Add `BatchMatMul` operation

Adding the `BatchMatMul` operation.
Expanding `BasicOperations` to test the new operation.

src/TensorFlowNET.Core/APIs/tf.linalg.cs

@@ -11,5 +11,8 @@ namespace Tensorflow
 
         public static Tensor matmul(Tensor a, Tensor b) 
             => gen_math_ops.mat_mul(a, b);
+
+        public static Tensor batch_matmul(Tensor x, Tensor y)
+            => gen_math_ops.batch_mat_mul(x, y);
     }
 }

src/TensorFlowNET.Core/Gradients/math_grad.cs

@@ -153,6 +153,11 @@ namespace Tensorflow.Gradients
             return new Tensor[] { grad_a, grad_b };
         }
 
+        public static Tensor[] _BatchMatMul(Operation op, Tensor[] grads)
+        {
+            throw new NotImplementedException();
+        }
+
         [RegisterGradient("Mean")]
         public static Tensor[] _MeanGrad(Operation op, Tensor[] grads)
         {

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -471,6 +471,41 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        /// <summary>
+        /// Multiply slices of the two matrices "x" and "y".
+        /// </summary>
+        /// <remarks>
+        /// The `BatchMatMul` operation is embedded into the
+        /// `MatMul` operation on the DLL side. However the expected
+        /// attributes are not the same, hence we need to expose this
+        /// method to have the right args list on the `_apply_op_helper`
+        /// function.
+        ///
+        /// For each rank > 2 the first rank - 2 dimensions are considered
+        /// as fixed, and have to be consistent across the two matrices. A
+        /// common matrix multiplication is then applied over the residual
+        /// 2 dimensions.
+        ///
+        /// e.g.
+        ///     x is (3, 6, 12); y is (3, 12, 6)
+        ///     batch_matmul(x, y) ==> (3, 6, 6)
+        /// </remarks>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <param name="adj_x"></param>
+        /// <param name="adj_y"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor batch_mat_mul(Tensor x, Tensor y, bool adj_x = false, bool adj_y = false, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper(
+                "BatchMatMul",
+                name,
+                args: new { x, y, adj_x, adj_y });
+
+            return _op.outputs[0];
+        }
+
         /// <summary>
         /// Returns the max of x and y (i.e. x > y ? x : y) element-wise.
         /// </summary>

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -497,6 +497,25 @@ namespace Tensorflow
             return result;
         }
 
+        public static Tensor batch_matmul(Tensor x, Tensor y,
+            bool adj_x = false, bool adj_y = false,
+            string name = null)
+        {
+            Tensor result = null;
+
+            with(ops.name_scope(name, "MatMul", new Tensor[] { x, y }), scope =>
+            {
+                name = scope;
+
+                x = ops.convert_to_tensor(x, name: "a");
+                y = ops.convert_to_tensor(y, name: "b");
+
+                result = gen_math_ops.batch_mat_mul(x, y, adj_x, adj_y, name);
+            });
+
+            return result;
+        }
+
         /// <summary>
         /// Returns the complex conjugate of a complex number.
         /// </summary>

test/TensorFlowNET.Examples/BasicOperations.cs

@@ -91,11 +91,69 @@ namespace TensorFlowNET.Examples
             // graph: the two constants and matmul.
             //
             // The output of the op is returned in 'result' as a numpy `ndarray` object.
-            return with(tf.Session(), sess =>
+            using (sess = tf.Session())
             {
                 var result = sess.run(product);
                 Console.WriteLine(result.ToString()); // ==> [[ 12.]]
-                return result.Data<int>()[0] == 12;
+            };
+
+            // `BatchMatMul` is actually embedded into the `MatMul` operation on the tensorflow.dll side. Every time we ask
+            // for a multiplication between matrices with rank > 2, the first rank - 2 dimensions are checked to be consistent
+            // across the two matrices and a common matrix multiplication is done on the residual 2 dimensions.
+            //
+            // np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9]).reshape(3, 3, 3)
+            // array([[[1, 2, 3],
+            //     [4, 5, 6],
+            //     [7, 8, 9]],
+            //
+            //     [[1, 2, 3],
+            //     [4, 5, 6],
+            //     [7, 8, 9]],
+            //
+            //     [[1, 2, 3],
+            //     [4, 5, 6],
+            //     [7, 8, 9]]])
+            var firstTensor = tf.convert_to_tensor(
+                np.reshape(
+                    np.array<float>(1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9),
+                    3, 3, 3));
+            //
+            // np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0]).reshape(3,3,2)
+            // array([[[0, 1],
+            //     [0, 1],
+            //     [0, 1]],
+            //
+            //     [[0, 1],
+            //     [0, 0],
+            //     [1, 0]],
+            //
+            //     [[1, 0],
+            //     [1, 0],
+            //     [1, 0]]])
+            var secondTensor = tf.convert_to_tensor(
+                np.reshape(
+                    np.array<float>(0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0),
+                    3, 3, 2));
+            var batchMul = tf.batch_matmul(firstTensor, secondTensor);
+            var checkTensor = np.array<float>(0, 6, 0, 15, 0, 24, 3, 1, 6, 4, 9, 7, 6, 0, 15, 0, 24, 0);
+            return with(tf.Session(), sess =>
+            {
+                var result = sess.run(batchMul);
+                Console.WriteLine(result.ToString());
+                //
+                // ==> array([[[0, 6],
+                //         [0, 15],
+                //         [0, 24]],
+                //
+                //         [[ 3,  1],
+                //         [ 6,  4],
+                //         [ 9,  7]],
+                //
+                //         [[ 6,  0],
+                //         [15,  0],
+                //         [24,  0]]])
+                return np.reshape(result, 18)
+                    .array_equal(checkTensor);
             });
         }