477d49c9ea
Here's an example of the new generated code:
AddN::AddN(const ::tensorflow::Scope& scope, ::tensorflow::InputList inputs) {
if (!scope.ok()) return;
auto _inputs = ::tensorflow::ops::AsNodeOutList(scope, inputs);
if (!scope.ok()) return;
::tensorflow::Node* ret;
const auto unique_name = scope.GetUniqueNameForOp("AddN");
auto builder = ::tensorflow::NodeBuilder(unique_name, "AddN")
.Input(_inputs)
;
scope.UpdateBuilder(&builder);
scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
if (!scope.ok()) return;
scope.UpdateStatus(scope.DoShapeInference(ret));
this->sum = Output(ret, 0);
}
Enabling shape inference unfortunately broke many tests. I fixed some of them, but for others I introduced a Scope::DisabledShapeInferenceScope() static method that returns a scope that doesn't perform shape inference. Eventually we should fix the tests that use this and remove it.
PiperOrigin-RevId: 165378429
76 lines
2.4 KiB
C++
76 lines
2.4 KiB
C++
/* Copyright 2016 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/cc/ops/const_op.h"
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#include "tensorflow/core/framework/types.h"
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namespace tensorflow {
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namespace ops {
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Output Const(const Scope& scope, const Input::Initializer& val) {
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if (!scope.ok()) return Output();
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if (!val.status.ok()) {
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scope.UpdateStatus(val.status);
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return Output();
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}
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Node* ret;
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Graph* graph = scope.graph();
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const string unique_name = scope.GetUniqueNameForOp("Const");
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auto builder = NodeBuilder(unique_name, "Const")
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.Attr("value", val.tensor)
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.Attr("dtype", val.tensor.dtype());
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scope.UpdateBuilder(&builder);
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scope.UpdateStatus(builder.Finalize(graph, &ret));
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if (!scope.ok()) return Output();
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scope.UpdateStatus(scope.DoShapeInference(ret));
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if (!scope.ok()) return Output();
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return Output(ret);
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}
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NodeBuilder::NodeOut AsNodeOut(const Scope& scope, const Input& inp) {
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if (!inp.status().ok()) {
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scope.UpdateStatus(inp.status());
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return NodeBuilder::NodeOut(inp.node(), inp.index());
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}
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if (inp.node()) {
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return NodeBuilder::NodeOut(inp.node(), inp.index());
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}
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if (!inp.node_name().empty()) {
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return NodeBuilder::NodeOut(inp.node_name(), inp.index(), inp.data_type());
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}
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auto transformed = Input{
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Const(scope.NewSubScope("Const"), Input::Initializer(inp.tensor()))};
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return NodeBuilder::NodeOut{transformed.node(), transformed.index()};
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}
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std::vector<NodeBuilder::NodeOut> AsNodeOutList(const Scope& scope,
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const InputList& inp) {
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std::vector<NodeBuilder::NodeOut> out;
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for (const auto& i : inp) {
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const auto node_out = AsNodeOut(scope, i);
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if (!scope.ok()) {
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return {};
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}
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out.push_back(node_out);
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}
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return out;
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}
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} // namespace ops
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} // namespace tensorflow
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