use log::trace;
use runmat_accelerate_api::{GpuTensorHandle, HostTensorView, ProviderPrecision};
use runmat_builtins::{CharArray, LogicalArray, Tensor, Value};
use runmat_macros::runtime_builtin;
use crate::builtins::common::{
gpu_helpers,
random_args::keyword_of,
spec::{
BroadcastSemantics, BuiltinFusionSpec, BuiltinGpuSpec, ConstantStrategy, FusionError,
FusionExprContext, FusionKernelTemplate, GpuOpKind, ProviderHook, ReductionNaN,
ResidencyPolicy, ScalarType, ShapeRequirements,
},
tensor,
};
use crate::builtins::math::type_resolvers::numeric_unary_type;
use crate::{build_runtime_error, BuiltinResult, RuntimeError};
#[runmat_macros::register_gpu_spec(builtin_path = "crate::builtins::math::elementwise::double")]
pub const GPU_SPEC: BuiltinGpuSpec = BuiltinGpuSpec {
name: "double",
op_kind: GpuOpKind::Elementwise,
supported_precisions: &[ScalarType::F64],
broadcast: BroadcastSemantics::Matlab,
provider_hooks: &[ProviderHook::Unary {
name: "unary_double",
}],
constant_strategy: ConstantStrategy::InlineLiteral,
residency: ResidencyPolicy::NewHandle,
nan_mode: ReductionNaN::Include,
two_pass_threshold: None,
workgroup_size: None,
accepts_nan_mode: false,
notes: "Casts inputs to float64. Providers without native float64 support gather to host; float64-capable providers keep results on device.",
};
#[runmat_macros::register_fusion_spec(builtin_path = "crate::builtins::math::elementwise::double")]
pub const FUSION_SPEC: BuiltinFusionSpec = BuiltinFusionSpec {
name: "double",
shape: ShapeRequirements::BroadcastCompatible,
constant_strategy: ConstantStrategy::InlineLiteral,
elementwise: Some(FusionKernelTemplate {
scalar_precisions: &[ScalarType::F64],
wgsl_body: |ctx: &FusionExprContext| {
let input = ctx.inputs.first().ok_or(FusionError::MissingInput(0))?;
Ok(format!("({input})"))
},
}),
reduction: None,
emits_nan: false,
notes: "Fusion treats double as an identity when the execution scalar type is already float64.",
};
const BUILTIN_NAME: &str = "double";
fn builtin_error(message: impl Into<String>) -> RuntimeError {
build_runtime_error(message)
.with_builtin(BUILTIN_NAME)
.build()
}
fn conversion_error(type_name: &str) -> RuntimeError {
builtin_error(format!(
"double: conversion to double from {type_name} is not possible"
))
}
#[runtime_builtin(
name = "double",
category = "math/elementwise",
summary = "Convert scalars, arrays, logical masks, and gpuArray values to double precision.",
keywords = "double,float64,cast,gpu",
accel = "unary",
type_resolver(numeric_unary_type),
builtin_path = "crate::builtins::math::elementwise::double"
)]
async fn double_builtin(value: Value, rest: Vec<Value>) -> BuiltinResult<Value> {
let template = parse_output_template(&rest)?;
let converted = match value {
Value::Num(n) => Ok(Value::Num(n)),
Value::Int(i) => Ok(Value::Num(i.to_f64())),
Value::Bool(flag) => Ok(Value::Num(if flag { 1.0 } else { 0.0 })),
Value::Tensor(tensor) => Ok(Value::Tensor(tensor)),
Value::Complex(re, im) => Ok(Value::Complex(re, im)),
Value::ComplexTensor(tensor) => Ok(Value::ComplexTensor(tensor)),
Value::LogicalArray(array) => double_from_logical(array),
Value::CharArray(chars) => double_from_char_array(chars),
Value::GpuTensor(handle) => double_from_gpu(handle).await,
Value::String(_) | Value::StringArray(_) => Err(conversion_error("string")),
Value::Cell(_) => Err(conversion_error("cell")),
Value::Struct(_) => Err(conversion_error("struct")),
Value::Object(obj) => Err(conversion_error(&obj.class_name)),
Value::HandleObject(handle) => Err(conversion_error(&handle.class_name)),
Value::Listener(_) => Err(conversion_error("event.listener")),
Value::FunctionHandle(_) | Value::Closure(_) => Err(conversion_error("function_handle")),
Value::ClassRef(_) => Err(conversion_error("meta.class")),
Value::MException(_) => Err(conversion_error("MException")),
Value::OutputList(_) => Err(conversion_error("OutputList")),
}?;
apply_output_template(converted, &template).await
}
fn double_from_logical(array: LogicalArray) -> BuiltinResult<Value> {
let tensor =
tensor::logical_to_tensor(&array).map_err(|e| builtin_error(format!("double: {e}")))?;
Ok(tensor::tensor_into_value(tensor))
}
fn double_from_char_array(chars: CharArray) -> BuiltinResult<Value> {
let data: Vec<f64> = chars.data.iter().map(|&ch| ch as u32 as f64).collect();
let tensor = Tensor::new(data, vec![chars.rows, chars.cols])
.map_err(|e| builtin_error(format!("double: {e}")))?;
Ok(tensor::tensor_into_value(tensor))
}
async fn double_from_gpu(handle: GpuTensorHandle) -> BuiltinResult<Value> {
let provider = runmat_accelerate_api::provider_for_handle(&handle);
if let Some(provider) = provider {
if provider.precision() == ProviderPrecision::F64 {
match provider.unary_double(&handle).await {
Ok(result) => {
return Ok(Value::GpuTensor(result));
}
Err(err) => {
trace!("double: provider unary_double unavailable ({err}); falling back to host conversion");
}
}
} else {
trace!(
"double: provider precision {:?} cannot store float64 values; gathering to host",
provider.precision()
);
}
}
let tensor = gpu_helpers::gather_tensor_async(&handle).await?;
if let Some(provider) = provider {
if provider.precision() == ProviderPrecision::F64 {
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
match provider.upload(&view) {
Ok(new_handle) => return Ok(Value::GpuTensor(new_handle)),
Err(err) => {
trace!("double: provider upload failed after gather ({err})");
}
}
} else {
trace!(
"double: provider precision {:?} does not support float64 outputs; returning host tensor",
provider.precision()
);
}
}
Ok(tensor::tensor_into_value(tensor))
}
#[derive(Clone)]
enum OutputTemplate {
Default,
Like(Value),
}
fn parse_output_template(args: &[Value]) -> BuiltinResult<OutputTemplate> {
match args.len() {
0 => Ok(OutputTemplate::Default),
1 => {
if matches!(keyword_of(&args[0]).as_deref(), Some("like")) {
Err(builtin_error("double: expected prototype after 'like'"))
} else {
Err(builtin_error("double: unrecognised argument for double"))
}
}
2 => {
if matches!(keyword_of(&args[0]).as_deref(), Some("like")) {
Ok(OutputTemplate::Like(args[1].clone()))
} else {
Err(builtin_error(
"double: unsupported option; only 'like' is accepted",
))
}
}
_ => Err(builtin_error("double: too many input arguments")),
}
}
async fn apply_output_template(value: Value, template: &OutputTemplate) -> BuiltinResult<Value> {
match template {
OutputTemplate::Default => Ok(value),
OutputTemplate::Like(proto) => match proto {
Value::GpuTensor(_) => convert_to_gpu(value),
Value::Tensor(_)
| Value::Num(_)
| Value::Int(_)
| Value::Bool(_)
| Value::LogicalArray(_) => convert_to_host_like(value).await,
Value::Complex(_, _) | Value::ComplexTensor(_) => Err(builtin_error(
"double: complex prototypes for 'like' are not supported yet",
)),
_ => Err(builtin_error(
"double: unsupported prototype for 'like'; provide a numeric or gpuArray prototype",
)),
},
}
}
fn convert_to_gpu(value: Value) -> BuiltinResult<Value> {
let provider = runmat_accelerate_api::provider().ok_or_else(|| {
builtin_error(
"double: GPU output requested via 'like' but no acceleration provider is active",
)
})?;
if provider.precision() != ProviderPrecision::F64 {
return Err(builtin_error(
"double: active acceleration provider does not support float64 storage",
));
}
match value {
Value::GpuTensor(handle) => Ok(Value::GpuTensor(handle)),
Value::Tensor(tensor) => {
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider
.upload(&view)
.map_err(|e| builtin_error(format!("double: {e}")))?;
Ok(Value::GpuTensor(handle))
}
Value::Num(n) => {
let tensor = Tensor::new(vec![n], vec![1, 1])
.map_err(|e| builtin_error(format!("double: {e}")))?;
convert_to_gpu(Value::Tensor(tensor))
}
Value::Int(i) => convert_to_gpu(Value::Num(i.to_f64())),
Value::Bool(b) => convert_to_gpu(Value::Num(if b { 1.0 } else { 0.0 })),
Value::LogicalArray(logical) => {
let tensor = tensor::logical_to_tensor(&logical)?;
convert_to_gpu(Value::Tensor(tensor))
}
Value::Complex(_, _) | Value::ComplexTensor(_) => Err(builtin_error(
"double: GPU prototypes for 'like' only support real numeric outputs",
)),
other => Err(builtin_error(format!(
"double: unsupported result type for GPU output via 'like' ({other:?})"
))),
}
}
async fn convert_to_host_like(value: Value) -> BuiltinResult<Value> {
match value {
Value::GpuTensor(handle) => {
let proxy = Value::GpuTensor(handle);
gpu_helpers::gather_value_async(&proxy)
.await
.map_err(|e| builtin_error(format!("double: {e}")))
}
other => Ok(other),
}
}
#[cfg(test)]
pub(crate) mod tests {
use super::*;
use crate::builtins::common::test_support;
use futures::executor::block_on;
use runmat_accelerate_api::HostTensorView;
#[cfg(feature = "wgpu")]
use runmat_accelerate_api::ProviderPrecision;
use runmat_builtins::{IntValue, ResolveContext, Type};
fn double_builtin(value: Value, rest: Vec<Value>) -> BuiltinResult<Value> {
block_on(super::double_builtin(value, rest))
}
#[test]
fn double_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 double_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 double_scalar_num_is_identity() {
let value = Value::Num(std::f64::consts::PI);
let result = double_builtin(value, Vec::new()).expect("double");
match result {
Value::Num(n) => assert_eq!(n, std::f64::consts::PI),
other => panic!("expected scalar Num, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_promotes_integers() {
let value = Value::Int(IntValue::I32(42));
let result = double_builtin(value, Vec::new()).expect("double");
match result {
Value::Num(n) => assert_eq!(n, 42.0),
other => panic!("expected scalar Num, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_logical_array_returns_tensor() {
let logical = LogicalArray::new(vec![0, 1, 1, 0], vec![2, 2]).unwrap();
let result = double_builtin(Value::LogicalArray(logical), Vec::new()).expect("double");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![2, 2]);
assert_eq!(t.data, vec![0.0, 1.0, 1.0, 0.0]);
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_char_array_converts_to_codes() {
let chars = CharArray::new_row("AB");
let result = double_builtin(Value::CharArray(chars), Vec::new()).expect("double");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![1, 2]);
assert_eq!(t.data, vec![65.0, 66.0]);
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_complex_scalar_is_identity() {
let result = double_builtin(Value::Complex(1.5, -2.5), Vec::new()).expect("double");
match result {
Value::Complex(re, im) => {
assert_eq!(re, 1.5);
assert_eq!(im, -2.5);
}
other => panic!("expected complex scalar, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_tensor_preserves_shape() {
let tensor = Tensor::new(vec![1.25, 2.5, 3.75, 4.5], vec![2, 2]).unwrap();
let result = double_builtin(Value::Tensor(tensor.clone()), Vec::new()).expect("double");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, tensor.shape);
assert_eq!(t.data, tensor.data);
}
other => panic!("expected tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_rejects_strings() {
let err = double_builtin(Value::String("hello".into()), Vec::new()).unwrap_err();
assert!(err
.message()
.contains("double: conversion to double from string is not possible"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_gpu_roundtrip() {
test_support::with_test_provider(|provider| {
let tensor = Tensor::new(vec![1.0, 4.0, 2.0, 5.0], vec![2, 2]).unwrap();
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let result = double_builtin(Value::GpuTensor(handle), Vec::new()).expect("double");
let gathered = test_support::gather(result).expect("gather");
assert_eq!(gathered.shape, vec![2, 2]);
assert_eq!(gathered.data, tensor.data);
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_like_gpu_prototype_keeps_residency() {
test_support::with_test_provider(|provider| {
let tensor = Tensor::new(vec![1.0, 2.0], vec![2, 1]).unwrap();
let proto = provider
.upload(&HostTensorView {
data: &[0.0],
shape: &[1, 1],
})
.expect("upload");
let result = double_builtin(
Value::Tensor(tensor.clone()),
vec![Value::from("like"), Value::GpuTensor(proto.clone())],
)
.expect("double");
match result {
Value::GpuTensor(h) => {
let gathered = test_support::gather(Value::GpuTensor(h)).expect("gather");
assert_eq!(gathered.data, tensor.data);
}
other => panic!("expected gpu tensor, got {other:?}"),
}
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_like_host_gathers_gpu_input() {
test_support::with_test_provider(|provider| {
let tensor = Tensor::new(vec![3.0], vec![1, 1]).unwrap();
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let result = double_builtin(
Value::GpuTensor(handle),
vec![Value::from("like"), Value::Num(0.0)],
)
.expect("double");
match result {
Value::Num(n) => assert_eq!(n, 3.0),
Value::Tensor(t) => {
assert_eq!(t.shape, vec![1, 1]);
assert_eq!(t.data, vec![3.0]);
}
other => panic!("expected scalar host value, got {other:?}"),
}
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_like_missing_prototype_errors() {
let err =
double_builtin(Value::Num(1.0), vec![Value::from("like")]).expect_err("expected error");
assert!(err.message().contains("expected prototype"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn double_like_rejects_extra_arguments() {
let err = double_builtin(
Value::Num(0.0),
vec![Value::from("like"), Value::Num(0.0), Value::Num(1.0)],
)
.expect_err("expected error");
assert!(err.message().contains("too many input arguments"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
#[cfg(feature = "wgpu")]
fn double_wgpu_matches_cpu() {
let _ = runmat_accelerate::backend::wgpu::provider::register_wgpu_provider(
runmat_accelerate::backend::wgpu::provider::WgpuProviderOptions::default(),
);
let provider = runmat_accelerate_api::provider().expect("wgpu provider");
let tensor = Tensor::new(vec![1.0, 2.5, -3.75, 4.125], vec![2, 2]).unwrap();
let cpu_value = double_builtin(Value::Tensor(tensor.clone()), Vec::new()).unwrap();
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let gpu_value = double_builtin(Value::GpuTensor(handle), Vec::new()).unwrap();
let gathered = test_support::gather(gpu_value.clone()).expect("gather");
match cpu_value {
Value::Tensor(ref ct) => {
assert_eq!(gathered.shape, ct.shape);
assert_eq!(gathered.data, ct.data);
}
Value::Num(n) => {
assert_eq!(gathered.data, vec![n]);
}
other => panic!("unexpected CPU reference value {other:?}"),
}
if provider.precision() == ProviderPrecision::F64 {
assert!(
matches!(gpu_value, Value::GpuTensor(_)),
"expected GPU residency under f64 precision"
);
} else {
assert!(
!matches!(gpu_value, Value::GpuTensor(_)),
"expected host fallback when f64 unsupported"
);
}
}
}