use runmat_accelerate_api::GpuTensorHandle;
use runmat_builtins::{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::{
broadcast::BroadcastPlan, gpu_helpers, map_control_flow_with_builtin, tensor,
};
use crate::builtins::math::type_resolvers::numeric_binary_type;
use crate::{build_runtime_error, BuiltinResult, RuntimeError};
#[runmat_macros::register_gpu_spec(builtin_path = "crate::builtins::math::elementwise::hypot")]
pub const GPU_SPEC: BuiltinGpuSpec = BuiltinGpuSpec {
name: "hypot",
op_kind: GpuOpKind::Elementwise,
supported_precisions: &[ScalarType::F32, ScalarType::F64],
broadcast: BroadcastSemantics::Matlab,
provider_hooks: &[ProviderHook::Binary {
name: "elem_hypot",
commutative: true,
}],
constant_strategy: ConstantStrategy::InlineLiteral,
residency: ResidencyPolicy::NewHandle,
nan_mode: ReductionNaN::Include,
two_pass_threshold: None,
workgroup_size: None,
accepts_nan_mode: false,
notes: "Providers can execute hypot in a single binary kernel; the runtime gathers to host when the hook is unavailable or shapes require implicit expansion.",
};
#[runmat_macros::register_fusion_spec(builtin_path = "crate::builtins::math::elementwise::hypot")]
pub const FUSION_SPEC: BuiltinFusionSpec = BuiltinFusionSpec {
name: "hypot",
shape: ShapeRequirements::BroadcastCompatible,
constant_strategy: ConstantStrategy::InlineLiteral,
elementwise: Some(FusionKernelTemplate {
scalar_precisions: &[ScalarType::F32, ScalarType::F64],
wgsl_body: |ctx: &FusionExprContext| {
let a = ctx.inputs.first().ok_or(FusionError::MissingInput(0))?;
let b = ctx.inputs.get(1).ok_or(FusionError::MissingInput(1))?;
Ok(format!("hypot({a}, {b})"))
},
}),
reduction: None,
emits_nan: false,
notes: "Fusion emits WGSL hypot(a, b); providers may override via elem_hypot.",
};
const BUILTIN_NAME: &str = "hypot";
fn builtin_error(message: impl Into<String>) -> RuntimeError {
build_runtime_error(message)
.with_builtin(BUILTIN_NAME)
.build()
}
#[runtime_builtin(
name = "hypot",
category = "math/elementwise",
summary = "Element-wise Euclidean norm sqrt(x.^2 + y.^2) with MATLAB-compatible broadcasting.",
keywords = "hypot,euclidean norm,distance,gpu",
accel = "binary",
type_resolver(numeric_binary_type),
builtin_path = "crate::builtins::math::elementwise::hypot"
)]
async fn hypot_builtin(lhs: Value, rhs: Value) -> BuiltinResult<Value> {
match (lhs, rhs) {
(Value::GpuTensor(a), Value::GpuTensor(b)) => hypot_gpu_pair(a, b).await,
(Value::GpuTensor(a), other) => {
let gathered = gpu_helpers::gather_tensor_async(&a)
.await
.map_err(|flow| map_control_flow_with_builtin(flow, BUILTIN_NAME))?;
Ok(hypot_host(Value::Tensor(gathered), other)?)
}
(other, Value::GpuTensor(b)) => {
let gathered = gpu_helpers::gather_tensor_async(&b)
.await
.map_err(|flow| map_control_flow_with_builtin(flow, BUILTIN_NAME))?;
Ok(hypot_host(other, Value::Tensor(gathered))?)
}
(left, right) => hypot_host(left, right),
}
}
async fn hypot_gpu_pair(a: GpuTensorHandle, b: GpuTensorHandle) -> BuiltinResult<Value> {
if let Some(provider) = runmat_accelerate_api::provider() {
if a.shape == b.shape {
if let Ok(handle) = provider.elem_hypot(&a, &b).await {
return Ok(gpu_helpers::resident_gpu_value(handle));
}
}
}
let left = gpu_helpers::gather_tensor_async(&a)
.await
.map_err(|flow| map_control_flow_with_builtin(flow, BUILTIN_NAME))?;
let right = gpu_helpers::gather_tensor_async(&b)
.await
.map_err(|flow| map_control_flow_with_builtin(flow, BUILTIN_NAME))?;
hypot_host(Value::Tensor(left), Value::Tensor(right))
}
fn hypot_host(lhs: Value, rhs: Value) -> BuiltinResult<Value> {
if let (Some(left), Some(right)) = (scalar_hypot_value(&lhs), scalar_hypot_value(&rhs)) {
return Ok(Value::Num(left.hypot(right)));
}
let tensor_a = value_into_hypot_tensor(lhs)?;
let tensor_b = value_into_hypot_tensor(rhs)?;
compute_hypot_tensor(&tensor_a, &tensor_b)
}
fn compute_hypot_tensor(a: &Tensor, b: &Tensor) -> BuiltinResult<Value> {
let plan = BroadcastPlan::new(&a.shape, &b.shape)
.map_err(|err| builtin_error(format!("hypot: {err}")))?;
if plan.is_empty() {
let tensor = Tensor::new(Vec::new(), plan.output_shape().to_vec())
.map_err(|e| builtin_error(format!("hypot: {e}")))?;
return Ok(tensor::tensor_into_value(tensor));
}
let mut result = vec![0.0f64; plan.len()];
for (out_idx, idx_a, idx_b) in plan.iter() {
result[out_idx] = a.data[idx_a].hypot(b.data[idx_b]);
}
let tensor = Tensor::new(result, plan.output_shape().to_vec())
.map_err(|e| builtin_error(format!("hypot: {e}")))?;
Ok(tensor::tensor_into_value(tensor))
}
fn value_into_hypot_tensor(value: Value) -> BuiltinResult<Tensor> {
match value {
Value::CharArray(ca) => {
let data: Vec<f64> = ca.data.iter().map(|&ch| ch as u32 as f64).collect();
Tensor::new(data, vec![ca.rows, ca.cols])
.map_err(|e| builtin_error(format!("hypot: {e}")))
}
Value::Complex(re, im) => Tensor::new(vec![complex_magnitude(re, im)], vec![1, 1])
.map_err(|e| builtin_error(format!("hypot: {e}"))),
Value::ComplexTensor(ct) => {
let data: Vec<f64> = ct.data.iter().map(|(re, im)| re.hypot(*im)).collect();
Tensor::new(data, ct.shape.clone()).map_err(|e| builtin_error(format!("hypot: {e}")))
}
other => {
if let Value::GpuTensor(_) = other {
return Err(builtin_error("hypot: internal error converting GPU tensor"));
}
tensor::value_into_tensor_for("hypot", other)
.map_err(|e| builtin_error(format!("hypot: {e}")))
}
}
}
fn complex_magnitude(re: f64, im: f64) -> f64 {
re.hypot(im)
}
fn scalar_hypot_value(value: &Value) -> Option<f64> {
match value {
Value::Num(n) => Some(*n),
Value::Int(i) => Some(i.to_f64()),
Value::Bool(b) => Some(if *b { 1.0 } else { 0.0 }),
Value::Tensor(t) if t.data.len() == 1 => t.data.first().copied(),
Value::LogicalArray(l) if l.data.len() == 1 => Some(if l.data[0] != 0 { 1.0 } else { 0.0 }),
Value::CharArray(ca) if ca.rows * ca.cols == 1 => {
Some(ca.data.first().map(|&ch| ch as u32 as f64).unwrap_or(0.0))
}
Value::Complex(re, im) => Some(complex_magnitude(*re, *im)),
Value::ComplexTensor(ct) if ct.data.len() == 1 => {
ct.data.first().map(|(re, im)| complex_magnitude(*re, *im))
}
_ => None,
}
}
#[cfg(test)]
pub(crate) mod tests {
use super::*;
use crate::builtins::common::test_support;
use futures::executor::block_on;
use runmat_builtins::{
CharArray, ComplexTensor, IntValue, LogicalArray, ResolveContext, Tensor, Type, Value,
};
fn hypot_builtin(lhs: Value, rhs: Value) -> BuiltinResult<Value> {
block_on(super::hypot_builtin(lhs, rhs))
}
#[test]
fn hypot_type_preserves_tensor_shape() {
let out = numeric_binary_type(
&[
Type::Tensor {
shape: Some(vec![Some(2), Some(3)]),
},
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 hypot_type_scalar_returns_num() {
let out = numeric_binary_type(&[Type::Num, Type::Int], &ResolveContext::new(Vec::new()));
assert_eq!(out, Type::Num);
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_scalar_pair() {
let result = hypot_builtin(Value::Num(3.0), Value::Num(4.0)).expect("hypot");
match result {
Value::Num(v) => assert!((v - 5.0).abs() < 1e-12),
other => panic!("expected scalar result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_matrix_elements() {
let lhs = Tensor::new(vec![1.0, 3.0, 2.0, 4.0], vec![2, 2]).unwrap();
let rhs = Tensor::new(vec![0.0, 0.0, 1.0, 1.0], vec![2, 2]).unwrap();
let result =
hypot_builtin(Value::Tensor(lhs), Value::Tensor(rhs)).expect("element-wise hypot");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![2, 2]);
let expected = [1.0, 3.0, (5.0f64).sqrt(), (17.0f64).sqrt()];
for (actual, expect) in t.data.iter().zip(expected.iter()) {
assert!((actual - expect).abs() < 1e-12, "{actual} vs {expect}");
}
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_scalar_broadcast() {
let matrix = Tensor::new(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2]).unwrap();
let result = hypot_builtin(Value::Tensor(matrix), Value::Num(4.0)).expect("broadcast");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![2, 2]);
let expected = [4.123105625617661, 4.47213595499958, 5.0, 5.656854249492381];
for (actual, expect) in t.data.iter().zip(expected.iter()) {
assert!((actual - expect).abs() < 1e-12);
}
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_row_vector_broadcasts_over_matrix() {
let matrix = Tensor::new(vec![1.0, 4.0, 2.0, 5.0, 3.0, 6.0], vec![2, 3]).unwrap();
let row = Tensor::new(vec![3.0, 4.0, 5.0], vec![1, 3]).unwrap();
let result =
hypot_builtin(Value::Tensor(matrix), Value::Tensor(row)).expect("row broadcast");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![2, 3]);
let expected = [
(1.0f64).hypot(3.0),
(4.0f64).hypot(3.0),
(2.0f64).hypot(4.0),
(5.0f64).hypot(4.0),
(3.0f64).hypot(5.0),
(6.0f64).hypot(5.0),
];
for (actual, expect) in t.data.iter().zip(expected.iter()) {
assert!((actual - expect).abs() < 1e-12, "{actual} vs {expect}");
}
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_complex_scalars() {
let left = (3.0, 4.0);
let right = (-1.0, 2.0);
let result = hypot_builtin(
Value::Complex(left.0, left.1),
Value::Complex(right.0, right.1),
)
.expect("complex hypot");
let expected = complex_magnitude(left.0, left.1).hypot(complex_magnitude(right.0, right.1));
match result {
Value::Num(v) => assert!((v - expected).abs() < 1e-12),
other => panic!("expected scalar norm, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_complex_tensor_with_real() {
let complex = ComplexTensor::new(vec![(3.0, 4.0), (5.0, 12.0)], vec![2, 1]).unwrap();
let real = Tensor::new(vec![0.0, 1.0], vec![2, 1]).unwrap();
let result =
hypot_builtin(Value::ComplexTensor(complex), Value::Tensor(real)).expect("mixed");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![2, 1]);
let expected = [
complex_magnitude(3.0, 4.0).hypot(0.0),
complex_magnitude(5.0, 12.0).hypot(1.0),
];
for (actual, expect) in t.data.iter().zip(expected.iter()) {
assert!((actual - expect).abs() < 1e-12);
}
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_char_array_inputs() {
let chars = CharArray::new("AB".chars().collect(), 1, 2).unwrap();
let result = hypot_builtin(Value::CharArray(chars), Value::Int(IntValue::I32(1)))
.expect("char hypot");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![1, 2]);
let expected = [
(65.0f64.powi(2) + 1.0).sqrt(),
(66.0f64.powi(2) + 1.0).sqrt(),
];
for (actual, expect) in t.data.iter().zip(expected.iter()) {
assert!((actual - expect).abs() < 1e-12);
}
}
other => panic!("expected tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_logical_inputs() {
let logical = LogicalArray::new(vec![1, 0, 0, 1], vec![2, 2]).expect("logical array");
let tensor = Tensor::new(vec![0.0, 1.0, 2.0, 3.0], vec![2, 2]).unwrap();
let result =
hypot_builtin(Value::LogicalArray(logical), Value::Tensor(tensor)).expect("logical");
match result {
Value::Tensor(out) => {
assert_eq!(out.shape, vec![2, 2]);
let expected = [
1.0_f64.hypot(0.0),
0.0_f64.hypot(1.0),
0.0_f64.hypot(2.0),
1.0_f64.hypot(3.0),
];
for (actual, expect) in out.data.iter().zip(expected.iter()) {
assert!((actual - expect).abs() < 1e-12, "{actual} vs {expect}");
}
}
other => panic!("expected tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_dimension_mismatch_errors() {
let lhs = Tensor::new(vec![1.0, 4.0, 2.0, 5.0], vec![2, 2]).unwrap();
let rhs = Tensor::new(vec![1.0, 2.0, 3.0], vec![3]).unwrap();
let err = hypot_builtin(Value::Tensor(lhs), Value::Tensor(rhs)).unwrap_err();
assert!(
err.message().contains("dimension"),
"unexpected error: {err}"
);
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_nan_propagates() {
let result = hypot_builtin(Value::Num(f64::NAN), Value::Num(1.0)).expect("nan propagation");
match result {
Value::Num(v) => assert!(v.is_nan()),
other => panic!("expected NaN scalar, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_both_infinite_is_inf() {
let result =
hypot_builtin(Value::Num(f64::INFINITY), Value::Num(f64::INFINITY)).expect("hypot inf");
match result {
Value::Num(v) => assert!(v.is_infinite() && v > 0.0, "expected +Inf, got {v}"),
other => panic!("expected +Inf scalar, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_one_infinite_is_inf() {
let result =
hypot_builtin(Value::Num(f64::INFINITY), Value::Num(3.0)).expect("hypot inf/finite");
match result {
Value::Num(v) => assert!(v.is_infinite() && v > 0.0, "expected +Inf, got {v}"),
other => panic!("expected +Inf scalar, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_gpu_provider_roundtrip() {
test_support::with_test_provider(|provider| {
let lhs = Tensor::new(vec![3.0, 5.0, 8.0, 7.0], vec![2, 2]).unwrap();
let rhs = Tensor::new(vec![4.0, 12.0, 15.0, 24.0], vec![2, 2]).unwrap();
let h_lhs = provider
.upload(&runmat_accelerate_api::HostTensorView {
data: &lhs.data,
shape: &lhs.shape,
})
.expect("upload lhs");
let h_rhs = provider
.upload(&runmat_accelerate_api::HostTensorView {
data: &rhs.data,
shape: &rhs.shape,
})
.expect("upload rhs");
let result =
hypot_builtin(Value::GpuTensor(h_lhs), Value::GpuTensor(h_rhs)).expect("gpu hypot");
let gathered = test_support::gather(result).expect("gathered result");
let expected = [5.0, 13.0, 17.0, 25.0];
assert_eq!(gathered.shape, vec![2, 2]);
for (actual, expect) in gathered.data.iter().zip(expected.iter()) {
assert!((actual - expect).abs() < 1e-12);
}
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_gpu_and_host_mix_falls_back() {
test_support::with_test_provider(|provider| {
let lhs = Tensor::new(vec![3.0, 4.0], vec![2, 1]).unwrap();
let handle = provider
.upload(&runmat_accelerate_api::HostTensorView {
data: &lhs.data,
shape: &lhs.shape,
})
.expect("upload");
let result =
hypot_builtin(Value::GpuTensor(handle), Value::Num(4.0)).expect("gpu + host hypot");
let gathered = test_support::gather(result).expect("gather");
assert_eq!(gathered.shape, vec![2, 1]);
let expected: Vec<f64> = lhs.data.iter().map(|&x| x.hypot(4.0)).collect();
for (actual, expect) in gathered.data.iter().zip(expected.iter()) {
assert!((actual - expect).abs() < 1e-12);
}
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn hypot_empty_tensor_result() {
let lhs = Tensor::new(Vec::new(), vec![0, 3]).unwrap();
let rhs = Tensor::new(Vec::new(), vec![0, 3]).unwrap();
let result =
hypot_builtin(Value::Tensor(lhs), Value::Tensor(rhs)).expect("empty hypot result");
match result {
Value::Tensor(out) => {
assert_eq!(out.shape, vec![0, 3]);
assert!(out.data.is_empty());
}
other => panic!("expected empty tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
#[cfg(feature = "wgpu")]
fn hypot_wgpu_matches_cpu_elementwise() {
let _ = runmat_accelerate::backend::wgpu::provider::register_wgpu_provider(
runmat_accelerate::backend::wgpu::provider::WgpuProviderOptions::default(),
);
let lhs = Tensor::new(vec![3.0, 4.0, 5.0, 12.0], vec![2, 2]).unwrap();
let rhs = Tensor::new(vec![4.0, 3.0, 12.0, 5.0], vec![2, 2]).unwrap();
let cpu_value = compute_hypot_tensor(&lhs, &rhs).expect("cpu hypot");
let expected = test_support::gather(cpu_value).expect("gather cpu result");
let provider = runmat_accelerate_api::provider().expect("wgpu provider");
let h_lhs = provider
.upload(&runmat_accelerate_api::HostTensorView {
data: &lhs.data,
shape: &lhs.shape,
})
.expect("upload lhs");
let h_rhs = provider
.upload(&runmat_accelerate_api::HostTensorView {
data: &rhs.data,
shape: &rhs.shape,
})
.expect("upload rhs");
let gpu_value =
hypot_builtin(Value::GpuTensor(h_lhs), Value::GpuTensor(h_rhs)).expect("gpu hypot");
let gathered = test_support::gather(gpu_value).expect("gather gpu result");
assert_eq!(gathered.shape, expected.shape);
let tol = match provider.precision() {
runmat_accelerate_api::ProviderPrecision::F64 => 1e-12,
runmat_accelerate_api::ProviderPrecision::F32 => 1e-5,
};
for (actual, expect) in gathered.data.iter().zip(expected.data.iter()) {
assert!(
(actual - expect).abs() < tol,
"|{actual} - {expect}| >= {tol}"
);
}
}
}