use super::common::{
download_provider_complex_tensor, gather_gpu_complex_tensor, parse_2d_lengths_from_data,
parse_length, transform_axes_complex_tensor, value_to_complex_tensor, TransformDirection,
};
use super::fft::fft_complex_tensor;
use crate::builtins::common::random_args::complex_tensor_into_value;
use crate::builtins::common::spec::{
BroadcastSemantics, BuiltinFusionSpec, BuiltinGpuSpec, ConstantStrategy, GpuOpKind,
ProviderHook, ReductionNaN, ResidencyPolicy, ScalarType, ShapeRequirements,
};
use crate::builtins::common::tensor;
use crate::builtins::math::fft::type_resolvers::fft2_type;
use crate::{build_runtime_error, BuiltinResult, RuntimeError};
use runmat_accelerate_api::GpuTensorHandle;
use runmat_builtins::{
BuiltinCompletionPolicy, BuiltinDescriptor, BuiltinErrorDescriptor, BuiltinOutputMode,
BuiltinParamArity, BuiltinParamDescriptor, BuiltinParamType, BuiltinSignatureDescriptor,
ComplexTensor, Value,
};
use runmat_macros::runtime_builtin;
#[runmat_macros::register_gpu_spec(builtin_path = "crate::builtins::math::fft::fft2")]
pub const GPU_SPEC: BuiltinGpuSpec = BuiltinGpuSpec {
name: "fft2",
op_kind: GpuOpKind::Custom("fft2"),
supported_precisions: &[ScalarType::F32, ScalarType::F64],
broadcast: BroadcastSemantics::Matlab,
provider_hooks: &[ProviderHook::Custom("fft_dim")],
constant_strategy: ConstantStrategy::InlineLiteral,
residency: ResidencyPolicy::NewHandle,
nan_mode: ReductionNaN::Include,
two_pass_threshold: None,
workgroup_size: None,
accepts_nan_mode: false,
notes: "Performs two sequential `fft_dim` passes (dimensions 0 and 1); falls back to host execution when the hook is missing.",
};
#[runmat_macros::register_fusion_spec(builtin_path = "crate::builtins::math::fft::fft2")]
pub const FUSION_SPEC: BuiltinFusionSpec = BuiltinFusionSpec {
name: "fft2",
shape: ShapeRequirements::Any,
constant_strategy: ConstantStrategy::InlineLiteral,
elementwise: None,
reduction: None,
emits_nan: false,
notes:
"fft2 terminates fusion plans; fused kernels are not generated for multi-dimensional FFTs.",
};
const BUILTIN_NAME: &str = "fft2";
const FFT2_OUTPUT: [BuiltinParamDescriptor; 1] = [BuiltinParamDescriptor {
name: "Y",
ty: BuiltinParamType::NumericArray,
arity: BuiltinParamArity::Required,
default: None,
description: "2-D complex Fourier spectrum output.",
}];
const FFT2_INPUTS_CORE: [BuiltinParamDescriptor; 1] = [BuiltinParamDescriptor {
name: "X",
ty: BuiltinParamType::Any,
arity: BuiltinParamArity::Required,
default: None,
description: "Input array.",
}];
const FFT2_INPUTS_SIZE: [BuiltinParamDescriptor; 2] = [
BuiltinParamDescriptor {
name: "X",
ty: BuiltinParamType::Any,
arity: BuiltinParamArity::Required,
default: None,
description: "Input array.",
},
BuiltinParamDescriptor {
name: "SIZE",
ty: BuiltinParamType::NumericArray,
arity: BuiltinParamArity::Optional,
default: Some("[]"),
description: "Scalar N or two-element [M N] size vector.",
},
];
const FFT2_INPUTS_M_N: [BuiltinParamDescriptor; 3] = [
BuiltinParamDescriptor {
name: "X",
ty: BuiltinParamType::Any,
arity: BuiltinParamArity::Required,
default: None,
description: "Input array.",
},
BuiltinParamDescriptor {
name: "M",
ty: BuiltinParamType::NumericScalar,
arity: BuiltinParamArity::Optional,
default: Some("[]"),
description: "Output row count for transform.",
},
BuiltinParamDescriptor {
name: "N",
ty: BuiltinParamType::NumericScalar,
arity: BuiltinParamArity::Optional,
default: Some("[]"),
description: "Output column count for transform.",
},
];
const FFT2_SIGNATURES: [BuiltinSignatureDescriptor; 3] = [
BuiltinSignatureDescriptor {
label: "Y = fft2(X)",
inputs: &FFT2_INPUTS_CORE,
outputs: &FFT2_OUTPUT,
},
BuiltinSignatureDescriptor {
label: "Y = fft2(X, SIZE)",
inputs: &FFT2_INPUTS_SIZE,
outputs: &FFT2_OUTPUT,
},
BuiltinSignatureDescriptor {
label: "Y = fft2(X, M, N)",
inputs: &FFT2_INPUTS_M_N,
outputs: &FFT2_OUTPUT,
},
];
const FFT2_ERROR_ARG_COUNT: BuiltinErrorDescriptor = BuiltinErrorDescriptor {
code: "RM.FFT2.ARG_COUNT",
identifier: Some("RunMat:fft2:ArgCount"),
when: "More than three input arguments are supplied.",
message: "fft2: invalid argument count",
};
const FFT2_ERROR_INVALID_LENGTH: BuiltinErrorDescriptor = BuiltinErrorDescriptor {
code: "RM.FFT2.INVALID_LENGTH",
identifier: Some("RunMat:fft2:InvalidLength"),
when: "Length/size arguments are invalid.",
message: "fft2: invalid transform length argument",
};
const FFT2_ERROR_INVALID_SIZE_VECTOR: BuiltinErrorDescriptor = BuiltinErrorDescriptor {
code: "RM.FFT2.INVALID_SIZE_VECTOR",
identifier: Some("RunMat:fft2:InvalidSizeVector"),
when: "Single SIZE argument is invalid.",
message: "fft2: invalid size vector argument",
};
const FFT2_ERROR_INVALID_INPUT: BuiltinErrorDescriptor = BuiltinErrorDescriptor {
code: "RM.FFT2.INVALID_INPUT",
identifier: Some("RunMat:fft2:InvalidInput"),
when: "Input cannot be converted to supported numeric/complex domain.",
message: "fft2: invalid input",
};
const FFT2_ERROR_INTERNAL: BuiltinErrorDescriptor = BuiltinErrorDescriptor {
code: "RM.FFT2.INTERNAL",
identifier: Some("RunMat:fft2:Internal"),
when: "FFT2 execution or tensor shaping fails.",
message: "fft2: internal error",
};
const FFT2_ERRORS: [BuiltinErrorDescriptor; 5] = [
FFT2_ERROR_ARG_COUNT,
FFT2_ERROR_INVALID_LENGTH,
FFT2_ERROR_INVALID_SIZE_VECTOR,
FFT2_ERROR_INVALID_INPUT,
FFT2_ERROR_INTERNAL,
];
pub const FFT2_DESCRIPTOR: BuiltinDescriptor = BuiltinDescriptor {
signatures: &FFT2_SIGNATURES,
output_mode: BuiltinOutputMode::Fixed,
completion_policy: BuiltinCompletionPolicy::Public,
errors: &FFT2_ERRORS,
};
fn fft2_error(error: &'static BuiltinErrorDescriptor) -> RuntimeError {
fft2_error_with_message(error.message, error)
}
fn fft2_error_with_detail(
error: &'static BuiltinErrorDescriptor,
detail: impl AsRef<str>,
) -> RuntimeError {
fft2_error_with_message(format!("{}: {}", error.message, detail.as_ref()), error)
}
fn fft2_error_with_source(
error: &'static BuiltinErrorDescriptor,
detail: impl AsRef<str>,
source: RuntimeError,
) -> RuntimeError {
let mut builder = build_runtime_error(format!("{}: {}", error.message, detail.as_ref()))
.with_builtin(BUILTIN_NAME)
.with_source(source);
if let Some(identifier) = error.identifier {
builder = builder.with_identifier(identifier);
}
builder.build()
}
fn fft2_error_with_message(
message: impl Into<String>,
error: &'static BuiltinErrorDescriptor,
) -> RuntimeError {
let mut builder = build_runtime_error(message).with_builtin(BUILTIN_NAME);
if let Some(identifier) = error.identifier {
builder = builder.with_identifier(identifier);
}
builder.build()
}
#[runtime_builtin(
name = "fft2",
category = "math/fft",
summary = "Compute two-dimensional discrete Fourier transforms.",
keywords = "fft2,2d fft,two-dimensional fourier transform,gpu",
type_resolver(fft2_type),
descriptor(crate::builtins::math::fft::fft2::FFT2_DESCRIPTOR),
builtin_path = "crate::builtins::math::fft::fft2"
)]
async fn fft2_builtin(value: Value, rest: Vec<Value>) -> crate::BuiltinResult<Value> {
let lengths = parse_fft2_arguments(&rest)?;
match value {
Value::GpuTensor(handle) => fft2_gpu(handle, lengths).await,
other => fft2_host(other, lengths),
}
}
fn fft2_host(value: Value, lengths: (Option<usize>, Option<usize>)) -> BuiltinResult<Value> {
let tensor = value_to_complex_tensor(value, BUILTIN_NAME).map_err(|source| {
fft2_error_with_source(&FFT2_ERROR_INVALID_INPUT, "input conversion failed", source)
})?;
let transformed = fft2_complex_tensor(tensor, lengths)?;
Ok(complex_tensor_into_value(transformed))
}
async fn fft2_gpu(
handle: GpuTensorHandle,
lengths: (Option<usize>, Option<usize>),
) -> BuiltinResult<Value> {
if matches!(lengths.0, Some(0)) || matches!(lengths.1, Some(0)) {
return fft2_gpu_fallback(handle, lengths).await;
}
if let Some(provider) = runmat_accelerate_api::provider() {
if let Ok(first) = provider.fft_dim(&handle, lengths.0, 0).await {
match provider.fft_dim(&first, lengths.1, 1).await {
Ok(second) => {
if first.buffer_id != second.buffer_id {
provider.free(&first).ok();
runmat_accelerate_api::clear_residency(&first);
}
return Ok(Value::GpuTensor(second));
}
Err(_) => {
let partial =
download_provider_complex_tensor(provider, &first, BUILTIN_NAME, true)
.await
.map_err(|source| {
fft2_error_with_source(
&FFT2_ERROR_INVALID_INPUT,
"provider download failed",
source,
)
})?;
let completed = fft_complex_tensor(partial, lengths.1, Some(2))?;
return Ok(complex_tensor_into_value(completed));
}
}
}
}
fft2_gpu_fallback(handle, lengths).await
}
async fn fft2_gpu_fallback(
handle: GpuTensorHandle,
lengths: (Option<usize>, Option<usize>),
) -> BuiltinResult<Value> {
let complex = gather_gpu_complex_tensor(&handle, BUILTIN_NAME)
.await
.map_err(|source| {
fft2_error_with_source(&FFT2_ERROR_INVALID_INPUT, "gpu gather failed", source)
})?;
let transformed = fft2_complex_tensor(complex, lengths)?;
Ok(complex_tensor_into_value(transformed))
}
fn fft2_complex_tensor(
tensor: ComplexTensor,
lengths: (Option<usize>, Option<usize>),
) -> BuiltinResult<ComplexTensor> {
let (len_rows, len_cols) = lengths;
transform_axes_complex_tensor(
tensor,
&[len_rows, len_cols],
TransformDirection::Forward,
BUILTIN_NAME,
)
.map_err(|source| fft2_error_with_source(&FFT2_ERROR_INTERNAL, "transform failed", source))
}
fn parse_fft2_arguments(args: &[Value]) -> BuiltinResult<(Option<usize>, Option<usize>)> {
match args.len() {
0 => Ok((None, None)),
1 => parse_fft2_single(&args[0]),
2 => {
let rows = parse_length(&args[0], BUILTIN_NAME).map_err(|source| {
fft2_error_with_source(
&FFT2_ERROR_INVALID_LENGTH,
"row-length parse failed",
source,
)
})?;
let cols = parse_length(&args[1], BUILTIN_NAME).map_err(|source| {
fft2_error_with_source(
&FFT2_ERROR_INVALID_LENGTH,
"column-length parse failed",
source,
)
})?;
Ok((rows, cols))
}
_ => Err(fft2_error(&FFT2_ERROR_ARG_COUNT)),
}
}
fn parse_fft2_single(value: &Value) -> BuiltinResult<(Option<usize>, Option<usize>)> {
match value {
Value::Tensor(tensor) => {
parse_2d_lengths_from_data(&tensor.data, BUILTIN_NAME).map_err(|source| {
fft2_error_with_detail(
&FFT2_ERROR_INVALID_SIZE_VECTOR,
format!("size vector parse failed: {source}"),
)
})
}
Value::LogicalArray(logical) => {
let tensor = tensor::logical_to_tensor(logical).map_err(|source| {
fft2_error_with_detail(
&FFT2_ERROR_INVALID_SIZE_VECTOR,
format!("logical size-vector conversion failed: {source}"),
)
})?;
parse_2d_lengths_from_data(&tensor.data, BUILTIN_NAME).map_err(|source| {
fft2_error_with_detail(
&FFT2_ERROR_INVALID_SIZE_VECTOR,
format!("size vector parse failed: {source}"),
)
})
}
Value::Num(_) | Value::Int(_) => {
let len = parse_length(value, BUILTIN_NAME).map_err(|source| {
fft2_error_with_source(&FFT2_ERROR_INVALID_LENGTH, "length parse failed", source)
})?;
Ok((len, len))
}
Value::Complex(re, im) => {
if im.abs() > f64::EPSILON {
return Err(fft2_error(&FFT2_ERROR_INVALID_LENGTH));
}
let scalar = Value::Num(*re);
let len = parse_length(&scalar, BUILTIN_NAME).map_err(|source| {
fft2_error_with_source(&FFT2_ERROR_INVALID_LENGTH, "length parse failed", source)
})?;
Ok((len, len))
}
Value::ComplexTensor(_) => Err(fft2_error(&FFT2_ERROR_INVALID_SIZE_VECTOR)),
Value::GpuTensor(_) => Err(fft2_error(&FFT2_ERROR_INVALID_SIZE_VECTOR)),
Value::Bool(_) => Err(fft2_error(&FFT2_ERROR_INVALID_LENGTH)),
Value::String(_)
| Value::StringArray(_)
| Value::CharArray(_)
| Value::Cell(_)
| Value::Struct(_)
| Value::FunctionHandle(_)
| Value::ExternalFunctionHandle(_)
| Value::MethodFunctionHandle(_)
| Value::BoundFunctionHandle { .. }
| Value::Closure(_)
| Value::HandleObject(_)
| Value::Listener(_)
| Value::Object(_)
| Value::ClassRef(_)
| Value::MException(_)
| Value::OutputList(_) => Err(fft2_error(&FFT2_ERROR_INVALID_LENGTH)),
}
}
#[cfg(test)]
pub(crate) mod tests {
use super::*;
use crate::builtins::common::test_support;
use crate::builtins::math::fft::common;
use futures::executor::block_on;
#[cfg(feature = "wgpu")]
use runmat_accelerate_api::AccelProvider;
use runmat_accelerate_api::HostTensorView;
use runmat_builtins::{builtin_function_by_name, IntValue, ResolveContext, Tensor, Type};
fn approx_eq(a: (f64, f64), b: (f64, f64), tol: f64) -> bool {
(a.0 - b.0).abs() <= tol && (a.1 - b.1).abs() <= tol
}
fn error_message(error: crate::RuntimeError) -> String {
error.message().to_string()
}
fn error_identifier(error: &crate::RuntimeError) -> Option<&str> {
error.identifier()
}
fn value_to_host_complex(value: Value) -> ComplexTensor {
match value {
Value::ComplexTensor(ct) => ct,
Value::GpuTensor(handle) => {
let provider = runmat_accelerate_api::provider_for_handle(&handle)
.or_else(runmat_accelerate_api::provider)
.expect("provider for gpu handle");
let host = block_on(provider.download(&handle)).expect("download gpu fft2 output");
common::host_to_complex_tensor(host, BUILTIN_NAME).expect("decode gpu complex")
}
other => panic!("expected complex value, got {other:?}"),
}
}
#[test]
fn fft2_type_pads_rank() {
let out = fft2_type(
&[Type::Tensor {
shape: Some(vec![Some(4)]),
}],
&ResolveContext::new(Vec::new()),
);
assert_eq!(
out,
Type::Tensor {
shape: Some(vec![Some(4), Some(1)])
}
);
}
#[test]
fn fft2_descriptor_signatures_and_errors() {
let builtin = builtin_function_by_name(BUILTIN_NAME).expect("fft2 builtin");
let descriptor = builtin.descriptor.expect("fft2 descriptor");
let labels: Vec<&str> = descriptor.signatures.iter().map(|sig| sig.label).collect();
assert!(labels.contains(&"Y = fft2(X)"));
assert!(labels.contains(&"Y = fft2(X, SIZE)"));
assert!(labels.contains(&"Y = fft2(X, M, N)"));
assert!(descriptor
.errors
.iter()
.any(|err| err.code == "RM.FFT2.INVALID_LENGTH"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_matches_sequential_fft() {
let tensor = Tensor::new(vec![1.0, 3.0, 2.0, 4.0], vec![2, 2]).unwrap();
let result = fft2_builtin(Value::Tensor(tensor.clone()), Vec::new()).expect("fft2");
let sequential = {
let complex = value_to_complex_tensor(Value::Tensor(tensor), "fft2").unwrap();
let first = fft_complex_tensor(complex, None, Some(1)).unwrap();
fft_complex_tensor(first, None, Some(2)).unwrap()
};
match result {
Value::ComplexTensor(out) => {
assert_eq!(out.shape, sequential.shape);
for (lhs, rhs) in out.data.iter().zip(sequential.data.iter()) {
assert!(approx_eq(*lhs, *rhs, 1e-12));
}
}
other => panic!("expected complex tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_accepts_scalar_length() {
let tensor = Tensor::new((0..9).map(|v| v as f64).collect(), vec![3, 3]).unwrap();
let result = fft2_builtin(
Value::Tensor(tensor.clone()),
vec![Value::Int(IntValue::I32(4))],
)
.expect("fft2");
match result {
Value::ComplexTensor(out) => {
assert_eq!(out.shape, vec![4, 4]);
assert_eq!(out.data.len(), 16);
}
other => panic!("expected complex tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_accepts_size_vector() {
let tensor = Tensor::new((0..6).map(|v| v as f64).collect(), vec![2, 3]).unwrap();
let size = Tensor::new(vec![4.0, 2.0], vec![1, 2]).unwrap();
let result =
fft2_builtin(Value::Tensor(tensor.clone()), vec![Value::Tensor(size)]).expect("fft2");
match result {
Value::ComplexTensor(out) => assert_eq!(out.shape, vec![4, 2]),
other => panic!("expected complex tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_accepts_empty_length_vector() {
let tensor = Tensor::new((0..6).map(|v| v as f64).collect(), vec![2, 3]).unwrap();
let empty = Tensor::new(Vec::new(), vec![0, 0]).unwrap();
let result =
fft2_builtin(Value::Tensor(tensor.clone()), vec![Value::Tensor(empty)]).expect("fft2");
match result {
Value::ComplexTensor(out) => assert_eq!(out.shape, tensor.shape),
other => panic!("expected complex tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_zero_length_returns_empty() {
let tensor = Tensor::new(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2]).unwrap();
let result = fft2_builtin(
Value::Tensor(tensor),
vec![Value::Int(IntValue::I32(0)), Value::Int(IntValue::I32(3))],
)
.expect("fft2");
match result {
Value::ComplexTensor(out) => {
assert_eq!(out.shape, vec![0, 3]);
assert!(out.data.is_empty());
}
other => panic!("expected complex tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_gpu_roundtrip_matches_cpu() {
test_support::with_test_provider(|provider| {
let tensor = Tensor::new((0..8).map(|v| v as f64).collect(), vec![2, 4]).unwrap();
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let gpu = fft2_builtin(Value::GpuTensor(handle), Vec::new()).expect("fft2 gpu");
let cpu = fft2_builtin(Value::Tensor(tensor), Vec::new()).expect("fft2 cpu");
let g = value_to_host_complex(gpu);
let c = value_to_host_complex(cpu);
assert_eq!(g.shape, c.shape);
let tol = 1e-10;
for (lhs, rhs) in g.data.iter().zip(c.data.iter()) {
assert!(approx_eq(*lhs, *rhs, tol), "{lhs:?} vs {rhs:?}");
}
});
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_rejects_size_vector_with_more_than_two_entries() {
let tensor = Tensor::new(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2]).unwrap();
let size = Tensor::new(vec![4.0, 2.0, 1.0], vec![1, 3]).unwrap();
let err = error_message(
fft2_builtin(Value::Tensor(tensor), vec![Value::Tensor(size)]).unwrap_err(),
);
assert!(err.contains("fft2"));
assert!(err.contains("two elements"));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_rejects_boolean_length_argument() {
let tensor = Tensor::new(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2]).unwrap();
let err = fft2_builtin(Value::Tensor(tensor), vec![Value::Bool(true)]).unwrap_err();
assert_eq!(error_identifier(&err), FFT2_ERROR_INVALID_LENGTH.identifier);
assert!(error_message(err).contains(FFT2_ERROR_INVALID_LENGTH.message));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_accepts_mixed_empty_and_length_arguments() {
let tensor = Tensor::new((0..6).map(|v| v as f64).collect(), vec![2, 3]).unwrap();
let empty = Tensor::new(Vec::new(), vec![0, 0]).unwrap();
let result = fft2_builtin(
Value::Tensor(tensor.clone()),
vec![Value::Tensor(empty), Value::Int(IntValue::I32(4))],
)
.expect("fft2");
match result {
Value::ComplexTensor(out) => assert_eq!(out.shape, vec![2, 4]),
other => panic!("expected complex tensor, got {other:?}"),
}
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
fn fft2_rejects_excess_arguments() {
let tensor = Tensor::new(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2]).unwrap();
let err = fft2_builtin(
Value::Tensor(tensor),
vec![
Value::Int(IntValue::I32(2)),
Value::Int(IntValue::I32(2)),
Value::Int(IntValue::I32(2)),
],
)
.unwrap_err();
assert_eq!(error_identifier(&err), FFT2_ERROR_ARG_COUNT.identifier);
assert!(error_message(err).contains(FFT2_ERROR_ARG_COUNT.message));
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
#[test]
#[cfg(feature = "wgpu")]
fn fft2_wgpu_matches_cpu() {
let provider = match std::panic::catch_unwind(|| {
runmat_accelerate::backend::wgpu::provider::ensure_wgpu_provider()
}) {
Ok(Ok(Some(provider))) => provider,
_ => return,
};
let tensor = Tensor::new((0..16).map(|v| v as f64).collect(), vec![4, 4]).expect("tensor");
let tensor_cpu = tensor.clone();
let view = HostTensorView {
data: &tensor.data,
shape: &tensor.shape,
};
let handle = provider.upload(&view).expect("upload");
let gpu_value =
fft2_builtin(Value::GpuTensor(handle.clone()), Vec::new()).expect("fft2 gpu");
let cpu_value = fft2_builtin(Value::Tensor(tensor_cpu), Vec::new()).expect("fft2 cpu");
let gpu_ct = value_to_host_complex(gpu_value);
let cpu_ct = value_to_host_complex(cpu_value);
assert_eq!(gpu_ct.shape, cpu_ct.shape);
let tol = match provider.precision() {
runmat_accelerate_api::ProviderPrecision::F64 => 1e-10,
runmat_accelerate_api::ProviderPrecision::F32 => 1e-5,
};
for (lhs, rhs) in gpu_ct.data.iter().zip(cpu_ct.data.iter()) {
assert!(approx_eq(*lhs, *rhs, tol), "{lhs:?} vs {rhs:?}");
}
provider.free(&handle).ok();
runmat_accelerate_api::clear_residency(&handle);
}
fn fft2_builtin(value: Value, rest: Vec<Value>) -> BuiltinResult<Value> {
block_on(super::fft2_builtin(value, rest))
}
}