
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ys1 = np_reshape(xs, (2, 4))
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ys2 = np_transpose(ys1)
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ys3 = ys2[::-1, 0]
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ys3[0] = -999.0
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output_ndarray_float_2(xs)
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output_ndarray_float_2(ys1)
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output_ndarray_float_2(ys2)
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output_ndarray_float_1(ys3)
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def test_ndarray_add():
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x = np_identity(2)
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y = x + np_ones([2, 2])
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@ -1653,6 +1685,7 @@ def run() -> int32:
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test_ndarray_transpose()
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test_ndarray_reshape()
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test_ndarray_broadcast_to()
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test_ndarray_subscript_assignment()
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test_ndarray_add()
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test_ndarray_add_broadcast()
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