use crate::encoder::EncoderExt;
use crate::kernels::utils::compute_broadcast_strides;
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 ScaledMaskedSoftmax;
impl ScaledMaskedSoftmax {
pub fn is_supported_dt(dt: DatumType) -> bool {
matches!(dt, DatumType::F32 | DatumType::F16)
}
pub fn kernel_name(&self, dt: DatumType) -> TractResult<String> {
ensure!(
Self::is_supported_dt(dt),
"Unsupported dt {:?} for metal scaled masked softmaxop",
dt
);
let tname = DeviceTensor::tname(dt)?;
Ok(format!("nn_ops::scaled_masked_softmax_nd3_{tname}"))
}
pub fn eval(
&self,
stream: &MetalStream,
input: &DeviceTensor,
scale: &Tensor,
mask: &DeviceTensor,
) -> TractResult<DeviceTensor> {
let output = unsafe { DeviceTensor::uninitialized_dt(input.datum_type(), input.shape())? };
self.dispatch_eval(stream, input, scale, mask, &output)?;
stream.wait_until_completed()?;
Ok(output)
}
pub fn dispatch_eval(
&self,
stream: &MetalStream,
input: &DeviceTensor,
scale: &Tensor,
mask: &DeviceTensor,
output: &DeviceTensor,
) -> TractResult<()> {
stream.retain_tensor(input);
stream.retain_tensor(mask);
stream.retain_tensor(output);
ensure!(output.shape() == input.shape());
ensure!(mask.rank() == 3 && input.rank() == 3);
ensure!(output.datum_type() == input.datum_type());
let shape = input.shape();
let strides = input.strides();
let mask_strides_nd3 = compute_broadcast_strides::<usize>(mask.shape(), mask.strides())?;
let pipeline =
stream.load_pipeline(LibraryName::NNOps, &self.kernel_name(input.datum_type())?)?;
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, mask, metal::MTLResourceUsage::Read);
encoder.set_tensor(2, scale);
encoder.set_metal_tensor(3, output, metal::MTLResourceUsage::Write);
encoder.set_slice(4, shape);
encoder.set_slice(5, strides);
encoder.set_slice(6, &mask_strides_nd3);
encoder.set_slice(7, output.strides());
let grid_size = MTLSize { width: 1 as _, height: shape[1] as _, depth: shape[0] as _ };
let group_size = MTLSize { width: usize::min(32, shape[2]) as _, 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 proptest::strategy::Strategy;
use tract_core::internal::Tensor;
use tract_transformers::ops::scaled_masked_softmax;
#[test]
fn test_scaled_masked_softmax_f32() -> TractResult<()> {
with_borrowed_metal_stream(|stream| {
let m = 4;
let n = 4;
let scale: Arc<_> = tensor0(0.125f32).into();
let mask = Tensor::from_shape(&[1, m, n], &vec![-1000f32; m * n])?.into_device()?;
let a =
Tensor::from_shape(&[1, m, n], &(0..m * n).map(|f| f as f32).collect::<Vec<_>>())?
.into_device()?;
let cpu = scaled_masked_softmax::ScaledMaskedSoftmax { scale: scale.clone() };
let cpu_output = cpu
.eval(tvec![a.to_host()?.into_tvalue(), mask.to_host()?.into_tvalue()])?[0]
.clone()
.into_tensor();
let metal_output = ScaledMaskedSoftmax.eval(stream, &a, &scale, &mask)?;
cpu_output
.close_enough(&metal_output.to_host()?.into_tensor(), Approximation::Approximate)?;
Ok(())
})
}
#[test]
fn test_scaled_masked_softmax_f32_2() -> TractResult<()> {
with_borrowed_metal_stream(|stream| {
let m = 4;
let n = 1024;
let scale: Arc<_> = tensor0(0.125f32).into();
let mask = Tensor::from_shape(&[1, m, n], &vec![-1000f32; m * n])?.into_device()?;
let a =
Tensor::from_shape(&[1, m, n], &(0..m * n).map(|f| f as f32).collect::<Vec<_>>())?
.into_device()?;
let cpu = scaled_masked_softmax::ScaledMaskedSoftmax { scale: scale.clone() };
let cpu_output = cpu
.eval(tvec![a.to_host()?.into_tvalue(), mask.to_host()?.into_tvalue()])?[0]
.clone()
.into_tensor();
let metal_output = ScaledMaskedSoftmax.eval(stream, &a, &scale, &mask)?;
cpu_output
.close_enough(&metal_output.to_host()?.into_tensor(), Approximation::Approximate)?;
Ok(())
})
}
proptest::proptest! {
#[test]
fn scaled_masked_softmax_prop_f32(pb in any::<ScaledMaskedSoftmaxProblem<f32>>()) {
fn run(pb: ScaledMaskedSoftmaxProblem<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 scaled_masked_softmax_prop_f16(pb in any::<ScaledMaskedSoftmaxProblem<f16>>()) {
fn run(pb: ScaledMaskedSoftmaxProblem<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 ScaledMaskedSoftmaxProblem<F: Datum + Float>
where
F: Datum + Float,
usize: AsPrimitive<F>,
{
pub shape: Vec<usize>,
pub mask_shape: Vec<usize>,
pub input: Vec<F>,
pub mask: Vec<F>,
}
impl<F> Arbitrary for ScaledMaskedSoftmaxProblem<F>
where
F: Datum + Float,
usize: AsPrimitive<F>,
{
type Parameters = ();
type Strategy = BoxedStrategy<Self>;
fn arbitrary_with(_: ()) -> Self::Strategy {
vec(1usize..10, 3..=3)
.prop_map(|shape| {
let mut mask_shape = shape.clone();
mask_shape[0] = 1;
let input = (0..shape.iter().product::<usize>())
.map(|f| f.as_() / 1000.as_())
.collect::<Vec<_>>();
let mask = (0..mask_shape.iter().product::<usize>())
.map(|f| f.as_() / 1000.as_())
.collect::<Vec<_>>();
Self { shape, input, mask_shape, mask }
})
.boxed()
}
}
impl<F> ScaledMaskedSoftmaxProblem<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 mask = Tensor::from_shape(self.mask_shape.as_slice(), &self.mask)?;
let scale: Arc<_> = tensor0::<F>(0.125f32.as_()).into();
let cpu_output = scaled_masked_softmax::ScaledMaskedSoftmax { scale }
.eval(tvec![a.into_tvalue(), mask.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 mask =
Tensor::from_shape(self.mask_shape.as_slice(), &self.mask)?.into_device()?;
let scale: Arc<_> = tensor0::<F>(0.125f32.as_()).into();
let metal_output = ScaledMaskedSoftmax.eval(stream, &a, &scale, &mask)?;
Ok(metal_output.to_host()?.into_tensor())
})
}
}
}