Move softmax bench to candle-nn

candle-core/benches/benchmarks/reduce.rs

@@ -1,8 +1,7 @@
 use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
-use candle_core::{DType, Device, Storage, Tensor};
+use candle_core::{DType, Device, 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) {
@@ -12,55 +11,10 @@ fn run_arg_min(a: &Tensor) {
     a.argmin_keepdim(2).unwrap();
 }
 
-// NOTE: Should this be removed? Softmax impls live in candle-nn.
-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 handler = BenchDeviceHandler::new().unwrap();
     let (lo, up) = (-1000.0f32, 1000.0f32);
     for device in handler.devices {
-        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), false);
         run_reduce(c, &device, (f16::from_f32(lo), f16::from_f32(up)), false);
         run_reduce(c, &device, (bf16::from_f32(lo), bf16::from_f32(up)), false);
@@ -79,41 +33,6 @@ fn criterion_benchmark(c: &mut Criterion) {
     }
 }
 
-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 = 1024;
-    let k = 1024;
-    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",
-    };
-    softmax(&a).unwrap();
-
-    let mut group = c.benchmark_group(device.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,

candle-nn/benches/bench_main.rs

@@ -1,4 +1,8 @@
 mod benchmarks;
 
 use criterion::criterion_main;
-criterion_main!(benchmarks::layer_norm::benches, benchmarks::conv::benches);
+criterion_main!(
+    benchmarks::softmax::benches,
+    benchmarks::layer_norm::benches,
+    benchmarks::conv::benches
+);

candle-nn/benches/benchmarks/mod.rs

@@ -1,5 +1,6 @@
 pub(crate) mod conv;
 pub(crate) mod layer_norm;
+pub(crate) mod softmax;
 
 use candle::{Device, Result};
 

candle-nn/benches/benchmarks/softmax.rs

@@ -0,0 +1,49 @@
+use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
+use candle::{DType, Device, Tensor};
+use candle_nn::ops::softmax_last_dim;
+use criterion::Throughput;
+use criterion::{black_box, criterion_group, Criterion};
+use std::time::Instant;
+
+fn run(input: &Tensor) {
+    let _ = softmax_last_dim(&input).unwrap();
+}
+
+const B: usize = 1;
+const M: usize = 1024;
+const K: usize = 1024;
+
+fn run_softmax_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
+    let elements = B * M * K;
+
+    let input = Tensor::rand(-1000.0f32, 1000.0f32, (B, M, K), &device)
+        .unwrap()
+        .to_dtype(dtype)
+        .unwrap();
+
+    let flops = elements * dtype.size_in_bytes();
+    let mut group = c.benchmark_group(device.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 {
+                run(black_box(&input));
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let device = BenchDeviceHandler::new().unwrap();
+    for d in device.devices {
+        run_softmax_benchmark(c, &d, DType::F32, "softmax_f32");
+        run_softmax_benchmark(c, &d, DType::BF16, "softmax_bf16");
+        run_softmax_benchmark(c, &d, DType::F16, "softmax_f16");
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);