From a044907ffce553a0394db3a1204f21e3691e54af Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 29 Aug 2023 16:12:11 +0100
Subject: Dilated convolutions (#657)

* Add the dilation parameter.

* Restore the basic optimizer example.

* Dilation support in cudnn.

* Use the dilation parameter in the cpu backend.

* More dilation support.

* No support for dilation in transposed convolutions.

* Add dilation to a test.

* Remove a print.

* Helper function.
---
 candle-core/examples/basics.rs         | 2 +-
 candle-core/examples/cpu_benchmarks.rs | 4 ++--
 candle-core/examples/cuda_basics.rs    | 6 +++---
 3 files changed, 6 insertions(+), 6 deletions(-)

(limited to 'candle-core/examples')

diff --git a/candle-core/examples/basics.rs b/candle-core/examples/basics.rs
index 9d4734de..ad008177 100644
--- a/candle-core/examples/basics.rs
+++ b/candle-core/examples/basics.rs
@@ -11,7 +11,7 @@ fn main() -> Result<()> {
     let inp = Tensor::randn(0f32, 1., (2, 320, 96, 96), &Device::Cpu)?;
     let w = Tensor::randn(0f32, 1., (320, 320, 3, 3), &Device::Cpu)?;
     let start = std::time::Instant::now();
-    let res = inp.conv2d(&w, 0, 1, 1)?;
+    let res = inp.conv2d(&w, 0, 1, 1, 1)?;
     println!("{:?}", start.elapsed());
     println!("{res:?}");
     Ok(())
diff --git a/candle-core/examples/cpu_benchmarks.rs b/candle-core/examples/cpu_benchmarks.rs
index 1ebd9b75..13175ac1 100644
--- a/candle-core/examples/cpu_benchmarks.rs
+++ b/candle-core/examples/cpu_benchmarks.rs
@@ -40,7 +40,7 @@ impl Benchmark for Conv1d {
     }
 
     fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
-        d.0.conv1d(&d.1, 0, 1, 1)
+        d.0.conv1d(&d.1, 0, 1, 1, 1)
     }
 
     const ITERS: usize = 5;
@@ -59,7 +59,7 @@ impl Benchmark for Conv2d {
     }
 
     fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
-        d.0.conv2d(&d.1, 0, 1, 1)
+        d.0.conv2d(&d.1, 0, 1, 1, 1)
     }
 
     const ITERS: usize = 1;
diff --git a/candle-core/examples/cuda_basics.rs b/candle-core/examples/cuda_basics.rs
index cbdafd64..ad207461 100644
--- a/candle-core/examples/cuda_basics.rs
+++ b/candle-core/examples/cuda_basics.rs
@@ -11,11 +11,11 @@ fn main() -> Result<()> {
     let device = Device::new_cuda(0)?;
     let in_t = Tensor::rand(-1f32, 1f32, (1, 3, 12, 7), &device)?;
     let k_t = Tensor::rand(-1f32, 1f32, (6, 3, 1, 1), &device)?;
-    let out_t = in_t.conv2d(&k_t, 0, 1, 1)?;
+    let out_t = in_t.conv2d(&k_t, 0, 1, 1, 1)?;
     println!("{out_t}");
     let in_t = in_t.to_device(&Device::Cpu)?;
     let k_t = k_t.to_device(&Device::Cpu)?;
-    let out_t2 = in_t.conv2d(&k_t, 0, 1, 1)?;
+    let out_t2 = in_t.conv2d(&k_t, 0, 1, 1, 1)?;
     let diff = (out_t.to_device(&Device::Cpu)? - out_t2)?
         .sqr()?
         .sum_all()?;
@@ -23,7 +23,7 @@ fn main() -> Result<()> {
 
     let t = Tensor::randn(0f32, 1f32, (2, 4, 96, 96), &device)?;
     let w = Tensor::randn(0f32, 1f32, (320, 4, 3, 3), &device)?;
-    let res = t.conv2d(&w, 1, 1, 1)?;
+    let res = t.conv2d(&w, 1, 1, 1, 1)?;
     println!("{res:?}");
     Ok(())
 }
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