48 lines
1.6 KiB
C++
48 lines
1.6 KiB
C++
/* Copyright 2018 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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#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
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#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
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#include "tensorflow/compiler/xla/client/lib/qr.h"
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namespace tensorflow {
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namespace {
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class QROp : public XlaOpKernel {
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public:
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explicit QROp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
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OP_REQUIRES_OK(ctx, ctx->GetAttr("full_matrices", &full_matrices_));
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}
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void Compile(XlaOpKernelContext* ctx) override {
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auto result = xla::QRDecomposition(ctx->Input(0), full_matrices_);
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if (!result.ok()) {
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ctx->SetStatus(result.status());
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return;
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}
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ctx->SetOutput(0, result.ValueOrDie().q);
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ctx->SetOutput(1, result.ValueOrDie().r);
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}
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private:
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// If true, compute full-sized q and r. If false, compute only the leading P
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// columns of q.
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bool full_matrices_;
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};
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REGISTER_XLA_OP(Name("Qr").TypeConstraint("T", kFloatTypes), QROp);
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} // namespace
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} // namespace tensorflow
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