[PyTorch] Remove device transfers from JNI (#105583)

Summary:
If a model was exported for Vulkan backend without (automatic or manual) device transfers, then the export is incorrect, and the JNI need not correct that.
(If this assumption is incorrect, please give feedback.)

Undo the changes from
- D23763771: automatic device transfers in JNI
- D39519168: `"requires_backend_transfers"` logic in JNI

Test Plan: Verify CUNET+ hybrid model from D47488843 works.

Reviewed By: SS-JIA

Differential Revision: D47527244

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105583
Approved by: https://github.com/SS-JIA

android/pytorch_android/src/main/cpp/pytorch_jni_common.cpp

@@ -348,7 +348,7 @@ facebook::jni::local_ref<JIValue> JIValue::newJIValueFromAtIValue(
     const auto& tensor = ivalue.toTensor();
     return jMethodTensor(
         JIValue::javaClassStatic(),
-        TensorHybrid::newJTensorFromAtTensor(tensor.cpu()));
+        TensorHybrid::newJTensorFromAtTensor(tensor));
   } else if (ivalue.isBool()) {
     static auto jMethodBool =
         JIValue::javaClassStatic()

android/pytorch_android/src/main/cpp/pytorch_jni_jit.cpp

@@ -195,18 +195,9 @@ class PytorchJni : public facebook::jni::HybridClass<PytorchJni> {
     std::vector<at::IValue> inputs{};
     size_t n = jinputs->size();
     inputs.reserve(n);
-    const bool requires_backend_transfers =
+    for (const auto i : c10::irange(n)) {
-        module_.attr("requires_backend_transfers", at::IValue(true)).toBool();
-    for (size_t i = 0; i < n; i++) {
       at::IValue atIValue = JIValue::JIValueToAtIValue(jinputs->getElement(i));
-      if (at::kVulkan == deviceType_ && requires_backend_transfers) {
+      inputs.push_back(std::move(atIValue));
-        inputs.push_back(
-            atIValue.isTensor() ? at::IValue{atIValue.toTensor().vulkan()}
-                                : std::move(atIValue));
-      } else {
-        TORCH_CHECK(at::kCPU == deviceType_ || !requires_backend_transfers);
-        inputs.push_back(std::move(atIValue));
-      }
     }
     auto output = [&]() {
       JITCallGuard guard;
@@ -225,18 +216,9 @@ class PytorchJni : public facebook::jni::HybridClass<PytorchJni> {
     std::vector<at::IValue> inputs{};
     size_t n = jinputs->size();
     inputs.reserve(n);
-    const bool requires_backend_transfers =
+    for (const auto i : c10::irange(n)) {
-        module_.attr("requires_backend_transfers", at::IValue(true)).toBool();
-    for (size_t i = 0; i < n; i++) {
       at::IValue atIValue = JIValue::JIValueToAtIValue(jinputs->getElement(i));
-      if (at::kVulkan == deviceType_ && requires_backend_transfers) {
+      inputs.push_back(std::move(atIValue));
-        inputs.push_back(
-            atIValue.isTensor() ? at::IValue{atIValue.toTensor().vulkan()}
-                                : std::move(atIValue));
-      } else {
-        TORCH_CHECK(at::kCPU == deviceType_ || !requires_backend_transfers);
-        inputs.push_back(std::move(atIValue));
-      }
     }
     if (auto method = module_.find_method(methodName)) {
       auto output = [&]() {

android/pytorch_android/src/main/cpp/pytorch_jni_lite.cpp

@@ -158,18 +158,9 @@ class PytorchJni : public facebook::jni::HybridClass<PytorchJni> {
     std::vector<at::IValue> inputs{};
     size_t n = jinputs->size();
     inputs.reserve(n);
-    const bool requires_backend_transfers =
-        module_.attr("requires_backend_transfers", at::IValue(true)).toBool();
     for (const auto i : c10::irange(n)) {
       at::IValue atIValue = JIValue::JIValueToAtIValue(jinputs->getElement(i));
-      if (at::kVulkan == deviceType_ && requires_backend_transfers) {
+      inputs.push_back(std::move(atIValue));
-        inputs.push_back(
-            atIValue.isTensor() ? at::IValue{atIValue.toTensor().vulkan()}
-                                : std::move(atIValue));
-      } else {
-        TORCH_CHECK(at::kCPU == deviceType_ || !requires_backend_transfers);
-        inputs.push_back(std::move(atIValue));
-      }
     }
 
     auto output = [&]() {
@@ -189,18 +180,9 @@ class PytorchJni : public facebook::jni::HybridClass<PytorchJni> {
     std::vector<at::IValue> inputs{};
     size_t n = jinputs->size();
     inputs.reserve(n);
-    const bool requires_backend_transfers =
-        module_.attr("requires_backend_transfers", at::IValue(true)).toBool();
     for (const auto i : c10::irange(n)) {
       at::IValue atIValue = JIValue::JIValueToAtIValue(jinputs->getElement(i));
-      if (at::kVulkan == deviceType_ && requires_backend_transfers) {
+      inputs.push_back(std::move(atIValue));
-        inputs.push_back(
-            atIValue.isTensor() ? at::IValue{atIValue.toTensor().vulkan()}
-                                : std::move(atIValue));
-      } else {
-        TORCH_CHECK(at::kCPU == deviceType_ || !requires_backend_transfers);
-        inputs.push_back(std::move(atIValue));
-      }
     }
     if (auto method = module_.find_method(methodName)) {
       auto output = [&]() {