8ba3108452
This changes moves the remaining modules in "core/graph/..." that depend on modules in "core/common_runtime/..." to "core/common_runtime/", and creates separate fine-grained targets for each of them:
* "gradients.{h.cc}"
* "graph_def_builder_util.{h,cc}"
* "mkl_{layout,tfconversion}_pass.{h,cc}"
* "quantize_training.{h,cc}"
PiperOrigin-RevId: 308841156
Change-Id: I7774e2498f465290e7addd331625bd91d90acbac
49 lines
1.9 KiB
C++
49 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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#include "pybind11/pybind11.h"
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#include "tensorflow/core/common_runtime/quantize_training.h"
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#include "tensorflow/python/lib/core/pybind11_lib.h"
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#include "tensorflow/python/lib/core/pybind11_status.h"
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namespace py = pybind11;
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namespace tensorflow {
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static PyObject* DoQuantizeTrainingOnGraphDefHelper(const string& input_graph,
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int num_bits) {
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string result;
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// TODO(suharshs): Make the QuantizeAndDequantizeV2 configurable.
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tensorflow::MaybeRaiseFromStatus(
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tensorflow::DoQuantizeTrainingOnSerializedGraphDef(
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input_graph, num_bits, "QuantizeAndDequantizeV2", &result));
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PyObject* py_str = PyBytes_FromStringAndSize(result.data(), result.size());
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if (!py_str) {
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tensorflow::MaybeRaiseFromStatus(tensorflow::errors::Internal(
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"Failed to generate serialized string of the rewritten graph."));
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}
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return py_str;
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}
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} // namespace tensorflow
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PYBIND11_MODULE(_pywrap_quantize_training, m) {
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m.def("DoQuantizeTrainingOnGraphDefHelper",
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[](const py::object input_graph, int num_bits) {
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return tensorflow::PyoOrThrow(
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tensorflow::DoQuantizeTrainingOnGraphDefHelper(
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input_graph.cast<std::string>(), num_bits));
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});
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};
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