# Copyright 2015 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 inplace_ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors 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 inplace_ops from tensorflow.python.platform import test as test_lib class InplaceOpsTest(test_util.TensorFlowTestCase): def testBasicUpdate(self): for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]: with self.test_session(use_gpu=True): x = array_ops.ones([7, 3], dtype) y = np.ones([7, 3], dtype.as_numpy_dtype) self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_update(x, [3], array_ops.ones([1, 3], dtype)) y[3, :] = 1 self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_update(x, [-1], array_ops.ones([1, 3], dtype) * 2) y[-1, :] = 2 self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_update(x, 5, array_ops.ones([3], dtype) * 7) y[5, :] = 7 self.assertAllClose(x.eval(), y) def testBasicUpdateBool(self): with self.test_session(use_gpu=True): x = array_ops.ones([7, 3], dtypes.bool) y = np.ones([7, 3], dtypes.bool.as_numpy_dtype) self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_update(x, [3], array_ops.ones([1, 3], dtypes.bool)) y[3, :] = True self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_update(x, [-1], array_ops.zeros([1, 3], dtypes.bool)) y[-1, :] = False self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_update(x, 5, array_ops.zeros([3], dtypes.bool)) y[5, :] = False self.assertAllClose(x.eval(), y) def testBasicAdd(self): for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]: with self.test_session(use_gpu=True): x = array_ops.ones([7, 3], dtype) y = np.ones([7, 3], dtype.as_numpy_dtype) self.assertAllClose(x.eval(), y) x = array_ops.inplace_add(x, [3], array_ops.ones([1, 3], dtype)) y[3, :] += 1 self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_add(x, [-1], array_ops.ones([1, 3], dtype) * 2) y[-1, :] += 2 self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_add(x, 5, array_ops.ones([3], dtype) * 7) y[5, :] += 7 self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_add(x, None, array_ops.ones([7, 3], dtype) * 99) y[:, :] += 99 self.assertAllClose(x.eval(), y) def testBasicSub(self): for dtype in [dtypes.float32, dtypes.int32, dtypes.int64]: with self.test_session(use_gpu=True): x = array_ops.ones([7, 3], dtype) y = np.ones([7, 3], dtype.as_numpy_dtype) self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_sub(x, [3], array_ops.ones([1, 3], dtype)) y[3, :] -= 1 self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_sub(x, [-1], array_ops.ones([1, 3], dtype) * 2) y[-1, :] -= 2 self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_sub(x, 5, array_ops.ones([3], dtype) * 7) y[5, :] -= 7 self.assertAllClose(x.eval(), y) x = inplace_ops.inplace_sub(x, None, array_ops.ones([7, 3], dtype) * 99) y[:, :] -= 99 self.assertAllClose(x.eval(), y) def testRandom(self): with self.test_session(use_gpu=True): d0, d1, d2 = 100, 3, 5 x = array_ops.zeros([d0, d1, d2]) y = np.zeros([d0, d1, d2]) for _ in xrange(20): idx = np.random.choice(d0, d0 // 10, replace=False) val = np.random.randint(10, size=(d0 // 10, d1, d2)) op = np.random.randint(3) if op == 0: x = inplace_ops.inplace_update(x, idx, val) y[idx, :] = val elif op == 1: x = inplace_ops.inplace_add(x, idx, val) y[idx, :] += val elif op == 2: x = inplace_ops.inplace_sub(x, idx, val) y[idx, :] -= val self.assertAllClose(x.eval(), y) def testRandom1D(self): with self.test_session(use_gpu=True): d0 = 100 x = array_ops.zeros([d0]) y = np.zeros([d0]) for _ in xrange(20): idx = np.random.choice(d0, d0 // 10, replace=False) val = np.random.randint(10, size=(d0 // 10)) op = np.random.randint(3) if op == 0: x = inplace_ops.inplace_update(x, idx, val) y[idx] = val elif op == 1: x = inplace_ops.inplace_add(x, idx, val) y[idx] += val elif op == 2: x = inplace_ops.inplace_sub(x, idx, val) y[idx] -= val self.assertAllClose(x.eval(), y) def testAlias(self): with self.test_session(use_gpu=True) as sess: x = array_ops.ones([2, 3]) y = inplace_ops.alias_inplace_add(x, [0], [[1, 2, 3]]) with ops.control_dependencies([y]): z = array_ops.identity(x) _, vy, vz = sess.run([x, y, z]) self.assertAllClose(vy, vz) def testError(self): with self.cached_session(): with self.assertRaisesRegexp(errors.InvalidArgumentError, "must be a vector"): _ = inplace_ops.inplace_update([[1.]], [[0]], [[10]]).eval() with self.assertRaisesRegexp(errors.InvalidArgumentError, "x and v shape doesn't match"): _ = inplace_ops.inplace_update([[1.]], [0], [10]).eval() with self.assertRaisesRegexp(errors.InvalidArgumentError, "i and x shape doesn't match"): _ = inplace_ops.inplace_update([[1.]], [0, 1], [[10]]).eval() def testEmpty(self): for dtype in [ dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64, dtypes.bool, dtypes.uint8 ]: with self.test_session(use_gpu=True): test_shapes = [(), (1,), (2, 3), (0, 2), (2, 3, 5), (2, 0, 5)] for shape in test_shapes: val = inplace_ops.empty(shape, dtype).eval() self.assertEqual(val.shape, shape) self.assertEqual(val.dtype, dtype.as_numpy_dtype) val = inplace_ops.empty(shape, dtype, init=True).eval() self.assertEqual(val.shape, shape) self.assertEqual(val.dtype, dtype.as_numpy_dtype) self.assertAllEqual(val, np.zeros(shape, dtype.as_numpy_dtype)) val = inplace_ops.empty_like(array_ops.zeros(shape, dtype)).eval() self.assertEqual(val.shape, shape) self.assertEqual(val.dtype, dtype.as_numpy_dtype) val = inplace_ops.empty_like( array_ops.zeros(shape, dtype), init=True).eval() self.assertEqual(val.shape, shape) self.assertEqual(val.dtype, dtype.as_numpy_dtype) self.assertAllEqual(val, np.zeros(shape, dtype.as_numpy_dtype)) with self.test_session(use_gpu=True): val = inplace_ops.empty((1, 2), dtypes.string, init=True).eval() self.assertEqual(val.tolist(), [[b"", b""]]) val = inplace_ops.empty((1, 2), dtypes.string, init=False).eval() self.assertEqual(val.tolist(), [[b"", b""]]) if __name__ == "__main__": test_lib.main()