[Relay] Fix a bug in tensor_array_scatter

  tensor_array_scatter constructs helper functions according to dtype
  and shape of element. When there are multiple scatter operations with
  same dtype and element shape but different indicies_shape, there will
  be name conflict in prelude.

Author: lixiaoquan

python/tvm/relay/prelude.py

@@ -383,6 +383,12 @@ def define_tensor_array_scatter(self, indices_shape=None, force_update=False):
             return
 
         tensor_array_scatter_helper_name = self.get_name("tensor_array_scatter_helper")
+
+        if indices_shape:
+            # Put indices_shape into variable name
+            tensor_array_scatter_name += "_" + str(indices_shape)
+            tensor_array_scatter_helper_name += "_" + str(indices_shape)
+
         tensor_array_scatter_helper_var = self._create_global_var(tensor_array_scatter_helper_name)
         ta = Var("ta", self.list(self.tensor_type_var()))
         current = Var("current", scalar_type("int32"))

tests/python/frontend/tensorflow/test_forward.py

@@ -1388,19 +1388,29 @@ def run(dtype_str, infer_shape):
                 element_shape = tf.TensorShape([tf.Dimension(None)])
             else:
                 element_shape = None
-            t = tf.constant(np.array([[1.0], [2.0], [3.0]]).astype(dtype_str), dtype=dtype)
-            indices = tf.constant([2, 1, 0])
-            ta1 = tf.TensorArray(
-                dtype=dtype, size=3, infer_shape=infer_shape, element_shape=element_shape
-            )
-            ta2 = ta1.scatter(indices, t)
-            out0 = ta2.read(0)
-            out1 = ta2.read(1)
-            out2 = ta2.read(2)
+            ta0 = _construct_scatter(dtype, dtype_str, element_shape, infer_shape, 3)
+            out0 = ta0.read(0)
+            out1 = ta0.read(1)
+            out2 = ta0.read(2)
+            ta1 = _construct_scatter(dtype, dtype_str, element_shape, infer_shape, 4)
+            out4 = ta1.read(0)
             g = tf.get_default_graph()
             compare_tf_with_tvm([], [], ["TensorArrayReadV3:0"], mode="vm")
             compare_tf_with_tvm([], [], ["TensorArrayReadV3_1:0"], mode="vm")
             compare_tf_with_tvm([], [], ["TensorArrayReadV3_2:0"], mode="vm")
+            compare_tf_with_tvm([], [], ["TensorArrayReadV3_2:0", out4.name], mode="vm")
+
+    def _construct_scatter(dtype, dtype_str, element_shape, infer_shape, size):
+        arr = [[float(i)] for i in range(size)]
+        indices_arr = [i for i in range(size - 1, -1, -1)]
+
+        t = tf.constant(np.array(arr).astype(dtype_str), dtype=dtype)
+        indices = tf.constant(indices_arr)
+        ta1 = tf.TensorArray(
+            dtype=dtype, size=size, infer_shape=infer_shape, element_shape=element_shape
+        )
+        ta2 = ta1.scatter(indices, t)
+        return ta2
 
     for dtype in ["float32", "int8"]:
         run(dtype, False)