[HuggingFace][Neuronx] Inference - Optimum Neuron 0.3.0 - Neuron sdk 2.24.0 - Transformers to 4.51.3

[JingyaHuang]

Issue #5273

• transformers: 4.51.3
• torch: 2.7.1
• diffusers: 0.35.1
• peft: 0.17.0

Note:

• If merging this PR should also close the associated Issue, please also add that Issue # to the Linked Issues section on the right.

• All PR's are checked weekly for staleness. This PR will be closed if not updated in 30 days.

Description

Tests Run

By default, docker image builds and tests are disabled. Two ways to run builds and tests:

1. Using dlc_developer_config.toml
2. Using this PR description (currently only supported for PyTorch, TensorFlow, vllm, and base images)

How to use the helper utility for updating dlc_developer_config.toml

Assuming your remote is called origin (you can find out more with git remote -v)...

• Run default builds and tests for a particular buildspec - also commits and pushes changes to remote; Example:

python src/prepare_dlc_dev_environment.py -b </path/to/buildspec.yml> -cp origin

• Enable specific tests for a buildspec or set of buildspecs - also commits and pushes changes to remote; Example:

python src/prepare_dlc_dev_environment.py -b </path/to/buildspec.yml> -t sanity_tests -cp origin

• Restore TOML file when ready to merge

python src/prepare_dlc_dev_environment.py -rcp origin

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

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

How to use PR description

Use the code block below to uncomment commands and run the PR CodeBuild jobs. There are two commands available:

• # /buildspec <buildspec_path>
  • e.g.: # /buildspec pytorch/training/buildspec.yml
  • If this line is commented out, dlc_developer_config.toml will be used.
• # /tests <test_list>
  • e.g.: # /tests sanity security ec2
  • If this line is commented out, it will run the default set of tests (same as the defaults in dlc_developer_config.toml): sanity, security, ec2, ecs, eks, sagemaker, sagemaker-local.

# /buildspec <buildspec_path>
# /tests <test_list>

Formatting

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

PR Checklist

Expand

• 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.

Pytest Marker Checklist

Expand

• (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.

[ahsan-z-khan]

The branch needs update, it shows around 30 commits. Hard to know which one is related. Can you rebase?

[ahsan-z-khan]

Overall, some of the pip commands can be combined to reduce layers

[JingyaHuang]

Hi @ahsan-z-khan, the branch is rebased and I addressed some of your comments. Thanks for the review. Do you have any idea about the sagemaker tests? I don't have access to the logs.

[ahsan-z-khan]

its looking for similar allowlist files - https://github.com/aws/deep-learning-containers/tree/master/huggingface/pytorch/inference/docker/2.1/py3/sdk2.20.0

[geetasg]

@JingyaHuang please let us know if you need more info on the allowlist files mentioned above. Looks like the PR is waiting on those before it can be merged.