Improve softmax kernel. 33%-39% higher thrpt

candle-core/benches/benchmarks/reduce.rs

@@ -1,6 +1,8 @@
 use crate::benchmarks::{bench_name, device, BenchDevice};
-use candle_core::{DType, Device, Tensor};
+use candle_core::{DType, Device, Storage, Tensor};
 use criterion::{black_box, criterion_group, Criterion, Throughput};
+use half::{bf16, f16};
+use std::ops::Deref;
 use std::time::Instant;
 
 fn run_sum(a: &Tensor) {
@@ -10,21 +12,114 @@ fn run_arg_min(a: &Tensor) {
     a.argmin(2).unwrap();
 }
 
+fn softmax(a: &Tensor) -> candle_core::Result<()> {
+    use candle_core::{backend::BackendStorage, DType};
+    let (storage, layout) = a.storage_and_layout();
+
+    let device = a.device();
+
+    if let (Device::Metal(device), Storage::Metal(storage)) = (device, storage.deref()) {
+        let command_buffer = device.command_buffer()?;
+        let kernels = device.kernels();
+        let name = match a.dtype() {
+            DType::F32 => "softmax_f32",
+            DType::F16 => "softmax_f16",
+            DType::BF16 => "softmax_bf16",
+            dtype => candle_core::bail!("softmax-last-dim is not implemented for {dtype:?}"),
+        };
+
+        let n = layout.stride().len();
+        if !(layout.is_contiguous() && layout.stride()[n - 1] == 1) {
+            candle_core::bail!("Non contiguous softmax-last-dim is not implemented");
+        }
+
+        let last_dim = layout.dims()[layout.shape().rank() - 1];
+        let elem_count = layout.shape().elem_count();
+        let output = device.new_buffer(elem_count, storage.dtype(), "softmax")?;
+        candle_metal_kernels::call_last_softmax(
+            device.metal_device(),
+            &command_buffer,
+            kernels,
+            name,
+            elem_count,
+            last_dim,
+            storage.buffer(),
+            layout.start_offset() * storage.dtype().size_in_bytes(),
+            &output,
+        )
+        .unwrap();
+    }
+    Ok(())
+}
+
 fn criterion_benchmark(c: &mut Criterion) {
     let device = device().unwrap();
-    run_reduce(c, &device);
-    run_arg_reduce(c, &device);
+
+    let (lo, up) = (-1000.0f32, 1000.0f32);
+    run_softmax(c, &device, (lo, up));
+    run_softmax(c, &device, (f16::from_f32(lo), f16::from_f32(up)));
+    run_softmax(c, &device, (bf16::from_f32(lo), bf16::from_f32(up)));
+
+    run_reduce(c, &device, (lo, up));
+    run_reduce(c, &device, (f16::from_f32(lo), f16::from_f32(up)));
+    run_reduce(c, &device, (bf16::from_f32(lo), bf16::from_f32(up)));
+
+    run_arg_reduce(c, &device, (lo, up));
+    run_arg_reduce(c, &device, (f16::from_f32(lo), f16::from_f32(up)));
+    run_arg_reduce(c, &device, (bf16::from_f32(lo), bf16::from_f32(up)));
 }
-fn run_reduce(c: &mut Criterion, device: &Device) {
+
+fn run_softmax<T: candle_core::FloatDType>(c: &mut Criterion, device: &Device, (lo, up): (T, T)) {
+    if !device.is_metal() {
+        return;
+    }
+
+    let b = 1;
+    let m = 2048;
+    let k = 2048;
+    let a = Tensor::rand(lo, up, (b, m, k), &device).unwrap();
+
+    let flops = b * m * k * T::DTYPE.size_in_bytes();
+
+    let name = match T::DTYPE {
+        DType::F32 => "softmax_f32",
+        DType::F16 => "softmax_f16",
+        DType::BF16 => "softmax_bf16",
+        _ => "softmax",
+    };
+
+    let mut group = c.benchmark_group(bench_name(name));
+    group.throughput(Throughput::Bytes(flops as u64));
+    group.bench_function("iter", move |b| {
+        b.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                softmax(black_box(&a)).unwrap();
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+fn run_reduce<T: candle_core::FloatDType>(c: &mut Criterion, device: &Device, (lo, up): (T, T)) {
     let b = 1;
     let m = 2048;
     let k = 2048;
 
-    let a = Tensor::rand(-1000.0f32, 1000.0f32, (b, m, k), &device).unwrap();
+    let a = Tensor::rand(lo, up, (b, m, k), &device).unwrap();
 
-    let flops = b * m * k * DType::F32.size_in_bytes();
+    let flops = b * m * k * T::DTYPE.size_in_bytes();
 
-    let mut group = c.benchmark_group(bench_name("reduce"));
+    let name = match T::DTYPE {
+        DType::F32 => "reduce_f32",
+        DType::F16 => "reduce_f16",
+        DType::BF16 => "reduce_bf16",
+        _ => "reduce",
+    };
+
+    let mut group = c.benchmark_group(bench_name(name));
     group.throughput(Throughput::Bytes(flops as u64));
     group.bench_function("iter", move |b| {
         b.iter_custom(|iters| {
@@ -39,16 +134,27 @@ fn run_reduce(c: &mut Criterion, device: &Device) {
     group.finish();
 }
 
-fn run_arg_reduce(c: &mut Criterion, device: &Device) {
+fn run_arg_reduce<T: candle_core::FloatDType>(
+    c: &mut Criterion,
+    device: &Device,
+    (lo, up): (T, T),
+) {
     let b = 1;
     let m = 2048;
     let k = 2048;
 
-    let a = Tensor::rand(-1000.0f32, 1000.0f32, (b, m, k), &device).unwrap();
+    let a = Tensor::rand(lo, up, (b, m, k), &device).unwrap();
 
-    let flops = b * m * k * DType::F32.size_in_bytes();
+    let flops = b * m * k * T::DTYPE.size_in_bytes();
 
-    let mut group = c.benchmark_group(bench_name("arg_reduce"));
+    let name = match T::DTYPE {
+        DType::F32 => "arg_reduce_f32",
+        DType::F16 => "arg_reduce_f16",
+        DType::BF16 => "arg_reduce_bf16",
+        _ => "reduce",
+    };
+
+    let mut group = c.benchmark_group(bench_name(name));
     group.throughput(Throughput::Bytes(flops as u64));
     group.bench_function("iter", move |b| {
         b.iter_custom(|iters| {