From a73694ab2db42b2a4ea560c6bbb36ed9175fc5fb Mon Sep 17 00:00:00 2001
From: Wanglongzhi2001 <583087864@qq.com>
Date: Fri, 20 Oct 2023 11:24:27 +0800
Subject: [PATCH] fix: add the implementation of the tile's grad

---
 .../Gradients/array_grad.cs                   | 24 +++++++++++++++++++
 .../Operations/array_ops.cs                   |  2 +-
 .../GradientTest/GradientEagerTest.cs         | 14 +++++++++++
 3 files changed, 39 insertions(+), 1 deletion(-)

diff --git a/src/TensorFlowNET.Core/Gradients/array_grad.cs b/src/TensorFlowNET.Core/Gradients/array_grad.cs
index 4b7027992..016e4f029 100644
--- a/src/TensorFlowNET.Core/Gradients/array_grad.cs
+++ b/src/TensorFlowNET.Core/Gradients/array_grad.cs
@@ -381,5 +381,29 @@ public static Tensor[] _ReverseV2Grad(Operation op, Tensor[] grads)
             var axis = op.inputs[1];
             return new Tensor[] { array_ops.reverse(grad, axis), null };
         }
+
+        [RegisterGradient("Tile")]
+        public static Tensor[] _TileGrad(Operation op, Tensor[] grads)
+        {
+            var grad = grads[0];
+            var input_shape = array_ops.shape(op.inputs[0], out_type: op.inputs[1].dtype);
+            var split_shape = array_ops.reshape(array_ops.transpose(array_ops.stack(new Tensor[] { op.inputs[1], input_shape })), new Shape(-1));
+            var axes = math_ops.range(0, array_ops.size(split_shape), 2);
+
+            //# Sum reduces grad along the first dimension for IndexedSlices
+            //if isinstance(grad, indexed_slices_lib.IndexedSlices):
+            //input_shape_0 = math_ops.cast(input_shape[0], grad.indices.dtype)
+            //grad = math_ops.unsorted_segment_sum(
+            //    grad.values, math_ops.mod(grad.indices, input_shape_0), input_shape_0)
+            //split_shape = array_ops.concat([[1], split_shape[1:]], axis = 0)
+
+            var input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes);
+            if (!tf.Context.executing_eagerly())
+            {
+                input_grad.set_shape(op.inputs[0].GetShape());
+            }
+            return new Tensor[] { input_grad, null };
+
+        }
     }
 }
diff --git a/src/TensorFlowNET.Core/Operations/array_ops.cs b/src/TensorFlowNET.Core/Operations/array_ops.cs
index fdc53cd7e..abf44c643 100644
--- a/src/TensorFlowNET.Core/Operations/array_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/array_ops.cs
@@ -990,7 +990,7 @@ public static Tensor gather(ResourceVariable @params, Tensor indices, string nam
             return @params.sparse_read(indices, name);
         }
 
-        public static Tensor transpose<T1>(T1 a, Axis perm, string name = "transpose", bool conjugate = false)
+        public static Tensor transpose<T1>(T1 a, Axis perm = null, string name = "transpose", bool conjugate = false)
         {
             return tf_with(ops.name_scope(name, "transpose", new { a }), scope =>
             {
diff --git a/test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs b/test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs
index e41e1d617..ed7599045 100644
--- a/test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs
+++ b/test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs
@@ -173,5 +173,19 @@ public void ConditionalMultiply()
             var result = grad(x, 4);
             Assert.AreEqual((float)result, 4.0f);
         }
+
+        [TestMethod]
+        public void Tile()
+        {
+            var a = tf.constant(new int[] { 1 }, TF_DataType.TF_FLOAT);
+            var b = tf.constant(new int[] { 2 });
+            using (var tape = tf.GradientTape())
+            {
+                tape.watch(a);
+                var y = tf.tile(a, b);
+                var grad = tape.gradient(y, a);
+                Assert.AreEqual((float)grad.numpy(), 2.0f);
+            }
+        }
     }
 }