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Updates GPU delegate documentation with experimental quant support

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PiperOrigin-RevId: 312165090
Change-Id: I8fb624f71101fce6a379ed24f6002f8f4b60245d
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Sachin Joglekar 2020-05-18 15:22:44 -07:00 committed by TensorFlower Gardener
parent 3c54ef5ab9
commit 4001e3dad3
3 changed files with 84 additions and 113 deletions
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2
tensorflow/lite/g3doc/performance/gpu.md
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@ -31,7 +31,7 @@ models.
For a step-by-step tutorial, watch the
[GPU Delegate for Android](https://youtu.be/Xkhgre8r5G0) video.
Note: This requires OpenGL ES 3.1 or higher.
Note: This requires OpenCL or OpenGL ES (3.1 or higher).
#### Step 1. Clone the TensorFlow source code and open it in Android Studio

189
tensorflow/lite/g3doc/performance/gpu_advanced.md
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@ -1,9 +1,9 @@
# TensorFlow Lite on GPU
[TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/) supports several
hardware accelerators. This document describes how to use the GPU backend using
the TensorFlow Lite delegate APIs on Android (requires OpenGL ES 3.1 or higher)
and iOS (requires iOS 8 or later).
hardware accelerators. This document describes how to use the GPU backend using
the TensorFlow Lite delegate APIs on Android (requires OpenCL or OpenGL ES 3.1
and higher) and iOS (requires iOS 8 or later).
## Benefits of GPU Acceleration
@ -35,25 +35,33 @@ power and generating less heat than the same task run on a CPU.
TensorFlow Lite on GPU supports the following ops in 16-bit and 32-bit float
precision:
* `ADD v1`
* `AVERAGE_POOL_2D v1`
* `CONCATENATION v1`
* `CONV_2D v1`
* `DEPTHWISE_CONV_2D v1-2`
* `FULLY_CONNECTED v1`
* `LOGISTIC v1`
* `MAX_POOL_2D v1`
* `MUL v1`
* `PAD v1`
* `PRELU v1`
* `RELU v1`
* `RELU6 v1`
* `RESHAPE v1`
* `RESIZE_BILINEAR v1`
* `SOFTMAX v1`
* `STRIDED_SLICE v1`
* `SUB v1`
* `TRANSPOSE_CONV v1`
* `ADD`
* `AVERAGE_POOL_2D`
* `CONCATENATION`
* `CONV_2D`
* `DEPTHWISE_CONV_2D v1-2`
* `EXP`
* `FULLY_CONNECTED`
* `LOGISTIC`
* `LSTM v2 (Basic LSTM only)`
* `MAX_POOL_2D`
* `MAXIMUM`
* `MINIMUM`
* `MUL`
* `PAD`
* `PRELU`
* `RELU`
* `RELU6`
* `RESHAPE`
* `RESIZE_BILINEAR v1-3`
* `SOFTMAX`
* `STRIDED_SLICE`
* `SUB`
* `TRANSPOSE_CONV`
By default, all ops are only supported at version 1. Enabling the
[experimental quantization support](gpu_advanced.md#running-quantized-models-experimental-android-only)
allows the appropriate versions; for example, ADD v2.
## Basic Usage
@ -82,8 +90,8 @@ delegate.close();
### Android (C/C++)
For C/C++ usage of TensorFlow Lite GPU on Android, the GPU delegate can be
created with `TfLiteGpuDelegateCreate()` and destroyed with
`TfLiteGpuDelegateDelete()`.
created with `TfLiteGpuDelegateV2Create()` and destroyed with
`TfLiteGpuDelegateV2Delete()`.
```c++
// Set up interpreter.
@ -94,15 +102,7 @@ std::unique_ptr<Interpreter> interpreter;
InterpreterBuilder(*model, op_resolver)(&interpreter);
// NEW: Prepare GPU delegate.
const TfLiteGpuDelegateOptions options = {
.metadata = NULL,
.compile_options = {
.precision_loss_allowed = 1, // FP16
.preferred_gl_object_type = TFLITE_GL_OBJECT_TYPE_FASTEST,
.dynamic_batch_enabled = 0, // Not fully functional yet
},
};
auto* delegate = TfLiteGpuDelegateCreate(&options);
auto* delegate = TfLiteGpuDelegateV2Create(/*default options=*/nullptr);
if (interpreter->ModifyGraphWithDelegate(delegate) != kTfLiteOk) return false;
// Run inference.
@ -111,9 +111,13 @@ if (interpreter->Invoke() != kTfLiteOk) return false;
ReadFromOutputTensor(interpreter->typed_output_tensor<float>(0));
// NEW: Clean up.
TfLiteGpuDelegateDelete(delegate);
TfLiteGpuDelegateV2Delete(delegate);
```
Take a look at `TfLiteGpuDelegateOptionsV2` to create a delegate instance with
custom options. You can initialize the default options with
`TfLiteGpuDelegateOptionsV2Default()` and then modify them as necessary.
TFLite GPU for Android C/C++ uses the [Bazel](https://bazel.io) build system.
The delegate can be built, for example, using the following command:
@ -165,6 +169,43 @@ called.
## Advanced Usage
### Running quantized models (Experimental, Android only)
The GPU delegate already supports
[float16 quantized](https://www.tensorflow.org/lite/performance/post_training_float16_quant)
models. There is experimental support on Android to run 8-bit quantized as well.
This includes all flavors of quantization, including:
* Models trained with
[Quantization-aware training](https://www.tensorflow.org/lite/convert/quantization)
* [Post-training dynamic-range quantization](https://www.tensorflow.org/lite/performance/post_training_quant)
* [Post-training full-integer quantization](https://www.tensorflow.org/lite/performance/post_training_integer_quant)
To optimize performance, use models that have floating-point input & output
tensors.
This feature can be enabled using delegate options as follows:
**C++ API**
```c++
// NEW: Prepare custom options with feature enabled.
TfLiteGpuDelegateOptionsV2 options = TfLiteGpuDelegateOptionsV2Default();
options.experimental_flags |= TFLITE_GPU_EXPERIMENTAL_FLAGS_ENABLE_QUANT;
auto* delegate = TfLiteGpuDelegateV2Create(options);
if (interpreter->ModifyGraphWithDelegate(delegate) != kTfLiteOk) return false;
```
**Java API**
```java
// NEW: Prepare GPU delegate with feature turned on.
GpuDelegate delegate = new GpuDelegate(new GpuDelegate.Options().setQuantizedModelsAllowed(true));
Interpreter.Options options = (new Interpreter.Options()).addDelegate(delegate);
```
### Delegate Options for iOS
`NewGpuDelegate()` accepts a `struct` of options.
@ -210,7 +251,7 @@ While it is convenient to use `nullptr`, we recommend that you explicitly set
the options, to avoid any unexpected behavior if default values are changed in
the future.
### Input/Output Buffers
### Input/Output Buffers (iOS only)
To do computation on the GPU, data must be made available to the GPU. This often
requires performing a memory copy. It is desirable not to cross the CPU/GPU
@ -229,80 +270,10 @@ 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.
#### Android
Assuming the image input is in the GPU memory, it must first be converted to an
OpenGL Shader Storage Buffer Object (SSBO). You can associate a TfLiteTensor to
a user-prepared SSBO with `Interpreter.bindGlBufferToTensor()`. Note that
`Interpreter.bindGlBufferToTensor()` must be called before
`Interpreter.modifyGraphWithDelegate()`.
```java
// Ensure a valid EGL rendering context.
EGLContext eglContext = eglGetCurrentContext();
if (eglContext.equals(EGL_NO_CONTEXT)) return false;
// Create an SSBO.
int[] id = new int[1];
glGenBuffers(id.length, id, 0);
glBindBuffer(GL_SHADER_STORAGE_BUFFER, id[0]);
glBufferData(GL_SHADER_STORAGE_BUFFER, inputSize, null, GL_STREAM_COPY);
glBindBuffer(GL_SHADER_STORAGE_BUFFER, 0); // unbind
int inputSsboId = id[0];
// Create interpreter.
Interpreter interpreter = new Interpreter(tfliteModel);
Tensor inputTensor = interpreter.getInputTensor(0);
GpuDelegate gpuDelegate = new GpuDelegate();
// The buffer must be bound before the delegate is installed.
gpuDelegate.bindGlBufferToTensor(inputTensor, inputSsboId);
interpreter.modifyGraphWithDelegate(gpuDelegate);
// Run inference; the null input argument indicates use of the bound buffer for input.
fillSsboWithCameraImageTexture(inputSsboId);
float[] outputArray = new float[outputSize];
interpreter.runInference(null, outputArray);
```
A similar approach can be applied to the output tensor. In that case,
`Interpreter.Options.setAllowBufferHandleOutput(true)` should be passed on, to
disable the default copying of the network's output from GPU memory to CPU
memory.
```java
// Ensure a valid EGL rendering context.
EGLContext eglContext = eglGetCurrentContext();
if (eglContext.equals(EGL_NO_CONTEXT)) return false;
// Create a SSBO.
int[] id = new int[1];
glGenBuffers(id.length, id, 0);
glBindBuffer(GL_SHADER_STORAGE_BUFFER, id[0]);
glBufferData(GL_SHADER_STORAGE_BUFFER, outputSize, null, GL_STREAM_COPY);
glBindBuffer(GL_SHADER_STORAGE_BUFFER, 0); // unbind
int outputSsboId = id[0];
// Create interpreter.
Interpreter.Options options = (new Interpreter.Options()).setAllowBufferHandleOutput(true);
Interpreter interpreter = new Interpreter(tfliteModel, options);
Tensor outputTensor = interpreter.getOutputTensor(0);
GpuDelegate gpuDelegate = new GpuDelegate();
// The buffer must be bound before the delegate is installed.
gpuDelegate.bindGlBufferToTensor(outputTensor, outputSsboId);
interpreter.modifyGraphWithDelegate(gpuDelegate);
// Run inference; the null output argument indicates use of the bound buffer for output.
ByteBuffer input = getCameraImageByteBuffer();
interpreter.runInference(input, null);
renderOutputSsbo(outputSsboId);
```
#### iOS
Assuming the image input is in GPU memory, it must first be converted to a
`MTLBuffer` object for Metal. You can associate a TfLiteTensor to a
user-prepared `MTLBuffer` with `BindMetalBufferToTensor()`. Note that
`BindMetalBufferToTensor()` must be called before
user-prepared `MTLBuffer` with `TFLGpuDelegateBindMetalBufferToTensor()`. Note
that `TFLGpuDelegateBindMetalBufferToTensor()` must be called before
`Interpreter::ModifyGraphWithDelegate()`. Additionally, the inference output is,
by default, copied from GPU memory to CPU memory. This behavior can be turned
off by calling `Interpreter::SetAllowBufferHandleOutput(true)` during
@ -312,8 +283,8 @@ initialization.
// Prepare GPU delegate.
auto* delegate = NewGpuDelegate(nullptr);
interpreter->SetAllowBufferHandleOutput(true); // disable default gpu->cpu copy
if (!BindMetalBufferToTensor(delegate, interpreter->inputs()[0], user_provided_input_buffer)) return false;
if (!BindMetalBufferToTensor(delegate, interpreter->outputs()[0], user_provided_output_buffer)) return false;
if (!TFLGpuDelegateBindMetalBufferToTensor(delegate, interpreter->inputs()[0], user_provided_input_buffer)) return false;
if (!TFLGpuDelegateBindMetalBufferToTensor(delegate, interpreter->outputs()[0], user_provided_output_buffer)) return false;
if (interpreter->ModifyGraphWithDelegate(delegate) != kTfLiteOk) return false;
// Run inference.

6
tensorflow/lite/g3doc/performance/model_optimization.md
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@ -89,9 +89,9 @@ The following types of quantization are available in TensorFlow Lite:
Technique | Data requirements | Size reduction | Accuracy | Supported hardware
------------------------------------------------------------------------------------------------------- | -------------------------------- | -------------- | --------------------------- | ------------------
[Post-training float16 quantization](post_training_float16_quant.ipynb) | No data | Up to 50% | Insignificant accuracy loss | CPU, GPU
[Post-training dynamic range quantization](post_training_quant.ipynb) | No data | Up to 75% | Accuracy loss | CPU
[Post-training integer quantization](post_training_integer_quant.ipynb) | Unlabelled representative sample | Up to 75% | Smaller accuracy loss | CPU, EdgeTPU, Hexagon DSP
[Quantization-aware training](http://www.tensorflow.org/model_optimization/guide/quantization/training) | Labelled training data | Up to 75% | Smallest accuracy loss | CPU, EdgeTPU, Hexagon DSP
[Post-training dynamic range quantization](post_training_quant.ipynb) | No data | Up to 75% | Accuracy loss | CPU, GPU (Android)
[Post-training integer quantization](post_training_integer_quant.ipynb) | Unlabelled representative sample | Up to 75% | Smaller accuracy loss | CPU, GPU (Android), EdgeTPU, Hexagon DSP
[Quantization-aware training](http://www.tensorflow.org/model_optimization/guide/quantization/training) | Labelled training data | Up to 75% | Smallest accuracy loss | CPU, GPU (Android), EdgeTPU, Hexagon DSP
Below are the latency and accuracy results for post-training quantization and
quantization-aware training on a few models. All latency numbers are measured on