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np_reshape(WIP) and np_transpose added

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This commit is contained in:
abdul124 2024-07-30 18:10:38 +08:00
parent 688e85d13c
commit 9e7cf4fcac
9 changed files with 669 additions and 181 deletions
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2
flake.nix
View File

@ -161,7 +161,9 @@
clippy
pre-commit
rustfmt
rust-analyzer
];
RUST_SRC_PATH = "${pkgs.rust.packages.stable.rustPlatform.rustLibSrc}";
};
devShells.x86_64-linux.msys2 = pkgs.mkShell {
name = "nac3-dev-shell-msys2";

387
nac3core/src/codegen/numpy.rs
View File

@ -2026,3 +2026,390 @@ pub fn gen_ndarray_fill<'ctx>(
Ok(())
}
pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
x1: (Type, BasicValueEnum<'ctx>),
) -> Result<BasicValueEnum<'ctx>, String> {
const FN_NAME: &str = "ndarray_transpose";
let (x1_ty, x1) = x1;
let llvm_usize = generator.get_size_type(ctx.ctx);
if let BasicValueEnum::PointerValue(n1) = x1 {
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
let out = create_ndarray_dyn_shape(
generator,
ctx,
elem_ty,
&n1,
|_, ctx, n| Ok(n.load_ndims(ctx)),
|generator, ctx, n, idx| {
let new_idx = ctx.builder.build_int_sub(n.load_ndims(ctx), idx, "").unwrap();
let new_idx = ctx
.builder
.build_int_sub(new_idx, new_idx.get_type().const_int(1, false), "")
.unwrap();
unsafe { Ok(n.dim_sizes().get_typed_unchecked(ctx, generator, &new_idx, None)) }
},
)
.unwrap();
gen_for_callback_incrementing(
generator,
ctx,
None,
llvm_usize.const_zero(),
(n_sz, false),
|generator, ctx, _, idx| {
let elem = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
// Calculate transposed idx
// 2, 3 => idx = row * num_col + col = 3 + 2 = 5
// 2, 3, 4 => idx = row * (num_col*num_z) + col * (num_z) + z => 12 + 8 + 3 | 18, [4, 2] 15, ,1,2
// num_z (col + num_col * row) + z
// 4D => 2, 3, 4, 5 = idx = num_w (num_z (col + num_col*row) + z) + w = 119
// [1, 1, 2]?
// 4 (1 + 3*1) + 2 = 6
// z = 2, col = 1, row = 0
// 0,1,
// 2, 3 => idx = row * num_col + col | 2, 3, 4 => idx = row * (num_col * num_z) + col * (num_z) + num_z
// ND => idx = 1 * (dim0 + dim1 + ... dimn) + dim[-1] * (dim0 + dim1 + ... + dimn-1) + ... + dim[1] * dim0
// 6 + 12 + 6 = 24 num_z * (row*num_col + col + 1) 4*6=24
// 2, 3, 4, 5 at idx 1 should go to
// 5, 4, 3, 2
// 18 => [2, 4] dim = 4
// 0 * 4 + 2 = 2
// 4 => [1, 1] dim = 3
// 2 * 3 + 1
let new_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
let rem_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
ctx.builder.build_store(new_idx, llvm_usize.const_zero()).unwrap();
ctx.builder.build_store(rem_idx, idx).unwrap();
gen_for_callback_incrementing(
generator,
ctx,
None,
llvm_usize.const_zero(),
(n1.load_ndims(ctx), false),
|generator, ctx, _, ndim| {
let ndim_rev =
ctx.builder.build_int_sub(n1.load_ndims(ctx), ndim, "").unwrap();
let ndim_rev = ctx
.builder
.build_int_sub(ndim_rev, llvm_usize.const_int(1, false), "")
.unwrap();
let dim = unsafe {
n1.dim_sizes().get_typed_unchecked(ctx, generator, &ndim_rev, None)
};
let rem_idx_val =
ctx.builder.build_load(rem_idx, "").unwrap().into_int_value();
let new_idx_val =
ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
let add_component =
ctx.builder.build_int_unsigned_rem(rem_idx_val, dim, "").unwrap();
let rem_idx_val =
ctx.builder.build_int_unsigned_div(rem_idx_val, dim, "").unwrap();
let new_idx_val = ctx.builder.build_int_mul(new_idx_val, dim, "").unwrap();
let new_idx_val =
ctx.builder.build_int_add(new_idx_val, add_component, "").unwrap();
ctx.builder.build_store(rem_idx, rem_idx_val).unwrap();
ctx.builder.build_store(new_idx, new_idx_val).unwrap();
Ok(())
},
llvm_usize.const_int(1, false),
)?;
let new_idx_val = ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
unsafe { out.data().set_unchecked(ctx, generator, &new_idx_val, elem) };
Ok(())
},
llvm_usize.const_int(1, false),
)?;
Ok(out.as_base_value().into())
} else {
unreachable!(
"{FN_NAME}() not supported for '{}'",
format!("'{}'", ctx.unifier.stringify(x1_ty))
)
}
}
pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
x1: (Type, BasicValueEnum<'ctx>),
shape: (Type, BasicValueEnum<'ctx>),
) -> Result<BasicValueEnum<'ctx>, String> {
const FN_NAME: &str = "ndarray_reshape";
let (x1_ty, x1) = x1;
let (_, shape) = shape;
let llvm_usize = generator.get_size_type(ctx.ctx);
if let BasicValueEnum::PointerValue(n1) = x1 {
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
// Check for -1 in the shapec
let ndim_ty = match shape {
BasicValueEnum::PointerValue(shape_list_ptr)
if ListValue::is_instance(shape_list_ptr, llvm_usize).is_ok() =>
{
let shape_list = ListValue::from_ptr_val(shape_list_ptr, llvm_usize, None);
shape_list
.data()
.get(ctx, generator, &llvm_usize.const_zero(), None)
.into_int_value()
.get_type()
}
BasicValueEnum::StructValue(shape_tuple) => ctx
.builder
.build_extract_value(shape_tuple, 0, "")
.unwrap()
.into_int_value()
.get_type(),
BasicValueEnum::IntValue(shape_int) => shape_int.get_type(),
_ => unreachable!(),
};
let acc = generator.gen_var_alloc(ctx, ndim_ty.into(), None)?;
let num_neg = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
ctx.builder.build_store(acc, ndim_ty.const_int(1, false)).unwrap();
ctx.builder.build_store(num_neg, llvm_usize.const_zero()).unwrap();
let out = match shape {
BasicValueEnum::PointerValue(shape_list_ptr)
if ListValue::is_instance(shape_list_ptr, llvm_usize).is_ok() =>
{
let shape_list = ListValue::from_ptr_val(shape_list_ptr, llvm_usize, None);
gen_for_callback_incrementing(
generator,
ctx,
None,
llvm_usize.const_zero(),
(shape_list.load_size(ctx, None), false),
|generator, ctx, _, idx| {
let ele =
shape_list.data().get(ctx, generator, &idx, None).into_int_value();
gen_if_else_expr_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(
IntPredicate::SLT,
ele,
ndim_ty.const_zero(),
"",
)
.unwrap())
},
|_, ctx| -> Result<Option<IntValue>, String> {
let num_neg_value =
ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
let num_neg_value = ctx
.builder
.build_int_add(
num_neg_value,
llvm_usize.const_int(1, false),
"",
)
.unwrap();
ctx.builder.build_store(num_neg, num_neg_value).unwrap();
Ok(None)
},
|_, ctx| {
let acc_value =
ctx.builder.build_load(acc, "").unwrap().into_int_value();
let acc_value =
ctx.builder.build_int_mul(acc_value, ele, "").unwrap();
ctx.builder.build_store(acc, acc_value).unwrap();
Ok(None)
},
)?;
Ok(())
},
llvm_usize.const_int(1, false),
)?;
let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
let rem = ctx.builder.build_int_sub(n_sz, acc_val, "").unwrap();
create_ndarray_dyn_shape(
generator,
ctx,
elem_ty,
&shape_list,
|_, ctx, _| Ok(shape_list.load_size(ctx, None)),
|generator, ctx, shape_list, idx| {
let dim =
shape_list.data().get(ctx, generator, &idx, None).into_int_value();
Ok(gen_if_else_expr_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(
IntPredicate::SLT,
dim,
ndim_ty.const_zero(),
"",
)
.unwrap())
},
|_, _| Ok(Some(rem)),
|_, _| Ok(Some(dim)),
)?
.unwrap()
.into_int_value())
},
)
}
BasicValueEnum::StructValue(shape_tuple) => {
let ndims = shape_tuple.get_type().count_fields();
let mut acc_val = ndim_ty.const_int(1, false);
for dim_i in 0..ndims {
let dim = ctx
.builder
.build_extract_value(shape_tuple, dim_i, "")
.unwrap()
.into_int_value();
gen_if_else_expr_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(IntPredicate::SLT, dim, ndim_ty.const_zero(), "")
.unwrap())
},
|_, ctx| -> Result<Option<IntValue>, String> {
let num_negs =
ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
let num_negs = ctx
.builder
.build_int_add(num_negs, llvm_usize.const_int(1, false), "")
.unwrap();
ctx.builder.build_store(num_neg, num_negs).unwrap();
Ok(None)
},
|_, ctx| {
acc_val = ctx.builder.build_int_mul(acc_val, dim, "").unwrap();
Ok(None)
},
)?;
}
let rem = ctx.builder.build_int_sub(n_sz, acc_val, "").unwrap();
let mut shape = Vec::with_capacity(ndims as usize);
for dim_i in 0..ndims {
let dim = ctx
.builder
.build_extract_value(shape_tuple, dim_i, "")
.unwrap()
.into_int_value();
let dim = gen_if_else_expr_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(IntPredicate::SLT, dim, ndim_ty.const_zero(), "")
.unwrap())
},
|_, _| Ok(Some(rem)),
|_, _| Ok(Some(dim)),
)?
.unwrap()
.into_int_value();
shape.push(dim);
}
create_ndarray_const_shape(generator, ctx, elem_ty, shape.as_slice())
}
BasicValueEnum::IntValue(shape_int) => {
let shape_int = gen_if_else_expr_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(
IntPredicate::SLT,
shape_int,
ndim_ty.const_zero(),
"",
)
.unwrap())
},
|_, _| Ok(Some(n_sz)),
|_, _| Ok(Some(shape_int)),
)?
.unwrap()
.into_int_value();
create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
}
_ => unreachable!(),
}
.unwrap();
let num_negs = ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
ctx.make_assert(
generator,
ctx.builder
.build_int_compare(IntPredicate::ULT, num_negs, llvm_usize.const_int(2, false), "")
.unwrap(),
"0:ValueError",
"can only specify one unknown dimension",
[None, None, None],
ctx.current_loc,
);
let out_sz = call_ndarray_calc_size(generator, ctx, &out.dim_sizes(), (None, None));
ctx.make_assert(
generator,
ctx.builder.build_int_compare(IntPredicate::EQ, out_sz, n_sz, "").unwrap(),
"0:ValueError",
"cannot reshape array of size {} into provided shape",
[Some(n_sz), None, None],
ctx.current_loc,
);
gen_for_callback_incrementing(
generator,
ctx,
None,
llvm_usize.const_zero(),
(n_sz, false),
|generator, ctx, _, idx| {
let elem = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
unsafe { out.data().set_unchecked(ctx, generator, &idx, elem) };
Ok(())
},
llvm_usize.const_int(1, false),
)?;
Ok(out.as_base_value().into())
} else {
unreachable!(
"{FN_NAME}() not supported for '{}'",
format!("'{}'", ctx.unifier.stringify(x1_ty))
)
}
}

47
nac3core/src/toplevel/builtins.rs
View File

@ -557,6 +557,10 @@ impl<'a> BuiltinBuilder<'a> {
| PrimDef::FunNpHypot
| PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
self.build_np_sp_ndarray_1ary_function(prim)
}
PrimDef::FunNpDot
| PrimDef::FunNpLinalgMatmul
| PrimDef::FunNpLinalgCholesky
@ -1885,6 +1889,49 @@ impl<'a> BuiltinBuilder<'a> {
}
}
/// Build 1-ary numpy/scipy functions that take in an ndarray and return a value of the same type as the input.
fn build_np_sp_ndarray_1ary_function(&mut self, prim: PrimDef) -> TopLevelDef {
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
let elem_type = self.unifier.get_fresh_var(Some("R".into()), None);
let ndarray_type = make_ndarray_ty(self.unifier, self.primitives, Some(elem_type.ty), None);
let ndarray_ty =
self.unifier.get_fresh_var_with_range(&[ndarray_type], Some("T".into()), None);
let var_map = into_var_map([elem_type, ndarray_ty]);
match prim {
PrimDef::FunNpTranspose => create_fn_by_codegen(
self.unifier,
&var_map,
prim.name(),
ndarray_ty.ty,
&[(ndarray_ty.ty, "x")],
Box::new(move |ctx, _, fun, args, generator| {
let arg_ty = fun.0.args[0].ty;
let arg_val = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
}),
),
PrimDef::FunNpReshape => create_fn_by_codegen(
self.unifier,
&var_map,
prim.name(),
ndarray_ty.ty,
&[(ndarray_ty.ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
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(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
}),
),
_ => unreachable!(),
}
}
/// Build `np_linalg` and `sp_linalg` functions
///
/// The input to these functions must be floating point `NDArray`

6
nac3core/src/toplevel/helper.rs
View File

@ -99,6 +99,8 @@ pub enum PrimDef {
FunNpLdExp,
FunNpHypot,
FunNpNextAfter,
FunNpTranspose,
FunNpReshape,
// Linalg functions
FunNpDot,
@ -282,6 +284,10 @@ impl PrimDef {
PrimDef::FunNpLdExp => fun("np_ldexp", None),
PrimDef::FunNpHypot => fun("np_hypot", None),
PrimDef::FunNpNextAfter => fun("np_nextafter", None),
PrimDef::FunNpTranspose => fun("np_transpose", None),
PrimDef::FunNpReshape => fun("np_reshape", None),
// Linalg functions
PrimDef::FunNpDot => fun("np_dot", None),
PrimDef::FunNpLinalgMatmul => fun("np_linalg_matmul", None),
PrimDef::FunNpLinalgCholesky => fun("np_linalg_cholesky", None),

2
nac3standalone/demo/interpret_demo.py
View File

@ -218,6 +218,8 @@ def patch(module):
module.np_ldexp = np.ldexp
module.np_hypot = np.hypot
module.np_nextafter = np.nextafter
module.np_transpose = np.transpose
module.np_reshape = np.reshape
# SciPy Math functions
module.sp_spec_erf = special.erf

10
nac3standalone/demo/interpreted.log Normal file
View File

@ -0,0 +1,10 @@
1.000000
2.000000
3.000000
4.000000
5.000000
6.000000
7.000000
8.000000
9.000000
10.000000

10
nac3standalone/demo/run_32.log Normal file
View File

@ -0,0 +1,10 @@
1.000000
2.000000
3.000000
4.000000
5.000000
6.000000
7.000000
8.000000
9.000000
10.000000

385
nac3standalone/demo/src/ndarray.py
View File

@ -68,6 +68,12 @@ def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
for c in range(len(n[r])):
output_float64(n[r][c])
def output_ndarray_float_3(n: ndarray[float, Literal[3]]):
for r in range(len(n)):
for c in range(len(n[r])):
for z in range(len(n[r][c])):
output_float64(n[r][c][z])
def consume_ndarray_1(n: ndarray[float, Literal[1]]):
pass
@ -1429,6 +1435,21 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
output_ndarray_float_2(nextafter_x_zeros)
output_ndarray_float_2(nextafter_x_ones)
def test_ndarray_transpose():
# x: ndarray[float, 3] = np_array([[[1., 2.], [3., 4.], [5., 6.]]])
x: ndarray[float, 1] = np_array([1., 2., 3.])
y = np_transpose(x)
output_ndarray_float_1(x)
output_ndarray_float_1(y)
def test_ndarray_reshape():
x: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
y = np_reshape(x, [2, 5])
output_ndarray_float_1(x)
# output_ndarray_float_1(y)
def test_ndarray_dot():
x: ndarray[float, 1] = np_array([5.0, 1.0])
y: ndarray[float, 1] = np_array([5.0, 1.0])
@ -1528,192 +1549,194 @@ def test_ndarray_svd():
def run() -> int32:
test_ndarray_ctor()
test_ndarray_empty()
test_ndarray_zeros()
test_ndarray_ones()
test_ndarray_full()
test_ndarray_eye()
test_ndarray_array()
test_ndarray_identity()
test_ndarray_fill()
test_ndarray_copy()
# test_ndarray_ctor()
# test_ndarray_empty()
# test_ndarray_zeros()
# test_ndarray_ones()
# test_ndarray_full()
# test_ndarray_eye()
# test_ndarray_array()
# test_ndarray_identity()
# test_ndarray_fill()
# test_ndarray_copy()
test_ndarray_neg_idx()
test_ndarray_slices()
test_ndarray_nd_idx()
# test_ndarray_neg_idx()
# test_ndarray_slices()
# test_ndarray_nd_idx()
test_ndarray_add()
test_ndarray_add_broadcast()
test_ndarray_add_broadcast_lhs_scalar()
test_ndarray_add_broadcast_rhs_scalar()
test_ndarray_iadd()
test_ndarray_iadd_broadcast()
test_ndarray_iadd_broadcast_scalar()
test_ndarray_sub()
test_ndarray_sub_broadcast()
test_ndarray_sub_broadcast_lhs_scalar()
test_ndarray_sub_broadcast_rhs_scalar()
test_ndarray_isub()
test_ndarray_isub_broadcast()
test_ndarray_isub_broadcast_scalar()
test_ndarray_mul()
test_ndarray_mul_broadcast()
test_ndarray_mul_broadcast_lhs_scalar()
test_ndarray_mul_broadcast_rhs_scalar()
test_ndarray_imul()
test_ndarray_imul_broadcast()
test_ndarray_imul_broadcast_scalar()
test_ndarray_truediv()
test_ndarray_truediv_broadcast()
test_ndarray_truediv_broadcast_lhs_scalar()
test_ndarray_truediv_broadcast_rhs_scalar()
test_ndarray_itruediv()
test_ndarray_itruediv_broadcast()
test_ndarray_itruediv_broadcast_scalar()
test_ndarray_floordiv()
test_ndarray_floordiv_broadcast()
test_ndarray_floordiv_broadcast_lhs_scalar()
test_ndarray_floordiv_broadcast_rhs_scalar()
test_ndarray_ifloordiv()
test_ndarray_ifloordiv_broadcast()
test_ndarray_ifloordiv_broadcast_scalar()
test_ndarray_mod()
test_ndarray_mod_broadcast()
test_ndarray_mod_broadcast_lhs_scalar()
test_ndarray_mod_broadcast_rhs_scalar()
test_ndarray_imod()
test_ndarray_imod_broadcast()
test_ndarray_imod_broadcast_scalar()
test_ndarray_pow()
test_ndarray_pow_broadcast()
test_ndarray_pow_broadcast_lhs_scalar()
test_ndarray_pow_broadcast_rhs_scalar()
test_ndarray_ipow()
test_ndarray_ipow_broadcast()
test_ndarray_ipow_broadcast_scalar()
test_ndarray_matmul()
test_ndarray_imatmul()
test_ndarray_pos()
test_ndarray_neg()
test_ndarray_inv()
test_ndarray_eq()
test_ndarray_eq_broadcast()
test_ndarray_eq_broadcast_lhs_scalar()
test_ndarray_eq_broadcast_rhs_scalar()
test_ndarray_ne()
test_ndarray_ne_broadcast()
test_ndarray_ne_broadcast_lhs_scalar()
test_ndarray_ne_broadcast_rhs_scalar()
test_ndarray_lt()
test_ndarray_lt_broadcast()
test_ndarray_lt_broadcast_lhs_scalar()
test_ndarray_lt_broadcast_rhs_scalar()
test_ndarray_lt()
test_ndarray_le_broadcast()
test_ndarray_le_broadcast_lhs_scalar()
test_ndarray_le_broadcast_rhs_scalar()
test_ndarray_gt()
test_ndarray_gt_broadcast()
test_ndarray_gt_broadcast_lhs_scalar()
test_ndarray_gt_broadcast_rhs_scalar()
test_ndarray_gt()
test_ndarray_ge_broadcast()
test_ndarray_ge_broadcast_lhs_scalar()
test_ndarray_ge_broadcast_rhs_scalar()
# test_ndarray_add()
# test_ndarray_add_broadcast()
# test_ndarray_add_broadcast_lhs_scalar()
# test_ndarray_add_broadcast_rhs_scalar()
# test_ndarray_iadd()
# test_ndarray_iadd_broadcast()
# test_ndarray_iadd_broadcast_scalar()
# test_ndarray_sub()
# test_ndarray_sub_broadcast()
# test_ndarray_sub_broadcast_lhs_scalar()
# test_ndarray_sub_broadcast_rhs_scalar()
# test_ndarray_isub()
# test_ndarray_isub_broadcast()
# test_ndarray_isub_broadcast_scalar()
# test_ndarray_mul()
# test_ndarray_mul_broadcast()
# test_ndarray_mul_broadcast_lhs_scalar()
# test_ndarray_mul_broadcast_rhs_scalar()
# test_ndarray_imul()
# test_ndarray_imul_broadcast()
# test_ndarray_imul_broadcast_scalar()
# test_ndarray_truediv()
# test_ndarray_truediv_broadcast()
# test_ndarray_truediv_broadcast_lhs_scalar()
# test_ndarray_truediv_broadcast_rhs_scalar()
# test_ndarray_itruediv()
# test_ndarray_itruediv_broadcast()
# test_ndarray_itruediv_broadcast_scalar()
# test_ndarray_floordiv()
# test_ndarray_floordiv_broadcast()
# test_ndarray_floordiv_broadcast_lhs_scalar()
# test_ndarray_floordiv_broadcast_rhs_scalar()
# test_ndarray_ifloordiv()
# test_ndarray_ifloordiv_broadcast()
# test_ndarray_ifloordiv_broadcast_scalar()
# test_ndarray_mod()
# test_ndarray_mod_broadcast()
# test_ndarray_mod_broadcast_lhs_scalar()
# test_ndarray_mod_broadcast_rhs_scalar()
# test_ndarray_imod()
# test_ndarray_imod_broadcast()
# test_ndarray_imod_broadcast_scalar()
# test_ndarray_pow()
# test_ndarray_pow_broadcast()
# test_ndarray_pow_broadcast_lhs_scalar()
# test_ndarray_pow_broadcast_rhs_scalar()
# test_ndarray_ipow()
# test_ndarray_ipow_broadcast()
# test_ndarray_ipow_broadcast_scalar()
# test_ndarray_matmul()
# test_ndarray_imatmul()
# test_ndarray_pos()
# test_ndarray_neg()
# test_ndarray_inv()
# test_ndarray_eq()
# test_ndarray_eq_broadcast()
# test_ndarray_eq_broadcast_lhs_scalar()
# test_ndarray_eq_broadcast_rhs_scalar()
# test_ndarray_ne()
# test_ndarray_ne_broadcast()
# test_ndarray_ne_broadcast_lhs_scalar()
# test_ndarray_ne_broadcast_rhs_scalar()
# test_ndarray_lt()
# test_ndarray_lt_broadcast()
# test_ndarray_lt_broadcast_lhs_scalar()
# test_ndarray_lt_broadcast_rhs_scalar()
# test_ndarray_lt()
# test_ndarray_le_broadcast()
# test_ndarray_le_broadcast_lhs_scalar()
# test_ndarray_le_broadcast_rhs_scalar()
# test_ndarray_gt()
# test_ndarray_gt_broadcast()
# test_ndarray_gt_broadcast_lhs_scalar()
# test_ndarray_gt_broadcast_rhs_scalar()
# test_ndarray_gt()
# test_ndarray_ge_broadcast()
# test_ndarray_ge_broadcast_lhs_scalar()
# test_ndarray_ge_broadcast_rhs_scalar()
test_ndarray_int32()
test_ndarray_int64()
test_ndarray_uint32()
test_ndarray_uint64()
test_ndarray_float()
test_ndarray_bool()
# test_ndarray_int32()
# test_ndarray_int64()
# test_ndarray_uint32()
# test_ndarray_uint64()
# test_ndarray_float()
# test_ndarray_bool()
test_ndarray_round()
test_ndarray_floor()
test_ndarray_min()
test_ndarray_minimum()
test_ndarray_minimum_broadcast()
test_ndarray_minimum_broadcast_lhs_scalar()
test_ndarray_minimum_broadcast_rhs_scalar()
test_ndarray_argmin()
test_ndarray_max()
test_ndarray_maximum()
test_ndarray_maximum_broadcast()
test_ndarray_maximum_broadcast_lhs_scalar()
test_ndarray_maximum_broadcast_rhs_scalar()
test_ndarray_argmax()
test_ndarray_abs()
test_ndarray_isnan()
test_ndarray_isinf()
# test_ndarray_round()
# test_ndarray_floor()
# test_ndarray_min()
# test_ndarray_minimum()
# test_ndarray_minimum_broadcast()
# test_ndarray_minimum_broadcast_lhs_scalar()
# test_ndarray_minimum_broadcast_rhs_scalar()
# test_ndarray_argmin()
# test_ndarray_max()
# test_ndarray_maximum()
# test_ndarray_maximum_broadcast()
# test_ndarray_maximum_broadcast_lhs_scalar()
# test_ndarray_maximum_broadcast_rhs_scalar()
# test_ndarray_argmax()
# test_ndarray_abs()
# test_ndarray_isnan()
# test_ndarray_isinf()
test_ndarray_sin()
test_ndarray_cos()
test_ndarray_exp()
test_ndarray_exp2()
test_ndarray_log()
test_ndarray_log10()
test_ndarray_log2()
test_ndarray_fabs()
test_ndarray_sqrt()
test_ndarray_rint()
test_ndarray_tan()
test_ndarray_arcsin()
test_ndarray_arccos()
test_ndarray_arctan()
test_ndarray_sinh()
test_ndarray_cosh()
test_ndarray_tanh()
test_ndarray_arcsinh()
test_ndarray_arccosh()
test_ndarray_arctanh()
test_ndarray_expm1()
test_ndarray_cbrt()
# test_ndarray_sin()
# test_ndarray_cos()
# test_ndarray_exp()
# test_ndarray_exp2()
# test_ndarray_log()
# test_ndarray_log10()
# test_ndarray_log2()
# test_ndarray_fabs()
# test_ndarray_sqrt()
# test_ndarray_rint()
# test_ndarray_tan()
# test_ndarray_arcsin()
# test_ndarray_arccos()
# test_ndarray_arctan()
# test_ndarray_sinh()
# test_ndarray_cosh()
# test_ndarray_tanh()
# test_ndarray_arcsinh()
# test_ndarray_arccosh()
# test_ndarray_arctanh()
# test_ndarray_expm1()
# test_ndarray_cbrt()
test_ndarray_erf()
test_ndarray_erfc()
test_ndarray_gamma()
test_ndarray_gammaln()
test_ndarray_j0()
test_ndarray_j1()
# test_ndarray_erf()
# test_ndarray_erfc()
# test_ndarray_gamma()
# test_ndarray_gammaln()
# test_ndarray_j0()
# test_ndarray_j1()
test_ndarray_arctan2()
test_ndarray_arctan2_broadcast()
test_ndarray_arctan2_broadcast_lhs_scalar()
test_ndarray_arctan2_broadcast_rhs_scalar()
test_ndarray_copysign()
test_ndarray_copysign_broadcast()
test_ndarray_copysign_broadcast_lhs_scalar()
test_ndarray_copysign_broadcast_rhs_scalar()
test_ndarray_fmax()
test_ndarray_fmax_broadcast()
test_ndarray_fmax_broadcast_lhs_scalar()
test_ndarray_fmax_broadcast_rhs_scalar()
test_ndarray_fmin()
test_ndarray_fmin_broadcast()
test_ndarray_fmin_broadcast_lhs_scalar()
test_ndarray_fmin_broadcast_rhs_scalar()
test_ndarray_ldexp()
test_ndarray_ldexp_broadcast()
test_ndarray_ldexp_broadcast_lhs_scalar()
test_ndarray_ldexp_broadcast_rhs_scalar()
test_ndarray_hypot()
test_ndarray_hypot_broadcast()
test_ndarray_hypot_broadcast_lhs_scalar()
test_ndarray_hypot_broadcast_rhs_scalar()
test_ndarray_nextafter()
test_ndarray_nextafter_broadcast()
test_ndarray_nextafter_broadcast_lhs_scalar()
test_ndarray_nextafter_broadcast_rhs_scalar()
test_ndarray_dot()
test_ndarray_linalg_matmul()
test_ndarray_cholesky()
test_ndarray_qr()
test_ndarray_svd()
test_ndarray_linalg_inv()
test_ndarray_pinv()
test_ndarray_lu()
test_ndarray_schur()
test_ndarray_hessenberg()
# test_ndarray_arctan2()
# test_ndarray_arctan2_broadcast()
# test_ndarray_arctan2_broadcast_lhs_scalar()
# test_ndarray_arctan2_broadcast_rhs_scalar()
# test_ndarray_copysign()
# test_ndarray_copysign_broadcast()
# test_ndarray_copysign_broadcast_lhs_scalar()
# test_ndarray_copysign_broadcast_rhs_scalar()
# test_ndarray_fmax()
# test_ndarray_fmax_broadcast()
# test_ndarray_fmax_broadcast_lhs_scalar()
# test_ndarray_fmax_broadcast_rhs_scalar()
# test_ndarray_fmin()
# test_ndarray_fmin_broadcast()
# test_ndarray_fmin_broadcast_lhs_scalar()
# test_ndarray_fmin_broadcast_rhs_scalar()
# test_ndarray_ldexp()
# test_ndarray_ldexp_broadcast()
# test_ndarray_ldexp_broadcast_lhs_scalar()
# test_ndarray_ldexp_broadcast_rhs_scalar()
# test_ndarray_hypot()
# test_ndarray_hypot_broadcast()
# test_ndarray_hypot_broadcast_lhs_scalar()
# test_ndarray_hypot_broadcast_rhs_scalar()
# test_ndarray_nextafter()
# test_ndarray_nextafter_broadcast()
# test_ndarray_nextafter_broadcast_lhs_scalar()
# test_ndarray_nextafter_broadcast_rhs_scalar()
# test_ndarray_transpose()
test_ndarray_reshape()
# test_ndarray_dot()
# test_ndarray_linalg_matmul()
# test_ndarray_cholesky()
# test_ndarray_qr()
# test_ndarray_svd()
# test_ndarray_linalg_inv()
# test_ndarray_pinv()
# test_ndarray_lu()
# test_ndarray_schur()
# test_ndarray_hessenberg()
return 0

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pyo3_output/nac3artiq.so Symbolic link
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../target/debug/libnac3artiq.so