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CPU implementation for the custom RMS example. (#228)

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* CPU implementation for the custom RMS example.

* Add the eps parameter.
This commit is contained in:
Laurent Mazare
2023-07-23 21:04:20 +02:00
committed by GitHub
parent fe87778223
commit b6f7dfb682
1 changed files with 20 additions and 5 deletions
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25
candle-examples/examples/custom-ops/main.rs
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@ -1,3 +1,7 @@
// This example illustrates how to implement custom operations. These operations can provide their
// own forward pass (CPU and GPU versions) as well as their backward pass.
//
// In this example we add the RMS normalization operation and implement it for f32.
#![allow(dead_code)]
#![allow(unused)]
@ -20,7 +24,9 @@ struct Args {
cpu: bool,
}
struct LayerNorm;
struct LayerNorm {
eps: f32,
}
impl CustomOp1 for LayerNorm {
fn name(&self) -> &'static str {
@ -28,12 +34,21 @@ impl CustomOp1 for LayerNorm {
}
fn cpu_fwd(&self, s: &CpuStorage, l: &Layout) -> Result<(CpuStorage, Shape)> {
let (dim1, dim2) = l.shape().dims2()?;
let s = s.as_slice::<f32>()?;
let _s = match l.contiguous_offsets() {
let src = match l.contiguous_offsets() {
None => Err(Error::Wrapped("input has to be contiguous".into()))?,
Some((o1, o2)) => &s[o1..o2],
};
todo!()
let mut dst = Vec::with_capacity(dim1 * dim2);
for idx1 in 0..dim1 {
let src = &src[idx1 * dim2..(idx1 + 1) * dim2];
let variance = src.iter().map(|x| x * x).sum::<f32>();
let s_variance = 1f32 / (variance / dim2 as f32 + self.eps).sqrt();
dst.extend(src.iter().map(|x| x * s_variance))
}
let storage = candle::WithDType::to_cpu_storage_owned(dst);
Ok((storage, l.shape().clone()))
}
#[cfg(feature = "cuda")]
@ -56,7 +71,7 @@ impl CustomOp1 for LayerNorm {
let elem_count = l.shape().elem_count();
let dst = unsafe { dev.alloc::<f32>(elem_count) }.w()?;
let func = dev.get_or_load_func("rms_f32", cuda_kernels::LAYERNORM_KERNELS)?;
let params = (&dst, &s, 1e-5f32, d1, d2);
let params = (&dst, &s, self.eps, d1, d2);
let cfg = LaunchConfig {
grid_dim: (d1, 1, 1),
block_dim: (d2, 1, 1),
@ -74,7 +89,7 @@ fn main() -> anyhow::Result<()> {
let device = candle_examples::device(args.cpu)?;
let t = Tensor::arange(0f32, 14f32, &device)?.reshape((2, 7))?;
println!("{t}");
let t = t.custom_op1(LayerNorm)?;
let t = t.custom_op1(LayerNorm { eps: 1e-5 })?;
println!("{t}");
Ok(())
}