Updating documentation for tf.scatter_nd_update to reflect the actual implementation, which is in case of duplicates we pick the first one. Also added tests for both CPU and GPU to assert this behavior.

PiperOrigin-RevId: 334021744
Change-Id: Ib045d04a9631b6244327d0acac6ad44ed4b5c65e

tensorflow/core/api_def/base_api/api_def_TensorScatterUpdate.pbtxt

@@ -33,12 +33,14 @@ This operation is very similar to `tf.scatter_nd`, except that the updates are
 scattered onto an existing tensor (as opposed to a zero-tensor). If the memory
 for the existing tensor cannot be re-used, a copy is made and updated.
 
-If `indices` contains duplicates, then their updates are accumulated (summed).
+If `indices` contains duplicates, then we pick the last update for the index.
 
-**WARNING**: The order in which updates are applied is nondeterministic, so the
-output will be nondeterministic if `indices` contains duplicates -- because
-of some numerical approximation issues, numbers summed in different order
-may yield different results.
+If an out of bound index is found, an error is returned.
+
+**WARNING**: There are some GPU specific semantics for this operation.
+- If an out of bound index is found, the index is ignored.
+- The order in which updates are applied is nondeterministic, so the output
+will be nondeterministic if `indices` contains duplicates.
 
 `indices` is an integer tensor containing indices into a new tensor of shape
 `shape`.  The last dimension of `indices` can be at most the rank of `shape`:
@@ -98,7 +100,5 @@ In Python, this scatter operation would look like this:
       [1 1 1 1]
       [1 1 1 1]]]
 
-Note that on CPU, if an out of bound index is found, an error is returned.
-On GPU, if an out of bound index is found, the index is ignored.
 END
 }

tensorflow/python/kernel_tests/array_ops/BUILD

@@ -76,6 +76,7 @@ cuda_py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
         "//tensorflow/python:resource_variable_ops",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:variables",

tensorflow/python/kernel_tests/array_ops/scatter_nd_ops_test.py

@@ -32,6 +32,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker_v2
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variables
@@ -909,6 +910,32 @@ class ScatterNdTensorTest(test.TestCase):
             "hello", "there", "hello", "there", "there", "hello", "hello", "12"
         ]))
 
+  @test_util.run_in_graph_and_eager_modes
+  def testUpdateRepeatedIndices1D(self):
+    if test_util.is_gpu_available():
+      self.skipTest("Duplicate indices scatter is non-deterministic on GPU")
+    a = array_ops.zeros([10, 1])
+    b = array_ops.tensor_scatter_update(a, [[5], [5]], [[4], [8]])
+    self.assertAllEqual(
+        b,
+        constant_op.constant([[0.], [0.], [0.], [0.], [0.], [8.], [0.], [0.],
+                              [0.], [0.]]))
+
+  @test_util.run_in_graph_and_eager_modes
+  def testUpdateRepeatedIndices2D(self):
+    if test_util.is_gpu_available():
+      self.skipTest("Duplicate indices scatter is non-deterministic on GPU")
+    a = array_ops.zeros([10, 10])
+    b = array_ops.tensor_scatter_update(
+        a, [[5], [6], [6]],
+        [math_ops.range(10),
+         math_ops.range(11, 21),
+         math_ops.range(10, 20)])
+    self.assertAllEqual(
+        b[6],
+        constant_op.constant(
+            [10., 11., 12., 13., 14., 15., 16., 17., 18., 19.]))
+
 
 if __name__ == "__main__":
   test.main()