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Fix the accelerate build (#678)

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* Cosmetic changes.

* Fix the accelerate build for tanh.
This commit is contained in:
Laurent Mazare
2023-08-30 18:31:14 +02:00
committed by GitHub
parent 7d753d3acd
commit 9874d843f1
2 changed files with 25 additions and 4 deletions
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22
candle-core/src/accelerate.rs
View File

@ -50,6 +50,8 @@ mod ffi {
pub fn vvcos(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvlogf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvlog(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vvtanhf(dst: *mut c_float, src: *const c_float, len: *const c_int);
pub fn vvtanh(dst: *mut c_double, src: *const c_double, len: *const c_int);
pub fn vDSP_vaddD(
_: *const c_double,
@ -308,6 +310,26 @@ pub fn vd_cos(a: &[f64], y: &mut [f64]) {
}
unsafe { ffi::vvcos(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_tanh(a: &[f32], y: &mut [f32]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvtanhf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vd_tanh(a: &[f64], y: &mut [f64]) {
let a_len = a.len();
let y_len = y.len();
if a_len != y_len {
panic!("a and y have different lengths {a_len} <> {y_len}")
}
unsafe { ffi::vvtanh(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
}
#[inline]
pub fn vs_ln(a: &[f32], y: &mut [f32]) {
let a_len = a.len();

7
candle-examples/examples/whisper/model.rs
View File

@ -1,4 +1,4 @@
use candle::{Device, IndexOp, Result, Tensor};
use candle::{Device, IndexOp, Result, Tensor, D};
use candle_nn::{ops::softmax, Conv1d, Conv1dConfig, Embedding, LayerNorm, Module, VarBuilder};
use serde::Deserialize;
@ -166,7 +166,7 @@ impl MultiHeadAttention {
}
let w = {
let _enter = self.softmax_span.enter();
softmax(&qk, candle::D::Minus1)?
softmax(&qk, D::Minus1)?
};
let wv = {
let _enter = self.matmul_span.enter();
@ -375,8 +375,7 @@ impl TextDecoder {
pub fn forward(&mut self, x: &Tensor, xa: &Tensor, flush_kv_cache: bool) -> Result<Tensor> {
let _enter = self.span.enter();
let x_dims = x.dims();
let last = x_dims[x_dims.len() - 1];
let last = x.dim(D::Minus1)?;
let token_embedding = self.token_embedding.forward(x)?;
let positional_embedding = self.positional_embedding.narrow(0, 0, last)?;
let mut x = token_embedding.broadcast_add(&positional_embedding)?;