huggingface/candle
1
0
Fork 0
mirror of https://github.com/huggingface/candle.git synced 2025-06-16 18:48:51 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #246 from LaurentMazare/rename_custom_op

Browse Source 
Rename exposed ops.
This commit is contained in:
Nicolas Patry
2023-07-26 14:20:29 +01:00
committed by GitHub
parent 2ce5f12513 1a5416ec35
commit 8b1d12bead
2 changed files with 18 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
candle-core/src/op.rs
View File

@ -109,11 +109,11 @@ pub trait CustomOp1: Send + Sync {
/// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides, /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it. /// offsets etc so the associated layout should be used to access it.
fn cpu_fwd(&self, s: &CpuStorage, l: &Layout) -> Result<(CpuStorage, Shape)>; fn cpu_fwd(&self, storage: &CpuStorage, layout: &Layout) -> Result<(CpuStorage, Shape)>;
/// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides, /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
/// offsets etc so the associated layout should be used to access it. /// offsets etc so the associated layout should be used to access it.
fn cuda_fwd(&self, _: &CudaStorage, _: &Layout) -> Result<(CudaStorage, Shape)> { fn cuda_fwd(&self, _storage: &CudaStorage, _layout: &Layout) -> Result<(CudaStorage, Shape)> {
Err(crate::Error::Cuda( Err(crate::Error::Cuda(
format!("no cuda implementation for {}", self.name()).into(), format!("no cuda implementation for {}", self.name()).into(),
)) ))

32
candle-examples/examples/custom-ops/main.rs
View File

@ -33,12 +33,12 @@ impl CustomOp1 for LayerNorm {
"layer-norm" "layer-norm"
} }
fn cpu_fwd(&self, s: &CpuStorage, l: &Layout) -> Result<(CpuStorage, Shape)> { fn cpu_fwd(&self, storage: &CpuStorage, layout: &Layout) -> Result<(CpuStorage, Shape)> {
let (dim1, dim2) = l.shape().dims2()?; let (dim1, dim2) = layout.shape().dims2()?;
let s = s.as_slice::<f32>()?; let slice = storage.as_slice::<f32>()?;
let src = match l.contiguous_offsets() { let src = match layout.contiguous_offsets() {
None => Err(Error::Wrapped("input has to be contiguous".into()))?, None => Err(Error::Wrapped("input has to be contiguous".into()))?,
Some((o1, o2)) => &s[o1..o2], Some((o1, o2)) => &slice[o1..o2],
}; };
let mut dst = Vec::with_capacity(dim1 * dim2); let mut dst = Vec::with_capacity(dim1 * dim2);
for idx1 in 0..dim1 { for idx1 in 0..dim1 {
@ -48,30 +48,30 @@ impl CustomOp1 for LayerNorm {
dst.extend(src.iter().map(|x| x * s_variance)) dst.extend(src.iter().map(|x| x * s_variance))
} }
let storage = candle::WithDType::to_cpu_storage_owned(dst); let storage = candle::WithDType::to_cpu_storage_owned(dst);
Ok((storage, l.shape().clone())) Ok((storage, layout.shape().clone()))
} }
#[cfg(feature = "cuda")] #[cfg(feature = "cuda")]
fn cuda_fwd( fn cuda_fwd(
&self, &self,
s: &candle::CudaStorage, storage: &candle::CudaStorage,
l: &Layout, layout: &Layout,
) -> Result<(candle::CudaStorage, Shape)> { ) -> Result<(candle::CudaStorage, Shape)> {
use candle::cuda_backend::{cudarc, WrapErr}; use candle::cuda_backend::{cudarc, WrapErr};
use cudarc::driver::{LaunchAsync, LaunchConfig}; use cudarc::driver::{LaunchAsync, LaunchConfig};
let (d1, d2) = l.shape().dims2()?; let (d1, d2) = layout.shape().dims2()?;
let d1 = d1 as u32; let d1 = d1 as u32;
let d2 = d2 as u32; let d2 = d2 as u32;
let dev = s.device().clone(); let dev = storage.device().clone();
let s = s.as_cuda_slice::<f32>()?; let slice = storage.as_cuda_slice::<f32>()?;
let s = match l.contiguous_offsets() { let slice = match layout.contiguous_offsets() {
None => Err(Error::Wrapped("input has to be contiguous".into()))?, None => Err(Error::Wrapped("input has to be contiguous".into()))?,
Some((o1, o2)) => s.slice(o1..o2), Some((o1, o2)) => slice.slice(o1..o2),
}; };
let elem_count = l.shape().elem_count(); let elem_count = layout.shape().elem_count();
let dst = unsafe { dev.alloc::<f32>(elem_count) }.w()?; let dst = unsafe { dev.alloc::<f32>(elem_count) }.w()?;
let func = dev.get_or_load_func("rms_f32", cuda_kernels::LAYERNORM_KERNELS)?; let func = dev.get_or_load_func("rms_f32", cuda_kernels::LAYERNORM_KERNELS)?;
let params = (&dst, &s, self.eps, d1, d2); let params = (&dst, &slice, self.eps, d1, d2);
let cfg = LaunchConfig { let cfg = LaunchConfig {
grid_dim: (d1, 1, 1), grid_dim: (d1, 1, 1),
block_dim: (d2, 1, 1), block_dim: (d2, 1, 1),
@ -80,7 +80,7 @@ impl CustomOp1 for LayerNorm {
unsafe { func.launch(cfg, params) }.w()?; unsafe { func.launch(cfg, params) }.w()?;
let dst = candle::CudaStorage::wrap_cuda_slice(dst, dev); let dst = candle::CudaStorage::wrap_cuda_slice(dst, dev);
Ok((dst, l.shape().clone())) Ok((dst, layout.shape().clone()))
} }
} }