diff --git a/tensorflow/python/ops/numpy_ops/BUILD b/tensorflow/python/ops/numpy_ops/BUILD
index 3479a622bc0..704e5a27b48 100644
--- a/tensorflow/python/ops/numpy_ops/BUILD
+++ b/tensorflow/python/ops/numpy_ops/BUILD
@@ -65,16 +65,6 @@ cuda_py_test(
],
)
-cuda_py_test(
- name = "np_backprop_test",
- srcs = ["np_backprop_test.py"],
- deps = [
- ":numpy",
- "//tensorflow/python:platform",
- "@absl_py//absl/testing:parameterized",
- ],
-)
-
cuda_py_test(
name = "np_logic_test",
srcs = ["np_logic_test.py"],
diff --git a/tensorflow/python/ops/numpy_ops/np_array_ops.py b/tensorflow/python/ops/numpy_ops/np_array_ops.py
index a87c72ed763..25241272699 100644
--- a/tensorflow/python/ops/numpy_ops/np_array_ops.py
+++ b/tensorflow/python/ops/numpy_ops/np_array_ops.py
@@ -852,66 +852,10 @@ def moveaxis(a, source, destination): # pylint: disable=missing-docstring
return np_utils.tensor_to_ndarray(a)
-def _setitem(arr, index, value):
- """Sets the `value` at `index` in the array `arr`.
-
- This works by replacing the slice at `index` in the tensor with `value`.
- Since tensors are immutable, this builds a new tensor using the `tf.concat`
- op. Currently, only 0-d and 1-d indices are supported.
-
- Note that this may break gradients e.g.
-
- a = tf_np.array([1, 2, 3])
- old_a_t = a.data
-
- with tf.GradientTape(persistent=True) as g:
- g.watch(a.data)
- b = a * 2
- a[0] = 5
- g.gradient(b.data, [a.data]) # [None]
- g.gradient(b.data, [old_a_t]) # [[2., 2., 2.]]
-
- Here `d_b / d_a` is `[None]` since a.data no longer points to the same
- tensor.
-
- Args:
- arr: array_like.
- index: scalar or 1-d integer array.
- value: value to set at index.
-
- Returns:
- ndarray
-
- Raises:
- ValueError: if `index` is not a scalar or 1-d array.
- """
- # TODO(srbs): Figure out a solution to the gradient problem.
- arr = asarray(arr)
- index = asarray(index)
- if index.ndim == 0:
- index = ravel(index)
- elif index.ndim > 1:
- raise ValueError('index must be a scalar or a 1-d array.')
- value = asarray(value, dtype=arr.dtype)
- if arr.shape[len(index):] != value.shape:
- value = full(arr.shape[len(index):], value)
- prefix_t = arr.data[:index.data[0]]
- postfix_t = arr.data[index.data[0] + 1:]
- if len(index) == 1:
- arr._data = array_ops.concat( # pylint: disable=protected-access
- [prefix_t, array_ops.expand_dims(value.data, 0), postfix_t], 0)
- else:
- subarray = arr[index.data[0]]
- _setitem(subarray, index[1:], value)
- arr._data = array_ops.concat( # pylint: disable=protected-access
- [prefix_t, array_ops.expand_dims(subarray.data, 0), postfix_t], 0)
-
-
# TODO(wangpeng): Make a custom `setattr` that also sets docstring for the
# method.
setattr(np_arrays.ndarray, 'transpose', transpose)
setattr(np_arrays.ndarray, 'reshape', _reshape_method_wrapper)
-setattr(np_arrays.ndarray, '__setitem__', _setitem)
@np_utils.np_doc('pad')
diff --git a/tensorflow/python/ops/numpy_ops/np_array_ops_test.py b/tensorflow/python/ops/numpy_ops/np_array_ops_test.py
index 25a6a53507a..d52e0c4ea83 100644
--- a/tensorflow/python/ops/numpy_ops/np_array_ops_test.py
+++ b/tensorflow/python/ops/numpy_ops/np_array_ops_test.py
@@ -911,26 +911,6 @@ class ArrayMethodsTest(test.TestCase):
run_test(np.arange(30).reshape(2, 3, 5).tolist(), [2, 0, 1])
run_test(np.arange(30).reshape(2, 3, 5).tolist(), [2, 1, 0])
- def testSetItem(self):
-
- def run_test(arr, index, value):
- for fn in self.array_transforms:
- value_arg = fn(value)
- tf_array = np_array_ops.array(arr)
- np_array = np.array(arr)
- tf_array[index] = value_arg
- # TODO(srbs): "setting an array element with a sequence" is thrown
- # if we do not wrap value_arg in a numpy array. Investigate how this can
- # be avoided.
- np_array[index] = np.array(value_arg)
- self.match(tf_array, np_array)
-
- run_test([1, 2, 3], 1, 5)
- run_test([[1, 2], [3, 4]], 0, [6, 7])
- run_test([[1, 2], [3, 4]], 1, [6, 7])
- run_test([[1, 2], [3, 4]], (0, 1), 6)
- run_test([[1, 2], [3, 4]], 0, 6) # Value needs to broadcast.
-
def match_shape(self, actual, expected, msg=None):
if msg:
msg = 'Shape match failed for: {}. Expected: {} Actual: {}'.format(
diff --git a/tensorflow/python/ops/numpy_ops/np_backprop_test.py b/tensorflow/python/ops/numpy_ops/np_backprop_test.py
deleted file mode 100644
index 65c532153a4..00000000000
--- a/tensorflow/python/ops/numpy_ops/np_backprop_test.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for backpropgration on tf-numpy functions."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.python.eager import backprop
-from tensorflow.python.framework import ops
-from tensorflow.python.ops.numpy_ops import np_array_ops
-# Required for operator overloads
-from tensorflow.python.ops.numpy_ops import np_math_ops # pylint: disable=unused-import
-from tensorflow.python.platform import test
-
-
-class BackpropTest(test.TestCase):
-
- def test_setitem(self):
- # Single integer index.
- a = np_array_ops.array([1., 2., 3.])
- b = np_array_ops.array(5.)
- c = np_array_ops.array(10.)
-
- tensors = [arr.data for arr in [a, b, c]]
- with backprop.GradientTape() as g:
- g.watch(tensors)
- a[1] = b + c
- loss = np_array_ops.sum(a)
-
- gradients = g.gradient(loss.data, tensors)
- self.assertSequenceEqual(
- np_array_ops.array(gradients[0]).tolist(), [1., 0., 1.])
- self.assertEqual(np_array_ops.array(gradients[1]).tolist(), 1.)
- self.assertEqual(np_array_ops.array(gradients[2]).tolist(), 1.)
-
- # Tuple index.
- a = np_array_ops.array([[[1., 2.], [3., 4.]], [[5., 6.],
- [7., 8.]]]) # 2x2x2 array.
- b = np_array_ops.array([10., 11.])
-
- tensors = [arr.data for arr in [a, b]]
- with backprop.GradientTape() as g:
- g.watch(tensors)
- a[(1, 0)] = b
- loss = np_array_ops.sum(a)
-
- gradients = g.gradient(loss.data, tensors)
- self.assertSequenceEqual(
- np_array_ops.array(gradients[0]).tolist(),
- [[[1., 1.], [1., 1.]], [[0., 0.], [1., 1.]]])
- self.assertEqual(np_array_ops.array(gradients[1]).tolist(), [1., 1.])
-
-
-if __name__ == '__main__':
- ops.enable_eager_execution()
- test.main()