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c_api.cc
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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/c/c_api.h"
#include <algorithm>
#include <cstring>
#include <limits>
#include <memory>
#include <optional>
#include <unordered_set>
#include <utility>
#include <vector>
#include "absl/strings/match.h"
// Required for IS_MOBILE_PLATFORM
#include "tensorflow/core/platform/platform.h" // NOLINT
#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
#include "tensorflow/c/experimental/filesystem/modular_filesystem.h"
#include "tensorflow/cc/framework/gradients.h"
#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/framework/scope_internal.h"
#include "tensorflow/cc/ops/while_loop.h"
#include "tensorflow/cc/saved_model/loader.h"
#include "tensorflow/core/distributed_runtime/server_lib.h"
#include "tensorflow/core/framework/logging.h"
#include "tensorflow/core/framework/op_gen_lib.h"
#endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/tf_buffer_internal.h"
#include "tensorflow/c/tf_status_internal.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/common_runtime/eval_const_tensor.h"
#include "tensorflow/core/common_runtime/graph_constructor.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/config/flag_defs.h"
#include "tensorflow/core/config/flags.h"
#include "tensorflow/core/framework/allocation_description.pb.h"
#include "tensorflow/core/framework/attr_value_util.h"
#include "tensorflow/core/framework/cpp_shape_inference.pb.h"
#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/framework/kernel_def.pb.h"
#include "tensorflow/core/framework/log_memory.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h" // NOLINT
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/versions.pb.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/graph/validate.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/platform/coding.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/mem.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/str_util.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/stringpiece.h"
#include "tensorflow/core/platform/thread_annotations.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/public/version.h"
// The implementation below is at the top level instead of the
// brain namespace because we are defining 'extern "C"' functions.
using tensorflow::AttrValueMap;
using tensorflow::DataType;
using tensorflow::ExtendSessionGraphHelper;
using tensorflow::FullTypeDef;
using tensorflow::Graph;
using tensorflow::GraphDef;
using tensorflow::mutex_lock;
using tensorflow::NameRangeMap;
using tensorflow::NameRangesForNode;
using tensorflow::NewSession;
using tensorflow::Node;
using tensorflow::NodeBuilder;
using tensorflow::OpDef;
using tensorflow::PartialTensorShape;
using tensorflow::RunMetadata;
using tensorflow::RunOptions;
using tensorflow::Session;
using tensorflow::Status;
using tensorflow::string;
using tensorflow::Tensor;
using tensorflow::TensorId;
using tensorflow::TensorShapeProto;
using tensorflow::VersionDef;
using tensorflow::errors::FailedPrecondition;
using tensorflow::errors::InvalidArgument;
using tensorflow::errors::OutOfRange;
using tensorflow::gtl::ArraySlice;
using tensorflow::strings::StrCat;
extern "C" {
// --------------------------------------------------------------------------
const char* TF_Version() { return TF_VERSION_STRING; }
// --------------------------------------------------------------------------
// --------------------------------------------------------------------------
TF_SessionOptions* TF_NewSessionOptions() {
TF_SessionOptions* out = new TF_SessionOptions;
// Disable optimizations for static graph to allow calls to Session::Extend.
out->options.config.mutable_experimental()
->set_disable_optimize_for_static_graph(true);
return out;
}
void TF_DeleteSessionOptions(TF_SessionOptions* opt) { delete opt; }
void TF_SetTarget(TF_SessionOptions* options, const char* target) {
options->options.target = target;
}
void TF_SetConfig(TF_SessionOptions* options, const void* proto,
size_t proto_len, TF_Status* status) {
if (!options->options.config.ParseFromArray(proto, proto_len)) {
status->status = InvalidArgument("Unparseable ConfigProto");
}
// Disable optimizations for static graph to allow calls to Session::Extend.
options->options.config.mutable_experimental()
->set_disable_optimize_for_static_graph(true);
}
void TF_TensorFromProto(const TF_Buffer* from, TF_Tensor* to,
TF_Status* status) {
TF_SetStatus(status, TF_OK, "");
tensorflow::TensorProto from_tensor_proto;
status->status = BufferToMessage(from, &from_tensor_proto);
if (!status->status.ok()) {
return;
}
status->status =
tensorflow::down_cast<tensorflow::TensorInterface*>(to->tensor)
->FromProto(from_tensor_proto);
}
// --------------------------------------------------------------------------
TF_DeprecatedSession* TF_NewDeprecatedSession(const TF_SessionOptions* opt,
TF_Status* status) {
Session* session;
status->status = NewSession(opt->options, &session);
if (status->status.ok()) {
return new TF_DeprecatedSession({session});
} else {
DCHECK_EQ(nullptr, session);
return nullptr;
}
}
void TF_CloseDeprecatedSession(TF_DeprecatedSession* s, TF_Status* status) {
status->status = s->session->Close();
}
void TF_DeleteDeprecatedSession(TF_DeprecatedSession* s, TF_Status* status) {
status->status = ::tensorflow::OkStatus();
if (s == nullptr) return;
delete s->session;
delete s;
}
void TF_ExtendGraph(TF_DeprecatedSession* s, const void* proto,
size_t proto_len, TF_Status* status) {
GraphDef g;
if (!tensorflow::ParseProtoUnlimited(&g, proto, proto_len)) {
status->status = InvalidArgument("Invalid GraphDef");
return;
}
status->status = s->session->Extend(g);
}
} // end extern "C"
// Reset helper for converting character arrays to string vectors.
static void TF_Reset_Helper(const TF_SessionOptions* opt,
const char** containers, int ncontainers,
TF_Status* status) {
std::vector<string> container_names(ncontainers);
for (int i = 0; i < ncontainers; ++i) {
container_names[i] = containers[i];
}
status->status = Reset(opt->options, container_names);
}
extern "C" {
void TF_Reset(const TF_SessionOptions* opt, const char** containers,
int ncontainers, TF_Status* status) {
TF_Reset_Helper(opt, containers, ncontainers, status);
}
} // end extern "C"
namespace tensorflow {
void RecordMutation(TF_Graph* graph, const TF_Operation& op,
const char* mutation_type) {
// If any session has already run this node_id, mark this session as
// unrunnable.
for (auto it : graph->sessions) {
mutex_lock session_lock(it.first->mu);
if (it.first->last_num_graph_nodes > op.node.id()) {
it.second = strings::StrCat(
"Operation '", op.node.DebugString(), "' was changed by ",
mutation_type,
" after it was run by a session. This mutation will have no effect, "
"and will trigger an error in the future. Either don't modify "
"nodes after running them or create a new session.");
}
}
}
namespace {
// Helper method that creates a shape handle for a shape described by dims.
tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims(
tensorflow::shape_inference::InferenceContext* ic, int num_dims,
const int64_t* dims) {
if (num_dims != -1) {
std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
dim_vec.reserve(num_dims);
for (int i = 0; i < num_dims; ++i) {
dim_vec.push_back(ic->MakeDim(dims[i]));
}
return ic->MakeShape(dim_vec);
} else {
return ic->UnknownShape();
}
}
} // namespace
void TF_GraphSetOutputHandleShapesAndTypes(TF_Graph* graph, TF_Output output,
int num_shapes_and_types,
const int64_t** shapes,
const int* ranks,
const TF_DataType* types,
TF_Status* status) {
Node* node = &output.oper->node;
mutex_lock l(graph->mu);
tensorflow::shape_inference::InferenceContext* ic =
graph->refiner.GetContext(node);
if (ic == nullptr) {
status->status =
InvalidArgument("Node ", node->name(), " was not found in the graph");
return;
}
auto shape_and_type_vec =
std::vector<tensorflow::shape_inference::ShapeAndType>(
num_shapes_and_types);
for (int i = 0; i < num_shapes_and_types; ++i) {
tensorflow::shape_inference::ShapeHandle shape_handle =
ShapeHandleFromDims(ic, ranks[i], shapes[i]);
shape_and_type_vec[i] = tensorflow::shape_inference::ShapeAndType(
shape_handle, static_cast<DataType>(types[i]));
}
ic->set_output_handle_shapes_and_types(output.index, shape_and_type_vec);
}
// Helpers for loading a TensorFlow plugin (a .so file).
Status LoadDynamicLibrary(const char* library_filename, void** result,
const void** buf, size_t* len);
// TODO(josh11b,mrry): Change Session to be able to use a Graph*
// directly, instead of requiring us to serialize to a GraphDef and
// call Session::Extend().
bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) {
if (session->graph != nullptr) {
// Take the graph lock before the session lock to avoid deadlock. This is
// safe since session->graph does not change.
session->graph->mu.lock();
mutex_lock session_lock(session->mu);
const Graph& graph = session->graph->graph;
const string& mutation_warning = session->graph->sessions[session];
if (!mutation_warning.empty()) {
// TODO(b/74949947): turn this back into an error status
LOG(WARNING) << mutation_warning;
session->graph->sessions[session].clear();
}
const auto num_nodes = graph.num_node_ids();
if (session->last_num_graph_nodes < num_nodes) {
// TODO(nolivia): check this on a subset of the graph instead of all of
// it.
status->status = graph::ValidateGraphHasNoCycle(session->graph->graph);
if (!status->status.ok()) {
session->graph->mu.unlock();
return false;
}
GraphDef graph_def;
*graph_def.mutable_versions() = graph.versions();
// Fill graph_def with nodes with ids in the range
// [session->last_num_graph_nodes, num_nodes), that is the nodes
// added since the last TF_SessionRun() call.
for (auto id = session->last_num_graph_nodes; id < num_nodes; ++id) {
Node* const node = graph.FindNodeId(id);
if (node != nullptr && node->IsOp()) {
NodeDef* const node_def = graph_def.add_node();
*node_def = node->def();
}
}
*graph_def.mutable_library() = graph.flib_def().ToProto();
if (flags::Global().more_stack_traces.value()) {
*graph_def.mutable_debug_info() = graph.BuildDebugInfo();
}
session->graph->mu.unlock();
status->status = session->session->Extend(std::move(graph_def));
if (!status->status.ok()) {
// Contract is we always delete input_values[i].
return false;
}
// Note: session->session is not modified if Extend() fails, so
// we only set last_num_graph_nodes if it succeeds.
session->last_num_graph_nodes = num_nodes;
} else {
session->graph->mu.unlock();
}
}
return true;
}
} // namespace tensorflow
static void TF_Run_Setup(int noutputs, TF_Tensor** c_outputs,
TF_Status* status) {
status->status = ::tensorflow::OkStatus();
for (int i = 0; i < noutputs; ++i) {
c_outputs[i] = nullptr;
}
}
// TF_TensorToTensorV1 decodes a string serialization to DT_RESOURCE.
// In the TFv1 convention, TF_Tensor can hold a string serialization of
// DT_RESOURCE. The string serialization is converted back to a
// ResourceHandle during Session run where the TF_Tensor is converted to a
// Tensor.
// TFv2 does not depend on this conversion. There is no matching
// TF_TensorFromTensorV1 because the conversion to string is performed by the
// python side of Session.
static Status TF_TensorToTensorV1(const TF_Tensor* src, Tensor* dst) {
Status status = TF_TensorToTensor(src, dst);
if (!status.ok()) {
return status;
}
if (dst->dtype() == tensorflow::DT_RESOURCE) {
const auto tensor_interface =
tensorflow::down_cast<const tensorflow::TensorInterface*>(src->tensor);
if (dst->dims() != 0) {
return InvalidArgument(
"Malformed TF_RESOURCE tensor: expected a scalar, got a tensor with "
"shape ",
dst->shape().DebugString());
}
*dst = tensorflow::Tensor(tensorflow::DT_RESOURCE, dst->shape());
if (!dst->scalar<tensorflow::ResourceHandle>()().ParseFromString(
string(static_cast<const char*>(tensor_interface->Data()),
tensor_interface->ByteSize()))) {
return InvalidArgument(
"Malformed TF_RESOURCE tensor: unable to parse resource handle");
}
return ::tensorflow::OkStatus();
}
return ::tensorflow::OkStatus();
}
static bool TF_Run_Inputs(TF_Tensor* const* c_inputs,
std::vector<std::pair<string, Tensor>>* input_pairs,
TF_Status* status) {
const int ninputs = input_pairs->size();
for (int i = 0; i < ninputs; ++i) {
status->status =
TF_TensorToTensorV1(c_inputs[i], &(*input_pairs)[i].second);
if (!status->status.ok()) return false;
}
return true;
}
// Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to
// result in a zero-sized tensor.
static TF_Tensor* EmptyTensor(TF_DataType dtype,
const tensorflow::TensorShape& shape) {
static char empty;
int64_t nelems = 1;
std::vector<int64_t> dims;
dims.reserve(shape.dims());
for (int i = 0; i < shape.dims(); ++i) {
dims.push_back(shape.dim_size(i));
nelems *= shape.dim_size(i);
}
CHECK_EQ(nelems, 0);
return TF_NewTensor(
dtype, reinterpret_cast<const int64_t*>(dims.data()), shape.dims(),
reinterpret_cast<void*>(&empty), 0, [](void*, size_t, void*) {}, nullptr);
}
static void TF_Run_Helper(
Session* session, const char* handle, const TF_Buffer* run_options,
// Input tensors
const std::vector<std::pair<string, Tensor>>& input_pairs,
// Output tensors
const std::vector<string>& output_tensor_names, TF_Tensor** c_outputs,
// Target nodes
const std::vector<string>& target_oper_names, TF_Buffer* run_metadata,
TF_Status* status) {
const int noutputs = output_tensor_names.size();
std::vector<Tensor> outputs(noutputs);
Status result;
if (handle == nullptr) {
RunOptions run_options_proto;
if (run_options != nullptr && !run_options_proto.ParseFromArray(
run_options->data, run_options->length)) {
status->status = InvalidArgument("Unparseable RunOptions proto");
return;
}
if (run_metadata != nullptr && run_metadata->data != nullptr) {
status->status =
InvalidArgument("Passing non-empty run_metadata is invalid.");
return;
}
RunMetadata run_metadata_proto;
result = session->Run(run_options_proto, input_pairs, output_tensor_names,
target_oper_names, &outputs, &run_metadata_proto);
// Serialize back to upstream client, who now owns the new buffer
if (run_metadata != nullptr) {
status->status = MessageToBuffer(run_metadata_proto, run_metadata);
if (!status->status.ok()) return;
}
} else {
// NOTE(zongheng): PRun does not support RunOptions yet.
result = session->PRun(handle, input_pairs, output_tensor_names, &outputs);
}
if (!result.ok()) {
status->status = result;
return;
}
// Store results in c_outputs[]
for (int i = 0; i < noutputs; ++i) {
const Tensor& src = outputs[i];
if (!src.IsInitialized() || src.NumElements() == 0) {
c_outputs[i] =
EmptyTensor(static_cast<TF_DataType>(src.dtype()), src.shape());
continue;
}
c_outputs[i] = TF_TensorFromTensor(src, &status->status);
if (!status->status.ok()) return;
}
}
extern "C" {
void TF_Run(TF_DeprecatedSession* s, const TF_Buffer* run_options,
// Input tensors
const char** c_input_names, TF_Tensor** c_inputs, int ninputs,
// Output tensors
const char** c_output_names, TF_Tensor** c_outputs, int noutputs,
// Target nodes
const char** c_target_oper_names, int ntargets,
TF_Buffer* run_metadata, TF_Status* status) {
TF_Run_Setup(noutputs, c_outputs, status);
std::vector<std::pair<string, Tensor>> input_pairs(ninputs);
if (!TF_Run_Inputs(c_inputs, &input_pairs, status)) return;
for (int i = 0; i < ninputs; ++i) {
input_pairs[i].first = c_input_names[i];
}
std::vector<string> output_names(noutputs);
for (int i = 0; i < noutputs; ++i) {
output_names[i] = c_output_names[i];
}
std::vector<string> target_oper_names(ntargets);
for (int i = 0; i < ntargets; ++i) {
target_oper_names[i] = c_target_oper_names[i];
}
TF_Run_Helper(s->session, nullptr, run_options, input_pairs, output_names,
c_outputs, target_oper_names, run_metadata, status);
}
void TF_PRunSetup(TF_DeprecatedSession* s,
// Input names
const char** c_input_names, int ninputs,
// Output names
const char** c_output_names, int noutputs,
// Target nodes
const char** c_target_oper_names, int ntargets,
const char** handle, TF_Status* status) {
*handle = nullptr;
std::vector<string> input_names(ninputs);
std::vector<string> output_names(noutputs);
std::vector<string> target_oper_names(ntargets);
for (int i = 0; i < ninputs; ++i) {
input_names[i] = c_input_names[i];
}
for (int i = 0; i < noutputs; ++i) {
output_names[i] = c_output_names[i];
}
for (int i = 0; i < ntargets; ++i) {
target_oper_names[i] = c_target_oper_names[i];
}
string new_handle;
status->status = s->session->PRunSetup(input_names, output_names,
target_oper_names, &new_handle);
if (status->status.ok()) {
char* buf = new char[new_handle.size() + 1];
memcpy(buf, new_handle.c_str(), new_handle.size() + 1);
*handle = buf;
}
}
void TF_PRun(TF_DeprecatedSession* s, const char* handle,
// Input tensors
const char** c_input_names, TF_Tensor** c_inputs, int ninputs,
// Output tensors
const char** c_output_names, TF_Tensor** c_outputs, int noutputs,
// Target nodes
const char** c_target_oper_names, int ntargets,
TF_Status* status) {
TF_Run_Setup(noutputs, c_outputs, status);
std::vector<std::pair<string, Tensor>> input_pairs(ninputs);
if (!TF_Run_Inputs(c_inputs, &input_pairs, status)) return;
for (int i = 0; i < ninputs; ++i) {
input_pairs[i].first = c_input_names[i];
}
std::vector<string> output_names(noutputs);
for (int i = 0; i < noutputs; ++i) {
output_names[i] = c_output_names[i];
}
std::vector<string> target_oper_names(ntargets);
for (int i = 0; i < ntargets; ++i) {
target_oper_names[i] = c_target_oper_names[i];
}
TF_Run_Helper(s->session, handle, nullptr, input_pairs, output_names,
c_outputs, target_oper_names, nullptr, status);
}
TF_Library* TF_LoadLibrary(const char* library_filename, TF_Status* status) {
TF_Library* lib_handle = new TF_Library;
status->status = tensorflow::LoadDynamicLibrary(
library_filename, &lib_handle->lib_handle, &lib_handle->op_list.data,
&lib_handle->op_list.length);
if (!status->status.ok()) {
delete lib_handle;
return nullptr;
}
return lib_handle;
}
TF_Buffer TF_GetOpList(TF_Library* lib_handle) { return lib_handle->op_list; }
void TF_DeleteLibraryHandle(TF_Library* lib_handle) {
if (lib_handle == nullptr) return;
tensorflow::port::Free(const_cast<void*>(lib_handle->op_list.data));
delete lib_handle;
}
TF_Buffer* TF_GetAllOpList() {
std::vector<tensorflow::OpDef> op_defs;
tensorflow::OpRegistry::Global()->GetRegisteredOps(&op_defs);
tensorflow::OpList op_list;
for (const auto& op : op_defs) {
*(op_list.add_op()) = op;
}
TF_Buffer* ret = TF_NewBuffer();
TF_CHECK_OK(MessageToBuffer(op_list, ret));
return ret;
}
// --------------------------------------------------------------------------
// ListDevices & SessionListDevices API
void TF_DeleteDeviceList(TF_DeviceList* list) { delete list; }
TF_DeviceList* TF_SessionListDevices(TF_Session* session, TF_Status* status) {
TF_DeviceList* response = new TF_DeviceList;
if (session && session->session)
status->status = session->session->ListDevices(&response->response);
return response;
}
TF_DeviceList* TF_DeprecatedSessionListDevices(TF_DeprecatedSession* session,
TF_Status* status) {
TF_DeviceList* response = new TF_DeviceList;
if (session && session->session)
status->status = session->session->ListDevices(&response->response);
return response;
}
int TF_DeviceListCount(const TF_DeviceList* list) {
return list->response.size();
}
#define TF_DEVICELIST_METHOD(return_type, method_name, accessor, err_val) \
return_type method_name(const TF_DeviceList* list, const int index, \
TF_Status* status) { \
if (list == nullptr) { \
status->status = InvalidArgument("list is null!"); \
return err_val; \
} \
if (index < 0 || index >= list->response.size()) { \
status->status = InvalidArgument("index out of bounds"); \
return err_val; \
} \
status->status = ::tensorflow::OkStatus(); \
return list->response[index].accessor; \
}
TF_DEVICELIST_METHOD(const char*, TF_DeviceListName, name().c_str(), nullptr);
TF_DEVICELIST_METHOD(const char*, TF_DeviceListType, device_type().c_str(),
nullptr);
TF_DEVICELIST_METHOD(int64_t, TF_DeviceListMemoryBytes, memory_limit(), -1);
TF_DEVICELIST_METHOD(uint64_t, TF_DeviceListIncarnation, incarnation(), 0);
#undef TF_DEVICELIST_METHOD
} // end extern "C"
// --------------------------------------------------------------------------
// New Graph and Session API
// Helper functions -----------------------------------------------------------
namespace {
TF_Operation* ToOperation(Node* node) {
return static_cast<TF_Operation*>(static_cast<void*>(node));
}
string OutputName(const TF_Output& output) {
return StrCat(output.oper->node.name(), ":", output.index);
}
const tensorflow::AttrValue* GetAttrValue(TF_Operation* oper,
const char* attr_name,
TF_Status* status) {
const tensorflow::AttrValue* attr = oper->node.attrs().Find(attr_name);
if (attr == nullptr) {
status->status = InvalidArgument("Operation '", oper->node.name(),
"' has no attr named '", attr_name, "'.");
}
return attr;
}
TensorId ToTensorId(const TF_Output& output) {
return TensorId(output.oper->node.name(), output.index);
}
#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
std::vector<tensorflow::Output> OutputsFromTFOutputs(TF_Output* tf_outputs,
int n) {
std::vector<tensorflow::Output> outputs(n);
for (int i = 0; i < n; ++i) {
outputs[i] =
tensorflow::Output(&tf_outputs[i].oper->node, tf_outputs[i].index);
}
return outputs;
}
void TFOutputsFromOutputs(const std::vector<tensorflow::Output>& outputs,
TF_Output* tf_outputs) {
for (int i = 0; i < outputs.size(); i++) {
tf_outputs[i].oper = ToOperation(outputs[i].node());
tf_outputs[i].index = outputs[i].index();
}
}
#endif // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
} // namespace
// Shape functions -----------------------------------------------------------
void TF_GraphSetTensorShape(TF_Graph* graph, TF_Output output,
const int64_t* dims, const int num_dims,
TF_Status* status) {
Node* node = &output.oper->node;
mutex_lock l(graph->mu);
tensorflow::shape_inference::InferenceContext* ic =
graph->refiner.GetContext(node);
if (ic == nullptr) {
status->status =
InvalidArgument("Node ", node->name(), " was not found in the graph");
return;
}
tensorflow::shape_inference::ShapeHandle new_shape =
tensorflow::ShapeHandleFromDims(ic, num_dims, dims);
status->status = graph->refiner.SetShape(node, output.index, new_shape);
}
int TF_GraphGetTensorNumDims(TF_Graph* graph, TF_Output output,
TF_Status* status) {
Node* node = &output.oper->node;
mutex_lock l(graph->mu);
tensorflow::shape_inference::InferenceContext* ic =
graph->refiner.GetContext(node);
if (ic == nullptr) {
status->status =
InvalidArgument("Node ", node->name(), " was not found in the graph");
return -1;
}
tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index);
// Unknown rank means the number of dimensions is -1.
if (!ic->RankKnown(shape)) {
return -1;
}
return ic->Rank(shape);
}
void TF_GraphGetTensorShape(TF_Graph* graph, TF_Output output, int64_t* dims,
int num_dims, TF_Status* status) {
Node* node = &output.oper->node;
mutex_lock l(graph->mu);
tensorflow::shape_inference::InferenceContext* ic =
graph->refiner.GetContext(node);
if (ic == nullptr) {
status->status =
InvalidArgument("Node ", node->name(), " was not found in the graph");
return;
}
tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index);
int rank = -1;
if (ic->RankKnown(shape)) {
rank = ic->Rank(shape);
}
if (num_dims != rank) {
status->status = InvalidArgument("Expected rank is ", num_dims,
" but actual rank is ", rank);
return;
}
if (num_dims == 0) {
// Output shape is a scalar.
return;
}
// Rank is greater than 0, so fill in the values, if known, and
// -1 for unknown values.
for (int i = 0; i < num_dims; ++i) {
auto dim = ic->Dim(shape, i);
int64_t value = -1;
if (ic->ValueKnown(dim)) {
value = ic->Value(dim);
}
dims[i] = value;
}
}
// TF_OperationDescription functions ------------------------------------------
extern "C" {
TF_OperationDescription* TF_NewOperationLocked(TF_Graph* graph,
const char* op_type,
const char* oper_name)
TF_EXCLUSIVE_LOCKS_REQUIRED(graph->mu) {
return new TF_OperationDescription(graph, op_type, oper_name);
}
TF_OperationDescription* TF_NewOperation(TF_Graph* graph, const char* op_type,
const char* oper_name) {
mutex_lock l(graph->mu);
return TF_NewOperationLocked(graph, op_type, oper_name);
}
void TF_SetDevice(TF_OperationDescription* desc, const char* device) {
desc->node_builder.Device(device);
}
void TF_AddInput(TF_OperationDescription* desc, TF_Output input) {
desc->node_builder.Input(&input.oper->node, input.index);
}
void TF_AddInputList(TF_OperationDescription* desc, const TF_Output* inputs,
int num_inputs) {
std::vector<NodeBuilder::NodeOut> input_list;
input_list.reserve(num_inputs);
for (int i = 0; i < num_inputs; ++i) {
input_list.emplace_back(&inputs[i].oper->node, inputs[i].index);
}
desc->node_builder.Input(input_list);
}
void TF_AddControlInput(TF_OperationDescription* desc, TF_Operation* input) {
desc->node_builder.ControlInput(&input->node);
}
void TF_ColocateWith(TF_OperationDescription* desc, TF_Operation* op) {
desc->colocation_constraints.emplace(
StrCat(tensorflow::kColocationGroupPrefix, op->node.name()));
}
void TF_SetAttrString(TF_OperationDescription* desc, const char* attr_name,
const void* value, size_t length) {
tensorflow::StringPiece s(static_cast<const char*>(value), length);
desc->node_builder.Attr(attr_name, s);
}
void TF_SetAttrStringList(TF_OperationDescription* desc, const char* attr_name,
const void* const* values, const size_t* lengths,
int num_values) {
if (strcmp(attr_name, tensorflow::kColocationAttrName) == 0) {
desc->colocation_constraints.clear();
for (int i = 0; i < num_values; ++i) {
desc->colocation_constraints.emplace(static_cast<const char*>(values[i]),
lengths[i]);
}
} else {
std::vector<tensorflow::StringPiece> v;
v.reserve(num_values);
for (int i = 0; i < num_values; ++i) {
v.emplace_back(static_cast<const char*>(values[i]), lengths[i]);
}
desc->node_builder.Attr(attr_name, v);
}
}
void TF_SetAttrInt(TF_OperationDescription* desc, const char* attr_name,
int64_t value) {
desc->node_builder.Attr(attr_name, static_cast<int64_t>(value));
}
void TF_SetAttrIntList(TF_OperationDescription* desc, const char* attr_name,
const int64_t* values, int num_values) {
desc->node_builder.Attr(
attr_name, ArraySlice<const int64_t>(
reinterpret_cast<const int64_t*>(values), num_values));
}
void TF_SetAttrFloat(TF_OperationDescription* desc, const char* attr_name,
float value) {
desc->node_builder.Attr(attr_name, value);
}
void TF_SetAttrFloatList(TF_OperationDescription* desc, const char* attr_name,
const float* values, int num_values) {
desc->node_builder.Attr(attr_name,
ArraySlice<const float>(values, num_values));
}
void TF_SetAttrBool(TF_OperationDescription* desc, const char* attr_name,
unsigned char value) {
desc->node_builder.Attr(attr_name, static_cast<bool>(value));
}
void TF_SetAttrBoolList(TF_OperationDescription* desc, const char* attr_name,
const unsigned char* values, int num_values) {
std::unique_ptr<bool[]> b(new bool[num_values]);
for (int i = 0; i < num_values; ++i) {
b[i] = values[i];
}
desc->node_builder.Attr(attr_name,
ArraySlice<const bool>(b.get(), num_values));
}
void TF_SetAttrType(TF_OperationDescription* desc, const char* attr_name,
TF_DataType value) {
desc->node_builder.Attr(attr_name, static_cast<DataType>(value));
}
void TF_SetAttrTypeList(TF_OperationDescription* desc, const char* attr_name,
const TF_DataType* values, int num_values) {
desc->node_builder.Attr(
attr_name, ArraySlice<const DataType>(
reinterpret_cast<const DataType*>(values), num_values));
}
void TF_SetAttrPlaceholder(TF_OperationDescription* desc, const char* attr_name,
const char* placeholder) {
tensorflow::AttrValue attr_value;
attr_value.set_placeholder(placeholder);
desc->node_builder.Attr(attr_name, attr_value);
}
void TF_SetAttrFuncName(TF_OperationDescription* desc, const char* attr_name,
const char* value, size_t length) {
tensorflow::NameAttrList func_name;
func_name.set_name(string(value, value + length));
desc->node_builder.Attr(attr_name, func_name);
}
void TF_SetAttrShape(TF_OperationDescription* desc, const char* attr_name,
const int64_t* dims, int num_dims) {
PartialTensorShape shape;
if (num_dims >= 0) {
shape = PartialTensorShape(
ArraySlice<int64_t>(reinterpret_cast<const int64_t*>(dims), num_dims));
}
desc->node_builder.Attr(attr_name, shape);
}
void TF_SetAttrShapeList(TF_OperationDescription* desc, const char* attr_name,
const int64_t* const* dims, const int* num_dims,
int num_shapes) {
std::vector<PartialTensorShape> shapes;
shapes.reserve(num_shapes);
for (int i = 0; i < num_shapes; ++i) {
if (num_dims[i] < 0) {
shapes.emplace_back();
} else {
shapes.emplace_back(ArraySlice<int64_t>(
reinterpret_cast<const int64_t*>(dims[i]), num_dims[i]));
}
}
desc->node_builder.Attr(attr_name, shapes);
}
void TF_SetAttrTensorShapeProto(TF_OperationDescription* desc,
const char* attr_name, const void* proto,
size_t proto_len, TF_Status* status) {
// shape.ParseFromArray takes an int as length, this function takes size_t,
// make sure there is no information loss.
if (proto_len > std::numeric_limits<int>::max()) {
status->status = InvalidArgument(
"proto_len (", proto_len,
" bytes) is too large to be parsed by the protocol buffer library");
return;
}
TensorShapeProto shape;
if (shape.ParseFromArray(proto, static_cast<int>(proto_len))) {
desc->node_builder.Attr(attr_name, shape);
status->status = ::tensorflow::OkStatus();
} else {
status->status = InvalidArgument("Unparseable TensorShapeProto");
}
}
void TF_SetAttrTensorShapeProtoList(TF_OperationDescription* desc,
const char* attr_name,
const void* const* protos,
const size_t* proto_lens, int num_shapes,
TF_Status* status) {
std::vector<TensorShapeProto> shapes;
shapes.resize(num_shapes);
for (int i = 0; i < num_shapes; ++i) {
if (proto_lens[i] > std::numeric_limits<int>::max()) {
status->status = InvalidArgument(
"length of element ", i, " in the list (", proto_lens[i],
" bytes) is too large to be parsed by the protocol buffer library");
return;
}
if (!shapes[i].ParseFromArray(protos[i], static_cast<int>(proto_lens[i]))) {
status->status =
InvalidArgument("Unparseable TensorShapeProto at index ", i);
return;
}
}
desc->node_builder.Attr(attr_name, shapes);
status->status = ::tensorflow::OkStatus();
}
void TF_SetAttrTensor(TF_OperationDescription* desc, const char* attr_name,
TF_Tensor* value, TF_Status* status) {
Tensor t;
status->status = TF_TensorToTensor(value, &t);
if (status->status.ok()) desc->node_builder.Attr(attr_name, t);
}
void TF_SetAttrTensorList(TF_OperationDescription* desc, const char* attr_name,
TF_Tensor* const* values, int num_values,