/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <functional>
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#include <memory>
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#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
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#include "tensorflow/core/framework/allocator.h"
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#include "tensorflow/core/framework/fake_input.h"
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#include "tensorflow/core/framework/node_def_builder.h"
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#include "tensorflow/core/framework/op_kernel.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/framework/types.h"
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#include "tensorflow/core/framework/types.pb.h"
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#include "tensorflow/core/graph/node_builder.h"
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#include "tensorflow/core/graph/testlib.h"
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#include "tensorflow/core/kernels/ops_testutil.h"
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#include "tensorflow/core/kernels/ops_util.h"
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#include "tensorflow/core/lib/core/status_test_util.h"
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#include "tensorflow/core/lib/random/simple_philox.h"
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#include "tensorflow/core/lib/strings/str_util.h"
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#include "tensorflow/core/platform/test.h"
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#include "tensorflow/core/platform/test_benchmark.h"
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namespace tensorflow {
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namespace {
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class DynamicPartitionOpTest : public OpsTestBase {
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protected:
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void MakeOp() {
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TF_ASSERT_OK(NodeDefBuilder("myop", "DynamicPartition")
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.Input(FakeInput(DT_FLOAT))
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.Input(FakeInput(DT_INT32))
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.Attr("num_partitions", 4)
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.Finalize(node_def()));
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TF_ASSERT_OK(InitOp());
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}
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};
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TEST_F(DynamicPartitionOpTest, Simple_OneD) {
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MakeOp();
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// Similar to how we would use this to split embedding ids to be looked up
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// Feed and run
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AddInputFromArray<float>(TensorShape({6}), {0, 13, 2, 39, 4, 17});
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AddInputFromArray<int32>(TensorShape({6}), {0, 0, 2, 3, 2, 1});
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TF_ASSERT_OK(RunOpKernel());
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// Check the output sizes
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{ // Output 0
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Tensor expected(allocator(), DT_FLOAT, TensorShape({2}));
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test::FillValues<float>(&expected, {0, 13});
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test::ExpectTensorEqual<float>(expected, *GetOutput(0));
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}
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{ // Output 1
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Tensor expected(allocator(), DT_FLOAT, TensorShape({1}));
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test::FillValues<float>(&expected, {17});
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test::ExpectTensorEqual<float>(expected, *GetOutput(1));
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}
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{ // Output 2
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Tensor expected(allocator(), DT_FLOAT, TensorShape({2}));
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test::FillValues<float>(&expected, {2, 4});
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test::ExpectTensorEqual<float>(expected, *GetOutput(2));
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}
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{ // Output 3
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Tensor expected(allocator(), DT_FLOAT, TensorShape({1}));
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test::FillValues<float>(&expected, {39});
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test::ExpectTensorEqual<float>(expected, *GetOutput(3));
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}
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}
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TEST_F(DynamicPartitionOpTest, Simple_TwoD) {
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MakeOp();
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// Feed and run
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AddInputFromArray<float>(
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TensorShape({6, 3}),
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{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17});
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AddInputFromArray<int32>(TensorShape({6}), {0, 0, 2, 3, 2, 1});
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TF_ASSERT_OK(RunOpKernel());
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// Check the output sizes
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{ // Output 0
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Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 3}));
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test::FillValues<float>(&expected, {0, 1, 2, 3, 4, 5});
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test::ExpectTensorEqual<float>(expected, *GetOutput(0));
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}
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{ // Output 1
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Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 3}));
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test::FillValues<float>(&expected, {15, 16, 17});
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test::ExpectTensorEqual<float>(expected, *GetOutput(1));
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}
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{ // Output 2
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Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 3}));
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test::FillValues<float>(&expected, {6, 7, 8, 12, 13, 14});
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test::ExpectTensorEqual<float>(expected, *GetOutput(2));
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}
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{ // Output 3
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Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 3}));
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test::FillValues<float>(&expected, {9, 10, 11});
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test::ExpectTensorEqual<float>(expected, *GetOutput(3));
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}
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}
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TEST_F(DynamicPartitionOpTest, SomeOutputsEmpty) {
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MakeOp();
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// Feed and run
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AddInputFromArray<float>(TensorShape({6}), {0, 13, 2, 39, 4, 17});
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AddInputFromArray<int32>(TensorShape({6}), {0, 0, 2, 2, 0, 2});
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TF_ASSERT_OK(RunOpKernel());
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TensorShape empty_one_dim;
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empty_one_dim.AddDim(0);
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Tensor expected_empty(allocator(), DT_FLOAT, empty_one_dim);
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// Check the output sizes
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{ // Output 0
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Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
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test::FillValues<float>(&expected, {0, 13, 4});
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test::ExpectTensorEqual<float>(expected, *GetOutput(0));
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}
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{ // Output 1
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test::ExpectTensorEqual<float>(expected_empty, *GetOutput(1));
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}
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{ // Output 2
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Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
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test::FillValues<float>(&expected, {2, 39, 17});
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test::ExpectTensorEqual<float>(expected, *GetOutput(2));
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}
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{ // Output 3
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test::ExpectTensorEqual<float>(expected_empty, *GetOutput(3));
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}
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}
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TEST_F(DynamicPartitionOpTest, Error_IndexOutOfRange) {
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MakeOp();
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// Feed and run
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AddInputFromArray<float>(TensorShape({5, 3}),
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{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14});
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AddInputFromArray<int32>(TensorShape({5}), {0, 2, 99, 2, 2});
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Status s = RunOpKernel();
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EXPECT_TRUE(str_util::StrContains(s.ToString(),
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"partitions[2] = 99 is not in [0, 4)"))
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<< s;
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}
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Node* DynamicPartitionNode(Graph* g, Node* in0, Node* in1, int num_partitions) {
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Node* ret;
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TF_CHECK_OK(NodeBuilder(g->NewName("n"), "DynamicPartition")
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.Input(in0)
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.Input(in1)
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.Attr("num_partitions", num_partitions)
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.Finalize(g, &ret));
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return ret;
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}
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template <typename T>
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static Graph* DynamicPartition(int num_partitions, int dim) {
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Graph* g = new Graph(OpRegistry::Global());
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// Always use a 128MB buffer.
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const int kRows = ((128 << 20) / sizeof(T)) / dim;
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Tensor data(DataTypeToEnum<T>::value, TensorShape({kRows, dim}));
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data.flat<T>().setRandom();
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random::PhiloxRandom philox(301, 17);
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random::SimplePhilox rnd(&philox);
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Tensor partitions(DT_INT32, TensorShape({kRows}));
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for (int i = 0; i < kRows; i++) {
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partitions.flat<int32>()(i) = rnd.Uniform(num_partitions);
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}
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DynamicPartitionNode(g, test::graph::Constant(g, data),
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test::graph::Constant(g, partitions), num_partitions);
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return g;
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}
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#define BM_DYNAMIC_PARTITION(DEVICE, T, num) \
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static void BM_##DEVICE##_dynpart_##T##_##num(int iters, int dim) { \
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const int64 items = ((128 << 20) / sizeof(T)); \
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const int64 tot = static_cast<int64>(iters) * items; \
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testing::ItemsProcessed(tot); \
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testing::UseRealTime(); \
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test::Benchmark(#DEVICE, DynamicPartition<T>(num, dim)).Run(iters); \
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} \
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BENCHMARK(BM_##DEVICE##_dynpart_##T##_##num)->Arg(1)->Arg(256)
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BM_DYNAMIC_PARTITION(cpu, float, 2);
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BM_DYNAMIC_PARTITION(cpu, float, 100);
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BM_DYNAMIC_PARTITION(cpu, double, 2);
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BM_DYNAMIC_PARTITION(cpu, double, 100);
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BM_DYNAMIC_PARTITION(cpu, complex64, 2);
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BM_DYNAMIC_PARTITION(cpu, complex64, 100);
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BM_DYNAMIC_PARTITION(gpu, float, 2);
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BM_DYNAMIC_PARTITION(gpu, float, 100);
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BM_DYNAMIC_PARTITION(gpu, double, 2);
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BM_DYNAMIC_PARTITION(gpu, double, 100);
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BM_DYNAMIC_PARTITION(gpu, complex64, 2);
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BM_DYNAMIC_PARTITION(gpu, complex64, 100);
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} // namespace
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} // namespace tensorflow
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