core/ndstrides: implement general matmul
This commit is contained in:
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@ -12,4 +12,5 @@
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#include "irrt/ndarray/array.hpp"
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#include "irrt/ndarray/reshape.hpp"
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#include "irrt/ndarray/broadcast.hpp"
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#include "irrt/ndarray/transpose.hpp"
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#include "irrt/ndarray/transpose.hpp"
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#include "irrt/ndarray/matmul.hpp"
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@ -0,0 +1,100 @@
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#pragma once
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#include "irrt/debug.hpp"
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#include "irrt/exception.hpp"
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#include "irrt/int_types.hpp"
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#include "irrt/ndarray/basic.hpp"
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#include "irrt/ndarray/broadcast.hpp"
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#include "irrt/ndarray/iter.hpp"
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// NOTE: Everything would be much easier and elegant if einsum is implemented.
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namespace {
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namespace ndarray {
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namespace matmul {
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/**
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* @brief Perform the broadcast in `np.einsum("...ij,...jk->...ik", a, b)`.
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*
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* Example:
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* Suppose `a_shape == [1, 97, 4, 2]`
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* and `b_shape == [99, 98, 1, 2, 5]`,
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*
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* ...then `new_a_shape == [99, 98, 97, 4, 2]`,
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* `new_b_shape == [99, 98, 97, 2, 5]`,
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* and `dst_shape == [99, 98, 97, 4, 5]`.
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* ^^^^^^^^^^ ^^^^
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* (broadcasted) (4x2 @ 2x5 => 4x5)
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*
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* @param a_ndims Length of `a_shape`.
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* @param a_shape Shape of `a`.
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* @param b_ndims Length of `b_shape`.
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* @param b_shape Shape of `b`.
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* @param final_ndims Should be equal to `max(a_ndims, b_ndims)`. This is the length of `new_a_shape`,
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* `new_b_shape`, and `dst_shape` - the number of dimensions after broadcasting.
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*/
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template<typename SizeT>
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void calculate_shapes(SizeT a_ndims,
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SizeT* a_shape,
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SizeT b_ndims,
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SizeT* b_shape,
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SizeT final_ndims,
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SizeT* new_a_shape,
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SizeT* new_b_shape,
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SizeT* dst_shape) {
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debug_assert(SizeT, a_ndims >= 2);
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debug_assert(SizeT, b_ndims >= 2);
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debug_assert_eq(SizeT, max(a_ndims, b_ndims), final_ndims);
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// Check that a and b are compatible for matmul
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if (a_shape[a_ndims - 1] != b_shape[b_ndims - 2]) {
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// This is a custom error message. Different from NumPy.
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raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot multiply LHS (shape ?x{0}) with RHS (shape {1}x?})",
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a_shape[a_ndims - 1], b_shape[b_ndims - 2], NO_PARAM);
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}
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const SizeT num_entries = 2;
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ShapeEntry<SizeT> entries[num_entries] = {{.ndims = a_ndims - 2, .shape = a_shape},
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{.ndims = b_ndims - 2, .shape = b_shape}};
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// TODO: Optimize this
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ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_a_shape);
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ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_b_shape);
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ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, dst_shape);
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new_a_shape[final_ndims - 2] = a_shape[a_ndims - 2];
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new_a_shape[final_ndims - 1] = a_shape[a_ndims - 1];
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new_b_shape[final_ndims - 2] = b_shape[b_ndims - 2];
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new_b_shape[final_ndims - 1] = b_shape[b_ndims - 1];
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dst_shape[final_ndims - 2] = a_shape[a_ndims - 2];
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dst_shape[final_ndims - 1] = b_shape[b_ndims - 1];
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}
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} // namespace matmul
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} // namespace ndarray
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} // namespace
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extern "C" {
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using namespace ndarray::matmul;
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void __nac3_ndarray_matmul_calculate_shapes(int32_t a_ndims,
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int32_t* a_shape,
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int32_t b_ndims,
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int32_t* b_shape,
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int32_t final_ndims,
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int32_t* new_a_shape,
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int32_t* new_b_shape,
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int32_t* dst_shape) {
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calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
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}
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void __nac3_ndarray_matmul_calculate_shapes64(int64_t a_ndims,
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int64_t* a_shape,
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int64_t b_ndims,
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int64_t* b_shape,
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int64_t final_ndims,
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int64_t* new_a_shape,
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int64_t* new_b_shape,
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int64_t* dst_shape) {
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calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
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}
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}
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@ -1572,7 +1572,11 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
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if op.base == Operator::MatMult {
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// Handle matrix multiplication.
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todo!()
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let left = left.to_ndarray(generator, ctx);
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let right = right.to_ndarray(generator, ctx);
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let result = NDArrayObject::matmul(generator, ctx, left, right, out)
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.split_unsized(generator, ctx);
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Ok(Some(ValueEnum::Dynamic(result.to_basic_value_enum())))
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} else {
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// For other operations, they are all elementwise operations.
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@ -1220,3 +1220,30 @@ pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
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.arg(axes)
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.returning_void();
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}
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#[allow(clippy::too_many_arguments)]
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pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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a_ndims: Instance<'ctx, Int<SizeT>>,
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a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
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b_ndims: Instance<'ctx, Int<SizeT>>,
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b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
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final_ndims: Instance<'ctx, Int<SizeT>>,
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new_a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
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new_b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
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dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
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) {
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let name =
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get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
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FnCall::builder(generator, ctx, &name)
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.arg(a_ndims)
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.arg(a_shape)
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.arg(b_ndims)
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.arg(b_shape)
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.arg(final_ndims)
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.arg(new_a_shape)
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.arg(new_b_shape)
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.arg(dst_shape)
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.returning_void();
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}
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@ -5,7 +5,6 @@ use crate::{
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ProxyType, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
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TypedArrayLikeMutator, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
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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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call_ndarray_calc_broadcast_index, call_ndarray_calc_nd_indices,
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@ -27,21 +26,18 @@ use crate::{
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numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
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DefinitionId,
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},
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typecheck::{
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magic_methods::Binop,
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typedef::{FunSignature, Type},
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},
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typecheck::typedef::{FunSignature, Type},
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};
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use inkwell::{
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types::BasicType,
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values::{BasicValueEnum, IntValue, PointerValue},
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AddressSpace, IntPredicate, OptimizationLevel,
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AddressSpace, IntPredicate,
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};
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use inkwell::{
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types::{AnyTypeEnum, BasicTypeEnum, PointerType},
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values::BasicValue,
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};
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use nac3parser::ast::{Operator, StrRef};
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use nac3parser::ast::StrRef;
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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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@ -1438,302 +1434,6 @@ where
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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
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.build_int_compare(IntPredicate::EQ, lhs_ndims, llvm_usize.const_int(2, false), "")
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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
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.build_int_compare(IntPredicate::EQ, rhs_ndims, llvm_usize.const_int(2, false), "")
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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(
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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 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(
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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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// res.ndims == 2
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ctx.make_assert(
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generator,
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ctx.builder
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.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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)
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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(IntPredicate::EQ, lhs_dim0, res_dim0, "").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(IntPredicate::EQ, rhs_dim1, res_dim1, "").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(
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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_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(IntPredicate::EQ, lhs_dim1, rhs_dim0, "").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_base_value() == lhs.as_base_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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|_, _, _| Ok(llvm_usize.const_int(2, false)),
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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
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.builder
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.build_int_compare(IntPredicate::EQ, idx, llvm_usize.const_zero(), "")
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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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)
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.map(|v| v.map(BasicValueEnum::into_int_value).unwrap())
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},
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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(generator, ctx, ndarray, |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(ctx, generator, &llvm_usize.const_zero(), None)
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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(ctx, generator, &llvm_usize.const_zero(), None);
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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 =
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idx.get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None);
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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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None,
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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(),
|
||||
);
|
||||
|
||||
rhs.data().get_unchecked(ctx, generator, &ab_idx, None)
|
||||
};
|
||||
|
||||
let a_mul_b = gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&Some(elem_ty), a),
|
||||
Binop::normal(Operator::Mult),
|
||||
(&Some(elem_ty), b),
|
||||
ctx.current_loc,
|
||||
)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, elem_ty)?;
|
||||
|
||||
let result = ctx.builder.build_load(result_addr, "").unwrap();
|
||||
let result = gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&Some(elem_ty), result),
|
||||
Binop::normal(Operator::Add),
|
||||
(&Some(elem_ty), a_mul_b),
|
||||
ctx.current_loc,
|
||||
)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, elem_ty)?;
|
||||
ctx.builder.build_store(result_addr, result).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
|
||||
let result = ctx.builder.build_load(result_addr, "").unwrap();
|
||||
Ok(result)
|
||||
})?;
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.empty`.
|
||||
pub fn gen_ndarray_empty<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,218 @@
|
|||
use std::cmp::max;
|
||||
|
||||
use nac3parser::ast::Operator;
|
||||
use util::gen_for_model;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
expr::gen_binop_expr_with_values, irrt::call_nac3_ndarray_matmul_calculate_shapes,
|
||||
model::*, object::ndarray::indexing::RustNDIndex, CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::{magic_methods::Binop, typedef::Type},
|
||||
};
|
||||
|
||||
use super::{NDArrayObject, NDArrayOut};
|
||||
|
||||
/// Perform `np.einsum("...ij,...jk->...ik", in_a, in_b)`.
|
||||
///
|
||||
/// `dst_dtype` defines the dtype of the returned ndarray.
|
||||
fn matmul_at_least_2d<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dst_dtype: Type,
|
||||
in_a: NDArrayObject<'ctx>,
|
||||
in_b: NDArrayObject<'ctx>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
assert!(in_a.ndims >= 2);
|
||||
assert!(in_b.ndims >= 2);
|
||||
|
||||
// Deduce ndims of the result of matmul.
|
||||
let ndims_int = max(in_a.ndims, in_b.ndims);
|
||||
let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int, false);
|
||||
|
||||
// Broadcasts `in_a.shape[:-2]` and `in_b.shape[:-2]` together and allocate the
|
||||
// destination ndarray to store the result of matmul.
|
||||
let (lhs, rhs, dst) = {
|
||||
let in_lhs_ndims = in_a.ndims_llvm(generator, ctx.ctx);
|
||||
let in_lhs_shape = in_a.instance.get(generator, ctx, |f| f.shape);
|
||||
let in_rhs_ndims = in_b.ndims_llvm(generator, ctx.ctx);
|
||||
let in_rhs_shape = in_b.instance.get(generator, ctx, |f| f.shape);
|
||||
let lhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
|
||||
let rhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
|
||||
let dst_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
|
||||
|
||||
// Matmul dimension compatibility is checked here.
|
||||
call_nac3_ndarray_matmul_calculate_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
in_lhs_ndims,
|
||||
in_lhs_shape,
|
||||
in_rhs_ndims,
|
||||
in_rhs_shape,
|
||||
ndims,
|
||||
lhs_shape,
|
||||
rhs_shape,
|
||||
dst_shape,
|
||||
);
|
||||
|
||||
let lhs = in_a.broadcast_to(generator, ctx, ndims_int, lhs_shape);
|
||||
let rhs = in_b.broadcast_to(generator, ctx, ndims_int, rhs_shape);
|
||||
|
||||
let dst = NDArrayObject::alloca(generator, ctx, dst_dtype, ndims_int);
|
||||
dst.copy_shape_from_array(generator, ctx, dst_shape);
|
||||
dst.create_data(generator, ctx);
|
||||
|
||||
(lhs, rhs, dst)
|
||||
};
|
||||
|
||||
let len = lhs.instance.get(generator, ctx, |f| f.shape).get_index_const(
|
||||
generator,
|
||||
ctx,
|
||||
i64::try_from(ndims_int - 1).unwrap(),
|
||||
);
|
||||
|
||||
let at_row = i64::try_from(ndims_int - 2).unwrap();
|
||||
let at_col = i64::try_from(ndims_int - 1).unwrap();
|
||||
|
||||
let dst_dtype_llvm = ctx.get_llvm_type(generator, dst_dtype);
|
||||
let dst_zero = dst_dtype_llvm.const_zero();
|
||||
|
||||
dst.foreach(generator, ctx, |generator, ctx, _, hdl| {
|
||||
let pdst_ij = hdl.get_pointer(generator, ctx);
|
||||
|
||||
ctx.builder.build_store(pdst_ij, dst_zero).unwrap();
|
||||
|
||||
let indices = hdl.get_indices();
|
||||
let i = indices.get_index_const(generator, ctx, at_row);
|
||||
let j = indices.get_index_const(generator, ctx, at_col);
|
||||
|
||||
let num_0 = Int(SizeT).const_int(generator, ctx.ctx, 0, false);
|
||||
let num_1 = Int(SizeT).const_int(generator, ctx.ctx, 1, false);
|
||||
|
||||
gen_for_model(generator, ctx, num_0, len, num_1, |generator, ctx, _, k| {
|
||||
// `indices` is modified to index into `a` and `b`, and restored.
|
||||
indices.set_index_const(ctx, at_row, i);
|
||||
indices.set_index_const(ctx, at_col, k);
|
||||
let a_ik = lhs.get_scalar_by_indices(generator, ctx, indices);
|
||||
|
||||
indices.set_index_const(ctx, at_row, k);
|
||||
indices.set_index_const(ctx, at_col, j);
|
||||
let b_kj = rhs.get_scalar_by_indices(generator, ctx, indices);
|
||||
|
||||
// Restore `indices`.
|
||||
indices.set_index_const(ctx, at_row, i);
|
||||
indices.set_index_const(ctx, at_col, j);
|
||||
|
||||
// x = a_[...]ik * b_[...]kj
|
||||
let x = gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&Some(lhs.dtype), a_ik.value),
|
||||
Binop::normal(Operator::Mult),
|
||||
(&Some(rhs.dtype), b_kj.value),
|
||||
ctx.current_loc,
|
||||
)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, dst_dtype)?;
|
||||
|
||||
// dst_[...]ij += x
|
||||
let dst_ij = ctx.builder.build_load(pdst_ij, "").unwrap();
|
||||
let dst_ij = gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&Some(dst_dtype), dst_ij),
|
||||
Binop::normal(Operator::Add),
|
||||
(&Some(dst_dtype), x),
|
||||
ctx.current_loc,
|
||||
)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, dst_dtype)?;
|
||||
ctx.builder.build_store(pdst_ij, dst_ij).unwrap();
|
||||
|
||||
Ok(())
|
||||
})
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
dst
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Perform `np.matmul` according to the rules in
|
||||
/// <https://numpy.org/doc/stable/reference/generated/numpy.matmul.html>.
|
||||
///
|
||||
/// This function always return an [`NDArrayObject`]. You may want to use [`NDArrayObject::split_unsized`]
|
||||
/// to handle when the output could be a scalar.
|
||||
///
|
||||
/// `dst_dtype` defines the dtype of the returned ndarray.
|
||||
pub fn matmul<G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a: Self,
|
||||
b: Self,
|
||||
out: NDArrayOut<'ctx>,
|
||||
) -> Self {
|
||||
// Sanity check, but type inference should prevent this.
|
||||
assert!(a.ndims > 0 && b.ndims > 0, "np.matmul disallows scalar input");
|
||||
|
||||
/*
|
||||
If both arguments are 2-D they are multiplied like conventional matrices.
|
||||
If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indices and broadcast accordingly.
|
||||
If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
|
||||
If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.
|
||||
*/
|
||||
|
||||
let new_a = if a.ndims == 1 {
|
||||
// Prepend 1 to its dimensions
|
||||
a.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis])
|
||||
} else {
|
||||
a
|
||||
};
|
||||
|
||||
let new_b = if b.ndims == 1 {
|
||||
// Append 1 to its dimensions
|
||||
b.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis])
|
||||
} else {
|
||||
b
|
||||
};
|
||||
|
||||
// NOTE: `result` will always be a newly allocated ndarray.
|
||||
// Current implementation cannot do in-place matrix muliplication.
|
||||
let mut result = matmul_at_least_2d(generator, ctx, out.get_dtype(), new_a, new_b);
|
||||
|
||||
// Postprocessing on the result to remove prepended/appended axes.
|
||||
let mut postindices = vec![];
|
||||
let zero = Int(Int32).const_0(generator, ctx.ctx);
|
||||
|
||||
if a.ndims == 1 {
|
||||
// Remove the prepended 1
|
||||
postindices.push(RustNDIndex::SingleElement(zero));
|
||||
}
|
||||
|
||||
if b.ndims == 1 {
|
||||
// Remove the appended 1
|
||||
postindices.push(RustNDIndex::Ellipsis);
|
||||
postindices.push(RustNDIndex::SingleElement(zero));
|
||||
}
|
||||
|
||||
if !postindices.is_empty() {
|
||||
result = result.index(generator, ctx, &postindices);
|
||||
}
|
||||
|
||||
match out {
|
||||
NDArrayOut::NewNDArray { .. } => result,
|
||||
NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
|
||||
let result_shape = result.instance.get(generator, ctx, |f| f.shape);
|
||||
out_ndarray.assert_can_be_written_by_out(
|
||||
generator,
|
||||
ctx,
|
||||
result.ndims,
|
||||
result_shape,
|
||||
);
|
||||
|
||||
out_ndarray.copy_data_from(generator, ctx, result);
|
||||
out_ndarray
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -3,6 +3,7 @@ pub mod broadcast;
|
|||
pub mod factory;
|
||||
pub mod indexing;
|
||||
pub mod map;
|
||||
pub mod matmul;
|
||||
pub mod nditer;
|
||||
pub mod shape_util;
|
||||
pub mod view;
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
use crate::toplevel::helper::{extract_ndims, PrimDef};
|
||||
use crate::toplevel::numpy::{make_ndarray_ty, unpack_ndarray_var_tys};
|
||||
use crate::typecheck::{
|
||||
type_inferencer::*,
|
||||
|
|
@ -13,6 +13,8 @@ use std::collections::HashMap;
|
|||
use std::rc::Rc;
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
use super::typedef::into_var_map;
|
||||
|
||||
/// The variant of a binary operator.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum BinopVariant {
|
||||
|
|
@ -171,19 +173,8 @@ pub fn impl_binop(
|
|||
ops: &[Operator],
|
||||
) {
|
||||
with_fields(unifier, ty, |unifier, fields| {
|
||||
let (other_ty, other_var_id) = if other_ty.len() == 1 {
|
||||
(other_ty[0], None)
|
||||
} else {
|
||||
let tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
|
||||
(tvar.ty, Some(tvar.id))
|
||||
};
|
||||
|
||||
let function_vars = if let Some(var_id) = other_var_id {
|
||||
vec![(var_id, other_ty)].into_iter().collect::<VarMap>()
|
||||
} else {
|
||||
VarMap::new()
|
||||
};
|
||||
|
||||
let other_tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
|
||||
let function_vars = into_var_map([other_tvar]);
|
||||
let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).ty);
|
||||
|
||||
for (base_op, variant) in iproduct!(ops, [BinopVariant::Normal, BinopVariant::AugAssign]) {
|
||||
|
|
@ -194,7 +185,7 @@ pub fn impl_binop(
|
|||
ret: ret_ty,
|
||||
vars: function_vars.clone(),
|
||||
args: vec![FuncArg {
|
||||
ty: other_ty,
|
||||
ty: other_tvar.ty,
|
||||
default_value: None,
|
||||
name: "other".into(),
|
||||
is_vararg: false,
|
||||
|
|
@ -537,36 +528,43 @@ pub fn typeof_binop(
|
|||
}
|
||||
}
|
||||
|
||||
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()
|
||||
let (lhs_dtype, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
|
||||
let lhs_ndims = extract_ndims(unifier, lhs_ndims);
|
||||
|
||||
let (rhs_dtype, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
|
||||
let rhs_ndims = extract_ndims(unifier, rhs_ndims);
|
||||
|
||||
if !(unifier.unioned(lhs_dtype, primitives.float)
|
||||
&& unifier.unioned(rhs_dtype, primitives.float))
|
||||
{
|
||||
return Err(format!(
|
||||
"ndarray.__matmul__ only supports float64 operations, but LHS has type {} and RHS has type {}",
|
||||
unifier.stringify(lhs),
|
||||
unifier.stringify(rhs)
|
||||
));
|
||||
}
|
||||
|
||||
// Deduce the ndims of the resulting ndarray.
|
||||
// If this is 0 (an unsized ndarray), matmul returns a scalar just like NumPy.
|
||||
let result_ndims = match (lhs_ndims, rhs_ndims) {
|
||||
(0, _) | (_, 0) => {
|
||||
return Err(
|
||||
"ndarray.__matmul__ does not allow unsized ndarray input".to_string()
|
||||
)
|
||||
}
|
||||
_ => 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!(),
|
||||
(1, 1) => 0,
|
||||
(1, _) => rhs_ndims - 1,
|
||||
(_, 1) => lhs_ndims - 1,
|
||||
(m, n) => max(m, n),
|
||||
};
|
||||
|
||||
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})",
|
||||
u8::from(rhs == 0)
|
||||
))
|
||||
}
|
||||
(lhs, rhs) => {
|
||||
return Err(format!(
|
||||
"ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"
|
||||
))
|
||||
}
|
||||
if result_ndims == 0 {
|
||||
// If the result is unsized, NumPy returns a scalar.
|
||||
primitives.float
|
||||
} else {
|
||||
let result_ndims_ty =
|
||||
unifier.get_fresh_literal(vec![SymbolValue::U64(result_ndims)], None);
|
||||
make_ndarray_ty(unifier, primitives, Some(primitives.float), Some(result_ndims_ty))
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -769,7 +767,7 @@ 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_matmul(unifier, store, ndarray_t, &[ndarray_unsized_t], None);
|
||||
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);
|
||||
|
|
|
|||
Loading…
Reference in New Issue