PyTorch Neuron release notes

This document lists the release notes for the Pytorch-Neuron package.

Known Issues and Limitations - Updated 3/4/2021

The following are not torch-neuron limitations, but may impact models you can successfully torch.neuron.trace

  • If you attempt to import torch.neuron from Python 3.5 you will see this error in 1.1.7.0 - please use Python 3.6 or greater:

File "/tmp/install_test_env/lib/python3.5/site-packages/torch_neuron/__init__.py", line 29
   f'Invalid dependency version torch=={torch.__version__}. '
                                                          ^
SyntaxError: invalid syntax

  • Torchvision has dropped support for Python 3.5

  • HuggingFace transformers has dropped support for Python 3.5

  • aten::max only correctly implements the simplest versions of that operator, the variants that return a tuple with arg max now return NotImplementedError during compilation

  • There is a dependency between versions of torchvision and the torch package that customers should be aware of when compiling torchvision models. These dependency rules can be managed through pip. At the time of writing torchvision==0.6.1 matched the torch==1.5.1 release, and torchvision==0.8.2 matched the torch==1.7.1 release

[1.7.1.1.2.16.0]

Date: 3/4/2021

Summary

  • Minor enhancements.

[1.7.1.1.2.15.0]

Date: 2/24/2021

Summary

  • Fix for CVE-2021-3177.

[1.7.1.1.2.3.0]

Date: 1/30/2021

Summary

  • Made changes to allow models with -inf scalar constants to correctly compile

  • Added new operator support. Please see Supported operators [PyTorch] for the complete list of operators.

[1.1.7.0]

Date: 12/23/2020

Summary

  • We are dropping support for Python 3.5 in this release

  • torch.neuron.trace behavior will now throw a RuntimeError in the case that no operators are compiled for neuron hardware

  • torch.neuron.trace will now display compilation progress indicators (dots) as default behavior (neuron-cc must updated to the December release to greater to see this feature)

  • Added new operator support. Please see Supported operators [PyTorch] for the complete list of operators.

  • Extended the BERT pretrained tutorial to demonstrate execution on multiple cores and batch modification, updated the tutorial to accomodate changes in the Hugging Face Transformers code for version 4.0

  • Added a tutorial for torch-serve which extends the BERT tutorial

  • Added support for PyTorch 1.7

[1.0.1978.0]

Date: 11/17/2020

Summary

  • Fixed bugs in comparison operators, and added remaining variantes (eq, ne, gt, ge, lt, le)

  • Added support for prim::PythonOp - note that this must be run on CPU and not Neuron. We recommend you replace this code with PyTorch operators if possible

  • Support for a series of new operators. Please see Supported operators [PyTorch] for the complete list of operators.

  • Performance improvements to the runtime library

  • Correction of a runtime library bug which caused models with large tensors to generate incorrect results in some cases

[1.0.1721.0]

Date: 09/22/2020

Summary

  • Various minor improvements to the Pytorch autopartitioner feature

  • Support for the operators aten::constant_pad_nd, aten::meshgrid

  • Improved performance on various torchvision models. Of note are resnet50 and vgg16

[1.0.1532.0]

Date: 08/08/2020

Summary

  • Various minor improvements to the Pytorch autopartitioner feature

  • Support for the aten:ones operator

[1.0.1522.0]

Date: 08/05/2020

Summary

Various minor improvements.

[1.0.1386.0]

Date: 07/16/2020

Summary

This release adds auto-partitioning, model analysis and PyTorch 1.5.1 support, along with a number of new operators

Major New Features

  • Support for Pytorch 1.5.1

  • Introduce an automated operator device placement mechanism in torch.neuron.trace to run sub-graphs that contain operators that are not supported by the neuron compiler in native PyTorch. This new mechanism is on by default and can be turned off by adding argument fallback=False to the compiler arguments.

  • Model analysis to find supported and unsupported operators in a model

Resolved Issues

[1.0.1168.0]

Date 6/11/2020

Summary

Major New Features

Resolved Issues

Known Issues and Limitations

[1.0.1001.0]

Date: 5/11/2020

Summary

Additional PyTorch operator support and improved support for model saving and reloading.

Major New Features

  • Added Neuron Compiler support for a number of previously unsupported PyTorch operators. Please see :ref:`neuron-cc-ops-pytorch`for the complete list of operators.

  • Add support for torch.neuron.trace on models which have previously been saved using torch.jit.save and then reloaded.

Resolved Issues

Known Issues and Limitations

[1.0.825.0]

Date: 3/26/2020

Summary

Major New Features

Resolved Issues

Known Issues and limitations

[1.0.763.0]

Date: 2/27/2020

Summary

Added Neuron Compiler support for a number of previously unsupported PyTorch operators. Please see Supported operators [PyTorch] for the complete list of operators.

Major new features

  • None

Resolved issues

  • None

[1.0.672.0]

Date: 1/27/2020

Summary

Major new features

Resolved issues

  • Python 3.5 and Python 3.7 are now supported.

Known issues and limitations

Other Notes

[1.0.627.0]

Date: 12/20/2019

Summary

This is the initial release of torch-neuron. It is not distributed on the DLAMI yet and needs to be installed from the neuron pip repository.

Note that we are currently using a TensorFlow as an intermediate format to pass to our compiler. This does not affect any runtime execution from PyTorch to Neuron Runtime and Inferentia. This is why the neuron-cc installation must include [tensorflow] for PyTorch.

Major new features

Resolved issues

Known issues and limitations

Models TESTED

The following models have successfully run on neuron-inferentia systems

  1. SqueezeNet

  2. ResNet50

  3. Wide ResNet50

Pytorch Serving

In this initial version there is no specific serving support. Inference works correctly through Python on Inf1 instances using the neuron runtime. Future releases will include support for production deployment and serving of models

Profiler support

Profiler support is not provided in this initial release and will be available in future releases

Automated partitioning

Automatic partitioning of graphs into supported and non-supported operations is not currently supported. A tutorial is available to provide guidance on how to manually parition a model graph. Please see pytorch-manual-partitioning-jn-tutorial

PyTorch dependency

Currently PyTorch support depends on a Neuron specific version of PyTorch v1.3.1. Future revisions will add support for 1.4 and future releases.

Trace behavior

In order to trace a model it must be in evaluation mode. For examples please see pytorch-getting-started

Six pip package is required

The Six package is required for the torch-neuron runtime, but it is not modeled in the package dependencies. This will be fixed in a future release.

Multiple NeuronCore support

If the num-neuroncores options is used the number of cores must be manually set in the calling shell environment variable for compilation and inference.

For example: Using the keyword argument compiler_args=[’—num-neuroncores’, ‘4’] in the trace call, requires NEURONCORE_GROUP_SIZES=4 to be set in the environment at compile time and runtime

CPU execution

At compilation time a constant output is generated for the purposes of tracing. Running inference on a non neuron instance will generate incorrect results. This must not be used. The following error message is generated to stderr:

Warning: Tensor output are ** NOT CALCULATED ** during CPU execution and only
indicate tensor shape

Other notes

  • Python version(s) supported:

    • 3.6

  • Linux distribution supported:

    • DLAMI Conda 26.0 and beyond running on Ubuntu 16, Ubuntu 18, Amazon Linux 2 (using Python 3.6 Conda environments)

    • Other AMIs based on Ubuntu 16, 18

    • For Amazon Linux 2 please install Conda and use Python 3.6 Conda environment