Update iOS GPU tutorial based on the lite interpreter support (pytorch#1628)

* Update iOS GPU tutorial based on the lite interpreter support

* few minor elabrations added

Author: husthyc

prototype_source/ios_gpu_workflow.rst

@@ -38,7 +38,7 @@ The next step is going to be converting the mobilenetv2 torchscript model to a M
     scripted_model = torch.jit.script(model)
     optimized_model = optimize_for_mobile(scripted_model, backend='metal')
     print(torch.jit.export_opnames(optimized_model))
-    torch.jit.save(optimized_model, './mobilenetv2_metal.pt')
+    torch.jit._save_for_lite_interpreter(optimized_model, './mobilenetv2_metal.pt')
 
 Note that the ``torch.jit.export_opnames(optimized_model)`` is going to dump all the optimized operators from the ``optimized_mobile``. If everything works well, you should be able to see the following ops being printed out from the console
 
@@ -65,31 +65,34 @@ In this section, we'll be using the `HelloWorld example <https://github.com/pyto
 
     IOS_ARCH=arm64 USE_PYTORCH_METAL=1 ./scripts/build_ios.sh
 
-Note ``IOS_ARCH`` tells the script to build a arm64 version of Libtorch. This is because in PyTorch, Metal is only available for the iOS devices that support the Apple A9 chip or above. Once the build finished, follow the `Build PyTorch iOS libraries from source <https://pytorch.org/mobile/ios/#build-pytorch-ios-libraries-from-source>`_ section from the iOS tutorial to setup the XCode settings properly. Don't forget to copy the `./mobilenetv2_metal.pt` to your XCode project.
+Note ``IOS_ARCH`` tells the script to build a arm64 version of Libtorch. This is because in PyTorch, Metal is only available for the iOS devices that support the Apple A9 chip or above. Once the build finished, follow the `Build PyTorch iOS libraries from source <https://pytorch.org/mobile/ios/#build-pytorch-ios-libraries-from-source>`_ section from the iOS tutorial to setup the XCode settings properly. Don't forget to copy the `./mobilenetv2_metal.pt` to your XCode project and modify the model file path accordingly.
 
 Next we need to make some changes in ``TorchModule.mm``
 
 .. code:: objective-c
 
-    // #import <Libtorch-Lite.h>
-    // If it's built from source with xcode, comment out the line above
+    ...
+    // #import <Libtorch-Lite/Libtorch-Lite.h>
+    // If it's built from source with Xcode, comment out the line above
     // and use following headers
     #include <torch/csrc/jit/mobile/import.h>
     #include <torch/csrc/jit/mobile/module.h>
     #include <torch/script.h>
+    ...
 
     - (NSArray<NSNumber*>*)predictImage:(void*)imageBuffer {
-      torch::jit::GraphOptimizerEnabledGuard opguard(false);
+      c10::InferenceMode mode;
       at::Tensor tensor = torch::from_blob(imageBuffer, {1, 3, 224, 224}, at::kFloat).metal();
       auto outputTensor = _impl.forward({tensor}).toTensor().cpu();
       ...
     }
+    ...
 
 As you can see, we simply just call ``.metal()`` to move our input tensor from CPU to GPU, and then call ``.cpu()`` to move the result back. Internally, ``.metal()`` will copy the input data from the CPU buffer to a GPU buffer with a GPU compatible memory format. When `.cpu()` is invoked, the GPU command buffer will be flushed and synced. After `forward` finished, the final result will then be copied back from the GPU buffer back to a CPU buffer.
 
-The last step we have to do is to add the `Accelerate.framework` and the `MetalShaderPerformance.framework` to your xcode project.
+The last step we have to do is to add the `Accelerate.framework` and the `MetalShaderPerformance.framework` to your xcode project. (Open your project via XCode, go to your project target’s "General" tab, locate the "Frameworks, Libraries and Embedded Content" section and click the "+" button)
 
-If everything works fine, you should be able to see the inference results on your phone. The result below was captured from an iPhone11 device
+If everything works fine, you should be able to see the inference results on your phone. The result below was captured from an iPhone 11 device
 
 .. code:: shell