Use torch::from_blob instead of shareExternalPointer, nits (#25973)

IvanKobzarev · facebook-github-bot · commit 0ea59786e808 · 2019-09-13T13:40:11.000-07:00
Summary: The main part is to switch at::Tensor creation from usage of `torch::empty(torch::IntArrayRef(...))->ShareExternalPointer(...) to torch::from_blob(...)` Removed explicit set of `device CPU` as `at::TensorOptions` by default `device CPU` And renaming of local variables removing `input` prefix to make them shorter Pull Request resolved: #25973 Differential Revision: D17356837 Pulled By: IvanKobzarev fbshipit-source-id: 679e099b8aebd787dbf8ed422dae07a81243e18f
diff --git a/android/pytorch_android/src/main/cpp/pytorch_jni.cpp b/android/pytorch_android/src/main/cpp/pytorch_jni.cpp
@@ -41,53 +41,51 @@ struct JHashMap
 };
 
 static at::Tensor newAtTensor(
-    facebook::jni::alias_ref<facebook::jni::JBuffer> inputData,
-    facebook::jni::alias_ref<jlongArray> inputDims,
+    facebook::jni::alias_ref<facebook::jni::JBuffer> jbuffer,
+    facebook::jni::alias_ref<jlongArray> jdims,
     jint typeCode) {
-  const auto inputDimsRank = inputDims->size();
-  const auto inputDimsArr = inputDims->getRegion(0, inputDimsRank);
-  std::vector<int64_t> inputDimsVec;
-  auto inputNumel = 1;
-  for (auto i = 0; i < inputDimsRank; ++i) {
-    inputDimsVec.push_back(inputDimsArr[i]);
-    inputNumel *= inputDimsArr[i];
+  const auto rank = jdims->size();
+  const auto dimsArr = jdims->getRegion(0, rank);
+  std::vector<int64_t> dimsVec{};
+  dimsVec.reserve(rank);
+  auto numel = 1;
+  for (auto i = 0; i < rank; ++i) {
+    dimsVec.push_back(dimsArr[i]);
+    numel *= dimsArr[i];
   }
   JNIEnv* jni = facebook::jni::Environment::current();
-  caffe2::TypeMeta inputTypeMeta{};
-  int inputDataElementSizeBytes = 0;
+  caffe2::TypeMeta typeMeta{};
+  int dataElementSizeBytes = 0;
   if (kTensorTypeCodeFloat32 == typeCode) {
-    inputDataElementSizeBytes = 4;
-    inputTypeMeta = caffe2::TypeMeta::Make<float>();
+    dataElementSizeBytes = 4;
+    typeMeta = caffe2::TypeMeta::Make<float>();
   } else if (kTensorTypeCodeInt32 == typeCode) {
-    inputDataElementSizeBytes = 4;
-    inputTypeMeta = caffe2::TypeMeta::Make<int>();
+    dataElementSizeBytes = 4;
+    typeMeta = caffe2::TypeMeta::Make<int>();
   } else if (kTensorTypeCodeByte == typeCode) {
-    inputDataElementSizeBytes = 1;
-    inputTypeMeta = caffe2::TypeMeta::Make<uint8_t>();
+    dataElementSizeBytes = 1;
+    typeMeta = caffe2::TypeMeta::Make<uint8_t>();
   } else {
     facebook::jni::throwNewJavaException(
         facebook::jni::gJavaLangIllegalArgumentException,
         "Unknown Tensor typeCode %d",
         typeCode);
   }
-  const auto inputDataCapacity = jni->GetDirectBufferCapacity(inputData.get());
-  if (inputDataCapacity != inputNumel) {
+  const auto dataCapacity = jni->GetDirectBufferCapacity(jbuffer.get());
+  if (dataCapacity != numel) {
     facebook::jni::throwNewJavaException(
         facebook::jni::gJavaLangIllegalArgumentException,
         "Tensor dimensions(elements number:%d, element byte size:%d, total "
         "bytes:%d) inconsistent with buffer capacity(%d)",
-        inputNumel,
-        inputDataElementSizeBytes,
-        inputNumel * inputDataElementSizeBytes,
-        inputDataCapacity);
+        numel,
+        dataElementSizeBytes,
+        numel * dataElementSizeBytes,
+        dataCapacity);
   }
-
-  at::Tensor inputTensor = torch::empty(torch::IntArrayRef(inputDimsVec));
-  inputTensor.unsafeGetTensorImpl()->ShareExternalPointer(
-      {jni->GetDirectBufferAddress(inputData.get()), at::DeviceType::CPU},
-      inputTypeMeta,
-      inputDataCapacity);
-  return inputTensor;
+  return torch::from_blob(
+      jni->GetDirectBufferAddress(jbuffer.get()),
+      torch::IntArrayRef(dimsVec),
+      at::TensorOptions(typeMeta));
 }
 
 class JTensor : public facebook::jni::JavaClass<JTensor> {
