Sonos' Neural Network inference engine.

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

tract is a Neural Network inference toolkit. It can read ONNX or NNEF, optimize them and run them.

• MobileNet v2 with ONNX
• BERT example with ONNX
• MobileNet v2 with TensorFlow
• From Keras and TensorFlow 2 to tract
• ResNet with PyTorch

There is also some technical documentation and blog posts.

As of today, 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.

Notable missing parts are operators dealing with Tensor Sequences and Optional Tensors : tract /really/ wants to flow Tensors and nothing else. This is structural. Changing it would be pretty difficult, and it's unclear whether it can be done without impairing performance or maintainability. We are not convinced these features have shown their interest in the wild yet, so we prefer to leave them aside.

Other dark corners are specific operators like "Resize" which fit perfectly in the framework but need a complex internal logic that is far from our core business. In these cases, we are happy to accept contributions and to help.

The following operators are implemented and tested.

Abs, Acos, Acosh, Add, And, ArgMax, ArgMin, ArrayFeatureExtractor, Asin, Asinh, Atan, Atanh, AveragePool, BatchNormalization, BitShift, BitwiseAnd, BitwiseNot, BitwiseOr, BitwiseXor, BlackmanWindow, Cast, CastLike, CategoryMapper, Ceil, Clip, Compress, Concat, Constant, ConstantLike, ConstantOfShape, Conv, ConvInteger, ConvTranspose, Cos, Cosh, CumSum, DFT, DepthToSpace, DequantizeLinear, Div, Dropout, DynamicQuantizeLinear, Einsum, Elu, Equal, Erf, Exp, Expand, EyeLike, Flatten, Floor, GRU, Gather, GatherElements, GatherND, Gemm, GlobalAveragePool, GlobalLpPool, GlobalMaxPool, Greater, GreaterOrEqual, HammingWindow, HannWindow, HardSigmoid, Hardmax, Identity, If, InstanceNormalization, IsInf, IsNaN, LRN, LSTM, LeakyRelu, Less, LessOrEqual, Log, LogSoftmax, MatMul, MatMulInteger, Max, MaxPool, Mean, MelWeightMatrix, Min, Mod, Mul, Multinomial, Neg, NonMaxSuppression, NonZero, Not, OneHot, Or, PRelu, Pad, ParametricSoftplus, Pow, QLinearConv, QLinearMatMul, QuantizeLinear, RNN, RandomNormal, RandomNormalLike, RandomUniform, RandomUniformLike, Range, Reciprocal, ReduceL1, ReduceL2, ReduceLogSum, ReduceLogSumExp, ReduceMax, ReduceMean, ReduceMin, ReduceProd, ReduceSum, ReduceSumSquare, Relu, Reshape, Resize, Round, Rsqrt, STFT, ScaledTanh, Scan, Scatter, ScatterElements, ScatterND, Selu, Shape, Shrink, Sigmoid, Sign, Sin, Sinh, Size, Slice, Softmax, Softplus, Softsign, SpaceToDepth, Split, Sqrt, Squeeze, Sub, Sum, Tan, Tanh, ThresholdedRelu, Tile, Transpose, TreeEnsembleClassifier, Unsqueeze, Where, Xor

We test these operators against from ONNX 1.4.1 (operator set 9), up to ONNX 1.13.0 (operator set 18).

We are using ONNX test suite, but it does not cover everything. We also deliberately ignore some tests, or restricting their scope depending on what we feel is realistic. Sometimes these decisions are just wrong, and sometimes they become wrong as time goes by and the fields moves in unexpected directions. So if you are puzzled by an ONNX model that does not work in tract, we are happy to take a look.

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 expressivity to 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 supports NNEF:

• tract_nnef can load and execute NNEF networks
• tract supports most of the NNEF specification, the most notable exception being the ROI operators
• tract introduces tract-OPL, a series of NNEF extensions to support other operators (or extend some operators semantics) in order to represent the full range of tract-core neural network support: any network understood by tract should be serializable to tract-OPL. This is a work in progress.
• tract command line can translate networks from TensorFlow or ONNX to NNEF/OPL.

A remainder: NNEF is not expressive enough to represent all ONNX. tract-OPL extends NNEF using proprietary to support what is missing. Notable extensions are pulse operators, recurring operators (as Scan) and symbolic extensions.

There is no strict check in place here, so... implementation is not bullet proof.

• NNEF part aims at being very stable. It is strongly constrained with compatibility with NNEF specification.

• tract-opl is a bit more in flux. Nevertheless we try to maintain the following golden rule:

  models serialized with tract 0.x.y should work with tract 0.x.z where z >= y

• in practice, breaking changes have been relatively rare so far. Most models are forward and retro compatible from when tract has acquired NNEF support.

Notable breakage occurred:

• 0.16.3 (forward compatible) on Scan operator
• 0.17.0 for binary decision tree classifier

Starting with 0.17.0, a model property is injected in tract-opl files (tract_nnef_ser_version) to tag which version of tract generated the file. As most models will remain compatible, tract will not do any version check. It is up to the application developer to do so.

A softer version tag exists as tract_nnef_format_version. pre-0.17.0 version set it to alpha1, post-0.17.0 set it beta1. Don't put too much emphasis into the "alpha-ness" naming of versions here.

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 relatively 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, Merge, 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, Switch, Tanh, Tile, Transpose, VariableV2

Additionally, the complexity of TensorFlow 2 make it very unlikely that a direct support will ever exist in tract. But many TensorFlow 2 models can be converted to ONNX and then loaded in tract.

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.

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

ARM demonstrated the capabilities 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 Raspberry Pi Zero, the "CNN M" model runs in about 70 micro-seconds, and 11 micro-seconds on a Raspberry Pi 3.

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.

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.

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 license statement.

All original work licensed under either of

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

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.