use crate::encoder::EncoderExt;
use crate::{LibraryName, MetalStream};
use metal::MTLSize;
use tract_core::internal::*;
use tract_gpu::tensor::DeviceTensor;
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub struct Silu;
impl Silu {
pub fn is_supported_dt(dt: DatumType) -> bool {
matches!(dt, DatumType::F32 | DatumType::F16)
}
pub fn kernel_name(&self, dt: DatumType, use_silu_4: bool) -> TractResult<String> {
ensure!(Self::is_supported_dt(dt), "Unsupported dt {:?} for metal siluop", dt);
let tname = DeviceTensor::tname(dt)?;
if use_silu_4 {
Ok(format!("nn_ops::silu_4_{tname}"))
} else {
Ok(format!("nn_ops::silu_{tname}"))
}
}
pub fn eval(&self, stream: &MetalStream, input: &DeviceTensor) -> TractResult<DeviceTensor> {
let output = unsafe { DeviceTensor::uninitialized_dt(input.datum_type(), input.shape())? };
self.dispatch_eval(stream, input, &output)?;
stream.wait_until_completed()?;
Ok(output)
}
pub fn dispatch_eval(
&self,
stream: &MetalStream,
input: &DeviceTensor,
output: &DeviceTensor,
) -> TractResult<()> {
stream.retain_tensor(input);
stream.retain_tensor(output);
ensure!(output.shape() == input.shape());
ensure!(output.datum_type() == input.datum_type());
let n_el = output.len();
let use_silu_4 = (n_el % 4 == 0) && (n_el as f32 > 2f32.powi(12));
let kernel_name = self.kernel_name(input.datum_type(), use_silu_4)?;
let n_threads = if use_silu_4 { n_el / 4 } else { n_el };
let pipeline = stream.load_pipeline(LibraryName::NNOps, &kernel_name)?;
let command_buffer = stream.command_buffer();
command_buffer.encode(|encoder| {
encoder.set_compute_pipeline_state(&pipeline);
encoder.set_metal_tensor(0, input, metal::MTLResourceUsage::Read);
encoder.set_metal_tensor(1, output, metal::MTLResourceUsage::Write);
let grid_size = MTLSize { width: n_threads as _, height: 1, depth: 1 };
let group_size = MTLSize { width: 1, height: 1, depth: 1 };
encoder.dispatch_thread_groups(grid_size, group_size);
});
Ok(())
}
}
#[cfg(test)]
mod tests {
use crate::utils::with_borrowed_metal_stream;
use tract_gpu::tensor::IntoDevice;
use super::*;
use derive_new::new;
use num_traits::AsPrimitive;
use num_traits::Float;
use proptest::collection::vec;
use proptest::prelude::*;
use tract_core::internal::Tensor;
fn test_case<F>(
shape: &[usize],
offset: f32,
scale: f32,
approximate: Approximation,
) -> TractResult<()>
where
F: Float + Datum,
usize: AsPrimitive<f32>,
f32: AsPrimitive<F>,
{
with_borrowed_metal_stream(|stream| {
let len = shape.iter().product::<usize>();
let a = Tensor::from_shape(
shape,
&(0..len)
.map(|f| -> F {
let v: f32 = f.as_();
(v * scale + offset).as_()
})
.collect::<Vec<_>>(),
)?
.into_device()?;
let cpu_output = tract_core::ops::nn::silu::silu()
.eval(tvec![a.to_host()?.into_tvalue()])?[0]
.clone()
.into_tensor();
let metal_output = Silu.eval(stream, &a)?;
cpu_output
.close_enough(&metal_output.to_host()?.into_tensor(), approximate)
.with_context(|| {
format!(
"Input: {:?}, scale: {:?} Cpu: {:?}, Metal: {:?}",
a.to_host().and_then(|it| it.dump(true)),
scale,
cpu_output.dump(true),
metal_output.to_host().and_then(|it| it.dump(true))
)
})?;
Ok(())
})
}
#[test]
fn test_silu() -> TractResult<()> {
test_case::<f32>(&[4, 4], -0.0, 1.0 / 100.0, Approximation::Approximate)?;
test_case::<f16>(&[4, 4], -6.0, 1.0 / 1000.0, Approximation::SuperApproximate)?;
Ok(())
}
proptest::proptest! {
#[test]
fn silu_prop_f32(pb in any::<SiluProblem<f32>>()) {
fn run(pb: SiluProblem<f32>) -> TractResult<()> {
let out = pb.run()?;
let reference = pb.reference()?;
out.close_enough(&reference, Approximation::Approximate)
.with_context(|| format!("Cpu: {:?}, Metal: {:?}", reference.dump(true), out.dump(true)))
}
run(pb).map_err(|e| TestCaseError::Fail(format!("{:?}", e).into()))?;
}
#[test]
fn silu_prop_f16(pb in any::<SiluProblem<f16>>()) {
fn run(pb: SiluProblem<f16>) -> TractResult<()> {
let out = pb.run()?;
let reference = pb.reference()?;
out.close_enough(&reference, Approximation::Approximate)
.with_context(|| format!("Cpu: {:?}, Metal: {:?}", reference.dump(true), out.dump(true)))
}
run(pb).map_err(|e| TestCaseError::Fail(format!("{:?}", e).into()))?;
}
}
#[derive(Debug, new)]
pub struct SiluProblem<F: Datum + Float>
where
F: Datum + Float,
usize: AsPrimitive<F>,
f32: AsPrimitive<F>,
{
pub shape: Vec<usize>,
pub input: Vec<F>,
}
impl<F> Arbitrary for SiluProblem<F>
where
F: Datum + Float,
usize: AsPrimitive<F>,
f32: AsPrimitive<F>,
{
type Parameters = ();
type Strategy = BoxedStrategy<Self>;
fn arbitrary_with(_: ()) -> Self::Strategy {
(0usize..3, 0usize..3)
.prop_flat_map(|(left, right)| {
let shape_len = usize::min(left + right + 1, 4);
let shape = 1usize..10;
vec(shape, shape_len..=shape_len)
})
.prop_map(|shape| {
let input = (0..shape.iter().product::<usize>())
.map(|f| f.as_() / 1000.as_())
.collect::<Vec<_>>();
Self { shape, input }
})
.boxed()
}
}
impl<F> SiluProblem<F>
where
F: Datum + Float + std::ops::AddAssign,
usize: AsPrimitive<F>,
f32: AsPrimitive<F>,
{
pub fn reference(&self) -> TractResult<Tensor> {
let a = Tensor::from_shape(self.shape.as_slice(), &self.input)?;
let silu = tract_core::ops::nn::silu::silu();
let cpu_output = silu.eval(tvec![a.into_tvalue()])?[0].clone().into_tensor();
Ok(cpu_output)
}
pub fn run(&self) -> TractResult<Tensor> {
with_borrowed_metal_stream(|stream| {
let a = Tensor::from_shape(self.shape.as_slice(), &self.input)?.into_device()?;
let metal_output = Silu.eval(stream, &a)?;
Ok(metal_output.to_host()?.into_tensor())
})
}
}
}