fix the issue of parallel executor

mindspore/lite/src/runtime/parallel_executor.cc

@@ -14,6 +14,7 @@
  * limitations under the License.
  */
 
+#include <utility>
 #include "src/runtime/parallel_executor.h"
 using mindspore::predict::ThreadPool;
 using mindspore::predict::TvmEnv;
@@ -25,25 +26,15 @@ ParallelExecutor::~ParallelExecutor() {
 }
 int ParallelExecutor::Prepare(std::vector<mindspore::kernel::LiteKernel *> &kernels) {
   pool = new ThreadPool();
-  pool->ConfigThreadPool(NO_BIND, MAX_THREAD_NUM);
-  for (mindspore::kernel::LiteKernel *kernel : kernels) {
-    refCount[kernel] = kernel->out_kernels().size();
+  if (pool == nullptr) {
+    MS_LOG(ERROR) << "Memory error: fail to new ThreadPool";
+    return RET_ERROR;
   }
+  pool->ConfigMaxThreadNum(MAX_THREAD_NUM);
+  pool->ConfigThreadPool(NO_BIND, MAX_THREAD_NUM);
   return RET_OK;
 }
 
-void ParallelExecutor::PrepareReadyKernels(const std::vector<mindspore::kernel::LiteKernel *> &kernels) {
-  for (auto iter = refCount.begin(); iter != refCount.end();) {
-    if (iter->second == 0) {
-      readyKernels.emplace_back(iter->first);
-      iter = refCount.erase(iter);
-    } else {
-      iter++;
-    }
-  }
-  results.resize(readyKernels.size());
-}
-
 static int RunKernel(int index, TvmEnv *env, void *data) {
   ParallelExecutor *executor = reinterpret_cast<ParallelExecutor *>(data);
   auto kernel = executor->GetReadyKernel(index);
@@ -83,27 +74,49 @@ int ParallelExecutor::Run(std::vector<tensor::Tensor *> &in_tensors, std::vector
   }
   kernel::LiteKernelUtil::InitTensorRefCount(kernels);
 
-  PrepareReadyKernels(kernels);
+  for (auto kernel : kernels) {
+    if (kernel->in_kernels().size() == 0) {
+      readyKernels.emplace_back(kernel);
+      continue;
+    }
+    refCount[kernel] = kernel->in_kernels().size();
+  }
+  std::vector<kernel::LiteKernel *> newReadyKernels;
   while (readyKernels.size() > 0) {
+    results.resize(readyKernels.size(), RET_OK);
     pool->LaunchWork(RunKernel, this, readyKernels.size());
 
     if (std::find_if(results.begin(), results.end(), [](const int &ret) { return (ret != 0); }) != results.end()) {
       return RET_ERROR;
     }
-    for (auto completedKernel : readyKernels) {
-      for (auto out : completedKernel->out_kernels()) {
+    newReadyKernels.clear();
+    for (auto completed : readyKernels) {
+      for (auto out : completed->out_kernels()) {
         auto iter = refCount.find(out);
         if (iter == refCount.end()) {
           continue;
         }
         (iter->second)--;
         if (iter->second <= 0) {
+          newReadyKernels.emplace_back(iter->first);
           refCount.erase(iter);
         }
       }
+
+      for (auto input_kernel : completed->in_kernels()) {
+        MS_ASSERT(input_kernel != nullptr);
+        if (input_kernel->is_model_output()) {
+          continue;
+        }
+        auto ret = input_kernel->DecOutTensorRefCount();
+        if (0 != ret) {
+          MS_LOG(WARNING) << "DecOutTensorRefCount for kernel" << completed->name() << " failed";
+          return -1;
+        }
+      }
     }
     readyKernels.clear();
-    PrepareReadyKernels(kernels);
+    readyKernels = std::move(newReadyKernels);
   }
 
   return RET_OK;

mindspore/lite/src/runtime/parallel_executor.h

@@ -39,9 +39,6 @@ class ParallelExecutor : public Executor {
   inline kernel::LiteKernel *GetReadyKernel(const int index) { return readyKernels.at(index); }
   inline void SetResult(const int index, const int result) { results.at(index) = result; }
 
- private:
-  void PrepareReadyKernels(const std::vector<kernel::LiteKernel *> &kernels);
-
  private:
   predict::ThreadPool *pool;
   std::unordered_map<kernel::LiteKernel *, size_t> refCount;