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math_ops_test.py
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math_ops_test.py
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# 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 tensorflow.ops.math_ops."""
from absl.testing import parameterized
import numpy as np
from tensorflow.core.framework import full_type_pb2
from tensorflow.python import tf2
from tensorflow.python.eager import backprop
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import indexed_slices
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor as tensor_lib
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import variables
from tensorflow.python.ops.ragged import ragged_factory_ops
from tensorflow.python.platform import googletest
@test_util.run_all_in_graph_and_eager_modes
class ReduceTest(test_util.TensorFlowTestCase):
def testReduceAllDims(self):
x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
with test_util.device(use_gpu=True):
y_tf = self.evaluate(math_ops.reduce_sum(x))
self.assertEqual(y_tf, 21)
def testReduceExtendType(self):
in_f32 = np.random.randn(1000, 1000).astype(np.float32)
in_bf16 = math_ops.cast(in_f32, dtypes.bfloat16)
out_f32 = self.evaluate(math_ops.reduce_sum(in_f32))
out_bf16 = self.evaluate(math_ops.reduce_sum(in_bf16))
expected = math_ops.cast(out_f32, dtypes.bfloat16)
self.assertAllClose(out_bf16, expected, 1e-3)
def testCountNonzero(self):
# simple case
x = np.array([[0, -2, 0], [4, 0, 0]], dtype=np.int32)
self.assertEqual(self.evaluate(math_ops.count_nonzero(x)), 2)
# boolean input
x = math_ops.not_equal(x, 0)
self.assertEqual(self.evaluate(math_ops.count_nonzero(x)), 2)
# would overflow if int8 would be used for internal calculations
x = 2 * np.ones(512, dtype=np.int8)
self.assertEqual(self.evaluate(math_ops.count_nonzero(x)), 512)
def testReduceExplicitAxes(self):
x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
with test_util.device(use_gpu=True):
for axis in (0, -2):
self.assertAllEqual(
self.evaluate(math_ops.reduce_sum(x, axis=axis)), [5, 7, 9])
for axis in (1, -1):
self.assertAllEqual(
self.evaluate(math_ops.reduce_sum(x, axis=axis)), [6, 15])
for axis in (None, (0, 1), (1, 0), (-1, 0), (0, -1), (-2, 1), (1, -2),
(-1, -2), (-2, -1)):
self.assertEqual(self.evaluate(math_ops.reduce_sum(x, axis=axis)), 21)
def testReduceInvalidAxis(self):
if context.executing_eagerly():
# The shape check is in run a graph construction time. In eager mode,
# it misses the check, magically return result given wrong shape.
return
x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
axis = np.array([[0], [1]])
with self.assertRaisesRegex(ValueError, "must be at most rank 1"):
math_ops.reduce_sum(x, axis)
def testReduceVar(self):
x = np.array([[0, 0, 0], [0, 0, 0]], "float32")
self.assertAllClose(self.evaluate(math_ops.reduce_variance(x)), 0)
self.assertAllClose(
self.evaluate(math_ops.reduce_variance(x, axis=0)), [0, 0, 0])
x = [[1, 2, 1, 1], [1, 1, 0, 1]]
with self.assertRaisesRegex(TypeError, "must be either real or complex"):
math_ops.reduce_variance(x)
x = [[1., 2., 1., 1.], [1., 1., 0., 1.]]
self.assertEqual(self.evaluate(math_ops.reduce_variance(x)), 0.25)
x_np = np.array(x)
self.assertEqual(np.var(x_np), 0.25)
self.assertEqual(self.evaluate(math_ops.reduce_variance(x_np)), 0.25)
x = ragged_factory_ops.constant([[5., 1., 4., 1.], [], [5., 9., 2.], [5.],
[]])
self.assertAllClose(math_ops.reduce_variance(x, axis=0), [0., 16., 1., 0.])
def testReduceVarComplex(self):
# Ensure that complex values are handled to be consistent with numpy
complex_ys = [([0 - 1j, 0 + 1j], dtypes.float64),
(np.array([0 - 1j, 0 + 1j], "complex64"), dtypes.float32),
(np.array([0 - 1j, 0 + 1j], "complex128"), dtypes.float64)]
for y, dtype in complex_ys:
y_result = math_ops.reduce_variance(y)
self.assertEqual(np.var(y), 1.0)
self.assertEqual(self.evaluate(y_result), 1.0)
self.assertEqual(y_result.dtype, dtype)
def testReduceStd(self):
x = np.array([[0, 0, 0], [0, 0, 0]], "float32")
self.assertAllClose(self.evaluate(math_ops.reduce_std(x)), 0)
self.assertAllClose(
self.evaluate(math_ops.reduce_std(x, axis=0)), [0, 0, 0])
x = [[1, 2, 1, 1], [1, 1, 0, 1]]
with self.assertRaisesRegex(TypeError, "must be either real or complex"):
math_ops.reduce_std(x)
x = [[1., 2., 1., 1.], [1., 1., 0., 1.]]
self.assertEqual(self.evaluate(math_ops.reduce_std(x)), 0.5)
x_np = np.array(x)
self.assertEqual(np.std(x_np), 0.5)
self.assertEqual(self.evaluate(math_ops.reduce_std(x_np)), 0.5)
x = ragged_factory_ops.constant([[5., 1., 4., 1.], [], [5., 9., 2.], [5.],
[]])
self.assertAllClose(math_ops.reduce_std(x, axis=0), [0., 4., 1., 0.])
def testReduceStdComplex(self):
# Ensure that complex values are handled to be consistent with numpy
complex_ys = [([0 - 1j, 0 + 1j], dtypes.float64),
(np.array([0 - 1j, 0 + 1j], "complex64"), dtypes.float32),
(np.array([0 - 1j, 0 + 1j], "complex128"), dtypes.float64)]
for y, dtype in complex_ys:
y_result = math_ops.reduce_std(y)
self.assertEqual(np.std(y), 1.0)
self.assertEqual(self.evaluate(y_result), 1.0)
self.assertEqual(y_result.dtype, dtype)
@test_util.run_all_in_graph_and_eager_modes
class LogSumExpTest(test_util.TensorFlowTestCase):
def testReduceLogSumExp(self):
for dtype in [np.float16, np.float32, np.double]:
x_np = np.random.rand(5, 5).astype(dtype)
with test_util.use_gpu():
y_tf_np = math_ops.reduce_logsumexp(x_np)
y_np = np.log(np.sum(np.exp(x_np)))
self.assertAllClose(y_tf_np, y_np)
def testReductionIndices(self):
for dtype in [np.float16, np.float32, np.double]:
x_np = np.random.rand(5, 5).astype(dtype)
with test_util.use_gpu():
y_tf = math_ops.reduce_logsumexp(x_np, axis=[0])
y_np = np.log(np.sum(np.exp(x_np), axis=0))
self.assertShapeEqual(y_np, y_tf)
y_tf_np = self.evaluate(y_tf)
self.assertAllClose(y_tf_np, y_np)
def testReductionIndices2(self):
for dtype in [np.float16, np.float32, np.double]:
x_np = np.random.rand(5, 5).astype(dtype)
with test_util.use_gpu():
y_tf = math_ops.reduce_logsumexp(x_np, axis=0)
y_np = np.log(np.sum(np.exp(x_np), axis=0))
self.assertShapeEqual(y_np, y_tf)
y_tf_np = self.evaluate(y_tf)
self.assertAllClose(y_tf_np, y_np)
def testKeepDims(self):
for dtype in [np.float16, np.float32, np.double]:
x_np = np.random.rand(5, 5).astype(dtype)
with test_util.use_gpu():
y_tf_np = math_ops.reduce_logsumexp(x_np, keepdims=True)
self.assertEqual(y_tf_np.shape.rank, x_np.ndim)
y_np = np.log(np.sum(np.exp(x_np), keepdims=True))
self.assertAllClose(y_tf_np, y_np)
def testOverflow(self):
x = [1000, 1001, 1002, 1003]
for dtype in [np.float16, np.float32, np.double]:
x_np = np.array(x, dtype=dtype)
max_np = np.max(x_np)
with self.assertRaisesRegex(RuntimeWarning,
"overflow encountered in exp"):
out = np.log(np.sum(np.exp(x_np)))
if out == np.inf:
raise RuntimeWarning("overflow encountered in exp")
with test_util.use_gpu():
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y_tf_np = math_ops.reduce_logsumexp(x_tf)
y_np = np.log(np.sum(np.exp(x_np - max_np))) + max_np
self.assertAllClose(y_tf_np, y_np)
def testUnderflow(self):
x = [-1000, -1001, -1002, -1003]
for dtype in [np.float16, np.float32, np.double]:
x_np = np.array(x, dtype=dtype)
max_np = np.max(x_np)
with self.assertRaisesRegex(RuntimeWarning,
"divide by zero encountered in log"):
out = np.log(np.sum(np.exp(x_np)))
if out == -np.inf:
raise RuntimeWarning("divide by zero encountered in log")
with test_util.use_gpu():
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y_tf_np = math_ops.reduce_logsumexp(x_tf)
y_np = np.log(np.sum(np.exp(x_np - max_np))) + max_np
self.assertAllClose(y_tf_np, y_np)
def testInfinity(self):
with test_util.use_gpu():
res = math_ops.reduce_logsumexp(-np.inf)
self.assertEqual(-np.inf, self.evaluate(res))
def testRaggedTensor(self):
for dtype in [dtypes.float16, dtypes.float32, dtypes.double]:
x_rt = ragged_factory_ops.constant([[1, 2], [], [3, 4, 5]], dtype=dtype)
x_np = np.array(self.evaluate(x_rt.flat_values))
with test_util.use_gpu():
y_rt = math_ops.reduce_logsumexp(x_rt)
y_np = np.log(np.sum(np.exp(x_np - np.max(x_np)))) + np.max(x_np)
self.assertAllClose(y_rt, y_np)
@test_util.run_all_in_graph_and_eager_modes
class RoundTest(test_util.TensorFlowTestCase):
def testRounding(self):
x = np.arange(-5.0, 5.0, .25)
for dtype in [np.float32, np.double, np.int32]:
x_np = np.array(x, dtype=dtype)
with test_util.device(use_gpu=True):
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y_tf = math_ops.round(x_tf)
y_tf_np = self.evaluate(y_tf)
y_np = np.round(x_np)
self.assertAllClose(y_tf_np, y_np, atol=1e-2)
@test_util.with_eager_op_as_function
@test_util.run_all_in_graph_and_eager_modes
class MatMulTest(test_util.TensorFlowTestCase, parameterized.TestCase):
"""Test for matmul."""
SUPPORTED_DTYPES = [
dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
dtypes.int64, dtypes.bfloat16, dtypes.complex64, dtypes.complex128
]
def testMatMul2D(self):
for dtype in self.SUPPORTED_DTYPES:
a = constant_op.constant([1, 2, 3, 4, 5, 6], shape=[2, 3], dtype=dtype)
b = constant_op.constant([7, 8, 9, 10, 11, 12], shape=[3, 2], dtype=dtype)
c = math_ops.matmul(a, b)
c_np = constant_op.constant([[58, 64], [139, 154]],
shape=(2, 2),
dtype=dtype)
self.assertAllClose(c, c_np, atol=1e-2)
def testBatchMatMul(self):
for dtype in self.SUPPORTED_DTYPES:
a = constant_op.constant(np.arange(1, 13), shape=[2, 2, 3], dtype=dtype)
b = constant_op.constant(np.arange(13, 25), shape=[2, 3, 2], dtype=dtype)
c = math_ops.matmul(a, b)
c_np = constant_op.constant(
[[[94, 100], [229, 244]], [[508, 532], [697, 730]]],
shape=[2, 2, 2],
dtype=dtype)
self.assertAllClose(c, c_np, atol=1e-2)
def testUnsupportedtypeMatmul(self):
a = constant_op.constant(
np.arange(1, 13), shape=[2, 2, 3], dtype=dtypes.int8)
b = constant_op.constant(
np.arange(13, 25), shape=[2, 3, 2], dtype=dtypes.int8)
with self.assertRaisesRegex((TypeError, errors.InvalidArgumentError),
"list of allowed values:"):
math_ops.matmul(a, b)
@parameterized.parameters((dtypes.int8, dtypes.int8),
(dtypes.int8, dtypes.uint8),
(dtypes.uint8, dtypes.int8))
# TODO(shivaniagrawal): matmul (dtypes.uint8, dtypes.uint8) fails in xla_gpu.
def testInt8MatMul2D(self, a_dtype, b_dtype):
a = constant_op.constant([1, 2, 3, 4, 5, 6], shape=[2, 3], dtype=a_dtype)
b = constant_op.constant([7, 8, 9, 10, 11, 12], shape=[3, 2], dtype=b_dtype)
c = math_ops.matmul(a, b, output_type=dtypes.int32)
c_np = constant_op.constant([[58, 64], [139, 154]],
shape=(2, 2),
dtype=dtypes.int32)
self.assertAllClose(c, c_np)
@parameterized.parameters((dtypes.int8), (dtypes.uint8))
def testMixPrecMatMul2D(self, b_dtype):
a = constant_op.constant([1, 2, 3, 4, 5, 6],
shape=[2, 3],
dtype=dtypes.bfloat16)
b = constant_op.constant([7, 8, 9, 10, 11, 12], shape=[3, 2], dtype=b_dtype)
c = math_ops.matmul(a, b, output_type=dtypes.bfloat16)
c_np = constant_op.constant([[58, 64], [139, 154]],
shape=(2, 2),
dtype=dtypes.bfloat16)
self.assertAllClose(c, c_np, atol=1e-2)
@parameterized.parameters((dtypes.int8, dtypes.int8),
(dtypes.int8, dtypes.uint8),
(dtypes.uint8, dtypes.int8))
# TODO(shivaniagrawal): matmul (dtypes.uint8, dtypes.uint8) fails in xla_gpu.
def testInt8BatchMatmul(self, a_dtype, b_dtype):
a = constant_op.constant(np.arange(1, 13), shape=[2, 2, 3], dtype=a_dtype)
b = constant_op.constant(np.arange(13, 25), shape=[2, 3, 2], dtype=b_dtype)
c_np = constant_op.constant(
[[[94, 100], [229, 244]], [[508, 532], [697, 730]]],
shape=[2, 2, 2],
dtype=dtypes.int32)
c = math_ops.matmul(a, b, output_type=dtypes.int32)
self.assertAllEqual(c, c_np)
@parameterized.parameters((dtypes.int8), (dtypes.uint8))
def testMixPrecBatchMatmul(self, b_dtype):
a = constant_op.constant(
np.arange(1, 13), shape=[2, 2, 3], dtype=dtypes.bfloat16)
b = constant_op.constant(np.arange(13, 25), shape=[2, 3, 2], dtype=b_dtype)
c_np = constant_op.constant(
[[[94, 100], [229, 244]], [[508, 532], [697, 730]]],
shape=[2, 2, 2],
dtype=dtypes.bfloat16)
c = math_ops.matmul(a, b, output_type=dtypes.bfloat16)
self.assertAllClose(c, c_np, atol=1e-2)
def testInvalidOutputTypeMatmul(self):
for dtype in [dtypes.int8, dtypes.bfloat16]:
a = constant_op.constant(np.arange(1, 13), shape=[2, 2, 3], dtype=dtype)
b = constant_op.constant(
np.arange(13, 25), shape=[2, 3, 2], dtype=dtypes.int8)
if context.executing_eagerly():
if context.is_tfrt_enabled():
with self.assertRaisesRegex(errors.InvalidArgumentError,
"NodeDef expected inputs"):
math_ops.matmul(a, b, output_type=dtypes.float32)
else:
with self.assertRaisesRegex(errors.NotFoundError,
"Could not find device for node:"):
math_ops.matmul(a, b, output_type=dtypes.float32)
else:
with self.assertRaisesRegex(errors.InvalidArgumentError,
"No OpKernel was registered to support Op"):
self.evaluate(math_ops.matmul(a, b, output_type=dtypes.float32))
@test_util.run_all_in_graph_and_eager_modes
class SampledADDMMTest(test_util.TensorFlowTestCase):
"""Test for sampled_addmm."""
SUPPORTED_DTYPES = [
dtypes.bfloat16,
dtypes.float16,
dtypes.float32,
dtypes.float64,
]
def sampledADDMMRef(
self,
indices,
values,
dense_shape,
mat1,
mat2,
beta=1.0,
alpha=1.0,
output_type=dtypes.float32,
):
dense = math_ops.matmul(mat1, mat2, output_type=output_type)
dense_vals = array_ops.gather_nd(dense, indices, batch_dims=dense.ndim - 2)
return alpha * dense_vals + beta * values
def testSampledADDMM2D(self):
for dtype in self.SUPPORTED_DTYPES:
indices = constant_op.constant([[0, 0], [1, 1]])
values = constant_op.constant([0.5, 0.3], dtype=dtype)
dense_shape = constant_op.constant([2, 2])
mat1 = constant_op.constant([1, 2, 3, 4, 5, 6], shape=[2, 3], dtype=dtype)
mat2 = constant_op.constant(
[7, 8, 9, 10, 11, 12], shape=[3, 2], dtype=dtype
)
alpha = 0.75
beta = 0.25
_, res, _ = math_ops.sampled_addmm(
indices,
values,
dense_shape,
mat1,
mat2,
beta=beta,
alpha=alpha,
output_type=dtype,
)
ref = self.sampledADDMMRef(
indices,
values,
dense_shape,
mat1,
mat2,
beta=beta,
alpha=alpha,
output_type=dtype,
)
self.assertAllClose(res, ref, atol=1e-2)
def testBatchSampledADDMM(self):
for dtype in self.SUPPORTED_DTYPES:
indices = constant_op.constant([[[0, 1], [1, 0]], [[0, 0], [1, 0]]])
values = constant_op.constant([[3, 5], [2, 7]], dtype=dtype)
dense_shape = constant_op.constant([2, 2])
mat1 = constant_op.constant(
np.arange(1, 13), shape=[2, 2, 3], dtype=dtype
)
mat2 = constant_op.constant(
np.arange(13, 25), shape=[2, 3, 2], dtype=dtype
)
alpha = 0.4
beta = 0.6
_, res, _ = math_ops.sampled_addmm(
indices,
values,
dense_shape,
mat1,
mat2,
beta=beta,
alpha=alpha,
output_type=dtype,
)
ref = self.sampledADDMMRef(
indices,
values,
dense_shape,
mat1,
mat2,
beta=beta,
alpha=alpha,
output_type=dtype,
)
self.assertAllClose(res, ref, atol=1e-2)
def testInvalidDenseShape(self):
for dtype in self.SUPPORTED_DTYPES:
indices = constant_op.constant([[[0, 1], [1, 0]], [[0, 0], [1, 0]]])
values = constant_op.constant([[3, 5], [2, 7]], dtype=dtype)
dense_shape = constant_op.constant([1, 2])
mat1 = constant_op.constant(
np.arange(1, 13), shape=[2, 2, 3], dtype=dtype
)
mat2 = constant_op.constant(
np.arange(13, 25), shape=[2, 3, 2], dtype=dtype
)
with self.assertRaisesRegex(ValueError, "does not match output shape"):
math_ops.sampled_addmm(
indices, values, dense_shape, mat1, mat2, output_type=dtype
)
@test_util.run_all_in_graph_and_eager_modes
class ModTest(test_util.TensorFlowTestCase):
def testFloat(self):
x = [0.5, 0.7, 0.3]
for dtype in [np.float32, np.double]:
# Test scalar and vector versions.
for denom in [x[0], [x[0]] * 3]:
x_np = np.array(x, dtype=dtype)
with test_util.use_gpu():
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y_tf = math_ops.mod(x_tf, denom)
y_tf_np = self.evaluate(y_tf)
y_np = np.fmod(x_np, denom)
self.assertAllClose(y_tf_np, y_np, atol=1e-2)
def testFixed(self):
x = [5, 10, 23]
for dtype in [np.int32, np.int64]:
# Test scalar and vector versions.
for denom in [x[0], x]:
x_np = np.array(x, dtype=dtype)
with test_util.use_gpu():
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y_tf = math_ops.mod(x_tf, denom)
y_tf_np = self.evaluate(y_tf)
y_np = np.mod(x_np, denom)
self.assertAllClose(y_tf_np, y_np)
@test_util.run_all_in_graph_and_eager_modes
class SquaredDifferenceTest(test_util.TensorFlowTestCase):
def testSquaredDifference(self):
for dtype in [
np.float16, np.float32, np.float64, dtypes.bfloat16.as_numpy_dtype,
np.int32, np.int64
]:
x = np.array([[1, 2, 3], [4, 5, 6]], dtype=dtype)
y = np.array([-3, -2, -1], dtype=dtype)
z = (x - y) * (x - y)
with test_util.device(use_gpu=True):
z_tf = self.evaluate(math_ops.squared_difference(x, y))
self.assertAllClose(z, z_tf)
def testComplexSquaredDifference(self):
for dtype in [np.complex64, np.complex128]:
x = np.array([[1 + 3j, 2 + 2j, 3 + 1j], [4 - 1j, 5 - 2j, 6 - 3j]],
dtype=dtype)
y = np.array([-3 + 1j, -2 + 2j, -1 + 3j], dtype=dtype)
z = np.conj(x - y) * (x - y)
with test_util.device(use_gpu=False):
z_tf = self.evaluate(math_ops.squared_difference(x, y))
self.assertAllClose(z, z_tf)
@test_util.with_eager_op_as_function
@test_util.run_all_in_graph_and_eager_modes
class ApproximateEqualTest(test_util.TensorFlowTestCase):
def testApproximateEqual(self):
for dtype in [np.float32, np.double]:
x = dtype(1)
y = dtype(1.00009)
z = False
with test_util.device(use_gpu=True):
# Default tolerance is 0.00001
z_tf = self.evaluate(math_ops.approximate_equal(x, y))
self.assertAllEqual(z, z_tf)
for dtype in [np.float32, np.double]:
x = dtype(1)
y = dtype(1.000009)
z = True
with test_util.device(use_gpu=True):
# Default tolerance is 0.00001
z_tf = self.evaluate(math_ops.approximate_equal(x, y))
self.assertAllEqual(z, z_tf)
for dtype in [np.float32, np.double]:
x = np.array([[[[-1, 2.00009999], [-3, 4.01]]]], dtype=dtype)
y = np.array([[[[-1.001, 2], [-3.00009, 4]]]], dtype=dtype)
z = np.array([[[[False, True], [True, False]]]], dtype=np.bool_)
with test_util.device(use_gpu=True):
z_tf = self.evaluate(math_ops.approximate_equal(x, y, tolerance=0.0001))
self.assertAllEqual(z, z_tf)
def testApproximateEqualShape(self):
for dtype in [np.float32, np.double]:
x = np.array([1, 2], dtype=dtype)
y = np.array([[1, 2]], dtype=dtype)
# The inputs 'x' and 'y' must have the same shape.
with self.assertRaisesRegex(
(ValueError, errors.InvalidArgumentError),
"Shapes must be equal rank|must be of the same shape"):
math_ops.approximate_equal(x, y)
def testApproximateEqualShapeXla(self):
@def_function.function(jit_compile=True)
def approximate_equal(x, y):
return math_ops.approximate_equal(x, y)
for dtype in [np.float32, np.double]:
x = np.array([1, 2], dtype=dtype)
y = np.array([[1, 2]], dtype=dtype)
with self.assertRaisesRegex(
(ValueError, errors.InvalidArgumentError),
"Shapes must be equal rank|must be of the same shape"):
approximate_equal(x, y)
@test_util.run_all_in_graph_and_eager_modes
class ScalarMulTest(test_util.TensorFlowTestCase):
def testAcceptsRefs(self):
if context.executing_eagerly():
var = resource_variable_ops.ResourceVariable(10, name="var")
else:
var = variables.Variable(10)
result = math_ops.scalar_mul(3, var)
init = variables.global_variables_initializer()
with test_util.device(use_gpu=True):
self.evaluate(init)
self.assertEqual(30, self.evaluate(result))
def testAcceptsConstant(self):
const = constant_op.constant(10)
result = math_ops.scalar_mul(3, const)
with test_util.device(use_gpu=True):
self.assertEqual(30, self.evaluate(result))
def testAcceptsTensor(self):
tensor = array_ops.ones([10, 10])
result = math_ops.scalar_mul(3, tensor)
expected = array_ops.ones([10, 10]) * 3
with test_util.device(use_gpu=True):
self.assertAllEqual(self.evaluate(expected), self.evaluate(result))
def testAcceptsIndexedSlices(self):
values = constant_op.constant([2, 3, 5, 7, 0, -1], shape=[3, 2])
indices = constant_op.constant([0, 2, 5])
x = math_ops.scalar_mul(-3, indexed_slices.IndexedSlices(values, indices))
with test_util.device(use_gpu=True):
self.assertAllEqual(
self.evaluate(x.values), [[-6, -9], [-15, -21], [0, 3]])
self.assertAllEqual(self.evaluate(x.indices), [0, 2, 5])
@test_util.run_all_in_graph_and_eager_modes
class AddNTest(test_util.TensorFlowTestCase):
def testPartials(self):
"""Test that previously revealed a bug in buffer forwarding for AddN."""
partials = []
for _ in range(98):
partials.append(math_ops.add_n([constant_op.constant(1)]))
partials.append(
math_ops.add_n([constant_op.constant(1),
constant_op.constant(1)]))
res = math_ops.add_n(partials) + constant_op.constant(0)
with test_util.use_gpu():
self.assertAllEqual(res, 100)
def testFloat(self):
np.random.seed(12345)
for num_inputs in range(1, 10):
x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(num_inputs)]
tf_x = ops.convert_n_to_tensor(x)
with test_util.use_gpu():
self.assertAllClose(sum(x), math_ops.add_n(tf_x))
self.assertAllClose(x[0] * num_inputs,
math_ops.add_n([tf_x[0]] * num_inputs))
def testInt(self):
np.random.seed(54321)
for num_inputs in range(1, 10):
x = [
np.random.randint(-128, 128, (5, 4, 3, 2, 1))
for _ in range(num_inputs)
]
tf_x = ops.convert_n_to_tensor(x)
with test_util.use_gpu():
self.assertAllEqual(sum(x), math_ops.add_n(tf_x))
self.assertAllEqual(x[0] * num_inputs,
math_ops.add_n([tf_x[0]] * num_inputs))
def testGrad(self):
np.random.seed(42)
for num_inputs in range(1, 10):
with test_util.use_gpu():
input_vars = [
variables.Variable(10.0 * np.random.random())
for _ in range(0, num_inputs)
]
self.evaluate(variables.global_variables_initializer())
if context.executing_eagerly():
with backprop.GradientTape() as tape:
tape.watch(input_vars)
addn = math_ops.add_n(input_vars)
add_n_grad = tape.gradient(addn, input_vars)
else:
addn = math_ops.add_n(input_vars)
add_n_grad = gradients.gradients(addn, input_vars)
self.assertAllEqual(
np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1
[self.evaluate(g) for g in add_n_grad])
def testIndexedSlices(self):
slc = indexed_slices.IndexedSlices(
array_ops.constant([1, 2], shape=[1, 2]), array_ops.constant([1]),
array_ops.constant([2, 2]))
slc_as_dense = np.array([[0, 0], [1, 2]])
with test_util.use_gpu():
# add_n currently always converts IndexedSlices to dense
self.assertAllEqual(slc_as_dense, math_ops.add_n([slc]))
self.assertAllEqual(2 * slc_as_dense, math_ops.add_n([slc, slc]))
def test_iterable(self):
"""Test that add_n supports iterables (e.g. generators and dict values)."""
def fn():
yield 1
yield 2
values_dict = {"a": 1, "b": 2}
with test_util.use_gpu():
self.assertAllEqual(3, math_ops.add_n(fn()))
self.assertAllEqual(3, math_ops.add_n(values_dict.values()))
@test_util.run_all_in_graph_and_eager_modes
class DivAndModTest(test_util.TensorFlowTestCase):
# TODO(aselle): Test more types before exposing new division operators.
def intTestData(self):
nums = np.arange(-10, 10, 1).reshape(20, 1)
divs = np.arange(-3, 4, 2).reshape(1, 4)
return nums, divs
def floatTestData(self):
nums = np.arange(-10, 10, .25).reshape(80, 1)
divs = np.arange(-3, 0, .25).reshape(1, 12)
return nums, divs
def numpySafeFloorDivInt(self, x, y):
z = x // y
# Numpy produces 0 for INT_MIN/-1, but we expect an overflow to INT_MIN
# so that (INT_MIN/-1) + (INT_MIN % -1) = INT_MIN + 0 = INT_MIN.
z[(x == np.iinfo(x.dtype).min) & (y == -1)] = np.iinfo(x.dtype).min
return z
def numpySafeFloorModInt(self, x, y):
# Numpy crashes with a FPE for INT_MIN % -1.
z = self.numpySafeFloorDivInt(x, y)
return x - z * y
def numpySafeTruncateDivInt(self, x, y):
z = self.numpySafeFloorDivInt(x, y)
# Round up if non-zero remainder and inputs have opposite signs.
z[(x != z * y) & ((x < 0) != (y < 0))] += 1
return z
def numpySafeTruncateModInt(self, x, y):
# Numpy crashes with a FPE for INT_MIN % -1.
z = self.numpySafeTruncateDivInt(x, y)
return x - z * y
def testFloorModInt(self):
nums, divs = self.intTestData()
for dtype in [np.int32, np.int64]:
x = nums.astype(dtype)
y = divs.astype(dtype)
tf_result = math_ops.floormod(x, y)
np_result = self.numpySafeFloorModInt(x, y)
self.assertAllEqual(tf_result, np_result)
tf2_result = (array_ops.constant(x) % array_ops.constant(y))
self.assertAllEqual(tf2_result, tf_result)
def testFloorModFloat(self):
nums, divs = self.floatTestData()
for dtype in [np.float16, np.float32, np.float64]:
x = nums.astype(dtype)
y = divs.astype(dtype)
tf_result = math_ops.floormod(x, y)
np_result = x % y
self.assertAllEqual(tf_result, np_result)
tf2_result = (array_ops.constant(x) % array_ops.constant(y))
self.assertAllEqual(tf2_result, tf_result)
def testFloorModBfloat16(self):
nums, divs = self.floatTestData()
tf_result = math_ops.floormod(
math_ops.cast(nums, dtypes.bfloat16),
math_ops.cast(divs, dtypes.bfloat16))
np_result = nums % divs
self.assertAllEqual(tf_result, np_result)
def testTruncateModInt(self):
nums, divs = self.intTestData()
tf_result = math_ops.truncatemod(nums, divs)
np_result = np.fmod(nums, divs)
self.assertAllEqual(tf_result, np_result)
def testTruncateModFloat(self):
nums, divs = self.floatTestData()
tf_result = math_ops.truncatemod(nums, divs)
np_result = np.fmod(nums, divs)
self.assertAllEqual(tf_result, np_result)
def testFloorDivideInt(self):
nums, divs = self.intTestData()
tf_result = math_ops.floor_div(nums, divs)
np_result = self.numpySafeFloorDivInt(nums, divs)
self.assertAllEqual(tf_result, np_result)
tf2_result = (array_ops.constant(nums) // array_ops.constant(divs))
self.assertAllEqual(tf2_result, tf_result)
def testTruncateDivideInt(self):
nums, divs = self.intTestData()
tf_result = math_ops.truncatediv(nums, divs)
np_result = self.numpySafeTruncateDivInt(nums, divs)
self.assertAllEqual(tf_result, np_result)
def testTruncateDivideFloat(self):
nums, divs = self.floatTestData()
tf_result = math_ops.truncatediv(nums, divs)
np_result = np.trunc(nums / divs)
self.assertAllEqual(tf_result, np_result)
@test_util.deprecated_graph_mode_only
def testDivideName(self):
op = math_ops.divide(
array_ops.constant(3), array_ops.constant(4), name="my_cool_divide")
self.assertEqual(op.name, "my_cool_divide:0")
def testRealDiv(self):
nums, divs = self.floatTestData()
tf_result = math_ops.realdiv(nums, divs)
np_result = np.divide(nums, divs)
self.assertAllClose(tf_result, np_result)
def testDivideType(self):
a = array_ops.constant([2], dtype=dtypes.int32)
# Since __future__.division is effect, we should always upgrade to float64
b = math_ops.divide(a, 1)
self.assertEqual(b.dtype, dtypes.float64)
self.assertEqual(2.0, self.evaluate(b))
c = math_ops.divide(a, 4)
self.assertEqual(c.dtype, dtypes.float64)
self.assertEqual(0.5, self.evaluate(c))
def testComplexDiv(self):
foo = array_ops.constant([1. + 3.j])
_ = math_ops.divide(foo, 1.)
_ = math_ops.div(foo, 2.)
def testFloorDivGrad(self):
a = variables.Variable(2.)
b = variables.Variable(4.)
input_vars = [a, b]
self.evaluate(variables.global_variables_initializer())
if context.executing_eagerly():
# TDOO(rmlarsen): Is there a more compact way of
# writing this for multiple expressions?
with backprop.GradientTape() as tape:
tape.watch(input_vars)
c_grad0 = tape.gradient(math_ops.divide(a, b), input_vars)
with backprop.GradientTape() as tape:
tape.watch(input_vars)
c_grad1 = tape.gradient(math_ops.div(a, b), input_vars)
with backprop.GradientTape() as tape:
tape.watch(input_vars)
c_grad2 = tape.gradient(math_ops.floordiv(a, b), input_vars)
else:
c_grad0 = gradients.gradients(math_ops.divide(a, b), input_vars)
c_grad1 = gradients.gradients(math_ops.div(a, b), input_vars)
c_grad2 = gradients.gradients(math_ops.floordiv(a, b), input_vars)
self.assertAllEqual([self.evaluate(x) for x in c_grad0], [.25, -.125])
self.assertAllEqual([self.evaluate(x) for x in c_grad1], [.25, -.125])
self.assertAllEqual(
[None if x is None else self.evaluate(x) for x in c_grad2],
[None, None])
def testConsistent(self):
nums, divs = self.intTestData()
tf_result = (
math_ops.floor_div(nums, divs) * divs + math_ops.floormod(nums, divs))
tf_nums = array_ops.constant(nums)
tf_divs = array_ops.constant(divs)
tf2_result = (tf_nums // tf_divs * tf_divs + tf_nums % tf_divs)
np_result = (nums // divs) * divs + (nums % divs)
# Consistent with numpy
self.assertAllEqual(tf_result, np_result)
# Consistent with two forms of divide
self.assertAllEqual(tf_result, tf2_result)
# consistency for truncation form
tf3_result = (
math_ops.truncatediv(nums, divs) * divs +
math_ops.truncatemod(nums, divs))
expanded_nums = np.reshape(
np.tile(nums, divs.shape[1]), (nums.shape[0], divs.shape[1]))
# Consistent with desire to get numerator
self.assertAllEqual(tf3_result, expanded_nums)
# Consistent with desire to get numerator
self.assertAllEqual(tf_result, expanded_nums)
def testWithPythonValue(self):
# Test case for https://github.com/tensorflow/tensorflow/issues/39475
x = math_ops.divide(5, 2)
self.assertIsInstance(x, tensor_lib.Tensor)
x = math_ops.divide(5, array_ops.constant(2.0))
self.assertIsInstance(x, tensor_lib.Tensor)
def intEdgeTestData(self, dtype):
"""Edge-case test data for integer types."""
# INT_MIN/-1 will produce signed-integer overflow, so we instead test
# (INT_MIN + 1) / -1.
nums = np.array(
[
[np.iinfo(dtype).min, -1, 1, np.iinfo(dtype).max],
[np.iinfo(dtype).min + 1, -1, 1, np.iinfo(dtype).max],
[np.iinfo(dtype).min, -1, 1, np.iinfo(dtype).max],
[np.iinfo(dtype).min, -1, 1, np.iinfo(dtype).max],
],
dtype=dtype,
)
divs = np.array(
[
[
np.iinfo(dtype).min,
np.iinfo(dtype).min,
np.iinfo(dtype).min,
np.iinfo(dtype).min,
],
[-1, -1, -1, -1],
[1, 1, 1, 1],
[
np.iinfo(dtype).max,
np.iinfo(dtype).max,
np.iinfo(dtype).max,
np.iinfo(dtype).max,
],
],
dtype=dtype,
)
return nums, divs
def testFloorDivModIntEdges(self):
for dtype in [np.int32, np.int64]:
x, y = self.intEdgeTestData(dtype)
tf_floor_div = math_ops.floor_div(x, y)
np_floor_div = self.numpySafeFloorDivInt(x, y)
self.assertAllEqual(tf_floor_div, np_floor_div)
tf_floor_mod = math_ops.floormod(x, y)
np_floor_mod = self.numpySafeFloorModInt(x, y)
self.assertAllEqual(tf_floor_mod, np_floor_mod)
def testTruncateDivModIntEdges(self):
for dtype in [np.int32, np.int64]:
x, y = self.intEdgeTestData(dtype)
tf_truncate_div = math_ops.truncatediv(x, y)
np_truncate_div = self.numpySafeTruncateDivInt(x, y)
self.assertAllEqual(tf_truncate_div, np_truncate_div)
tf_truncate_mod = math_ops.truncatemod(x, y)
np_truncate_mod = self.numpySafeTruncateModInt(x, y)
self.assertAllEqual(tf_truncate_mod, np_truncate_mod)
@test_util.run_all_in_graph_and_eager_modes
class DivNoNanTest(test_util.TensorFlowTestCase, parameterized.TestCase):
_SUPPORTED_DTYPES = [dtypes.int8, dtypes.uint8,
dtypes.int16, dtypes.uint16,
dtypes.int32, dtypes.uint32,
dtypes.int64, dtypes.uint64,
dtypes.bfloat16, dtypes.float16,
dtypes.float32, dtypes.float64,
dtypes.complex64, dtypes.complex128]
@parameterized.parameters(*_SUPPORTED_DTYPES)
def testBasic(self, dtype):
if dtype.is_unsigned:
nums = np.arange(0, 120, 3).reshape(40, 1)
divs = np.arange(0, 48, 4).reshape(1, 12)
elif dtype.is_integer:
nums = np.arange(-120, 120, 3).reshape(80, 1)
divs = np.arange(-48, 48, 4).reshape(1, 24)
else:
nums = np.arange(-10, 10, .25).reshape(80, 1)
divs = np.arange(-3, 3, .25).reshape(1, 24)
assert 0 in divs, "Bad test set-up"
tf_nums = constant_op.constant(nums, dtype=dtype)
tf_divs = constant_op.constant(divs, dtype=dtype)
# Use tf versions for expected value to ensure inputs are identical
# (e.g. in the case of bfloat16).
np_nums = self.evaluate(tf_nums)
np_divs = self.evaluate(tf_divs)
np_result = np.true_divide(np_nums, np_divs)
np_result[:, np_divs[0] == 0] = 0
with test_util.use_gpu():
tf_result = math_ops.div_no_nan(tf_nums, tf_divs)
self.assertAllCloseAccordingToType(tf_result, np_result)
@parameterized.product(
type_x=_SUPPORTED_DTYPES + [float, int],