More accelerate optimizations (#427)

* Add more tracing to the whisper example.

* Support accelerate in more examples.

* Use accelerate for pointwise functions.

* Use accelerate for binary operations too.

* Bugfix for binary operation: use the rhs before the lhs.

candle-core/examples/cuda_basics.rs

@@ -1,3 +1,6 @@
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 

candle-core/examples/cuda_sum_benchmark.rs

@@ -1,6 +1,9 @@
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
 use std::str::FromStr;
 
 use anyhow::Result;

candle-core/src/accelerate.rs

@@ -1,5 +1,5 @@
 #![allow(dead_code)]
-use libc::{c_char, c_double, c_float, c_int};
+use libc::{c_char, c_double, c_float, c_int, c_long, c_ulong};
 
 mod ffi {
     use super::*;
@@ -39,6 +39,90 @@ mod ffi {
             c: *mut c_double,
             ldc: *const c_int,
         );
+
+        pub fn vvexpf(dst: *mut c_float, src: *const c_float, len: *const c_int);
+        pub fn vvexp(dst: *mut c_double, src: *const c_double, len: *const c_int);
+        pub fn vvsqrtf(dst: *mut c_float, src: *const c_float, len: *const c_int);
+        pub fn vvsqrt(dst: *mut c_double, src: *const c_double, len: *const c_int);
+        pub fn vvsinf(dst: *mut c_float, src: *const c_float, len: *const c_int);
+        pub fn vvsin(dst: *mut c_double, src: *const c_double, len: *const c_int);
+        pub fn vvcosf(dst: *mut c_float, src: *const c_float, len: *const c_int);
+        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 vDSP_vaddD(
+            _: *const c_double,
+            _: c_long,
+            _: *const c_double,
+            _: c_long,
+            _: *mut c_double,
+            _: c_long,
+            _: c_ulong,
+        );
+        pub fn vDSP_vadd(
+            _: *const c_float,
+            _: c_long,
+            _: *const c_float,
+            _: c_long,
+            _: *mut c_float,
+            _: c_long,
+            _: c_ulong,
+        );
+        pub fn vDSP_vsubD(
+            _: *const c_double,
+            _: c_long,
+            _: *const c_double,
+            _: c_long,
+            _: *mut c_double,
+            _: c_long,
+            _: c_ulong,
+        );
+        pub fn vDSP_vsub(
+            _: *const c_float,
+            _: c_long,
+            _: *const c_float,
+            _: c_long,
+            _: *mut c_float,
+            _: c_long,
+            _: c_ulong,
+        );
+        pub fn vDSP_vmulD(
+            _: *const c_double,
+            _: c_long,
+            _: *const c_double,
+            _: c_long,
+            _: *mut c_double,
+            _: c_long,
+            _: c_ulong,
+        );
+        pub fn vDSP_vmul(
+            _: *const c_float,
+            _: c_long,
+            _: *const c_float,
+            _: c_long,
+            _: *mut c_float,
+            _: c_long,
+            _: c_ulong,
+        );
+        pub fn vDSP_vdivD(
+            _: *const c_double,
+            _: c_long,
+            _: *const c_double,
+            _: c_long,
+            _: *mut c_double,
+            _: c_long,
+            _: c_ulong,
+        );
+        pub fn vDSP_vdiv(
+            _: *const c_float,
+            _: c_long,
+            _: *const c_float,
+            _: c_long,
+            _: *mut c_float,
+            _: c_long,
+            _: c_ulong,
+        );
     }
 }
 
@@ -109,3 +193,158 @@ pub unsafe fn dgemm(
         &ldc,
     )
 }
+
+#[inline]
+pub fn vs_exp(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::vvexpf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
+}
+
+#[inline]
+pub fn vd_exp(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::vvexp(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
+}
+
+#[inline]
+pub fn vs_sqrt(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::vvsqrtf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
+}
+
+#[inline]
+pub fn vd_sqrt(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::vvsqrt(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
+}
+
+#[inline]
+pub fn vs_sin(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::vvsinf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
+}
+
+#[inline]
+pub fn vd_sin(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::vvsin(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
+}
+#[inline]
+pub fn vs_cos(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::vvcosf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
+}
+
+#[inline]
+pub fn vd_cos(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::vvcos(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();
+    let y_len = y.len();
+    if a_len != y_len {
+        panic!("a and y have different lengths {a_len} <> {y_len}")
+    }
+    unsafe { ffi::vvlogf(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
+}
+
+#[inline]
+pub fn vd_ln(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::vvlog(y.as_mut_ptr(), a.as_ptr(), &(a_len as i32)) }
+}
+
+#[inline]
+pub fn vs_sqr(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}")
+    }
+    y.iter_mut().zip(a.iter()).for_each(|(y, a)| *y = *a * *a)
+}
+
+#[inline]
+pub fn vd_sqr(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}")
+    }
+    y.iter_mut().zip(a.iter()).for_each(|(y, a)| *y = *a * *a)
+}
+
+macro_rules! binary_op {
+    ($fn_name:ident, $ty:ty, $accelerate_name:ident) => {
+        #[inline]
+        pub fn $fn_name(a: &[$ty], b: &[$ty], y: &mut [$ty]) {
+            let a_len = a.len();
+            let b_len = b.len();
+            let y_len = y.len();
+            if a_len != y_len || b_len != y_len {
+                panic!(
+                    "{} a,b,y len mismatch {a_len} {b_len} {y_len}",
+                    stringify!($fn_name)
+                );
+            }
+            unsafe {
+                // Weird quirk of accelerate, the rhs comes before the lhs.
+                ffi::$accelerate_name(
+                    b.as_ptr(),
+                    1,
+                    a.as_ptr(),
+                    1,
+                    y.as_mut_ptr(),
+                    1,
+                    a_len as u64,
+                )
+            }
+        }
+    };
+}
+binary_op!(vs_add, f32, vDSP_vadd);
+binary_op!(vd_add, f64, vDSP_vaddD);
+binary_op!(vs_sub, f32, vDSP_vsub);
+binary_op!(vd_sub, f64, vDSP_vsubD);
+binary_op!(vs_mul, f32, vDSP_vmul);
+binary_op!(vd_mul, f64, vDSP_vmulD);
+binary_op!(vs_div, f32, vDSP_vdiv);
+binary_op!(vd_div, f64, vDSP_vdivD);

candle-core/src/op.rs

@@ -338,6 +338,21 @@ macro_rules! bin_op {
             fn f64_vec(xs1: &[f64], xs2: &[f64], ys: &mut [f64]) {
                 crate::mkl::$f64_vec(xs1, xs2, ys)
             }
+
+            #[cfg(feature = "accelerate")]
+            const F32_VEC: bool = true;
+            #[cfg(feature = "accelerate")]
+            const F64_VEC: bool = true;
+            #[cfg(feature = "accelerate")]
+            #[inline(always)]
+            fn f32_vec(xs1: &[f32], xs2: &[f32], ys: &mut [f32]) {
+                crate::accelerate::$f32_vec(xs1, xs2, ys)
+            }
+            #[cfg(feature = "accelerate")]
+            #[inline(always)]
+            fn f64_vec(xs1: &[f64], xs2: &[f64], ys: &mut [f64]) {
+                crate::accelerate::$f64_vec(xs1, xs2, ys)
+            }
         }
     };
 }
@@ -424,6 +439,21 @@ macro_rules! unary_op {
             fn f64_vec(xs: &[f64], ys: &mut [f64]) {
                 crate::mkl::$f64_vec(xs, ys)
             }
+
+            #[cfg(feature = "accelerate")]
+            const F32_VEC: bool = true;
+            #[cfg(feature = "accelerate")]
+            const F64_VEC: bool = true;
+            #[cfg(feature = "accelerate")]
+            #[inline(always)]
+            fn f32_vec(xs: &[f32], ys: &mut [f32]) {
+                crate::accelerate::$f32_vec(xs, ys)
+            }
+            #[cfg(feature = "accelerate")]
+            #[inline(always)]
+            fn f64_vec(xs: &[f64], ys: &mut [f64]) {
+                crate::accelerate::$f64_vec(xs, ys)
+            }
         }
     };
 }

candle-core/tests/grad_tests.rs

@@ -85,8 +85,14 @@ fn unary_grad(device: &Device) -> Result<()> {
     let y = (x.log()? + 1.)?;
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
-    assert_eq!(y.to_vec1::<f32>()?, [2.0986123, 1.0, 2.3862944, -0.89712]);
+    assert_eq!(
-    assert_eq!(grad_x.to_vec1::<f32>()?, [0.33333334, 1.0, 0.25, 6.6666665]);
+        test_utils::to_vec1_round(&y, 4)?,
+        [2.0986, 1.0, 2.3863, -0.8971]
+    );
+    assert_eq!(
+        test_utils::to_vec1_round(grad_x, 4)?,
+        [0.3333, 1.0, 0.25, 6.6667]
+    );
     let y = x.exp()?;
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
@@ -141,7 +147,7 @@ fn unary_grad(device: &Device) -> Result<()> {
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
     assert_eq!(y.to_vec1::<f32>()?, [3.0, 1.0, 4.0, 0.15]);
-    assert_eq!(grad_x.to_vec1::<f32>()?, [1.0, 1.0, 1.0, 1.0]);
+    assert_eq!(test_utils::to_vec1_round(grad_x, 4)?, [1.0, 1.0, 1.0, 1.0]);
     let y = x.neg()?;
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
@@ -155,7 +161,10 @@ fn unary_grad(device: &Device) -> Result<()> {
     let y = Tensor::new(1f32, device)?.broadcast_div(x)?;
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
-    assert_eq!(y.to_vec1::<f32>()?, [0.33333334, 1.0, 0.25, 6.6666665]);
+    assert_eq!(
+        test_utils::to_vec1_round(&y, 4)?,
+        [0.3333, 1.0, 0.25, 6.6667]
+    );
     assert_eq!(
         grad_x.to_vec1::<f32>()?,
         [-0.11111111, -1.0, -0.0625, -44.444443],

candle-core/tests/test_utils.rs

@@ -1,5 +1,8 @@
 #![allow(dead_code)]
 
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
 use candle_core::{Result, Tensor};
 
 #[macro_export]

candle-examples/examples/falcon/main.rs

@@ -1,5 +1,8 @@
 // TODO: Add an offline mode.
 
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 

candle-examples/examples/whisper/main.rs

@@ -1,9 +1,11 @@
 // https://github.com/openai/whisper/blob/main/whisper/model.py/rgs
 // TODO:
-// - kv-cache support?
 // - Batch size greater than 1.
 // - More token filters (SuppressBlanks, ApplyTimestampRules).
 
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 

candle-examples/examples/whisper/model.rs

@@ -1,4 +1,4 @@
-use candle::{Device, Result, Tensor};
+use candle::{Device, IndexOp, Result, Tensor};
 use candle_nn::{ops::softmax, Conv1d, Conv1dConfig, Embedding, LayerNorm, VarBuilder};
 use serde::Deserialize;
 
@@ -105,12 +105,16 @@ struct MultiHeadAttention {
     out: Linear,
     n_head: usize,
     span: tracing::Span,
+    softmax_span: tracing::Span,
+    matmul_span: tracing::Span,
     kv_cache: Option<(Tensor, Tensor)>,
 }
 
 impl MultiHeadAttention {
     fn load(n_state: usize, n_head: usize, vb: VarBuilder) -> Result<Self> {
         let span = tracing::span!(tracing::Level::TRACE, "multi-head-attn");
+        let softmax_span = tracing::span!(tracing::Level::TRACE, "multi-head-attn-softmax");
+        let matmul_span = tracing::span!(tracing::Level::TRACE, "multi-head-attn-matmul");
         let query = linear(n_state, n_state, vb.pp("q_proj"))?;
         let value = linear(n_state, n_state, vb.pp("v_proj"))?;
         let key = linear_no_bias(n_state, n_state, vb.pp("k_proj"))?;
@@ -122,6 +126,8 @@ impl MultiHeadAttention {
             out,
             n_head,
             span,
+            softmax_span,
+            matmul_span,
             kv_cache: None,
         })
     }
@@ -178,13 +184,24 @@ impl MultiHeadAttention {
         let q = (self.reshape_head(q)? * scale)?;
         let k = (self.reshape_head(k)?.transpose(2, 3)? * scale)?;
         let v = self.reshape_head(v)?.contiguous()?;
-        let mut qk = q.matmul(&k)?;
+        let mut qk = {
+            let _enter = self.matmul_span.enter();
+            q.matmul(&k)?
+        };
         if let Some(mask) = mask {
-            let mask = mask.narrow(0, 0, n_ctx)?.narrow(1, 0, n_ctx)?;
+            let mask = mask.i((0..n_ctx, 0..n_ctx))?;
             qk = qk.broadcast_add(&mask)?
         }
-        let w = softmax(&qk, candle::D::Minus1)?;
+        let w = {
-        let wv = w.matmul(&v)?.transpose(1, 2)?.flatten_from(2)?;
+            let _enter = self.softmax_span.enter();
+            softmax(&qk, candle::D::Minus1)?
+        };
+        let wv = {
+            let _enter = self.matmul_span.enter();
+            w.matmul(&v)?
+        }
+        .transpose(1, 2)?
+        .flatten_from(2)?;
         Ok(wv)
     }
 }