1/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations under the License.
14==============================================================================*/
15
16#ifndef TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_
17#define TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_
18
19// Must be included first
20#include "tensorflow/python/lib/core/numpy.h"
21
22#include "tensorflow/c/c_api.h"
23#include "tensorflow/core/framework/graph.pb.h"
24#include "tensorflow/core/lib/core/errors.h"
25#include "tensorflow/core/lib/core/status.h"
26#include "tensorflow/core/lib/gtl/inlined_vector.h"
27
28namespace tensorflow {
29
30// Container types for the various arguments and temporary values used
31// in the wrapper.
32
33// A NameVector is a vector of tensor or operation names, as borrowed
34// C strings.
35typedef tensorflow::gtl::InlinedVector<const char*, 8> NameVector;
36
37// A PyObjectVector is a vector of borrowed pointers to PyObjects.
38typedef tensorflow::gtl::InlinedVector<PyObject*, 8> PyObjectVector;
39
40// A TF_TensorVector is a vector of borrowed pointers to TF_Tensors.
41typedef gtl::InlinedVector<TF_Tensor*, 8> TF_TensorVector;
42
43TF_Session* TF_NewSessionRef(TF_Graph* graph, const TF_SessionOptions* opts,
44 TF_Status* status);
45
46// Run the graph associated with the session starting with the
47// supplied inputs[]. Regardless of success or failure, inputs[] are
48// stolen by the implementation (i.e. the implementation will
49// eventually call Py_DECREF on each array input).
50//
51// The PyObject* feed_dict must be a dictionary mapping strings to
52// NumPy arrays. This function does not modify its reference count.
53//
54// On success, the tensors corresponding to output_names[0,noutputs-1]
55// are placed in out_values[], and these outputs[] become the property
56// of the caller (the caller must eventually call Py_DECREF on them).
57//
58// On failure, out_status contains a tensorflow::Status with an error
59// message.
60void TF_Run_wrapper(TF_DeprecatedSession* session, const TF_Buffer* run_options,
61 PyObject* feed_dict, const NameVector& output_names,
62 const NameVector& target_nodes, TF_Status* out_status,
63 PyObjectVector* out_values, TF_Buffer* run_outputs);
64
65// Python wrappers for the `Session::MakeCallable()` API.
66void TF_DeprecatedSessionMakeCallable(TF_DeprecatedSession* session,
67 const TF_Buffer* callable_options,
68 int64_t* out_handle, TF_Status* status);
69void TF_SessionMakeCallable(TF_Session* session,
70 const TF_Buffer* callable_options,
71 int64_t* out_handle, TF_Status* status);
72
73// Python wrappers for the `Session::RunCallable()` API.
74void TF_DeprecatedSessionRunCallable(TF_DeprecatedSession* session,
75 int64_t handle, PyObject* feed_values,
76 PyObjectVector* out_values,
77 TF_Buffer* run_metadata,
78 TF_Status* status);
79void TF_SessionRunCallable(TF_Session* session, int64_t handle,
80 PyObject* feed_values, PyObjectVector* out_values,
81 TF_Buffer* run_metadata, TF_Status* status);
82
83// Python wrappers for the `Session::ReleaseCallable()` API.
84void TF_DeprecatedSessionReleaseCallable(TF_DeprecatedSession* session,
85 int64_t handle, TF_Status* status);
86void TF_SessionReleaseCallable(TF_Session* session, int64_t handle,
87 TF_Status* status);
88
89// Set up the graph with the intended feeds and fetches for partial run.
90// *out_handle is owned by the caller.
91//
92// On success, returns a handle that is used for subsequent PRun calls.
93//
94// On failure, out_status contains a tensorflow::Status with an error
95// message.
96void TF_PRunSetup_wrapper(TF_DeprecatedSession* session,
97 const NameVector& input_names,
98 const NameVector& output_names,
99 const NameVector& target_nodes, TF_Status* out_status,
100 const char** out_handle);
101
102// Continue to run the graph with additional feeds and fetches. The
103// execution state is uniquely identified by the handle.
104//
105// The PyObject* feed_dict must be a dictionary mapping strings to
106// NumPy arrays. This function does not modify its reference count.
107//
108// On success, the tensors corresponding to output_names[0,noutputs-1]
109// are placed in out_values[], and these outputs[] become the property
110// of the caller (the caller must eventually call Py_DECREF on them).
111//
112// On failure, out_status contains a tensorflow::Status with an error
113// message.
114void TF_PRun_wrapper(TF_DeprecatedSession* session, const char* handle,
115 PyObject* feed_dict, const NameVector& output_names,
116 TF_Status* out_status, PyObjectVector* out_values);
117
118// Wrapper for TF_Reset that converts the string vectors to character arrays.
119void TF_Reset_wrapper(const TF_SessionOptions* opt,
120 const NameVector& containers, TF_Status* status);
121
122// Convenience wrapper around EqualGraphDef to make it easier to wrap.
123// Returns an explanation if a difference is found, or the empty string
124// for no difference.
125string EqualGraphDefWrapper(const string& actual, const string& expected);
126
127// Convenience wrapper around AreAttrValuesEqual to make it easier to wrap.
128// The actual and expected strings must correspond to a serialized binary
129// representation of two AttrValue proto instances.
130// Returns an explanation if a difference is found, or the empty string
131// for no difference.
132string EqualAttrValueWrapper(const string& actual, const string& expected);
133
134// Gets shape from C API Graph object.
135//
136// If shape is known, returns shape vector where -1 means "unknown
137// dimension". Sets unknown_shape to false.
138//
139// If shape is unknown, sets unknown_shape to true.
140tensorflow::gtl::InlinedVector<int64_t, 6> TF_GraphGetTensorShapeHelper(
141 TF_Graph* graph, TF_Output output, TF_Status* status, bool* unknown_shape);
142
143// Runs the graph associated with the session starting with the supplied inputs.
144// On success, `py_outputs` is populated with a numpy ndarray for each output
145// (the caller must decref these ndarrays, although this will likely be handled
146// by the Python gc). `session`, `out_status`, and `py_outputs` must be
147// non-null. `py_outputs` should be empty.
148void TF_SessionRun_wrapper(TF_Session* session, const TF_Buffer* run_options,
149 const std::vector<TF_Output>& inputs,
150 const std::vector<PyObject*>& input_ndarrays,
151 const std::vector<TF_Output>& outputs,
152 const std::vector<TF_Operation*>& targets,
153 TF_Buffer* run_metadata, TF_Status* status,
154 std::vector<PyObject*>* py_outputs);
155
156// Set up the graph with the intended feeds (inputs) and fetches (output) for
157// a sequence of partial run calls.
158//
159// On success, returns a handle that can be used for subsequent PRun calls. The
160// handle is owned by the caller and should be deleted with TF_DeletePRunHandle
161// when it is no longer needed.
162//
163// On failure, out_status contains a tensorflow::Status with an error
164// message.
165void TF_SessionPRunSetup_wrapper(TF_Session* session,
166 const std::vector<TF_Output>& inputs,
167 const std::vector<TF_Output>& outputs,
168 const std::vector<TF_Operation*>& targets,
169 const char** out_handle, TF_Status* status);
170
171// Continue to run the graph with additional feeds and fetches. The
172// execution state is uniquely identified by the handle.
173//
174// On success, `py_outputs` is populated with a numpy ndarray for each output
175// (the caller must decref these ndarrays, although this will likely be handled
176// by the Python gc). `session`, `handle`, `out_status`, and `py_outputs` must
177// be non-null. `py_outputs` should be empty.
178//
179// On failure, out_status contains a tensorflow::Status with an error
180// message.
181void TF_SessionPRun_wrapper(TF_Session* session, const char* handle,
182 const std::vector<TF_Output>& inputs,
183 const std::vector<PyObject*>& input_ndarrays,
184 const std::vector<TF_Output>& outputs,
185 TF_Status* status,
186 std::vector<PyObject*>* py_outputs);
187
188// Retrieves the inputs of this operation.
189std::vector<TF_Output> GetOperationInputs(TF_Operation* oper);
190
191// Retrieves the control inputs of this operation.
192std::vector<TF_Operation*> TF_OperationGetControlInputs_wrapper(
193 TF_Operation* oper);
194
195// Retrieves the control outputs of this operation.
196std::vector<TF_Operation*> TF_OperationGetControlOutputs_wrapper(
197 TF_Operation* oper);
198
199// Retrieves the op names of the consumers of `oper_out`. The returned strings
200// have the lifetime of the underlying TF_Graph.
201std::vector<const char*> TF_OperationOutputConsumers_wrapper(
202 TF_Output oper_out);
203
204// `opers` equaling NULL are converted to `nopers = -1`.
205// `output_names` must be empty or have the same length as `outputs`.
206TF_Function* TF_GraphToFunction_wrapper(
207 const TF_Graph* fn_body, const char* fn_name, bool append_hash_to_fn_name,
208 const std::vector<TF_Operation*>* opers,
209 const std::vector<TF_Output>& inputs, const std::vector<TF_Output>& outputs,
210 const NameVector& output_names,
211 const std::vector<TF_Operation*>* control_outputs,
212 const NameVector& control_output_names, const TF_FunctionOptions* opts,
213 const char* description, TF_Status* status);
214
215// Set the shapes and types for the output's handle.
216//
217// The sizes of 'shapes', 'ranks', and 'types' must be equal; `shapes[i]`
218// contains the shape of the handle's i-th value, `ranks[i]` contains the i-th
219// shape's rank, and `types[i]` contains the i-th value's dtype. If the i-th
220// shape is unknown, then `ranks[i]` must be equal to -1.
221//
222// The space between the double angle brackets below looks extraneous, but
223// our version of SWIG cannot parse ">>".
224void TF_GraphSetOutputHandleShapesAndTypes_wrapper(
225 TF_Graph* graph, TF_Output output,
226 const std::vector<std::vector<int64_t> >& shapes,
227 const std::vector<int>& ranks, const std::vector<TF_DataType>& types,
228 TF_Status* status);
229
230// Creates Placeholders with specified types in the Graph.
231//
232// This is an internal API used to speed up creation of unused placeholders
233// in while_v2 cond graph and is subject to change/removal.
234std::vector<TF_Output> TF_CreatePlaceholders(TF_Graph* graph, PyObject* dtypes,
235 const char* prefix,
236 TF_Status* status);
237
238// Set the shape of output. If unknown is true, `num_dims` must be set to
239// -1 and `dims` is set to nullptr.
240void TF_GraphSetTensorShape_wrapper(TF_Graph* graph, TF_Output output,
241 const std::vector<int64_t>& dims,
242 bool unknown_shape, TF_Status* status);
243
244// Returns the string representations of the missing unused input mappings.
245std::vector<string> TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper(
246 TF_ImportGraphDefResults* results);
247
248// If evaluation was possible, returns the numpy ndarray of the evaluated
249// result. Otherwise returns None.
250PyObject* TF_TryEvaluateConstant_wrapper(TF_Graph* graph, TF_Output output,
251 TF_Status* status);
252
253} // namespace tensorflow
254
255#endif // TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_
256