[burn-autodiff] Fix abs NaN when output is 0 (#2249)

crates/burn-autodiff/src/ops/tensor.rs

@@ -1816,8 +1816,7 @@ impl<B: Backend, C: CheckpointStrategy> FloatTensorOps<Self> for Autodiff<B, C>
             ) {
                 let tensor: B::FloatTensorPrimitive<D> =
                     checkpointer.retrieve_node_output(ops.state);
-                let output = B::float_abs(tensor.clone());
+                let state = B::float_sign(tensor);
-                let state = B::float_div(tensor, output);
                 unary::<B, D, D, _>(ops.parents, ops.node, grads, |grad| {
                     B::float_mul(grad, state)
                 });

crates/burn-autodiff/src/tests/abs.rs

@@ -25,4 +25,28 @@ mod tests {
         let expected = TensorData::from([[84.0, 42.0], [90.0, 54.0]]);
         grad_2.to_data().assert_approx_eq(&expected, 3);
     }
+
+    #[test]
+    fn should_diff_abs_no_nans() {
+        let data_1 = TensorData::from([[6.0, 7.0], [9.0, -10.0]]);
+        let data_2 = TensorData::from([[0.0, -1.0], [3.0, 4.0]]);
+
+        let device = Default::default();
+        let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
+        let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
+
+        let tensor_3 = tensor_1.clone().matmul(tensor_2.clone().abs());
+        let grads = tensor_3.backward();
+
+        let grad_1 = tensor_1.grad(&grads).unwrap();
+        let grad_2 = tensor_2.grad(&grads).unwrap();
+
+        let expected = TensorData::from([[1.0, 7.0], [1.0, 7.0]]);
+        grad_1.to_data().assert_approx_eq(&expected, 3);
+
+        let expected = TensorData::from([[0.0, -15.0], [-3.0, -3.0]]);
+        grad_2.to_data().assert_approx_eq(&expected, 3);
+
+        assert_eq!(grad_2.contains_nan().into_scalar(), false);
+    }
 }