use runmat_builtins::{CharArray, ComplexTensor, LogicalArray, 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::logical::type_resolvers::logical_unary_type;
use crate::{build_runtime_error, BuiltinResult, RuntimeError};
#[runmat_macros::register_gpu_spec(builtin_path = "crate::builtins::logical::bit::not")]
pub const GPU_SPEC: BuiltinGpuSpec = BuiltinGpuSpec {
name: "not",
op_kind: GpuOpKind::Elementwise,
supported_precisions: &[ScalarType::F32, ScalarType::F64],
broadcast: BroadcastSemantics::Matlab,
provider_hooks: &[ProviderHook::Unary {
name: "logical_not",
}],
constant_strategy: ConstantStrategy::InlineLiteral,
residency: ResidencyPolicy::NewHandle,
nan_mode: ReductionNaN::Include,
two_pass_threshold: None,
workgroup_size: None,
accepts_nan_mode: false,
notes:
"Dispatches to the provider `logical_not` hook when available; otherwise the runtime gathers to host and performs the negation on the CPU.",
};
#[runmat_macros::register_fusion_spec(builtin_path = "crate::builtins::logical::bit::not")]
pub const FUSION_SPEC: BuiltinFusionSpec = BuiltinFusionSpec {
name: "not",
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))?;
let (zero, one) = match ctx.scalar_ty {
ScalarType::F32 => ("0.0".to_string(), "1.0".to_string()),
ScalarType::F64 => ("f64(0.0)".to_string(), "f64(1.0)".to_string()),
_ => return Err(FusionError::UnsupportedPrecision(ctx.scalar_ty)),
};
let cond = format!("({input} != {zero})");
Ok(format!("select({one}, {zero}, {cond})"))
},
}),
reduction: None,
emits_nan: false,
notes: "Fusion kernels treat any non-zero input as true and write 0/1 outputs, matching MATLAB logical semantics.",
};
#[runtime_builtin(
name = "not",
category = "logical/bit",
summary = "Element-wise logical negation for scalars, arrays, and gpuArray values.",
keywords = "logical,not,boolean,gpu",
accel = "elementwise",
type_resolver(logical_unary_type),
builtin_path = "crate::builtins::logical::bit::not"
)]
async fn not_builtin(value: Value) -> BuiltinResult<Value> {
if let Value::GpuTensor(ref handle) = value {
if let Some(provider) = runmat_accelerate_api::provider() {
if let Ok(device_out) = provider.logical_not(handle) {
return Ok(gpu_helpers::logical_gpu_value(device_out));
}
}
}
not_host(value).await
}
async fn not_host(value: Value) -> BuiltinResult<Value> {
let buffer = logical_buffer_from("not", value).await?;
let LogicalBuffer { data, shape } = buffer;
let total = tensor::element_count(&shape);
if total == 0 {
return logical_value("not", Vec::new(), shape);
}
let mapped = data
.into_iter()
.map(|bit| if bit == 0 { 1 } else { 0 })
.collect::<Vec<_>>();
logical_value("not", mapped, shape)
}
fn builtin_error(fn_name: &str, message: impl Into<String>) -> RuntimeError {
build_runtime_error(message).with_builtin(fn_name).build()
}
fn logical_value(fn_name: &str, data: Vec<u8>, shape: Vec<usize>) -> BuiltinResult<Value> {
if data.len() == 1 && tensor::element_count(&shape) == 1 {
Ok(Value::Bool(data[0] != 0))
} else {
LogicalArray::new(data, shape)
.map(Value::LogicalArray)
.map_err(|e| builtin_error(fn_name, format!("{fn_name}: {e}")))
}
}
struct LogicalBuffer {
data: Vec<u8>,
shape: Vec<usize>,
}
async fn logical_buffer_from(name: &str, value: Value) -> BuiltinResult<LogicalBuffer> {
match value {
Value::LogicalArray(array) => {
let LogicalArray { data, shape } = array;
Ok(LogicalBuffer { data, shape })
}
Value::Bool(flag) => Ok(LogicalBuffer {
data: vec![if flag { 1 } else { 0 }],
shape: vec![1, 1],
}),
Value::Num(n) => Ok(LogicalBuffer {
data: vec![logical_from_f64(n)],
shape: vec![1, 1],
}),
Value::Int(i) => Ok(LogicalBuffer {
data: vec![if i.to_i64() != 0 { 1 } else { 0 }],
shape: vec![1, 1],
}),
Value::Complex(re, im) => Ok(LogicalBuffer {
data: vec![logical_from_complex(re, im)],
shape: vec![1, 1],
}),
Value::Tensor(tensor) => tensor_to_logical_buffer(tensor),
Value::ComplexTensor(tensor) => complex_tensor_to_logical_buffer(tensor),
Value::CharArray(array) => char_array_to_logical_buffer(array),
Value::GpuTensor(handle) => {
let tensor = gpu_helpers::gather_tensor_async(&handle)
.await
.map_err(|err| builtin_error(name, format!("{name}: {err}")))?;
tensor_to_logical_buffer(tensor)
}
other => Err(builtin_error(
name,
format!(
"{name}: unsupported input type {:?}; expected logical, numeric, complex, or character data",
other
),
)),
}
}
fn tensor_to_logical_buffer(tensor: Tensor) -> BuiltinResult<LogicalBuffer> {
let Tensor { data, shape, .. } = tensor;
let mapped = data
.into_iter()
.map(|v| if v != 0.0 { 1 } else { 0 })
.collect();
Ok(LogicalBuffer {
data: mapped,
shape,
})
}
fn complex_tensor_to_logical_buffer(tensor: ComplexTensor) -> BuiltinResult<LogicalBuffer> {
let ComplexTensor { data, shape, .. } = tensor;
let mapped = data
.into_iter()
.map(|(re, im)| if re != 0.0 || im != 0.0 { 1 } else { 0 })
.collect();
Ok(LogicalBuffer {
data: mapped,
shape,
})
}
fn char_array_to_logical_buffer(array: CharArray) -> BuiltinResult<LogicalBuffer> {
let CharArray { data, rows, cols } = array;
let mapped = data
.into_iter()
.map(|ch| if ch == '\0' { 0 } else { 1 })
.collect();
Ok(LogicalBuffer {
data: mapped,
shape: vec![rows, cols],
})
}
#[inline]
fn logical_from_f64(value: f64) -> u8 {
if value != 0.0 {
1
} else {
0
}
}
#[inline]
fn logical_from_complex(re: f64, im: f64) -> u8 {
if re != 0.0 || im != 0.0 {
1
} else {
0
}
}
#[cfg(test)]
pub(crate) mod tests {
use super::*;
#[cfg(feature = "wgpu")]
use crate::builtins::common::tensor;
use crate::builtins::common::test_support;
use crate::RuntimeError;
use futures::executor::block_on;
use runmat_accelerate_api::HostTensorView;
fn assert_error_contains(err: RuntimeError, expected: &str) {
assert!(
err.message().contains(expected),
"unexpected error: {}",
err.message()
);
}
fn run_not(value: Value) -> BuiltinResult<Value> {
block_on(super::not_builtin(value))
}
#[cfg(feature = "wgpu")]
fn run_not_host(value: Value) -> BuiltinResult<Value> {
block_on(not_host(value))
}
#[cfg(feature = "wgpu")]
use runmat_accelerate_api::ProviderPrecision;
use runmat_builtins::{CharArray, ComplexTensor, IntValue, LogicalArray, Tensor};
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_of_booleans() {
assert_eq!(run_not(Value::Bool(true)).unwrap(), Value::Bool(false));
assert_eq!(run_not(Value::Bool(false)).unwrap(), Value::Bool(true));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_numeric_array() {
let tensor = Tensor::new(vec![0.0, 1.0, 2.0, 0.0], vec![2, 2]).unwrap();
let result = run_not(Value::Tensor(tensor)).unwrap();
match result {
Value::LogicalArray(array) => {
assert_eq!(array.shape, vec![2, 2]);
assert_eq!(array.data, vec![1, 0, 0, 1]);
}
other => panic!("expected logical array, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_complex_scalar() {
let result = run_not(Value::Complex(0.0, 0.0)).expect("not complex zero should succeed");
assert_eq!(result, Value::Bool(true));
let result = run_not(Value::Complex(1.0, 0.0)).expect("not complex nonzero should succeed");
assert_eq!(result, Value::Bool(false));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_nan_yields_false() {
let result = run_not(Value::Num(f64::NAN)).unwrap();
assert_eq!(result, Value::Bool(false));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_char_array() {
let chars = CharArray::new_row("A\0C");
let result = run_not(Value::CharArray(chars)).unwrap();
match result {
Value::LogicalArray(array) => {
assert_eq!(array.shape, vec![1, 3]);
assert_eq!(array.data, vec![0, 1, 0]);
}
other => panic!("expected logical array, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_gpu_roundtrip() {
test_support::with_test_provider(|provider| {
let tensor = Tensor::new(vec![0.0, 1.0, 0.0, 2.0], vec![2, 2]).unwrap();
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let result = run_not(Value::GpuTensor(handle)).expect("not on gpu");
let gathered = test_support::gather(result).expect("gather");
assert_eq!(gathered.shape, vec![2, 2]);
assert_eq!(gathered.data, vec![1.0, 0.0, 1.0, 0.0]);
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_accepts_int_inputs() {
let value = Value::Int(IntValue::I32(0));
assert_eq!(run_not(value).unwrap(), Value::Bool(true));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_tensor_scalar_returns_bool() {
let tensor = Tensor::new(vec![2.0], vec![1, 1]).unwrap();
assert_eq!(run_not(Value::Tensor(tensor)).unwrap(), Value::Bool(false));
let tensor = Tensor::new(vec![0.0], vec![1, 1]).unwrap();
assert_eq!(run_not(Value::Tensor(tensor)).unwrap(), Value::Bool(true));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_empty_tensor_preserves_shape() {
let tensor = Tensor::new(Vec::<f64>::new(), vec![0, 3]).unwrap();
let result = run_not(Value::Tensor(tensor)).unwrap();
match result {
Value::LogicalArray(array) => {
assert_eq!(array.shape, vec![0, 3]);
assert!(array.data.is_empty());
}
other => panic!("expected logical array, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_complex_tensor() {
let tensor =
ComplexTensor::new(vec![(0.0, 0.0), (1.0, 0.0), (0.0, -2.0)], vec![3, 1]).unwrap();
let result = run_not(Value::ComplexTensor(tensor)).unwrap();
match result {
Value::LogicalArray(array) => {
assert_eq!(array.shape, vec![3, 1]);
assert_eq!(array.data, vec![1, 0, 0]);
}
other => panic!("expected logical array, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_logical_array_flips_bits() {
let array = LogicalArray::new(vec![1, 0, 1, 1], vec![2, 2]).unwrap();
let result = run_not(Value::LogicalArray(array)).unwrap();
match result {
Value::LogicalArray(out) => {
assert_eq!(out.shape, vec![2, 2]);
assert_eq!(out.data, vec![0, 1, 0, 0]);
}
other => panic!("expected logical array, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn not_rejects_string_input() {
let err = run_not(Value::String("abc".into())).unwrap_err();
assert_error_contains(err, "unsupported input type");
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
#[cfg(feature = "wgpu")]
fn not_wgpu_matches_host_path() {
let _ = runmat_accelerate::backend::wgpu::provider::register_wgpu_provider(
runmat_accelerate::backend::wgpu::provider::WgpuProviderOptions::default(),
);
let tensor = Tensor::new(vec![0.0, 3.0, 0.0, -1.0], vec![2, 2]).unwrap();
let cpu = run_not_host(Value::Tensor(tensor.clone())).unwrap();
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = runmat_accelerate_api::provider()
.unwrap()
.upload(&view)
.unwrap();
let gpu = run_not(Value::GpuTensor(handle)).unwrap();
let gathered = test_support::gather(gpu).expect("gather");
let cpu_tensor = tensor::value_to_tensor(&cpu).expect("cpu tensor");
assert_eq!(gathered.shape, cpu_tensor.shape);
let tol = match runmat_accelerate_api::provider().unwrap().precision() {
ProviderPrecision::F64 => 1e-12,
ProviderPrecision::F32 => 1e-5,
};
for (expected, actual) in cpu_tensor.data.iter().zip(gathered.data.iter()) {
assert!((*expected - *actual).abs() < tol, "{expected} vs {actual}");
}
}
}