use singe_cuda::{context::Context as CudaContext, memory::DeviceMemory};
use singe_cutensor::{
context::Context,
error::Result,
operation::{ComputeDescriptor, OperationDescriptor, TensorOperand},
plan::{Plan, PlanPreference},
tensor::TensorDescriptor,
types::{Operator, WorkspacePreference},
};
fn packed_offset(indices: &[u64], extents: &[u64]) -> usize {
let mut stride = 1_usize;
let mut offset = 0_usize;
for (&index, &extent) in indices.iter().zip(extents) {
offset += index as usize * stride;
stride *= extent as usize;
}
offset
}
fn main() -> Result<()> {
let cuda_context = CudaContext::create()?;
let context = Context::create(&cuda_context)?;
let stream = cuda_context.create_stream()?;
let extent_a = vec![2, 3, 2];
let extent_c = vec![2, 2];
let mode_a = vec!['m'.into(), 'h'.into(), 'v'.into()];
let mode_c = vec!['m'.into(), 'v'.into()];
let host_a = (0..extent_a.iter().product::<u64>())
.map(|index| index as f32 + 1.0)
.collect::<Vec<_>>();
let host_c = vec![0.5_f32, -1.0, 1.5, -2.0];
let device_a = DeviceMemory::from_slice(&host_a)?;
let device_c_input = DeviceMemory::from_slice(&host_c)?;
let mut device_c = DeviceMemory::from_slice(&host_c)?;
const ALIGNMENT: u32 = 128;
let descriptor_a = TensorDescriptor::create_for::<f32>(&context, &extent_a, ALIGNMENT)?;
let descriptor_c = TensorDescriptor::create_for::<f32>(&context, &extent_c, ALIGNMENT)?;
let reduction = OperationDescriptor::reduction(
&context,
TensorOperand::identity(&descriptor_a, &mode_a),
TensorOperand::identity(&descriptor_c, &mode_c),
TensorOperand::identity(&descriptor_c, &mode_c),
Operator::Add,
ComputeDescriptor::f32(),
)?;
let preference = PlanPreference::create_default(&context)?;
let workspace_size = Plan::estimate_workspace_size(
&context,
&reduction,
&preference,
WorkspacePreference::Default,
)?;
let plan = Plan::create(&context, &reduction, &preference, workspace_size)?;
let mut workspace = if plan.required_workspace_size() > 0 {
Some(DeviceMemory::<u8>::create(
plan.required_workspace_size_bytes()?,
)?)
} else {
None
};
let alpha = 1.25_f32;
let beta = -0.5_f32;
plan.reduce(
&alpha,
&device_a,
&beta,
&device_c_input,
&mut device_c,
workspace.as_mut(),
&stream,
)?;
stream.synchronize()?;
let result = device_c.copy_to_host_vec()?;
let mut expected = vec![0.0_f32; extent_c.iter().product::<u64>() as usize];
for m in 0..extent_c[0] as usize {
for v in 0..extent_c[1] as usize {
let mut sum = 0.0_f32;
for h in 0..extent_a[1] {
let a_offset = packed_offset(&[m as u64, h, v as u64], &extent_a);
sum += host_a[a_offset];
}
let c_offset = packed_offset(&[m as u64, v as u64], &extent_c);
expected[c_offset] = alpha * sum + beta * host_c[c_offset];
}
}
assert_eq!(result.len(), expected.len());
for (actual, reference) in result.iter().zip(&expected) {
assert!((actual - reference).abs() < 1.0e-5);
}
println!("reduction output verified for {} elements", result.len());
Ok(())
}