path: root/tensorflow/python/client/tf_session.i

/* Copyright 2015 Google Inc. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

%include "tensorflow/python/platform/base.i"

%{

#include "tensorflow/python/client/tf_session_helper.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/public/version.h"

%}

// Implements the StatusNotOK exception.
%import(module="tensorflow.python.pywrap_tensorflow") "tensorflow/python/lib/core/status.i"

// Required to use PyArray_* functions.
%include "tensorflow/python/platform/numpy.i"
%init %{
tensorflow::ImportNumpy();
%}

// TensorFlow version and GraphDef versions
%constant const char* __version__ = TF_VERSION_STRING;
%constant int GRAPH_DEF_VERSION = TF_GRAPH_DEF_VERSION;
%constant int GRAPH_DEF_VERSION_MIN_CONSUMER = TF_GRAPH_DEF_VERSION_MIN_CONSUMER;
%constant int GRAPH_DEF_VERSION_MIN_PRODUCER = TF_GRAPH_DEF_VERSION_MIN_PRODUCER;

// Release the Python GIL for the duration of most methods.
%exception {
  Py_BEGIN_ALLOW_THREADS;
  $action
  Py_END_ALLOW_THREADS;
}

// Proto input arguments to C API functions are passed as a (const
// void*, size_t) pair. In Python, typemap these to a single string
// argument.
%typemap(in) (const void* proto, size_t proto_len) {
  char* c_string;
  Py_ssize_t py_size;
  if (PyBytes_AsStringAndSize($input, &c_string, &py_size) == -1) {
    // Python has raised an error (likely TypeError or UnicodeEncodeError).
    SWIG_fail;
  }
  $1 = static_cast<void*>(c_string);
  $2 = static_cast<size_t>(py_size);
}

////////////////////////////////////////////////////////////////////////////////
// BEGIN TYPEMAPS FOR tensorflow::TF_Run_wrapper()
////////////////////////////////////////////////////////////////////////////////

// The wrapper takes a vector of pairs of feed names and feed
// values. In Python this is represented as dictionary mapping strings
// to numpy arrays.
%typemap(in) const tensorflow::FeedVector& inputs (
    tensorflow::FeedVector temp,
    tensorflow::Safe_PyObjectPtr temp_string_list(tensorflow::make_safe(nullptr)),
    tensorflow::Safe_PyObjectPtr temp_array_list(tensorflow::make_safe(nullptr))) {
  if (!PyDict_Check($input)) {
    SWIG_fail;
  }

  temp_string_list = tensorflow::make_safe(PyList_New(0));
  if (!temp_string_list) {
    SWIG_fail;
  }
  temp_array_list = tensorflow::make_safe(PyList_New(0));
  if (!temp_array_list) {
    SWIG_fail;
  }

  PyObject* key;
  PyObject* value;
  Py_ssize_t pos = 0;
  while (PyDict_Next($input, &pos, &key, &value)) {
    char* key_string = PyBytes_AsString(key);
    if (!key_string) {
      SWIG_fail;
    }

    // The ndarray must be stored as contiguous bytes in C (row-major) order.
    PyObject* array_object = PyArray_FromAny(
        value, nullptr, 0, 0, NPY_ARRAY_CARRAY, nullptr);
    if (!array_object) {
      SWIG_fail;
    }
    PyArrayObject* array = reinterpret_cast<PyArrayObject*>(array_object);

    // Keep a reference to the key and the array, in case the incoming dict is
    // modified, and/or to avoid leaking references on failure.
    if (PyList_Append(temp_string_list.get(), key) == -1) {
      SWIG_fail;
    }
    if (PyList_Append(temp_array_list.get(), array_object) == -1) {
      SWIG_fail;
    }

    temp.push_back(std::make_pair(key_string, array));
  }
  $1 = &temp;
}

// The wrapper also takes a list of fetch and target names.  In Python this is
// represented as a list of strings.
%typemap(in) const tensorflow::NameVector& (
    tensorflow::NameVector temp,
    tensorflow::Safe_PyObjectPtr temp_string_list(tensorflow::make_safe(nullptr))) {
  if (!PyList_Check($input)) {
    SWIG_fail;
  }

  Py_ssize_t len = PyList_Size($input);

  temp_string_list = tensorflow::make_safe(PyList_New(len));
  if (!temp_string_list) {
    SWIG_fail;
  }

  for (Py_ssize_t i = 0; i < len; ++i) {
    PyObject* elem = PyList_GetItem($input, i);
    if (!elem) {
      SWIG_fail;
    }

    // Keep a reference to the string in case the incoming list is modified.
    PyList_SET_ITEM(temp_string_list.get(), i, elem);
    Py_INCREF(elem);

    char* fetch_name = PyBytes_AsString(elem);
    if (!fetch_name) {
      PyErr_SetString(PyExc_TypeError,
                      "a fetch or target name was not a string");
      SWIG_fail;
    }

    // TODO(mrry): Avoid copying the fetch name in, if this impacts performance.
    temp.push_back(fetch_name);
  }
  $1 = &temp;
}


// The wrapper has two outputs: a tensorflow::Status, and a vector of
// PyObjects containing the fetch results (iff the status is OK). Since
// the interpretation of the vector depends on the status, we define
// them as two consecutive out arguments, so that they can be accessed
// together in a typemap.

// Define temporaries for the argout outputs.
%typemap(in, numinputs=0) tensorflow::Status* out_status (
    tensorflow::Status temp) {
  $1 = &temp;
}
%typemap(in, numinputs=0) tensorflow::PyObjectVector* out_values (
    tensorflow::PyObjectVector temp) {
  $1 = &temp;
}
%typemap(in, numinputs=0) char** out_handle (
    char* temp) {
  $1 = &temp;
}

// Raise a StatusNotOK exception if the out_status is not OK;
// otherwise build a Python list of outputs and return it.
%typemap(argout, fragment="StatusNotOK") (
    tensorflow::Status* out_status, tensorflow::PyObjectVector* out_values) {
  if (!$1->ok()) {
    RaiseStatusNotOK(*$1, $descriptor(tensorflow::Status*));
    SWIG_fail;
  } else {
    tensorflow::Safe_PyObjectVector out_values_safe;
    for (size_t i = 0; i < $2->size(); ++i) {
      out_values_safe.emplace_back(tensorflow::make_safe($2->at(i)));
    }

    $result = PyList_New($2->size());
    if (!$result) {
      SWIG_fail;
    }

    for (size_t i = 0; i < $2->size(); ++i) {
      PyList_SET_ITEM($result, i, $2->at(i));
      out_values_safe[i].release();
    }
  }
}

// Raise a StatusNotOK exception if the out_status is not OK;
// otherwise return the handle as a python string object.
%typemap(argout, fragment="StatusNotOK") (
    tensorflow::Status* out_status, char** out_handle) {
  if (!$1->ok()) {
    RaiseStatusNotOK(*$1, $descriptor(tensorflow::Status*));
    SWIG_fail;
  } else {
    $result = PyString_FromStringAndSize(*$2, strlen(*$2));
    delete *$2;
  }
}

////////////////////////////////////////////////////////////////////////////////
// END TYPEMAPS FOR tensorflow::TF_Run_wrapper()
////////////////////////////////////////////////////////////////////////////////

// Typemaps for TF_GetOpList.
// The wrapped function TF_GetOpList returns a TF_Buffer pointer. This typemap
// creates a Python string from the TF_Buffer and returns it.
%typemap(out) TF_Buffer TF_GetOpList {
%#if PY_MAJOR_VERSION < 3
  $result = PyString_FromStringAndSize(
%#else
  $result = PyUnicode_FromStringAndSize(
%#endif
      reinterpret_cast<const char*>($1.data), $1.length);
}

// Include the functions from tensor_c_api.h, except TF_Run.
%ignoreall
%unignore TF_Code;
%unignore TF_Status;
%unignore TF_NewStatus;
%unignore TF_DeleteStatus;
%unignore TF_GetCode;
%unignore TF_Message;
%unignore TF_SessionOptions;
%rename("_TF_SetTarget") TF_SetTarget;
%rename("_TF_SetConfig") TF_SetConfig;
%rename("_TF_NewSessionOptions") TF_NewSessionOptions;
%unignore TF_DeleteSessionOptions;
%unignore TF_NewSession;
%unignore TF_CloseSession;
%unignore TF_DeleteSession;
%unignore TF_ExtendGraph;
%unignore TF_NewLibrary;
%unignore TF_LoadLibrary;
%unignore TF_GetOpList;
%include "tensorflow/core/public/tensor_c_api.h"
%ignoreall

%insert("python") %{
  def TF_NewSessionOptions(target=None, config=None):
    opts = _TF_NewSessionOptions()
    if target is not None:
      _TF_SetTarget(opts, target)
    if config is not None:
      from tensorflow.core.framework import config_pb2
      if not isinstance(config, config_pb2.ConfigProto):
        raise TypeError("Expected config_pb2.ConfigProto, "
                        "but got %s" % type(config))
      status = TF_NewStatus()
      config_str = config.SerializeToString()
      _TF_SetConfig(opts, config_str, status)
      if TF_GetCode(status) != 0:
        raise ValueError(TF_Message(status))
    return opts
%}

// Include the wrapper for TF_Run from tf_session_helper.h.

// The %exception block above releases the Python GIL for the length
// of each wrapped method. We disable this behavior for TF_Run
// because it uses the Python allocator.
%noexception tensorflow::TF_Run_wrapper;
%rename(TF_Run) tensorflow::TF_Run_wrapper;
%unignore tensorflow;
%unignore TF_Run;
%unignore EqualGraphDefWrapper;

// Include the wrapper for TF_PRunSetup from tf_session_helper.h.

// The %exception block above releases the Python GIL for the length
// of each wrapped method. We disable this behavior for TF_PRunSetup
// because it uses the Python allocator.
%noexception tensorflow::TF_PRunSetup_wrapper;
%rename(TF_PRunSetup) tensorflow::TF_PRunSetup_wrapper;
%unignore tensorflow;
%unignore TF_PRunSetup;

// Include the wrapper for TF_PRun from tf_session_helper.h.

// The %exception block above releases the Python GIL for the length
// of each wrapped method. We disable this behavior for TF_PRun
// because it uses the Python allocator.
%noexception tensorflow::TF_PRun_wrapper;
%rename(TF_PRun) tensorflow::TF_PRun_wrapper;
%unignore tensorflow;
%unignore TF_PRun;

%include "tensorflow/python/client/tf_session_helper.h"

%unignoreall