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Merge pull request #38288 from yongtang:26069-equal-dtype 

PiperOrigin-RevId: 307638517
Change-Id: I57e294157e5ead1a285009994ecdb90b7577a232
This commit is contained in:
TensorFlower Gardener 2020-04-21 11:00:34 -07:00
parent 121ee5d9b3 9319da2277
commit 9c925a52e8
4 changed files with 43 additions and 2 deletions
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1
tensorflow/core/kernels/cwise_op_equal_to_1.cc
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@ -18,6 +18,7 @@ limitations under the License.
namespace tensorflow {
REGISTER7(BinaryOp, CPU, "Equal", functor::equal_to, float, Eigen::half, double,
uint8, int8, int16, bfloat16);
REGISTER3(BinaryOp, CPU, "Equal", functor::equal_to, uint16, uint32, uint64);
REGISTER_KERNEL_BUILDER(
Name("ApproximateEqual").Device(DEVICE_CPU).TypeConstraint<float>("T"),
ApproximateEqualOp<CPUDevice, float>);

2
tensorflow/core/kernels/cwise_op_not_equal_to_1.cc
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@ -18,6 +18,8 @@ limitations under the License.
namespace tensorflow {
REGISTER7(BinaryOp, CPU, "NotEqual", functor::not_equal_to, float, Eigen::half,
double, uint8, int8, int16, bfloat16);
REGISTER3(BinaryOp, CPU, "NotEqual", functor::not_equal_to, uint16, uint32,
uint64);
#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
REGISTER4(BinaryOp, GPU, "NotEqual", functor::not_equal_to, float, Eigen::half,
double, uint8);

4
tensorflow/core/ops/math_ops.cc
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@ -717,8 +717,8 @@ REGISTER_OP("GreaterEqual").COMPARISON();
.SetIsCommutative() \
.Attr( \
"T: {bfloat16, half, float, double, uint8, int8, int16, int32, " \
"int64, complex64, quint8, qint8, qint32, string, bool, " \
"complex128}") \
"int64, uint16, uint32, uint64, complex64, " \
"quint8, qint8, qint32, string, bool, complex128}") \
.Attr("incompatible_shape_error: bool = true") \
.SetShapeFn([](InferenceContext* c) { \
ShapeHandle x = c->input(0); \

38
tensorflow/python/kernel_tests/cwise_ops_binary_test.py
View File

@ -953,6 +953,44 @@ class ComparisonOpTest(test.TestCase):
"Incompatible shapes|Dimensions must be equal"):
f(x.astype(t), y.astype(t))
def testEqualDType(self):
dtypes = [
np.float16,
np.float32,
np.float64,
np.int8,
np.int16,
np.int32,
np.int64,
np.uint8,
np.uint16,
np.uint32,
np.uint64,
np.bool,
]
x = np.asarray([0, 1, 2, 3, 4])
y = np.asarray([0, 1, 2, 3, 4])
for dtype in dtypes:
xt = x.astype(dtype)
yt = y.astype(dtype)
cmp_eq = math_ops.equal(xt, yt)
cmp_ne = math_ops.not_equal(xt, yt)
values = self.evaluate([cmp_eq, cmp_ne])
self.assertAllEqual(
[[True, True, True, True, True], [False, False, False, False, False]],
values)
for dtype in [np.complex64, np.complex128]:
xt = x.astype(dtype)
xt -= 1j * xt
yt = y.astype(dtype)
yt -= 1j * yt
cmp_eq = math_ops.equal(xt, yt)
cmp_ne = math_ops.not_equal(xt, yt)
values = self.evaluate([cmp_eq, cmp_ne])
self.assertAllEqual(
[[True, True, True, True, True], [False, False, False, False, False]],
values)
if __name__ == "__main__":
test.main()