50 lines
1.9 KiB
C++
50 lines
1.9 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SVD_H_
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#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SVD_H_
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#include "tensorflow/compiler/xla/client/xla_builder.h"
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#include "tensorflow/compiler/xla/xla_data.pb.h"
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namespace xla {
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// The singular value decomposition of a given matrix A[..., M, N], the original
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// matrix is recovered by u * diag(d) * v_t, where the first dims(A) - 2
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// dimensions are batch dimensions.
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struct SVDResult {
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// The columns of U are the left-singular vectors, e.g.,
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// U[..., :, :]_T * U[..., :, :] = I.
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XlaOp u;
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// Vector(s) with the singular values, within each vector sorted in descending
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// order. The first dims(D) - 1 dimensions have the same size as the batch
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// dimensions of A. And U[..., :, i] * D[..., i] = A[..., :, :] * V[..., :,
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// i].
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XlaOp d;
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// The columns of V are the right-singular vectors. e.g.,
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// V[..., :, :]_T * U[..., :, :] = I.
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XlaOp v;
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};
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// TODO(kuny): Add a bool flag that supports SVD with economy (reduced)
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// representation, which is more memory efficient, especially in the case of
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// tall-skinny matrices.
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SVDResult SVD(XlaOp a, int64 max_iter = 100, float epsilon = 1e-6,
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PrecisionConfig::Precision precision = PrecisionConfig::HIGHEST);
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} // namespace xla
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#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SVD_H_
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