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core: remove np_linalg_matmul

This commit is contained in:
abdul124 2024-08-01 18:43:06 +08:00
parent f8d3a374e6
commit 2237137f1a
7 changed files with 5 additions and 139 deletions
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49
nac3core/src/codegen/builtin_fns.rs
View File

@ -1867,55 +1867,6 @@ fn build_output_struct<'ctx>(
out_ptr
}
/// Invokes the `np_linalg_matmul` linalg function
pub fn call_np_linalg_matmul<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
x1: (Type, BasicValueEnum<'ctx>),
x2: (Type, BasicValueEnum<'ctx>),
) -> Result<BasicValueEnum<'ctx>, String> {
const FN_NAME: &str = "np_linalg_matmul";
let (x1_ty, x1) = x1;
let (x2_ty, x2) = x2;
let llvm_usize = generator.get_size_type(ctx.ctx);
if let (BasicValueEnum::PointerValue(n1), BasicValueEnum::PointerValue(n2)) = (x1, x2) {
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
let (n2_elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x2_ty);
let n2_elem_ty = ctx.get_llvm_type(generator, n2_elem_ty);
let (BasicTypeEnum::FloatType(_), BasicTypeEnum::FloatType(_)) = (n1_elem_ty, n2_elem_ty)
else {
unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty]);
};
let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
let n2 = NDArrayValue::from_ptr_val(n2, llvm_usize, None);
let outdim0 = unsafe {
n1.dim_sizes()
.get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
.into_int_value()
};
let outdim1 = unsafe {
n2.dim_sizes()
.get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
.into_int_value()
};
let out = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[outdim0, outdim1])
.unwrap()
.as_base_value()
.as_basic_value_enum();
extern_fns::call_np_linalg_matmul(ctx, x1, x2, out, None);
Ok(out)
} else {
unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty])
}
}
/// Invokes the `np_linalg_cholesky` linalg function
pub fn call_np_linalg_cholesky<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,

1
nac3core/src/codegen/extern_fns.rs
View File

@ -179,7 +179,6 @@ macro_rules! generate_linalg_extern_fn {
};
}
generate_linalg_extern_fn!(call_np_linalg_matmul, "np_linalg_matmul", 3);
generate_linalg_extern_fn!(call_np_linalg_cholesky, "np_linalg_cholesky", 2);
generate_linalg_extern_fn!(call_np_linalg_qr, "np_linalg_qr", 3);
generate_linalg_extern_fn!(call_np_linalg_svd, "np_linalg_svd", 4);

23
nac3core/src/toplevel/builtins.rs
View File

@ -562,7 +562,6 @@ impl<'a> BuiltinBuilder<'a> {
}
PrimDef::FunNpDot
| PrimDef::FunNpLinalgMatmul
| PrimDef::FunNpLinalgCholesky
| PrimDef::FunNpLinalgQr
| PrimDef::FunNpLinalgSvd
@ -1950,7 +1949,6 @@ impl<'a> BuiltinBuilder<'a> {
prim,
&[
PrimDef::FunNpDot,
PrimDef::FunNpLinalgMatmul,
PrimDef::FunNpLinalgCholesky,
PrimDef::FunNpLinalgQr,
PrimDef::FunNpLinalgSvd,
@ -1981,27 +1979,6 @@ impl<'a> BuiltinBuilder<'a> {
}),
),
PrimDef::FunNpLinalgMatmul => create_fn_by_codegen(
self.unifier,
&VarMap::new(),
prim.name(),
self.ndarray_float_2d,
&[(self.ndarray_float_2d, "x1"), (self.ndarray_float_2d, "x2")],
Box::new(move |ctx, _, fun, args, generator| {
let x1_ty = fun.0.args[0].ty;
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
let x2_ty = fun.0.args[1].ty;
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
Ok(Some(builtin_fns::call_np_linalg_matmul(
generator,
ctx,
(x1_ty, x1_val),
(x2_ty, x2_val),
)?))
}),
),
PrimDef::FunNpLinalgCholesky | PrimDef::FunNpLinalgInv | PrimDef::FunNpLinalgPinv => {
create_fn_by_codegen(
self.unifier,

2
nac3core/src/toplevel/helper.rs
View File

@ -104,7 +104,6 @@ pub enum PrimDef {
// Linalg functions
FunNpDot,
FunNpLinalgMatmul,
FunNpLinalgCholesky,
FunNpLinalgQr,
FunNpLinalgSvd,
@ -291,7 +290,6 @@ impl PrimDef {
// Linalg functions
PrimDef::FunNpDot => fun("np_dot", None),
PrimDef::FunNpLinalgMatmul => fun("np_linalg_matmul", None),
PrimDef::FunNpLinalgCholesky => fun("np_linalg_cholesky", None),
PrimDef::FunNpLinalgQr => fun("np_linalg_qr", None),
PrimDef::FunNpLinalgSvd => fun("np_linalg_svd", None),

3
nac3standalone/demo/interpret_demo.py
View File

@ -5,8 +5,8 @@ import importlib.util
import importlib.machinery
import math
import numpy as np
import scipy as sp
import numpy.typing as npt
import scipy as sp
import pathlib
from numpy import int32, int64, uint32, uint64
@ -231,7 +231,6 @@ def patch(module):
# Linalg functions
module.np_dot = np.dot
module.np_linalg_matmul = np.matmul
module.np_linalg_cholesky = np.linalg.cholesky
module.np_linalg_qr = np.linalg.qr
module.np_linalg_svd = np.linalg.svd

45
nac3standalone/demo/linalg/src/lib.rs
View File

@ -34,51 +34,6 @@ impl InputMatrix {
}
}
/// # Safety
///
/// `mat1` and `mat2` should point to a valid 2DArray of `f64` floats in row-major order
#[no_mangle]
pub unsafe extern "C" fn np_linalg_matmul(
mat1: *mut InputMatrix,
mat2: *mut InputMatrix,
out: *mut InputMatrix,
) {
let mat1 = mat1.as_mut().unwrap();
let mat2 = mat2.as_mut().unwrap();
let out = out.as_mut().unwrap();
if !(mat1.ndims == 2 && mat2.ndims == 2) {
let err_msg = format!(
"expected 2D Vector Input, but received {}D and {}D input",
mat1.ndims, mat2.ndims
);
report_error("ValueError", "np_matmul", file!(), line!(), column!(), &err_msg);
}
let dim1 = (*mat1).get_dims();
let dim2 = (*mat2).get_dims();
if dim1[1] != dim2[0] {
let err_msg = format!(
"shapes ({},{}) and ({},{}) not aligned: {} (dim 1) != {} (dim 0)",
dim1[0], dim1[1], dim2[0], dim2[1], dim1[1], dim2[0]
);
report_error("ValueError", "np_matmul", file!(), line!(), column!(), &err_msg);
}
let outdim = out.get_dims();
let out_slice = unsafe { slice::from_raw_parts_mut(out.data, outdim[0] * outdim[1]) };
let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
let data_slice2 = unsafe { slice::from_raw_parts_mut(mat2.data, dim2[0] * dim2[1]) };
let matrix1 = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
let matrix2 = DMatrix::from_row_slice(dim2[0], dim2[1], data_slice2);
let mut result = DMatrix::<f64>::zeros(outdim[0], outdim[1]);
matrix1.mul_to(&matrix2, &mut result);
out_slice.copy_from_slice(result.transpose().as_slice());
}
/// # Safety
///
/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order

21
nac3standalone/demo/src/ndarray.py
View File

@ -1474,18 +1474,6 @@ def test_ndarray_dot():
output_float64(z5)
output_bool(z6)
def test_ndarray_linalg_matmul():
x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
y: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
z = np_linalg_matmul(x, y)
m = np_argmax(z)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
output_ndarray_float_2(z)
output_int64(m)
def test_ndarray_cholesky():
x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
y = np_linalg_cholesky(x)
@ -1501,7 +1489,7 @@ def test_ndarray_qr():
# QR Factorization is not unique and gives different results in numpy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(y, z)
a = y @ z
output_ndarray_float_2(a)
def test_ndarray_linalg_inv():
@ -1540,7 +1528,7 @@ def test_ndarray_schur():
# Schur Factorization is not unique and gives different results in scipy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(np_linalg_matmul(z, t), np_linalg_inv(z))
a = (z @ t) @ np_linalg_inv(z)
output_ndarray_float_2(a)
def test_ndarray_hessenberg():
@ -1551,7 +1539,7 @@ def test_ndarray_hessenberg():
# Hessenberg Factorization is not unique and gives different results in scipy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(np_linalg_matmul(q, h), np_linalg_inv(q))
a = (q @ h) @ np_linalg_inv(q)
output_ndarray_float_2(a)
@ -1572,7 +1560,7 @@ def test_ndarray_svd():
# SVD Factorization is not unique and gives different results in numpy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(x, z)
a = x @ z
output_ndarray_float_2(a)
output_ndarray_float_1(y)
@ -1759,7 +1747,6 @@ def run() -> int32:
test_ndarray_reshape()
test_ndarray_dot()
test_ndarray_linalg_matmul()
test_ndarray_cholesky()
test_ndarray_qr()
test_ndarray_svd()