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#include <functional>
#include "tensorflow/core/public/session_options.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/device_factory.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/tensor_testutil.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/platform/test_benchmark.h"
#include "tensorflow/core/public/tensor.h"
#include <gtest/gtest.h>
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/graph/testlib.h"
#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
namespace tensorflow {
template <typename Index>
static void BM_SegmentReduction(int iters, string reduction, Index num_rows,
Index num_cols, Index segment_size) {
testing::StopTiming();
std::unique_ptr<Device> device(
DeviceFactory::NewDevice("CPU", {}, "/job:a/replica:0/task:0"));
// Create inputs
gtl::InlinedVector<TensorValue, 4> reduction_inputs;
TensorShape shape1({num_rows, num_cols});
Tensor input1(DT_FLOAT, shape1);
reduction_inputs.push_back({nullptr, &input1});
TensorShape shape2({num_rows});
Tensor input2(DataTypeToEnum<Index>::v(), shape2);
test::FillFn<Index>(&input2, [&num_rows, &segment_size](Index i) -> Index {
return std::min(i / segment_size, num_rows - 1);
});
reduction_inputs.push_back({nullptr, &input2});
NodeDef reduction_node_def;
TF_CHECK_OK(NodeDefBuilder(reduction, reduction)
.Input(FakeInput(DT_FLOAT))
.Input(FakeInput(DataTypeToEnum<Index>::v()))
.Finalize(&reduction_node_def));
Status status;
std::unique_ptr<OpKernel> reduction_op(CreateOpKernel(
DEVICE_CPU, device.get(), cpu_allocator(), reduction_node_def, &status));
OpKernelContext::Params params;
params.device = device.get();
params.frame_iter = FrameAndIter(0, 0);
params.inputs = &reduction_inputs;
params.op_kernel = reduction_op.get();
params.output_alloc_attr = [&device, &reduction_op, ¶ms](int index) {
AllocatorAttributes attr;
const bool on_host =
(reduction_op->output_memory_types()[index] == HOST_MEMORY);
attr.set_on_host(on_host);
return attr;
};
std::unique_ptr<OpKernelContext> reduction_context(
new OpKernelContext(params));
reduction_op->Compute(reduction_context.get());
TF_CHECK_OK(reduction_context->status());
testing::StartTiming();
for (int i = 0; i < iters; ++i) {
delete reduction_context->release_output(0).tensor;
reduction_op->Compute(reduction_context.get());
}
int64 bytes_per_iter =
static_cast<int64>(num_rows * num_cols * sizeof(float));
testing::BytesProcessed(bytes_per_iter * iters);
}
#define BM_Reduce(O, R, C, S) \
static void BM_Reduce_##O##_##R##_##C##_##S##_int32(int iters) { \
BM_SegmentReduction<int32>(iters, #O, R, C, S); \
} \
static void BM_Reduce_##O##_##R##_##C##_##S##_int64(int iters) { \
BM_SegmentReduction<int64>(iters, #O, R, C, S); \
} \
BENCHMARK(BM_Reduce_##O##_##R##_##C##_##S##_int32); \
BENCHMARK(BM_Reduce_##O##_##R##_##C##_##S##_int64);
#define BM_Reduce_Arg(R, C, S) \
BM_Reduce(SegmentSum, R, C, S); \
BM_Reduce(SegmentMean, R, C, S);
BM_Reduce_Arg(64, 32, 1);
BM_Reduce_Arg(4096, 128, 1);
BM_Reduce_Arg(16, 8, 2);
BM_Reduce_Arg(64, 32, 2);
BM_Reduce_Arg(4096, 32, 2);
BM_Reduce_Arg(4096, 128, 2);
static void SparseSegmentMeanGradHelper(int iters, float uniqueness, int size) {
testing::StopTiming();
RequireDefaultOps();
Graph* g = new Graph(OpRegistry::Global());
CHECK_LE(uniqueness, 1.0);
CHECK_GT(uniqueness, 0.0);
const int kNumIndices = size;
Tensor indices(DT_INT32, TensorShape({kNumIndices}));
auto indices_flat = indices.flat<int32>();
Tensor segments(DT_INT32, TensorShape({kNumIndices}));
auto segments_flat = segments.flat<int32>();
int kUniqueIndices = uniqueness * kNumIndices;
Tensor output_dim0(DT_INT32, TensorShape({}));
output_dim0.scalar<int32>()() = kUniqueIndices;
for (int i = 0; i < kNumIndices; ++i) {
indices_flat(i) = (i * 31) % kUniqueIndices;
segments_flat(i) = i * .8;
}
const int kDim1 = segments_flat(kNumIndices - 1) + 1;
const int kDim2 = 128;
Tensor input(DT_FLOAT, TensorShape({kDim1, kDim2}));
input.flat<float>().setRandom();
Node* node;
TF_CHECK_OK(NodeBuilder(g->NewName("n"), "SparseSegmentMeanGrad")
.Input(test::graph::Constant(g, input))
.Input(test::graph::Constant(g, indices))
.Input(test::graph::Constant(g, segments))
.Input(test::graph::Constant(g, output_dim0))
.Attr("T", DT_FLOAT)
.Finalize(g, &node));
testing::UseRealTime();
testing::BytesProcessed(static_cast<int64>(iters) * (kDim1 * kDim2) *
sizeof(float));
testing::StartTiming();
test::Benchmark("cpu", g).Run(iters);
}
static void BM_SparseSegmentMeanGrad_Low(int iters, int size) {
return SparseSegmentMeanGradHelper(iters, 1.0, size);
}
static void BM_SparseSegmentMeanGrad_High(int iters, int size) {
return SparseSegmentMeanGradHelper(iters, 0.01, size);
}
BENCHMARK(BM_SparseSegmentMeanGrad_Low)->Arg(1000)->Arg(100000);
BENCHMARK(BM_SparseSegmentMeanGrad_High)->Arg(1000)->Arg(100000);
} // namespace tensorflow
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