diff --git a/tensorflow/core/kernels/reduction_ops.h b/tensorflow/core/kernels/reduction_ops.h
index 164359f601a..3c62dcfc081 100644
--- a/tensorflow/core/kernels/reduction_ops.h
+++ b/tensorflow/core/kernels/reduction_ops.h
@@ -26,6 +26,11 @@ limitations under the License.
namespace tensorflow {
namespace functor {
+template <typename Reducer>
+struct ReducerTraits {
+ enum { IsScalarIdentity = true };
+};
+
// Dummy class used for template specialization for mean reduction, which is
// accomplished by SumReducer and on-the-fly division by the reduction factor.
template <typename Scalar>
@@ -39,6 +44,11 @@ struct EuclideanNormReducer {
Scalar initialize() const { return Scalar(0); }
};
+template <typename Scalar>
+struct ReducerTraits<EuclideanNormReducer<Scalar>> {
+ enum { IsScalarIdentity = false };
+};
+
template <typename Device, typename OUT_T, typename IN_T,
typename ReductionAxes, typename Reducer>
struct ReduceEigenImpl {
diff --git a/tensorflow/core/kernels/reduction_ops_common.h b/tensorflow/core/kernels/reduction_ops_common.h
index c6c36ec29a7..072699288db 100644
--- a/tensorflow/core/kernels/reduction_ops_common.h
+++ b/tensorflow/core/kernels/reduction_ops_common.h
@@ -155,12 +155,12 @@ class ReductionOp : public OpKernel {
OP_REQUIRES_OK(ctx, helper.Simplify(data, axes, keep_dims_));
CHECK_GE(helper.ndims(), 0);
- if (helper.ndims() == 0 ||
- (helper.ndims() == 1 && !helper.reduce_first_axis())) {
- // Special case. Reduces nothing. It is unclear why this is
- // necessary, but tests fail without it. Look into why this
- // case occurs.
+ bool is_scalar_identity = functor::ReducerTraits<Reducer>::IsScalarIdentity;
+ bool is_trivial = helper.ndims() == 0 ||
+ (helper.ndims() == 1 && !helper.reduce_first_axis());
+ if (is_scalar_identity && is_trivial) {
Tensor out;
+ // Special case. Reduces nothing and does not alter the input values.
if (!out.CopyFrom(data, helper.out_shape())) {
ctx->SetStatus(errors::Internal("Error during reduction copy."));
}
@@ -172,73 +172,83 @@ class ReductionOp : public OpKernel {
// output(0) because it is returned as output(0) in the end.
const AllocatorAttributes alloc_attr = ctx->output_alloc_attr(0);
- // A temporary tensor whose size matches the size of the reduced
- // output.
Tensor tmp_out;
- OP_REQUIRES_OK(
- ctx, ctx->allocate_temp(ctx->expected_output_dtype(0),
- helper.out_reshape(), &tmp_out, alloc_attr));
-
typedef functor::ReduceFunctor<Device, Reducer> Functor;
Constants<Device> constants;
const Device& d = ctx->eigen_device<Device>();
Reducer reducer;
- if (tmp_out.NumElements() == 0) {
- // Nothing to do, fall through to final reshaping.
- } else if (data.NumElements() == 0) {
- // Degenerate reduction where the input is empty but the output is
- // nonempty (thus tmp_out.NumElements() > 0), and we must fill the output
- // with identity elements. Example: tf.reduce_sum(tf.zeros((0, 3)), [0]).
- // Eigen sometimes crashes in this case, so we do it manually.
- Functor::FillIdentity(d, tmp_out.flat<T>(), reducer);
- } else if ((helper.ndims() == 1) && helper.reduce_first_axis()) {
- // Reduce to a scalar.
- Functor::Reduce(ctx, helper.out<T, 0>(&tmp_out), helper.in<T, 1>(data),
- constants.kZero, reducer);
- } else if ((helper.ndims() == 2) && helper.reduce_first_axis()) {
- // Can be viewed as a reduction of a matrix along 1st dimension.
- Functor::Reduce(ctx, helper.out<T, 1>(&tmp_out), helper.in<T, 2>(data),
- constants.kZero, reducer);
- } else if ((helper.ndims() == 2) && !helper.reduce_first_axis()) {
- // Can be viewed as a reduction of a matrix along 2nd dimension.
- Functor::Reduce(ctx, helper.out<T, 1>(&tmp_out), helper.in<T, 2>(data),
- constants.kOne, reducer);
- } else if ((helper.ndims() == 3) && helper.reduce_first_axis()) {
- // Can be viewed as a reduction of a 3D tensor along 1st and 3rd
- // dimensions.
- Functor::Reduce(ctx, helper.out<T, 1>(&tmp_out), helper.in<T, 3>(data),
- constants.kZeroTwo, reducer);
- } else if ((helper.ndims() == 3) && !helper.reduce_first_axis()) {
- // Can be viewed as a reduction of a 3D tensor along 2nd dimension.
- Functor::Reduce(ctx, helper.out<T, 2>(&tmp_out), helper.in<T, 3>(data),
- constants.kOne, reducer);
- } else {
- // If we don't hit one of the cases above, transpose the data so that
- // all reduced dimensions are last and reuse the 2-D -> 1-D case.
- Tensor data_reshaped;
- CHECK(data_reshaped.CopyFrom(data, helper.data_reshape()));
- Tensor shuffled;
- OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
- helper.shuffled_shape(), &shuffled,
- alloc_attr));
- OP_REQUIRES_OK(
- ctx, DoTranspose(d, data_reshaped, helper.permutation(), &shuffled));
- const int64 unreduced = tmp_out.NumElements();
- const int64 reduced = shuffled.NumElements() / unreduced;
- const Tensor& const_shuffled = shuffled;
+ if (data.NumElements() > 0 && is_trivial && !is_scalar_identity) {
+ OP_REQUIRES_OK(ctx, ctx->allocate_temp(ctx->expected_output_dtype(0),
+ TensorShape({data.NumElements()}),
+ &tmp_out, alloc_attr));
Functor::Reduce(ctx, tmp_out.flat<T>(),
- const_shuffled.shaped<T, 2>({unreduced, reduced}),
- constants.kOne, reducer);
+ data.shaped<T, 2>({1, data.NumElements()}),
+ constants.kZero, reducer);
+ } else {
+ // A temporary tensor whose size matches the size of the reduced
+ // output.
+ OP_REQUIRES_OK(
+ ctx, ctx->allocate_temp(ctx->expected_output_dtype(0),
+ helper.out_reshape(), &tmp_out, alloc_attr));
+
+ if (tmp_out.NumElements() == 0) {
+ // Nothing to do, fall through to final reshaping.
+ } else if (data.NumElements() == 0) {
+ // Degenerate reduction where the input is empty but the output is
+ // nonempty (thus tmp_out.NumElements() > 0), and we must fill the
+ // output with identity elements. Example: tf.reduce_sum(tf.zeros((0,
+ // 3)), [0]). Eigen sometimes crashes in this case, so we do it
+ // manually.
+ Functor::FillIdentity(d, tmp_out.flat<T>(), reducer);
+ } else if ((helper.ndims() == 1) && helper.reduce_first_axis()) {
+ // Reduce to a scalar.
+ Functor::Reduce(ctx, helper.out<T, 0>(&tmp_out), helper.in<T, 1>(data),
+ constants.kZero, reducer);
+ } else if ((helper.ndims() == 2) && helper.reduce_first_axis()) {
+ // Can be viewed as a reduction of a matrix along 1st dimension.
+ Functor::Reduce(ctx, helper.out<T, 1>(&tmp_out), helper.in<T, 2>(data),
+ constants.kZero, reducer);
+ } else if ((helper.ndims() == 2) && !helper.reduce_first_axis()) {
+ // Can be viewed as a reduction of a matrix along 2nd dimension.
+ Functor::Reduce(ctx, helper.out<T, 1>(&tmp_out), helper.in<T, 2>(data),
+ constants.kOne, reducer);
+ } else if ((helper.ndims() == 3) && helper.reduce_first_axis()) {
+ // Can be viewed as a reduction of a 3D tensor along 1st and 3rd
+ // dimensions.
+ Functor::Reduce(ctx, helper.out<T, 1>(&tmp_out), helper.in<T, 3>(data),
+ constants.kZeroTwo, reducer);
+ } else if ((helper.ndims() == 3) && !helper.reduce_first_axis()) {
+ // Can be viewed as a reduction of a 3D tensor along 2nd dimension.
+ Functor::Reduce(ctx, helper.out<T, 2>(&tmp_out), helper.in<T, 3>(data),
+ constants.kOne, reducer);
+ } else {
+ // If we don't hit one of the cases above, transpose the data so that
+ // all reduced dimensions are last and reuse the 2-D -> 1-D case.
+ Tensor data_reshaped;
+ OP_REQUIRES(ctx, data_reshaped.CopyFrom(data, helper.data_reshape()),
+ errors::Internal("Error during reduction copy."));
+ Tensor shuffled;
+ OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
+ helper.shuffled_shape(),
+ &shuffled, alloc_attr));
+ OP_REQUIRES_OK(ctx, DoTranspose(d, data_reshaped, helper.permutation(),
+ &shuffled));
+ const int64 unreduced = tmp_out.NumElements();
+ const int64 reduced = shuffled.NumElements() / unreduced;
+ const Tensor& const_shuffled = shuffled;
+ Functor::Reduce(ctx, tmp_out.flat<T>(),
+ const_shuffled.shaped<T, 2>({unreduced, reduced}),
+ constants.kOne, reducer);
+ }
}
// Set the real output using the contents of the reduction but the
// real expected output shape. The number of elements should
// match between the two shapes.
Tensor out;
- if (!out.CopyFrom(tmp_out, helper.out_shape())) {
- ctx->SetStatus(errors::Internal("Error during reduction copy."));
- }
+ OP_REQUIRES(ctx, out.CopyFrom(tmp_out, helper.out_shape()),
+ errors::Internal("Error during reduction copy."));
ctx->set_output(0, out);
}
diff --git a/tensorflow/python/kernel_tests/reduction_ops_test.py b/tensorflow/python/kernel_tests/reduction_ops_test.py
index ca052ab9445..acc66b7c3e6 100644
--- a/tensorflow/python/kernel_tests/reduction_ops_test.py
+++ b/tensorflow/python/kernel_tests/reduction_ops_test.py
@@ -497,11 +497,8 @@ class EuclideanNormReductionTest(BaseReductionTest):
if isinstance(reduction_axes, list) or isinstance(reduction_axes,
np.ndarray):
reduction_axes = tuple(reduction_axes)
- if reduction_axes is None or reduction_axes != tuple():
- np_fro = np.sqrt(
- np.sum(x * np.conj(x), axis=reduction_axes, keepdims=keepdims))
- else:
- np_fro = x
+ np_fro = np.sqrt(
+ np.sum(x * np.conj(x), axis=reduction_axes, keepdims=keepdims))
if np.issubdtype(x.dtype, np.integer):
np_fro = np.floor(np_fro)
return np_fro
@@ -522,6 +519,12 @@ class EuclideanNormReductionTest(BaseReductionTest):
np_arr = np.array([special_value_x, special_value_y]).astype(dtype)
self._compareAll(np_arr, None)
+ @test_util.run_deprecated_v1
+ def testSingleton(self):
+ for dtype in [np.float32, np.float64]:
+ np_arr = np.array([-1.]).astype(dtype)
+ self._compareAll(np_arr, None)
+
@test_util.run_deprecated_v1
def testInt32(self):
for rank in range(1, _MAX_RANK + 1):