Add some epsilon tolerance to grad tests so that they work on cuda / …

…mkl.

candle-core/src/tensor.rs

@@ -482,6 +482,11 @@ impl Tensor {
         }
     }
 
+    /// An alias for `to_scalar`.
+    pub fn to_vec0<S: crate::WithDType>(&self) -> Result<S> {
+        self.to_scalar::<S>()
+    }
+
     /// This operation multiplies the input tensor by `mul` then adds `add` and return the result.
     /// The input values `mul` and `add` are casted to the appropriate type so some rounding might
     /// be performed.

candle-core/tests/grad_tests.rs

@@ -114,23 +114,23 @@ fn unary_grad(device: &Device) -> Result<()> {
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
     assert_eq!(
-        y.to_vec1::<f32>()?,
-        [0.14112, 0.84147096, -0.7568025, 0.14943814],
+        test_utils::to_vec1_round(&y, 4)?,
+        [0.1411, 0.8415, -0.7568, 0.1494],
     );
     assert_eq!(
-        grad_x.to_vec1::<f32>()?,
-        [-0.9899925, 0.5403023, -0.6536436, 0.9887711],
+        test_utils::to_vec1_round(grad_x, 4)?,
+        [-0.99, 0.5403, -0.6536, 0.9888],
     );
     let y = x.cos()?;
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
     assert_eq!(
-        y.to_vec1::<f32>()?,
-        [-0.9899925, 0.5403023, -0.6536436, 0.9887711],
+        test_utils::to_vec1_round(&y, 4)?,
+        [-0.99, 0.5403, -0.6536, 0.9888],
     );
     assert_eq!(
-        grad_x.to_vec1::<f32>()?,
-        [-0.14112, -0.84147096, 0.7568025, -0.14943814],
+        test_utils::to_vec1_round(grad_x, 4)?,
+        [-0.1411, -0.8415, 0.7568, -0.1494],
     );
     let y = x.sqr()?;
     let grads = y.backward()?;

candle-core/tests/test_utils.rs

@@ -20,6 +20,23 @@ macro_rules! test_device {
     };
 }
 
+pub fn to_vec1_round(t: &Tensor, digits: i32) -> Result<Vec<f32>> {
+    let b = 10f32.powi(digits);
+    let t = t.to_vec1::<f32>()?;
+    let t = t.iter().map(|t| f32::round(t * b) / b).collect();
+    Ok(t)
+}
+
+pub fn to_vec2_round(t: &Tensor, digits: i32) -> Result<Vec<Vec<f32>>> {
+    let b = 10f32.powi(digits);
+    let t = t.to_vec2::<f32>()?;
+    let t = t
+        .iter()
+        .map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
+        .collect();
+    Ok(t)
+}
+
 pub fn to_vec3_round(t: Tensor, digits: i32) -> Result<Vec<Vec<Vec<f32>>>> {
     let b = 10f32.powi(digits);
     let t = t.to_vec3::<f32>()?;