1 | /* Copyright 2015 The TensorFlow Authors. All Rights Reserved. |
2 | |
3 | Licensed under the Apache License, Version 2.0 (the "License"); |
4 | you may not use this file except in compliance with the License. |
5 | You may obtain a copy of the License at |
6 | |
7 | http://www.apache.org/licenses/LICENSE-2.0 |
8 | |
9 | Unless required by applicable law or agreed to in writing, software |
10 | distributed under the License is distributed on an "AS IS" BASIS, |
11 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
12 | See the License for the specific language governing permissions and |
13 | limitations under the License. |
14 | ==============================================================================*/ |
15 | |
16 | #include "tensorflow/c/c_api.h" |
17 | |
18 | #include <algorithm> |
19 | #include <limits> |
20 | #include <memory> |
21 | #include <vector> |
22 | |
23 | #include "absl/strings/match.h" |
24 | // Required for IS_MOBILE_PLATFORM |
25 | #include "tensorflow/core/platform/platform.h" // NOLINT |
26 | |
27 | #if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
28 | #include "tensorflow/c/experimental/filesystem/modular_filesystem.h" |
29 | #include "tensorflow/cc/framework/gradients.h" |
30 | #include "tensorflow/cc/framework/ops.h" |
31 | #include "tensorflow/cc/framework/scope_internal.h" |
32 | #include "tensorflow/cc/ops/while_loop.h" |
33 | #include "tensorflow/cc/saved_model/loader.h" |
34 | #include "tensorflow/core/distributed_runtime/server_lib.h" |
35 | #include "tensorflow/core/framework/logging.h" |
36 | #include "tensorflow/core/framework/op_gen_lib.h" |
37 | #endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
38 | #include "tensorflow/c/c_api_internal.h" |
39 | #include "tensorflow/c/tf_buffer_internal.h" |
40 | #include "tensorflow/c/tf_status_internal.h" |
41 | #include "tensorflow/c/tf_tensor.h" |
42 | #include "tensorflow/core/common_runtime/device_mgr.h" |
43 | #include "tensorflow/core/common_runtime/eval_const_tensor.h" |
44 | #include "tensorflow/core/common_runtime/graph_constructor.h" |
45 | #include "tensorflow/core/common_runtime/shape_refiner.h" |
46 | #include "tensorflow/core/framework/allocation_description.pb.h" |
47 | #include "tensorflow/core/framework/kernel_def.pb.h" |
48 | #include "tensorflow/core/framework/log_memory.h" |
49 | #include "tensorflow/core/framework/node_def_util.h" |
50 | #include "tensorflow/core/framework/op_kernel.h" |
51 | #include "tensorflow/core/framework/partial_tensor_shape.h" |
52 | #include "tensorflow/core/framework/tensor.h" |
53 | #include "tensorflow/core/framework/tensor.pb.h" // NOLINT |
54 | #include "tensorflow/core/framework/tensor_shape.h" |
55 | #include "tensorflow/core/framework/tensor_shape.pb.h" |
56 | #include "tensorflow/core/framework/types.h" |
57 | #include "tensorflow/core/framework/versions.pb.h" |
58 | #include "tensorflow/core/graph/graph.h" |
59 | #include "tensorflow/core/graph/node_builder.h" |
60 | #include "tensorflow/core/graph/validate.h" |
61 | #include "tensorflow/core/lib/gtl/array_slice.h" |
62 | #include "tensorflow/core/platform/coding.h" |
63 | #include "tensorflow/core/platform/errors.h" |
64 | #include "tensorflow/core/platform/mem.h" |
65 | #include "tensorflow/core/platform/mutex.h" |
66 | #include "tensorflow/core/platform/protobuf.h" |
67 | #include "tensorflow/core/platform/status.h" |
68 | #include "tensorflow/core/platform/str_util.h" |
69 | #include "tensorflow/core/platform/strcat.h" |
70 | #include "tensorflow/core/platform/stringpiece.h" |
71 | #include "tensorflow/core/platform/thread_annotations.h" |
72 | #include "tensorflow/core/platform/types.h" |
73 | #include "tensorflow/core/public/session.h" |
74 | #include "tensorflow/core/public/version.h" |
75 | |
76 | // The implementation below is at the top level instead of the |
77 | // brain namespace because we are defining 'extern "C"' functions. |
78 | using tensorflow::AllocationDescription; |
79 | using tensorflow::AttrValueMap; |
80 | using tensorflow::DataType; |
81 | using tensorflow::ExtendSessionGraphHelper; |
82 | using tensorflow::Graph; |
83 | using tensorflow::GraphDef; |
84 | using tensorflow::mutex_lock; |
85 | using tensorflow::NameRangeMap; |
86 | using tensorflow::NameRangesForNode; |
87 | using tensorflow::NewSession; |
88 | using tensorflow::Node; |
89 | using tensorflow::NodeBuilder; |
90 | using tensorflow::NodeDef; |
91 | using tensorflow::OpDef; |
92 | using tensorflow::OpRegistry; |
93 | using tensorflow::OutputTensor; |
94 | using tensorflow::PartialTensorShape; |
95 | using tensorflow::RunMetadata; |
96 | using tensorflow::RunOptions; |
97 | using tensorflow::Session; |
98 | using tensorflow::Status; |
99 | using tensorflow::string; |
100 | using tensorflow::Tensor; |
101 | using tensorflow::TensorBuffer; |
102 | using tensorflow::TensorId; |
103 | using tensorflow::TensorShape; |
104 | using tensorflow::TensorShapeProto; |
105 | using tensorflow::VersionDef; |
106 | using tensorflow::errors::FailedPrecondition; |
107 | using tensorflow::errors::InvalidArgument; |
108 | using tensorflow::errors::OutOfRange; |
109 | using tensorflow::gtl::ArraySlice; |
110 | using tensorflow::strings::StrCat; |
111 | |
112 | extern "C" { |
113 | |
114 | // -------------------------------------------------------------------------- |
115 | const char* TF_Version() { return TF_VERSION_STRING; } |
116 | |
117 | // -------------------------------------------------------------------------- |
118 | |
119 | // -------------------------------------------------------------------------- |
120 | TF_SessionOptions* TF_NewSessionOptions() { return new TF_SessionOptions; } |
121 | void TF_DeleteSessionOptions(TF_SessionOptions* opt) { delete opt; } |
122 | |
123 | void TF_SetTarget(TF_SessionOptions* options, const char* target) { |
124 | options->options.target = target; |
125 | } |
126 | |
127 | void TF_SetConfig(TF_SessionOptions* options, const void* proto, |
128 | size_t proto_len, TF_Status* status) { |
129 | if (!options->options.config.ParseFromArray(proto, proto_len)) { |
130 | status->status = InvalidArgument("Unparseable ConfigProto" ); |
131 | } |
132 | } |
133 | |
134 | void TF_TensorFromProto(const TF_Buffer* from, TF_Tensor* to, |
135 | TF_Status* status) { |
136 | TF_SetStatus(status, TF_OK, "" ); |
137 | tensorflow::TensorProto from_tensor_proto; |
138 | status->status = BufferToMessage(from, &from_tensor_proto); |
139 | if (!status->status.ok()) { |
140 | return; |
141 | } |
142 | status->status = |
143 | tensorflow::down_cast<tensorflow::TensorInterface*>(to->tensor) |
144 | ->FromProto(from_tensor_proto); |
145 | } |
146 | // -------------------------------------------------------------------------- |
147 | |
148 | TF_DeprecatedSession* TF_NewDeprecatedSession(const TF_SessionOptions* opt, |
149 | TF_Status* status) { |
150 | Session* session; |
151 | status->status = NewSession(opt->options, &session); |
152 | if (status->status.ok()) { |
153 | return new TF_DeprecatedSession({session}); |
154 | } else { |
155 | DCHECK_EQ(nullptr, session); |
156 | return nullptr; |
157 | } |
158 | } |
159 | |
160 | void TF_CloseDeprecatedSession(TF_DeprecatedSession* s, TF_Status* status) { |
161 | status->status = s->session->Close(); |
162 | } |
163 | |
164 | void TF_DeleteDeprecatedSession(TF_DeprecatedSession* s, TF_Status* status) { |
165 | status->status = ::tensorflow::OkStatus(); |
166 | if (s == nullptr) return; |
167 | delete s->session; |
168 | delete s; |
169 | } |
170 | |
171 | void TF_ExtendGraph(TF_DeprecatedSession* s, const void* proto, |
172 | size_t proto_len, TF_Status* status) { |
173 | GraphDef g; |
174 | if (!tensorflow::ParseProtoUnlimited(&g, proto, proto_len)) { |
175 | status->status = InvalidArgument("Invalid GraphDef" ); |
176 | return; |
177 | } |
178 | status->status = s->session->Extend(g); |
179 | } |
180 | |
181 | } // end extern "C" |
182 | |
183 | // Reset helper for converting character arrays to string vectors. |
184 | static void TF_Reset_Helper(const TF_SessionOptions* opt, |
185 | const char** containers, int ncontainers, |
186 | TF_Status* status) { |
187 | std::vector<string> container_names(ncontainers); |
188 | for (int i = 0; i < ncontainers; ++i) { |
189 | container_names[i] = containers[i]; |
190 | } |
191 | |
192 | status->status = Reset(opt->options, container_names); |
193 | } |
194 | |
195 | extern "C" { |
196 | |
197 | void TF_Reset(const TF_SessionOptions* opt, const char** containers, |
198 | int ncontainers, TF_Status* status) { |
199 | TF_Reset_Helper(opt, containers, ncontainers, status); |
200 | } |
201 | |
202 | } // end extern "C" |
203 | |
204 | namespace tensorflow { |
205 | |
206 | void RecordMutation(TF_Graph* graph, const TF_Operation& op, |
207 | const char* mutation_type) { |
208 | // If any session has already run this node_id, mark this session as |
209 | // unrunnable. |
210 | for (auto it : graph->sessions) { |
211 | mutex_lock session_lock(it.first->mu); |
212 | if (it.first->last_num_graph_nodes > op.node.id()) { |
213 | it.second = strings::StrCat( |
214 | "Operation '" , op.node.DebugString(), "' was changed by " , |
215 | mutation_type, |
216 | " after it was run by a session. This mutation will have no effect, " |
217 | "and will trigger an error in the future. Either don't modify " |
218 | "nodes after running them or create a new session." ); |
219 | } |
220 | } |
221 | } |
222 | |
223 | namespace { |
224 | |
225 | // Helper method that creates a shape handle for a shape described by dims. |
226 | tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims( |
227 | tensorflow::shape_inference::InferenceContext* ic, int num_dims, |
228 | const int64_t* dims) { |
229 | if (num_dims != -1) { |
230 | std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec; |
231 | dim_vec.reserve(num_dims); |
232 | for (int i = 0; i < num_dims; ++i) { |
233 | dim_vec.push_back(ic->MakeDim(dims[i])); |
234 | } |
235 | return ic->MakeShape(dim_vec); |
236 | } else { |
237 | return ic->UnknownShape(); |
238 | } |
239 | } |
240 | |
241 | } // namespace |
242 | |
243 | void TF_GraphSetOutputHandleShapesAndTypes(TF_Graph* graph, TF_Output output, |
244 | int num_shapes_and_types, |
245 | const int64_t** shapes, |
246 | const int* ranks, |
247 | const TF_DataType* types, |
248 | TF_Status* status) { |
249 | Node* node = &output.oper->node; |
250 | |
251 | mutex_lock l(graph->mu); |
252 | tensorflow::shape_inference::InferenceContext* ic = |
253 | graph->refiner.GetContext(node); |
254 | if (ic == nullptr) { |
255 | status->status = |
256 | InvalidArgument("Node " , node->name(), " was not found in the graph" ); |
257 | return; |
258 | } |
259 | |
260 | auto shape_and_type_vec = |
261 | std::vector<tensorflow::shape_inference::ShapeAndType>( |
262 | num_shapes_and_types); |
263 | for (int i = 0; i < num_shapes_and_types; ++i) { |
264 | tensorflow::shape_inference::ShapeHandle shape_handle = |
265 | ShapeHandleFromDims(ic, ranks[i], shapes[i]); |
266 | shape_and_type_vec[i] = tensorflow::shape_inference::ShapeAndType( |
267 | shape_handle, static_cast<DataType>(types[i])); |
268 | } |
269 | |
270 | ic->set_output_handle_shapes_and_types(output.index, shape_and_type_vec); |
271 | } |
272 | |
273 | // Helpers for loading a TensorFlow plugin (a .so file). |
274 | Status LoadDynamicLibrary(const char* library_filename, void** result, |
275 | const void** buf, size_t* len); |
276 | |
277 | // TODO(josh11b,mrry): Change Session to be able to use a Graph* |
278 | // directly, instead of requiring us to serialize to a GraphDef and |
279 | // call Session::Extend(). |
280 | bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) { |
281 | if (session->graph != nullptr) { |
282 | // Take the graph lock before the session lock to avoid deadlock. This is |
283 | // safe since session->graph does not change. |
284 | session->graph->mu.lock(); |
285 | mutex_lock session_lock(session->mu); |
286 | const Graph& graph = session->graph->graph; |
287 | |
288 | const string& mutation_warning = session->graph->sessions[session]; |
289 | if (!mutation_warning.empty()) { |
290 | // TODO(b/74949947): turn this back into an error status |
291 | LOG(WARNING) << mutation_warning; |
292 | session->graph->sessions[session].clear(); |
293 | } |
294 | |
295 | const auto num_nodes = graph.num_node_ids(); |
296 | if (session->last_num_graph_nodes < num_nodes) { |
297 | // TODO(nolivia): check this on a subset of the graph instead of all of |
298 | // it. |
299 | status->status = graph::ValidateGraphHasNoCycle(session->graph->graph); |
300 | if (!status->status.ok()) { |
301 | session->graph->mu.unlock(); |
302 | return false; |
303 | } |
304 | |
305 | GraphDef graph_def; |
306 | *graph_def.mutable_versions() = graph.versions(); |
307 | // Fill graph_def with nodes with ids in the range |
308 | // [session->last_num_graph_nodes, num_nodes), that is the nodes |
309 | // added since the last TF_SessionRun() call. |
310 | for (auto id = session->last_num_graph_nodes; id < num_nodes; ++id) { |
311 | Node* const node = graph.FindNodeId(id); |
312 | if (node != nullptr && node->IsOp()) { |
313 | NodeDef* const node_def = graph_def.add_node(); |
314 | *node_def = node->def(); |
315 | } |
316 | } |
317 | *graph_def.mutable_library() = graph.flib_def().ToProto(); |
318 | session->graph->mu.unlock(); |
319 | status->status = session->session->Extend(std::move(graph_def)); |
320 | if (!status->status.ok()) { |
321 | // Contract is we always delete input_values[i]. |
322 | return false; |
323 | } |
324 | // Note: session->session is not modified if Extend() fails, so |
325 | // we only set last_num_graph_nodes if it succeeds. |
326 | session->last_num_graph_nodes = num_nodes; |
327 | } else { |
328 | session->graph->mu.unlock(); |
329 | } |
330 | } |
331 | return true; |
332 | } |
333 | |
334 | } // namespace tensorflow |
335 | |
336 | static void TF_Run_Setup(int noutputs, TF_Tensor** c_outputs, |
337 | TF_Status* status) { |
338 | status->status = ::tensorflow::OkStatus(); |
339 | for (int i = 0; i < noutputs; ++i) { |
340 | c_outputs[i] = nullptr; |
341 | } |
342 | } |
343 | |
344 | // TF_TensorToTensorV1 decodes a string serialization to DT_RESOURCE. |
345 | // In the TFv1 convention, TF_Tensor can hold a string serialization of |
346 | // DT_RESOURCE. The string serialization is converted back to a |
347 | // ResourceHandle during Session run where the TF_Tensor is converted to a |
348 | // Tensor. |
349 | // TFv2 does not depend on this conversion. There is no matching |
350 | // TF_TensorFromTensorV1 because the conversion to string is performed by the |
351 | // python side of Session. |
352 | static Status TF_TensorToTensorV1(const TF_Tensor* src, Tensor* dst) { |
353 | Status status = TF_TensorToTensor(src, dst); |
354 | if (!status.ok()) { |
355 | return status; |
356 | } |
357 | if (dst->dtype() == tensorflow::DT_RESOURCE) { |
358 | const auto tensor_interface = |
359 | tensorflow::down_cast<const tensorflow::TensorInterface*>(src->tensor); |
360 | |
361 | if (dst->dims() != 0) { |
362 | return InvalidArgument( |
363 | "Malformed TF_RESOURCE tensor: expected a scalar, got a tensor with " |
364 | "shape " , |
365 | dst->shape().DebugString()); |
366 | } |
367 | *dst = tensorflow::Tensor(tensorflow::DT_RESOURCE, dst->shape()); |
368 | if (!dst->scalar<tensorflow::ResourceHandle>()().ParseFromString( |
369 | string(static_cast<const char*>(tensor_interface->Data()), |
370 | tensor_interface->ByteSize()))) { |
371 | return InvalidArgument( |
372 | "Malformed TF_RESOURCE tensor: unable to parse resource handle" ); |
373 | } |
374 | return ::tensorflow::OkStatus(); |
375 | } |
376 | return ::tensorflow::OkStatus(); |
377 | } |
378 | |
379 | static bool TF_Run_Inputs(TF_Tensor* const* c_inputs, |
380 | std::vector<std::pair<string, Tensor>>* input_pairs, |
381 | TF_Status* status) { |
382 | const int ninputs = input_pairs->size(); |
383 | for (int i = 0; i < ninputs; ++i) { |
384 | status->status = |
385 | TF_TensorToTensorV1(c_inputs[i], &(*input_pairs)[i].second); |
386 | if (!status->status.ok()) return false; |
387 | } |
388 | return true; |
389 | } |
390 | |
391 | // Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to |
392 | // result in a zero-sized tensor. |
393 | static TF_Tensor* EmptyTensor(TF_DataType dtype, |
394 | const tensorflow::TensorShape& shape) { |
395 | static char empty; |
396 | int64_t nelems = 1; |
397 | std::vector<int64_t> dims; |
398 | dims.reserve(shape.dims()); |
399 | for (int i = 0; i < shape.dims(); ++i) { |
400 | dims.push_back(shape.dim_size(i)); |
401 | nelems *= shape.dim_size(i); |
402 | } |
403 | CHECK_EQ(nelems, 0); |
404 | return TF_NewTensor( |
405 | dtype, reinterpret_cast<const int64_t*>(dims.data()), shape.dims(), |
406 | reinterpret_cast<void*>(&empty), 0, [](void*, size_t, void*) {}, nullptr); |
407 | } |
408 | |
409 | static void TF_Run_Helper( |
410 | Session* session, const char* handle, const TF_Buffer* run_options, |
411 | // Input tensors |
412 | const std::vector<std::pair<string, Tensor>>& input_pairs, |
413 | // Output tensors |
414 | const std::vector<string>& output_tensor_names, TF_Tensor** c_outputs, |
415 | // Target nodes |
416 | const std::vector<string>& target_oper_names, TF_Buffer* run_metadata, |
417 | TF_Status* status) { |
418 | const int noutputs = output_tensor_names.size(); |
419 | std::vector<Tensor> outputs(noutputs); |
420 | Status result; |
421 | |
422 | if (handle == nullptr) { |
423 | RunOptions run_options_proto; |
424 | if (run_options != nullptr && !run_options_proto.ParseFromArray( |
425 | run_options->data, run_options->length)) { |
426 | status->status = InvalidArgument("Unparseable RunOptions proto" ); |
427 | return; |
428 | } |
429 | if (run_metadata != nullptr && run_metadata->data != nullptr) { |
430 | status->status = |
431 | InvalidArgument("Passing non-empty run_metadata is invalid." ); |
432 | return; |
433 | } |
434 | |
435 | RunMetadata run_metadata_proto; |
436 | result = session->Run(run_options_proto, input_pairs, output_tensor_names, |
437 | target_oper_names, &outputs, &run_metadata_proto); |
438 | |
439 | // Serialize back to upstream client, who now owns the new buffer |
440 | if (run_metadata != nullptr) { |
441 | status->status = MessageToBuffer(run_metadata_proto, run_metadata); |
442 | if (!status->status.ok()) return; |
443 | } |
444 | } else { |
445 | // NOTE(zongheng): PRun does not support RunOptions yet. |
446 | result = session->PRun(handle, input_pairs, output_tensor_names, &outputs); |
447 | } |
448 | if (!result.ok()) { |
449 | status->status = result; |
450 | return; |
451 | } |
452 | |
453 | // Store results in c_outputs[] |
454 | for (int i = 0; i < noutputs; ++i) { |
455 | const Tensor& src = outputs[i]; |
456 | if (!src.IsInitialized() || src.NumElements() == 0) { |
457 | c_outputs[i] = |
458 | EmptyTensor(static_cast<TF_DataType>(src.dtype()), src.shape()); |
459 | continue; |
460 | } |
461 | c_outputs[i] = TF_TensorFromTensor(src, &status->status); |
462 | if (!status->status.ok()) return; |
463 | } |
464 | } |
465 | |
466 | extern "C" { |
467 | |
468 | void TF_Run(TF_DeprecatedSession* s, const TF_Buffer* run_options, |
469 | // Input tensors |
470 | const char** c_input_names, TF_Tensor** c_inputs, int ninputs, |
471 | // Output tensors |
472 | const char** c_output_names, TF_Tensor** c_outputs, int noutputs, |
473 | // Target nodes |
474 | const char** c_target_oper_names, int ntargets, |
475 | TF_Buffer* run_metadata, TF_Status* status) { |
476 | TF_Run_Setup(noutputs, c_outputs, status); |
477 | std::vector<std::pair<string, Tensor>> input_pairs(ninputs); |
478 | if (!TF_Run_Inputs(c_inputs, &input_pairs, status)) return; |
479 | for (int i = 0; i < ninputs; ++i) { |
480 | input_pairs[i].first = c_input_names[i]; |
481 | } |
482 | std::vector<string> output_names(noutputs); |
483 | for (int i = 0; i < noutputs; ++i) { |
484 | output_names[i] = c_output_names[i]; |
485 | } |
486 | std::vector<string> target_oper_names(ntargets); |
487 | for (int i = 0; i < ntargets; ++i) { |
488 | target_oper_names[i] = c_target_oper_names[i]; |
489 | } |
490 | TF_Run_Helper(s->session, nullptr, run_options, input_pairs, output_names, |
491 | c_outputs, target_oper_names, run_metadata, status); |
492 | } |
493 | |
494 | void TF_PRunSetup(TF_DeprecatedSession* s, |
495 | // Input names |
496 | const char** c_input_names, int ninputs, |
497 | // Output names |
498 | const char** c_output_names, int noutputs, |
499 | // Target nodes |
500 | const char** c_target_oper_names, int ntargets, |
501 | const char** handle, TF_Status* status) { |
502 | *handle = nullptr; |
503 | |
504 | std::vector<string> input_names(ninputs); |
505 | std::vector<string> output_names(noutputs); |
506 | std::vector<string> target_oper_names(ntargets); |
507 | for (int i = 0; i < ninputs; ++i) { |
508 | input_names[i] = c_input_names[i]; |
509 | } |
510 | for (int i = 0; i < noutputs; ++i) { |
511 | output_names[i] = c_output_names[i]; |
512 | } |
513 | for (int i = 0; i < ntargets; ++i) { |
514 | target_oper_names[i] = c_target_oper_names[i]; |
515 | } |
516 | string new_handle; |
517 | status->status = s->session->PRunSetup(input_names, output_names, |
518 | target_oper_names, &new_handle); |
519 | if (status->status.ok()) { |
520 | char* buf = new char[new_handle.size() + 1]; |
521 | memcpy(buf, new_handle.c_str(), new_handle.size() + 1); |
522 | *handle = buf; |
523 | } |
524 | } |
525 | |
526 | void TF_PRun(TF_DeprecatedSession* s, const char* handle, |
527 | // Input tensors |
528 | const char** c_input_names, TF_Tensor** c_inputs, int ninputs, |
529 | // Output tensors |
530 | const char** c_output_names, TF_Tensor** c_outputs, int noutputs, |
531 | // Target nodes |
532 | const char** c_target_oper_names, int ntargets, |
533 | TF_Status* status) { |
534 | TF_Run_Setup(noutputs, c_outputs, status); |
535 | std::vector<std::pair<string, Tensor>> input_pairs(ninputs); |
536 | if (!TF_Run_Inputs(c_inputs, &input_pairs, status)) return; |
537 | for (int i = 0; i < ninputs; ++i) { |
538 | input_pairs[i].first = c_input_names[i]; |
539 | } |
540 | |
541 | std::vector<string> output_names(noutputs); |
542 | for (int i = 0; i < noutputs; ++i) { |
543 | output_names[i] = c_output_names[i]; |
544 | } |
545 | std::vector<string> target_oper_names(ntargets); |
546 | for (int i = 0; i < ntargets; ++i) { |
547 | target_oper_names[i] = c_target_oper_names[i]; |
548 | } |
549 | TF_Run_Helper(s->session, handle, nullptr, input_pairs, output_names, |
550 | c_outputs, target_oper_names, nullptr, status); |
551 | } |
552 | |
553 | TF_Library* TF_LoadLibrary(const char* library_filename, TF_Status* status) { |
554 | TF_Library* lib_handle = new TF_Library; |
555 | status->status = tensorflow::LoadDynamicLibrary( |
556 | library_filename, &lib_handle->lib_handle, &lib_handle->op_list.data, |
557 | &lib_handle->op_list.length); |
558 | if (!status->status.ok()) { |
559 | delete lib_handle; |
560 | return nullptr; |
561 | } |
562 | return lib_handle; |
563 | } |
564 | |
565 | TF_Buffer TF_GetOpList(TF_Library* lib_handle) { return lib_handle->op_list; } |
566 | |
567 | void TF_DeleteLibraryHandle(TF_Library* lib_handle) { |
568 | if (lib_handle == nullptr) return; |
569 | tensorflow::port::Free(const_cast<void*>(lib_handle->op_list.data)); |
570 | delete lib_handle; |
571 | } |
572 | |
573 | TF_Buffer* TF_GetAllOpList() { |
574 | std::vector<tensorflow::OpDef> op_defs; |
575 | tensorflow::OpRegistry::Global()->GetRegisteredOps(&op_defs); |
576 | tensorflow::OpList op_list; |
577 | for (const auto& op : op_defs) { |
578 | *(op_list.add_op()) = op; |
579 | } |
580 | TF_Buffer* ret = TF_NewBuffer(); |
581 | TF_CHECK_OK(MessageToBuffer(op_list, ret)); |
582 | return ret; |
583 | } |
584 | |
585 | // -------------------------------------------------------------------------- |
586 | // ListDevices & SessionListDevices API |
587 | |
588 | void TF_DeleteDeviceList(TF_DeviceList* list) { delete list; } |
589 | |
590 | TF_DeviceList* TF_SessionListDevices(TF_Session* session, TF_Status* status) { |
591 | TF_DeviceList* response = new TF_DeviceList; |
592 | if (session && session->session) |
593 | status->status = session->session->ListDevices(&response->response); |
594 | return response; |
595 | } |
596 | |
597 | TF_DeviceList* TF_DeprecatedSessionListDevices(TF_DeprecatedSession* session, |
598 | TF_Status* status) { |
599 | TF_DeviceList* response = new TF_DeviceList; |
600 | if (session && session->session) |
601 | status->status = session->session->ListDevices(&response->response); |
602 | return response; |
603 | } |
604 | |
605 | int TF_DeviceListCount(const TF_DeviceList* list) { |
606 | return list->response.size(); |
607 | } |
608 | |
609 | #define TF_DEVICELIST_METHOD(return_type, method_name, accessor, err_val) \ |
610 | return_type method_name(const TF_DeviceList* list, const int index, \ |
611 | TF_Status* status) { \ |
612 | if (list == nullptr) { \ |
613 | status->status = InvalidArgument("list is null!"); \ |
614 | return err_val; \ |
615 | } \ |
616 | if (index < 0 || index >= list->response.size()) { \ |
617 | status->status = InvalidArgument("index out of bounds"); \ |
618 | return err_val; \ |
619 | } \ |
620 | status->status = ::tensorflow::OkStatus(); \ |
621 | return list->response[index].accessor; \ |
622 | } |
623 | |
624 | TF_DEVICELIST_METHOD(const char*, TF_DeviceListName, name().c_str(), nullptr); |
625 | TF_DEVICELIST_METHOD(const char*, TF_DeviceListType, device_type().c_str(), |
626 | nullptr); |
627 | TF_DEVICELIST_METHOD(int64_t, TF_DeviceListMemoryBytes, memory_limit(), -1); |
628 | TF_DEVICELIST_METHOD(uint64_t, TF_DeviceListIncarnation, incarnation(), 0); |
629 | |
630 | #undef TF_DEVICELIST_METHOD |
631 | |
632 | } // end extern "C" |
633 | |
634 | // -------------------------------------------------------------------------- |
635 | // New Graph and Session API |
636 | |
637 | // Helper functions ----------------------------------------------------------- |
638 | |
639 | namespace { |
640 | |
641 | TF_Operation* ToOperation(Node* node) { |
642 | return static_cast<TF_Operation*>(static_cast<void*>(node)); |
643 | } |
644 | |
645 | string OutputName(const TF_Output& output) { |
646 | return StrCat(output.oper->node.name(), ":" , output.index); |
647 | } |
648 | |
649 | const tensorflow::AttrValue* GetAttrValue(TF_Operation* oper, |
650 | const char* attr_name, |
651 | TF_Status* status) { |
652 | const tensorflow::AttrValue* attr = oper->node.attrs().Find(attr_name); |
653 | if (attr == nullptr) { |
654 | status->status = InvalidArgument("Operation '" , oper->node.name(), |
655 | "' has no attr named '" , attr_name, "'." ); |
656 | } |
657 | return attr; |
658 | } |
659 | |
660 | TensorId ToTensorId(const TF_Output& output) { |
661 | return TensorId(output.oper->node.name(), output.index); |
662 | } |
663 | |
664 | #if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
665 | std::vector<tensorflow::Output> OutputsFromTFOutputs(TF_Output* tf_outputs, |
666 | int n) { |
667 | std::vector<tensorflow::Output> outputs(n); |
668 | for (int i = 0; i < n; ++i) { |
669 | outputs[i] = |
670 | tensorflow::Output(&tf_outputs[i].oper->node, tf_outputs[i].index); |
671 | } |
672 | return outputs; |
673 | } |
674 | |
675 | void TFOutputsFromOutputs(const std::vector<tensorflow::Output>& outputs, |
676 | TF_Output* tf_outputs) { |
677 | for (int i = 0; i < outputs.size(); i++) { |
678 | tf_outputs[i].oper = ToOperation(outputs[i].node()); |
679 | tf_outputs[i].index = outputs[i].index(); |
680 | } |
681 | } |
682 | #endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
683 | |
684 | } // namespace |
685 | |
686 | // Shape functions ----------------------------------------------------------- |
687 | |
688 | void TF_GraphSetTensorShape(TF_Graph* graph, TF_Output output, |
689 | const int64_t* dims, const int num_dims, |
690 | TF_Status* status) { |
691 | Node* node = &output.oper->node; |
692 | |
693 | mutex_lock l(graph->mu); |
694 | tensorflow::shape_inference::InferenceContext* ic = |
695 | graph->refiner.GetContext(node); |
696 | if (ic == nullptr) { |
697 | status->status = |
698 | InvalidArgument("Node " , node->name(), " was not found in the graph" ); |
699 | return; |
700 | } |
701 | tensorflow::shape_inference::ShapeHandle new_shape = |
702 | tensorflow::ShapeHandleFromDims(ic, num_dims, dims); |
703 | status->status = graph->refiner.SetShape(node, output.index, new_shape); |
704 | } |
705 | |
706 | int TF_GraphGetTensorNumDims(TF_Graph* graph, TF_Output output, |
707 | TF_Status* status) { |
708 | Node* node = &output.oper->node; |
709 | |
710 | mutex_lock l(graph->mu); |
711 | tensorflow::shape_inference::InferenceContext* ic = |
712 | graph->refiner.GetContext(node); |
713 | if (ic == nullptr) { |
714 | status->status = |
715 | InvalidArgument("Node " , node->name(), " was not found in the graph" ); |
716 | return -1; |
717 | } |
718 | |
719 | tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index); |
720 | |
721 | // Unknown rank means the number of dimensions is -1. |
722 | if (!ic->RankKnown(shape)) { |
723 | return -1; |
724 | } |
725 | |
726 | return ic->Rank(shape); |
727 | } |
728 | |
729 | void TF_GraphGetTensorShape(TF_Graph* graph, TF_Output output, int64_t* dims, |
730 | int num_dims, TF_Status* status) { |
731 | Node* node = &output.oper->node; |
732 | |
733 | mutex_lock l(graph->mu); |
734 | tensorflow::shape_inference::InferenceContext* ic = |
735 | graph->refiner.GetContext(node); |
736 | if (ic == nullptr) { |
737 | status->status = |
738 | InvalidArgument("Node " , node->name(), " was not found in the graph" ); |
739 | return; |
740 | } |
741 | |
742 | tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index); |
743 | |
744 | int rank = -1; |
745 | if (ic->RankKnown(shape)) { |
746 | rank = ic->Rank(shape); |
747 | } |
748 | |
749 | if (num_dims != rank) { |
750 | status->status = InvalidArgument("Expected rank is " , num_dims, |
751 | " but actual rank is " , rank); |
752 | return; |
753 | } |
754 | |
755 | if (num_dims == 0) { |
756 | // Output shape is a scalar. |
757 | return; |
758 | } |
759 | |
760 | // Rank is greater than 0, so fill in the values, if known, and |
761 | // -1 for unknown values. |
762 | for (int i = 0; i < num_dims; ++i) { |
763 | auto dim = ic->Dim(shape, i); |
764 | int64_t value = -1; |
765 | if (ic->ValueKnown(dim)) { |
766 | value = ic->Value(dim); |
767 | } |
768 | dims[i] = value; |
769 | } |
770 | } |
771 | |
772 | // TF_OperationDescription functions ------------------------------------------ |
773 | |
774 | extern "C" { |
775 | |
776 | TF_OperationDescription* TF_NewOperationLocked(TF_Graph* graph, |
777 | const char* op_type, |
778 | const char* oper_name) |
779 | TF_EXCLUSIVE_LOCKS_REQUIRED(graph->mu) { |
780 | return new TF_OperationDescription(graph, op_type, oper_name); |
781 | } |
782 | |
783 | TF_OperationDescription* TF_NewOperation(TF_Graph* graph, const char* op_type, |
784 | const char* oper_name) { |
785 | mutex_lock l(graph->mu); |
786 | return TF_NewOperationLocked(graph, op_type, oper_name); |
787 | } |
788 | |
789 | void TF_SetDevice(TF_OperationDescription* desc, const char* device) { |
790 | desc->node_builder.Device(device); |
791 | } |
792 | |
793 | void TF_AddInput(TF_OperationDescription* desc, TF_Output input) { |
794 | desc->node_builder.Input(&input.oper->node, input.index); |
795 | } |
796 | |
797 | void TF_AddInputList(TF_OperationDescription* desc, const TF_Output* inputs, |
798 | int num_inputs) { |
799 | std::vector<NodeBuilder::NodeOut> input_list; |
800 | input_list.reserve(num_inputs); |
801 | for (int i = 0; i < num_inputs; ++i) { |
802 | input_list.emplace_back(&inputs[i].oper->node, inputs[i].index); |
803 | } |
804 | desc->node_builder.Input(input_list); |
805 | } |
806 | |
807 | void TF_AddControlInput(TF_OperationDescription* desc, TF_Operation* input) { |
808 | desc->node_builder.ControlInput(&input->node); |
809 | } |
810 | |
811 | void TF_ColocateWith(TF_OperationDescription* desc, TF_Operation* op) { |
812 | desc->colocation_constraints.emplace( |
813 | StrCat(tensorflow::kColocationGroupPrefix, op->node.name())); |
814 | } |
815 | |
816 | void TF_SetAttrString(TF_OperationDescription* desc, const char* attr_name, |
817 | const void* value, size_t length) { |
818 | tensorflow::StringPiece s(static_cast<const char*>(value), length); |
819 | desc->node_builder.Attr(attr_name, s); |
820 | } |
821 | |
822 | void TF_SetAttrStringList(TF_OperationDescription* desc, const char* attr_name, |
823 | const void* const* values, const size_t* lengths, |
824 | int num_values) { |
825 | if (strcmp(attr_name, tensorflow::kColocationAttrName) == 0) { |
826 | desc->colocation_constraints.clear(); |
827 | for (int i = 0; i < num_values; ++i) { |
828 | desc->colocation_constraints.emplace(static_cast<const char*>(values[i]), |
829 | lengths[i]); |
830 | } |
831 | } else { |
832 | std::vector<tensorflow::StringPiece> v; |
833 | v.reserve(num_values); |
834 | for (int i = 0; i < num_values; ++i) { |
835 | v.emplace_back(static_cast<const char*>(values[i]), lengths[i]); |
836 | } |
837 | desc->node_builder.Attr(attr_name, v); |
838 | } |
839 | } |
840 | |
841 | void TF_SetAttrInt(TF_OperationDescription* desc, const char* attr_name, |
842 | int64_t value) { |
843 | desc->node_builder.Attr(attr_name, static_cast<int64_t>(value)); |
844 | } |
845 | |
846 | void TF_SetAttrIntList(TF_OperationDescription* desc, const char* attr_name, |
847 | const int64_t* values, int num_values) { |
848 | desc->node_builder.Attr( |
849 | attr_name, ArraySlice<const int64_t>( |
850 | reinterpret_cast<const int64_t*>(values), num_values)); |
851 | } |
852 | |
853 | void TF_SetAttrFloat(TF_OperationDescription* desc, const char* attr_name, |
854 | float value) { |
855 | desc->node_builder.Attr(attr_name, value); |
856 | } |
857 | |
858 | void TF_SetAttrFloatList(TF_OperationDescription* desc, const char* attr_name, |
859 | const float* values, int num_values) { |
860 | desc->node_builder.Attr(attr_name, |
861 | ArraySlice<const float>(values, num_values)); |
862 | } |
863 | |
864 | void TF_SetAttrBool(TF_OperationDescription* desc, const char* attr_name, |
865 | unsigned char value) { |
866 | desc->node_builder.Attr(attr_name, static_cast<bool>(value)); |
867 | } |
868 | |
869 | void TF_SetAttrBoolList(TF_OperationDescription* desc, const char* attr_name, |
870 | const unsigned char* values, int num_values) { |
871 | std::unique_ptr<bool[]> b(new bool[num_values]); |
872 | for (int i = 0; i < num_values; ++i) { |
873 | b[i] = values[i]; |
874 | } |
875 | desc->node_builder.Attr(attr_name, |
876 | ArraySlice<const bool>(b.get(), num_values)); |
877 | } |
878 | |
879 | void TF_SetAttrType(TF_OperationDescription* desc, const char* attr_name, |
880 | TF_DataType value) { |
881 | desc->node_builder.Attr(attr_name, static_cast<DataType>(value)); |
882 | } |
883 | |
884 | void TF_SetAttrTypeList(TF_OperationDescription* desc, const char* attr_name, |
885 | const TF_DataType* values, int num_values) { |
886 | desc->node_builder.Attr( |
887 | attr_name, ArraySlice<const DataType>( |
888 | reinterpret_cast<const DataType*>(values), num_values)); |
889 | } |
890 | |
891 | void TF_SetAttrPlaceholder(TF_OperationDescription* desc, const char* attr_name, |
892 | const char* placeholder) { |
893 | tensorflow::AttrValue attr_value; |
894 | attr_value.set_placeholder(placeholder); |
895 | desc->node_builder.Attr(attr_name, attr_value); |
896 | } |
897 | |
898 | void TF_SetAttrFuncName(TF_OperationDescription* desc, const char* attr_name, |
899 | const char* value, size_t length) { |
900 | tensorflow::NameAttrList func_name; |
901 | func_name.set_name(string(value, value + length)); |
902 | desc->node_builder.Attr(attr_name, func_name); |
903 | } |
904 | |
905 | void TF_SetAttrShape(TF_OperationDescription* desc, const char* attr_name, |
906 | const int64_t* dims, int num_dims) { |
907 | PartialTensorShape shape; |
908 | if (num_dims >= 0) { |
909 | shape = PartialTensorShape( |
910 | ArraySlice<int64_t>(reinterpret_cast<const int64_t*>(dims), num_dims)); |
911 | } |
912 | desc->node_builder.Attr(attr_name, shape); |
913 | } |
914 | |
915 | void TF_SetAttrShapeList(TF_OperationDescription* desc, const char* attr_name, |
916 | const int64_t* const* dims, const int* num_dims, |
917 | int num_shapes) { |
918 | std::vector<PartialTensorShape> shapes; |
919 | shapes.reserve(num_shapes); |
920 | for (int i = 0; i < num_shapes; ++i) { |
921 | if (num_dims[i] < 0) { |
922 | shapes.emplace_back(); |
923 | } else { |
924 | shapes.emplace_back(ArraySlice<int64_t>( |
925 | reinterpret_cast<const int64_t*>(dims[i]), num_dims[i])); |
926 | } |
927 | } |
928 | desc->node_builder.Attr(attr_name, shapes); |
929 | } |
930 | |
931 | void TF_SetAttrTensorShapeProto(TF_OperationDescription* desc, |
932 | const char* attr_name, const void* proto, |
933 | size_t proto_len, TF_Status* status) { |
934 | // shape.ParseFromArray takes an int as length, this function takes size_t, |
935 | // make sure there is no information loss. |
936 | if (proto_len > std::numeric_limits<int>::max()) { |
937 | status->status = InvalidArgument( |
938 | "proto_len (" , proto_len, |
939 | " bytes) is too large to be parsed by the protocol buffer library" ); |
940 | return; |
941 | } |
942 | TensorShapeProto shape; |
943 | if (shape.ParseFromArray(proto, static_cast<int>(proto_len))) { |
944 | desc->node_builder.Attr(attr_name, shape); |
945 | status->status = ::tensorflow::OkStatus(); |
946 | } else { |
947 | status->status = InvalidArgument("Unparseable TensorShapeProto" ); |
948 | } |
949 | } |
950 | |
951 | void TF_SetAttrTensorShapeProtoList(TF_OperationDescription* desc, |
952 | const char* attr_name, |
953 | const void* const* protos, |
954 | const size_t* proto_lens, int num_shapes, |
955 | TF_Status* status) { |
956 | std::vector<TensorShapeProto> shapes; |
957 | shapes.resize(num_shapes); |
958 | for (int i = 0; i < num_shapes; ++i) { |
959 | if (proto_lens[i] > std::numeric_limits<int>::max()) { |
960 | status->status = InvalidArgument( |
961 | "length of element " , i, " in the list (" , proto_lens[i], |
962 | " bytes) is too large to be parsed by the protocol buffer library" ); |
963 | return; |
964 | } |
965 | if (!shapes[i].ParseFromArray(protos[i], static_cast<int>(proto_lens[i]))) { |
966 | status->status = |
967 | InvalidArgument("Unparseable TensorShapeProto at index " , i); |
968 | return; |
969 | } |
970 | } |
971 | desc->node_builder.Attr(attr_name, shapes); |
972 | status->status = ::tensorflow::OkStatus(); |
973 | } |
974 | |
975 | void TF_SetAttrTensor(TF_OperationDescription* desc, const char* attr_name, |
976 | TF_Tensor* value, TF_Status* status) { |
977 | Tensor t; |
978 | status->status = TF_TensorToTensor(value, &t); |
979 | if (status->status.ok()) desc->node_builder.Attr(attr_name, t); |
980 | } |
981 | |
982 | void TF_SetAttrTensorList(TF_OperationDescription* desc, const char* attr_name, |
983 | TF_Tensor* const* values, int num_values, |
984 | TF_Status* status) { |
985 | status->status = ::tensorflow::OkStatus(); |
986 | std::vector<Tensor> t; |
987 | t.reserve(num_values); |
988 | |
989 | for (int i = 0; i < num_values && status->status.ok(); ++i) { |
990 | Tensor v; |
991 | status->status = TF_TensorToTensor(values[i], &v); |
992 | t.emplace_back(v); |
993 | } |
994 | |
995 | if (status->status.ok()) desc->node_builder.Attr(attr_name, t); |
996 | } |
997 | |
998 | void TF_SetAttrValueProto(TF_OperationDescription* desc, const char* attr_name, |
999 | const void* proto, size_t proto_len, |
1000 | TF_Status* status) { |
1001 | tensorflow::AttrValue attr_value; |
1002 | if (!attr_value.ParseFromArray(proto, proto_len)) { |
1003 | status->status = InvalidArgument("Unparseable AttrValue proto" ); |
1004 | return; |
1005 | } |
1006 | |
1007 | if (strcmp(attr_name, tensorflow::kColocationAttrName) == 0) { |
1008 | if (attr_value.value_case() != tensorflow::AttrValue::kList && |
1009 | attr_value.value_case() != tensorflow::AttrValue::VALUE_NOT_SET) { |
1010 | status->status = |
1011 | InvalidArgument("Expected \"list\" field for \"" , |
1012 | tensorflow::kColocationAttrName, "\" attribute" ); |
1013 | return; |
1014 | } |
1015 | desc->colocation_constraints.clear(); |
1016 | for (const string& location : attr_value.list().s()) { |
1017 | desc->colocation_constraints.insert(location); |
1018 | } |
1019 | } else { |
1020 | desc->node_builder.Attr(attr_name, std::move(attr_value)); |
1021 | } |
1022 | |
1023 | status->status = ::tensorflow::OkStatus(); |
1024 | } |
1025 | |
1026 | TF_Operation* TF_FinishOperationLocked(TF_OperationDescription* desc, |
1027 | TF_Status* status) |
1028 | TF_EXCLUSIVE_LOCKS_REQUIRED(desc->graph->mu) { |
1029 | Node* ret = nullptr; |
1030 | |
1031 | if (desc->graph->name_map.count(desc->node_builder.node_name())) { |
1032 | status->status = InvalidArgument("Duplicate node name in graph: '" , |
1033 | desc->node_builder.node_name(), "'" ); |
1034 | } else { |
1035 | if (!desc->colocation_constraints.empty()) { |
1036 | desc->node_builder.Attr( |
1037 | tensorflow::kColocationAttrName, |
1038 | std::vector<string>(desc->colocation_constraints.begin(), |
1039 | desc->colocation_constraints.end())); |
1040 | } |
1041 | status->status = desc->node_builder.Finalize(&desc->graph->graph, &ret, |
1042 | /*consume=*/true); |
1043 | |
1044 | if (status->status.ok()) { |
1045 | // Run shape inference function for newly added node. |
1046 | status->status = desc->graph->refiner.AddNode(ret); |
1047 | } |
1048 | if (status->status.ok()) { |
1049 | // Add the node to the name-to-node mapping. |
1050 | desc->graph->name_map[ret->name()] = ret; |
1051 | } else if (ret != nullptr) { |
1052 | desc->graph->graph.RemoveNode(ret); |
1053 | ret = nullptr; |
1054 | } |
1055 | } |
1056 | |
1057 | delete desc; |
1058 | |
1059 | return ToOperation(ret); |
1060 | } |
1061 | |
1062 | TF_Operation* TF_FinishOperation(TF_OperationDescription* desc, |
1063 | TF_Status* status) { |
1064 | mutex_lock l(desc->graph->mu); |
1065 | return TF_FinishOperationLocked(desc, status); |
1066 | } |
1067 | |
1068 | // TF_Operation functions |
1069 | // ---------------------------------------------------------- |
1070 | |
1071 | const char* TF_OperationName(TF_Operation* oper) { |
1072 | return oper->node.name().c_str(); |
1073 | } |
1074 | |
1075 | const char* TF_OperationOpType(TF_Operation* oper) { |
1076 | return oper->node.type_string().c_str(); |
1077 | } |
1078 | |
1079 | const char* TF_OperationDevice(TF_Operation* oper) { |
1080 | return oper->node.requested_device().c_str(); |
1081 | } |
1082 | |
1083 | int TF_OperationNumOutputs(TF_Operation* oper) { |
1084 | return oper->node.num_outputs(); |
1085 | } |
1086 | |
1087 | TF_DataType TF_OperationOutputType(TF_Output oper_out) { |
1088 | return static_cast<TF_DataType>( |
1089 | oper_out.oper->node.output_type(oper_out.index)); |
1090 | } |
1091 | |
1092 | int TF_OperationOutputListLength(TF_Operation* oper, const char* arg_name, |
1093 | TF_Status* status) { |
1094 | NameRangeMap name_ranges; |
1095 | status->status = |
1096 | NameRangesForNode(oper->node, oper->node.op_def(), nullptr, &name_ranges); |
1097 | if (!status->status.ok()) return -1; |
1098 | auto iter = name_ranges.find(arg_name); |
1099 | if (iter == name_ranges.end()) { |
1100 | status->status = InvalidArgument("Output arg '" , arg_name, "' not found" ); |
1101 | return -1; |
1102 | } |
1103 | return iter->second.second - iter->second.first; |
1104 | } |
1105 | |
1106 | int TF_OperationNumInputs(TF_Operation* oper) { |
1107 | return oper->node.num_inputs(); |
1108 | } |
1109 | |
1110 | TF_DataType TF_OperationInputType(TF_Input oper_in) { |
1111 | return static_cast<TF_DataType>(oper_in.oper->node.input_type(oper_in.index)); |
1112 | } |
1113 | |
1114 | int TF_OperationInputListLength(TF_Operation* oper, const char* arg_name, |
1115 | TF_Status* status) { |
1116 | NameRangeMap name_ranges; |
1117 | status->status = |
1118 | NameRangesForNode(oper->node, oper->node.op_def(), &name_ranges, nullptr); |
1119 | if (!status->status.ok()) return -1; |
1120 | auto iter = name_ranges.find(arg_name); |
1121 | if (iter == name_ranges.end()) { |
1122 | status->status = InvalidArgument("Input arg '" , arg_name, "' not found" ); |
1123 | return -1; |
1124 | } |
1125 | return iter->second.second - iter->second.first; |
1126 | } |
1127 | |
1128 | TF_Output TF_OperationInput(TF_Input oper_in) { |
1129 | const tensorflow::Edge* edge; |
1130 | Status s = oper_in.oper->node.input_edge(oper_in.index, &edge); |
1131 | if (!s.ok()) { |
1132 | return {nullptr, -1}; |
1133 | } |
1134 | |
1135 | return {ToOperation(edge->src()), edge->src_output()}; |
1136 | } |
1137 | |
1138 | void TF_OperationAllInputs(TF_Operation* oper, TF_Output* inputs, |
1139 | int max_inputs) { |
1140 | for (auto* edge : oper->node.in_edges()) { |
1141 | if (edge->dst_input() >= 0 && edge->dst_input() < max_inputs) { |
1142 | inputs[edge->dst_input()] = {ToOperation(edge->src()), |
1143 | edge->src_output()}; |
1144 | } |
1145 | } |
1146 | } |
1147 | |
1148 | int TF_OperationOutputNumConsumers(TF_Output oper_out) { |
1149 | int count = 0; |
1150 | for (const auto* edge : oper_out.oper->node.out_edges()) { |
1151 | if (edge->src_output() == oper_out.index) { |
1152 | ++count; |
1153 | } |
1154 | } |
1155 | return count; |
1156 | } |
1157 | |
1158 | int TF_OperationOutputConsumers(TF_Output oper_out, TF_Input* consumers, |
1159 | int max_consumers) { |
1160 | int count = 0; |
1161 | for (const auto* edge : oper_out.oper->node.out_edges()) { |
1162 | if (edge->src_output() == oper_out.index) { |
1163 | if (count < max_consumers) { |
1164 | consumers[count] = {ToOperation(edge->dst()), edge->dst_input()}; |
1165 | } |
1166 | ++count; |
1167 | } |
1168 | } |
1169 | return count; |
1170 | } |
1171 | |
1172 | int TF_OperationNumControlInputs(TF_Operation* oper) { |
1173 | int count = 0; |
1174 | for (const auto* edge : oper->node.in_edges()) { |
1175 | if (edge->IsControlEdge() && !edge->src()->IsSource()) { |
1176 | ++count; |
1177 | } |
1178 | } |
1179 | return count; |
1180 | } |
1181 | |
1182 | int TF_OperationGetControlInputs(TF_Operation* oper, |
1183 | TF_Operation** control_inputs, |
1184 | int max_control_inputs) { |
1185 | int count = 0; |
1186 | for (const auto* edge : oper->node.in_edges()) { |
1187 | if (edge->IsControlEdge() && !edge->src()->IsSource()) { |
1188 | if (count < max_control_inputs) { |
1189 | control_inputs[count] = ToOperation(edge->src()); |
1190 | } |
1191 | ++count; |
1192 | } |
1193 | } |
1194 | return count; |
1195 | } |
1196 | |
1197 | int TF_OperationNumControlOutputs(TF_Operation* oper) { |
1198 | int count = 0; |
1199 | for (const auto* edge : oper->node.out_edges()) { |
1200 | if (edge->IsControlEdge() && !edge->dst()->IsSink()) { |
1201 | ++count; |
1202 | } |
1203 | } |
1204 | return count; |
1205 | } |
1206 | |
1207 | int TF_OperationGetControlOutputs(TF_Operation* oper, |
1208 | TF_Operation** control_outputs, |
1209 | int max_control_outputs) { |
1210 | int count = 0; |
1211 | for (const auto* edge : oper->node.out_edges()) { |
1212 | if (edge->IsControlEdge() && !edge->dst()->IsSink()) { |
1213 | if (count < max_control_outputs) { |
1214 | control_outputs[count] = ToOperation(edge->dst()); |
1215 | } |
1216 | ++count; |
1217 | } |
1218 | } |
1219 | return count; |
1220 | } |
1221 | |
1222 | TF_AttrMetadata TF_OperationGetAttrMetadata(TF_Operation* oper, |
1223 | const char* attr_name, |
1224 | TF_Status* status) { |
1225 | TF_AttrMetadata metadata; |
1226 | const auto* attr = GetAttrValue(oper, attr_name, status); |
1227 | if (!status->status.ok()) return metadata; |
1228 | switch (attr->value_case()) { |
1229 | #define SINGLE_CASE(kK, attr_type, size_expr) \ |
1230 | case tensorflow::AttrValue::kK: \ |
1231 | metadata.is_list = 0; \ |
1232 | metadata.list_size = -1; \ |
1233 | metadata.type = attr_type; \ |
1234 | metadata.total_size = size_expr; \ |
1235 | break; |
1236 | |
1237 | SINGLE_CASE(kS, TF_ATTR_STRING, attr->s().length()); |
1238 | SINGLE_CASE(kI, TF_ATTR_INT, -1); |
1239 | SINGLE_CASE(kF, TF_ATTR_FLOAT, -1); |
1240 | SINGLE_CASE(kB, TF_ATTR_BOOL, -1); |
1241 | SINGLE_CASE(kType, TF_ATTR_TYPE, -1); |
1242 | SINGLE_CASE(kShape, TF_ATTR_SHAPE, |
1243 | attr->shape().unknown_rank() ? -1 : attr->shape().dim_size()); |
1244 | SINGLE_CASE(kTensor, TF_ATTR_TENSOR, -1); |
1245 | #undef SINGLE_CASE |
1246 | |
1247 | case tensorflow::AttrValue::kList: |
1248 | metadata.is_list = 1; |
1249 | metadata.list_size = 0; |
1250 | metadata.total_size = -1; |
1251 | #define LIST_CASE(field, attr_type, ...) \ |
1252 | if (attr->list().field##_size() > 0) { \ |
1253 | metadata.type = attr_type; \ |
1254 | metadata.list_size = attr->list().field##_size(); \ |
1255 | __VA_ARGS__; \ |
1256 | break; \ |
1257 | } |
1258 | |
1259 | LIST_CASE( |
1260 | s, TF_ATTR_STRING, metadata.total_size = 0; |
1261 | for (int i = 0; i < attr->list().s_size(); |
1262 | ++i) { metadata.total_size += attr->list().s(i).size(); }); |
1263 | LIST_CASE(i, TF_ATTR_INT); |
1264 | LIST_CASE(f, TF_ATTR_FLOAT); |
1265 | LIST_CASE(b, TF_ATTR_BOOL); |
1266 | LIST_CASE(type, TF_ATTR_TYPE); |
1267 | LIST_CASE( |
1268 | shape, TF_ATTR_SHAPE, metadata.total_size = 0; |
1269 | for (int i = 0; i < attr->list().shape_size(); ++i) { |
1270 | const auto& s = attr->list().shape(i); |
1271 | metadata.total_size += s.unknown_rank() ? 0 : s.dim_size(); |
1272 | }); |
1273 | LIST_CASE(tensor, TF_ATTR_TENSOR); |
1274 | LIST_CASE(tensor, TF_ATTR_FUNC); |
1275 | #undef LIST_CASE |
1276 | // All lists empty, determine the type from the OpDef. |
1277 | if (metadata.list_size == 0) { |
1278 | for (int i = 0; i < oper->node.op_def().attr_size(); ++i) { |
1279 | const auto& a = oper->node.op_def().attr(i); |
1280 | if (a.name() != attr_name) continue; |
1281 | const string& typestr = a.type(); |
1282 | if (typestr == "list(string)" ) { |
1283 | metadata.type = TF_ATTR_STRING; |
1284 | } else if (typestr == "list(int)" ) { |
1285 | metadata.type = TF_ATTR_INT; |
1286 | } else if (typestr == "list(float)" ) { |
1287 | metadata.type = TF_ATTR_FLOAT; |
1288 | } else if (typestr == "list(bool)" ) { |
1289 | metadata.type = TF_ATTR_BOOL; |
1290 | } else if (typestr == "list(type)" ) { |
1291 | metadata.type = TF_ATTR_TYPE; |
1292 | } else if (typestr == "list(shape)" ) { |
1293 | metadata.type = TF_ATTR_SHAPE; |
1294 | } else if (typestr == "list(tensor)" ) { |
1295 | metadata.type = TF_ATTR_TENSOR; |
1296 | } else if (typestr == "list(func)" ) { |
1297 | metadata.type = TF_ATTR_FUNC; |
1298 | } else { |
1299 | status->status = InvalidArgument( |
1300 | "Attribute '" , attr_name, |
1301 | "' has an empty value of an unrecognized type '" , typestr, "'" ); |
1302 | return metadata; |
1303 | } |
1304 | } |
1305 | } |
1306 | break; |
1307 | |
1308 | case tensorflow::AttrValue::kPlaceholder: |
1309 | metadata.is_list = 0; |
1310 | metadata.list_size = -1; |
1311 | metadata.type = TF_ATTR_PLACEHOLDER; |
1312 | metadata.total_size = -1; |
1313 | break; |
1314 | |
1315 | case tensorflow::AttrValue::kFunc: |
1316 | metadata.is_list = 0; |
1317 | metadata.list_size = -1; |
1318 | metadata.type = TF_ATTR_FUNC; |
1319 | metadata.total_size = -1; |
1320 | break; |
1321 | |
1322 | case tensorflow::AttrValue::VALUE_NOT_SET: |
1323 | status->status = |
1324 | InvalidArgument("Attribute '" , attr_name, "' has no value set" ); |
1325 | break; |
1326 | } |
1327 | return metadata; |
1328 | } |
1329 | |
1330 | void TF_OperationGetAttrString(TF_Operation* oper, const char* attr_name, |
1331 | void* value, size_t max_length, |
1332 | TF_Status* status) { |
1333 | const auto* attr = GetAttrValue(oper, attr_name, status); |
1334 | if (!status->status.ok()) return; |
1335 | if (attr->value_case() != tensorflow::AttrValue::kS) { |
1336 | status->status = |
1337 | InvalidArgument("Attribute '" , attr_name, "' is not a string" ); |
1338 | return; |
1339 | } |
1340 | if (max_length <= 0) { |
1341 | return; |
1342 | } |
1343 | const auto& s = attr->s(); |
1344 | std::memcpy(value, s.data(), std::min<size_t>(s.length(), max_length)); |
1345 | } |
1346 | |
1347 | void TF_OperationGetAttrStringList(TF_Operation* oper, const char* attr_name, |
1348 | void** values, size_t* lengths, |
1349 | int max_values, void* storage, |
1350 | size_t storage_size, TF_Status* status) { |
1351 | const auto* attr = GetAttrValue(oper, attr_name, status); |
1352 | if (!status->status.ok()) return; |
1353 | if (attr->value_case() != tensorflow::AttrValue::kList) { |
1354 | status->status = |
1355 | InvalidArgument("Value for '" , attr_name, "' is not a list" ); |
1356 | return; |
1357 | } |
1358 | const auto len = std::min(max_values, attr->list().s_size()); |
1359 | char* p = static_cast<char*>(storage); |
1360 | for (int i = 0; i < len; ++i) { |
1361 | const string& s = attr->list().s(i); |
1362 | values[i] = p; |
1363 | lengths[i] = s.size(); |
1364 | if ((p + s.size()) > (static_cast<char*>(storage) + storage_size)) { |
1365 | status->status = InvalidArgument( |
1366 | "Not enough storage to hold the requested list of strings" ); |
1367 | return; |
1368 | } |
1369 | memcpy(values[i], s.data(), s.size()); |
1370 | p += s.size(); |
1371 | } |
1372 | } |
1373 | |
1374 | #define DEFINE_GETATTR(func, c_type, cpp_type, list_field) \ |
1375 | void func(TF_Operation* oper, const char* attr_name, c_type* value, \ |
1376 | TF_Status* status) { \ |
1377 | cpp_type v; \ |
1378 | status->status = \ |
1379 | tensorflow::GetNodeAttr(oper->node.attrs(), attr_name, &v); \ |
1380 | if (!status->status.ok()) return; \ |
1381 | *value = static_cast<c_type>(v); \ |
1382 | } \ |
1383 | void func##List(TF_Operation* oper, const char* attr_name, c_type* values, \ |
1384 | int max_values, TF_Status* status) { \ |
1385 | const auto* attr = GetAttrValue(oper, attr_name, status); \ |
1386 | if (!status->status.ok()) return; \ |
1387 | if (attr->value_case() != tensorflow::AttrValue::kList) { \ |
1388 | status->status = \ |
1389 | InvalidArgument("Value for '", attr_name, "' is not a list."); \ |
1390 | return; \ |
1391 | } \ |
1392 | const auto len = std::min(max_values, attr->list().list_field##_size()); \ |
1393 | for (int i = 0; i < len; ++i) { \ |
1394 | values[i] = static_cast<c_type>(attr->list().list_field(i)); \ |
1395 | } \ |
1396 | } |
1397 | DEFINE_GETATTR(TF_OperationGetAttrInt, int64_t, int64_t, i); |
1398 | DEFINE_GETATTR(TF_OperationGetAttrFloat, float, float, f); |
1399 | DEFINE_GETATTR(TF_OperationGetAttrBool, unsigned char, bool, b); |
1400 | DEFINE_GETATTR(TF_OperationGetAttrType, TF_DataType, DataType, type); |
1401 | #undef DEFINE_GETATTR |
1402 | |
1403 | void TF_OperationGetAttrShape(TF_Operation* oper, const char* attr_name, |
1404 | int64_t* value, int num_dims, TF_Status* status) { |
1405 | PartialTensorShape shape; |
1406 | status->status = |
1407 | tensorflow::GetNodeAttr(oper->node.attrs(), attr_name, &shape); |
1408 | if (!status->status.ok()) return; |
1409 | auto len = std::min(shape.dims(), num_dims); |
1410 | for (int i = 0; i < len; ++i) { |
1411 | value[i] = shape.dim_size(i); |
1412 | } |
1413 | } |
1414 | |
1415 | void TF_OperationGetAttrShapeList(TF_Operation* oper, const char* attr_name, |
1416 | int64_t** dims, int* num_dims, int num_shapes, |
1417 | int64_t* storage, int storage_size, |
1418 | TF_Status* status) { |
1419 | std::vector<PartialTensorShape> shapes; |
1420 | status->status = |
1421 | tensorflow::GetNodeAttr(oper->node.attrs(), attr_name, &shapes); |
1422 | if (!status->status.ok()) return; |
1423 | auto len = std::min(static_cast<int>(shapes.size()), num_shapes); |
1424 | int64_t* p = storage; |
1425 | int storage_left = storage_size; |
1426 | for (int i = 0; i < len; ++i) { |
1427 | // shapes[i].dims() == -1 for shapes with an unknown rank. |
1428 | int64_t n = shapes[i].dims(); |
1429 | num_dims[i] = n; |
1430 | dims[i] = p; |
1431 | if (n < 0) { |
1432 | continue; |
1433 | } |
1434 | if (storage_left < n) { |
1435 | status->status = InvalidArgument( |
1436 | "Not enough storage to hold the requested list of shapes" ); |
1437 | return; |
1438 | } |
1439 | storage_left -= n; |
1440 | for (int j = 0; j < n; ++j, ++p) { |
1441 | *p = shapes[i].dim_size(j); |
1442 | } |
1443 | } |
1444 | } |
1445 | |
1446 | void TF_OperationGetAttrTensorShapeProto(TF_Operation* oper, |
1447 | const char* attr_name, |
1448 | TF_Buffer* value, TF_Status* status) { |
1449 | const auto* attr = GetAttrValue(oper, attr_name, status); |
1450 | if (!status->status.ok()) return; |
1451 | if (attr->value_case() != tensorflow::AttrValue::kShape) { |
1452 | status->status = |
1453 | InvalidArgument("Value for '" , attr_name, "' is not a shape." ); |
1454 | return; |
1455 | } |
1456 | status->status = MessageToBuffer(attr->shape(), value); |
1457 | } |
1458 | |
1459 | void TF_OperationGetAttrTensorShapeProtoList(TF_Operation* oper, |
1460 | const char* attr_name, |
1461 | TF_Buffer** values, int max_values, |
1462 | TF_Status* status) { |
1463 | const auto* attr = GetAttrValue(oper, attr_name, status); |
1464 | if (!status->status.ok()) return; |
1465 | if (attr->value_case() != tensorflow::AttrValue::kList) { |
1466 | status->status = |
1467 | InvalidArgument("Value for '" , attr_name, "' is not a list" ); |
1468 | return; |
1469 | } |
1470 | const auto len = std::min(max_values, attr->list().shape_size()); |
1471 | for (int i = 0; i < len; ++i) { |
1472 | values[i] = TF_NewBuffer(); |
1473 | status->status = MessageToBuffer(attr->list().shape(i), values[i]); |
1474 | if (!status->status.ok()) { |
1475 | // Delete everything allocated to far, the operation has failed. |
1476 | for (int j = 0; j <= i; ++j) { |
1477 | TF_DeleteBuffer(values[j]); |
1478 | } |
1479 | return; |
1480 | } |
1481 | } |
1482 | } |
1483 | |
1484 | void TF_OperationGetAttrTensor(TF_Operation* oper, const char* attr_name, |
1485 | TF_Tensor** value, TF_Status* status) { |
1486 | *value = nullptr; |
1487 | Tensor t; |
1488 | status->status = tensorflow::GetNodeAttr(oper->node.attrs(), attr_name, &t); |
1489 | if (!status->status.ok()) return; |
1490 | *value = TF_TensorFromTensor(t, &status->status); |
1491 | } |
1492 | |
1493 | void TF_OperationGetAttrTensorList(TF_Operation* oper, const char* attr_name, |
1494 | TF_Tensor** values, int max_values, |
1495 | TF_Status* status) { |
1496 | std::vector<Tensor> ts; |
1497 | status->status = tensorflow::GetNodeAttr(oper->node.attrs(), attr_name, &ts); |
1498 | if (!status->status.ok()) return; |
1499 | const auto len = std::min(max_values, static_cast<int>(ts.size())); |
1500 | for (int i = 0; i < len; ++i) { |
1501 | values[i] = TF_TensorFromTensor(ts[i], &status->status); |
1502 | } |
1503 | } |
1504 | |
1505 | void TF_OperationGetAttrValueProto(TF_Operation* oper, const char* attr_name, |
1506 | TF_Buffer* output_attr_value, |
1507 | TF_Status* status) { |
1508 | const auto* attr = GetAttrValue(oper, attr_name, status); |
1509 | if (!status->status.ok()) return; |
1510 | status->status = MessageToBuffer(*attr, output_attr_value); |
1511 | } |
1512 | |
1513 | int TF_OperationGetNumAttrs(TF_Operation* oper) { |
1514 | return oper->node.attrs().size(); |
1515 | } |
1516 | |
1517 | int TF_OperationGetAttrNameLength(TF_Operation* oper, int i) { |
1518 | auto attrs = oper->node.attrs(); |
1519 | int count = 0; |
1520 | AttrValueMap::const_iterator it; |
1521 | for (it = attrs.begin(); it != attrs.end(); it++) { |
1522 | if (count == i) { |
1523 | return it->first.length(); |
1524 | } |
1525 | count++; |
1526 | } |
1527 | return -1; |
1528 | } |
1529 | |
1530 | void TF_OperationGetAttrName(TF_Operation* oper, int i, char* output, |
1531 | TF_Status* status) { |
1532 | auto attrs = oper->node.attrs(); |
1533 | int count = 0; |
1534 | AttrValueMap::const_iterator it; |
1535 | for (it = attrs.begin(); it != attrs.end(); it++) { |
1536 | if (count == i) { |
1537 | strncpy(output, it->first.c_str(), it->first.length()); |
1538 | status->status = ::tensorflow::OkStatus(); |
1539 | return; |
1540 | } |
1541 | count++; |
1542 | } |
1543 | status->status = OutOfRange("Operation only has " , count, |
1544 | " attributes, can't get the " , i, "th" ); |
1545 | } |
1546 | |
1547 | void TF_OperationToNodeDef(TF_Operation* oper, TF_Buffer* output_node_def, |
1548 | TF_Status* status) { |
1549 | status->status = MessageToBuffer(oper->node.def(), output_node_def); |
1550 | } |
1551 | |
1552 | // TF_Graph functions --------------------------------------------------------- |
1553 | |
1554 | TF_Graph::TF_Graph() |
1555 | : graph(tensorflow::OpRegistry::Global()), |
1556 | refiner(graph.versions().producer(), graph.op_registry()), |
1557 | delete_requested(false), |
1558 | parent(nullptr), |
1559 | parent_inputs(nullptr) { |
1560 | // Tell the shape refiner to also run shape inference on functions. |
1561 | refiner.set_function_library_for_shape_inference(&graph.flib_def()); |
1562 | } |
1563 | |
1564 | TF_Graph* TF_NewGraph() { return new TF_Graph; } |
1565 | |
1566 | void TF_DeleteGraph(TF_Graph* g) { |
1567 | if (g == nullptr) return; |
1568 | g->mu.lock(); |
1569 | g->delete_requested = true; |
1570 | const bool del = g->sessions.empty(); |
1571 | g->mu.unlock(); |
1572 | if (del) delete g; |
1573 | } |
1574 | |
1575 | TF_Operation* TF_GraphOperationByName(TF_Graph* graph, const char* oper_name) { |
1576 | mutex_lock l(graph->mu); |
1577 | auto iter = graph->name_map.find(oper_name); |
1578 | if (iter == graph->name_map.end()) { |
1579 | return nullptr; |
1580 | } else { |
1581 | return ToOperation(iter->second); |
1582 | } |
1583 | } |
1584 | |
1585 | TF_Operation* TF_GraphNextOperation(TF_Graph* graph, size_t* pos) { |
1586 | if (*pos == 0) { |
1587 | // Advance past the first sentinel nodes in every graph (the source & sink). |
1588 | *pos += 2; |
1589 | } else { |
1590 | // Advance to the next node. |
1591 | *pos += 1; |
1592 | } |
1593 | |
1594 | mutex_lock l(graph->mu); |
1595 | while (*pos < static_cast<size_t>(graph->graph.num_node_ids())) { |
1596 | Node* node = graph->graph.FindNodeId(*pos); |
1597 | // FindNodeId() returns nullptr for nodes that have been deleted. |
1598 | // We aren't currently allowing nodes to be deleted, but it is safer |
1599 | // to still check. |
1600 | if (node != nullptr) return ToOperation(node); |
1601 | *pos += 1; |
1602 | } |
1603 | |
1604 | // No more nodes. |
1605 | return nullptr; |
1606 | } |
1607 | |
1608 | void TF_GraphToGraphDef(TF_Graph* graph, TF_Buffer* output_graph_def, |
1609 | TF_Status* status) { |
1610 | GraphDef def; |
1611 | { |
1612 | mutex_lock l(graph->mu); |
1613 | graph->graph.ToGraphDef(&def); |
1614 | } |
1615 | status->status = MessageToBuffer(def, output_graph_def); |
1616 | } |
1617 | |
1618 | void TF_GraphGetOpDef(TF_Graph* graph, const char* op_name, |
1619 | TF_Buffer* output_op_def, TF_Status* status) { |
1620 | const OpDef* op_def; |
1621 | { |
1622 | mutex_lock l(graph->mu); |
1623 | status->status = graph->graph.op_registry()->LookUpOpDef(op_name, &op_def); |
1624 | if (!status->status.ok()) return; |
1625 | } |
1626 | status->status = MessageToBuffer(*op_def, output_op_def); |
1627 | } |
1628 | |
1629 | void TF_GraphVersions(TF_Graph* graph, TF_Buffer* output_version_def, |
1630 | TF_Status* status) { |
1631 | VersionDef versions; |
1632 | { |
1633 | mutex_lock l(graph->mu); |
1634 | versions = graph->graph.versions(); |
1635 | } |
1636 | status->status = MessageToBuffer(versions, output_version_def); |
1637 | } |
1638 | |
1639 | TF_ImportGraphDefOptions* TF_NewImportGraphDefOptions() { |
1640 | return new TF_ImportGraphDefOptions; |
1641 | } |
1642 | void TF_DeleteImportGraphDefOptions(TF_ImportGraphDefOptions* opts) { |
1643 | delete opts; |
1644 | } |
1645 | void TF_ImportGraphDefOptionsSetPrefix(TF_ImportGraphDefOptions* opts, |
1646 | const char* prefix) { |
1647 | opts->opts.prefix = prefix; |
1648 | } |
1649 | void TF_ImportGraphDefOptionsSetDefaultDevice(TF_ImportGraphDefOptions* opts, |
1650 | const char* device) { |
1651 | opts->opts.default_device = device; |
1652 | } |
1653 | |
1654 | void TF_ImportGraphDefOptionsSetUniquifyNames(TF_ImportGraphDefOptions* opts, |
1655 | unsigned char uniquify_names) { |
1656 | opts->opts.uniquify_names = uniquify_names; |
1657 | } |
1658 | |
1659 | void TF_ImportGraphDefOptionsSetUniquifyPrefix(TF_ImportGraphDefOptions* opts, |
1660 | unsigned char uniquify_prefix) { |
1661 | opts->opts.uniquify_prefix = uniquify_prefix; |
1662 | } |
1663 | |
1664 | void TF_ImportGraphDefOptionsAddInputMapping(TF_ImportGraphDefOptions* opts, |
1665 | const char* src_name, |
1666 | int src_index, TF_Output dst) { |
1667 | opts->tensor_id_data.push_back(src_name); |
1668 | const string& src_name_str = opts->tensor_id_data.back(); |
1669 | // We don't need to store dst's name in tensor_id_data, since `dst` must |
1670 | // outlive the ImportGraphDef call. |
1671 | opts->opts.input_map[TensorId(src_name_str, src_index)] = ToTensorId(dst); |
1672 | } |
1673 | |
1674 | void TF_ImportGraphDefOptionsRemapControlDependency( |
1675 | TF_ImportGraphDefOptions* opts, const char* src_name, TF_Operation* dst) { |
1676 | opts->opts.input_map[TensorId(src_name, tensorflow::Graph::kControlSlot)] = |
1677 | TensorId(dst->node.name(), tensorflow::Graph::kControlSlot); |
1678 | } |
1679 | |
1680 | extern void TF_ImportGraphDefOptionsAddControlDependency( |
1681 | TF_ImportGraphDefOptions* opts, TF_Operation* oper) { |
1682 | opts->opts.control_dependencies.push_back(oper->node.name()); |
1683 | } |
1684 | |
1685 | void TF_ImportGraphDefOptionsAddReturnOutput(TF_ImportGraphDefOptions* opts, |
1686 | const char* oper_name, int index) { |
1687 | opts->tensor_id_data.push_back(oper_name); |
1688 | const string& oper_name_str = opts->tensor_id_data.back(); |
1689 | opts->opts.return_tensors.emplace_back(oper_name_str, index); |
1690 | } |
1691 | |
1692 | int TF_ImportGraphDefOptionsNumReturnOutputs( |
1693 | const TF_ImportGraphDefOptions* opts) { |
1694 | return opts->opts.return_tensors.size(); |
1695 | } |
1696 | |
1697 | void TF_ImportGraphDefOptionsAddReturnOperation(TF_ImportGraphDefOptions* opts, |
1698 | const char* oper_name) { |
1699 | opts->opts.return_nodes.push_back(oper_name); |
1700 | } |
1701 | |
1702 | int TF_ImportGraphDefOptionsNumReturnOperations( |
1703 | const TF_ImportGraphDefOptions* opts) { |
1704 | return opts->opts.return_nodes.size(); |
1705 | } |
1706 | |
1707 | void TF_ImportGraphDefResultsReturnOutputs(TF_ImportGraphDefResults* results, |
1708 | int* num_outputs, |
1709 | TF_Output** outputs) { |
1710 | *num_outputs = results->return_tensors.size(); |
1711 | *outputs = results->return_tensors.data(); |
1712 | } |
1713 | |
1714 | void TF_ImportGraphDefResultsReturnOperations(TF_ImportGraphDefResults* results, |
1715 | int* num_opers, |
1716 | TF_Operation*** opers) { |
1717 | *num_opers = results->return_nodes.size(); |
1718 | *opers = results->return_nodes.data(); |
1719 | } |
1720 | |
1721 | void TF_ImportGraphDefResultsMissingUnusedInputMappings( |
1722 | TF_ImportGraphDefResults* results, int* num_missing_unused_input_mappings, |
1723 | const char*** src_names, int** src_indexes) { |
1724 | *num_missing_unused_input_mappings = results->missing_unused_key_names.size(); |
1725 | *src_names = results->missing_unused_key_names.data(); |
1726 | *src_indexes = results->missing_unused_key_indexes.data(); |
1727 | } |
1728 | |
1729 | void TF_DeleteImportGraphDefResults(TF_ImportGraphDefResults* results) { |
1730 | delete results; |
1731 | } |
1732 | |
1733 | static void GraphImportGraphDefLocked(TF_Graph* graph, const GraphDef& def, |
1734 | const TF_ImportGraphDefOptions* opts, |
1735 | TF_ImportGraphDefResults* tf_results, |
1736 | TF_Status* status) |
1737 | TF_EXCLUSIVE_LOCKS_REQUIRED(graph->mu) { |
1738 | const int last_node_id = graph->graph.num_node_ids(); |
1739 | tensorflow::ImportGraphDefResults results; |
1740 | status->status = tensorflow::ImportGraphDef(opts->opts, def, &graph->graph, |
1741 | &graph->refiner, &results); |
1742 | if (!status->status.ok()) return; |
1743 | |
1744 | // Add new nodes to name_map |
1745 | for (int i = last_node_id; i < graph->graph.num_node_ids(); ++i) { |
1746 | auto* node = graph->graph.FindNodeId(i); |
1747 | if (node != nullptr) graph->name_map[node->name()] = node; |
1748 | } |
1749 | |
1750 | // Populate return_tensors |
1751 | DCHECK(tf_results->return_tensors.empty()); |
1752 | tf_results->return_tensors.resize(results.return_tensors.size()); |
1753 | for (int i = 0; i < results.return_tensors.size(); ++i) { |
1754 | tf_results->return_tensors[i].oper = |
1755 | ToOperation(results.return_tensors[i].first); |
1756 | tf_results->return_tensors[i].index = results.return_tensors[i].second; |
1757 | } |
1758 | |
1759 | // Populate return_nodes |
1760 | DCHECK(tf_results->return_nodes.empty()); |
1761 | tf_results->return_nodes.resize(results.return_nodes.size()); |
1762 | for (int i = 0; i < results.return_nodes.size(); ++i) { |
1763 | tf_results->return_nodes[i] = ToOperation(results.return_nodes[i]); |
1764 | } |
1765 | |
1766 | // Populate missing unused map keys |
1767 | DCHECK(tf_results->missing_unused_key_names.empty()); |
1768 | DCHECK(tf_results->missing_unused_key_indexes.empty()); |
1769 | DCHECK(tf_results->missing_unused_key_names_data.empty()); |
1770 | |
1771 | size_t size = results.missing_unused_input_map_keys.size(); |
1772 | tf_results->missing_unused_key_names.resize(size); |
1773 | tf_results->missing_unused_key_indexes.resize(size); |
1774 | |
1775 | for (int i = 0; i < size; ++i) { |
1776 | TensorId id = results.missing_unused_input_map_keys[i]; |
1777 | tf_results->missing_unused_key_names_data.emplace_back(id.first); |
1778 | tf_results->missing_unused_key_names[i] = |
1779 | tf_results->missing_unused_key_names_data.back().c_str(); |
1780 | tf_results->missing_unused_key_indexes[i] = id.second; |
1781 | } |
1782 | } |
1783 | |
1784 | TF_ImportGraphDefResults* TF_GraphImportGraphDefWithResults( |
1785 | TF_Graph* graph, const TF_Buffer* graph_def, |
1786 | const TF_ImportGraphDefOptions* options, TF_Status* status) { |
1787 | GraphDef def; |
1788 | if (!tensorflow::ParseProtoUnlimited(&def, graph_def->data, |
1789 | graph_def->length)) { |
1790 | status->status = InvalidArgument("Invalid GraphDef" ); |
1791 | return nullptr; |
1792 | } |
1793 | auto results = new TF_ImportGraphDefResults(); |
1794 | mutex_lock l(graph->mu); |
1795 | GraphImportGraphDefLocked(graph, def, options, results, status); |
1796 | if (!status->status.ok()) { |
1797 | delete results; |
1798 | return nullptr; |
1799 | } |
1800 | return results; |
1801 | } |
1802 | |
1803 | void TF_GraphImportGraphDefWithReturnOutputs( |
1804 | TF_Graph* graph, const TF_Buffer* graph_def, |
1805 | const TF_ImportGraphDefOptions* options, TF_Output* return_outputs, |
1806 | int num_return_outputs, TF_Status* status) { |
1807 | if (num_return_outputs != options->opts.return_tensors.size()) { |
1808 | status->status = InvalidArgument("Expected 'num_return_outputs' to be " , |
1809 | options->opts.return_tensors.size(), |
1810 | ", got " , num_return_outputs); |
1811 | return; |
1812 | } |
1813 | if (num_return_outputs > 0 && return_outputs == nullptr) { |
1814 | status->status = InvalidArgument( |
1815 | "'return_outputs' must be preallocated to length " , num_return_outputs); |
1816 | return; |
1817 | } |
1818 | GraphDef def; |
1819 | if (!tensorflow::ParseProtoUnlimited(&def, graph_def->data, |
1820 | graph_def->length)) { |
1821 | status->status = InvalidArgument("Invalid GraphDef" ); |
1822 | return; |
1823 | } |
1824 | TF_ImportGraphDefResults results; |
1825 | mutex_lock l(graph->mu); |
1826 | GraphImportGraphDefLocked(graph, def, options, &results, status); |
1827 | DCHECK_EQ(results.return_tensors.size(), num_return_outputs); |
1828 | memcpy(return_outputs, results.return_tensors.data(), |
1829 | num_return_outputs * sizeof(TF_Output)); |
1830 | } |
1831 | |
1832 | void TF_GraphImportGraphDef(TF_Graph* graph, const TF_Buffer* graph_def, |
1833 | const TF_ImportGraphDefOptions* options, |
1834 | TF_Status* status) { |
1835 | TF_ImportGraphDefResults* results = |
1836 | TF_GraphImportGraphDefWithResults(graph, graph_def, options, status); |
1837 | TF_DeleteImportGraphDefResults(results); |
1838 | } |
1839 | |
1840 | // While loop functions ------------------------------------------------------- |
1841 | |
1842 | namespace { |
1843 | |
1844 | #if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
1845 | |
1846 | // Creates a placeholder representing an input to the cond or body graph. |
1847 | // TODO(skyewm): remove these from final graph |
1848 | bool CreateInput(const TF_Output& parent_input, TF_Graph* g, const char* name, |
1849 | TF_Output* input, TF_Status* status) { |
1850 | TF_OperationDescription* desc = TF_NewOperation(g, "Placeholder" , name); |
1851 | TF_SetAttrType(desc, "dtype" , TF_OperationOutputType(parent_input)); |
1852 | // TODO(skyewm): set placeholder shape |
1853 | TF_Operation* oper = TF_FinishOperation(desc, status); |
1854 | if (!status->status.ok()) return false; |
1855 | *input = {oper, 0}; |
1856 | return true; |
1857 | } |
1858 | |
1859 | // Copies `src_graph` into `dst_graph`. Any node in `src_graph` with input |
1860 | // `src_inputs[i]` will have that input replaced with `dst_inputs[i]`. `prefix` |
1861 | // will be prepended to copied node names. `control_deps` are nodes in |
1862 | // `dst_graph` that the copied `src_graph` nodes will have control dependencies |
1863 | // on. `return_nodes` are nodes in `src_graph`, and the new corresponding nodes |
1864 | // in `dst_graph` will be returned. `return_nodes` must be non-null. |
1865 | Status CopyGraph(Graph* src_graph, Graph* dst_graph, |
1866 | tensorflow::ShapeRefiner* dst_refiner, |
1867 | const TF_Output* src_inputs, |
1868 | const std::vector<tensorflow::Output>& dst_inputs, |
1869 | const string& prefix, |
1870 | const std::vector<tensorflow::Operation>& control_deps, |
1871 | const TF_Output* nodes_to_return, int nreturn_nodes, |
1872 | std::vector<tensorflow::Output>* return_nodes) { |
1873 | DCHECK(return_nodes != nullptr); |
1874 | GraphDef gdef; |
1875 | src_graph->ToGraphDef(&gdef); |
1876 | |
1877 | tensorflow::ImportGraphDefOptions opts; |
1878 | opts.prefix = prefix; |
1879 | |
1880 | for (int i = 0; i < dst_inputs.size(); ++i) { |
1881 | opts.input_map[ToTensorId(src_inputs[i])] = |
1882 | TensorId(dst_inputs[i].node()->name(), dst_inputs[i].index()); |
1883 | } |
1884 | opts.skip_mapped_nodes = true; |
1885 | |
1886 | for (const tensorflow::Operation& op : control_deps) { |
1887 | opts.control_dependencies.push_back(op.node()->name()); |
1888 | } |
1889 | |
1890 | for (int i = 0; i < nreturn_nodes; ++i) { |
1891 | opts.return_tensors.push_back(ToTensorId(nodes_to_return[i])); |
1892 | } |
1893 | |
1894 | // TODO(skyewm): change to OutputTensor |
1895 | tensorflow::ImportGraphDefResults results; |
1896 | TF_RETURN_IF_ERROR( |
1897 | ImportGraphDef(opts, gdef, dst_graph, dst_refiner, &results)); |
1898 | |
1899 | for (const auto& pair : results.return_tensors) { |
1900 | return_nodes->emplace_back(pair.first, pair.second); |
1901 | } |
1902 | return ::tensorflow::OkStatus(); |
1903 | } |
1904 | |
1905 | bool ValidateConstWhileParams(const TF_WhileParams& params, TF_Status* s) { |
1906 | if (params.cond_graph == nullptr || params.body_graph == nullptr || |
1907 | params.cond_graph->parent == nullptr || |
1908 | params.cond_graph->parent != params.body_graph->parent || |
1909 | params.cond_graph->parent_inputs != params.body_graph->parent_inputs || |
1910 | params.ninputs <= 0 || params.cond_inputs == nullptr || |
1911 | params.body_inputs == nullptr || params.body_outputs == nullptr) { |
1912 | s->status = InvalidArgument( |
1913 | "TF_WhileParams must be created by successful TF_NewWhile() call" ); |
1914 | return false; |
1915 | } |
1916 | return true; |
1917 | } |
1918 | |
1919 | bool ValidateInputWhileParams(const TF_WhileParams& params, TF_Status* s) { |
1920 | if (params.cond_output.oper == nullptr) { |
1921 | s->status = InvalidArgument("TF_WhileParams `cond_output` field isn't set" ); |
1922 | return false; |
1923 | } |
1924 | for (int i = 0; i < params.ninputs; ++i) { |
1925 | if (params.body_outputs[i].oper == nullptr) { |
1926 | s->status = InvalidArgument("TF_WhileParams `body_outputs[" , i, "]` " , |
1927 | "field isn't set" ); |
1928 | return false; |
1929 | } |
1930 | } |
1931 | if (params.name == nullptr) { |
1932 | s->status = InvalidArgument("TF_WhileParams `name` field is null" ); |
1933 | return false; |
1934 | } |
1935 | return true; |
1936 | } |
1937 | |
1938 | #endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
1939 | |
1940 | void FreeWhileResources(const TF_WhileParams* params) { |
1941 | TF_DeleteGraph(params->cond_graph); |
1942 | TF_DeleteGraph(params->body_graph); |
1943 | delete[] params->cond_inputs; |
1944 | delete[] params->body_inputs; |
1945 | delete[] params->body_outputs; |
1946 | } |
1947 | |
1948 | TF_WhileParams EmptyWhileParams() { |
1949 | return {0, nullptr, nullptr, {nullptr, 0}, |
1950 | nullptr, nullptr, nullptr, nullptr}; |
1951 | } |
1952 | |
1953 | } // namespace |
1954 | |
1955 | TF_WhileParams TF_NewWhile(TF_Graph* g, TF_Output* inputs, int ninputs, |
1956 | TF_Status* status) { |
1957 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
1958 | status->status = tensorflow::errors::Unimplemented( |
1959 | "Creating while loops is not supported on mobile. File a bug at " |
1960 | "https://github.com/tensorflow/tensorflow/issues if this feature is " |
1961 | "important to you" ); |
1962 | return EmptyWhileParams(); |
1963 | #else |
1964 | if (ninputs == 0) { |
1965 | status->status = |
1966 | InvalidArgument("TF_NewWhile() must be passed at least one input" ); |
1967 | return EmptyWhileParams(); |
1968 | } |
1969 | |
1970 | TF_Graph* cond_graph = TF_NewGraph(); |
1971 | TF_Graph* body_graph = TF_NewGraph(); |
1972 | cond_graph->parent = g; |
1973 | cond_graph->parent_inputs = inputs; |
1974 | body_graph->parent = g; |
1975 | body_graph->parent_inputs = inputs; |
1976 | |
1977 | TF_Output* cond_inputs = new TF_Output[ninputs]; |
1978 | TF_Output cond_output = {nullptr, -1}; |
1979 | TF_Output* body_inputs = new TF_Output[ninputs]; |
1980 | TF_Output* body_outputs = new TF_Output[ninputs]; |
1981 | for (int i = 0; i < ninputs; ++i) body_outputs[i] = {nullptr, -1}; |
1982 | const char* name = nullptr; |
1983 | |
1984 | for (int i = 0; i < ninputs; ++i) { |
1985 | // TODO(skyewm): prefix names with underscore (requires some plumbing) |
1986 | if (!CreateInput(inputs[i], cond_graph, StrCat("cond_input" , i).c_str(), |
1987 | &cond_inputs[i], status)) { |
1988 | break; |
1989 | } |
1990 | if (!CreateInput(inputs[i], body_graph, StrCat("body_input" , i).c_str(), |
1991 | &body_inputs[i], status)) { |
1992 | break; |
1993 | } |
1994 | } |
1995 | |
1996 | TF_WhileParams params = {ninputs, cond_graph, cond_inputs, cond_output, |
1997 | body_graph, body_inputs, body_outputs, name}; |
1998 | |
1999 | if (!status->status.ok()) { |
2000 | FreeWhileResources(¶ms); |
2001 | return EmptyWhileParams(); |
2002 | } |
2003 | return params; |
2004 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2005 | } |
2006 | |
2007 | #if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
2008 | namespace { |
2009 | |
2010 | // TODO(skyewm): make nodes in while loop unfetchable like in Python version |
2011 | void TF_FinishWhileHelper(const TF_WhileParams* params, TF_Status* status, |
2012 | TF_Output* outputs) { |
2013 | if (!ValidateInputWhileParams(*params, status)) return; |
2014 | |
2015 | TF_Graph* parent = params->cond_graph->parent; |
2016 | TF_Output* parent_inputs = params->cond_graph->parent_inputs; |
2017 | int num_loop_vars = params->ninputs; |
2018 | |
2019 | mutex_lock l(parent->mu); |
2020 | |
2021 | // 'cond_fn' copies the cond graph into the parent graph. |
2022 | tensorflow::ops::CondGraphBuilderFn cond_fn = |
2023 | [params, parent](const tensorflow::Scope& scope, |
2024 | const std::vector<tensorflow::Output>& inputs, |
2025 | tensorflow::Output* output) { |
2026 | DCHECK_EQ(scope.graph(), &parent->graph); |
2027 | std::vector<tensorflow::Output> cond_output; |
2028 | TF_RETURN_IF_ERROR(CopyGraph( |
2029 | ¶ms->cond_graph->graph, &parent->graph, &parent->refiner, |
2030 | params->cond_inputs, inputs, scope.impl()->name(), |
2031 | scope.impl()->control_deps(), ¶ms->cond_output, |
2032 | /* nreturn_nodes */ 1, &cond_output)); |
2033 | *output = cond_output[0]; |
2034 | return ::tensorflow::OkStatus(); |
2035 | }; |
2036 | |
2037 | // 'body_fn' copies the body graph into the parent graph. |
2038 | tensorflow::ops::BodyGraphBuilderFn body_fn = |
2039 | [params, parent, num_loop_vars]( |
2040 | const tensorflow::Scope& scope, |
2041 | const std::vector<tensorflow::Output>& inputs, |
2042 | std::vector<tensorflow::Output>* outputs) { |
2043 | DCHECK_EQ(scope.graph(), &parent->graph); |
2044 | TF_RETURN_IF_ERROR( |
2045 | CopyGraph(¶ms->body_graph->graph, &parent->graph, |
2046 | &parent->refiner, params->body_inputs, inputs, |
2047 | scope.impl()->name(), scope.impl()->control_deps(), |
2048 | params->body_outputs, num_loop_vars, outputs)); |
2049 | return ::tensorflow::OkStatus(); |
2050 | }; |
2051 | |
2052 | // Create the while loop using an internal scope. |
2053 | tensorflow::Scope scope = |
2054 | NewInternalScope(&parent->graph, &status->status, &parent->refiner) |
2055 | .NewSubScope(params->name); |
2056 | |
2057 | const int first_new_node_id = parent->graph.num_node_ids(); |
2058 | |
2059 | tensorflow::OutputList loop_outputs; |
2060 | status->status = tensorflow::ops::BuildWhileLoop( |
2061 | scope, OutputsFromTFOutputs(parent_inputs, num_loop_vars), cond_fn, |
2062 | body_fn, params->name, &loop_outputs); |
2063 | |
2064 | // Update name_map with newly-created ops. |
2065 | // TODO(skyewm): right now BuildWhileLoop() may alter the graph if it returns |
2066 | // a bad status. Once we fix this, we may want to return early instead of |
2067 | // executing the following code. |
2068 | for (int i = first_new_node_id; i < parent->graph.num_node_ids(); ++i) { |
2069 | Node* new_node = parent->graph.FindNodeId(i); |
2070 | if (new_node == nullptr) continue; |
2071 | parent->name_map[new_node->name()] = new_node; |
2072 | } |
2073 | |
2074 | // Populate 'outputs'. |
2075 | DCHECK_LE(loop_outputs.size(), num_loop_vars); |
2076 | for (int i = 0; i < loop_outputs.size(); ++i) { |
2077 | outputs[i] = {ToOperation(loop_outputs[i].node()), loop_outputs[i].index()}; |
2078 | } |
2079 | } |
2080 | |
2081 | } // namespace |
2082 | #endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
2083 | |
2084 | void TF_FinishWhile(const TF_WhileParams* params, TF_Status* status, |
2085 | TF_Output* outputs) { |
2086 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2087 | status->status = tensorflow::errors::Unimplemented( |
2088 | "Creating while loops is not supported on mobile. File a bug at " |
2089 | "https://github.com/tensorflow/tensorflow/issues if this feature is " |
2090 | "important to you" ); |
2091 | #else |
2092 | // If it appears the caller created or modified `params`, don't free resources |
2093 | if (!ValidateConstWhileParams(*params, status)) return; |
2094 | TF_FinishWhileHelper(params, status, outputs); |
2095 | FreeWhileResources(params); |
2096 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2097 | } |
2098 | |
2099 | void TF_AbortWhile(const TF_WhileParams* params) { FreeWhileResources(params); } |
2100 | |
2101 | void TF_AddGradients(TF_Graph* g, TF_Output* y, int ny, TF_Output* x, int nx, |
2102 | TF_Output* dx, TF_Status* status, TF_Output* dy) { |
2103 | TF_AddGradientsWithPrefix(g, nullptr, y, ny, x, nx, dx, status, dy); |
2104 | } |
2105 | |
2106 | void TF_AddGradientsWithPrefix(TF_Graph* g, const char* prefix, TF_Output* y, |
2107 | int ny, TF_Output* x, int nx, TF_Output* dx, |
2108 | TF_Status* status, TF_Output* dy) { |
2109 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2110 | status->status = tensorflow::errors::Unimplemented( |
2111 | "Adding gradients is not supported on mobile. File a bug at " |
2112 | "https://github.com/tensorflow/tensorflow/issues if this feature is " |
2113 | "important to you" ); |
2114 | #else |
2115 | std::vector<tensorflow::Output> y_arg = OutputsFromTFOutputs(y, ny); |
2116 | std::vector<tensorflow::Output> x_arg = OutputsFromTFOutputs(x, nx); |
2117 | std::vector<tensorflow::Output> dy_arg; |
2118 | |
2119 | { |
2120 | // We need to hold on to the lock while we have a scope that uses TF_Graph. |
2121 | mutex_lock graph_lock(g->mu); |
2122 | |
2123 | const int first_new_node_id = g->graph.num_node_ids(); |
2124 | |
2125 | string prefix_cmp; |
2126 | const char* child_scope_name; |
2127 | if (prefix == nullptr) { |
2128 | child_scope_name = "gradients" ; |
2129 | } else { |
2130 | prefix_cmp = string(prefix) + "/" ; |
2131 | // The operation should fail if the provided name prefix has already been |
2132 | // used in this graph |
2133 | for (const auto& pair : g->name_map) { |
2134 | const string& name = pair.first; |
2135 | if ((name == prefix) || absl::StartsWith(name, prefix_cmp)) { |
2136 | status->status = InvalidArgument( |
2137 | "prefix [" , prefix, |
2138 | "] conflicts with existing node in the graph named [" , name, "]" ); |
2139 | return; |
2140 | } |
2141 | } |
2142 | child_scope_name = prefix; |
2143 | } |
2144 | tensorflow::Scope scope = |
2145 | NewInternalScope(&g->graph, &status->status, &g->refiner) |
2146 | .NewSubScope(child_scope_name); |
2147 | |
2148 | if (dx != nullptr) { |
2149 | std::vector<tensorflow::Output> dx_arg = OutputsFromTFOutputs(dx, ny); |
2150 | status->status = |
2151 | AddSymbolicGradients(scope, y_arg, x_arg, dx_arg, &dy_arg); |
2152 | } else { |
2153 | status->status = AddSymbolicGradients(scope, y_arg, x_arg, &dy_arg); |
2154 | } |
2155 | |
2156 | // Update g->name_map with the name_map from the scope, which will contain |
2157 | // the new gradient ops. |
2158 | for (int i = first_new_node_id; i < g->graph.num_node_ids(); ++i) { |
2159 | Node* n = g->graph.FindNodeId(i); |
2160 | if (n == nullptr) continue; |
2161 | |
2162 | // Adding the gradients to the graph can alter the prefix to prevent |
2163 | // name collisions only if this prefix has not been provided explicitly |
2164 | // by the user. If it was provided, assert that it remained intact. |
2165 | if (prefix != nullptr && !absl::StartsWith(n->name(), prefix_cmp)) { |
2166 | status->status = tensorflow::errors::Internal( |
2167 | "BUG: The gradients prefix have been unexpectedly altered when " |
2168 | "adding the nodes to the graph. This is a bug. Please file an " |
2169 | "issue at https://github.com/tensorflow/tensorflow/issues." ); |
2170 | return; |
2171 | } |
2172 | // We have a convoluted scheme here: Using the C++ graph construction API |
2173 | // to add potentially many nodes to the graph without running the checks |
2174 | // (such as uniqueness of the names of nodes) we run with other functions |
2175 | // that add a node to the graph (like TF_FinishOperation). |
2176 | if (!g->name_map.insert(std::make_pair(n->name(), n)).second) { |
2177 | status->status = tensorflow::errors::Internal( |
2178 | "BUG: The API allowed construction of a graph with duplicate node " |
2179 | "names (" , |
2180 | n->name(), |
2181 | "). This is a bug. Please file an issue at " |
2182 | "https://github.com/tensorflow/tensorflow/issues." ); |
2183 | } |
2184 | } |
2185 | } |
2186 | |
2187 | // Unpack the results from grad_outputs_arg. |
2188 | TFOutputsFromOutputs(dy_arg, dy); |
2189 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2190 | } |
2191 | |
2192 | // TF_Session functions ---------------------------------------------- |
2193 | |
2194 | TF_Session::TF_Session(tensorflow::Session* s, TF_Graph* g) |
2195 | : session(s), graph(g), last_num_graph_nodes(0), extend_before_run(true) {} |
2196 | |
2197 | TF_Session* TF_NewSession(TF_Graph* graph, const TF_SessionOptions* opt, |
2198 | TF_Status* status) { |
2199 | Session* session; |
2200 | status->status = NewSession(opt->options, &session); |
2201 | if (status->status.ok()) { |
2202 | TF_Session* new_session = new TF_Session(session, graph); |
2203 | if (graph != nullptr) { |
2204 | mutex_lock l(graph->mu); |
2205 | graph->sessions[new_session] = "" ; |
2206 | } |
2207 | return new_session; |
2208 | } else { |
2209 | LOG(ERROR) << status->status; |
2210 | DCHECK_EQ(nullptr, session); |
2211 | return nullptr; |
2212 | } |
2213 | } |
2214 | |
2215 | TF_Session* TF_LoadSessionFromSavedModel( |
2216 | const TF_SessionOptions* session_options, const TF_Buffer* run_options, |
2217 | const char* export_dir, const char* const* tags, int tags_len, |
2218 | TF_Graph* graph, TF_Buffer* meta_graph_def, TF_Status* status) { |
2219 | // TODO(sjr): Remove the IS_MOBILE_PLATFORM guard. This will require ensuring |
2220 | // that the tensorflow/cc/saved_model:loader build target is mobile friendly. |
2221 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2222 | status->status = tensorflow::errors::Unimplemented( |
2223 | "Loading a SavedModel is not supported on mobile. File a bug at " |
2224 | "https://github.com/tensorflow/tensorflow/issues if this feature is " |
2225 | "important to you" ); |
2226 | return nullptr; |
2227 | #else |
2228 | mutex_lock l(graph->mu); |
2229 | if (!graph->name_map.empty()) { |
2230 | status->status = InvalidArgument("Graph is non-empty." ); |
2231 | return nullptr; |
2232 | } |
2233 | |
2234 | RunOptions run_options_proto; |
2235 | if (run_options != nullptr && !run_options_proto.ParseFromArray( |
2236 | run_options->data, run_options->length)) { |
2237 | status->status = InvalidArgument("Unparseable RunOptions proto" ); |
2238 | return nullptr; |
2239 | } |
2240 | |
2241 | std::unordered_set<string> tag_set; |
2242 | for (int i = 0; i < tags_len; i++) { |
2243 | tag_set.insert(string(tags[i])); |
2244 | } |
2245 | |
2246 | tensorflow::SavedModelBundle bundle; |
2247 | status->status = |
2248 | tensorflow::LoadSavedModel(session_options->options, run_options_proto, |
2249 | export_dir, tag_set, &bundle); |
2250 | if (!status->status.ok()) return nullptr; |
2251 | |
2252 | // Create a TF_Graph from the MetaGraphDef. This is safe as long as Session |
2253 | // extends using GraphDefs. The Graph instance is different, but equivalent |
2254 | // to the one used to create the session. |
2255 | // |
2256 | // TODO(jhseu): When Session is modified to take Graphs instead of |
2257 | // GraphDefs, return the Graph generated in LoadSavedModel(). |
2258 | TF_ImportGraphDefOptions* import_opts = TF_NewImportGraphDefOptions(); |
2259 | TF_ImportGraphDefResults results; |
2260 | GraphImportGraphDefLocked(graph, bundle.meta_graph_def.graph_def(), |
2261 | import_opts, &results, status); |
2262 | TF_DeleteImportGraphDefOptions(import_opts); |
2263 | if (!status->status.ok()) return nullptr; |
2264 | |
2265 | if (meta_graph_def != nullptr) { |
2266 | status->status = MessageToBuffer(bundle.meta_graph_def, meta_graph_def); |
2267 | if (!status->status.ok()) return nullptr; |
2268 | } |
2269 | |
2270 | TF_Session* session = new TF_Session(bundle.session.release(), graph); |
2271 | |
2272 | graph->sessions[session] = "" ; |
2273 | session->last_num_graph_nodes = graph->graph.num_node_ids(); |
2274 | return session; |
2275 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2276 | } |
2277 | |
2278 | void TF_CloseSession(TF_Session* s, TF_Status* status) { |
2279 | status->status = s->session->Close(); |
2280 | } |
2281 | |
2282 | void TF_DeleteSession(TF_Session* s, TF_Status* status) { |
2283 | status->status = ::tensorflow::OkStatus(); |
2284 | if (s == nullptr) return; |
2285 | TF_Graph* const graph = s->graph; |
2286 | if (graph != nullptr) { |
2287 | graph->mu.lock(); |
2288 | graph->sessions.erase(s); |
2289 | const bool del = graph->delete_requested && graph->sessions.empty(); |
2290 | graph->mu.unlock(); |
2291 | if (del) delete graph; |
2292 | } |
2293 | delete s->session; |
2294 | delete s; |
2295 | } |
2296 | |
2297 | void TF_SessionRun(TF_Session* session, const TF_Buffer* run_options, |
2298 | const TF_Output* inputs, TF_Tensor* const* input_values, |
2299 | int ninputs, const TF_Output* outputs, |
2300 | TF_Tensor** output_values, int noutputs, |
2301 | const TF_Operation* const* target_opers, int ntargets, |
2302 | TF_Buffer* run_metadata, TF_Status* status) { |
2303 | // TODO(josh11b,mrry): Change Session to be able to use a Graph* |
2304 | // directly, instead of requiring us to serialize to a GraphDef and |
2305 | // call Session::Extend(). |
2306 | if (session->extend_before_run && |
2307 | !ExtendSessionGraphHelper(session, status)) { |
2308 | return; |
2309 | } |
2310 | |
2311 | TF_Run_Setup(noutputs, output_values, status); |
2312 | |
2313 | // Convert from TF_Output and TF_Tensor to a string and Tensor. |
2314 | std::vector<std::pair<string, Tensor>> input_pairs(ninputs); |
2315 | if (!TF_Run_Inputs(input_values, &input_pairs, status)) return; |
2316 | for (int i = 0; i < ninputs; ++i) { |
2317 | input_pairs[i].first = OutputName(inputs[i]); |
2318 | } |
2319 | |
2320 | // Convert from TF_Output to string names. |
2321 | std::vector<string> output_names(noutputs); |
2322 | for (int i = 0; i < noutputs; ++i) { |
2323 | output_names[i] = OutputName(outputs[i]); |
2324 | } |
2325 | |
2326 | // Convert from TF_Operation* to string names. |
2327 | std::vector<string> target_names(ntargets); |
2328 | for (int i = 0; i < ntargets; ++i) { |
2329 | target_names[i] = target_opers[i]->node.name(); |
2330 | } |
2331 | |
2332 | // Actually run. |
2333 | TF_Run_Helper(session->session, nullptr, run_options, input_pairs, |
2334 | output_names, output_values, target_names, run_metadata, |
2335 | status); |
2336 | } |
2337 | |
2338 | void TF_SessionPRunSetup(TF_Session* session, const TF_Output* inputs, |
2339 | int ninputs, const TF_Output* outputs, int noutputs, |
2340 | const TF_Operation* const* target_opers, int ntargets, |
2341 | const char** handle, TF_Status* status) { |
2342 | *handle = nullptr; |
2343 | |
2344 | if (session->extend_before_run && |
2345 | !ExtendSessionGraphHelper(session, status)) { |
2346 | return; |
2347 | } |
2348 | |
2349 | std::vector<string> input_names(ninputs); |
2350 | for (int i = 0; i < ninputs; ++i) { |
2351 | input_names[i] = OutputName(inputs[i]); |
2352 | } |
2353 | |
2354 | std::vector<string> output_names(noutputs); |
2355 | for (int i = 0; i < noutputs; ++i) { |
2356 | output_names[i] = OutputName(outputs[i]); |
2357 | } |
2358 | |
2359 | std::vector<string> target_names(ntargets); |
2360 | for (int i = 0; i < ntargets; ++i) { |
2361 | target_names[i] = target_opers[i]->node.name(); |
2362 | } |
2363 | |
2364 | string new_handle; |
2365 | status->status = session->session->PRunSetup(input_names, output_names, |
2366 | target_names, &new_handle); |
2367 | if (status->status.ok()) { |
2368 | char* buf = new char[new_handle.size() + 1]; |
2369 | memcpy(buf, new_handle.c_str(), new_handle.size() + 1); |
2370 | *handle = buf; |
2371 | } |
2372 | } |
2373 | |
2374 | void TF_DeletePRunHandle(const char* handle) { |
2375 | delete[] handle; |
2376 | // TODO(suharshs): Free up any resources held by the partial run state. |
2377 | } |
2378 | |
2379 | void TF_SessionPRun(TF_Session* session, const char* handle, |
2380 | const TF_Output* inputs, TF_Tensor* const* input_values, |
2381 | int ninputs, const TF_Output* outputs, |
2382 | TF_Tensor** output_values, int noutputs, |
2383 | const TF_Operation* const* target_opers, int ntargets, |
2384 | TF_Status* status) { |
2385 | // TODO(josh11b,mrry): Change Session to be able to use a Graph* |
2386 | // directly, instead of requiring us to serialize to a GraphDef and |
2387 | // call Session::Extend(). |
2388 | if (session->extend_before_run && |
2389 | !ExtendSessionGraphHelper(session, status)) { |
2390 | return; |
2391 | } |
2392 | |
2393 | TF_Run_Setup(noutputs, output_values, status); |
2394 | |
2395 | // Convert from TF_Output and TF_Tensor to a string and Tensor. |
2396 | std::vector<std::pair<string, Tensor>> input_pairs(ninputs); |
2397 | if (!TF_Run_Inputs(input_values, &input_pairs, status)) return; |
2398 | for (int i = 0; i < ninputs; ++i) { |
2399 | input_pairs[i].first = OutputName(inputs[i]); |
2400 | } |
2401 | |
2402 | // Convert from TF_Output to string names. |
2403 | std::vector<string> output_names(noutputs); |
2404 | for (int i = 0; i < noutputs; ++i) { |
2405 | output_names[i] = OutputName(outputs[i]); |
2406 | } |
2407 | |
2408 | // Convert from TF_Operation* to string names. |
2409 | std::vector<string> target_names(ntargets); |
2410 | for (int i = 0; i < ntargets; ++i) { |
2411 | target_names[i] = target_opers[i]->node.name(); |
2412 | } |
2413 | |
2414 | TF_Run_Helper(session->session, handle, nullptr, input_pairs, output_names, |
2415 | output_values, target_names, nullptr, status); |
2416 | } |
2417 | |
2418 | unsigned char TF_TryEvaluateConstant(TF_Graph* graph, TF_Output output, |
2419 | TF_Tensor** result, TF_Status* status) { |
2420 | *result = nullptr; |
2421 | mutex_lock l(graph->mu); |
2422 | OutputTensor tensor(&output.oper->node, output.index); |
2423 | bool evaluated; |
2424 | Tensor result_tensor; |
2425 | status->status = EvaluateConstantTensor( |
2426 | tensor, graph->refiner, *graph->graph.op_registry(), |
2427 | graph->graph.versions().producer(), &evaluated, &result_tensor); |
2428 | if (evaluated) { |
2429 | DCHECK(status->status.ok()); |
2430 | *result = TF_TensorFromTensor(result_tensor, &status->status); |
2431 | if (!status->status.ok()) evaluated = false; |
2432 | } |
2433 | return evaluated; |
2434 | } |
2435 | |
2436 | TF_ApiDefMap* TF_NewApiDefMap(TF_Buffer* op_list_buffer, TF_Status* status) { |
2437 | tensorflow::OpList op_list; |
2438 | if (!op_list.ParseFromArray(op_list_buffer->data, op_list_buffer->length)) { |
2439 | status->status = InvalidArgument("Unparseable OpList" ); |
2440 | return nullptr; |
2441 | } |
2442 | status->status = ::tensorflow::OkStatus(); |
2443 | return new TF_ApiDefMap(op_list); |
2444 | } |
2445 | |
2446 | void TF_DeleteApiDefMap(TF_ApiDefMap* apimap) { delete apimap; } |
2447 | |
2448 | void TF_ApiDefMapPut(TF_ApiDefMap* api_def_map, const char* text, |
2449 | size_t text_len, TF_Status* status) { |
2450 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2451 | status->status = tensorflow::errors::Unimplemented( |
2452 | "ApiDefMap is not supported on mobile." ); |
2453 | #else |
2454 | mutex_lock l(api_def_map->lock); |
2455 | if (api_def_map->update_docs_called) { |
2456 | status->status = FailedPrecondition( |
2457 | "TF_ApiDefMapPut cannot be called after TF_ApiDefMapGet has been " |
2458 | "called." ); |
2459 | return; |
2460 | } |
2461 | string api_def_text(text, text_len); |
2462 | status->status = api_def_map->api_def_map.LoadApiDef(api_def_text); |
2463 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2464 | } |
2465 | |
2466 | TF_Buffer* TF_ApiDefMapGet(TF_ApiDefMap* api_def_map, const char* name, |
2467 | size_t name_len, TF_Status* status) { |
2468 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2469 | status->status = tensorflow::errors::Unimplemented( |
2470 | "ApiDefMap is not supported on mobile." ); |
2471 | return nullptr; |
2472 | #else |
2473 | mutex_lock l(api_def_map->lock); |
2474 | if (!api_def_map->update_docs_called) { |
2475 | api_def_map->api_def_map.UpdateDocs(); |
2476 | api_def_map->update_docs_called = true; |
2477 | } |
2478 | string name_str(name, name_len); |
2479 | const auto* api_def = api_def_map->api_def_map.GetApiDef(name_str); |
2480 | if (api_def == nullptr) { |
2481 | return nullptr; |
2482 | } |
2483 | |
2484 | TF_Buffer* ret = TF_NewBuffer(); |
2485 | status->status = MessageToBuffer(*api_def, ret); |
2486 | if (!status->status.ok()) { |
2487 | TF_DeleteBuffer(ret); |
2488 | return nullptr; |
2489 | } |
2490 | return ret; |
2491 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2492 | } |
2493 | |
2494 | TF_Buffer* TF_GetAllRegisteredKernels(TF_Status* status) { |
2495 | tensorflow::KernelList kernel_list = tensorflow::GetAllRegisteredKernels(); |
2496 | TF_Buffer* ret = TF_NewBuffer(); |
2497 | status->status = MessageToBuffer(kernel_list, ret); |
2498 | if (!status->status.ok()) { |
2499 | TF_DeleteBuffer(ret); |
2500 | return nullptr; |
2501 | } |
2502 | return ret; |
2503 | } |
2504 | |
2505 | TF_Buffer* TF_GetRegisteredKernelsForOp(const char* name, TF_Status* status) { |
2506 | tensorflow::KernelList kernel_list = |
2507 | tensorflow::GetRegisteredKernelsForOp(name); |
2508 | TF_Buffer* ret = TF_NewBuffer(); |
2509 | status->status = MessageToBuffer(kernel_list, ret); |
2510 | if (!status->status.ok()) { |
2511 | TF_DeleteBuffer(ret); |
2512 | return nullptr; |
2513 | } |
2514 | return ret; |
2515 | } |
2516 | |
2517 | void TF_UpdateEdge(TF_Graph* graph, TF_Output new_src, TF_Input dst, |
2518 | TF_Status* status) { |
2519 | using tensorflow::RecordMutation; |
2520 | mutex_lock l(graph->mu); |
2521 | tensorflow::shape_inference::InferenceContext* ic = |
2522 | graph->refiner.GetContext(&new_src.oper->node); |
2523 | |
2524 | if (ic->num_outputs() <= new_src.index) { |
2525 | status->status = tensorflow::errors::OutOfRange( |
2526 | "Cannot update edge. Output index [" , new_src.index, |
2527 | "] is greater than the number of total outputs [" , ic->num_outputs(), |
2528 | "]." ); |
2529 | return; |
2530 | } |
2531 | tensorflow::shape_inference::ShapeHandle shape = ic->output(new_src.index); |
2532 | |
2533 | tensorflow::shape_inference::InferenceContext* ic_dst = |
2534 | graph->refiner.GetContext(&dst.oper->node); |
2535 | if (ic_dst->num_inputs() <= dst.index) { |
2536 | status->status = tensorflow::errors::OutOfRange( |
2537 | "Cannot update edge. Input index [" , dst.index, |
2538 | "] is greater than the number of total inputs [" , ic_dst->num_inputs(), |
2539 | "]." ); |
2540 | return; |
2541 | } |
2542 | if (!ic_dst->MergeInput(dst.index, shape)) { |
2543 | status->status = tensorflow::errors::InvalidArgument( |
2544 | "Cannot update edge, incompatible shapes: " , ic_dst->DebugString(shape), |
2545 | " and " , ic_dst->DebugString(ic_dst->input(dst.index)), "." ); |
2546 | return; |
2547 | } |
2548 | status->status = graph->graph.UpdateEdge(&new_src.oper->node, new_src.index, |
2549 | &dst.oper->node, dst.index); |
2550 | |
2551 | if (TF_GetCode(status) == TF_OK) { |
2552 | // This modification only updates the destination node for |
2553 | // the purposes of running this graph in a session. Thus, we don't |
2554 | // record the source node as being modified. |
2555 | RecordMutation(graph, *dst.oper, "updating input tensor" ); |
2556 | } |
2557 | } |
2558 | |
2559 | // TF_Server functions ---------------------------------------------- |
2560 | |
2561 | #if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
2562 | TF_Server::TF_Server(std::unique_ptr<tensorflow::ServerInterface> server) |
2563 | : target(server->target()), server(std::move(server)) {} |
2564 | #endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
2565 | |
2566 | TF_Server* TF_NewServer(const void* proto, size_t proto_len, |
2567 | TF_Status* status) { |
2568 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2569 | status->status = tensorflow::errors::Unimplemented( |
2570 | "Server functionality is not supported on mobile" ); |
2571 | return nullptr; |
2572 | #else |
2573 | tensorflow::ServerDef server_def; |
2574 | if (!server_def.ParseFromArray(proto, static_cast<int>(proto_len))) { |
2575 | status->status = InvalidArgument( |
2576 | "Could not parse provided bytes into a ServerDef protocol buffer" ); |
2577 | return nullptr; |
2578 | } |
2579 | |
2580 | std::unique_ptr<tensorflow::ServerInterface> out_server; |
2581 | status->status = tensorflow::NewServer(server_def, &out_server); |
2582 | if (!status->status.ok()) return nullptr; |
2583 | |
2584 | return new TF_Server(std::move(out_server)); |
2585 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2586 | } |
2587 | |
2588 | void TF_ServerStart(TF_Server* server, TF_Status* status) { |
2589 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2590 | status->status = tensorflow::errors::Unimplemented( |
2591 | "Server functionality is not supported on mobile" ); |
2592 | #else |
2593 | status->status = server->server->Start(); |
2594 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2595 | } |
2596 | |
2597 | void TF_ServerStop(TF_Server* server, TF_Status* status) { |
2598 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2599 | status->status = tensorflow::errors::Unimplemented( |
2600 | "Server functionality is not supported on mobile" ); |
2601 | #else |
2602 | status->status = server->server->Stop(); |
2603 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2604 | } |
2605 | |
2606 | void TF_ServerJoin(TF_Server* server, TF_Status* status) { |
2607 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2608 | status->status = tensorflow::errors::Unimplemented( |
2609 | "Server functionality is not supported on mobile" ); |
2610 | #else |
2611 | status->status = server->server->Join(); |
2612 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2613 | } |
2614 | |
2615 | const char* TF_ServerTarget(TF_Server* server) { |
2616 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2617 | return nullptr; |
2618 | #else |
2619 | return server->target.c_str(); |
2620 | #endif |
2621 | } |
2622 | |
2623 | void TF_DeleteServer(TF_Server* server) { |
2624 | #if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
2625 | delete server; |
2626 | #endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
2627 | } |
2628 | |
2629 | void TF_RegisterLogListener(void (*listener)(const char*)) { |
2630 | #if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
2631 | tensorflow::logging::RegisterListener(listener); |
2632 | #endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD) |
2633 | } |
2634 | |
2635 | void TF_RegisterFilesystemPlugin(const char* plugin_filename, |
2636 | TF_Status* status) { |
2637 | #if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2638 | status->status = tensorflow::errors::Unimplemented( |
2639 | "FileSystem plugin functionality is not supported on mobile" ); |
2640 | #else |
2641 | status->status = tensorflow::RegisterFilesystemPlugin(plugin_filename); |
2642 | #endif // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD) |
2643 | } |
2644 | |
2645 | } // end extern "C" |
2646 | |