Made GEMM routine tuning a bit more generic in preparation of possible separate batched tuning arguments

2 files changed, 93 insertions, 47 deletions

diff --git a/src/tuning/routines/routine_tuner.hpp b/src/tuning/routines/routine_tuner.hpp
index c02c1b84..2aa0b3ce 100644
--- a/src/tuning/routines/routine_tuner.hpp
+++ b/src/tuning/routines/routine_tuner.hpp
@@ -28,41 +28,44 @@ namespace clblast {
 
 template <typename T>
 void ForceSelectIndirectFrom(const size_t minimum_size, const Device &device,
-                             const std::string &name, const std::string& parameter_name) {
-  const auto override_status = OverrideParameters(device(), name + "Routine", PrecisionValue<T>(),
+                             const std::string &tuner_name, const std::string& parameter_name) {
+  const auto override_status = OverrideParameters(device(), tuner_name, PrecisionValue<T>(),
                                                   {{parameter_name, minimum_size}});
   if (override_status != StatusCode::kSuccess) {
     throw RuntimeError("OverrideParameters failed with status " + ToString(override_status));
   }
 }
 
-template <typename T, typename F>
-std::vector<TuningResult> TuneKernelSelection(const Device& device, const Context& context,
-                                              Queue& queue, const Precision precision,
-                                              F const &routine, const size_t num_runs,
-                                              const std::string &name,
-                                              const std::string& parameter_name) {
+// Computes the best switching point
+TuningResult GetBestResult(const std::vector<TuningResult>& scores) {
+  auto comparison = [](const TuningResult& lhs, const TuningResult& rhs) { return lhs.score < rhs.score; };
+  const auto best_configuration = std::min_element(scores.begin(), scores.end(), comparison);
+  return *best_configuration;
+}
 
-  // Values for m, n, and k
-  const auto from = size_t{64};
-  const auto to = size_t{2048};
-  const auto step = size_t{64};
+// Tunes at kernel-level
+template <typename T, typename F>
+void TuneKernelSelection(const Platform& platform, const Device& device, const Context& context,
+                         Queue& queue, const Precision precision, F const &routine,
+                         const size_t from, const size_t to, const size_t step, const size_t batch_count,
+                         const size_t num_runs, const std::string &name, const std::string &tuner_name,
+                         const std::string &family_name, const std::string& parameter_name) {
 
   // Buffers
   auto buffers = std::vector<Buffer<T>>{
-      Buffer<T>(context, to * to),
-      Buffer<T>(context, to * to),
-      Buffer<T>(context, to * to)
+      Buffer<T>(context, to * to * batch_count),
+      Buffer<T>(context, to * to * batch_count),
+      Buffer<T>(context, to * to * batch_count)
   };
 
   // In-direct version
   printf("\n* Testing the in-direct %s routine for m=n=k\n", name.c_str());
-  ForceSelectIndirectFrom<T>(0, device, name, parameter_name);
+  ForceSelectIndirectFrom<T>(0, device, tuner_name, parameter_name);
   const auto indirect = TimeRoutine(from, to, step, num_runs, queue, buffers, routine);
 
   // Direct version
   printf("\n* Testing the direct %s routine for m=n=k\n", name.c_str());
-  ForceSelectIndirectFrom<T>(to + 1, device, name, parameter_name);
+  ForceSelectIndirectFrom<T>(batch_count * to + 1, device, tuner_name, parameter_name);
   const auto direct = TimeRoutine(from, to, step, num_runs, queue, buffers, routine);
 
   // Determining final score and best kernel selection point
@@ -90,29 +93,40 @@ std::vector<TuningResult> TuneKernelSelection(const Device& device, const Contex
   }
 
   // Displaying results
-  printf("|         || %8s indirect || %8s direct ||          |\n", name.c_str(), name.c_str());
-  printf("|   m=n=k ||   ms   |  GFLOPS  ||   ms   |  GFLOPS  ||  score   | (lowest score == best switching point)\n");
-  printf("x---------xx--------x----------xx--------x----------xx----------x\n");
+  printf("|         || %12s indirect || %12s direct ||          |\n", name.c_str(), name.c_str());
+  printf("|   m=n=k ||    ms    |   GFLOPS   ||    ms    |  GFLOPS  ||  score   | (lowest score == best switching point)\n");
+  printf("x---------xx----------x------------xx----------x----------xx----------x\n");
   for (auto i = size_t{0}; i < indirect.size(); ++i) {
     assert(indirect[i].first == direct[i].first);
     const auto value = indirect[i].first;
     if (indirect[i].second != -1 && direct[i].second != -1) {
       const auto gflops_indirect = (2 * value * value * value) / (indirect[i].second * 1.0e6);
       const auto gflops_direct = (2 * value * value * value) / (direct[i].second * 1.0e6);
-      printf("| %7zu || %6.2lf | %8.1lf || %6.2lf | %8.1lf || %8.3lf |\n",
+      printf("| %7zu || %8.2lf | %10.1lf || %8.2lf | %8.1lf || %8.3lf |\n",
              value, indirect[i].second, gflops_indirect, direct[i].second, gflops_direct, scores[i].score);
     }
   }
-  printf("x---------xx--------x----------xx--------x----------xx----------x\n");
+  printf("x---------xx----------x------------xx----------x----------xx----------x\n");
   printf("\n");
-  return scores;
-}
 
-// Computes the best switching point
-TuningResult GetBestResult(const std::vector<TuningResult>& scores) {
-  auto comparison = [](const TuningResult& lhs, const TuningResult& rhs) { return lhs.score < rhs.score; };
-  const auto best_configuration = std::min_element(scores.begin(), scores.end(), comparison);
-  return *best_configuration;
+  const auto best_result = GetBestResult(scores);
+  const auto best_switching_point = best_result.config.at(parameter_name);
+  const auto best_string = parameter_name + "=" + ToString(best_switching_point);
+
+  // Outputs the results as JSON to disk, including some meta-data
+  const auto precision_string = std::to_string(static_cast<size_t>(precision));
+  auto metadata = std::vector<std::pair<std::string,std::string>>{
+      {"kernel_family", family_name},
+      {"precision", precision_string},
+      {"arg_from", ToString(from)},
+      {"arg_to", ToString(to)},
+      {"arg_step", ToString(step)},
+      {"best_kernel", best_result.name},
+      {"best_time", ToString(best_result.score)},
+      {"best_parameters", best_string}
+  };
+  PrintTimingsToFileAsJSON("clblast_" + family_name + "_" + precision_string + ".json",
+                           device, platform, metadata, scores);
 }
 
 // =================================================================================================
diff --git a/src/tuning/routines/xgemm.cpp b/src/tuning/routines/xgemm.cpp
index de4bef71..0721ad7c 100644
--- a/src/tuning/routines/xgemm.cpp
+++ b/src/tuning/routines/xgemm.cpp
@@ -39,6 +39,46 @@ void RunGemmRoutine(const size_t value, const Queue& queue, const std::vector<Bu
   clReleaseEvent(event);
 }
 
+template <typename T, size_t batch_count>
+void RunGemmBatchedRoutine(const size_t value, const Queue& queue, const std::vector<Buffer<T>>& buffers) {
+  auto offsets = std::vector<size_t>(batch_count);
+  auto factors = std::vector<T>(batch_count);
+  for (auto i = size_t{0}; i < batch_count; ++i) {
+    offsets[i] = batch_count * value;
+    factors[i] = ConstantOne<T>();
+  }
+  auto queue_plain = queue();
+  auto event = cl_event{};
+  auto status = GemmBatched(Layout::kRowMajor, Transpose::kNo, Transpose::kNo,
+                            value, value, value, factors.data(),
+                            buffers[0](), offsets.data(), value,
+                            buffers[1](), offsets.data(), value, factors.data(),
+                            buffers[2](), offsets.data(), value, batch_count,
+                            &queue_plain, &event);
+  if (status != StatusCode::kSuccess) {
+    throw RuntimeError("GemmBatched failed with status " + ToString(status));
+  }
+  clWaitForEvents(1, &event);
+  clReleaseEvent(event);
+}
+
+template <typename T, size_t batch_count>
+void RunGemmStridedBatchedRoutine(const size_t value, const Queue& queue, const std::vector<Buffer<T>>& buffers) {
+  auto queue_plain = queue();
+  auto event = cl_event{};
+  auto status = GemmStridedBatched(Layout::kRowMajor, Transpose::kNo, Transpose::kNo,
+                                   value, value, value, ConstantOne<T>(),
+                                   buffers[0](), 0, value, value * value,
+                                   buffers[1](), 0, value, value * value, ConstantOne<T>(),
+                                   buffers[2](), 0, value, value * value, batch_count,
+                                   &queue_plain, &event);
+  if (status != StatusCode::kSuccess) {
+    throw RuntimeError("Gemm failed with status " + ToString(status));
+  }
+  clWaitForEvents(1, &event);
+  clReleaseEvent(event);
+}
+
 // =================================================================================================
 
 template <typename T>
@@ -61,24 +101,16 @@ void TuneXgemm(int argc, char* argv[]) {
   const auto context = Context(device);
   auto queue = Queue(context, device);
 
-  // Run the tuners for the XGEMM routine
-  const auto scores = TuneKernelSelection<T>(device, context, queue, precision, RunGemmRoutine<T>,
-                                             num_runs, "Gemm", "XGEMM_MIN_INDIRECT_SIZE");
-  const auto xgemm_best = GetBestResult(scores);
-  const auto xgemm_switching_point = xgemm_best.config.at("XGEMM_MIN_INDIRECT_SIZE");
-  const auto xgemm_string = "XGEMM_MIN_INDIRECT_SIZE=" + ToString(xgemm_switching_point);
-
-  // Outputs the results as JSON to disk, including some meta-data
-  const auto precision_string = std::to_string(static_cast<size_t>(precision));
-  auto metadata = std::vector<std::pair<std::string,std::string>>{
-      {"kernel_family", "gemm_routine"},
-      {"precision", precision_string},
-      {"best_kernel", xgemm_best.name},
-      {"best_time", ToString(xgemm_best.score)},
-      {"best_parameters", xgemm_string}
-  };
-  PrintTimingsToFileAsJSON("clblast_routine_gemm_" + precision_string + ".json",
-                           device, platform, metadata, scores);
+  // Run the tuners for the XGEMM routines
+  TuneKernelSelection<T>(platform, device, context, queue, precision, RunGemmRoutine<T>,
+                         64, 2048, 64, 1, num_runs,
+                         "gemm", "GemmRoutine", "gemm_routine", "XGEMM_MIN_INDIRECT_SIZE");
+  //TuneKernelSelection<T>(platform, device, context, queue, precision, RunGemmBatchedRoutine<T, 30>,
+  //                       16, 128, 32, 30, num_runs,
+  //                       "gemmbatched", "GemmRoutine", "gemm_routine_2", "XGEMMBATCHED_MIN_INDIRECT_SIZE");
+  //TuneKernelSelection<T>(platform, device, context, queue, precision, RunGemmStridedBatchedRoutine<T, 30>,
+  //                       16, 128, 32, 30, num_runs,
+  //                       "gemmstridedbatched", "GemmRoutine", "gemm_routine_3", "XGEMMSTRIDEDBATCHED_MIN_INDIRECT_SIZE");
 
   printf("* Completed tuning process\n");
   printf("\n");