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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 "tensorflow/c/c_test_util.h"

#include "tensorflow/core/framework/function.pb.h"

#include "tensorflow/core/framework/op_def.pb.h"

#include "tensorflow/core/lib/core/status.h"

#include "tensorflow/core/lib/hash/hash.h"

#include "tensorflow/core/lib/strings/proto_serialization.h"

#include "tensorflow/core/lib/strings/str_util.h"

#include "tensorflow/core/lib/strings/strcat.h"

#include "tensorflow/core/platform/logging.h"

#include "tensorflow/core/platform/test.h"

namespace tensorflow {

namespace {

// Specification for expected input/output and its type.

// DataType value of DT_INVALID signifies that we don't want to

// check the data type.

typedef std::pair<string, DataType> IOSpec;

std::vector<IOSpec> M(const std::initializer_list<string>& names) {

std::vector<IOSpec> v;

for (const string& name : names) {

v.push_back(IOSpec(name, DT_INVALID));

}

return v;

}

// Specification for an expected edge.

// src is either:

// - input name (as it appears in FunctionDef)

// - name of output tensor (in nested "add:z:0" format)

// dst is either:

// - output name (as it appears in FunctionDef)

// - <name_of_node>:<index_of_this_input_into_node> (this looks the same as

// output tensor naming, but it the index is actually an input index)

struct EdgeSpec : public std::pair<string, string> {

typedef std::pair<string, string> Base;

// Inherit the set of constructors

using Base::pair;

string ToString() const { return strings::StrCat(first, "->", second); }

};

class CApiFunctionTest : public ::testing::Test {

protected:

CApiFunctionTest()

: s_(TF_NewStatus()),

func_graph_(TF_NewGraph()),

host_graph_(TF_NewGraph()),

func_(nullptr) {}

void SetUp() override {}

~CApiFunctionTest() override {

TF_DeleteFunction(func_);

TF_DeleteGraph(host_graph_);

TF_DeleteGraph(func_graph_);

TF_DeleteStatus(s_);

}

void Run(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,

TF_Operation* output, int32_t expected_result) {

Run(inputs, {{output, 0}}, {expected_result});

}

// Run the host graph, which now contains a function and check that

// outputs are as expected.

// 'T' stands for 'tensor' since the outputs are tensors, not scalars.

void RunT(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,

std::initializer_list<TF_Output> outputs,

const std::vector<std::vector<int32_t>>& expected_results) {

// Create a session for this graph

CSession csession(host_graph_, s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

// Run

csession.SetInputs(inputs);

csession.SetOutputs(outputs);

csession.Run(s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

// Check results

for (int i = 0; i < expected_results.size(); ++i) {

TF_Tensor* out = csession.output_tensor(i);

ASSERT_TRUE(out != nullptr);

EXPECT_EQ(TF_INT32, TF_TensorType(out));

EXPECT_EQ(1, TF_NumDims(out));

CompareInt32Tensor(expected_results[i], out);

}

// Run the host graph, which now contains a function and check that

// outputs are as expected.

void Run(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,

std::initializer_list<TF_Output> outputs,

const std::vector<int32_t>& expected_results) {

// Create a session for this graph.

CSession csession(host_graph_, s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

csession.SetInputs(inputs);

csession.SetOutputs(outputs);

csession.Run(s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

for (int i = 0; i < expected_results.size(); ++i) {

TF_Tensor* out = csession.output_tensor(i);

ASSERT_TRUE(out != nullptr);

EXPECT_EQ(TF_INT32, TF_TensorType(out));

EXPECT_EQ(0, TF_NumDims(out)); // scalar

ASSERT_EQ(sizeof(int32_t), TF_TensorByteSize(out));

int32_t* output_contents = static_cast<int32_t*>(TF_TensorData(out));

EXPECT_EQ(expected_results[i], *output_contents);

}

void CompareInt32Tensor(const std::vector<int32_t>& expected, TF_Tensor* t) {

int32_t* data = static_cast<int32_t*>(TF_TensorData(t));

size_t size = TF_TensorByteSize(t);

ASSERT_EQ(expected.size() * sizeof(int32_t), size);

for (int i = 0; i < expected.size(); ++i) {

ASSERT_EQ(expected[i], data[i]) << "Different data at index " << i;

}

std::vector<TF_Output> ToOutput(const std::vector<TF_Operation*> ops) {

std::vector<TF_Output> out;

for (auto op : ops) {

out.push_back({op, 0});

}

return out;

}

void Define(int num_opers, const std::vector<TF_Operation*>& opers,

const std::vector<TF_Operation*>& inputs,

const std::vector<TF_Operation*>& outputs,

const std::vector<string>& output_names,

bool expect_failure = false) {

DefineT(num_opers, opers, ToOutput(inputs), ToOutput(outputs), output_names,

expect_failure);

}

// Caller must delete[] the returned value

static const char** ToArray(const std::vector<string>& strs) {

const char** ptr = nullptr;

if (!strs.empty()) {

ptr = new const char*[strs.size()];

for (size_t i = 0; i < strs.size(); ++i) {

ptr[i] = strs[i].c_str();

}

return ptr;

}

// An explicit `num_opers` is needed so that we can distinguish between the

// case of no operations specified (-1) and the case of an empty set of

// operations specified (0).

void DefineT(int num_opers, const std::vector<TF_Operation*>& opers,

const std::vector<TF_Output>& inputs,

const std::vector<TF_Output>& outputs,

const std::vector<string>& output_names,

bool expect_failure = false) {

ASSERT_EQ(func_, nullptr);

const char** output_names_ptr = ToArray(output_names);

func_ = TF_GraphToFunction(func_graph_, func_name_, false, num_opers,

num_opers == -1 ? nullptr : opers.data(),

inputs.size(), inputs.data(), outputs.size(),

outputs.data(), output_names_ptr,

/*opts=*/nullptr, /*description=*/nullptr, s_);

delete[] output_names_ptr;

if (expect_failure) {

ASSERT_EQ(func_, nullptr);

return;

}

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

ASSERT_NE(func_, nullptr);

TF_GraphCopyFunction(host_graph_, func_, nullptr, s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

}

TF_Operation* Use(const std::vector<TF_Operation*>& inputs) {

return UseT(ToOutput(inputs));

}

TF_Operation* UseT(const std::vector<TF_Output>& inputs) {

TF_Operation* op;

UseHelper(inputs, &op);

return op;

}

// All the *Helper methods are used as a workaround for the restrictions that

// one cannot call ASSERT_* methods in non-void-returning functions (when

// exceptions are disabled during compilation)

void UseHelper(const std::vector<TF_Output>& inputs, TF_Operation** op) {

TF_OperationDescription* desc =

TF_NewOperation(host_graph_, func_name_, func_node_name_);

for (auto input : inputs) {

TF_AddInput(desc, input);

}

// Set device to CPU because some ops inside the function might not be

// available on GPU.

TF_SetDevice(desc, "/cpu:0");

*op = TF_FinishOperation(desc, s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

ASSERT_NE(*op, nullptr);

}

FunctionDef fdef() {

tensorflow::FunctionDef fdef;

EXPECT_TRUE(GetFunctionDef(func_, &fdef));

return fdef;

}

// logging utility

template <class Container>

string ToString(const Container& v) {

std::stringstream ss;

ss << "{";

size_t i = 0;

for (const auto& e : v) {

if (i != 0) {

ss << ", ";

}

ss << e.ToString();

++i;

}

ss << "}";

return ss.str();

}

void VerifyFDefNodes(const tensorflow::FunctionDef& fdef,

const std::unordered_set<string>& nodes) {

ASSERT_EQ(nodes.size(), fdef.node_def_size())

<< "Got unexpected number of nodes. Expected: ["

<< str_util::Join(nodes, ", ")

<< "] Actual nodes in fdef: " << fdef.DebugString();

for (const NodeDef& node_def : fdef.node_def()) {

ASSERT_TRUE(nodes.find(node_def.name()) != nodes.end())

<< "Got unexpected node: " << node_def.name()

<< " in fdef: " << fdef.DebugString();

}

void VerifyFDefInputs(const tensorflow::FunctionDef& fdef,

const std::vector<IOSpec>& inputs) {

const OpDef& signature = fdef.signature();

ASSERT_EQ(inputs.size(), signature.input_arg_size());

for (int i = 0; i < inputs.size(); ++i) {

const OpDef::ArgDef& arg = signature.input_arg(i);

const IOSpec& in = inputs[i];

if (in.second != DT_INVALID) {

ASSERT_EQ(arg.type(), in.second)

<< "Got unexpected type for input " << i

<< ". fdef: " << fdef.DebugString();

}

ASSERT_EQ(arg.name(), in.first) << "Got unexpected name for input " << i

<< ". fdef: " << fdef.DebugString();

}

void VerifyFDefOutputs(const tensorflow::FunctionDef& fdef,

const std::vector<IOSpec>& outputs) {

const OpDef& signature = fdef.signature();

ASSERT_EQ(outputs.size(), signature.output_arg_size());

for (int i = 0; i < outputs.size(); ++i) {

const OpDef::ArgDef& arg = signature.output_arg(i);

const IOSpec& out = outputs[i];

if (out.second != DT_INVALID) {

ASSERT_EQ(arg.type(), out.second)

<< "Got unexpected type for output " << i

<< ". fdef: " << fdef.DebugString();

}

ASSERT_EQ(arg.name(), out.first) << "Got unexpected name for output " << i

<< ". fdef: " << fdef.DebugString();

}

void VerifyFDefEdges(

const tensorflow::FunctionDef& fdef,

const std::vector<EdgeSpec>& e_edges, // expected edges

const std::vector<EdgeSpec>& c_edges, // expected ctrl edges

bool is_exact_edges = true) {

// Build a set of edges from fdef

std::set<EdgeSpec> a_edges; // actual edges

// Get edges from inputs to body nodes and between body nodes

for (const NodeDef& node_def : fdef.node_def()) {

for (int i = 0; i < node_def.input_size(); ++i) {

const string& in = node_def.input(i);

const auto& v =

a_edges.insert({in, strings::StrCat(node_def.name(), ":", i)});

ASSERT_TRUE(v.second) << "Duplicate edge " << in << " -> "

<< strings::StrCat(node_def.name(), ":", i)

<< ". fdef: " << fdef.DebugString();

}

// Get edges from body nodes to outputs and from inputs to outputs

for (const OpDef::ArgDef& arg : fdef.signature().output_arg()) {

const auto& iter = fdef.ret().find(arg.name());

if (iter != fdef.ret().end()) {

const auto& v = a_edges.insert({iter->second, arg.name()});

ASSERT_TRUE(v.second) << "Duplicate edge " << iter->second << " -> "

<< arg.name() << ". fdef: " << fdef.DebugString();

} else {

const auto& v = a_edges.insert({arg.name(), arg.name()});

ASSERT_TRUE(v.second) << "Duplicate edge " << arg.name() << " -> "

<< arg.name() << ". fdef: " << fdef.DebugString();

}

// Verify edges

for (const EdgeSpec& e : e_edges) {

ASSERT_TRUE(a_edges.find(e) != a_edges.end())

<< "Failed to find expected edge " << e.ToString()

<< " in fdef: " << fdef.DebugString();

}

for (const EdgeSpec& e : c_edges) {

ASSERT_TRUE(a_edges.find(e) != a_edges.end())

<< "Failed to find expected control edge " << e.ToString()

<< " in fdef: " << fdef.DebugString();

}

// If caller specified all edges, check that we have seen all

if (is_exact_edges) {

ASSERT_EQ(e_edges.size() + c_edges.size(), a_edges.size())

<< "Expected edges: " << ToString(e_edges)

<< " Expected Control edges: " << ToString(c_edges)

<< " Actual edges: " << ToString(a_edges)

<< " in fdef: " << fdef.DebugString();

}

void VerifyFDef(const std::unordered_set<string>& nodes,

const std::vector<IOSpec>& inputs,

const std::vector<IOSpec>& outputs,

const std::vector<EdgeSpec>& e_edges, // expected edges

const std::vector<EdgeSpec>& c_edges, // expected ctrl edges

bool is_exact_edges = true) {

tensorflow::FunctionDef fdef;

ASSERT_TRUE(GetFunctionDef(func_, &fdef));

VerifyFDefNodes(fdef, nodes);

VerifyFDefInputs(fdef, inputs);

VerifyFDefOutputs(fdef, outputs);

VerifyFDefEdges(fdef, e_edges, c_edges, is_exact_edges);

}

// Serialize func_ to fdef and import it back

void Reincarnate() {

// func_ -> fdef

tensorflow::FunctionDef fdef;

ASSERT_TRUE(GetFunctionDef(func_, &fdef));

TF_DeleteFunction(func_);

// fdef -> func_

string buf;

ASSERT_TRUE(fdef.SerializeToString(&buf));

func_ = TF_FunctionImportFunctionDef(buf.data(), buf.size(), s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

}

void GetAttr(const char* attr_name, AttrValue* out_attr) {

TF_Buffer* attr_buf = TF_NewBuffer();

TF_FunctionGetAttrValueProto(func_, attr_name, attr_buf, s_);

ASSERT_TRUE(out_attr->ParseFromArray(attr_buf->data, attr_buf->length));

TF_DeleteBuffer(attr_buf);

}

const char* func_name_ = "MyFunc";

const char* func_node_name_ = "MyFunc_0";

TF_Status* s_;

TF_Graph* func_graph_;

TF_Graph* host_graph_;

TF_Function* func_;

// Workaround for not being able to initialize empty map using {}

std::unordered_set<string> empty_;

};

TEST_F(CApiFunctionTest, OneOp_ZeroInputs_OneOutput) {

* constant

* |

* v

// Define

TF_Operation* c = ScalarConst(10, func_graph_, s_, "scalar10");

Define(-1, {}, {}, {c}, {});

// Use, run, and verify

TF_Operation* func_op = Use({});

Run({}, func_op, 10);

VerifyFDef({"scalar10_0"}, {}, {{"scalar10", DT_INT32}},

{{"scalar10_0:output:0", "scalar10"}}, {});

}

TEST_F(CApiFunctionTest, OneOp_OneInput_OneOutput) {

* |

* v

* negate

* |

* v

// Define

TF_Operation* feed = Placeholder(func_graph_, s_);

TF_Operation* neg = Neg(feed, func_graph_, s_);

Define(-1, {}, {feed}, {neg}, {});

// Use, run, and verify

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({func_feed});

Run({{func_feed, Int32Tensor(3)}}, func_op, -3);

VerifyFDef({"neg_0"}, {{"feed", DT_INT32}}, {{"neg", DT_INT32}},

{{"feed", "neg_0:0"}, {"neg_0:y:0", "neg"}}, {});

}

TEST_F(CApiFunctionTest, OneOutput_OutputNames) {

* |

* v

* negate

* |

* v

// Define

TF_Operation* feed = Placeholder(func_graph_, s_);

TF_Operation* neg = Neg(feed, func_graph_, s_);

Define(-1, {}, {feed}, {neg}, {"negated_num"});

// Use, run, and verify

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({func_feed});

Run({{func_feed, Int32Tensor(3)}}, func_op, -3);

VerifyFDef({"neg"}, {{"feed", DT_INT32}}, {{"negated_num", DT_INT32}},

{{"feed", "neg:0"}, {"neg:y:0", "negated_num"}}, {});

}

TEST_F(CApiFunctionTest, OutputNames_SameNameAsInput) {

* |

* v

* negate

* |

* v

// Define

TF_Operation* feed = Placeholder(func_graph_, s_, "negation");

TF_Operation* neg = Neg(feed, func_graph_, s_, "neg");

Define(-1, {}, {feed}, {neg}, {"negation"});

// Use, run, and verify

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({func_feed});

Run({{func_feed, Int32Tensor(3)}}, func_op, -3);

VerifyFDef({"neg"}, {{"negation_0", DT_INT32}}, {{"negation", DT_INT32}},

{{"negation_0", "neg:0"}, {"neg:y:0", "negation"}}, {});

}

TEST_F(CApiFunctionTest, ZeroOps_Identity) {

* |

* v

// Define

TF_Operation* feed = Placeholder(func_graph_, s_);

Define(-1, {}, {feed}, {feed}, {});

// Use, run, and verify

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({func_feed});

Run({{func_feed, Int32Tensor(3)}}, func_op, 3);

VerifyFDef(empty_, {{"feed_0", DT_INT32}}, {{"feed", DT_INT32}},

{{"feed_0", "feed"}}, {});

}

TEST_F(CApiFunctionTest, ZeroOps_Permutation) {

* | |

* \ /

* \/

* x

* /\

* / \

* | |

* v v

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

Define(-1, {}, {feed1, feed2}, {feed2, feed1}, {});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_);

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, func_feed});

Run({{func_feed, Int32Tensor(3)}}, {{func_op, 0}, {func_op, 1}}, {3, 2});

VerifyFDef(empty_, M({{"feed1_0"}, {"feed2_0"}}), M({{"feed2"}, {"feed1"}}),

{{"feed1_0", "feed1"}, {"feed2_0", "feed2"}}, {});

}

TEST_F(CApiFunctionTest, ZeroOps_Permutation_OutputNames) {

* | |

* \ /

* \/

* x

* /\

* / \

* | |

* v v

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

Define(-1, {}, {feed1, feed2}, {feed2, feed1}, {"first", "second"});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_);

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, func_feed});

Run({{func_feed, Int32Tensor(3)}}, {{func_op, 0}, {func_op, 1}}, {3, 2});

VerifyFDef(empty_, M({{"feed1"}, {"feed2"}}), M({{"first"}, {"second"}}),

{{"feed1", "second"}, {"feed2", "first"}}, {});

}

TEST_F(CApiFunctionTest, OneOp_TwoInputs_OneOutput) {

* | |

* v v

* add

* |

* v

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

TF_Operation* add = Add(feed1, feed2, func_graph_, s_);

Define(-1, {}, {feed1, feed2}, {add}, {});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_);

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, func_feed});

Run({{func_feed, Int32Tensor(3)}}, func_op, 2 + 3);

VerifyFDef(

{"add_0"}, M({{"feed1"}, {"feed2"}}), M({{"add"}}),

{{"feed1", "add_0:0"}, {"feed2", "add_0:1"}, {"add_0:sum:0", "add"}}, {});

}

TEST_F(CApiFunctionTest, OneOp_TwoInputs_ZeroOutputs) {

* | |

* v v

* add

* (output ignored)

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

Add(feed1, feed2, func_graph_, s_);

Define(-1, {}, {feed1, feed2}, {}, {});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_);

TF_Operation* func_feed = Placeholder(host_graph_, s_);

Use({two, func_feed});

VerifyFDef({"add"}, M({{"feed1"}, {"feed2"}}), {},

{{"feed1", "add:0"}, {"feed2", "add:1"}}, {});

}

TEST_F(CApiFunctionTest, TwoOps_ThreeInputs_OneOutput) {

* | | |

* v v /

* add1 /

* | |

* v v

* add2

* |

* v

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

TF_Operation* feed3 = Placeholder(func_graph_, s_, "feed3");

TF_Operation* add1 = Add(feed1, feed2, func_graph_, s_, "add1");

TF_Operation* add2 = Add(add1, feed3, func_graph_, s_, "add2");

Define(-1, {}, {feed1, feed2, feed3}, {add2}, {});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_, "two");

TF_Operation* ten = ScalarConst(10, host_graph_, s_, "ten");

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, ten, func_feed});

Run({{func_feed, Int32Tensor(3)}}, func_op, 2 + 10 + 3);

VerifyFDef({"add1", "add2_0"}, M({{"feed1"}, {"feed2"}, {"feed3"}}),

M({{"add2"}}),

{{"feed1", "add1:0"},

{"feed2", "add1:1"},

{"add1:sum:0", "add2_0:0"},

{"feed3", "add2_0:1"},

{"add2_0:sum:0", "add2"}},

{});

}

TEST_F(CApiFunctionTest, OneOp_TwoInputs_TwoDuplicateOutputs) {

* | |

* v v

* add

* |

* +-+-+

* | |

* v v

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

TF_Operation* add = Add(feed1, feed2, func_graph_, s_);

Define(-1, {}, {feed1, feed2}, {add, add}, {});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_);

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, func_feed});

Run({{func_feed, Int32Tensor(3)}}, {{func_op, 0}, {func_op, 1}}, {5, 5});

VerifyFDef({"add_1"}, M({{"feed1"}, {"feed2"}}), M({{"add"}, {"add_0"}}),

{{"feed1", "add_1:0"},

{"feed2", "add_1:1"},

{"add_1:sum:0", "add"},

{"add_1:sum:0", "add_0"}},

{});

}

TEST_F(CApiFunctionTest, TwoDuplicateOutputs_OutputNames) {

* | |

* v v

* add

* |

* +-+-+

* | |

* v v

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

TF_Operation* add = Add(feed1, feed2, func_graph_, s_);

Define(-1, {}, {feed1, feed2}, {add, add}, {"out1", "out2"});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_);

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, func_feed});

Run({{func_feed, Int32Tensor(3)}}, {{func_op, 0}, {func_op, 1}}, {5, 5});

VerifyFDef({"add"}, M({{"feed1"}, {"feed2"}}), M({{"out1"}, {"out2"}}),

{{"feed1", "add:0"},

{"feed2", "add:1"},

{"add:sum:0", "out1"},

{"add:sum:0", "out2"}},

{});

}

TEST_F(CApiFunctionTest, TwoOps_ThreeInputs_TwoOutputs) {

* | | |

* v v /

* add /

* | |

* +-+ |

* | | |

* | v v

* | add

* | |

* v v

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

TF_Operation* feed3 = Placeholder(func_graph_, s_, "feed3");

TF_Operation* add1 = Add(feed1, feed2, func_graph_, s_, "add1");

TF_Operation* add2 = Add(add1, feed3, func_graph_, s_, "add2");

Define(-1, {}, {feed1, feed2, feed3}, {add1, add2}, {});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_, "two");

TF_Operation* ten = ScalarConst(10, host_graph_, s_, "ten");

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, ten, func_feed});

Run({{func_feed, Int32Tensor(3)}}, {{func_op, 0}, {func_op, 1}}, {12, 15});

VerifyFDef({"add1_0", "add2_0"}, M({{"feed1"}, {"feed2"}, {"feed3"}}),

M({{"add1"}, {"add2"}}),

{{"feed1", "add1_0:0"},

{"feed2", "add1_0:1"},

{"add1_0:sum:0", "add2_0:0"},

{"feed3", "add2_0:1"},

{"add1_0:sum:0", "add1"},

{"add2_0:sum:0", "add2"}},

{});

}

TEST_F(CApiFunctionTest, FromSubsetOfOps) {

* | | |

* v v /

* add /

* | |

* +---+--+---+

* Ops used | | | |

* for func | v v |

* | | add |

* +-------> | | |

* | v |

* | |

* +----------+

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

TF_Operation* feed3 = Placeholder(func_graph_, s_, "feed3");

TF_Operation* add1 = Add(feed1, feed2, func_graph_, s_, "add1");

TF_Operation* add2 = Add(add1, feed3, func_graph_, s_, "add2");

Define(1, {add2}, {add1, feed3}, {add2}, {});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_, "two");

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, func_feed});

Run({{func_feed, Int32Tensor(3)}}, func_op, 2 + 3);

VerifyFDef(

{"add2_0"}, M({{"add1"}, {"feed3"}}), M({{"add2"}}),

{{"add1", "add2_0:0"}, {"feed3", "add2_0:1"}, {"add2_0:sum:0", "add2"}},

{});

}

TEST_F(CApiFunctionTest, UsingOneOutputOfSplit) {

* feed

* |

* +---------+---+

* | const0 | |

* | | | |

* | v / |

* | split |

* | | | | |

* | v | v |

* | | |

* +------+------+

* |

* v

* Only the second output from split is used as function output

// Define

TF_Operation* feed = Placeholder(func_graph_, s_);

TF_Operation* split = Split3(feed, func_graph_, s_);

DefineT(-1, {}, {{feed, 0}}, {{split, 1}}, {});

// Use, run, and verify

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({func_feed});

RunT({{func_feed, Int32Tensor({1, 2, 3, 4, 5, 6})}}, {{func_op, 0}},

{{3, 4}});

VerifyFDef({"split3_const0", "split3_0"}, M({{"feed"}}), M({{"split3"}}),

{{"split3_const0:output:0", "split3_0:0"},

{"feed", "split3_0:1"},

{"split3_0:output:1", "split3"}},

{});

}

TEST_F(CApiFunctionTest, UsingTwoOutputsOfSplit) {

* feed

* |

* +---------+---+

* | const0 | |

* | | | |

* | v / |

* | split |

* | | | | |

* | | v | |

* | | | |

* +---+-----+---+

* | |

* v v

* Second output from split is not used as function output

// Define

TF_Operation* feed = Placeholder(func_graph_, s_);

TF_Operation* split = Split3(feed, func_graph_, s_);

DefineT(-1, {}, {{feed, 0}}, {{split, 0}, {split, 2}}, {});

// Use, run, and verify

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({func_feed});

RunT({{func_feed, Int32Tensor({1, 2, 3, 4, 5, 6})}},

{{func_op, 0}, {func_op, 1}}, {{1, 2}, {5, 6}});

VerifyFDef({"split3_const0", "split3_1"}, M({{"feed"}}),

M({{"split3"}, {"split3_0"}}),

{{"split3_const0:output:0", "split3_1:0"},

{"feed", "split3_1:1"},

{"split3_1:output:0", "split3"},

{"split3_1:output:2", "split3_0"}},

{});

}

TEST_F(CApiFunctionTest, UsingTwoOutputsOfSplitAsInputs) {

* |

* v

* split

* | | |

* | v |

* | |

* +---+-----+---+

* | | | |

* | v v |

* | add |

* | | |

* +------+------+

* |

* v

// Define

TF_Operation* feed = Placeholder(func_graph_, s_);

TF_Operation* split = Split3(feed, func_graph_, s_);

TF_Operation* add = Add({split, 0}, {split, 2}, func_graph_, s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

DefineT(1, {add}, {{split, 0}, {split, 2}}, {{add, 0}}, {});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_, "two");

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, func_feed});

Run({{func_feed, Int32Tensor(3)}}, func_op, 2 + 3);

VerifyFDef(

{"add_0"}, M({{"split3"}, {"split3_0"}}), M({{"add"}}),

{{"split3", "add_0:0"}, {"split3_0", "add_0:1"}, {"add_0:sum:0", "add"}},

{});

}

TEST_F(CApiFunctionTest, NodesUsedInInputsMustHaveSingleOutput) {

* |

* v

* split

* | | |

* | v |

* | |

* input --->| |<--- input

* | |

* v v

* add

* |

* v

// Define

TF_Tensor* tensor_123 = Int32Tensor({1, 2, 3});

TF_Operation* c = Const(tensor_123, func_graph_, s_, "const_array");

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

TF_Operation* split = Split3(c, func_graph_, s_);

TF_Operation* add = Add({split, 0}, {split, 2}, func_graph_, s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

DefineT(-1, {}, {{split, 0}, {split, 2}}, {{add, 0}}, {}, true);

EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));

EXPECT_EQ(string("When `num_opers` is set to -1, nodes referenced in "

"`inputs` must have a single output. Node split3 has "

"3 outputs. Encountered while creating function 'MyFunc'"),

string(TF_Message(s_)));

TF_DeleteTensor(tensor_123);

}

TEST_F(CApiFunctionTest, FunctionWithWhileLoop) {

// Inputs to the while loop and the function as a whole

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

// Outputs of the while loop corresponding to the two inputs above

// The first one will the function's output

std::vector<TF_Output> outputs;

// Add while loop to func_graph_

{

// The inputs to the while loop

std::vector<TF_Output> inputs = {{feed1, 0}, {feed2, 0}};

std::unique_ptr<TF_WhileParams> params(new TF_WhileParams(

TF_NewWhile(func_graph_, &inputs[0], inputs.size(), s_)));

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

params->name = "test_loop";

// Initialize outputs so we can easily detect errors/bugs

outputs.resize(2, {nullptr, -1});

// Create loop: while (input1 < input2) input1 += input2 + 1

TF_Operation* less_than = LessThan(

params->cond_inputs[0], params->cond_inputs[1], params->cond_graph, s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

params->cond_output = {less_than, 0};

TF_Operation* add1 = Add(params->body_inputs[0], params->body_inputs[1],

params->body_graph, s_, "add1");

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

TF_Operation* one = ScalarConst(1, params->body_graph, s_);

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

TF_Operation* add2 = Add(add1, one, params->body_graph, s_, "add2");

ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

params->body_outputs[0] = {add2, 0};

params->body_outputs[1] = params->body_inputs[1];

// Finalize while loop

TF_FinishWhile(params.get(), s_, &outputs[0]);

EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

}

// Define function, use it in graph, and run

DefineT(-1, {}, {{feed1, 0}, {feed2, 0}}, {outputs[0]}, {});

TF_Operation* five = ScalarConst(5, host_graph_, s_, "five");

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({func_feed, five});

Run({{func_feed, Int32Tensor(2)}}, func_op, 2 /*+=*/ + 5 + 1);

// Verify input, output, and subset of edges in fdef.

// The subset of edges we verify is a chain between feed1 and output to

// make sure that the correct output is picked.

tensorflow::FunctionDef fdef;

ASSERT_TRUE(GetFunctionDef(func_, &fdef));

VerifyFDefInputs(fdef, M({{"feed1"}, {"feed2"}}));

VerifyFDefOutputs(fdef, M({{"test_loop_exit"}}));

VerifyFDefEdges(fdef,

{{"feed1", "test_loop/Enter:0"},

{"test_loop/Enter:output:0", "test_loop/Merge:0"},

{"test_loop/Merge:output:0", "test_loop/Switch:0"},

{"test_loop/Switch:output_false:0", "test_loop/Exit:0"},

{"test_loop/Exit:output:0", "test_loop_exit"}},

{}, false);

}

TEST_F(CApiFunctionTest, ControlDependency) {

* | | scalar

* | | .

* v v . <---- control dependency

* add < -

* |

* v

// Define

TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

TF_Operation* five = ScalarConst(5, func_graph_, s_);

TF_Operation* add =

AddWithCtrlDependency(feed1, feed2, func_graph_, five, s_);

EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

Define(-1, {}, {feed1, feed2}, {add}, {});

// Use, run, and verify

TF_Operation* two = ScalarConst(2, host_graph_, s_);

TF_Operation* func_feed = Placeholder(host_graph_, s_);

TF_Operation* func_op = Use({two, func_feed});

Run({{func_feed, Int32Tensor(3)}}, func_op, 2 + 3);

VerifyFDef(

{"add_0", "scalar"}, M({{"feed1"}, {"feed2"}}), M({{"add"}}),

{{"feed1", "add_0:0"}, {"feed2", "add_0:1"}, {"add_0:sum:0", "add"}},

{{"^scalar", "add_0:2"}});

}

TEST_F(CApiFunctionTest, ControlDependencyOutsideOfBody) {

* | | scalar

* | | .

* v v . <---- control dependency

* add < -

* |

* v

View remainder of file in raw view

tensorflow/tensorflow/c/c_api_function_test.cc at master · codeplaysoftware/tensorflow · GitHub

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