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#include <functional>
#include <memory>
#include <vector>
#include <gtest/gtest.h>
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/fake_input.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/kernels/ops_testutil.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/lib/random/simple_philox.h"
#include "tensorflow/core/platform/test_benchmark.h"
#include "tensorflow/core/public/tensor.h"
namespace tensorflow {
namespace {
class GatherOpTest : public OpsTestBase {
protected:
void MakeOp(DataType index_type) {
RequireDefaultOps();
ASSERT_OK(NodeDefBuilder("myop", "Gather")
.Input(FakeInput(DT_FLOAT))
.Input(FakeInput(index_type))
.Finalize(node_def()));
ASSERT_OK(InitOp());
}
};
TEST_F(GatherOpTest, ScalarIndices) {
MakeOp(DT_INT32);
// Feed and run
AddInputFromArray<float>(TensorShape({5}), {0, 1, 2, 3, 4});
AddInputFromArray<int32>(TensorShape({}), {3});
ASSERT_OK(RunOpKernel());
// Check the output.
Tensor expected(allocator(), DT_FLOAT, TensorShape({}));
test::FillValues<float>(&expected, {3});
test::ExpectTensorEqual<float>(expected, *GetOutput(0));
}
TEST_F(GatherOpTest, Simple_TwoD32) {
MakeOp(DT_INT32);
// Feed and run
AddInputFromArray<float>(TensorShape({5, 3}),
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14});
AddInputFromArray<int32>(TensorShape({4}), {0, 4, 0, 2});
ASSERT_OK(RunOpKernel());
// Check the output.
Tensor expected(allocator(), DT_FLOAT, TensorShape({4, 3}));
test::FillValues<float>(&expected, {0, 1, 2, 12, 13, 14, 0, 1, 2, 6, 7, 8});
test::ExpectTensorEqual<float>(expected, *GetOutput(0));
}
TEST_F(GatherOpTest, Simple_TwoD64) {
MakeOp(DT_INT64);
// Feed and run
AddInputFromArray<float>(TensorShape({5, 3}),
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14});
AddInputFromArray<int64>(TensorShape({4}), {0, 4, 0, 2});
ASSERT_OK(RunOpKernel());
// Check the output.
Tensor expected(allocator(), DT_FLOAT, TensorShape({4, 3}));
test::FillValues<float>(&expected, {0, 1, 2, 12, 13, 14, 0, 1, 2, 6, 7, 8});
test::ExpectTensorEqual<float>(expected, *GetOutput(0));
}
TEST_F(GatherOpTest, HighRank) {
MakeOp(DT_INT32);
// Feed and run
AddInputFromArray<float>(TensorShape({4}), {0, 1, 2, 3});
AddInputFromArray<int32>(TensorShape({2, 3}), {1, 2, 0, 2, 3, 0});
ASSERT_OK(RunOpKernel());
// Check the output
Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 3}));
test::FillValues<float>(&expected, {1, 2, 0, 2, 3, 0});
test::ExpectTensorEqual<float>(expected, *GetOutput(0));
}
TEST_F(GatherOpTest, Error_IndexOutOfRange) {
MakeOp(DT_INT32);
// Feed and run
AddInputFromArray<float>(TensorShape({5, 3}),
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14});
AddInputFromArray<int32>(TensorShape({4}), {0, 4, 99, 2});
Status s = RunOpKernel();
EXPECT_TRUE(StringPiece(s.ToString())
.contains("Index 99 at offset 2 in Tindices is out of range"))
<< s;
}
class GatherOpForBenchmark : public GatherOpTest {
public:
void TestBody() override { // not used }
}
void PublicMakeOp(DataType index_type) { MakeOp(index_type); }
};
static const int kSorted = 0x8000; // Mask for arg to specify sorting vs. not
template <typename Index>
void BM_Gather(int iters, int arg) {
testing::StopTiming();
bool sorted = ((arg & kSorted) != 0);
int dim = arg & ~kSorted;
GatherOpForBenchmark t;
t.PublicMakeOp(DataTypeToEnum<Index>::v());
// Use a 512 MB table, regardless of dim
const int kRows = ((1 << 29) / sizeof(float)) / dim;
std::vector<float> data(kRows * dim, 1.0f);
t.AddInputFromArray<float>(TensorShape({kRows, dim}), data);
const int kLookups = 2000;
const int kBatches = 1000000 / kLookups;
random::PhiloxRandom philox(301, 17);
random::SimplePhilox rnd(&philox);
std::vector<std::vector<Index>> all_ids(kBatches);
for (int i = 0; i < kBatches; ++i) {
std::vector<Index>* ids = &all_ids[i];
ids->resize(kLookups);
for (int j = 0; j < kLookups; ++j) {
(*ids)[j] = rnd.Uniform(kRows);
}
if (sorted) {
sort(ids->begin(), ids->end());
}
}
t.AddInput<Index>(TensorShape({kLookups}), [](int i) { return 0; });
if (sorted) {
testing::SetLabel("sorted by id");
}
testing::BytesProcessed(static_cast<int64>(iters) * kLookups * dim *
sizeof(float));
testing::StartTiming();
while (--iters > 0) {
const std::vector<Index>& b = all_ids[iters % kBatches];
TensorValue input = t.mutable_input(1);
gtl::MutableArraySlice<Index> slice(&input->vec<Index>()(0),
input->NumElements());
for (int i = 0; i < kLookups; i++) {
slice[i] = b[i];
}
Status s = t.RunOpKernel();
}
}
static void BM_Gather32(int iters, int arg) { BM_Gather<int32>(iters, arg); }
static void BM_Gather64(int iters, int arg) { BM_Gather<int64>(iters, arg); }
BENCHMARK(BM_Gather32)
->Arg(10)
->Arg(10 | kSorted)
->Arg(20)
->Arg(40)
->Arg(63)
->Arg(63 | kSorted)
->Arg(64)
->Arg(64 | kSorted)
->Arg(65)
->Arg(65 | kSorted)
->Arg(100)
->Arg(100 | kSorted)
->Arg(127)
->Arg(127 | kSorted)
->Arg(128)
->Arg(128 | kSorted)
->Arg(129)
->Arg(129 | kSorted)
->Arg(1000)
->Arg(1000 | kSorted);
BENCHMARK(BM_Gather64)
->Arg(10)
->Arg(10 | kSorted)
->Arg(20)
->Arg(40)
->Arg(63)
->Arg(63 | kSorted)
->Arg(64)
->Arg(64 | kSorted)
->Arg(65)
->Arg(65 | kSorted)
->Arg(100)
->Arg(100 | kSorted)
->Arg(127)
->Arg(127 | kSorted)
->Arg(128)
->Arg(128 | kSorted)
->Arg(129)
->Arg(129 | kSorted)
->Arg(1000)
->Arg(1000 | kSorted);
} // namespace
} // namespace tensorflow
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