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 Tensorflow 1, ONNX
or NNEF, optimize them and run data through them.
- MobileNet v2 with ONNX
- MobileNet v2 with ONNX and batch
- BERT example with ONNX
- MobileNet v2 with TensorFlow
- From Keras and TensorFlow 1 in Jupyter to tract
- From Keras and TensorFlow 2 in Jupyter to tract
- ResNet with PyTorch
There is also some technical documentation and blog posts.
As of today (October 2020), 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, ArrayFeatureExtractor, Asin, Asinh, Atan, Atanh, AveragePool, BatchNormalization, BitShift, Cast, CategoryMapper, Ceil, Clip, Compress, Concat, Constant, ConstantLike, ConstantOfShape, Conv, ConvInteger, ConvTranspose, Cos, Cosh, CumSum, DepthToSpace, DequantizeLinear, Div, Dropout, DynamicQuantizeLinear, Einsum, Elu, Equal, Erf, Exp, Expand, EyeLike, Flatten, Floor, GRU, Gather, GatherElements, GatherND, Gemm, GlobalAveragePool, GlobalLpPool, GlobalMaxPool, Greater, GreaterOrEqual, HardSigmoid, Hardmax, Identity, If, InstanceNormalization, IsInf, IsNaN, LRN, LSTM, LeakyRelu, Less, LessOrEqual, Log, LogSoftmax, MatMul, MatMulInteger, Max, MaxPool, Mean, Min, Mod, Mul, Neg, NonZero, Not, OneHot, Or, PRelu, Pad, ParametricSoftplus, Pow, QLinearConv, QLinearMatMul, QuantizeLinear, RNN, Range, Reciprocal, ReduceL1, ReduceL2, ReduceLogSum, ReduceLogSumExp, ReduceMax, ReduceMean, ReduceMin, ReduceProd, ReduceSum, ReduceSumSquare, Relu, Reshape, Resize, Round, Rsqrt, 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 Onnx 1.4.1 (operator set 9), Onnx 1.5.0 (operator set 10), Onnx 1.6.0 (operator set 11), Onnx 1.7.0 (operator set 12), Onnx 1.8.1 (operator set 13), Onnx 1.9.0 (operator set 14), and Onnx 1.10.1 (operator set 15). Many networks in operator set 8 are also working.
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
Addiotionaly, the complexity of TensorFlow 2 make it very unlikely that a direct support will ever exist in tract. Many TensorFlow 2 nets can be converted to ONNX and loaded in tract.
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 stricts 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 practise, breaking changes have been relatively rare so far. Most models are forward and retro compatible from when tract has acquired NNEF support.
Notable breakage occured:
- 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 developper 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.
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 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.
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.
| Device | Family | TensorFlow-lite | tract |
|---|---|---|---|
| Raspberry Pi Zero | Armv6 VFP | 113s | 39s |
| Raspberry Pi 2 | Armv7 NEON | 25s | 7s |
| Raspberry Pi 3 | aarch32 NEON | 5s | 5s |
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 licence statement.
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.
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.