[Huggingface][Neuronx] Training - update for Optimum-Neuron 0.0.21 - Neuron sdk 2.18.0 - Transformers to 4.36.2

[JingyaHuang]

GitHub Issue #3822 :

Description

This PR updates Hugginface's PyTorch DLC for training on neuron devices. Here are the corresponding updated dependencies versions:

Dependencies versions:

aws-neuronx-runtime-discovery 2.9
datasets                      2.18.0
diffusers                     0.27.2
libneuronxla                  0.5.971
neuronx-cc                    2.13.66.0+6dfecc895
neuronx-distributed           0.7.0
optimum                       1.18.1.3
optimum-neuron                0.0.21.dev0
sentence-transformers         2.6.1
torch                         1.13.1
torch-neuronx                 1.13.1.1.14.0
torch-xla                     1.13.1+torchneurone
torchvision                   0.14.1
transformers                  4.36.2
transformers-neuronx          0.10.0.21

aws-neuronx-collectives/unknown,now 2.20.22.0-c101c322e amd64 [installed]
aws-neuronx-dkms/unknown,now 2.16.7.0 amd64 [installed]
aws-neuronx-runtime-lib/unknown,now 2.20.22.0-1b3ca6425 amd64 [installed]
aws-neuronx-tools/unknown,now 2.17.1.0 amd64 [installed]

cc. @catalin-manciu-aws @haixiw @arjkesh

Tests run

NOTE: By default, docker builds are disabled. In order to build your container, please update dlc_developer_config.toml and specify the framework to build in "build_frameworks"

• I have run builds/tests on commit for my changes.

NOTE: If you are creating a PR for a new framework version, please ensure success of the standard, rc, and efa sagemaker remote tests by updating the dlc_developer_config.toml file:

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• sagemaker_remote_tests = true
• sagemaker_efa_tests = true
• sagemaker_rc_tests = true

Additionally, please run the sagemaker local tests in at least one revision:

• sagemaker_local_tests = true

Formatting

• I have run black -l 100 on my code (formatting tool: https://black.readthedocs.io/en/stable/getting_started.html)

DLC image/dockerfile

Builds to Execute

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Click the checkbox to enable a build to execute upon merge.

Note: By default, pipelines are set to "latest". Replace with major.minor framework version if you do not want "latest".

• build_pytorch_training_latest
• build_pytorch_inference_latest
• build_tensorflow_training_latest
• build_tensorflow_inference_latest

Additional context

PR Checklist

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• I've prepended PR tag with frameworks/job this applies to : [mxnet, tensorflow, pytorch] | [ei/neuron/graviton] | [build] | [test] | [benchmark] | [ec2, ecs, eks, sagemaker]
• If the PR changes affects SM test, I've modified dlc_developer_config.toml in my PR branch by setting sagemaker_tests = true and efa_tests = true
• If this PR changes existing code, the change fully backward compatible with pre-existing code. (Non backward-compatible changes need special approval.)
• (If applicable) I've documented below the DLC image/dockerfile this relates to
• (If applicable) I've documented below the tests I've run on the DLC image
• (If applicable) I've reviewed the licenses of updated and new binaries and their dependencies to make sure all licenses are on the Apache Software Foundation Third Party License Policy Category A or Category B license list. See https://www.apache.org/legal/resolved.html.
• (If applicable) I've scanned the updated and new binaries to make sure they do not have vulnerabilities associated with them.

NEURON/GRAVITON Testing Checklist

• When creating a PR:

• I've modified dlc_developer_config.toml in my PR branch by setting neuron_mode = true or graviton_mode = true

Benchmark Testing Checklist

• When creating a PR:

• I've modified dlc_developer_config.toml in my PR branch by setting ec2_benchmark_tests = true or sagemaker_benchmark_tests = true

Pytest Marker Checklist

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• (If applicable) I have added the marker @pytest.mark.model("<model-type>") to the new tests which I have added, to specify the Deep Learning model that is used in the test (use "N/A" if the test doesn't use a model)
• (If applicable) I have added the marker @pytest.mark.integration("<feature-being-tested>") to the new tests which I have added, to specify the feature that will be tested
• (If applicable) I have added the marker @pytest.mark.multinode(<integer-num-nodes>) to the new tests which I have added, to specify the number of nodes used on a multi-node test
• (If applicable) I have added the marker @pytest.mark.processor(<"cpu"/"gpu"/"eia"/"neuron">) to the new tests which I have added, if a test is specifically applicable to only one processor type

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license. I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.