# GPU acceleration delegate for iOS Using graphics processing units (GPUs) to run your machine learning (ML) models can dramatically improve the performance of your model and the user experience of your ML-enabled applications. On iOS devices, you can enable use of GPU-accelerated execution of your models using a [*delegate*](../../performance/delegates). Delegates act as hardware drivers for TensorFlow Lite, allowing you to run the code of your model on GPU processors. This page describes how to enable GPU acceleration for TensorFlow Lite models in iOS apps. For more information about using the GPU delegate for TensorFlow Lite, including best practices and advanced techniques, see the [GPU delegates](../../performance/gpu) page. ## Use GPU with Interpreter API The TensorFlow Lite [Interpreter API](../../api_docs/swift/Classes/Interpreter) provides a set of general purpose APIs for building a machine learning applications. The following instructions guide you through adding GPU support to an iOS app. This guide assumes you already have an iOS app that can successfully execute an ML model with TensorFlow Lite. Note: If you don't already have an iOS app that uses TensorFlow Lite, follow the [iOS quickStart](https://www.tensorflow.org/lite/guide/ios) and build the demo app. After completing the tutorial, you can follow along with these instructions to enable GPU support. ### Modify the Podfile to include GPU support Starting with the TensorFlow Lite 2.3.0 release, the GPU delegate is excluded from the pod to reduce the binary size. You can include them by specifying a subspec for the `TensorFlowLiteSwift` pod: ```ruby pod 'TensorFlowLiteSwift/Metal', '~> 0.0.1-nightly', ``` OR ```ruby pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => ['Metal'] ``` You can also use `TensorFlowLiteObjC` or `TensorFlowLiteC` if you want to use the Objective-C, which is available for versions 2.4.0 and higher, or the C API. Note: For TensorFlow Lite versions 2.1.0 to 2.2.0, GPU delegate is *included* in the `TensorFlowLiteC` pod. You can choose between `TensorFlowLiteC` and `TensorFlowLiteSwift` depending on what programming language you use. ### Initialize and use GPU delegate You can use the GPU delegate with the TensorFlow Lite [Interpreter API](../../api_docs/swift/Classes/Interpreter) with a number of programming languages. Swift and Objective-C are recommended, but you can also use C++ and C. Using C is required if you are using a version of TensorFlow Lite earlier than 2.4. The following code examples outline how to use the delegate with each of these languages.

Swift

import TensorFlowLite

// Load model ...

// Initialize TensorFlow Lite interpreter with the GPU delegate.
let delegate = MetalDelegate()
if let interpreter = try Interpreter(modelPath: modelPath,
                                      delegates: [delegate]) {
  // Run inference ...
}
      

Objective-C

// Import module when using CocoaPods with module support
@import TFLTensorFlowLite;

// Or import following headers manually
#import "tensorflow/lite/objc/apis/TFLMetalDelegate.h"
#import "tensorflow/lite/objc/apis/TFLTensorFlowLite.h"

// Initialize GPU delegate
TFLMetalDelegate* metalDelegate = [[TFLMetalDelegate alloc] init];

// Initialize interpreter with model path and GPU delegate
TFLInterpreterOptions* options = [[TFLInterpreterOptions alloc] init];
NSError* error = nil;
TFLInterpreter* interpreter = [[TFLInterpreter alloc]
                                initWithModelPath:modelPath
                                          options:options
                                        delegates:@[ metalDelegate ]
                                            error:&error];
if (error != nil) { /* Error handling... */ }

if (![interpreter allocateTensorsWithError:&error]) { /* Error handling... */ }
if (error != nil) { /* Error handling... */ }

// Run inference ...
      

C++

// Set up interpreter.
auto model = FlatBufferModel::BuildFromFile(model_path);
if (!model) return false;
tflite::ops::builtin::BuiltinOpResolver op_resolver;
std::unique_ptr<Interpreter> interpreter;
InterpreterBuilder(*model, op_resolver)(&interpreter);

// Prepare GPU delegate.
auto* delegate = TFLGpuDelegateCreate(/*default options=*/nullptr);
if (interpreter->ModifyGraphWithDelegate(delegate) != kTfLiteOk) return false;

// Run inference.
WriteToInputTensor(interpreter->typed_input_tensor<float>(0));
if (interpreter->Invoke() != kTfLiteOk) return false;
ReadFromOutputTensor(interpreter->typed_output_tensor<float>(0));

// Clean up.
TFLGpuDelegateDelete(delegate);
      

C (before 2.4.0)

#include "tensorflow/lite/c/c_api.h"
#include "tensorflow/lite/delegates/gpu/metal_delegate.h"

// Initialize model
TfLiteModel* model = TfLiteModelCreateFromFile(model_path);

// Initialize interpreter with GPU delegate
TfLiteInterpreterOptions* options = TfLiteInterpreterOptionsCreate();
TfLiteDelegate* delegate = TFLGPUDelegateCreate(nil);  // default config
TfLiteInterpreterOptionsAddDelegate(options, metal_delegate);
TfLiteInterpreter* interpreter = TfLiteInterpreterCreate(model, options);
TfLiteInterpreterOptionsDelete(options);

TfLiteInterpreterAllocateTensors(interpreter);

NSMutableData *input_data = [NSMutableData dataWithLength:input_size * sizeof(float)];
NSMutableData *output_data = [NSMutableData dataWithLength:output_size * sizeof(float)];
TfLiteTensor* input = TfLiteInterpreterGetInputTensor(interpreter, 0);
const TfLiteTensor* output = TfLiteInterpreterGetOutputTensor(interpreter, 0);

// Run inference
TfLiteTensorCopyFromBuffer(input, inputData.bytes, inputData.length);
TfLiteInterpreterInvoke(interpreter);
TfLiteTensorCopyToBuffer(output, outputData.mutableBytes, outputData.length);

// Clean up
TfLiteInterpreterDelete(interpreter);
TFLGpuDelegateDelete(metal_delegate);
TfLiteModelDelete(model);
      

#### GPU API language use notes - TensorFlow Lite versions prior to 2.4.0 can only use the C API for Objective-C. - The C++ API is only available when you are using bazel or build TensorFlow Lite by yourself. C++ API can't be used with CocoaPods. - When using TensorFlow Lite with the GPU delegate with C++, get the GPU delegate via the `TFLGpuDelegateCreate()` function and then pass it to `Interpreter::ModifyGraphWithDelegate()`, instead of calling `Interpreter::AllocateTensors()`. ### Build and test with release mode Change to a release build with the appropriate Metal API accelerator settings to get better performance and for final testing. This section explains how to enable a release build and configure setting for Metal acceleration. Note: These instructions require XCode v10.1 or later. To change to a release build: 1. Edit the build settings by selecting **Product > Scheme > Edit Scheme...** and then selecting **Run**. 1. On the **Info** tab, change **Build Configuration** to **Release** and uncheck **Debug executable**. ![setting up release](../../../images/lite/ios/iosdebug.png) 1. Click the **Options** tab and change **GPU Frame Capture** to **Disabled** and **Metal API Validation** to **Disabled**.
![setting up metal options](../../../images/lite/ios/iosmetal.png) 1. Make sure to select Release-only builds on 64-bit architecture. Under **Project navigator > tflite_camera_example > PROJECT > your_project_name > Build Settings** set **Build Active Architecture Only > Release** to **Yes**. ![setting up release options](../../../images/lite/ios/iosrelease.png) ## Advanced GPU support This section covers advanced uses of the GPU delegate for iOS, including delegate options, input and output buffers, and use of quantized models. ### Delegate Options for iOS The constructor for GPU delegate accepts a `struct` of options in the [Swift API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/swift/Sources/MetalDelegate.swift), [Objective-C API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/objc/apis/TFLMetalDelegate.h), and [C API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/delegates/gpu/metal_delegate.h). Passing `nullptr` (C API) or nothing (Objective-C and Swift API) to the initializer sets the default options (which are explicated in the Basic Usage example above).

Swift

// THIS:
var options = MetalDelegate.Options()
options.isPrecisionLossAllowed = false
options.waitType = .passive
options.isQuantizationEnabled = true
let delegate = MetalDelegate(options: options)

// IS THE SAME AS THIS:
let delegate = MetalDelegate()
      

Objective-C

// THIS:
TFLMetalDelegateOptions* options = [[TFLMetalDelegateOptions alloc] init];
options.precisionLossAllowed = false;
options.waitType = TFLMetalDelegateThreadWaitTypePassive;
options.quantizationEnabled = true;

TFLMetalDelegate* delegate = [[TFLMetalDelegate alloc] initWithOptions:options];

// IS THE SAME AS THIS:
TFLMetalDelegate* delegate = [[TFLMetalDelegate alloc] init];
      

C

// THIS:
const TFLGpuDelegateOptions options = {
  .allow_precision_loss = false,
  .wait_type = TFLGpuDelegateWaitType::TFLGpuDelegateWaitTypePassive,
  .enable_quantization = true,
};

TfLiteDelegate* delegate = TFLGpuDelegateCreate(options);

// IS THE SAME AS THIS:
TfLiteDelegate* delegate = TFLGpuDelegateCreate(nullptr);
      

Tip: While it is convenient to use `nullptr` or default constructors, you should explicitly set the options to avoid any unexpected behavior if default values are changed in the future. ### Input/Output buffers using C++ API Computation on the GPU requires that the data is available to the GPU. This requirement often means you must perform a memory copy. You should avoid having your data cross the CPU/GPU memory boundary if possible, as this can take up a significant amount of time. Usually, such crossing is inevitable, but in some special cases, one or the other can be omitted. Note: The following technique is only available when you are using Bazel or building TensorFlow Lite yourself. C++ API can't be used with CocoaPods. If the network's input is an image already loaded in the GPU memory (for example, a GPU texture containing the camera feed) it can stay in the GPU memory without ever entering the CPU memory. Similarly, if the network's output is in the form of a renderable image, such as a [image style transfer](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) operation, you can directly display the result on screen. To achieve best performance, TensorFlow Lite makes it possible for users to directly read from and write to the TensorFlow hardware buffer and bypass avoidable memory copies. Assuming the image input is in GPU memory, you must first convert it to a `MTLBuffer` object for Metal. You can associate a `TfLiteTensor` to a user-prepared `MTLBuffer` with the `TFLGpuDelegateBindMetalBufferToTensor()` function. Note that this function *must* be called after `Interpreter::ModifyGraphWithDelegate()`. Additionally, the inference output is, by default, copied from GPU memory to CPU memory. You can turn this behavior off by calling `Interpreter::SetAllowBufferHandleOutput(true)` during initialization.

C++

#include "tensorflow/lite/delegates/gpu/metal_delegate.h"
#include "tensorflow/lite/delegates/gpu/metal_delegate_internal.h"

// ...

// Prepare GPU delegate.
auto* delegate = TFLGpuDelegateCreate(nullptr);

if (interpreter->ModifyGraphWithDelegate(delegate) != kTfLiteOk) return false;

interpreter->SetAllowBufferHandleOutput(true);  // disable default gpu->cpu copy
if (!TFLGpuDelegateBindMetalBufferToTensor(
        delegate, interpreter->inputs()[0], user_provided_input_buffer)) {
  return false;
}
if (!TFLGpuDelegateBindMetalBufferToTensor(
        delegate, interpreter->outputs()[0], user_provided_output_buffer)) {
  return false;
}

// Run inference.
if (interpreter->Invoke() != kTfLiteOk) return false;
      

Once the default behavior is turned off, copying the inference output from GPU memory to CPU memory requires an explicit call to `Interpreter::EnsureTensorDataIsReadable()` for each output tensor. This approach also works for quantized models, but you still need to use a **float32 sized buffer with float32 data**, because the buffer is bound to the internal de-quantized buffer. ### Quantized models {:#quantized-models} The iOS GPU delegate libraries *support quantized models by default*. You do not need to make any code changes to use quantized models with the GPU delegate. The following section explains how to disable quantized support for testing or experimental purposes. #### Disable quantized model support The following code shows how to ***disable*** support for quantized models.

Swift

    var options = MetalDelegate.Options()
    options.isQuantizationEnabled = false
    let delegate = MetalDelegate(options: options)
      

Objective-C

    TFLMetalDelegateOptions* options = [[TFLMetalDelegateOptions alloc] init];
    options.quantizationEnabled = false;
      

C

    TFLGpuDelegateOptions options = TFLGpuDelegateOptionsDefault();
    options.enable_quantization = false;

    TfLiteDelegate* delegate = TFLGpuDelegateCreate(options);
      

For more information about running quantized models with GPU acceleration, see [GPU delegate](../../performance/gpu#quantized-models) overview.