Faster Quantized Inference with XNNPACK

Posted by Marat Dukhan and Frank Barchard, software engineers

Quantization is among the most popular methods to speedup neural network inference on CPUs. A year ago TensorFlow Lite increased performance for floating-point models with the integration of XNNPACK backend. Today, we are extending the XNNPACK backend to quantized models with, on average across computer vision models, 30% speedup on ARM64 mobile phones, 5X speedup on x86-64 laptop and desktop systems, and 20X speedup for in-browser inference with WebAssembly SIMD compared to the default TensorFlow Lite quantized kernels.

Quantized inference in XNNPACK is optimized for symmetric quantization schemas used by the TensorFlow Model Optimization Toolkit. XNNPACK supports both the traditional per-tensor quantization schema and the newer accuracy-optimized schema with per-channel quantization of weights and per-tensor quantization of activations. Additionally, XNNPACK supports the asymmetric quantization schema, albeit with reduced efficiency.

Performance improvements

We evaluated XNNPACK-acclerated quantized inference on a number of edge devices and neural network architectures. Below, we present benchmarks on four public and two internal quantized models covering common computer vision tasks:

  1. EfficientNet-Lite0 image classification [download]
  2. EfficientDet-Lite0 object detection [download]
  3. DeepLab v3 segmentation with MobileNet v2 feature extractor [download]
  4. CartoonGAN image style transfer [download]
  5. Quantized version of the Face Mesh landmarks
  6. Quantized version of the Video Segmentation
Speedup from XNNPACK on single-threaded inference of quantized computer vision models on Android/ARM64 mobile phones.
Speedup from XNNPACK on single-threaded inference of quantized computer vision models on Android/ARM64 mobile phones.

Across the six Android ARM64 mobile devices XNNPACK delivers, on average, 30% speedup over the default TensorFlow Lite quantized kernels.

Speedup from XNNPACK on single-threaded inference of quantized computer vision models on x86-64 laptop and desktop systems.
Speedup from XNNPACK on single-threaded inference of quantized computer vision models on x86-64 laptop and desktop systems.

XNNPACK offers even greater improvements on laptop and desktop systems with x86 processors. On the 5 x86 processors in our benchmarks XNNPACK accelerated inference on average by 5 times. Notably, low-end and older processors which don’t support AVX instructions see over 20X speedup from switching quantized inference to XNNPACK: while the previous TensorFlow Lite inference backend had optimized implementations only for AVX, AVX2, and AVX512 instruction sets, XNNPACK provides optimized implementations for all x86-64 processors.

Speedup from XNNPACK on single-threaded WebAssembly SIMD inference of quantized computer vision models on mobile phones, laptops, and desktops when running through V8.
Speedup from XNNPACK on single-threaded WebAssembly SIMD inference of quantized computer vision models on mobile phones, laptops, and desktops when running through V8.

Besides the traditional mobile and laptop/desktop platforms, XNNPACK brings accelerated quantized inference to the Web platform through the TensorFlow Lite Web API. The above plot demonstrates a geomean speedup of 20X over the default TensorFlow Lite implementation when running WebAssembly SIMD benchmarks through the V8 JavaScript engine on 3 x86-64 and 2 ARM64 systems.

Two years of optimizations

XNNPACK started its life as a fork of QNNPACK library, but as the first version of XNNPACK focused on floating-point inference and QNNPACK focused on quantized inference, it was not possible to compare the two. Now with XNNPACK introducing support for quantized inference, we can directly evaluate and attribute the two further years of performance optimizations.

Graph showing xxnpack speedup over qnnpack

To compare the two quantized inference backends, we ported randomized MobileNet v1 and MobileNet v2 models from XNNPACK API to QNNPACK API, and benchmarked their single-threaded performance on two ARM64 Android phones and two x86-64 systems. The results are presented in the plot above, and the progress made by XNNPACK in two years is striking. XNNPACK is 50% faster on the older Pixel 3a phone and 4-5X faster on the newer Pixel 4a phone, 2.5X faster on the x86-64 laptop, and over 3X faster on the x86-64 workstation. These improvements are the result of a multiple optimizations XNNPACK gained in the two years since it forked from QNNPACK:

  • XNNPACK retained the optimizations in QNNPACK, like the Indirect Convolution algorithm and microarchitecture-specific microkernel selection, and further augmented them with Indirect Deconvolution algorithm, and more flexible capabilities, like built-in numpy-like broadcasting in the quantized addition and quantized multiplication operators.
  • Convolution, Deconvolution, and Fully Connected operators accumulate products of 8-bit activations and weights into a 32-bit number, and in the end this number needs to be converted back, or requantized, to an 8-bit number. There are multiple ways how requantization can be implemented, but QNNPACK adapted schema from the GEMMLOWP library, which pioneered quantized computations for neural network inference. However, it has since been discovered that GEMMLOWP requantization schema is suboptimal in terms of both accuracy and performance, and XNNPACK replaced it with more performant and accurate alternatives
  • Whereas QNNPACK targeted asymmetric quantization schema, where both activations and weights are represented as unsigned integers with zero point and scale quantization parameters, XNNPACK’s optimizations focus on symmetric quantization, where both activations and weights are signed integers, and weights have additional restrictions: the zero point of the weights is always zero and the quantized weights elements are limited to the [-127, 127] range (-128 is excluded even though it can be represented as a signed 8-bit integer). Symmetric quantization offers two computational advantages exploited in XNNPACK. First, when the filter weights are static, the results of accumulating the product of input zero point by the filter weights can be completely fused into the bias term in the Convolution, Deconvolution, and Fully Connected operators. Thus, zero point parameters are completely absent from the inference computations. Secondly, the product of a signed 8-bit input element by the weight element restricted to [-127, 127] fits into 15 bits. This enables the microkernels for Convolution, Deconvolution, and Fully Connected operators to do half of the accumulations on 16-bit variables rather than always extending the products to 32 bits.
  • QNNPACK microkernels were optimized NEON SIMD instructions on ARM and SSE2 SIMD instructions on x86, but XNNPACK supports a much wider set of instruction set-specific optimizations. Most quantized microkernels in XNNPACK are optimized for SSE2, SSE4.1, AVX, XOP, AVX2, and AVX512 instructions on x86/x86-64, for NEON, NEON V8, and NEON dot product instructions on ARM/ARM64, and for WebAssembly SIMD instructions. Additionally, XNNPACK provides scalar implementations for WebAssembly 1.0 and pre-NEON ARM processors.
  • QNNPACK introduced the idea of specialized assembly microkernels for high-end ARM and low-end ARM cores, but XNNPACK takes this idea much further. XNNPACK not only includes specialized expert-tuned software pipelined assembly microkernels for Cortex-A53, Cortex-A55, and high-end cores with and without NEON dot product instructions, but even supports switching between them on the fly. When a thread doing inference migrates from a big to a little core, XNNPACK automatically adapts from using a microkernel optimized for the big core to the one optimized for the little core.
  • QNNPACK mainly focused on multi-threaded inference and organized computations as a large number of small tasks, each computing a tiny tile of the output tensor. XNNPACK reworked parallelisation and made the tasks flexible: they can be fine-grained or coarse-grained depending on the number of threads participating in the parallelization. Through dynamic adjustment of task granularity, XNNPACK archives low overhead in single-threaded execution and high parallelization efficiency for multi-threaded inference.

Taken together, these optimizations make XNNPACK the new state-of-art for quantized inference, and turn TensorFlow Lite into the most versatile quantized inference solution, covering systems from Raspberry Pi Zero to Chromebooks to workstations with server-class processors.

How can you use it?

Quantized XNNPACK inference is enabled by default in the CMake builds of TensorFlow Lite for all platforms, in the Bazel builds of TensorFlow Lite for the Web platform, and will be available in TensorFlow Lite Web API in the 2.7 release. In Bazel builds for other platforms, quantized XNNPACK inference is enabled via a build-time opt-in mechanism. When building TensorFlow Lite with Bazel, add --define tflite_with_xnnpack=true --define xnn_enable_qs8=true, and the TensorFlow Lite interpreter will use the XNNPACK backend by default for supported operators with symmetric quantization. Limited support for operators with asymmetric quantization is available via the --define xnn_enable_qu8=true Bazel option.

Which operations are accelerated?

The XNNPACK backend currently supports a subset of quantized TensorFlow Lite operators (see documentation for details and limitations). XNNPACK supports models produced by the Model Optimization Toolkit through post-training integer quantization and quantization-aware training, but not post-training dynamic range quantization.

Future work

This is the third version of the XNNPACK integration into TensorFlow Lite following the initial release of the floating-point implementation and the subsequent release that brought sparse inference support. In the following versions we plan to add the following improvements:

  • Half-precision inference on the recent ARM processors
  • Sparse quantized inference.
  • Even faster dense inference.

We encourage you to leave your thoughts and comments on our GitHub and StackOverflow pages, and you can ask questions on

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