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c_api_test.cc
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c_api_test.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 <cstddef>
#include <iterator>
#include <memory>
#include <vector>
#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/c_test_util.h"
#include "tensorflow/c/tf_buffer.h"
#include "tensorflow/c/tf_buffer_internal.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/cc/saved_model/signature_constants.h"
#include "tensorflow/cc/saved_model/tag_constants.h"
#include "tensorflow/core/example/example.pb.h"
#include "tensorflow/core/example/feature.pb.h"
#include "tensorflow/core/framework/api_def.pb.h"
#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/kernel_def.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/graph/tensor_id.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/resource_loader.h"
#include "tensorflow/core/platform/str_util.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/error_codes.pb.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/util/equal_graph_def.h"
namespace tensorflow {
TF_Tensor* TF_TensorFromTensor(const Tensor& src, Status* status);
Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);
namespace {
static void ExpectHasSubstr(StringPiece s, StringPiece expected) {
EXPECT_TRUE(absl::StrContains(s, expected))
<< "'" << s << "' does not contain '" << expected << "'";
}
// Returns the GPU device name if there is one (with arbitrary tie breaking if
// there are more than one), or "" otherwise.
string GPUDeviceName(TF_Session* session) {
std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
TF_NewStatus(), TF_DeleteStatus);
TF_Status* s = status.get();
std::unique_ptr<TF_DeviceList, decltype(&TF_DeleteDeviceList)> list(
TF_SessionListDevices(session, s), TF_DeleteDeviceList);
TF_DeviceList* device_list = list.get();
CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
const int num_devices = TF_DeviceListCount(device_list);
LOG(INFO) << "There are " << num_devices << " devices.";
for (int i = 0; i < num_devices; ++i) {
const char* device_name = TF_DeviceListName(device_list, i, s);
CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
const char* device_type = TF_DeviceListType(device_list, i, s);
CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
LOG(INFO) << "Device " << i << " has name " << device_name << ", type "
<< device_type;
if (string(device_type) == DEVICE_GPU) {
return device_name;
}
}
// No GPU device found.
return "";
}
string GPUDeviceName() {
std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
TF_NewStatus(), TF_DeleteStatus);
TF_Status* s = status.get();
std::unique_ptr<TF_Graph, decltype(&TF_DeleteGraph)> graph(TF_NewGraph(),
TF_DeleteGraph);
TF_SessionOptions* opts = TF_NewSessionOptions();
TF_Session* sess = TF_NewSession(graph.get(), opts, s);
TF_DeleteSessionOptions(opts);
const string gpu_device_name = GPUDeviceName(sess);
TF_DeleteSession(sess, s);
CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
return gpu_device_name;
}
TEST(CAPI, Version) { EXPECT_STRNE("", TF_Version()); }
TEST(CAPI, Status) {
TF_Status* s = TF_NewStatus();
EXPECT_EQ(TF_OK, TF_GetCode(s));
EXPECT_EQ(string(), TF_Message(s));
TF_SetStatus(s, TF_CANCELLED, "cancel");
EXPECT_EQ(TF_CANCELLED, TF_GetCode(s));
EXPECT_EQ(string("cancel"), TF_Message(s));
TF_DeleteStatus(s);
}
void Deallocator(void* data, size_t, void* arg) {
tensorflow::cpu_allocator()->DeallocateRaw(data);
*reinterpret_cast<bool*>(arg) = true;
}
TEST(CAPI, Tensor) {
const int num_bytes = 6 * sizeof(float);
float* values =
reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(
EIGEN_MAX_ALIGN_BYTES, num_bytes));
int64_t dims[] = {2, 3};
bool deallocator_called = false;
TF_Tensor* t = TF_NewTensor(TF_FLOAT, dims, 2, values, num_bytes,
&Deallocator, &deallocator_called);
EXPECT_FALSE(deallocator_called);
EXPECT_EQ(TF_FLOAT, TF_TensorType(t));
EXPECT_EQ(2, TF_NumDims(t));
EXPECT_EQ(dims[0], TF_Dim(t, 0));
EXPECT_EQ(dims[1], TF_Dim(t, 1));
EXPECT_EQ(num_bytes, TF_TensorByteSize(t));
EXPECT_EQ(static_cast<void*>(values), TF_TensorData(t));
TF_DeleteTensor(t);
EXPECT_TRUE(deallocator_called);
}
void NoOpDeallocator(void* data, size_t, void*) {}
TEST(CAPI, MalformedTensor) {
// See https://github.com/tensorflow/tensorflow/issues/7394
// num_dims = 0 implies a scalar, so should be backed by at least 4 bytes of
// data.
TF_Tensor* t =
TF_NewTensor(TF_FLOAT, nullptr, 0, nullptr, 0, &NoOpDeallocator, nullptr);
ASSERT_TRUE(t == nullptr);
}
TEST(CAPI, AllocateTensor) {
const int num_bytes = 6 * sizeof(float);
int64_t dims[] = {2, 3};
TF_Tensor* t = TF_AllocateTensor(TF_FLOAT, dims, 2, num_bytes);
EXPECT_EQ(TF_FLOAT, TF_TensorType(t));
EXPECT_EQ(2, TF_NumDims(t));
EXPECT_EQ(dims[0], TF_Dim(t, 0));
EXPECT_EQ(dims[1], TF_Dim(t, 1));
EXPECT_EQ(num_bytes, TF_TensorByteSize(t));
EXPECT_EQ(6, TF_TensorElementCount(t));
TF_DeleteTensor(t);
}
TEST(CAPI, MaybeMove) {
const int num_bytes = 6 * sizeof(float);
float* values =
reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(
EIGEN_MAX_ALIGN_BYTES, num_bytes));
int64_t dims[] = {2, 3};
bool deallocator_called = false;
TF_Tensor* t = TF_NewTensor(TF_FLOAT, dims, 2, values, num_bytes,
&Deallocator, &deallocator_called);
TF_Tensor* o = TF_TensorMaybeMove(t);
ASSERT_TRUE(o == nullptr); // It is unsafe to move memory TF might not own.
TF_DeleteTensor(t);
EXPECT_TRUE(deallocator_called);
}
TEST(CAPI, LibraryLoadFunctions) {
// TODO(b/73318067): Fix linking for the GPU test generated by the
// tf_cuda_cc_test() bazel rule and remove the next line.
if (!GPUDeviceName().empty()) return;
#if !defined(TENSORFLOW_NO_SHARED_OBJECTS)
{
// Load the library.
TF_Status* status = TF_NewStatus();
string lib_path = tensorflow::GetDataDependencyFilepath(
tensorflow::io::JoinPath("tensorflow", "c", "test_op1.so"));
TF_Library* lib = TF_LoadLibrary(lib_path.c_str(), status);
TF_Code code = TF_GetCode(status);
string status_msg(TF_Message(status));
TF_DeleteStatus(status);
ASSERT_EQ(TF_OK, code) << status_msg;
// Test op list.
TF_Buffer op_list_buf = TF_GetOpList(lib);
tensorflow::OpList op_list;
EXPECT_TRUE(op_list.ParseFromArray(op_list_buf.data, op_list_buf.length));
ASSERT_EQ(op_list.op_size(), 1);
EXPECT_EQ("TestCApi1", op_list.op(0).name());
TF_DeleteLibraryHandle(lib);
}
#endif // !defined(TENSORFLOW_NO_SHARED_OBJECTS)
{
TF_Buffer* op_list_buffer = TF_GetAllOpList();
tensorflow::OpList op_list;
op_list.ParseFromArray(op_list_buffer->data, op_list_buffer->length);
ASSERT_GE(op_list.op_size(), 1);
typedef tensorflow::protobuf::RepeatedPtrField<tensorflow::OpDef> OpDefs;
const OpDefs& ops = op_list.op();
bool found = std::find_if(ops.begin(), ops.end(),
[](const tensorflow::OpDef& op_def) {
return op_def.name() == "TestCApi";
}) != ops.end();
EXPECT_TRUE(found);
TF_DeleteBuffer(op_list_buffer);
}
}
void TestEncodeDecode(int line, const std::vector<string>& data) {
const int64_t n = data.size();
Status status;
for (const std::vector<int64_t>& dims :
std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
// Create C++ Tensor
Tensor src(tensorflow::DT_STRING, TensorShape(dims));
for (int64_t i = 0; i < src.NumElements(); ++i) {
src.flat<tstring>()(i) = data[i];
}
TF_Tensor* dst = TF_TensorFromTensor(src, &status);
ASSERT_TRUE(status.ok()) << status.message();
// Convert back to a C++ Tensor and ensure we get expected output.
Tensor output;
ASSERT_EQ(OkStatus(), TF_TensorToTensor(dst, &output)) << line;
ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
for (int64_t i = 0; i < src.NumElements(); ++i) {
ASSERT_EQ(data[i], output.flat<tstring>()(i)) << line;
}
TF_DeleteTensor(dst);
}
}
TEST(CAPI, TensorEncodeDecodeStrings) {
TestEncodeDecode(__LINE__, {});
TestEncodeDecode(__LINE__, {"hello"});
TestEncodeDecode(__LINE__,
{"the", "quick", "brown", "fox", "jumped", "over"});
string big(1000, 'a');
TestEncodeDecode(__LINE__, {"small", big, "small2"});
}
TEST(CAPI, SessionOptions) {
TF_SessionOptions* opt = TF_NewSessionOptions();
TF_DeleteSessionOptions(opt);
}
TEST(CAPI, DeprecatedSession) {
TF_Status* s = TF_NewStatus();
TF_SessionOptions* opt = TF_NewSessionOptions();
TF_DeprecatedSession* session = TF_NewDeprecatedSession(opt, s);
TF_DeleteSessionOptions(opt);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Buffer* run_options = TF_NewBufferFromString("", 0);
TF_Buffer* run_metadata = TF_NewBuffer();
TF_Run(session, run_options, nullptr, nullptr, 0, nullptr, nullptr, 0,
nullptr, 0, run_metadata, s);
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ("Session was not created with a graph before Run()!",
string(TF_Message(s)));
TF_DeleteBuffer(run_metadata);
TF_DeleteBuffer(run_options);
TF_DeleteDeprecatedSession(session, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_DeleteStatus(s);
}
TEST(CAPI, DataTypeEnum) {
EXPECT_EQ(TF_FLOAT, static_cast<TF_DataType>(tensorflow::DT_FLOAT));
EXPECT_EQ(TF_DOUBLE, static_cast<TF_DataType>(tensorflow::DT_DOUBLE));
EXPECT_EQ(TF_INT32, static_cast<TF_DataType>(tensorflow::DT_INT32));
EXPECT_EQ(TF_UINT8, static_cast<TF_DataType>(tensorflow::DT_UINT8));
EXPECT_EQ(TF_INT16, static_cast<TF_DataType>(tensorflow::DT_INT16));
EXPECT_EQ(TF_INT8, static_cast<TF_DataType>(tensorflow::DT_INT8));
EXPECT_EQ(TF_STRING, static_cast<TF_DataType>(tensorflow::DT_STRING));
EXPECT_EQ(TF_COMPLEX64, static_cast<TF_DataType>(tensorflow::DT_COMPLEX64));
EXPECT_EQ(TF_COMPLEX, TF_COMPLEX64);
EXPECT_EQ(TF_INT64, static_cast<TF_DataType>(tensorflow::DT_INT64));
EXPECT_EQ(TF_BOOL, static_cast<TF_DataType>(tensorflow::DT_BOOL));
EXPECT_EQ(TF_QINT8, static_cast<TF_DataType>(tensorflow::DT_QINT8));
EXPECT_EQ(TF_QUINT8, static_cast<TF_DataType>(tensorflow::DT_QUINT8));
EXPECT_EQ(TF_QINT32, static_cast<TF_DataType>(tensorflow::DT_QINT32));
EXPECT_EQ(TF_BFLOAT16, static_cast<TF_DataType>(tensorflow::DT_BFLOAT16));
EXPECT_EQ(TF_QINT16, static_cast<TF_DataType>(tensorflow::DT_QINT16));
EXPECT_EQ(TF_QUINT16, static_cast<TF_DataType>(tensorflow::DT_QUINT16));
EXPECT_EQ(TF_UINT16, static_cast<TF_DataType>(tensorflow::DT_UINT16));
EXPECT_EQ(TF_COMPLEX128, static_cast<TF_DataType>(tensorflow::DT_COMPLEX128));
EXPECT_EQ(TF_HALF, static_cast<TF_DataType>(tensorflow::DT_HALF));
EXPECT_EQ(TF_DataTypeSize(TF_DOUBLE),
tensorflow::DataTypeSize(tensorflow::DT_DOUBLE));
EXPECT_EQ(TF_DataTypeSize(TF_STRING),
tensorflow::DataTypeSize(tensorflow::DT_STRING));
// Test with invalid type; should always return 0 as documented
EXPECT_EQ(TF_DataTypeSize(static_cast<TF_DataType>(0)), 0);
}
TEST(CAPI, StatusEnum) {
EXPECT_EQ(TF_OK, static_cast<TF_Code>(tensorflow::error::OK));
EXPECT_EQ(TF_CANCELLED, static_cast<TF_Code>(tensorflow::error::CANCELLED));
EXPECT_EQ(TF_UNKNOWN, static_cast<TF_Code>(tensorflow::error::UNKNOWN));
EXPECT_EQ(TF_INVALID_ARGUMENT,
static_cast<TF_Code>(absl::StatusCode::kInvalidArgument));
EXPECT_EQ(TF_DEADLINE_EXCEEDED,
static_cast<TF_Code>(tensorflow::error::DEADLINE_EXCEEDED));
EXPECT_EQ(TF_NOT_FOUND, static_cast<TF_Code>(tensorflow::error::NOT_FOUND));
EXPECT_EQ(TF_ALREADY_EXISTS,
static_cast<TF_Code>(tensorflow::error::ALREADY_EXISTS));
EXPECT_EQ(TF_PERMISSION_DENIED,
static_cast<TF_Code>(tensorflow::error::PERMISSION_DENIED));
EXPECT_EQ(TF_UNAUTHENTICATED,
static_cast<TF_Code>(tensorflow::error::UNAUTHENTICATED));
EXPECT_EQ(TF_RESOURCE_EXHAUSTED,
static_cast<TF_Code>(tensorflow::error::RESOURCE_EXHAUSTED));
EXPECT_EQ(TF_FAILED_PRECONDITION,
static_cast<TF_Code>(tensorflow::error::FAILED_PRECONDITION));
EXPECT_EQ(TF_ABORTED, static_cast<TF_Code>(tensorflow::error::ABORTED));
EXPECT_EQ(TF_OUT_OF_RANGE,
static_cast<TF_Code>(tensorflow::error::OUT_OF_RANGE));
EXPECT_EQ(TF_UNIMPLEMENTED,
static_cast<TF_Code>(tensorflow::error::UNIMPLEMENTED));
EXPECT_EQ(TF_INTERNAL, static_cast<TF_Code>(tensorflow::error::INTERNAL));
EXPECT_EQ(TF_UNAVAILABLE,
static_cast<TF_Code>(tensorflow::error::UNAVAILABLE));
EXPECT_EQ(TF_DATA_LOSS, static_cast<TF_Code>(tensorflow::error::DATA_LOSS));
}
TEST(CAPI, GetAllOpList) {
TF_Buffer* buf = TF_GetAllOpList();
tensorflow::OpList op_list;
EXPECT_TRUE(op_list.ParseFromArray(buf->data, buf->length));
EXPECT_GT(op_list.op_size(), 0);
TF_DeleteBuffer(buf);
}
TEST(CAPI, SetShape) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
TF_Operation* feed = Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Output feed_out_0 = TF_Output{feed, 0};
int num_dims;
// Fetch the shape, it should be completely unknown.
num_dims = TF_GraphGetTensorNumDims(graph, feed_out_0, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(-1, num_dims);
// Set the shape to be unknown, expect no change.
TF_GraphSetTensorShape(graph, feed_out_0, /*dims=*/nullptr, -1, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
num_dims = TF_GraphGetTensorNumDims(graph, feed_out_0, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(-1, num_dims);
// Set the shape to be 2 x Unknown
int64_t dims[] = {2, -1};
TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Fetch the shape and validate it is 2 by -1.
num_dims = TF_GraphGetTensorNumDims(graph, feed_out_0, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(2, num_dims);
// Resize the dimension vector appropriately.
int64_t returned_dims[2];
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(dims[0], returned_dims[0]);
EXPECT_EQ(dims[1], returned_dims[1]);
// Set to a new valid shape: [2, 3]
dims[1] = 3;
TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Fetch and see that the new value is returned.
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(dims[0], returned_dims[0]);
EXPECT_EQ(dims[1], returned_dims[1]);
// Try to set 'unknown' with unknown rank on the shape and see that
// it doesn't change.
TF_GraphSetTensorShape(graph, feed_out_0, /*dims=*/nullptr, -1, s);
EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(2, num_dims);
EXPECT_EQ(2, returned_dims[0]);
EXPECT_EQ(3, returned_dims[1]);
// Try to set 'unknown' with same rank on the shape and see that
// it doesn't change.
dims[0] = -1;
dims[1] = -1;
TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Fetch and see that the new value is returned.
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(2, num_dims);
EXPECT_EQ(2, returned_dims[0]);
EXPECT_EQ(3, returned_dims[1]);
// Try to fetch a shape with the wrong num_dims
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s);
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);
// Try to set an invalid shape (cannot change 2x3 to a 2x5).
dims[1] = 5;
TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);
// Test for a scalar.
TF_Operation* three = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Output three_out_0 = TF_Output{three, 0};
num_dims = TF_GraphGetTensorNumDims(graph, three_out_0, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(0, num_dims);
TF_GraphGetTensorShape(graph, three_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Clean up
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, Graph) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Make a placeholder operation.
TF_Operation* feed = Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Test TF_Operation*() query functions.
EXPECT_EQ(string("feed"), string(TF_OperationName(feed)));
EXPECT_EQ(string("Placeholder"), string(TF_OperationOpType(feed)));
EXPECT_EQ(string(""), string(TF_OperationDevice(feed)));
EXPECT_EQ(1, TF_OperationNumOutputs(feed));
EXPECT_EQ(TF_INT32, TF_OperationOutputType(TF_Output{feed, 0}));
EXPECT_EQ(1, TF_OperationOutputListLength(feed, "output", s));
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(0, TF_OperationNumInputs(feed));
EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));
EXPECT_EQ(0, TF_OperationNumControlInputs(feed));
EXPECT_EQ(0, TF_OperationNumControlOutputs(feed));
tensorflow::AttrValue attr_value;
ASSERT_TRUE(GetAttrValue(feed, "dtype", &attr_value, s)) << TF_Message(s);
EXPECT_EQ(attr_value.type(), tensorflow::DT_INT32);
// Test not found errors in TF_Operation*() query functions.
EXPECT_EQ(-1, TF_OperationOutputListLength(feed, "bogus", s));
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s));
ASSERT_FALSE(GetAttrValue(feed, "missing", &attr_value, s));
EXPECT_EQ(string("Operation 'feed' has no attr named 'missing'."),
string(TF_Message(s)));
// Make a constant oper with the scalar "3".
TF_Operation* three = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Add oper.
TF_Operation* add = Add(feed, three, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Test TF_Operation*() query functions.
EXPECT_EQ(string("add"), string(TF_OperationName(add)));
EXPECT_EQ(string("AddN"), string(TF_OperationOpType(add)));
EXPECT_EQ(string(""), string(TF_OperationDevice(add)));
EXPECT_EQ(1, TF_OperationNumOutputs(add));
EXPECT_EQ(TF_INT32, TF_OperationOutputType(TF_Output{add, 0}));
EXPECT_EQ(1, TF_OperationOutputListLength(add, "sum", s));
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(2, TF_OperationNumInputs(add));
EXPECT_EQ(2, TF_OperationInputListLength(add, "inputs", s));
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(TF_INT32, TF_OperationInputType(TF_Input{add, 0}));
EXPECT_EQ(TF_INT32, TF_OperationInputType(TF_Input{add, 1}));
TF_Output add_in_0 = TF_OperationInput(TF_Input{add, 0});
EXPECT_EQ(feed, add_in_0.oper);
EXPECT_EQ(0, add_in_0.index);
TF_Output add_in_1 = TF_OperationInput(TF_Input{add, 1});
EXPECT_EQ(three, add_in_1.oper);
EXPECT_EQ(0, add_in_1.index);
EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{add, 0}));
EXPECT_EQ(0, TF_OperationNumControlInputs(add));
EXPECT_EQ(0, TF_OperationNumControlOutputs(add));
ASSERT_TRUE(GetAttrValue(add, "T", &attr_value, s)) << TF_Message(s);
EXPECT_EQ(attr_value.type(), tensorflow::DT_INT32);
ASSERT_TRUE(GetAttrValue(add, "N", &attr_value, s)) << TF_Message(s);
EXPECT_EQ(attr_value.i(), 2);
// Placeholder oper now has a consumer.
ASSERT_EQ(1, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));
TF_Input feed_port;
EXPECT_EQ(1, TF_OperationOutputConsumers(TF_Output{feed, 0}, &feed_port, 1));
EXPECT_EQ(add, feed_port.oper);
EXPECT_EQ(0, feed_port.index);
// The scalar const oper also has a consumer.
ASSERT_EQ(1, TF_OperationOutputNumConsumers(TF_Output{three, 0}));
TF_Input three_port;
EXPECT_EQ(1,
TF_OperationOutputConsumers(TF_Output{three, 0}, &three_port, 1));
EXPECT_EQ(add, three_port.oper);
EXPECT_EQ(1, three_port.index);
// Serialize to GraphDef.
GraphDef graph_def;
ASSERT_TRUE(GetGraphDef(graph, &graph_def));
// Validate GraphDef is what we expect.
bool found_placeholder = false;
bool found_scalar_const = false;
bool found_add = false;
for (const auto& n : graph_def.node()) {
if (IsPlaceholder(n)) {
EXPECT_FALSE(found_placeholder);
found_placeholder = true;
} else if (IsScalarConst(n, 3)) {
EXPECT_FALSE(found_scalar_const);
found_scalar_const = true;
} else if (IsAddN(n, 2)) {
EXPECT_FALSE(found_add);
found_add = true;
} else {
ADD_FAILURE() << "Unexpected NodeDef: " << n.DebugString();
}
}
EXPECT_TRUE(found_placeholder);
EXPECT_TRUE(found_scalar_const);
EXPECT_TRUE(found_add);
// Add another oper to the graph.
TF_Operation* neg = Neg(add, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Serialize to NodeDef.
NodeDef node_def;
ASSERT_TRUE(GetNodeDef(neg, &node_def));
// Validate NodeDef is what we expect.
EXPECT_TRUE(IsNeg(node_def, "add"));
// Serialize to GraphDef.
GraphDef graph_def2;
ASSERT_TRUE(GetGraphDef(graph, &graph_def2));
// Compare with first GraphDef + added NodeDef.
NodeDef* added_node = graph_def.add_node();
*added_node = node_def;
EXPECT_EQ(graph_def.DebugString(), graph_def2.DebugString());
// Look up some nodes by name.
TF_Operation* neg2 = TF_GraphOperationByName(graph, "neg");
EXPECT_TRUE(neg == neg2);
NodeDef node_def2;
ASSERT_TRUE(GetNodeDef(neg2, &node_def2));
EXPECT_EQ(node_def.DebugString(), node_def2.DebugString());
TF_Operation* feed2 = TF_GraphOperationByName(graph, "feed");
EXPECT_TRUE(feed == feed2);
ASSERT_TRUE(GetNodeDef(feed, &node_def));
ASSERT_TRUE(GetNodeDef(feed2, &node_def2));
EXPECT_EQ(node_def.DebugString(), node_def2.DebugString());
// Test iterating through the nodes of a graph.
found_placeholder = false;
found_scalar_const = false;
found_add = false;
bool found_neg = false;
size_t pos = 0;
TF_Operation* oper;
while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) {
if (oper == feed) {
EXPECT_FALSE(found_placeholder);
found_placeholder = true;
} else if (oper == three) {
EXPECT_FALSE(found_scalar_const);
found_scalar_const = true;
} else if (oper == add) {
EXPECT_FALSE(found_add);
found_add = true;
} else if (oper == neg) {
EXPECT_FALSE(found_neg);
found_neg = true;
} else {
ASSERT_TRUE(GetNodeDef(oper, &node_def));
ADD_FAILURE() << "Unexpected Node: " << node_def.DebugString();
}
}
EXPECT_TRUE(found_placeholder);
EXPECT_TRUE(found_scalar_const);
EXPECT_TRUE(found_add);
EXPECT_TRUE(found_neg);
// Clean up
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, UpdateEdge) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Make two scalar constants.
TF_Operation* one = ScalarConst(1, graph, s, "one");
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* two = ScalarConst(2, graph, s, "two");
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Add oper.
TF_Operation* add = Add(one, two, graph, s, "add");
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Add another oper to the graph.
TF_Operation* neg = Neg(add, graph, s, "neg");
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
NodeDef node_def_neg;
ASSERT_TRUE(GetNodeDef(neg, &node_def_neg));
EXPECT_EQ(string("add"), node_def_neg.input(0));
// update edge of neg
TF_UpdateEdge(graph, TF_Output{one, 0}, TF_Input{neg, 0}, s);
ASSERT_TRUE(GetNodeDef(neg, &node_def_neg));
EXPECT_EQ(string("one:0"), node_def_neg.input(0));
// Clean up
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
/*
TODO(skyewm): this test currently DCHECKs, change to bad status
TEST(CAPI, InputFromDifferentGraphError) {
TF_Status* s = TF_NewStatus();
TF_Graph* g1 = TF_NewGraph();
TF_Graph* g2 = TF_NewGraph();
TF_Operation* feed = Placeholder(g1, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Attempt to create node in g2 with input from g1
Neg(feed, g2, s);
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s));
EXPECT_STREQ("foo", TF_Message(s));
TF_DeleteGraph(g1);
TF_DeleteGraph(g2);
TF_DeleteStatus(s);
}
*/
TEST(CAPI, ImportGraphDef) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Create a simple graph.
Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "feed") != nullptr);
TF_Operation* oper = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "scalar") != nullptr);
Neg(oper, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "neg") != nullptr);
// Export to a GraphDef.
TF_Buffer* graph_def = TF_NewBuffer();
TF_GraphToGraphDef(graph, graph_def, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Import it, with a prefix, in a fresh graph.
TF_DeleteGraph(graph);
graph = TF_NewGraph();
TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported");
TF_GraphImportGraphDef(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar = TF_GraphOperationByName(graph, "imported/scalar");
TF_Operation* feed = TF_GraphOperationByName(graph, "imported/feed");
TF_Operation* neg = TF_GraphOperationByName(graph, "imported/neg");
ASSERT_TRUE(scalar != nullptr);
ASSERT_TRUE(feed != nullptr);
ASSERT_TRUE(neg != nullptr);
// Test basic structure of the imported graph.
EXPECT_EQ(0, TF_OperationNumInputs(scalar));
EXPECT_EQ(0, TF_OperationNumInputs(feed));
ASSERT_EQ(1, TF_OperationNumInputs(neg));
TF_Output neg_input = TF_OperationInput({neg, 0});
EXPECT_EQ(scalar, neg_input.oper);
EXPECT_EQ(0, neg_input.index);
// Test that we can't see control edges involving the source and sink nodes.
TF_Operation* control_ops[100];
EXPECT_EQ(0, TF_OperationNumControlInputs(scalar));
EXPECT_EQ(0, TF_OperationGetControlInputs(scalar, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlOutputs(scalar));
EXPECT_EQ(0, TF_OperationGetControlOutputs(scalar, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlInputs(feed));
EXPECT_EQ(0, TF_OperationGetControlInputs(feed, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlOutputs(feed));
EXPECT_EQ(0, TF_OperationGetControlOutputs(feed, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlInputs(neg));
EXPECT_EQ(0, TF_OperationGetControlInputs(neg, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlOutputs(neg));
EXPECT_EQ(0, TF_OperationGetControlOutputs(neg, control_ops, 100));
// Import it again, with an input mapping, return outputs, and a return
// operation, into the same graph.
TF_DeleteImportGraphDefOptions(opts);
opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported2");
TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
TF_ImportGraphDefOptionsAddReturnOutput(opts, "feed", 0);
TF_ImportGraphDefOptionsAddReturnOutput(opts, "scalar", 0);
EXPECT_EQ(2, TF_ImportGraphDefOptionsNumReturnOutputs(opts));
TF_ImportGraphDefOptionsAddReturnOperation(opts, "scalar");
EXPECT_EQ(1, TF_ImportGraphDefOptionsNumReturnOperations(opts));
tensorflow::GraphDef graph_def_proto;
ASSERT_TRUE(tensorflow::ParseProtoUnlimited(&graph_def_proto, graph_def->data,
graph_def->length));
TF_Buffer graph_def_buffer;
graph_def_buffer.data = reinterpret_cast<const void*>(&graph_def_proto);
graph_def_buffer.length = sizeof(tensorflow::GraphDef*);
TF_ImportGraphDefResults* results =
TF_GraphImportGraphDefWithResultsNoSerialization(graph, &graph_def_buffer,
opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar2 = TF_GraphOperationByName(graph, "imported2/scalar");
TF_Operation* feed2 = TF_GraphOperationByName(graph, "imported2/feed");
TF_Operation* neg2 = TF_GraphOperationByName(graph, "imported2/neg");
ASSERT_TRUE(scalar2 != nullptr);
ASSERT_TRUE(feed2 != nullptr);
ASSERT_TRUE(neg2 != nullptr);
// Check input mapping
neg_input = TF_OperationInput({neg, 0});
EXPECT_EQ(scalar, neg_input.oper);
EXPECT_EQ(0, neg_input.index);
// Check return outputs
TF_Output* return_outputs;
int num_return_outputs;
TF_ImportGraphDefResultsReturnOutputs(results, &num_return_outputs,
&return_outputs);
ASSERT_EQ(2, num_return_outputs);
EXPECT_EQ(feed2, return_outputs[0].oper);
EXPECT_EQ(0, return_outputs[0].index);
EXPECT_EQ(scalar, return_outputs[1].oper); // remapped
EXPECT_EQ(0, return_outputs[1].index);
// Check return operation
TF_Operation** return_opers;
int num_return_opers;
TF_ImportGraphDefResultsReturnOperations(results, &num_return_opers,
&return_opers);
ASSERT_EQ(1, num_return_opers);
EXPECT_EQ(scalar2, return_opers[0]); // not remapped
TF_DeleteImportGraphDefResults(results);
// Import again, with control dependencies, into the same graph.
TF_DeleteImportGraphDefOptions(opts);
opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported3");
TF_ImportGraphDefOptionsAddControlDependency(opts, feed);
TF_ImportGraphDefOptionsAddControlDependency(opts, feed2);
TF_GraphImportGraphDef(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar3 = TF_GraphOperationByName(graph, "imported3/scalar");
TF_Operation* feed3 = TF_GraphOperationByName(graph, "imported3/feed");
TF_Operation* neg3 = TF_GraphOperationByName(graph, "imported3/neg");
ASSERT_TRUE(scalar3 != nullptr);
ASSERT_TRUE(feed3 != nullptr);
ASSERT_TRUE(neg3 != nullptr);
// Check that newly-imported scalar and feed have control deps (neg3 will
// inherit them from input)
TF_Operation* control_inputs[100];
int num_control_inputs = TF_OperationGetControlInputs(
scalar3, control_inputs, TF_OperationNumControlInputs(scalar3));
ASSERT_EQ(2, num_control_inputs);
EXPECT_EQ(feed, control_inputs[0]);
EXPECT_EQ(feed2, control_inputs[1]);
num_control_inputs = TF_OperationGetControlInputs(
feed3, control_inputs, TF_OperationNumControlInputs(feed3));
ASSERT_EQ(2, num_control_inputs);
EXPECT_EQ(feed, control_inputs[0]);
EXPECT_EQ(feed2, control_inputs[1]);
// Export to a graph def so we can import a graph with control dependencies
TF_DeleteBuffer(graph_def);
graph_def = TF_NewBuffer();
TF_GraphToGraphDef(graph, graph_def, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Import again, with remapped control dependency, into the same graph
TF_DeleteImportGraphDefOptions(opts);
opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported4");
TF_ImportGraphDefOptionsRemapControlDependency(opts, "imported/feed", feed);
TF_GraphImportGraphDef(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar4 =
TF_GraphOperationByName(graph, "imported4/imported3/scalar");
TF_Operation* feed4 =
TF_GraphOperationByName(graph, "imported4/imported2/feed");
// Check that imported `imported3/scalar` has remapped control dep from
// original graph and imported control dep
num_control_inputs = TF_OperationGetControlInputs(
scalar4, control_inputs, TF_OperationNumControlInputs(scalar4));
ASSERT_EQ(2, num_control_inputs);
EXPECT_EQ(feed, control_inputs[0]);
EXPECT_EQ(feed4, control_inputs[1]);
TF_DeleteImportGraphDefOptions(opts);
TF_DeleteBuffer(graph_def);
// Can add nodes to the imported graph without trouble.
Add(feed, scalar, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, ImportGraphDef_WithReturnOutputs) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Create a graph with two nodes: x and 3
Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "feed") != nullptr);
TF_Operation* oper = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "scalar") != nullptr);
Neg(oper, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "neg") != nullptr);
// Export to a GraphDef.
TF_Buffer* graph_def = TF_NewBuffer();
TF_GraphToGraphDef(graph, graph_def, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Import it in a fresh graph with return outputs.
TF_DeleteGraph(graph);
graph = TF_NewGraph();
TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsAddReturnOutput(opts, "feed", 0);
TF_ImportGraphDefOptionsAddReturnOutput(opts, "scalar", 0);
EXPECT_EQ(2, TF_ImportGraphDefOptionsNumReturnOutputs(opts));
TF_Output return_outputs[2];
TF_GraphImportGraphDefWithReturnOutputs(graph, graph_def, opts,
return_outputs, 2, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar = TF_GraphOperationByName(graph, "scalar");
TF_Operation* feed = TF_GraphOperationByName(graph, "feed");
TF_Operation* neg = TF_GraphOperationByName(graph, "neg");
ASSERT_TRUE(scalar != nullptr);
ASSERT_TRUE(feed != nullptr);
ASSERT_TRUE(neg != nullptr);
// Check return outputs
EXPECT_EQ(feed, return_outputs[0].oper);
EXPECT_EQ(0, return_outputs[0].index);
EXPECT_EQ(scalar, return_outputs[1].oper);
EXPECT_EQ(0, return_outputs[1].index);
TF_DeleteImportGraphDefOptions(opts);
TF_DeleteBuffer(graph_def);
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, ImportGraphDef_MissingUnusedInputMappings) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Create a graph with two nodes: x and 3
Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "feed") != nullptr);
TF_Operation* oper = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "scalar") != nullptr);
Neg(oper, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "neg") != nullptr);
// Export to a GraphDef.
TF_Buffer* graph_def = TF_NewBuffer();
TF_GraphToGraphDef(graph, graph_def, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Import it in a fresh graph.
TF_DeleteGraph(graph);
graph = TF_NewGraph();
TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions();
TF_GraphImportGraphDef(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar = TF_GraphOperationByName(graph, "scalar");
// Import it in a fresh graph with an unused input mapping.
TF_DeleteImportGraphDefOptions(opts);
opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported");
TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
TF_ImportGraphDefOptionsAddInputMapping(opts, "fake", 0, {scalar, 0});
tensorflow::GraphDef graph_def_proto;
ASSERT_TRUE(tensorflow::ParseProtoUnlimited(&graph_def_proto, graph_def->data,
graph_def->length));
TF_Buffer graph_def_buffer;
graph_def_buffer.data = reinterpret_cast<const void*>(&graph_def_proto);
graph_def_buffer.length = sizeof(tensorflow::GraphDef*);
TF_ImportGraphDefResults* results =
TF_GraphImportGraphDefWithResultsNoSerialization(graph, &graph_def_buffer,
opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Check unused input mappings
int num_unused_input_mappings;
const char** src_names;
int* src_indexes;
TF_ImportGraphDefResultsMissingUnusedInputMappings(
results, &num_unused_input_mappings, &src_names, &src_indexes);
ASSERT_EQ(1, num_unused_input_mappings);
EXPECT_EQ(string("fake"), string(src_names[0]));
EXPECT_EQ(0, src_indexes[0]);
TF_DeleteImportGraphDefResults(results);
TF_DeleteImportGraphDefOptions(opts);
TF_DeleteBuffer(graph_def);
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, Session) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Make a placeholder operation.