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Add support for conv_transpose2d on Metal backend (#1903)
* add support for conv transpose 2d and add bench mark for float types * update bench calculation * enable testing all conv operations on metal
This commit is contained in:
59
candle-core/benches/benchmarks/conv_transpose2d.rs
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59
candle-core/benches/benchmarks/conv_transpose2d.rs
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use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
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use candle_core::{DType, Device, Tensor};
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use criterion::{black_box, criterion_group, Criterion, Throughput};
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use std::time::Instant;
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fn run(
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x: &Tensor,
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k: &Tensor,
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padding: usize,
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output_padding: usize,
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stride: usize,
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dilation: usize,
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) {
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x.conv_transpose2d(k, padding, output_padding, stride, dilation)
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.unwrap();
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}
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fn run_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
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let t = Tensor::arange(0.0f32, 10000.0, device)
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.unwrap()
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.reshape((1, 4, 50, 50))
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.unwrap()
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.to_dtype(dtype)
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.unwrap();
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let kernel = Tensor::arange(0.0f32, 100.0, device)
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.unwrap()
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.reshape((4, 1, 5, 5))
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.unwrap()
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.to_dtype(dtype)
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.unwrap();
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let flops = t.dims().iter().product::<usize>() * dtype.size_in_bytes();
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let mut group = c.benchmark_group(device.bench_name(name));
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group.throughput(Throughput::Bytes(flops as u64));
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group.bench_function("iter", move |b| {
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b.iter_custom(|iters| {
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let start = Instant::now();
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for _i in 0..iters {
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run(black_box(&t), black_box(&kernel), 1, 0, 1, 2);
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}
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device.sync().unwrap();
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start.elapsed()
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})
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});
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group.finish();
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}
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fn criterion_benchmark(c: &mut Criterion) {
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let handler = BenchDeviceHandler::new().unwrap();
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for device in handler.devices {
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run_benchmark(c, &device, DType::F32, "conv_transpose2d_f32");
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run_benchmark(c, &device, DType::F16, "conv_transpose2d_f16");
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run_benchmark(c, &device, DType::BF16, "conv_transpose2d_bf16");
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}
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}
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criterion_group!(benches, criterion_benchmark);
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