Snips' tiny TensorFlow and ONNX inference engine.

This project used to be called tfdeploy, or Tensorflow-deploy-rust.

What ?

tract is a tensorflow- and ONNX- compatible inference library. It loads a Tensorflow or ONNX frozen model from the regular protobuf format, and flows data through it.

Quick start

• MobileNet v2 with TensorFlow
• From Keras in Jupyter to tract
• ResNet with PyTorch

Real-time streaming support

This is a semi-experimental support for real-time applications like voice processing. In many real time voice applications, processing must happen "as you go". One can not wait for the end of the incoming audio signal to start decoding.

While Kaldi has built its inference engine around this streaming constraint, our approach to the same issue is a bit different. tract graph analyser and optimiser will reason on "streamed" tensors, in order to generate an equivalent stateful "pulsing" network that will propagate small time slices ("pulses") of data. This makes optimisation efforts on pulsing and "finite" tensor modes mutually benefit each other.

Obviously, this conversion only makes sense for a subset of operators, so not all networks can be converted to a pulse network: for instance, an aggregation (like a SoftMax) on the time dimension can only be given a value when the signal has been processed up to the end.

Status and compatibility

ONNX

As of today (October 2019), tract passes successfully about 85% of ONNX backends tests. All "real life" integration tests in Onnx test suite are passing: bvlc_alexnet, densenet121, inception_v1, inception_v2, resnet50, shufflenet, squeezenet, vgg19, zfnet512.

The following operators are implemented and tested.

Abs, Acos, Acosh, Add, And, ArgMax, ArgMin, Asin, Asinh, Atan, Atanh, AveragePool, BatchNormalization, Cast, CategoryMapper, Ceil, Clip, Compress, Concat, Constant, ConstantLike, ConstantOfShape, Conv, Cos, Cosh, DequantizeLinear, Div, Dropout, Elu, Equal, Erf, Exp, Expand, EyeLike, Flatten, Floor, GRU, Gather, Gemm, GlobalAveragePool, GlobalLpPool, GlobalMaxPool, Greater, HardSigmoid, Hardmax, Identity, IsNaN, LRN, LSTM, LeakyRelu, Less, Log, LogSoftmax, MatMul, Max, MaxPool, Mean, Min, Mul, Neg, Not, Or, PRelu, Pad, ParametricSoftplus, Pow, QuantizeLinear, RNN, Reciprocal, ReduceL1, ReduceL2, ReduceLogSum, ReduceLogSumExp, ReduceMax, ReduceMean, ReduceMin, ReduceProd, ReduceSum, ReduceSumSquare, Relu, Reshape, Rsqrt, ScaledTanh, Scan, Selu, Shape, Shrink, Sigmoid, Sign, Sin, Sinh, Size, Slice, Softmax, Softplus, Softsign, Split, Sqrt, Squeeze, Sub, Sum, Tan, Tanh, ThresholdedRelu, Tile, Transpose, Unsqueeze, Where, Xor

We test these operators against Onnx 1.4.1 (operator set 9) and Onnx 1.5.0 (operator set 10).

TensorFlow

Even if tract is very far from supporting any arbitrary model, it can run Google Inception v3 and Snips wake word models. Missing operators are easy to add. The lack of easy to reuse test suite, and the wide diversity of operators in Tensorflow make it difficult to target a full support.

The following operators are implemented and tested:

Abs, Add, AddN, AddV2, Assign, AvgPool, BatchToSpaceND, BiasAdd, BlockLSTM, Cast, Ceil, ConcatV2, Const, Conv2D, DepthwiseConv2dNative, Div, Enter, Equal, Exit, ExpandDims, FakeQuantWithMinMaxVars, Fill, FloorMod, FusedBatchNorm, GatherNd, GatherV2, Greater, GreaterEqual, Identity, Less, LessEqual, Log, LogicalAnd, LogicalOr, LoopCond, MatMul, Max, MaxPool, Maximum, Mean, Min, Minimum, Mul, Neg, NoOp, Pack, Pad, Placeholder, Pow, Prod, RandomUniform, RandomUniformInt, Range, RealDiv, Relu, Relu6, Reshape, Rsqrt, Shape, Sigmoid, Slice, Softmax, SpaceToBatchND, Squeeze, StridedSlice, Sub, Sum, Tanh, Tile, Transpose, VariableV2

TensorFlow-Lite

TensorFlow-Lite is a TensorFlow subproject that also focuses on inference on smaller devices. It uses a precompiler to transform a TensorFlow network to its own format. It only supports a subset of operators from TensorFlow though, and is only optimised for devices with Arm Neon support.

Tract supports a wider subset of TensorFlow operators, and has been optimised for CPU of the previous generation (ARM VFP), also targetting devices in the Raspberry Pi Zero family.

NNEF

Long story short, TensorFlow and Onnx formats are good for designing and training networks. They need to move fast to follow the research field, tend to integrate new features and operators greedily. They also exhibit a high level of expressibity to make facilitate network design.

On the other hand, only a subset of operators and network features actually reach production, so systems running production network do not have to deal with so many operators. Furthermore, some information required for training can be stripped from the network before going to production for prediction.

NNEF tries to bridge the gap between training frameworks and inference by proposing a format dedicated to production and prediction.

Tract NNEF support is partial, and alpha level:

• tract_nnef can load and execute networks NNEF networks
• tract command line can translate networks from TensorFlow or ONNX to NNEF
• tract supports most of the NNEF specification, the most notable exception being the ROI operators and deconvolution
• tract needs to extend NNEF with other operators (or extend some operators semantics) in order to support the subset of ONNX and TensorFlow that tract supports.

Example of supported networks

These models among others, are used to track tract performance evolution as part of the Continuous Integration jobs. See .travis/README.md and .travis/bundle-entrypoint.sh for more information.

Keyword spotting on Arm Cortex-M Microcontrollers

https://github.com/ARM-software/ML-KWS-for-MCU

ARM demonstrated the capabilited of the Cortex-M family by providing tutorials and pre-trained models for keyword spotting. While the exercise is ultimately meant for micro-controllers, tract can run the intermediate TensorFlow models.

For instance, on a Rasperry Pi Zero, the "CNN M" model runs in about 70 micro-seconds, and 11 micro-seconds on a Raspberry Pi 3.

Snips wake word models

https://arxiv.org/abs/1811.07684

Snips uses tract to run the wake word detectors. While earlier models were class-based and did not require any special treatment, tract pulsing capabilities made it possible to run WaveNet models efficiently enough for a Raspberry Pi Zero.

Inception v3

Notes:

• while the Raspberry Pi 3 is an Armv8 device, this bench is running on Raspbian, an armv6 operating system, crippling the performance of both benches
• there exists other benches on the internet that show better performance results for TensorFlow (not -Lite) on the Pi 3. They use all four cores of the device. Both TensorFlow-Lite and tract here have been made to run on a single-core.

Roadmap

One important guiding cross-concern: this library must cross-compile as easily as practical to small-ish devices (think 20$ boards).

• nearly complete ONNX support, and wraps it as a backend
• integrate other TF models to use as example, test and benches
  • https://github.com/ARM-software/ML-KWS-for-MCU
  • https://github.com/mozilla/DeepSpeech
• consider acting as kaldi backend

License

Note: files in the tensorflow/protos directory are copied from the TensorFlow project and are not covered by the following licence statement.

Note: files in the onnx/protos directory are copied from the ONNX project and are not covered by the following licence statement.

Apache 2.0/MIT

All original work licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT) at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.