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::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 mode_c = vec!['m'.into(), 'u'.into(), 'n'.into(), 'v'.into()];
let mode_a = vec!['m'.into(), 'h'.into(), 'k'.into(), 'n'.into()];
let mode_b = vec!['u'.into(), 'k'.into(), 'v'.into(), 'h'.into()];
let extent_c = vec![2, 2, 3, 2];
let extent_a = vec![2, 2, 2, 3];
let extent_b = vec![2, 2, 2, 2];
let elements_a = extent_a.iter().product::<u64>();
let elements_b = extent_b.iter().product::<u64>();
let elements_c = extent_c.iter().product::<u64>();
let host_a = (0..elements_a)
.map(|index| (index as f32 + 1.0) * 0.25)
.collect::<Vec<_>>();
let host_b = (0..elements_b)
.map(|index| ((index % 7) as f32 - 3.0) * 0.5)
.collect::<Vec<_>>();
let host_c = (0..elements_c)
.map(|index| (index as f32 - 4.0) * 0.125)
.collect::<Vec<_>>();
let device_a = DeviceMemory::from_slice(&host_a)?;
let device_b = DeviceMemory::from_slice(&host_b)?;
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_b = TensorDescriptor::create_for::<f32>(&context, &extent_b, ALIGNMENT)?;
let descriptor_c = TensorDescriptor::create_for::<f32>(&context, &extent_c, ALIGNMENT)?;
let contraction = OperationDescriptor::contraction(
&context,
TensorOperand::identity(&descriptor_a, &mode_a),
TensorOperand::identity(&descriptor_b, &mode_b),
TensorOperand::identity(&descriptor_c, &mode_c),
TensorOperand::identity(&descriptor_c, &mode_c),
ComputeDescriptor::f32(),
)?;
let preference = PlanPreference::create_default(&context)?;
let workspace_size = Plan::estimate_workspace_size(
&context,
&contraction,
&preference,
WorkspacePreference::Default,
)?;
let plan = Plan::create(&context, &contraction, &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 = 1.1_f32;
let beta = -0.25_f32;
plan.contract(
&alpha,
&device_a,
&device_b,
&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; elements_c as usize];
for m in 0..extent_c[0] {
for u in 0..extent_c[1] {
for n in 0..extent_c[2] {
for v in 0..extent_c[3] {
let mut sum = 0.0_f32;
for h in 0..extent_a[1] {
for k in 0..extent_a[2] {
let a_offset = packed_offset(&[m, h, k, n], &extent_a);
let b_offset = packed_offset(&[u, k, v, h], &extent_b);
sum += host_a[a_offset] * host_b[b_offset];
}
}
let c_offset = packed_offset(&[m, u, n, v], &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!("contraction output verified for {} elements", result.len());
Ok(())
}