Fix tests (#1089)

* Fix tests * Fix fmt * Fix CI
tracel-ai · Dec 21, 2023 · d82e6b1 · d82e6b1
1 parent 1fd07fc
commit d82e6b1
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Showing 34 changed files with 166 additions and 198 deletions.
diff --git a/burn-autodiff/src/tests/log1p.rs b/burn-autodiff/src/tests/log1p.rs
@@ -5,11 +5,8 @@ mod tests {
 
     #[test]
     fn should_diff_log1p() {
-        let data_1 = Data::<f32, 2>::from([[0.0, 1.0], [3.0, 4.0]]);
-        let data_2 = Data::<f32, 2>::from([[6.0, 7.0], [9.0, 10.0]]);
-
-        let tensor_1 = TestAutodiffTensor::from_data_devauto(data_1).require_grad();
-        let tensor_2 = TestAutodiffTensor::from_data_devauto(data_2).require_grad();
+        let tensor_1 = TestAutodiffTensor::from([[0.0, 1.0], [3.0, 4.0]]).require_grad();
+        let tensor_2 = TestAutodiffTensor::from([[6.0, 7.0], [9.0, 10.0]]).require_grad();
 
         let tensor_3 = tensor_1.clone().matmul(tensor_2.clone().log1p());
         let tensor_4 = tensor_3.matmul(tensor_2.clone());

diff --git a/burn-candle/src/backend.rs b/burn-candle/src/backend.rs
@@ -50,7 +50,7 @@ impl From<candle_core::Device> for CandleDevice {
         match device.location() {
             DeviceLocation::Cpu => CandleDevice::Cpu,
             DeviceLocation::Cuda { gpu_id } => CandleDevice::Cuda(gpu_id),
-            DeviceLocation::Metal => panic!("Metal unsupported"),
+            _ => panic!("Device unsupported: {device:?}"),
         }
     }
 }

diff --git a/burn-candle/src/lib.rs b/burn-candle/src/lib.rs
@@ -24,6 +24,8 @@ mod tests {
     pub type TestTensor<const D: usize> = burn_tensor::Tensor<TestBackend, D>;
     pub type ReferenceTensor<const D: usize> = burn_tensor::Tensor<ReferenceBackend, D>;
     pub type TestTensorInt<const D: usize> = burn_tensor::Tensor<TestBackend, D, burn_tensor::Int>;
+    pub type TestTensorBool<const D: usize> =
+        burn_tensor::Tensor<TestBackend, D, burn_tensor::Bool>;
 
     type TestAutodiffBackend = burn_autodiff::Autodiff<TestBackend>;
     type TestAutodiffTensor<const D: usize> = burn_tensor::Tensor<TestAutodiffBackend, D>;

diff --git a/burn-ndarray/src/lib.rs b/burn-ndarray/src/lib.rs
@@ -32,6 +32,8 @@ mod tests {
     type TestBackend = crate::NdArray<f32>;
     type TestTensor<const D: usize> = burn_tensor::Tensor<TestBackend, D>;
     type TestTensorInt<const D: usize> = burn_tensor::Tensor<TestBackend, D, burn_tensor::Int>;
+    type TestTensorBool<const D: usize> = burn_tensor::Tensor<TestBackend, D, burn_tensor::Bool>;
+
     use alloc::format;
     use alloc::vec;
 

diff --git a/burn-tch/src/lib.rs b/burn-tch/src/lib.rs
@@ -19,6 +19,7 @@ mod tests {
     type TestBackend = crate::LibTorch<f32>;
     type TestTensor<const D: usize> = burn_tensor::Tensor<TestBackend, D>;
     type TestTensorInt<const D: usize> = burn_tensor::Tensor<TestBackend, D, burn_tensor::Int>;
+    type TestTensorBool<const D: usize> = burn_tensor::Tensor<TestBackend, D, burn_tensor::Bool>;
 
     burn_tensor::testgen_all!();
     burn_autodiff::testgen_all!();

diff --git a/burn-tensor/src/tensor/api/base.rs b/burn-tensor/src/tensor/api/base.rs
@@ -27,6 +27,17 @@ where
     pub(crate) primitive: K::Primitive<D>,
 }
 
+impl<B, const D: usize, K, T> From<T> for Tensor<B, D, K>
+where
+    B: Backend,
+    K: BasicOps<B>,
+    T: Into<Data<K::Elem, D>>,
+{
+    fn from(value: T) -> Self {
+        Tensor::from_data(value.into(), &Default::default())
+    }
+}
+
 impl<B, const D: usize, K> Tensor<B, D, K>
 where
     B: Backend,

diff --git a/burn-tensor/src/tests/activation/gelu.rs b/burn-tensor/src/tests/activation/gelu.rs
@@ -5,14 +5,10 @@ mod tests {
 
     #[test]
     fn test_gelu() {
-        let data = Data::from([[
+        let tensor = TestTensor::from([[
             0.5447, 0.9809, 0.4114, 0.1398, 0.8045, 0.4103, 0.2388, 0.5262, 0.6677, 0.6737,
         ]]);
-        let tensor = Tensor::<TestBackend, 2>::from_data_devauto(data)
-            .clone()
-            .clone();
-
-        let data_actual = activation::gelu(tensor).to_data();
+        let data_actual = activation::gelu(tensor).into_data();
 
         let data_expected = Data::from([[
             0.3851, 0.8207, 0.2714, 0.0777, 0.6351, 0.2704, 0.1419, 0.3687, 0.4993, 0.5051,

diff --git a/burn-tensor/src/tests/activation/mish.rs b/burn-tensor/src/tests/activation/mish.rs
@@ -5,10 +5,9 @@ mod tests {
 
     #[test]
     fn test_mish() {
-        let data = Data::from([[-0.4240, -0.9574, -0.2215], [-0.5767, 0.7218, -0.1620]]);
-        let tensor = Tensor::<TestBackend, 2>::from_data(data);
+        let tensor = TestTensor::from([[-0.4240, -0.9574, -0.2215], [-0.5767, 0.7218, -0.1620]]);
 
-        let data_actual = activation::mish(tensor).to_data();
+        let data_actual = activation::mish(tensor).into_data();
 
         let data_expected = Data::from([[-0.1971, -0.3006, -0.1172], [-0.2413, 0.5823, -0.0888]]);
         data_actual.assert_approx_eq(&data_expected, 4);

diff --git a/burn-tensor/src/tests/activation/quiet_softmax.rs b/burn-tensor/src/tests/activation/quiet_softmax.rs
@@ -5,10 +5,9 @@ mod tests {
 
     #[test]
     fn test_quiet_softmax_d2() {
-        let data = Data::from([[1.0, 7.0], [13.0, -3.0]]);
-        let tensor = Tensor::<TestBackend, 2>::from_data(data);
+        let tensor = TestTensor::from([[1.0, 7.0], [13.0, -3.0]]);
 
-        let data_actual = activation::quiet_softmax(tensor, 1).to_data();
+        let data_actual = activation::quiet_softmax(tensor, 1).into_data();
 
         let data_expected = Data::from([[2.47e-03, 9.975e-01], [1.0, 1.1254e-07]]);
         data_actual.assert_approx_eq(&data_expected, 4);

diff --git a/burn-tensor/src/tests/activation/relu.rs b/burn-tensor/src/tests/activation/relu.rs
@@ -5,10 +5,9 @@ mod tests {
 
     #[test]
     fn test_relu_d2() {
-        let data = Data::from([[0.0, -1.0, 2.0], [3.0, -4.0, 5.0]]);
-        let tensor = Tensor::<TestBackend, 2>::from_data_devauto(data);
+        let tensor = TestTensor::from([[0.0, -1.0, 2.0], [3.0, -4.0, 5.0]]);
 
-        let data_actual = activation::relu(tensor).to_data();
+        let data_actual = activation::relu(tensor).into_data();
 
         let data_expected = Data::from([[0.0, 0.0, 2.0], [3.0, 0.0, 5.0]]);
         assert_eq!(data_expected, data_actual);

diff --git a/burn-tensor/src/tests/activation/sigmoid.rs b/burn-tensor/src/tests/activation/sigmoid.rs
@@ -5,21 +5,19 @@ mod tests {
 
     #[test]
     fn test_sigmoid() {
-        let data = Data::from([[1.0, 7.0], [13.0, -3.0]]);
-        let tensor = Tensor::<TestBackend, 2>::from_data_devauto(data);
+        let tensor = TestTensor::from([[1.0, 7.0], [13.0, -3.0]]);
 
-        let data_actual = activation::sigmoid(tensor).to_data();
+        let data_actual = activation::sigmoid(tensor).into_data();
 
         let data_expected = Data::from([[0.7311, 0.9991], [1.0, 0.0474]]);
         data_actual.assert_approx_eq(&data_expected, 4);
     }
 
     #[test]
     fn test_sigmoid_overflow() {
-        let data = Data::from([f32::MAX, f32::MIN]);
-        let tensor = Tensor::<TestBackend, 1>::from_data_devauto(data);
+        let tensor = TestTensor::from([f32::MAX, f32::MIN]);
 
-        let data_actual = activation::sigmoid(tensor).to_data();
+        let data_actual = activation::sigmoid(tensor).into_data();
 
         let data_expected = Data::from([1.0, 0.0]);
         data_actual.assert_approx_eq(&data_expected, 4);

diff --git a/burn-tensor/src/tests/activation/silu.rs b/burn-tensor/src/tests/activation/silu.rs
@@ -5,10 +5,9 @@ mod tests {
 
     #[test]
     fn test_silu() {
-        let data = Data::from([[1.0, 2.0], [3.0, 4.0]]);
-        let tensor = Tensor::<TestBackend, 2>::from_data_devauto(data);
+        let tensor = TestTensor::from([[1.0, 2.0], [3.0, 4.0]]);
 
-        let data_actual = activation::silu(tensor).to_data();
+        let data_actual = activation::silu(tensor).into_data();
 
         let data_expected = Data::from([[0.7311, 1.7616], [2.8577, 3.9281]]);
         data_actual.assert_approx_eq(&data_expected, 4);

diff --git a/burn-tensor/src/tests/activation/softmax.rs b/burn-tensor/src/tests/activation/softmax.rs
@@ -5,10 +5,9 @@ mod tests {
 
     #[test]
     fn test_softmax_d2() {
-        let data = Data::from([[1.0, 7.0], [13.0, -3.0]]);
-        let tensor = Tensor::<TestBackend, 2>::from_data_devauto(data);
+        let tensor = TestTensor::from([[1.0, 7.0], [13.0, -3.0]]);
 
-        let data_actual = activation::softmax(tensor, 1).to_data();
+        let data_actual = activation::softmax(tensor, 1).into_data();
 
         let data_expected = Data::from([[2.47e-03, 9.975e-01], [1.0, 1.1254e-07]]);
         data_actual.assert_approx_eq(&data_expected, 4);

diff --git a/burn-tensor/src/tests/activation/softplus.rs b/burn-tensor/src/tests/activation/softplus.rs
@@ -5,10 +5,12 @@ mod tests {
 
     #[test]
     fn test_softplus_d2() {
-        let data = Data::from([[-0.4240, -0.9574, -0.2215], [-0.5767, 0.7218, -0.1620]]);
-        let tensor = Tensor::<TestBackend, 2>::from_data(data);
+        let tensor = Tensor::<TestBackend, 2>::from([
+            [-0.4240, -0.9574, -0.2215],
+            [-0.5767, 0.7218, -0.1620],
+        ]);
 
-        let data_actual_beta1 = activation::softplus(tensor.clone(), 1.0).to_data();
+        let data_actual_beta1 = activation::softplus(tensor.clone(), 1.0).into_data();
         let data_expected_beta1 = Data::from([[0.5034, 0.3249, 0.5885], [0.4458, 1.1178, 0.6154]]);
         data_actual_beta1.assert_approx_eq(&data_expected_beta1, 4);
 

diff --git a/burn-tensor/src/tests/activation/tanh_activation.rs b/burn-tensor/src/tests/activation/tanh_activation.rs
@@ -5,10 +5,9 @@ mod tests {
 
     #[test]
     fn test_tanh() {
-        let data = Data::from([[1., 2.], [3., 4.]]);
-        let tensor = Tensor::<TestBackend, 2>::from_data_devauto(data);
+        let tensor = TestTensor::from([[1., 2.], [3., 4.]]);
 
-        let data_actual = activation::tanh(tensor).to_data();
+        let data_actual = activation::tanh(tensor).into_data();
 
         let data_expected = Data::from([[0.7616, 0.9640], [0.9951, 0.9993]]);
         data_actual.assert_approx_eq(&data_expected, 4);

diff --git a/burn-tensor/src/tests/module/adaptive_avgpool1d.rs b/burn-tensor/src/tests/module/adaptive_avgpool1d.rs
@@ -13,7 +13,7 @@ mod tests {
             length_out: 4,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[
+        test.assert_output(TestTensor::from([[
             [0.5, 2.5, 4.5, 6.5],
             [8.5, 10.5, 12.5, 14.5],
         ]]));
@@ -28,10 +28,7 @@ mod tests {
             length_out: 3,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[
-            [1.0, 3.0, 5.0],
-            [8.0, 10.0, 12.0],
-        ]]));
+        test.assert_output(TestTensor::from([[[1.0, 3.0, 5.0], [8.0, 10.0, 12.0]]]));
     }
 
     #[test]
@@ -43,7 +40,7 @@ mod tests {
             length_out: 8,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[
+        test.assert_output(TestTensor::from([[
             [0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0],
             [4.0, 4.0, 5.0, 5.0, 6.0, 6.0, 7.0, 7.0],
         ]]));

diff --git a/burn-tensor/src/tests/module/adaptive_avgpool2d.rs b/burn-tensor/src/tests/module/adaptive_avgpool2d.rs
@@ -15,7 +15,7 @@ mod tests {
             width_out: 4,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[
+        test.assert_output(TestTensor::from([[
             [
                 [3.5000, 4.5000, 6.5000, 7.5000],
                 [15.5000, 16.5000, 18.5000, 19.5000],
@@ -42,7 +42,7 @@ mod tests {
             width_out: 2,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[
+        test.assert_output(TestTensor::from([[
             [[5.0000, 8.0000], [15.5000, 18.5000], [26.0000, 29.0000]],
             [[40.0000, 43.0000], [50.5000, 53.5000], [61.0000, 64.0000]],
         ]]));
@@ -59,7 +59,7 @@ mod tests {
             width_out: 4,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[
+        test.assert_output(TestTensor::from([[
             [
                 [0.0000, 0.5000, 1.5000, 2.0000],
                 [1.5000, 2.0000, 3.0000, 3.5000],
@@ -89,7 +89,7 @@ mod tests {
     impl AdaptiveAvgPool2dTestCase {
         fn assert_output(self, y: TestTensor<4>) {
             let shape_x = Shape::new([self.batch_size, self.channels, self.height, self.width]);
-            let x = TestTensor::from_data_devauto(
+            let x = TestTensor::from(
                 TestTensorInt::arange_devauto(0..shape_x.num_elements())
                     .reshape(shape_x)
                     .into_data()

diff --git a/burn-tensor/src/tests/module/avgpool1d.rs b/burn-tensor/src/tests/module/avgpool1d.rs
@@ -16,7 +16,7 @@ mod tests {
             count_include_pad: true,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[[1., 2., 3., 4.]]]));
+        test.assert_output(TestTensor::from([[[1., 2., 3., 4.]]]));
     }
 
     #[test]
@@ -31,7 +31,7 @@ mod tests {
             count_include_pad: true,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[
+        test.assert_output(TestTensor::from([[
             [0.3333, 2.0000, 4.0000],
             [4.3333, 8.0000, 10.0000],
         ]]));
@@ -49,7 +49,7 @@ mod tests {
             count_include_pad: false,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[
+        test.assert_output(TestTensor::from([[
             [0.5000, 2.0000, 4.0000],
             [6.5000, 8.0000, 10.0000],
         ]]));
@@ -68,7 +68,7 @@ mod tests {
     impl AvgPool1dTestCase {
         fn assert_output(self, y: TestTensor<3>) {
             let shape_x = Shape::new([self.batch_size, self.channels, self.length]);
-            let x = TestTensor::from_data_devauto(
+            let x = TestTensor::from(
                 TestTensorInt::arange_devauto(0..shape_x.num_elements())
                     .reshape(shape_x)
                     .into_data()

diff --git a/burn-tensor/src/tests/module/avgpool2d.rs b/burn-tensor/src/tests/module/avgpool2d.rs
@@ -20,7 +20,7 @@ mod tests {
             count_include_pad: true,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[[
+        test.assert_output(TestTensor::from([[[
             [7., 8., 9., 10.],
             [13., 14., 15., 16.],
             [19., 20., 21., 22.],
@@ -44,7 +44,7 @@ mod tests {
             count_include_pad: true,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[[
+        test.assert_output(TestTensor::from([[[
             [1.1667, 3.0000, 4.3333, 2.5000],
             [3.2500, 7.5000, 9.5000, 5.2500],
             [6.2500, 13.5000, 15.5000, 8.2500],
@@ -68,7 +68,7 @@ mod tests {
             count_include_pad: false,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([[[
+        test.assert_output(TestTensor::from([[[
             [3.5000, 4.5000, 6.5000, 7.5000],
             [6.5000, 7.5000, 9.5000, 10.5000],
             [12.5000, 13.5000, 15.5000, 16.5000],
@@ -93,7 +93,7 @@ mod tests {
     impl AvgPool2dTestCase {
         fn assert_output(self, y: TestTensor<4>) {
             let shape_x = Shape::new([self.batch_size, self.channels, self.height, self.width]);
-            let x = TestTensor::from_data_devauto(
+            let x = TestTensor::from(
                 TestTensorInt::arange_devauto(0..shape_x.num_elements())
                     .reshape(shape_x)
                     .into_data()

diff --git a/burn-tensor/src/tests/module/conv1d.rs b/burn-tensor/src/tests/module/conv1d.rs
@@ -19,7 +19,7 @@ mod tests {
             length: 4,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([
+        test.assert_output(TestTensor::from([
             [[43., 67., 82., 49.], [104., 176., 227., 158.]],
             [[139., 187., 202., 113.], [392., 584., 635., 414.]],
         ]));
@@ -39,7 +39,7 @@ mod tests {
             length: 4,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([
+        test.assert_output(TestTensor::from([
             [[62., 38.], [159., 111.]],
             [[158., 102.], [447., 367.]],
         ]));
@@ -59,7 +59,7 @@ mod tests {
             length: 4,
         };
 
-        test.assert_output(TestTensor::from_floats_devauto([
+        test.assert_output(TestTensor::from([
             [[2., 5., 8., 3.], [42., 63., 75., 47.]],
             [[26., 29., 32., 11.], [114., 159., 171., 103.]],
         ]));