Add Threadpool::TrySimpleParallelFor

docs/NotesOnThreading.md

@@ -3,14 +3,15 @@
 This document is intended for ORT developers.
 
 ORT allows the usage of either OpenMP or non-OpenMP (ORT) threads for execution. Threadpool management
-is abstracted behind: (1) ThreadPool class in threadpool.h and (2) functions in thread_utils.h.
+is abstracted behind: (1) ThreadPool class in [threadpool.h](https://github.com/microsoft/onnxruntime/blob/master/include/onnxruntime/core/platform/threadpool.h) and (2) functions in [thread_utils.h](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/core/util/thread_utils.h).
 
 When developing an op, please use these abstractions to parallelize your code. These abstractions centralize 2 things.
 When OpenMP is enabled, they resort to using OpenMP. When OpenMP is disabled they resort to sequential execution if the threadpool ptr is NULL or schedule the tasks on the threadpool otherwise.
 
-Examples of these abstractions are: (threadpool.h has more documentation for these)
+Examples of these abstractions are: ([threadpool.h](https://github.com/microsoft/onnxruntime/blob/master/include/onnxruntime/core/platform/threadpool.h) has more documentation for these)
 * TryBatchParallelFor
 * TryParallelFor
+* TrySimpleParallelFor
 * static version of NumThreads
 
 **Please do not write #ifdef pragma omp in operator code**.

include/onnxruntime/core/platform/threadpool.h

@@ -190,11 +190,11 @@ class ThreadPool {
 
   // Similar to ParallelFor above, but takes the specified scheduling strategy
   // into account.
-  void
-  ParallelFor(std::ptrdiff_t total, const SchedulingParams& scheduling_params,
-              const std::function<void(std::ptrdiff_t, std::ptrdiff_t)>& fn);
+  void ParallelFor(std::ptrdiff_t total, const SchedulingParams& scheduling_params,
+                   const std::function<void(std::ptrdiff_t, std::ptrdiff_t)>& fn);
 
-  static void TryParallelFor(concurrency::ThreadPool* tp, std::ptrdiff_t total, const SchedulingParams& scheduling_params,
+  static void TryParallelFor(concurrency::ThreadPool* tp, std::ptrdiff_t total,
+                             const SchedulingParams& scheduling_params,
                              const std::function<void(std::ptrdiff_t first, std::ptrdiff_t last)>& fn) {
 #ifdef _OPENMP
     ORT_UNUSED_PARAMETER(scheduling_params);
@@ -216,7 +216,7 @@ class ThreadPool {
     }
     tp->ParallelFor(total, scheduling_params, fn);
 #endif
-  }  // namespace concurrency
+  }
 
   // Prefer using this API to get the number of threads unless you know what you're doing.
   // This API takes into account if openmp is enabled/disabled and if the thread pool ptr is nullptr.
@@ -236,7 +236,27 @@ class ThreadPool {
 
   // Directly schedule the 'total' tasks to the underlying threadpool, without
   // cutting them by halves
-  void SimpleParallelFor(std::ptrdiff_t total, std::function<void(std::ptrdiff_t)> fn);
+  void SimpleParallelFor(std::ptrdiff_t total, const std::function<void(std::ptrdiff_t)>& fn);
+
+  inline static void TrySimpleParallelFor(ThreadPool* tp, std::ptrdiff_t total,
+                                          const std::function<void(std::ptrdiff_t)>& fn) {
+#ifdef _OPENMP
+    ORT_UNUSED_PARAMETER(tp);
+#pragma omp parallel for
+    for (std::ptrdiff_t i = 0; i < total; ++i) {
+      fn(i);
+    }
+#else
+    if (tp != nullptr) {
+      tp->SimpleParallelFor(total, fn);
+    } else {
+      for (std::ptrdiff_t i = 0; i < total; ++i) {
+        // In many cases, fn can be inlined here.
+        fn(i);
+      }
+    }
+#endif
+  }
 
   /**
    * Tries to call the given function in parallel, with calls split into (num_batches) batches.

onnxruntime/core/common/threadpool.cc

@@ -109,7 +109,8 @@ ThreadPool::ThreadPool(Eigen::ThreadPoolInterface* user_threadpool, Eigen::Alloc
 }
 
 ThreadPool::~ThreadPool() = default;
-void ThreadPool::SimpleParallelFor(std::ptrdiff_t total, std::function<void(std::ptrdiff_t)> fn) {
+
+void ThreadPool::SimpleParallelFor(std::ptrdiff_t total, const std::function<void(std::ptrdiff_t)>& fn) {
   if (total <= 0)
     return;
 
@@ -320,4 +321,4 @@ Eigen::ThreadPoolInterface* ThreadPool::AsEigenThreadPool() const {
   return underlying_threadpool_;
 }
 }  // namespace concurrency
-}  // namespace onnxruntime
+}  // namespace onnxruntime

onnxruntime/core/providers/cpu/math/top_k.cc

@@ -164,7 +164,7 @@ static void FindTopKElements(const Tensor* input, const TensorShape& input_shape
   const int64_t num_blocks = input_shape[axis_parsed];
   const int64_t block_slice = reduced_cols / k;
 
-  int64_t tp_threads = threadpool != nullptr ? threadpool->NumThreads() : 1;
+  int64_t tp_threads = concurrency::ThreadPool::NumThreads(threadpool);
   int64_t num_threads = std::min(tp_threads, rows);  // split on rows so can't have more threads than rows
 
   // rough attempt to make sure there's enough work for each thread. if there's insufficient work the usage of
@@ -326,7 +326,8 @@ static void FindTopKElements(const Tensor* input, const TensorShape& input_shape
     // we want to re-use the storage variables in each lambda as much as possible to minimize allocations
     // on each iteration, so the lambda does multiple rows. e.g. the data_holder and indices_data vectors.
     // the alternative would be to use TryBatchParallelFor with the lambda doing one row.
-    threadpool->SimpleParallelFor(num_threads, find_top_k);
+    // Use TrySimpleParallelFor so openmp is supported correctly
+    concurrency::ThreadPool::TrySimpleParallelFor(threadpool, num_threads, find_top_k);
   }
 }