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Fix reduce_euclidean_norm for scalars - it should return the absolute value of the inpus.

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PiperOrigin-RevId: 266200918
This commit is contained in:
A. Unique TensorFlower 2019-08-29 12:25:57 -07:00 committed by TensorFlower Gardener
parent 79ba1e5243
commit a82ae4be43
3 changed files with 87 additions and 64 deletions
tensorflow
core/kernels
reduction_ops.hreduction_ops_common.h
python/kernel_tests
reduction_ops_test.py

10
tensorflow/core/kernels/reduction_ops.h
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@ -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 {

128
tensorflow/core/kernels/reduction_ops_common.h
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@ -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);
}

13
tensorflow/python/kernel_tests/reduction_ops_test.py
View File

@ -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):