Add some conv1d test + bugfix using padding. (#349)

candle-core/examples/basics.rs

@@ -8,25 +8,11 @@ use anyhow::Result;
 use candle_core::{Device, Tensor};
 
 fn main() -> Result<()> {
-    let a = Tensor::randn(0f32, 1., (2, 3), &Device::Cpu)?;
+    let inp = Tensor::randn(0f32, 1., (2, 320, 96, 96), &Device::Cpu)?;
-    let b = Tensor::randn(0f32, 1., (3, 4), &Device::Cpu)?;
+    let w = Tensor::randn(0f32, 1., (320, 320, 3, 3), &Device::Cpu)?;
-    let c = a.matmul(&b)?;
+    let start = std::time::Instant::now();
-    println!("{a} {b} {c}");
+    let res = inp.conv2d(&w, 0, 1);
-
+    println!("{:?}", start.elapsed());
-    let data = &[[3f32, 1., 4., 1., 5.], [2., 7., 1., 8., 2.]];
+    println!("{res:?}");
-    let t1 = Tensor::new(data, &Device::Cpu)?;
-    let data2 = &[[5f32, 5., 5., 5., 5.], [2., 7., 1., 8., 2.]];
-    let t2 = Tensor::new(data2, &Device::Cpu)?;
-    assert_eq!(
-        Tensor::cat(&[&t1.t()?, &t2.t()?], 1)?
-            .t()?
-            .to_vec2::<f32>()?,
-        [
-            [3.0, 1.0, 4.0, 1.0, 5.0],
-            [2.0, 7.0, 1.0, 8.0, 2.0],
-            [5.0, 5.0, 5.0, 5.0, 5.0],
-            [2.0, 7.0, 1.0, 8.0, 2.0]
-        ]
-    );
     Ok(())
 }

candle-core/src/cpu_backend.rs

@@ -1011,7 +1011,7 @@ impl<'a> Map2 for Conv1D<'a> {
                     let dst_idx = dst_idx + dst_l;
                     let mut d = T::zero();
                     for offset in 0..p.k_size {
-                        let src_l_plus = p.stride * dst_l + offset;
+                        let src_l_plus = p.stride * dst_l + offset + k_over_2 - p.padding;
                         // inp[bidx, src_c_idx, dst_l + offset - k//2] * k[dst_c_idx, src_c_idx, offset]
                         if k_over_2 <= src_l_plus && src_l_plus < k_over_2 + p.l_in {
                             let src_l = src_l_plus - k_over_2;

candle-core/tests/conv_tests.rs

@@ -6,6 +6,46 @@ use candle_core::{Device, Tensor};
 import torch
 torch.manual_seed(4242)
 
+t = torch.randn((1, 4, 5))
+w = torch.randn((2, 4, 3))
+print(t.flatten())
+print(w.flatten())
+res = torch.nn.functional.conv1d(t, w)
+print(res.flatten())
+*/
+#[test]
+fn conv1d() -> Result<()> {
+    let dev = &Device::Cpu;
+    let t = Tensor::new(
+        &[
+            0.4056f32, -0.8689, -0.0773, -1.5630, 1.2279, -0.9287, -1.7030, 0.1370, 0.1866, 0.4145,
+            1.8025, -0.1536, 2.2013, -0.6836, 0.2477, 1.3127, -0.6957, 0.3278, -1.0124, 0.5599,
+        ],
+        dev,
+    )?
+    .reshape((1, 4, 5))?;
+    let w = Tensor::new(
+        &[
+            -0.8404f32, -0.3490, 0.0130, 1.3123, 0.1763, -1.9249, 1.4270, 0.9421, 0.8670, -0.7181,
+            -1.1111, 0.8869, -1.2429, 1.8357, 1.6052, -1.3844, 0.3951, -1.2036, 0.6686, 1.6261,
+            -0.6451, -0.0840, -1.4247, 0.5512,
+        ],
+        dev,
+    )?
+    .reshape((2, 4, 3))?;
+    let res = t.conv1d(&w, 0, 1)?;
+    assert_eq!(res.dims(), [1, 2, 3]);
+    assert_eq!(
+        test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
+        [2.6357, -1.3336, 4.1393, -1.1784, 3.5675, 0.5069]
+    );
+    Ok(())
+}
+
+/* This test is based on the following script.
+import torch
+torch.manual_seed(4242)
+
 t = torch.randn((1, 4, 5, 5))
 w = torch.randn((2, 4, 3, 3))
 print(t.flatten())