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, map_control_flow_with_builtin, 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::real")]
pub const GPU_SPEC: BuiltinGpuSpec = BuiltinGpuSpec {
name: "real",
op_kind: GpuOpKind::Elementwise,
supported_precisions: &[ScalarType::F32, ScalarType::F64],
broadcast: BroadcastSemantics::Matlab,
provider_hooks: &[ProviderHook::Unary { name: "unary_real" }],
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 real in-place via unary_real; the runtime gathers to the host when the hook is absent or when host-only conversions (e.g. complex tensors) are required.",
};
#[runmat_macros::register_fusion_spec(builtin_path = "crate::builtins::math::elementwise::real")]
pub const FUSION_SPEC: BuiltinFusionSpec = BuiltinFusionSpec {
name: "real",
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!("({input})"))
},
}),
reduction: None,
emits_nan: false,
notes: "Fusion kernels treat real as an identity transform for real tensors; providers can override via fused pipelines when advantageous.",
};
const BUILTIN_NAME: &str = "real";
fn builtin_error(message: impl Into<String>) -> RuntimeError {
build_runtime_error(message)
.with_builtin(BUILTIN_NAME)
.build()
}
#[runtime_builtin(
name = "real",
category = "math/elementwise",
summary = "Extract the real part of scalars, vectors, matrices, or N-D tensors.",
keywords = "real,real part,complex,elementwise,gpu",
accel = "unary",
type_resolver(numeric_unary_type),
builtin_path = "crate::builtins::math::elementwise::real"
)]
async fn real_builtin(value: Value) -> BuiltinResult<Value> {
match value {
Value::GpuTensor(handle) => real_gpu(handle).await,
Value::Complex(re, _) => Ok(Value::Num(re)),
Value::ComplexTensor(ct) => real_complex_tensor(ct),
Value::CharArray(ca) => real_char_array(ca),
Value::String(_) | Value::StringArray(_) => {
Err(builtin_error("real: expected numeric input"))
}
x @ (Value::Tensor(_)
| Value::LogicalArray(_)
| Value::Num(_)
| Value::Int(_)
| Value::Bool(_)) => real_real(x),
other => Err(builtin_error(format!(
"real: unsupported input type {:?}; expected numeric, logical, or char input",
other
))),
}
}
async fn real_gpu(handle: GpuTensorHandle) -> BuiltinResult<Value> {
if let Some(provider) = runmat_accelerate_api::provider_for_handle(&handle) {
if let Ok(out) = provider.unary_real(&handle).await {
return Ok(Value::GpuTensor(out));
}
}
let tensor = gpu_helpers::gather_tensor_async(&handle)
.await
.map_err(|flow| map_control_flow_with_builtin(flow, BUILTIN_NAME))?;
Ok(tensor::tensor_into_value(real_tensor(tensor)?))
}
fn real_real(value: Value) -> BuiltinResult<Value> {
let tensor = tensor::value_into_tensor_for("real", value)
.map_err(|e| builtin_error(format!("real: {e}")))?;
Ok(tensor::tensor_into_value(real_tensor(tensor)?))
}
fn real_tensor(tensor: Tensor) -> BuiltinResult<Tensor> {
Ok(tensor)
}
fn real_complex_tensor(ct: ComplexTensor) -> BuiltinResult<Value> {
let data = ct.data.iter().map(|&(re, _)| re).collect::<Vec<_>>();
let tensor =
Tensor::new(data, ct.shape.clone()).map_err(|e| builtin_error(format!("real: {e}")))?;
Ok(tensor::tensor_into_value(tensor))
}
fn real_char_array(ca: CharArray) -> BuiltinResult<Value> {
let data = ca
.data
.iter()
.map(|&ch| ch as u32 as f64)
.collect::<Vec<_>>();
let tensor = Tensor::new(data, vec![ca.rows, ca.cols])
.map_err(|e| builtin_error(format!("real: {e}")))?;
Ok(tensor::tensor_into_value(tensor))
}
#[cfg(test)]
pub(crate) mod tests {
use super::*;
use crate::builtins::common::test_support;
use futures::executor::block_on;
use runmat_builtins::{IntValue, LogicalArray, ResolveContext, Type};
fn real_builtin(value: Value) -> BuiltinResult<Value> {
block_on(super::real_builtin(value))
}
#[test]
fn real_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 real_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 real_scalar_num() {
let result = real_builtin(Value::Num(-2.5)).expect("real");
match result {
Value::Num(n) => assert!((n + 2.5).abs() < 1e-12),
other => panic!("expected scalar result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn real_complex_scalar() {
let result = real_builtin(Value::Complex(3.0, 4.0)).expect("real");
match result {
Value::Num(n) => assert!((n - 3.0).abs() < 1e-12),
other => panic!("expected scalar result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn real_int_promotes_to_double() {
let result = real_builtin(Value::Int(IntValue::I32(7))).expect("real");
match result {
Value::Num(n) => assert!((n - 7.0).abs() < 1e-12),
other => panic!("expected scalar result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn real_complex_tensor_to_real_tensor() {
let complex =
ComplexTensor::new(vec![(1.0, 2.0), (-3.0, 4.0)], vec![2, 1]).expect("complex tensor");
let result = real_builtin(Value::ComplexTensor(complex)).expect("real");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![2, 1]);
assert!((t.data[0] - 1.0).abs() < 1e-12);
assert!((t.data[1] + 3.0).abs() < 1e-12);
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn real_logical_array_to_numeric() {
let logical = LogicalArray::new(vec![0, 1, 1, 0], vec![2, 2]).expect("logical array");
let result = real_builtin(Value::LogicalArray(logical)).expect("real");
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 real_char_array_codes() {
let chars = CharArray::new("AZ".chars().collect(), 1, 2).expect("char array");
let result = real_builtin(Value::CharArray(chars)).expect("real");
match result {
Value::Tensor(t) => {
assert_eq!(t.shape, vec![1, 2]);
assert_eq!(t.data, vec![65.0, 90.0]);
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn real_string_error() {
let err = real_builtin(Value::from("hello")).expect_err("real should error");
assert!(err.message().contains("expected numeric"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn real_gpu_provider_roundtrip() {
test_support::with_test_provider(|provider| {
let tensor = Tensor::new(vec![1.0, -2.0, 3.5, -4.25], vec![4, 1]).unwrap();
let view = runmat_accelerate_api::HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let result = real_builtin(Value::GpuTensor(handle)).expect("real");
let gathered = test_support::gather(result).expect("gather");
assert_eq!(gathered.shape, vec![4, 1]);
assert_eq!(gathered.data, tensor.data);
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
#[cfg(feature = "wgpu")]
fn real_wgpu_matches_cpu_identity() {
let _ = runmat_accelerate::backend::wgpu::provider::register_wgpu_provider(
runmat_accelerate::backend::wgpu::provider::WgpuProviderOptions::default(),
);
let tensor = Tensor::new(vec![0.0, 1.0, -2.5, 4.0], vec![4, 1]).unwrap();
let cpu = real_real(Value::Tensor(tensor.clone())).unwrap();
let view = runmat_accelerate_api::HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let h = runmat_accelerate_api::provider()
.unwrap()
.upload(&view)
.unwrap();
let gpu = block_on(real_gpu(h)).unwrap();
let gathered = test_support::gather(gpu).expect("gather");
let cpu_tensor = match cpu {
Value::Tensor(t) => t,
Value::Num(n) => Tensor::new(vec![n], vec![1, 1]).unwrap(),
other => panic!("unexpected cpu value {other:?}"),
};
assert_eq!(gathered.shape, cpu_tensor.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 (a, b) in gathered.data.iter().zip(cpu_tensor.data.iter()) {
assert!((a - b).abs() < tol, "|{} - {}| >= {}", a, b, tol);
}
}
}