benchmark modifications to make it compilable in a standalone fashion.

5 files changed, 318 insertions, 56 deletions

diff --git a/bench/tensors/benchmark.h b/bench/tensors/benchmark.h
new file mode 100644
index 000000000..d8b4fd4c6
--- /dev/null
+++ b/bench/tensors/benchmark.h
@@ -0,0 +1,48 @@
+/*
+ * Copyright (C) 2012 The Android Open Source Project
+ *
+ * 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 <stdint.h>
+#include <vector>
+
+namespace testing {
+class Benchmark {
+ public:
+  Benchmark(const char* name, void (*fn)(int)) {
+    Register(name, fn, NULL);
+  }
+  Benchmark(const char* name, void (*fn_range)(int, int)) {
+    Register(name, NULL, fn_range);
+  }
+  Benchmark* Arg(int x);
+  Benchmark* Range(int lo, int hi);
+  const char* Name();
+  bool ShouldRun(int argc, char* argv[]);
+  void Run();
+ private:
+  const char* name_;
+  void (*fn_)(int);
+  void (*fn_range_)(int, int);
+  std::vector<int> args_;
+  void Register(const char* name, void (*fn)(int), void (*fn_range)(int, int));
+  void RunRepeatedlyWithArg(int iterations, int arg);
+  void RunWithArg(int arg);
+};
+}  // namespace testing
+void SetBenchmarkBytesProcessed(int64_t);
+void StopBenchmarkTiming();
+void StartBenchmarkTiming();
+#define BENCHMARK(f) \
+    static ::testing::Benchmark* _benchmark_##f __attribute__((unused)) = \
+        (new ::testing::Benchmark(#f, f))
\ No newline at end of file
diff --git a/bench/tensors/benchmark_main.cc b/bench/tensors/benchmark_main.cc
new file mode 100644
index 000000000..0fc12960e
--- /dev/null
+++ b/bench/tensors/benchmark_main.cc
@@ -0,0 +1,215 @@
+/*
+ * Copyright (C) 2012 The Android Open Source Project
+ *
+ * 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 "benchmark.h"
+#include <regex.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string>
+#include <inttypes.h>
+#include <time.h>
+#include <map>
+
+static int64_t g_bytes_processed;
+static int64_t g_benchmark_total_time_ns;
+static int64_t g_benchmark_start_time_ns;
+typedef std::map<std::string, ::testing::Benchmark*> BenchmarkMap;
+typedef BenchmarkMap::iterator BenchmarkMapIt;
+static BenchmarkMap g_benchmarks;
+static int g_name_column_width = 20;
+static int Round(int n) {
+  int base = 1;
+  while (base*10 < n) {
+    base *= 10;
+  }
+  if (n < 2*base) {
+    return 2*base;
+  }
+  if (n < 5*base) {
+    return 5*base;
+  }
+  return 10*base;
+}
+static int64_t NanoTime() {
+  struct timespec t;
+  t.tv_sec = t.tv_nsec = 0;
+  clock_gettime(CLOCK_MONOTONIC, &t);
+  return static_cast<int64_t>(t.tv_sec) * 1000000000LL + t.tv_nsec;
+}
+namespace testing {
+Benchmark* Benchmark::Arg(int arg) {
+  args_.push_back(arg);
+  return this;
+}
+
+Benchmark* Benchmark::Range(int lo, int hi) {
+  const int kRangeMultiplier = 8;
+  if (hi < lo) {
+    int temp = hi;
+    hi = lo;
+    lo = temp;
+  }
+  while (lo < hi) {
+    args_.push_back(lo);
+    lo *= kRangeMultiplier;
+  }
+  // We always run the hi number.
+  args_.push_back(hi);
+  return this;
+}
+
+const char* Benchmark::Name() {
+  return name_;
+}
+bool Benchmark::ShouldRun(int argc, char* argv[]) {
+  if (argc == 1) {
+    return true;  // With no arguments, we run all benchmarks.
+  }
+  // Otherwise, we interpret each argument as a regular expression and
+  // see if any of our benchmarks match.
+  for (int i = 1; i < argc; i++) {
+    regex_t re;
+    if (regcomp(&re, argv[i], 0) != 0) {
+      fprintf(stderr, "couldn't compile \"%s\" as a regular expression!\n", argv[i]);
+      exit(EXIT_FAILURE);
+    }
+    int match = regexec(&re, name_, 0, NULL, 0);
+    regfree(&re);
+    if (match != REG_NOMATCH) {
+      return true;
+    }
+  }
+  return false;
+}
+void Benchmark::Register(const char* name, void (*fn)(int), void (*fn_range)(int, int)) {
+  name_ = name;
+  fn_ = fn;
+  fn_range_ = fn_range;
+  if (fn_ == NULL && fn_range_ == NULL) {
+    fprintf(stderr, "%s: missing function\n", name_);
+    exit(EXIT_FAILURE);
+  }
+  g_benchmarks.insert(std::make_pair(name, this));
+}
+void Benchmark::Run() {
+  if (fn_ != NULL) {
+    RunWithArg(0);
+  } else {
+    if (args_.empty()) {
+      fprintf(stderr, "%s: no args!\n", name_);
+      exit(EXIT_FAILURE);
+    }
+    for (size_t i = 0; i < args_.size(); ++i) {
+      RunWithArg(args_[i]);
+    }
+  }
+}
+void Benchmark::RunRepeatedlyWithArg(int iterations, int arg) {
+  g_bytes_processed = 0;
+  g_benchmark_total_time_ns = 0;
+  g_benchmark_start_time_ns = NanoTime();
+  if (fn_ != NULL) {
+    fn_(iterations);
+  } else {
+    fn_range_(iterations, arg);
+  }
+  if (g_benchmark_start_time_ns != 0) {
+    g_benchmark_total_time_ns += NanoTime() - g_benchmark_start_time_ns;
+  }
+}
+void Benchmark::RunWithArg(int arg) {
+  // run once in case it's expensive
+  int iterations = 1;
+  RunRepeatedlyWithArg(iterations, arg);
+  while (g_benchmark_total_time_ns < 1e9 && iterations < 1e9) {
+    int last = iterations;
+    if (g_benchmark_total_time_ns/iterations == 0) {
+      iterations = 1e9;
+    } else {
+      iterations = 1e9 / (g_benchmark_total_time_ns/iterations);
+    }
+    iterations = std::max(last + 1, std::min(iterations + iterations/2, 100*last));
+    iterations = Round(iterations);
+    RunRepeatedlyWithArg(iterations, arg);
+  }
+  char throughput[100];
+  throughput[0] = '\0';
+  if (g_benchmark_total_time_ns > 0 && g_bytes_processed > 0) {
+    double mib_processed = static_cast<double>(g_bytes_processed)/1e6;
+    double seconds = static_cast<double>(g_benchmark_total_time_ns)/1e9;
+    snprintf(throughput, sizeof(throughput), " %8.2f MiB/s", mib_processed/seconds);
+  }
+  char full_name[100];
+  if (fn_range_ != NULL) {
+    if (arg >= (1<<20)) {
+      snprintf(full_name, sizeof(full_name), "%s/%dM", name_, arg/(1<<20));
+    } else if (arg >= (1<<10)) {
+      snprintf(full_name, sizeof(full_name), "%s/%dK", name_, arg/(1<<10));
+    } else {
+      snprintf(full_name, sizeof(full_name), "%s/%d", name_, arg);
+    }
+  } else {
+    snprintf(full_name, sizeof(full_name), "%s", name_);
+  }
+  printf("%-*s %10d %10" PRId64 "%s\n", g_name_column_width, full_name,
+         iterations, g_benchmark_total_time_ns/iterations, throughput);
+  fflush(stdout);
+}
+}  // namespace testing
+void SetBenchmarkBytesProcessed(int64_t x) {
+  g_bytes_processed = x;
+}
+void StopBenchmarkTiming() {
+  if (g_benchmark_start_time_ns != 0) {
+    g_benchmark_total_time_ns += NanoTime() - g_benchmark_start_time_ns;
+  }
+  g_benchmark_start_time_ns = 0;
+}
+void StartBenchmarkTiming() {
+  if (g_benchmark_start_time_ns == 0) {
+    g_benchmark_start_time_ns = NanoTime();
+  }
+}
+int main(int argc, char* argv[]) {
+  if (g_benchmarks.empty()) {
+    fprintf(stderr, "No benchmarks registered!\n");
+    exit(EXIT_FAILURE);
+  }
+  for (BenchmarkMapIt it = g_benchmarks.begin(); it != g_benchmarks.end(); ++it) {
+    int name_width = static_cast<int>(strlen(it->second->Name()));
+    g_name_column_width = std::max(g_name_column_width, name_width);
+  }
+  bool need_header = true;
+  for (BenchmarkMapIt it = g_benchmarks.begin(); it != g_benchmarks.end(); ++it) {
+    ::testing::Benchmark* b = it->second;
+    if (b->ShouldRun(argc, argv)) {
+      if (need_header) {
+        printf("%-*s %10s %10s\n", g_name_column_width, "", "iterations", "ns/op");
+        fflush(stdout);
+        need_header = false;
+      }
+      b->Run();
+    }
+  }
+  if (need_header) {
+    fprintf(stderr, "No matching benchmarks!\n");
+    fprintf(stderr, "Available benchmarks:\n");
+    for (BenchmarkMapIt it = g_benchmarks.begin(); it != g_benchmarks.end(); ++it) {
+      fprintf(stderr, "  %s\n", it->second->Name());
+    }
+    exit(EXIT_FAILURE);
+  }
+  return 0;
+}
\ No newline at end of file
diff --git a/bench/tensors/tensor_benchmarks.h b/bench/tensors/tensor_benchmarks.h
index 525b9acda..a1696afda 100644
--- a/bench/tensors/tensor_benchmarks.h
+++ b/bench/tensors/tensor_benchmarks.h
@@ -4,12 +4,23 @@
 typedef int TensorIndex;
 #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
 
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "testing/base/public/benchmark.h"
+#include "unsupported/Eigen/CXX11/Tensor"
+#include "benchmark.h"
+
+#define BENCHMARK_RANGE(bench, lo, hi) \
+  BENCHMARK(bench)->Range(lo, hi)
+
+template <typename... Args>
+std::string StrCat(const Args... args) {
+  std::stringstream ss;
+  StrCatRecursive(ss, args...);
+  return ss.str();
+}
 
 using Eigen::Tensor;
 using Eigen::TensorMap;
 
+typedef int64_t int64;
 
 // TODO(bsteiner): also templatize on the input type since we have users
 // for int8 as well as floats.
@@ -43,7 +54,7 @@ template <typename Device> class BenchmarkSuite {
 
   void random(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
 
     StartBenchmarkTiming();
@@ -56,16 +67,16 @@ template <typename Device> class BenchmarkSuite {
 
   void slicing(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
 
-    const Eigen::DSizes<TensorIndex, 2> quarter_sizes(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
-    const Eigen::DSizes<TensorIndex, 2> first_quadrant(Eigen::array<TensorIndex, 2>(0, 0));
-    const Eigen::DSizes<TensorIndex, 2> second_quadrant(Eigen::array<TensorIndex, 2>(0, m_/2));
-    const Eigen::DSizes<TensorIndex, 2> third_quadrant(Eigen::array<TensorIndex, 2>(m_/2, 0));
-    const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
+    const Eigen::DSizes<TensorIndex, 2> quarter_sizes(m_/2, m_/2);
+    const Eigen::DSizes<TensorIndex, 2> first_quadrant(0, 0);
+    const Eigen::DSizes<TensorIndex, 2> second_quadrant(0, m_/2);
+    const Eigen::DSizes<TensorIndex, 2> third_quadrant(m_/2, 0);
+    const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(m_/2, m_/2);
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -85,12 +96,12 @@ template <typename Device> class BenchmarkSuite {
 
   void shuffling(int num_iters) {
     eigen_assert(m_ == n_);
-    const Eigen::array<TensorIndex, 2> size_a(m_, k_);
+    const Eigen::array<TensorIndex, 2> size_a = {{m_, k_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
-    const Eigen::array<TensorIndex, 2> size_b(k_, m_);
+    const Eigen::array<TensorIndex, 2> size_b = {{k_, m_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
 
-    const Eigen::array<int, 2> shuffle(1, 0);
+    const Eigen::array<int, 2> shuffle = {{1, 0}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -102,9 +113,9 @@ template <typename Device> class BenchmarkSuite {
 
  void padding(int num_iters) {
     eigen_assert(m_ == k_);
-    const Eigen::array<TensorIndex, 2> size_a(m_, k_-3);
+    const Eigen::array<TensorIndex, 2> size_a = {{m_, k_-3}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
-    const Eigen::array<TensorIndex, 2> size_b(k_, m_);
+    const Eigen::array<TensorIndex, 2> size_b = {{k_, m_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
 
     Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
@@ -121,12 +132,12 @@ template <typename Device> class BenchmarkSuite {
 
  void striding(int num_iters) {
     eigen_assert(m_ == k_);
-    const Eigen::array<TensorIndex, 2> size_a(m_, k_);
+    const Eigen::array<TensorIndex, 2> size_a = {{m_, k_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
-    const Eigen::array<TensorIndex, 2> size_b(m_, k_ / 2);
+    const Eigen::array<TensorIndex, 2> size_b = {{m_, k_ / 2}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
 
-    const Eigen::array<TensorIndex, 2> strides(1, 2);
+    const Eigen::array<TensorIndex, 2> strides = {{1, 2}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -137,14 +148,14 @@ template <typename Device> class BenchmarkSuite {
   }
 
   void broadcasting(int num_iters) {
-    const Eigen::array<TensorIndex, 2> size_a(m_, 1);
+    const Eigen::array<TensorIndex, 2> size_a = {{m_, 1}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
-    const Eigen::array<TensorIndex, 2> size_c(m_, n_);
+    const Eigen::array<TensorIndex, 2> size_c = {{m_, n_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, size_c);
 
-#if defined(__CUDACC__)
+#ifndef EIGEN_HAS_INDEX_LIST
     // nvcc doesn't support cxx11
-    const Eigen::array<int, 2> broadcast(1, n_);
+    const Eigen::array<int, 2> broadcast = {{1, n_}};
 #else
     // Take advantage of cxx11 to give the compiler information it can use to
     // optimize the code.
@@ -162,7 +173,7 @@ template <typename Device> class BenchmarkSuite {
 
   void coeffWiseOp(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
@@ -178,7 +189,7 @@ template <typename Device> class BenchmarkSuite {
 
   void algebraicFunc(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
@@ -194,7 +205,7 @@ template <typename Device> class BenchmarkSuite {
 
   void transcendentalFunc(int num_iters) {
     eigen_assert(m_ == k_ && k_ == n_);
-    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
@@ -210,12 +221,12 @@ template <typename Device> class BenchmarkSuite {
 
   // Simple reduction
   void reduction(int num_iters) {
-    const Eigen::array<TensorIndex, 2> input_size(k_, n_);
+    const Eigen::array<TensorIndex, 2> input_size = {{k_, n_}};
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, input_size);
-    const Eigen::array<TensorIndex, 1> output_size(n_);
+    const Eigen::array<TensorIndex, 1> output_size = {{n_}};
     TensorMap<Tensor<float, 1>, Eigen::Aligned> C(c_, output_size);
 
-    const Eigen::array<TensorIndex, 1> sum_along_dim(0);
+    const Eigen::array<TensorIndex, 1> sum_along_dim = {{0}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -228,16 +239,16 @@ template <typename Device> class BenchmarkSuite {
 
   // do a contraction which is equivalent to a matrix multiplication
   void contraction(int num_iters) {
-    const Eigen::array<TensorIndex, 2> sizeA(m_, k_);
-    const Eigen::array<TensorIndex, 2> sizeB(k_, n_);
-    const Eigen::array<TensorIndex, 2> sizeC(m_, n_);
+    const Eigen::array<TensorIndex, 2> sizeA = {{m_, k_}};
+    const Eigen::array<TensorIndex, 2> sizeB = {{k_, n_}};
+    const Eigen::array<TensorIndex, 2> sizeC = {{m_, n_}};
 
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizeA);
     const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizeB);
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizeC);
 
     typedef typename Tensor<float, 2>::DimensionPair DimPair;
-    const Eigen::array<DimPair, 1> dims(DimPair(1, 0));
+    const Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -249,14 +260,14 @@ template <typename Device> class BenchmarkSuite {
   }
 
   void convolution(int num_iters, int kernel_x, int kernel_y) {
-    const Eigen::array<TensorIndex, 2> input_sizes(m_, n_);
+    const Eigen::array<TensorIndex, 2> input_sizes = {{m_, n_}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, input_sizes);
-    const Eigen::array<TensorIndex, 2> kernel_sizes(kernel_x, kernel_y);
+    const Eigen::array<TensorIndex, 2> kernel_sizes = {{kernel_x, kernel_y}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, kernel_sizes);
-    const Eigen::array<TensorIndex, 2> result_sizes(
-        m_ - kernel_x + 1, n_ - kernel_y + 1);
+    const Eigen::array<TensorIndex, 2> result_sizes =
+        {{m_ - kernel_x + 1, n_ - kernel_y + 1}};
     TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, result_sizes);
-    Eigen::array<Tensor<float, 2>::Index, 2> dims(0, 1);
+    Eigen::array<Tensor<float, 2>::Index, 2> dims = {{0, 1}};
 
     StartBenchmarkTiming();
     for (int iter = 0; iter < num_iters; ++iter) {
@@ -280,7 +291,7 @@ template <typename Device> class BenchmarkSuite {
     device_.memset(b_, 23, k_ * n_ * sizeof(float));
     device_.memset(c_, 31, m_ * n_ * sizeof(float));
 
-    BenchmarkUseRealTime();
+    //BenchmarkUseRealTime();
   }
 
   inline void finalizeBenchmark(int64 num_items) {
@@ -290,7 +301,7 @@ template <typename Device> class BenchmarkSuite {
     }
 #endif
     StopBenchmarkTiming();
-    SetBenchmarkItemsProcessed(num_items);
+    SetBenchmarkBytesProcessed(num_items);
   }
 
 
diff --git a/bench/tensors/tensor_benchmarks_cpu.cc b/bench/tensors/tensor_benchmarks_cpu.cc
index 68653ba15..248a63861 100644
--- a/bench/tensors/tensor_benchmarks_cpu.cc
+++ b/bench/tensors/tensor_benchmarks_cpu.cc
@@ -1,19 +1,12 @@
 #define EIGEN_USE_THREADS
 
-#include "base/sysinfo.h"
-#include "strings/strcat.h"
-#include "third_party/eigen3/tensor_benchmarks.h"
-#include "thread/threadpool.h"
+#include <string>
+
+#include "tensor_benchmarks.h"
 
-#ifdef __ANDROID__
-#define CREATE_THREAD_POOL(threads)             \
-Eigen::ThreadPoolDevice device(threads);
-#else
 #define CREATE_THREAD_POOL(threads)             \
-ThreadPool tp(threads);                         \
-tp.StartWorkers();                              \
-Eigen::ThreadPoolDevice device(&tp, threads);
-#endif
+Eigen::ThreadPool pool(threads);                \
+Eigen::ThreadPoolDevice device(&pool, threads);
 
 // Simple functions
 #define BM_FuncCPU(FUNC, THREADS)                                \
@@ -22,7 +15,6 @@ Eigen::ThreadPoolDevice device(&tp, threads);
     CREATE_THREAD_POOL(THREADS);                                 \
     BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, N);    \
     suite.FUNC(iters);                                           \
-    SetBenchmarkLabel(StrCat("using ", THREADS, " threads"));    \
   }                                                              \
   BENCHMARK_RANGE(BM_##FUNC##_##THREADS##T, 10, 5000);
 
@@ -84,7 +76,6 @@ BM_FuncCPU(reduction, 12);
       BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, D1, D2, D3);       \
       suite.FUNC(iters);                                                       \
     }                                                                          \
-    SetBenchmarkLabel(StrCat("using ", THREADS, " threads"));                  \
   }                                                                            \
   BENCHMARK_RANGE(BM_##FUNC##_##D1##x##D2##x##D3##_##THREADS##T, 10, 5000);
 
@@ -127,7 +118,6 @@ BM_FuncWithInputDimsCPU(contraction, N, N, 1, 16);
     CREATE_THREAD_POOL(THREADS);                                               \
     BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, N);                  \
     suite.FUNC(iters, DIM1, DIM2);                                             \
-    SetBenchmarkLabel(StrCat("using ", THREADS, " threads"));                  \
   }                                                                            \
   BENCHMARK_RANGE(BM_##FUNC##_##DIM1##x##DIM2##_##THREADS##T, 128, 5000);
 
diff --git a/bench/tensors/tensor_benchmarks_gpu.cc b/bench/tensors/tensor_benchmarks_gpu.cc
index adea754ad..9fe8f84d9 100644
--- a/bench/tensors/tensor_benchmarks_gpu.cc
+++ b/bench/tensors/tensor_benchmarks_gpu.cc
@@ -3,10 +3,8 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 #include <iostream>
-#include "strings/strcat.h"
-#include "third_party/eigen3/tensor_benchmarks.h"
-
 
+#include "tensor_benchmarks.h"
 
 // Simple functions
 #define BM_FuncGPU(FUNC)                                                       \