96f40ae009
We add the TensorInterface & TensorHandleInterface classes and keep them as the sole member of TF_Tensor and TFE_TensorHandle structs to keep those structs simple. This allows us to keep most of the C API functions as simple wrappers around C++ classes. PiperOrigin-RevId: 288903948 Change-Id: I9f4d8914c447145df63c8518bcde60656f7098f9
102 lines
3.3 KiB
C++
102 lines
3.3 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_PYTHON_EAGER_PYWRAP_TENSOR_CONVERSION_H_
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#define TENSORFLOW_PYTHON_EAGER_PYWRAP_TENSOR_CONVERSION_H_
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// Place `<locale>` before <Python.h> to avoid build failure in macOS.
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#include <locale>
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// The empty line above is on purpose as otherwise clang-format will
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// automatically move <Python.h> before <locale>.
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#include <Python.h>
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#include "absl/container/flat_hash_map.h"
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#include "absl/hash/hash.h"
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#include "absl/strings/string_view.h"
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#include "tensorflow/c/eager/c_api.h"
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#include "tensorflow/core/common_runtime/eager/tensor_handle.h"
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#include "tensorflow/core/framework/types.pb.h"
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namespace tensorflow {
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// Wrapper-class allowing to use Python hashing/comparison functions
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// for PyObject*.
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//
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// Note that unlike Safe_PyObjectPtr this class does not steal a
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// reference to a Python object. The caller is responsible for doing
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// Py_INCREF/Py_DECREF.
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struct PyObjectPtr {
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template <typename H>
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friend H AbslHashValue(H h, const PyObjectPtr& obj) {
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return H::combine(std::move(h), PyObject_Hash(obj.ptr));
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}
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explicit PyObjectPtr(PyObject* ptr) : ptr(ptr) {}
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explicit inline operator PyObject*() const { return ptr; }
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inline bool operator==(const PyObjectPtr& other) const {
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// We require exact type equality to account for 0 == 0.0 == False.
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if (Py_TYPE(ptr) != Py_TYPE(other.ptr)) {
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return false;
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}
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bool result = PyObject_RichCompareBool(ptr, other.ptr, Py_EQ) > 0;
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CHECK(!PyErr_Occurred());
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return result;
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}
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private:
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PyObject* ptr;
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};
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// Cache mapping PyObject* to the corresponding on-device TFE_TensorHandles.
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// Used to speed up ConvertToEagerTensor for scalars.
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// TODO(slebedev): move ConvertToEagerTensor here.
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struct TFE_TensorHandleCache {
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static TFE_TensorHandleCache* Get();
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TFE_TensorHandleCache() { cache.reserve(64); }
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~TFE_TensorHandleCache() { DecrefUnrefAll(); }
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TFE_TensorHandle* Lookup(PyObject* value, tensorflow::DataType dtype,
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absl::string_view device_name) const;
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void Insert(PyObject* value, tensorflow::DataType dtype,
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absl::string_view device_name, TFE_TensorHandle* h);
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void Clear();
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private:
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// TODO(slebedev): should the key depend on TFE_Context?
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using Key = std::tuple<PyObjectPtr, tensorflow::DataType, absl::string_view>;
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void DecrefUnrefAll() {
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for (const auto& p : cache) {
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Py_DECREF(static_cast<PyObject*>(std::get<0>(p.first)));
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TFE_DeleteTensorHandle(p.second);
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}
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}
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// Not guarded by a mutex because the code is only used while the
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// GIL is held.
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absl::flat_hash_map<Key, TFE_TensorHandle*> cache;
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};
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} // namespace tensorflow
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#endif // TENSORFLOW_PYTHON_EAGER_PYWRAP_TENSOR_CONVERSION_H_
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