use once_cell::sync::Lazy;
use runmat_accelerate_api::{GpuTensorHandle, HostTensorView};
use runmat_builtins::{Tensor, Value};
use runmat_macros::runtime_builtin;
use crate::builtins::common::random_args::keyword_of;
use crate::builtins::common::spec::{
BroadcastSemantics, BuiltinFusionSpec, BuiltinGpuSpec, ConstantStrategy, 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};
const MAX_FACTORIAL_N: usize = 170;
static FACT_TABLE: Lazy<[f64; MAX_FACTORIAL_N + 1]> = Lazy::new(|| {
let mut table = [1.0f64; MAX_FACTORIAL_N + 1];
let mut acc = 1.0;
for (n, slot) in table.iter_mut().enumerate().skip(1) {
acc *= n as f64;
*slot = acc;
}
table
});
#[runmat_macros::register_gpu_spec(builtin_path = "crate::builtins::math::elementwise::factorial")]
pub const GPU_SPEC: BuiltinGpuSpec = BuiltinGpuSpec {
name: "factorial",
op_kind: GpuOpKind::Elementwise,
supported_precisions: &[ScalarType::F32, ScalarType::F64],
broadcast: BroadcastSemantics::Matlab,
provider_hooks: &[ProviderHook::Unary {
name: "unary_factorial",
}],
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 implement unary_factorial; otherwise the runtime gathers to host and mirrors MATLAB overflow/NaN behaviour.",
};
#[runmat_macros::register_fusion_spec(
builtin_path = "crate::builtins::math::elementwise::factorial"
)]
pub const FUSION_SPEC: BuiltinFusionSpec = BuiltinFusionSpec {
name: "factorial",
shape: ShapeRequirements::BroadcastCompatible,
constant_strategy: ConstantStrategy::InlineLiteral,
elementwise: None,
reduction: None,
emits_nan: true,
notes: "Factorial is evaluated as a scalar helper; fusion currently bypasses it and executes the standalone host or provider kernel.",
};
const BUILTIN_NAME: &str = "factorial";
fn builtin_error(message: impl Into<String>) -> RuntimeError {
build_runtime_error(message)
.with_builtin(BUILTIN_NAME)
.build()
}
#[runtime_builtin(
name = "factorial",
category = "math/elementwise",
summary = "Element-wise factorial for non-negative integers.",
keywords = "factorial,n!,permutation,gpu",
accel = "unary",
type_resolver(numeric_unary_type),
builtin_path = "crate::builtins::math::elementwise::factorial"
)]
async fn factorial_builtin(value: Value, rest: Vec<Value>) -> BuiltinResult<Value> {
let output = parse_output_template(&rest)?;
let base = match value {
Value::GpuTensor(handle) => factorial_gpu(handle).await?,
Value::Complex(_, _) | Value::ComplexTensor(_) => {
return Err(builtin_error(
"factorial: complex inputs are not supported; use gamma(z + 1) instead",
))
}
Value::String(_) | Value::StringArray(_) | Value::CharArray(_) => {
return Err(builtin_error(
"factorial: expected numeric or logical input",
))
}
other => {
let tensor = tensor::value_into_tensor_for("factorial", other)
.map_err(|e| builtin_error(format!("factorial: {e}")))?;
factorial_tensor(tensor).map(tensor::tensor_into_value)?
}
};
apply_output_template(base, &output).await
}
#[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("factorial: expected prototype after 'like'"))
} else {
Err(builtin_error(
"factorial: unrecognised option; only 'like' is supported",
))
}
}
2 => {
if matches!(keyword_of(&args[0]).as_deref(), Some("like")) {
Ok(OutputTemplate::Like(args[1].clone()))
} else {
Err(builtin_error(
"factorial: unrecognised option; only 'like' is supported",
))
}
}
_ => Err(builtin_error("factorial: too many input arguments")),
}
}
async fn factorial_gpu(handle: GpuTensorHandle) -> BuiltinResult<Value> {
if let Some(provider) = runmat_accelerate_api::provider_for_handle(&handle) {
if let Ok(out) = provider.unary_factorial(&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(factorial_tensor(tensor)?))
}
fn factorial_tensor(tensor: Tensor) -> BuiltinResult<Tensor> {
let mut data = Vec::with_capacity(tensor.data.len());
for &value in &tensor.data {
data.push(factorial_scalar(value));
}
Tensor::new(data, tensor.shape.clone()).map_err(|e| builtin_error(format!("factorial: {e}")))
}
fn factorial_scalar(value: f64) -> f64 {
if value.is_nan() {
return f64::NAN;
}
if value == 0.0 {
return 1.0;
}
if value.is_infinite() {
return if value.is_sign_positive() {
f64::INFINITY
} else {
f64::NAN
};
}
if value < 0.0 {
return f64::NAN;
}
let Some(n) = classify_nonnegative_integer(value) else {
return f64::NAN;
};
if n > MAX_FACTORIAL_N {
return f64::INFINITY;
}
FACT_TABLE[n]
}
fn classify_nonnegative_integer(value: f64) -> Option<usize> {
if !value.is_finite() {
return None;
}
if value < 0.0 {
return None;
}
let rounded = value.round();
let tol = f64::EPSILON * value.abs().max(1.0);
if (value - rounded).abs() > tol {
return None;
}
if rounded < 0.0 {
return None;
}
Some(rounded as usize)
}
async fn apply_output_template(value: Value, template: &OutputTemplate) -> BuiltinResult<Value> {
match template {
OutputTemplate::Default => Ok(value),
OutputTemplate::Like(proto) => {
let analysis = analyse_like_prototype(proto).await?;
match analysis.device {
DevicePreference::Host => convert_to_host_like(value).await,
DevicePreference::Gpu => convert_to_gpu_like(value),
}
}
}
}
#[derive(Clone, Copy)]
enum DevicePreference {
Host,
Gpu,
}
struct LikeAnalysis {
device: DevicePreference,
}
#[async_recursion::async_recursion(?Send)]
async fn analyse_like_prototype(proto: &Value) -> BuiltinResult<LikeAnalysis> {
match proto {
Value::GpuTensor(_) => Ok(LikeAnalysis {
device: DevicePreference::Gpu,
}),
Value::Tensor(_) | Value::Num(_) | Value::Int(_) | Value::Bool(_) => Ok(LikeAnalysis {
device: DevicePreference::Host,
}),
Value::LogicalArray(_) => Ok(LikeAnalysis {
device: DevicePreference::Host,
}),
Value::Complex(_, _) | Value::ComplexTensor(_) => Err(builtin_error(
"factorial: complex prototypes for 'like' are not supported; results are always real",
)),
Value::String(_) | Value::StringArray(_) | Value::CharArray(_) => Err(builtin_error(
"factorial: prototype must be numeric or a gpuArray",
)),
other => {
let gathered = gpu_helpers::gather_value_async(other)
.await
.map_err(|flow| map_control_flow_with_builtin(flow, BUILTIN_NAME))?;
analyse_like_prototype(&gathered).await
}
}
}
async fn convert_to_host_like(value: Value) -> BuiltinResult<Value> {
match value {
Value::GpuTensor(handle) => gpu_helpers::gather_value_async(&Value::GpuTensor(handle))
.await
.map_err(|flow| map_control_flow_with_builtin(flow, BUILTIN_NAME)),
other => Ok(other),
}
}
fn convert_to_gpu_like(value: Value) -> BuiltinResult<Value> {
let provider = runmat_accelerate_api::provider().ok_or_else(|| {
builtin_error(
"factorial: GPU output requested via 'like' but no acceleration provider is active",
)
})?;
match value {
Value::GpuTensor(handle) => Ok(Value::GpuTensor(handle)),
Value::Tensor(tensor) => upload_tensor(provider, tensor),
Value::Num(n) => {
let tensor = Tensor::new(vec![n], vec![1, 1])
.map_err(|e| builtin_error(format!("factorial: {e}")))?;
upload_tensor(provider, tensor)
}
Value::Int(i) => convert_to_gpu_like(Value::Num(i.to_f64())),
Value::Bool(b) => convert_to_gpu_like(Value::Num(if b { 1.0 } else { 0.0 })),
Value::LogicalArray(logical) => {
let tensor = tensor::logical_to_tensor(&logical)
.map_err(|e| builtin_error(format!("factorial: {e}")))?;
upload_tensor(provider, tensor)
}
other => Err(builtin_error(format!(
"factorial: cannot place value {other:?} on the GPU via 'like'"
))),
}
}
fn upload_tensor(
provider: &'static dyn runmat_accelerate_api::AccelProvider,
tensor: Tensor,
) -> BuiltinResult<Value> {
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider
.upload(&view)
.map_err(|e| builtin_error(format!("factorial: failed to upload GPU result: {e}")))?;
Ok(Value::GpuTensor(handle))
}
#[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, Tensor, Type};
fn factorial_builtin(value: Value, rest: Vec<Value>) -> BuiltinResult<Value> {
block_on(super::factorial_builtin(value, rest))
}
#[test]
fn factorial_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 factorial_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 factorial_scalar_positive() {
let result = factorial_builtin(Value::Num(5.0), Vec::new()).expect("factorial");
assert_eq!(result, Value::Num(120.0));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_zero_is_one() {
let result = factorial_builtin(Value::Num(0.0), Vec::new()).expect("factorial");
assert_eq!(result, Value::Num(1.0));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_vector_inputs() {
let tensor = Tensor::new(vec![0.0, 1.0, 3.0, 5.0], vec![4, 1]).unwrap();
let result = factorial_builtin(Value::Tensor(tensor), Vec::new()).expect("factorial");
match result {
Value::Tensor(out) => {
assert_eq!(out.shape, vec![4, 1]);
assert_eq!(out.data, vec![1.0, 1.0, 6.0, 120.0]);
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_non_integer_produces_nan() {
let result = factorial_builtin(Value::Num(2.5), Vec::new()).expect("factorial");
match result {
Value::Num(v) => assert!(v.is_nan()),
other => panic!("expected scalar NaN, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_negative_produces_nan() {
let tensor = Tensor::new(vec![-1.0, 3.0], vec![2, 1]).unwrap();
let result = factorial_builtin(Value::Tensor(tensor), Vec::new()).expect("factorial");
match result {
Value::Tensor(out) => {
assert!(out.data[0].is_nan());
assert_eq!(out.data[1], 6.0);
}
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_small_positive_non_integer_nan() {
let result = factorial_builtin(Value::Num(1e-12), Vec::new()).expect("factorial");
match result {
Value::Num(v) => assert!(v.is_nan()),
other => panic!("expected scalar NaN, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_overflow_returns_inf() {
let result = factorial_builtin(Value::Num(171.0), Vec::new()).expect("factorial");
match result {
Value::Num(v) => assert!(v.is_infinite()),
other => panic!("expected scalar Inf, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_like_missing_prototype_errors() {
let err = factorial_builtin(Value::Num(3.0), vec![Value::from("like")])
.expect_err("expected error");
assert!(err.message().contains("prototype"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_like_gpu_prototype_uploads() {
test_support::with_test_provider(|provider| {
let tensor = Tensor::new(vec![3.0, 4.0], vec![2, 1]).unwrap();
let proto_view = HostTensorView {
data: &[0.0],
shape: &[1, 1],
};
let proto = provider.upload(&proto_view).expect("upload");
let result = factorial_builtin(
Value::Tensor(tensor.clone()),
vec![Value::from("like"), Value::GpuTensor(proto)],
)
.expect("factorial");
match result {
Value::GpuTensor(handle) => {
let gathered = test_support::gather(Value::GpuTensor(handle)).expect("gather");
assert_eq!(gathered.shape, vec![2, 1]);
assert_eq!(gathered.data, vec![6.0, 24.0]);
}
other => panic!("expected GPU tensor, got {other:?}"),
}
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_gpu_provider_roundtrip() {
test_support::with_test_provider(|provider| {
let tensor = Tensor::new(vec![0.0, 1.0, 3.0, 5.0], vec![4, 1]).unwrap();
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let result = factorial_builtin(Value::GpuTensor(handle), Vec::new()).expect("fact");
let gathered = test_support::gather(result).expect("gather");
assert_eq!(gathered.shape, vec![4, 1]);
assert_eq!(gathered.data, vec![1.0, 1.0, 6.0, 120.0]);
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_like_host_with_gpu_input_gathers() {
test_support::with_test_provider(|provider| {
let tensor = Tensor::new(vec![3.0, 4.0], vec![2, 1]).unwrap();
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let result = factorial_builtin(
Value::GpuTensor(handle),
vec![Value::from("like"), Value::Num(0.0)],
)
.expect("factorial");
match result {
Value::Tensor(t) => {
assert_eq!(t.data, vec![6.0, 24.0]);
}
other => panic!("expected host tensor, got {other:?}"),
}
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_logical_input_promotes() {
let logical = LogicalArray::new(vec![1, 0, 1], vec![3, 1]).unwrap();
let result = factorial_builtin(Value::LogicalArray(logical), Vec::new()).expect("fact");
match result {
Value::Tensor(t) => assert_eq!(t.data, vec![1.0, 1.0, 1.0]),
other => panic!("expected tensor result, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_int_input_promotes_to_double() {
let value = Value::Int(IntValue::U16(5));
let result = factorial_builtin(value, Vec::new()).expect("factorial");
assert_eq!(result, Value::Num(120.0));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_nan_propagates() {
let result = factorial_builtin(Value::Num(f64::NAN), Vec::new()).expect("factorial");
match result {
Value::Num(v) => assert!(v.is_nan()),
other => panic!("expected scalar NaN, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_complex_input_errors() {
let err = factorial_builtin(Value::Complex(1.0, 0.5), Vec::new())
.expect_err("expected complex rejection");
assert!(err.message().contains("complex"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_string_input_errors() {
let err = factorial_builtin(Value::from("hello"), Vec::new())
.expect_err("expected string rejection");
assert!(err.message().contains("numeric"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn factorial_like_complex_prototype_rejected() {
let err = factorial_builtin(
Value::Num(3.0),
vec![Value::from("like"), Value::Complex(0.0, 1.0)],
)
.expect_err("expected complex prototype rejection");
assert!(err.message().contains("complex"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
#[cfg(feature = "wgpu")]
fn factorial_wgpu_matches_cpu_after_gather() {
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, 4.0], vec![3, 1]).unwrap();
let cpu = factorial_builtin(Value::Tensor(tensor.clone()), Vec::new()).expect("cpu");
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = runmat_accelerate_api::provider()
.unwrap()
.upload(&view)
.unwrap();
let gpu = block_on(factorial_gpu(handle)).expect("gpu");
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 result {other:?}"),
};
assert_eq!(gathered.shape, cpu_tensor.shape);
assert_eq!(gathered.data, cpu_tensor.data);
}
}