use num_complex::Complex64;
use runmat_accelerate_api::GpuTensorHandle;
use runmat_builtins::{CharArray, ComplexTensor, Tensor, Value};
use runmat_macros::runtime_builtin;
use crate::builtins::common::spec::{
BroadcastSemantics, BuiltinFusionSpec, BuiltinGpuSpec, ConstantStrategy, FusionError,
FusionExprContext, FusionKernelTemplate, GpuOpKind, ProviderHook, ReductionNaN,
ResidencyPolicy, ScalarType, ShapeRequirements,
};
use crate::builtins::common::{gpu_helpers, tensor};
use crate::builtins::math::type_resolvers::numeric_unary_type;
use crate::{build_runtime_error, BuiltinResult, RuntimeError};
const BUILTIN_NAME: &str = "asinh";
#[runmat_macros::register_gpu_spec(builtin_path = "crate::builtins::math::trigonometry::asinh")]
pub const GPU_SPEC: BuiltinGpuSpec = BuiltinGpuSpec {
name: "asinh",
op_kind: GpuOpKind::Elementwise,
supported_precisions: &[ScalarType::F32, ScalarType::F64],
broadcast: BroadcastSemantics::Matlab,
provider_hooks: &[ProviderHook::Unary { name: "unary_asinh" }],
constant_strategy: ConstantStrategy::InlineLiteral,
residency: ResidencyPolicy::NewHandle,
nan_mode: ReductionNaN::Include,
two_pass_threshold: None,
workgroup_size: None,
accepts_nan_mode: false,
notes:
"Providers may execute asinh directly on device buffers; runtimes gather to host when unary_asinh is unavailable.",
};
fn runtime_error_for(message: impl Into<String>) -> RuntimeError {
build_runtime_error(message)
.with_builtin(BUILTIN_NAME)
.build()
}
#[runmat_macros::register_fusion_spec(builtin_path = "crate::builtins::math::trigonometry::asinh")]
pub const FUSION_SPEC: BuiltinFusionSpec = BuiltinFusionSpec {
name: "asinh",
shape: ShapeRequirements::BroadcastCompatible,
constant_strategy: ConstantStrategy::InlineLiteral,
elementwise: Some(FusionKernelTemplate {
scalar_precisions: &[ScalarType::F32, ScalarType::F64],
wgsl_body: |ctx: &FusionExprContext| {
let input = ctx.inputs.first().ok_or(FusionError::MissingInput(0))?;
Ok(format!("asinh({input})"))
},
}),
reduction: None,
emits_nan: false,
notes: "Fusion planner emits WGSL `asinh` calls; providers may override via fused elementwise kernels.",
};
#[runtime_builtin(
name = "asinh",
category = "math/trigonometry",
summary = "Inverse hyperbolic sine of scalars, vectors, matrices, or N-D tensors (element-wise).",
keywords = "asinh,arcsinh,inverse hyperbolic sine,trigonometry,gpu",
accel = "unary",
type_resolver(numeric_unary_type),
builtin_path = "crate::builtins::math::trigonometry::asinh"
)]
async fn asinh_builtin(value: Value) -> BuiltinResult<Value> {
match value {
Value::GpuTensor(handle) => asinh_gpu(handle).await,
Value::Complex(re, im) => Ok(complex_asinh_scalar(re, im)),
Value::ComplexTensor(ct) => asinh_complex_tensor(ct),
Value::CharArray(ca) => asinh_char_array(ca),
Value::String(_) | Value::StringArray(_) => {
Err(runtime_error_for("asinh: expected numeric input"))
}
other => asinh_real(other),
}
}
async fn asinh_gpu(handle: GpuTensorHandle) -> BuiltinResult<Value> {
if let Some(provider) = runmat_accelerate_api::provider_for_handle(&handle) {
if let Ok(out) = provider.unary_asinh(&handle).await {
return Ok(gpu_helpers::resident_gpu_value(out));
}
}
let tensor = gpu_helpers::gather_tensor_async(&handle).await?;
asinh_tensor(tensor).map(tensor::tensor_into_value)
}
fn asinh_real(value: Value) -> BuiltinResult<Value> {
let tensor = tensor::value_into_tensor_for("asinh", value).map_err(runtime_error_for)?;
asinh_tensor(tensor).map(tensor::tensor_into_value)
}
fn asinh_tensor(tensor: Tensor) -> BuiltinResult<Tensor> {
let data = tensor.data.iter().map(|&v| v.asinh()).collect::<Vec<_>>();
Tensor::new(data, tensor.shape.clone()).map_err(|e| runtime_error_for(format!("asinh: {e}")))
}
fn asinh_complex_tensor(ct: ComplexTensor) -> BuiltinResult<Value> {
let mapped = ct
.data
.iter()
.map(|&(re, im)| {
let res = Complex64::new(re, im).asinh();
(res.re, res.im)
})
.collect::<Vec<_>>();
let tensor = ComplexTensor::new(mapped, ct.shape.clone())
.map_err(|e| runtime_error_for(format!("asinh: {e}")))?;
Ok(Value::ComplexTensor(tensor))
}
fn asinh_char_array(ca: CharArray) -> BuiltinResult<Value> {
let data = ca
.data
.iter()
.map(|&ch| (ch as u32 as f64).asinh())
.collect::<Vec<_>>();
let tensor = Tensor::new(data, vec![ca.rows, ca.cols])
.map_err(|e| runtime_error_for(format!("asinh: {e}")))?;
Ok(Value::Tensor(tensor))
}
fn complex_asinh_scalar(re: f64, im: f64) -> Value {
let result = Complex64::new(re, im).asinh();
Value::Complex(result.re, result.im)
}
#[cfg(test)]
pub(crate) mod tests {
use super::*;
use crate::builtins::common::test_support;
use futures::executor::block_on;
use num_complex::Complex64;
use runmat_builtins::{LogicalArray, ResolveContext, Type};
fn asinh_builtin(value: Value) -> BuiltinResult<Value> {
block_on(super::asinh_builtin(value))
}
fn error_message(err: RuntimeError) -> String {
err.message().to_string()
}
#[test]
fn asinh_type_preserves_tensor_shape() {
let out = numeric_unary_type(
&[Type::Tensor {
shape: Some(vec![Some(2), Some(3)]),
}],
&ResolveContext::new(Vec::new()),
);
assert_eq!(
out,
Type::Tensor {
shape: Some(vec![Some(2), Some(3)])
}
);
}
#[test]
fn asinh_type_scalar_tensor_returns_num() {
let out = numeric_unary_type(
&[Type::Tensor {
shape: Some(vec![Some(1), Some(1)]),
}],
&ResolveContext::new(Vec::new()),
);
assert_eq!(out, Type::Num);
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn asinh_scalar() {
let value = Value::Num(0.5);
let result = asinh_builtin(value).expect("asinh");
match result {
Value::Num(v) => assert!((v - 0.48121182505960347).abs() < 1e-12),
other => panic!("expected scalar result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn asinh_tensor_values() {
let tensor =
Tensor::new(vec![0.0, -0.5, 1.0, 3.0], vec![2, 2]).expect("tensor construction");
let result = asinh_builtin(Value::Tensor(tensor)).expect("asinh");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![2, 2]);
let expected = [
0.0,
-0.48121182505960347,
0.881373587019543,
1.8184464592320668,
];
for (actual, exp) in t.data.iter().zip(expected.iter()) {
assert!((actual - exp).abs() < 1e-12);
}
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn asinh_complex_inputs() {
let inputs = [Complex64::new(1.0, 2.0), Complex64::new(-0.5, 0.75)];
let complex = ComplexTensor::new(inputs.iter().map(|c| (c.re, c.im)).collect(), vec![1, 2])
.expect("complex tensor");
let result = asinh_builtin(Value::ComplexTensor(complex)).expect("asinh");
match result {
Value::ComplexTensor(t) => {
assert_eq!(t.shape, vec![1, 2]);
for (actual, input) in t.data.iter().zip(inputs.iter()) {
let expected = input.asinh();
assert!((actual.0 - expected.re).abs() < 1e-12);
assert!((actual.1 - expected.im).abs() < 1e-12);
}
}
other => panic!("expected complex tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn asinh_char_array_roundtrip() {
let chars = CharArray::new("az".chars().collect(), 1, 2).expect("char array");
let result = asinh_builtin(Value::CharArray(chars)).expect("asinh");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![1, 2]);
let expected = [(('a' as u32) as f64).asinh(), (('z' as u32) as f64).asinh()];
for (actual, exp) in t.data.iter().zip(expected.iter()) {
assert!((actual - exp).abs() < 1e-12);
}
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn asinh_gpu_provider_roundtrip() {
test_support::with_test_provider(|provider| {
let tensor =
Tensor::new(vec![0.0, 0.5, 1.0, 2.0], vec![2, 2]).expect("tensor construction");
let view = runmat_accelerate_api::HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let result = asinh_builtin(Value::GpuTensor(handle)).expect("asinh");
let gathered = test_support::gather(result).expect("gather");
let expected = tensor.data.iter().map(|&v| v.asinh()).collect::<Vec<_>>();
assert_eq!(gathered.shape, vec![2, 2]);
for (actual, exp) in gathered.data.iter().zip(expected.iter()) {
assert!((actual - exp).abs() < 1e-12);
}
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn asinh_logical_array_promotes() {
let logical =
LogicalArray::new(vec![1, 0, 1, 1], vec![2, 2]).expect("logical array construction");
let result = asinh_builtin(Value::LogicalArray(logical)).expect("asinh");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![2, 2]);
let expected = [
1.0f64.asinh(),
0.0f64.asinh(),
1.0f64.asinh(),
1.0f64.asinh(),
];
for (actual, exp) in t.data.iter().zip(expected.iter()) {
assert!((actual - exp).abs() < 1e-12);
}
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn asinh_string_errors() {
let err = asinh_builtin(Value::from("not numeric")).expect_err("expected error");
let message = error_message(err);
assert!(
message.contains("expected numeric input"),
"unexpected error: {message}"
);
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
#[cfg(feature = "wgpu")]
fn asinh_wgpu_matches_cpu() {
let _ = runmat_accelerate::backend::wgpu::provider::register_wgpu_provider(
runmat_accelerate::backend::wgpu::provider::WgpuProviderOptions::default(),
);
let tensor = Tensor::new(vec![-3.0, -1.0, 0.0, 1.0, 3.0], vec![5, 1]).unwrap();
let cpu = asinh_real(Value::Tensor(tensor.clone())).unwrap();
let view = runmat_accelerate_api::HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = runmat_accelerate_api::provider()
.expect("provider")
.upload(&view)
.expect("upload");
let gpu = block_on(asinh_gpu(handle)).unwrap();
let gathered = test_support::gather(gpu).expect("gather");
match cpu {
Value::Tensor(ct) => {
assert_eq!(gathered.shape, ct.shape);
let tol = match runmat_accelerate_api::provider().unwrap().precision() {
runmat_accelerate_api::ProviderPrecision::F64 => 1e-12,
runmat_accelerate_api::ProviderPrecision::F32 => 1e-5,
};
for (actual, expected) in gathered.data.iter().zip(ct.data.iter()) {
assert!(
(actual - expected).abs() < tol,
"|{} - {}| >= {}",
actual,
expected,
tol
);
}
}
other => panic!("expected tensor result, got {other:?}"),
}
}
}