diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.circleci/parse_test_outputs.py b/tasks/tasksmith-333d8be2d5aa/tests/source/.circleci/parse_test_outputs.py new file mode 100644 index 0000000000000000000000000000000000000000..a69da1a3eafb27f37ee7115472aad11b8736690b --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.circleci/parse_test_outputs.py @@ -0,0 +1,70 @@ +import re +import argparse + +def parse_pytest_output(file_path): + skipped_tests = {} + skipped_count = 0 + with open(file_path, 'r') as file: + for line in file: + match = re.match(r'^SKIPPED \[(\d+)\] (tests/.*): (.*)$', line) + if match: + skipped_count += 1 + test_file, test_line, reason = match.groups() + skipped_tests[reason] = skipped_tests.get(reason, []) + [(test_file, test_line)] + for k,v in sorted(skipped_tests.items(), key=lambda x:len(x[1])): + print(f"{len(v):4} skipped because: {k}") + print("Number of skipped tests:", skipped_count) + +def parse_pytest_failure_output(file_path): + failed_tests = {} + failed_count = 0 + with open(file_path, 'r') as file: + for line in file: + match = re.match(r'^FAILED (tests/.*) - (.*): (.*)$', line) + if match: + failed_count += 1 + _, error, reason = match.groups() + failed_tests[reason] = failed_tests.get(reason, []) + [error] + for k,v in sorted(failed_tests.items(), key=lambda x:len(x[1])): + print(f"{len(v):4} failed because `{v[0]}` -> {k}") + print("Number of failed tests:", failed_count) + if failed_count>0: + exit(1) + +def parse_pytest_errors_output(file_path): + print(file_path) + error_tests = {} + error_count = 0 + with open(file_path, 'r') as file: + for line in file: + match = re.match(r'^ERROR (tests/.*) - (.*): (.*)$', line) + if match: + error_count += 1 + _, test_error, reason = match.groups() + error_tests[reason] = error_tests.get(reason, []) + [test_error] + for k,v in sorted(error_tests.items(), key=lambda x:len(x[1])): + print(f"{len(v):4} errored out because of `{v[0]}` -> {k}") + print("Number of errors:", error_count) + if error_count>0: + exit(1) + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--file", help="file to parse") + parser.add_argument("--skip", action="store_true", help="show skipped reasons") + parser.add_argument("--fail", action="store_true", help="show failed tests") + parser.add_argument("--errors", action="store_true", help="show failed tests") + args = parser.parse_args() + + if args.skip: + parse_pytest_output(args.file) + + if args.fail: + parse_pytest_failure_output(args.file) + + if args.errors: + parse_pytest_errors_output(args.file) + + +if __name__ == "__main__": + main() diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.gitattributes b/tasks/tasksmith-333d8be2d5aa/tests/source/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..6505bc7edf9af5694cee1a93b90c565872fbbeb9 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.gitattributes @@ -0,0 +1,4 @@ +*.py eol=lf +*.rst eol=lf +*.md eol=lf +*.mdx eol=lf \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/bug-report.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/bug-report.yml new file mode 100644 index 0000000000000000000000000000000000000000..ecc795ae63253e6e71265269a0fe579122d39e90 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/bug-report.yml @@ -0,0 +1,127 @@ +name: "\U0001F41B Bug Report" +description: Submit a bug report to help us improve transformers +labels: [ "bug" ] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! 🤗 + + Before you submit your bug report: + + - If it is your first time submitting, be sure to check our [bug report guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#did-you-find-a-bug) + - Try our [docs bot](https://huggingface.co/spaces/huggingchat/hf-docs-chat) -- it might be able to help you with your issue + + - type: textarea + id: system-info + attributes: + label: System Info + description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below. + placeholder: transformers version, platform, python version, ... + validations: + required: true + + - type: textarea + id: who-can-help + attributes: + label: Who can help? + description: | + Your issue will be replied to more quickly if you can figure out the right person to tag with @ + If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. + + All issues are read by one of the core maintainers, so if you don't know who to tag, just leave this blank and + a core maintainer will ping the right person. + + Please tag fewer than 3 people. + + Models: + + - text models: @ArthurZucker + - vision models: @amyeroberts, @qubvel + - speech models: @ylacombe, @eustlb + - graph models: @clefourrier + + Library: + + - flax: @sanchit-gandhi + - generate: @zucchini-nlp (visual-language models) or @gante (all others) + - pipelines: @Rocketknight1 + - tensorflow: @gante and @Rocketknight1 + - tokenizers: @ArthurZucker and @itazap + - trainer: @muellerzr @SunMarc + + Integrations: + + - deepspeed: HF Trainer/Accelerate: @muellerzr + - ray/raytune: @richardliaw, @amogkam + - Big Model Inference: @SunMarc + - quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber + + Documentation: @stevhliu + + Model hub: + + - for issues with a model, report at https://discuss.huggingface.co/ and tag the model's creator. + + HF projects: + + - accelerate: [different repo](https://github.com/huggingface/accelerate) + - datasets: [different repo](https://github.com/huggingface/datasets) + - diffusers: [different repo](https://github.com/huggingface/diffusers) + - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) + + Maintained examples (not research project or legacy): + + - Flax: @sanchit-gandhi + - PyTorch: See Models above and tag the person corresponding to the modality of the example. + - TensorFlow: @Rocketknight1 + + Research projects are not maintained and should be taken as is. + + placeholder: "@Username ..." + + - type: checkboxes + id: information-scripts-examples + attributes: + label: Information + description: 'The problem arises when using:' + options: + - label: "The official example scripts" + - label: "My own modified scripts" + + - type: checkboxes + id: information-tasks + attributes: + label: Tasks + description: "The tasks I am working on are:" + options: + - label: "An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)" + - label: "My own task or dataset (give details below)" + + - type: textarea + id: reproduction + validations: + required: true + attributes: + label: Reproduction + description: | + Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet. + If you have code snippets, error messages, stack traces please provide them here as well. + Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting + Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code. + + placeholder: | + Steps to reproduce the behavior: + + 1. + 2. + 3. + + + - type: textarea + id: expected-behavior + validations: + required: true + attributes: + label: Expected behavior + description: "A clear and concise description of what you would expect to happen." diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/config.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..1b9386d11221fc6be29a88ee0b77e768a4d5eb9d --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,12 @@ +blank_issues_enabled: true +version: 2.1 +contact_links: + - name: Model checkpoints on the Hugging Face Hub + url: https://huggingface.co/models + about: Open a Pull request / Discussion related to a specific model checkpoint directly on the Hugging Face Hub + - name: Website Related + url: https://github.com/huggingface/hub-docs/issues + about: Feature requests and bug reports related to the website + - name: Forum + url: https://discuss.huggingface.co/ + about: General usage questions and community discussions diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/feature-request.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/feature-request.yml new file mode 100644 index 0000000000000000000000000000000000000000..ff0d452a807f6e12958b8e91dd8f18bcbf7962a4 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/feature-request.yml @@ -0,0 +1,31 @@ +name: "\U0001F680 Feature request" +description: Submit a proposal/request for a new transformers feature +labels: [ "Feature request" ] +body: + - type: textarea + id: feature-request + validations: + required: true + attributes: + label: Feature request + description: | + A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. + + - type: textarea + id: motivation + validations: + required: true + attributes: + label: Motivation + description: | + Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too. + + + - type: textarea + id: contribution + validations: + required: true + attributes: + label: Your contribution + description: | + Is there any way that you could help, e.g. by submitting a PR? Make sure to read the CONTRIBUTING.MD [readme](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/i18n.md b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/i18n.md new file mode 100644 index 0000000000000000000000000000000000000000..5b91427d55b73c616d9320cefbecf61787c51c1e --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/i18n.md @@ -0,0 +1,46 @@ +--- +name: 🌐 Translating a new language? +about: Start a new translation effort in your language +title: '[i18n-] Translating docs to ' +labels: WIP +assignees: '' + +--- + + + +Hi! + +Let's bring the documentation to all the -speaking community 🌐 (currently 0 out of 267 complete) + +Who would want to translate? Please follow the 🤗 [TRANSLATING guide](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list. + +Some notes: + +* Please translate using an informal tone (imagine you are talking with a friend about transformers 🤗). +* Please translate in a gender-neutral way. +* Add your translations to the folder called `` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source). +* Register your translation in `/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml). +* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu and @MKhalusova for review. +* 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/). + +## Get Started section + +- [ ] [index.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/index.md) https://github.com/huggingface/transformers/pull/20180 +- [ ] [quicktour.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.md) (waiting for initial PR to go through) +- [ ] [installation.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/installation.md). + +## Tutorial section +- [ ] [pipeline_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/pipeline_tutorial.md) +- [ ] [autoclass_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/autoclass_tutorial.md) +- [ ] [preprocessing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/preprocessing.md) +- [ ] [training.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/training.md) +- [ ] [accelerate.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/accelerate.md) +- [ ] [model_sharing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_sharing.md) +- [ ] [multilingual.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/multilingual.md) + + diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/migration.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/migration.yml new file mode 100644 index 0000000000000000000000000000000000000000..778413141b1f3b42f71c123a4d83f61bc01663e1 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/migration.yml @@ -0,0 +1,72 @@ +name: "\U0001F4DA Migration from pytorch-pretrained-bert or pytorch-transformers" +description: Report a problem when migrating from pytorch-pretrained-bert or pytorch-transformers to transformers +labels: [ "migration" ] +body: + - type: textarea + id: system-info + attributes: + label: System Info + description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below. + render: shell + placeholder: transformers version, platform, python version, ... + validations: + required: true + + - type: checkboxes + id: information-scripts-examples + attributes: + label: Information + description: 'The problem arises when using:' + options: + - label: "The official example scripts" + - label: "My own modified scripts" + + - type: checkboxes + id: information-tasks + attributes: + label: Tasks + description: "The tasks I am working on are:" + options: + - label: "An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)" + - label: "My own task or dataset (give details below)" + + - type: textarea + id: reproduction + validations: + required: true + attributes: + label: Reproduction + description: | + Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet. + If you have code snippets, error messages, stack traces please provide them here as well. + Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting + Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code. + + placeholder: | + Steps to reproduce the behavior: + + 1. + 2. + 3. + + + - type: textarea + id: expected-behavior + validations: + required: true + attributes: + label: Expected behavior + description: "A clear and concise description of what you would expect to happen." + render: shell + + - type: checkboxes + id: checklist + attributes: + label: Checklist + options: + - label: "I have read the migration guide in the readme. + ([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers); + [pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))" + required: true + - label: "I checked if a related official extension example runs on my machine." + required: true diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/new-model-addition.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/new-model-addition.yml new file mode 100644 index 0000000000000000000000000000000000000000..2f3476d3ab095ffb109b2b498247a0984cd0b500 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/ISSUE_TEMPLATE/new-model-addition.yml @@ -0,0 +1,31 @@ +name: "\U0001F31F New model addition" +description: Submit a proposal/request to implement a new model +labels: [ "New model" ] + +body: + - type: textarea + id: description-request + validations: + required: true + attributes: + label: Model description + description: | + Put any and all important information relative to the model + + - type: checkboxes + id: information-tasks + attributes: + label: Open source status + description: | + Please note that if the model implementation isn't available or if the weights aren't open-source, we are less likely to implement it in `transformers`. + options: + - label: "The model implementation is available" + - label: "The model weights are available" + + - type: textarea + id: additional-info + attributes: + label: Provide useful links for the implementation + description: | + Please provide information regarding the implementation, the weights, and the authors. + Please mention the authors by @gh-username if you're aware of their usernames. diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/PULL_REQUEST_TEMPLATE.md b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000000000000000000000000000000000000..ee7a7eaae1139bd7497bcc1becae6cfd27ff4479 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,79 @@ +# What does this PR do? + + + + + +Fixes # (issue) + + +## Before submitting +- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). +- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request), + Pull Request section? +- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link + to it if that's the case. +- [ ] Did you make sure to update the documentation with your changes? Here are the + [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and + [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). +- [ ] Did you write any new necessary tests? + + +## Who can review? + +Anyone in the community is free to review the PR once the tests have passed. Feel free to tag +members/contributors who may be interested in your PR. + + diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/conda/build.sh b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/conda/build.sh new file mode 100644 index 0000000000000000000000000000000000000000..a40f1097a8631648d5c2cecb1be4dd144cc632c2 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/conda/build.sh @@ -0,0 +1 @@ +$PYTHON setup.py install # Python command to install the script. diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/conda/meta.yaml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/conda/meta.yaml new file mode 100644 index 0000000000000000000000000000000000000000..89dc353b127774528faa0c979e28215be12de6f0 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/conda/meta.yaml @@ -0,0 +1,56 @@ +{% set name = "transformers" %} + +package: + name: "{{ name|lower }}" + version: "{{ TRANSFORMERS_VERSION }}" + +source: + path: ../../ + +build: + noarch: python + +requirements: + host: + - python + - pip + - numpy >=1.17 + - dataclasses + - huggingface_hub + - packaging + - filelock + - requests + - tqdm >=4.27 + - sacremoses + - regex !=2019.12.17 + - protobuf + - tokenizers >=0.11.1,!=0.11.3,<0.13 + - pyyaml >=5.1 + - safetensors + - fsspec + run: + - python + - numpy >=1.17 + - dataclasses + - huggingface_hub + - packaging + - filelock + - requests + - tqdm >=4.27 + - sacremoses + - regex !=2019.12.17 + - protobuf + - tokenizers >=0.11.1,!=0.11.3,<0.13 + - pyyaml >=5.1 + - safetensors + - fsspec + +test: + imports: + - transformers + +about: + home: https://huggingface.co + license: Apache License 2.0 + license_file: LICENSE + summary: "🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0." diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/TROUBLESHOOT.md b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/TROUBLESHOOT.md new file mode 100644 index 0000000000000000000000000000000000000000..f6101e6d70b59af8ef1878c1461c914090f9fa22 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/TROUBLESHOOT.md @@ -0,0 +1,9 @@ +# Troubleshooting + +This is a document explaining how to deal with various issues on github-actions self-hosted CI. The entries may include actual solutions or pointers to Issues that cover those. + +## GitHub Actions (self-hosted CI) + +* Deepspeed + + - if jit build hangs, clear out `rm -rf ~/.cache/torch_extensions/` reference: https://github.com/huggingface/transformers/pull/12723 diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/add-model-like.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/add-model-like.yml new file mode 100644 index 0000000000000000000000000000000000000000..cd6768317844065146ed425701f59cb87aec61bf --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/add-model-like.yml @@ -0,0 +1,80 @@ +name: Add model like runner + +on: + push: + branches: + - none # put main here when this is fixed + #pull_request: + # paths: + # - "src/**" + # - "tests/**" + # - ".github/**" + # types: [opened, synchronize, reopened] + +jobs: + run_tests_templates_like: + name: "Add new model like template tests" + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v4 + + - name: Install dependencies + run: | + sudo apt -y update && sudo apt install -y libsndfile1-dev + + - name: Load cached virtual environment + uses: actions/cache@v4 + id: cache + with: + path: ~/venv/ + key: v4-tests_model_like-${{ hashFiles('setup.py') }} + + - name: Create virtual environment on cache miss + if: steps.cache.outputs.cache-hit != 'true' + run: | + python -m venv ~/venv && . ~/venv/bin/activate + pip install --upgrade pip!=21.3 + pip install -e .[dev] + + - name: Check transformers location + # make `transformers` available as package (required since we use `-e` flag) and check it's indeed from the repo. + run: | + . ~/venv/bin/activate + python setup.py develop + transformers_install=$(pip list -e | grep transformers) + transformers_install_array=($transformers_install) + transformers_loc=${transformers_install_array[-1]} + transformers_repo_loc=$(pwd .) + if [ "$transformers_loc" != "$transformers_repo_loc" ]; then + echo "transformers is from $transformers_loc but it shoud be from $transformers_repo_loc/src." + echo "A fix is required. Stop testing." + exit 1 + fi + + - name: Create model files + run: | + . ~/venv/bin/activate + transformers-cli add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo . + make style + make fix-copies + + - name: Run all PyTorch modeling test + run: | + . ~/venv/bin/activate + python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_new_models tests/bert_new/test_modeling_bert_new.py + + - name: Run style changes + run: | + . ~/venv/bin/activate + make style && make quality && make repo-consistency + + - name: Failure short reports + if: ${{ always() }} + run: cat reports/tests_new_models/failures_short.txt + + - name: Test suite reports artifacts + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: run_all_tests_new_models_test_reports + path: reports/tests_new_models diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/benchmark.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/benchmark.yml new file mode 100644 index 0000000000000000000000000000000000000000..1bbd1c1e94d08ca21b8d3d99a2c7484d8d9a45e3 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/benchmark.yml @@ -0,0 +1,74 @@ +name: Self-hosted runner (benchmark) + +on: + push: + branches: [main] + pull_request: + types: [ opened, labeled, reopened, synchronize ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + +env: + HF_HOME: /mnt/cache + +jobs: + benchmark: + name: Benchmark + strategy: + matrix: + group: [aws-g5-4xlarge-cache, aws-p4d-24xlarge-plus] + runs-on: + group: ${{ matrix.group }} + if: | + (github.event_name == 'pull_request' && contains( github.event.pull_request.labels.*.name, 'run-benchmark') )|| + (github.event_name == 'push' && github.ref == 'refs/heads/main') + container: + image: huggingface/transformers-pytorch-gpu + options: --gpus all --privileged --ipc host + steps: + - name: Get repo + uses: actions/checkout@v4 + with: + ref: ${{ github.event.pull_request.head.sha || github.sha }} + + - name: Install libpq-dev & psql + run: | + apt update + apt install -y libpq-dev postgresql-client + + - name: Install benchmark script dependencies + run: python3 -m pip install -r benchmark/requirements.txt + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e ".[torch]" + + - name: Run database init script + run: | + psql -f benchmark/init_db.sql + env: + PGDATABASE: metrics + PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }} + PGUSER: transformers_benchmarks + PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }} + + - name: Run benchmark + run: | + git config --global --add safe.directory /__w/transformers/transformers + if [ "$GITHUB_EVENT_NAME" = "pull_request" ]; then + commit_id=$(echo "${{ github.event.pull_request.head.sha }}") + elif [ "$GITHUB_EVENT_NAME" = "push" ]; then + commit_id=$GITHUB_SHA + fi + commit_msg=$(git show -s --format=%s | cut -c1-70) + python3 benchmark/benchmarks_entrypoint.py "${{ github.head_ref || github.ref_name }}" "$commit_id" "$commit_msg" + env: + HF_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + # Enable this to see debug logs + # HF_HUB_VERBOSITY: debug + # TRANSFORMERS_VERBOSITY: debug + PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }} + PGUSER: transformers_benchmarks + PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-ci-docker-images.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-ci-docker-images.yml new file mode 100644 index 0000000000000000000000000000000000000000..9d947684ee867e0f4add03499c1aa7bdc1263164 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-ci-docker-images.yml @@ -0,0 +1,77 @@ +name: Build pr ci-docker + +on: + push: + branches: + - push-ci-image # for now let's only build on this branch + repository_dispatch: + workflow_call: + inputs: + image_postfix: + required: true + type: string + schedule: + - cron: "6 0 * * *" + + +concurrency: + group: ${{ github.workflow }} + cancel-in-progress: true + +jobs: + build: + runs-on: ubuntu-22.04 + + if: ${{ contains(github.event.head_commit.message, '[build-ci-image]') || contains(github.event.head_commit.message, '[push-ci-image]') && '!cancelled()' || github.event_name == 'schedule' }} + + strategy: + matrix: + file: ["quality", "consistency", "custom-tokenizers", "torch-light", "tf-light", "exotic-models", "torch-tf-light", "torch-jax-light", "jax-light", "examples-torch", "examples-tf"] + continue-on-error: true + + steps: + - + name: Set tag + run: | + if ${{contains(github.event.head_commit.message, '[build-ci-image]')}}; then + echo "TAG=huggingface/transformers-${{ matrix.file }}:dev" >> "$GITHUB_ENV" + echo "setting it to DEV!" + else + echo "TAG=huggingface/transformers-${{ matrix.file }}" >> "$GITHUB_ENV" + + fi + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build ${{ matrix.file }}.dockerfile + uses: docker/build-push-action@v5 + with: + context: ./docker + build-args: | + REF=${{ github.sha }} + file: "./docker/${{ matrix.file }}.dockerfile" + push: ${{ contains(github.event.head_commit.message, 'ci-image]') || github.event_name == 'schedule' }} + tags: ${{ env.TAG }} + + notify: + runs-on: ubuntu-22.04 + if: ${{ contains(github.event.head_commit.message, '[build-ci-image]') || contains(github.event.head_commit.message, '[push-ci-image]') && '!cancelled()' || github.event_name == 'schedule' }} + steps: + - name: Post to Slack + if: ${{ contains(github.event.head_commit.message, '[push-ci-image]') && github.event_name != 'schedule' }} + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: "#transformers-ci-circleci-images" + title: 🤗 New docker images for CircleCI are pushed. + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-docker-images.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-docker-images.yml new file mode 100644 index 0000000000000000000000000000000000000000..c21faf2d7479426bd7c995e018cef935446ba3be --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-docker-images.yml @@ -0,0 +1,393 @@ +name: Build docker images (scheduled) + +on: + push: + branches: + - build_ci_docker_image* + repository_dispatch: + workflow_call: + inputs: + image_postfix: + required: true + type: string + schedule: + - cron: "17 0 * * *" + +concurrency: + group: docker-images-builds + cancel-in-progress: false + +jobs: + latest-docker: + name: "Latest PyTorch + TensorFlow [dev]" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-all-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-all-latest-gpu${{ inputs.image_postfix }} + # Push CI images still need to be re-built daily + - + name: Build and push (for Push CI) in a daily basis + # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. + # The later case is useful for manual image building for debugging purpose. Use another tag in this case! + if: inputs.image_postfix != '-push-ci' + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-all-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-all-latest-gpu-push-ci + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the transformers-all-latest-gpu-push-ci docker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-torch-deepspeed-docker: + name: "Latest PyTorch + DeepSpeed" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-deepspeed-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-latest-gpu${{ inputs.image_postfix }} + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER}} + title: 🤗 Results of the transformers-pytorch-deepspeed-latest-gpu docker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + # Can't build 2 images in a single job `latest-torch-deepspeed-docker` (for `nvcr.io/nvidia`) + latest-torch-deepspeed-docker-for-push-ci-daily-build: + name: "Latest PyTorch + DeepSpeed (Push CI - Daily Build)" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + # Push CI images still need to be re-built daily + - + name: Build and push (for Push CI) in a daily basis + # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. + # The later case is useful for manual image building for debugging purpose. Use another tag in this case! + if: inputs.image_postfix != '-push-ci' + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-deepspeed-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the transformers-pytorch-deepspeed-latest-gpu-push-ci docker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + doc-builder: + name: "Doc builder" + # Push CI doesn't need this image + if: inputs.image_postfix != '-push-ci' + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-doc-builder + push: true + tags: huggingface/transformers-doc-builder + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the huggingface/transformers-doc-builder docker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-pytorch: + name: "Latest PyTorch [dev]" + # Push CI doesn't need this image + if: inputs.image_postfix != '-push-ci' + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-gpu + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the huggingface/transformers-pytorch-gpudocker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-pytorch-amd: + name: "Latest PyTorch (AMD) [dev]" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-amd-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-amd-gpu${{ inputs.image_postfix }} + # Push CI images still need to be re-built daily + - + name: Build and push (for Push CI) in a daily basis + # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. + # The later case is useful for manual image building for debugging purpose. Use another tag in this case! + if: inputs.image_postfix != '-push-ci' + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-amd-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-amd-gpu-push-ci + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the huggingface/transformers-pytorch-amd-gpu-push-ci build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-tensorflow: + name: "Latest TensorFlow [dev]" + # Push CI doesn't need this image + if: inputs.image_postfix != '-push-ci' + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-tensorflow-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-tensorflow-gpu + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the huggingface/transformers-tensorflow-gpu build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-pytorch-deepspeed-amd: + name: "PyTorch + DeepSpeed (AMD) [dev]" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-deepspeed-amd-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-amd-gpu${{ inputs.image_postfix }} + # Push CI images still need to be re-built daily + - + name: Build and push (for Push CI) in a daily basis + # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. + # The later case is useful for manual image building for debugging purpose. Use another tag in this case! + if: inputs.image_postfix != '-push-ci' + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-deepspeed-amd-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-amd-gpu-push-ci + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the transformers-pytorch-deepspeed-amd-gpu build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-quantization-torch-docker: + name: "Latest Pytorch + Quantization [dev]" + # Push CI doesn't need this image + if: inputs.image_postfix != '-push-ci' + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-quantization-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-quantization-latest-gpu${{ inputs.image_postfix }} + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the transformers-quantization-latest-gpu build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-nightly-ci-docker-images.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-nightly-ci-docker-images.yml new file mode 100644 index 0000000000000000000000000000000000000000..4b00a6d3fae36639fd2bf3a14b5517fdac0d1665 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-nightly-ci-docker-images.yml @@ -0,0 +1,67 @@ +name: Build docker images (Nightly CI) + +on: + workflow_call: + push: + branches: + - build_nightly_ci_docker_image* + +concurrency: + group: docker-images-builds + cancel-in-progress: false + +jobs: + latest-with-torch-nightly-docker: + name: "Nightly PyTorch + Stable TensorFlow" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v3 + with: + context: ./docker/transformers-all-latest-gpu + build-args: | + REF=main + PYTORCH=pre + push: true + tags: huggingface/transformers-all-latest-torch-nightly-gpu + + nightly-torch-deepspeed-docker: + name: "Nightly PyTorch + DeepSpeed" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v3 + with: + context: ./docker/transformers-pytorch-deepspeed-nightly-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-nightly-gpu diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-past-ci-docker-images.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-past-ci-docker-images.yml new file mode 100644 index 0000000000000000000000000000000000000000..c4f0b78986caea24724dfa38465b1e27b88be335 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build-past-ci-docker-images.yml @@ -0,0 +1,101 @@ +name: Build docker images (Past CI) + +on: + push: + branches: + - build_past_ci_docker_image* + +concurrency: + group: docker-images-builds + cancel-in-progress: false + +jobs: + past-pytorch-docker: + name: "Past PyTorch Docker" + strategy: + fail-fast: false + matrix: + version: ["1.13", "1.12", "1.11"] + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + - + name: Check out code + uses: actions/checkout@v4 + - + id: get-base-image + name: Get Base Image + env: + framework_version: ${{ matrix.version }} + run: | + echo "base_image=$(python3 -c 'import os; from utils.past_ci_versions import past_versions_testing; base_image = past_versions_testing["pytorch"][os.environ["framework_version"]]["base_image"]; print(base_image)')" >> $GITHUB_OUTPUT + - + name: Print Base Image + run: | + echo ${{ steps.get-base-image.outputs.base_image }} + - + name: Login to DockerHub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v3 + with: + context: ./docker/transformers-past-gpu + build-args: | + REF=main + BASE_DOCKER_IMAGE=${{ steps.get-base-image.outputs.base_image }} + FRAMEWORK=pytorch + VERSION=${{ matrix.version }} + push: true + tags: huggingface/transformers-pytorch-past-${{ matrix.version }}-gpu + + past-tensorflow-docker: + name: "Past TensorFlow Docker" + strategy: + fail-fast: false + matrix: + version: ["2.11", "2.10", "2.9", "2.8", "2.7", "2.6", "2.5"] + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + - + name: Check out code + uses: actions/checkout@v4 + - + id: get-base-image + name: Get Base Image + env: + framework_version: ${{ matrix.version }} + run: | + echo "base_image=$(python3 -c 'import os; from utils.past_ci_versions import past_versions_testing; base_image = past_versions_testing["tensorflow"][os.environ["framework_version"]]["base_image"]; print(base_image)')" >> $GITHUB_OUTPUT + - + name: Print Base Image + run: | + echo ${{ steps.get-base-image.outputs.base_image }} + - + name: Login to DockerHub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v3 + with: + context: ./docker/transformers-past-gpu + build-args: | + REF=main + BASE_DOCKER_IMAGE=${{ steps.get-base-image.outputs.base_image }} + FRAMEWORK=tensorflow + VERSION=${{ matrix.version }} + push: true + tags: huggingface/transformers-tensorflow-past-${{ matrix.version }}-gpu diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build_documentation.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build_documentation.yml new file mode 100644 index 0000000000000000000000000000000000000000..c55638ded1497c574639d8f352dbe1ed303a06fb --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build_documentation.yml @@ -0,0 +1,23 @@ +name: Build documentation + +on: + workflow_dispatch: + push: + branches: + - main + - doc-builder* + - v*-release + - use_templates + +jobs: + build: + uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main + with: + commit_sha: ${{ github.sha }} + package: transformers + notebook_folder: transformers_doc + languages: ar de en es fr hi it ko pt tr zh ja te + custom_container: huggingface/transformers-doc-builder + secrets: + token: ${{ secrets.HUGGINGFACE_PUSH }} + hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build_pr_documentation.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build_pr_documentation.yml new file mode 100644 index 0000000000000000000000000000000000000000..f698f860b2f93c64a8b4d5c9dfeace0133c99eed --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/build_pr_documentation.yml @@ -0,0 +1,18 @@ +name: Build PR Documentation + +on: + pull_request: + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + +jobs: + build: + uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main + with: + commit_sha: ${{ github.event.pull_request.head.sha }} + pr_number: ${{ github.event.number }} + package: transformers + languages: ar de en es fr hi it ko pt tr zh ja te + custom_container: huggingface/transformers-doc-builder diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/check_failed_model_tests.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/check_failed_model_tests.yml new file mode 100644 index 0000000000000000000000000000000000000000..f3ea8646900ad2eda2af957078dc4778b7b154ee --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/check_failed_model_tests.yml @@ -0,0 +1,129 @@ +name: Process failed tests + +on: + workflow_call: + inputs: + docker: + required: true + type: string + start_sha: + required: true + type: string + + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. + # This token is created under the bot `hf-transformers-bot`. + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + RUN_PT_TF_CROSS_TESTS: 1 + CUDA_VISIBLE_DEVICES: 0,1 + + +jobs: + run_models_gpu: + name: " " + runs-on: + group: aws-g4dn-2xlarge-cache + container: + image: ${{ inputs.docker }} + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - uses: actions/download-artifact@v4 + with: + name: ci_results_run_models_gpu + path: /transformers/ci_results_run_models_gpu + + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Get target commit + working-directory: /transformers/utils + run: | + echo "END_SHA=$(TOKEN=${{ secrets.ACCESS_REPO_INFO_TOKEN }} python3 -c 'import os; from get_previous_daily_ci import get_last_daily_ci_run_commit; commit=get_last_daily_ci_run_commit(token=os.environ["TOKEN"]); print(commit)')" >> $GITHUB_ENV + + - name: Checkout to `start_sha` + working-directory: /transformers + run: git fetch && git checkout ${{ inputs.start_sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Check failed tests + working-directory: /transformers + run: python3 utils/check_bad_commit.py --start_commit ${{ inputs.start_sha }} --end_commit ${{ env.END_SHA }} --file ci_results_run_models_gpu/new_model_failures.json --output_file new_model_failures_with_bad_commit.json + + - name: Show results + working-directory: /transformers + run: | + ls -l new_model_failures_with_bad_commit.json + cat new_model_failures_with_bad_commit.json + + - name: Checkout back + working-directory: /transformers + run: | + git checkout ${{ inputs.start_sha }} + + - name: Process report + shell: bash + working-directory: /transformers + env: + TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }} + run: | + python3 utils/process_bad_commit_report.py + + - name: Process report + shell: bash + working-directory: /transformers + env: + TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }} + run: | + { + echo 'REPORT_TEXT<> "$GITHUB_ENV" + + - name: Send processed report + if: ${{ !endsWith(env.REPORT_TEXT, '{}') }} + uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001 + with: + # Slack channel id, channel name, or user id to post message. + # See also: https://api.slack.com/methods/chat.postMessage#channels + channel-id: '#transformers-ci-feedback-tests' + # For posting a rich message using Block Kit + payload: | + { + "blocks": [ + { + "type": "section", + "text": { + "type": "mrkdwn", + "text": "${{ env.REPORT_TEXT }}" + } + } + ] + } + env: + SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/check_tiny_models.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/check_tiny_models.yml new file mode 100644 index 0000000000000000000000000000000000000000..a2b4846051a05460d62a86cdc45d59b1fa323487 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/check_tiny_models.yml @@ -0,0 +1,82 @@ +name: Check Tiny Models + +on: + push: + branches: + - check_tiny_models* + repository_dispatch: + schedule: + - cron: "0 2 * * *" + +env: + TOKEN: ${{ secrets.TRANSFORMERS_HUB_BOT_HF_TOKEN }} + +jobs: + check_tiny_models: + name: Check tiny models + runs-on: ubuntu-22.04 + steps: + - name: Checkout transformers + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - uses: actions/checkout@v4 + - name: Set up Python 3.8 + uses: actions/setup-python@v5 + with: + # Semantic version range syntax or exact version of a Python version + python-version: '3.8' + # Optional - x64 or x86 architecture, defaults to x64 + architecture: 'x64' + + - name: Install + run: | + sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng cmake + pip install --upgrade pip + python -m pip install -U .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm,video,tf-cpu] + pip install tensorflow_probability + python -m pip install -U 'natten<0.15.0' + + - name: Create all tiny models (locally) + run: | + python utils/create_dummy_models.py tiny_local_models --all --num_workers 2 + + - name: Local tiny model reports artifacts + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: tiny_local_model_creation_reports + path: tiny_local_models/reports + + # GitHub-hosted runners have 2-core CPUs + - name: Run pipeline tests against all new (local) tiny models + run: | + OMP_NUM_THREADS=1 TRANSFORMERS_TINY_MODEL_PATH=tiny_local_models python -m pytest --max-worker-restart=0 -n 2 --dist=loadfile -s -rA --make-reports=tests_pipelines tests/models -m is_pipeline_test -k "test_pipeline_" | tee tests_output.txt + + - name: Test suite reports artifacts + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: tiny_local_model_creation_reports + path: reports/tests_pipelines + + - name: Create + Upload tiny models for new model architecture(s) + run: | + python utils/update_tiny_models.py --num_workers 2 + + - name: Full report + run: cat tiny_models/reports/tiny_model_creation_report.json + + - name: Failure report + run: cat tiny_models/reports/simple_failed_report.txt + + - name: Summary report + run: cat tiny_models/reports/tiny_model_summary.json + + - name: New tiny model creation reports artifacts + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: tiny_model_creation_reports + path: tiny_models/reports diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/doctest_job.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/doctest_job.yml new file mode 100644 index 0000000000000000000000000000000000000000..eb62b797b8eb5575a33a6c0452563d809ce7fce7 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/doctest_job.yml @@ -0,0 +1,83 @@ +name: Doctest job + +on: + workflow_call: + inputs: + job_splits: + required: true + type: string + split_keys: + required: true + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + RUN_SLOW: yes + OMP_NUM_THREADS: 16 + MKL_NUM_THREADS: 16 + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + +jobs: + run_doctests: + name: " " + strategy: + max-parallel: 8 # 8 jobs at a time + fail-fast: false + matrix: + split_keys: ${{ fromJson(inputs.split_keys) }} + runs-on: + group: aws-g4dn-2xlarge-cache + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .[flax] + + - name: GPU visibility + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + run: pip freeze + + - name: Get doctest files + working-directory: /transformers + run: | + echo "${{ toJson(fromJson(inputs.job_splits)[matrix.split_keys]) }}" > doc_tests.txt + cat doc_tests.txt + + - name: Set `split_keys` + shell: bash + run: | + echo "${{ matrix.split_keys }}" + split_keys=${{ matrix.split_keys }} + split_keys=${split_keys//'/'/'_'} + echo "split_keys" + echo "split_keys=$split_keys" >> $GITHUB_ENV + + - name: Run doctests + working-directory: /transformers + run: | + cat doc_tests.txt + python3 -m pytest -v --make-reports doc_tests_gpu_${{ env.split_keys }} --doctest-modules $(cat doc_tests.txt) -sv --doctest-continue-on-failure --doctest-glob="*.md" + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/doc_tests_gpu_${{ env.split_keys }}/failures_short.txt + + - name: "Test suite reports artifacts: doc_tests_gpu_test_reports_${{ env.split_keys }}" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: doc_tests_gpu_test_reports_${{ env.split_keys }} + path: /transformers/reports/doc_tests_gpu_${{ env.split_keys }} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/doctests.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/doctests.yml new file mode 100644 index 0000000000000000000000000000000000000000..472b07684ed12ada716cc5f561665e5947c2f835 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/doctests.yml @@ -0,0 +1,89 @@ +name: Doctests + +on: + push: + branches: + - run_doctest* + repository_dispatch: + schedule: + - cron: "17 2 * * *" + +env: + NUM_SLICES: 3 + +jobs: + setup: + name: Setup + runs-on: + group: aws-g4dn-2xlarge-cache + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + outputs: + job_splits: ${{ steps.set-matrix.outputs.job_splits }} + split_keys: ${{ steps.set-matrix.outputs.split_keys }} + steps: + - name: Update clone + working-directory: /transformers + run: | + git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Check values for matrix + working-directory: /transformers + run: | + python3 utils/split_doctest_jobs.py + python3 utils/split_doctest_jobs.py --only_return_keys --num_splits ${{ env.NUM_SLICES }} + + - id: set-matrix + working-directory: /transformers + name: Set values for matrix + run: | + echo "job_splits=$(python3 utils/split_doctest_jobs.py)" >> $GITHUB_OUTPUT + echo "split_keys=$(python3 utils/split_doctest_jobs.py --only_return_keys --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT + + call_doctest_job: + name: "Call doctest jobs" + needs: setup + strategy: + max-parallel: 1 # 1 split at a time (in `doctest_job.yml`, we set `8` to run 8 jobs at the same time) + fail-fast: false + matrix: + split_keys: ${{ fromJson(needs.setup.outputs.split_keys) }} + uses: ./.github/workflows/doctest_job.yml + with: + job_splits: ${{ needs.setup.outputs.job_splits }} + split_keys: ${{ toJson(matrix.split_keys) }} + secrets: inherit + + send_results: + name: Send results to webhook + runs-on: ubuntu-22.04 + if: always() + needs: [call_doctest_job] + steps: + - uses: actions/checkout@v4 + - uses: actions/download-artifact@v4 + - name: Send message to Slack + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + # Use `CI_SLACK_CHANNEL_DUMMY_TESTS` when doing experimentation + SLACK_REPORT_CHANNEL: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY_DOCS }} + run: | + pip install slack_sdk + python utils/notification_service_doc_tests.py + + - name: "Upload results" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: doc_test_results + path: doc_test_results diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/model_jobs.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/model_jobs.yml new file mode 100644 index 0000000000000000000000000000000000000000..001e2c531d9bc894b6628656780ab230adb5c162 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/model_jobs.yml @@ -0,0 +1,139 @@ +name: model jobs + +on: + workflow_call: + inputs: + folder_slices: + required: true + type: string + machine_type: + required: true + type: string + slice_id: + required: true + type: number + runner: + required: true + type: string + docker: + required: true + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. + # This token is created under the bot `hf-transformers-bot`. + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + RUN_PT_TF_CROSS_TESTS: 1 + CUDA_VISIBLE_DEVICES: 0,1 + +jobs: + run_models_gpu: + name: " " + strategy: + max-parallel: 8 + fail-fast: false + matrix: + folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }} + runs-on: + group: '${{ inputs.machine_type }}' + container: + image: ${{ inputs.docker }} + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Echo input and matrix info + shell: bash + run: | + echo "${{ inputs.folder_slices }}" + echo "${{ matrix.folders }}" + echo "${{ toJson(fromJson(inputs.folder_slices)[inputs.slice_id]) }}" + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') }} + working-directory: /transformers + run: | + python3 -m pip install -U datasets + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') && contains(inputs.docker, '-pytorch-') }} + working-directory: /transformers + run: | + python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ inputs.machine_type }}" + + if [ "${{ inputs.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ inputs.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ inputs.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run all tests on GPU + working-directory: /transformers + run: python3 -m pytest -rsfE -v --make-reports=${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: Run test + shell: bash + run: | + mkdir -p /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports + echo "hello" > /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt + echo "${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports" + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/model_jobs_amd.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/model_jobs_amd.yml new file mode 100644 index 0000000000000000000000000000000000000000..a7e6c7b1ccd5761c9aa8af818e2a54a6755e1e7d --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/model_jobs_amd.yml @@ -0,0 +1,129 @@ +name: model jobs + +on: + workflow_call: + inputs: + folder_slices: + required: true + type: string + machine_type: + required: true + type: string + slice_id: + required: true + type: number + runner: + required: true + type: string + docker: + required: true + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. + # This token is created under the bot `hf-transformers-bot`. + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + RUN_PT_TF_CROSS_TESTS: 1 + CUDA_VISIBLE_DEVICES: 0,1 + +jobs: + run_models_gpu: + name: " " + strategy: + max-parallel: 1 # For now, not to parallelize. Can change later if it works well. + fail-fast: false + matrix: + folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }} + runs-on: ['${{ inputs.machine_type }}', self-hosted, amd-gpu, '${{ inputs.runner }}'] + container: + image: ${{ inputs.docker }} + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Echo input and matrix info + shell: bash + run: | + echo "${{ inputs.folder_slices }}" + echo "${{ matrix.folders }}" + echo "${{ toJson(fromJson(inputs.folder_slices)[inputs.slice_id]) }}" + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') }} + working-directory: /transformers + run: | + python3 -m pip install -U datasets + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') && contains(inputs.docker, '-pytorch-') }} + working-directory: /transformers + run: | + python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + + - name: ROCM-SMI + run: | + rocm-smi + + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all tests on GPU + working-directory: /transformers + run: python3 -m pytest -rsfE -v --make-reports=${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} -m "not not_device_test" + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: Run test + shell: bash + run: | + mkdir -p /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports + echo "hello" > /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt + echo "${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports" + + - name: "Test suite reports artifacts: ${{ inputs.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ inputs.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/push-important-models.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/push-important-models.yml new file mode 100644 index 0000000000000000000000000000000000000000..7294777655e18398753a832010f581c4959d90f3 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/push-important-models.yml @@ -0,0 +1,136 @@ +name: Slow tests on important models (on Push - A10) + +on: + push: + branches: [ main ] + +env: + OUTPUT_SLACK_CHANNEL_ID: "C06L2SGMEEA" + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. # This token is created under the bot `hf-transformers-bot`. + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + RUN_PT_TF_CROSS_TESTS: 1 + +jobs: + get_modified_models: + name: "Get all modified files" + runs-on: ubuntu-latest + outputs: + matrix: ${{ steps.set-matrix.outputs.matrix }} + steps: + - name: Check out code + uses: actions/checkout@v4 + + - name: Get changed files + id: changed-files + uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42 + with: + files: src/transformers/models/** + + - name: Run step if only the files listed above change + if: steps.changed-files.outputs.any_changed == 'true' + id: set-matrix + env: + ALL_CHANGED_FILES: ${{ steps.changed-files.outputs.all_changed_files }} + run: | + model_arrays=() + for file in $ALL_CHANGED_FILES; do + model_path="${file#*models/}" + model_path="models/${model_path%%/*}" + if grep -qFx "$model_path" utils/important_models.txt; then + # Append the file to the matrix string + model_arrays+=("$model_path") + fi + done + matrix_string=$(printf '"%s", ' "${model_arrays[@]}" | sed 's/, $//') + echo "matrix=[$matrix_string]" >> $GITHUB_OUTPUT + test_modified_files: + needs: get_modified_models + name: Slow & FA2 tests + runs-on: + group: aws-g5-4xlarge-cache + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + if: ${{ needs.get_modified_models.outputs.matrix != '[]' && needs.get_modified_models.outputs.matrix != '' && fromJson(needs.get_modified_models.outputs.matrix)[0] != null }} + strategy: + fail-fast: false + matrix: + model-name: ${{ fromJson(needs.get_modified_models.outputs.matrix) }} + + steps: + - name: Check out code + uses: actions/checkout@v4 + + - name: Install locally transformers & other libs + run: | + apt install sudo + sudo -H pip install --upgrade pip + sudo -H pip uninstall -y transformers + sudo -H pip install -U -e ".[testing]" + MAX_JOBS=4 pip install flash-attn --no-build-isolation + pip install bitsandbytes + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Show installed libraries and their versions + run: pip freeze + + - name: Run FA2 tests + id: run_fa2_tests + run: + pytest -rsfE -m "flash_attn_test" --make-reports=${{ matrix.model-name }}_fa2_tests/ tests/${{ matrix.model-name }}/test_modeling_* + + - name: "Test suite reports artifacts: ${{ matrix.model-name }}_fa2_tests" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ matrix.model-name }}_fa2_tests + path: /transformers/reports/${{ matrix.model-name }}_fa2_tests + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ env.OUTPUT_SLACK_CHANNEL_ID }} + title: 🤗 Results of the FA2 tests - ${{ matrix.model-name }} + status: ${{ steps.run_fa2_tests.conclusion}} + slack_token: ${{ secrets.CI_SLACK_BOT_TOKEN }} + + - name: Run integration tests + id: run_integration_tests + if: always() + run: + pytest -rsfE -k "IntegrationTest" --make-reports=tests_integration_${{ matrix.model-name }} tests/${{ matrix.model-name }}/test_modeling_* + + - name: "Test suite reports artifacts: tests_integration_${{ matrix.model-name }}" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: tests_integration_${{ matrix.model-name }} + path: /transformers/reports/tests_integration_${{ matrix.model-name }} + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ env.OUTPUT_SLACK_CHANNEL_ID }} + title: 🤗 Results of the Integration tests - ${{ matrix.model-name }} + status: ${{ steps.run_integration_tests.conclusion}} + slack_token: ${{ secrets.CI_SLACK_BOT_TOKEN }} + + - name: Tailscale # In order to be able to SSH when a test fails + if: ${{ runner.debug == '1'}} + uses: huggingface/tailscale-action@v1 + with: + authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }} + slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }} + slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + waitForSSH: true diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/release-conda.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/release-conda.yml new file mode 100644 index 0000000000000000000000000000000000000000..c0e28d7a510d7fae787b7b007ac4235360a5d666 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/release-conda.yml @@ -0,0 +1,47 @@ +name: Release - Conda + +on: + push: + tags: + - v* + branches: + - conda_* + +env: + ANACONDA_API_TOKEN: ${{ secrets.ANACONDA_API_TOKEN }} + +jobs: + build_and_package: + runs-on: ubuntu-22.04 + defaults: + run: + shell: bash -l {0} + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Install miniconda + uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + auto-activate-base: false + python-version: 3.8 + activate-environment: "build-transformers" + channels: huggingface + + - name: Setup conda env + run: | + conda install -c defaults anaconda-client conda-build + + - name: Extract version + run: echo "TRANSFORMERS_VERSION=`python setup.py --version`" >> $GITHUB_ENV + + - name: Build conda packages + run: | + conda info + conda list + conda-build .github/conda + + - name: Upload to Anaconda + run: anaconda upload `conda-build .github/conda --output` --force diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-nightly-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-nightly-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..5538e2d56e749091d0b7fdccdf24ed95b39e5a3d --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-nightly-caller.yml @@ -0,0 +1,43 @@ +name: Self-hosted runner (nightly-ci) + + +on: + repository_dispatch: + schedule: + - cron: "17 2 * * *" + push: + branches: + - run_nightly_ci* + +jobs: + build_nightly_ci_images: + name: Build Nightly CI Docker Images + if: (github.event_name == 'schedule') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_nightly_ci')) + uses: ./.github/workflows/build-nightly-ci-docker-images.yml + secrets: inherit + + model-ci: + name: Model CI + needs: [build_nightly_ci_images] + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_models_gpu + slack_report_channel: "#transformers-ci-past-future" + runner: ci + docker: huggingface/transformers-all-latest-torch-nightly-gpu + ci_event: Nightly CI + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + needs: [build_nightly_ci_images] + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#transformers-ci-past-future" + runner: ci + # test deepspeed nightly build with the latest release torch + docker: huggingface/transformers-pytorch-deepspeed-latest-gpu + ci_event: Nightly CI + working-directory-prefix: /workspace + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-nightly-past-ci-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-nightly-past-ci-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..46d811d4a43394a86f256669b69d19cf5d109fab --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-nightly-past-ci-caller.yml @@ -0,0 +1,99 @@ +name: Self-hosted runner (nightly-past-ci-caller) + +on: + schedule: + - cron: "17 2,14 * * *" + push: + branches: + - run_past_ci* + +jobs: + get_number: + name: Get number + runs-on: ubuntu-22.04 + outputs: + run_number: ${{ steps.get_number.outputs.run_number }} + steps: + - name: Get number + id: get_number + run: | + echo "${{ github.run_number }}" + echo "$(python3 -c 'print(int(${{ github.run_number }}) % 10)')" + echo "run_number=$(python3 -c 'print(int(${{ github.run_number }}) % 10)')" >> $GITHUB_OUTPUT + + run_past_ci_tensorflow_2-11: + name: TensorFlow 2.11 + needs: get_number + if: needs.get_number.outputs.run_number == 3 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.11" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-10: + name: TensorFlow 2.10 + needs: get_number + if: needs.get_number.outputs.run_number == 4 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.10" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-9: + name: TensorFlow 2.9 + needs: get_number + if: needs.get_number.outputs.run_number == 5 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.9" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-8: + name: TensorFlow 2.8 + needs: get_number + if: needs.get_number.outputs.run_number == 6 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.8" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-7: + name: TensorFlow 2.7 + needs: get_number + if: needs.get_number.outputs.run_number == 7 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.7" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-6: + name: TensorFlow 2.6 + needs: get_number + if: needs.get_number.outputs.run_number == 8 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.6" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-5: + name: TensorFlow 2.5 + needs: get_number + if: needs.get_number.outputs.run_number == 9 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.5" + sha: ${{ github.sha }} + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-past-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-past-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..1929a01c34d94726d1e820924933cec1412a02d6 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-past-caller.yml @@ -0,0 +1,40 @@ +name: Self-hosted runner (past-ci) + + +on: + workflow_call: + inputs: + framework: + required: true + type: string + version: + required: true + type: string + # Use this to control the commit to test against + sha: + default: 'main' + required: false + type: string + +jobs: + model-ci: + name: Model CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_models_gpu + slack_report_channel: "#transformers-ci-past-future" + runner: past-ci + docker: huggingface/transformers-${{ inputs.framework }}-past-${{ inputs.version }}-gpu + ci_event: Past CI - ${{ inputs.framework }}-${{ inputs.version }} + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#transformers-ci-past-future" + runner: past-ci + docker: huggingface/transformers-${{ inputs.framework }}-past-${{ inputs.version }}-gpu + ci_event: Past CI - ${{ inputs.framework }}-${{ inputs.version }} + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd-mi210-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd-mi210-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..45b325f7b357bf838f2f876bb7f4748ed6204ee2 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd-mi210-caller.yml @@ -0,0 +1,25 @@ +name: Self-hosted runner (AMD mi210 CI caller) + +on: + #workflow_run: + # workflows: ["Self-hosted runner (push-caller)"] + # branches: ["main"] + # types: [completed] + push: + branches: + - run_amd_push_ci_caller* + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + +jobs: + run_amd_ci: + name: AMD mi210 + if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_amd_push_ci_caller'))) + uses: ./.github/workflows/self-push-amd.yml + with: + gpu_flavor: mi210 + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd-mi250-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd-mi250-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..91b978b593d0b5b37d2415677a35c8f73c434dac --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd-mi250-caller.yml @@ -0,0 +1,25 @@ +name: Self-hosted runner (AMD mi250 CI caller) + +on: + #workflow_run: + # workflows: ["Self-hosted runner (push-caller)"] + # branches: ["main"] + # types: [completed] + push: + branches: + - run_amd_push_ci_caller* + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + +jobs: + run_amd_ci: + name: AMD mi250 + if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_amd_push_ci_caller'))) + uses: ./.github/workflows/self-push-amd.yml + with: + gpu_flavor: mi250 + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd-mi300-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd-mi300-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..797916125a24fbaf75ae74e87eddbd46a7507154 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd-mi300-caller.yml @@ -0,0 +1,25 @@ +name: Self-hosted runner (AMD mi300 CI caller) + +on: + #workflow_run: + # workflows: ["Self-hosted runner (push-caller)"] + # branches: ["main"] + # types: [completed] + push: + branches: + - run_amd_push_ci_caller* + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + +jobs: + run_amd_ci: + name: AMD mi300 + if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && (startsWith(github.ref_name, 'run_amd_push_ci_caller') || startsWith(github.ref_name, 'mi300-ci')))) + uses: ./.github/workflows/self-push-amd.yml + with: + gpu_flavor: mi300 + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd.yml new file mode 100644 index 0000000000000000000000000000000000000000..6931c2f3eadcad4832960d2d236d63bf576b65f2 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-amd.yml @@ -0,0 +1,335 @@ +name: Self-hosted runner AMD GPU (push) + +on: + workflow_call: + inputs: + gpu_flavor: + required: true + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + PYTEST_TIMEOUT: 60 + TF_FORCE_GPU_ALLOW_GROWTH: true + RUN_PT_TF_CROSS_TESTS: 1 + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + +jobs: + check_runner_status: + name: Check Runner Status + runs-on: ubuntu-22.04 + steps: + - name: Checkout transformers + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - name: Check Runner Status + run: python utils/check_self_hosted_runner.py --target_runners amd-mi210-single-gpu-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + + check_runners: + name: Check Runners + needs: check_runner_status + strategy: + matrix: + machine_type: [single-gpu, multi-gpu] + runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}'] + container: + image: huggingface/transformers-pytorch-amd-gpu-push-ci # <--- We test only for PyTorch for now + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: ROCM-SMI + run: | + rocm-smi + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + setup_gpu: + name: Setup + needs: check_runners + strategy: + matrix: + machine_type: [single-gpu, multi-gpu] + runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}'] + container: + image: huggingface/transformers-pytorch-amd-gpu-push-ci # <--- We test only for PyTorch for now + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + outputs: + matrix: ${{ steps.set-matrix.outputs.matrix }} + test_map: ${{ steps.set-matrix.outputs.test_map }} + env: + # `CI_BRANCH_PUSH`: The branch name from the push event + # `CI_BRANCH_WORKFLOW_RUN`: The name of the branch on which this workflow is triggered by `workflow_run` event + # `CI_SHA_PUSH`: The commit SHA from the push event + # `CI_SHA_WORKFLOW_RUN`: The commit SHA that triggers this workflow by `workflow_run` event + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # `CI_BRANCH`: The non-empty branch name from the above two (one and only one of them is empty) + # `CI_SHA`: The non-empty commit SHA from the above two (one and only one of them is empty) + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Cleanup + working-directory: /transformers + run: | + rm -rf tests/__pycache__ + rm -rf tests/models/__pycache__ + rm -rf reports + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Fetch the tests to run + working-directory: /transformers + # TODO: add `git-python` in the docker images + run: | + pip install --upgrade git-python + python3 utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt + + - name: Report fetched tests + uses: actions/upload-artifact@v4 + with: + name: test_fetched + path: /transformers/test_preparation.txt + + - id: set-matrix + name: Organize tests into models + working-directory: /transformers + # The `keys` is used as GitHub actions matrix for jobs, i.e. `models/bert`, `tokenization`, `pipeline`, etc. + # The `test_map` is used to get the actual identified test files under each key. + # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail) + run: | + if [ -f test_map.json ]; then + keys=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); d = list(test_map.keys()); print(d)') + test_map=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); print(test_map)') + else + keys=$(python3 -c 'keys = ["dummy"]; print(keys)') + test_map=$(python3 -c 'test_map = {"dummy": []}; print(test_map)') + fi + echo $keys + echo $test_map + echo "matrix=$keys" >> $GITHUB_OUTPUT + echo "test_map=$test_map" >> $GITHUB_OUTPUT + + run_models_gpu: + name: Model tests + needs: setup_gpu + # `dummy` means there is no test to run + if: contains(fromJson(needs.setup_gpu.outputs.matrix), 'dummy') != true + strategy: + fail-fast: false + matrix: + folders: ${{ fromJson(needs.setup_gpu.outputs.matrix) }} + machine_type: [single-gpu, multi-gpu] + runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}'] + container: + image: huggingface/transformers-pytorch-amd-gpu-push-ci # <--- We test only for PyTorch for now + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + echo "${{ fromJson(needs.setup_gpu.outputs.test_map)[matrix.folders] }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: ROCM-SMI + run: | + rocm-smi + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports ${{ fromJson(needs.setup_gpu.outputs.test_map)[matrix.folders] }} -m "not not_device_test" + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports + + send_results: + name: Send results to webhook + runs-on: ubuntu-22.04 + if: always() + needs: [ + check_runner_status, + check_runners, + setup_gpu, + run_models_gpu, +# run_tests_torch_cuda_extensions_single_gpu, +# run_tests_torch_cuda_extensions_multi_gpu + ] + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + - name: Preliminary job status + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + echo "Runner availability: ${{ needs.check_runner_status.result }}" + echo "Setup status: ${{ needs.setup_gpu.result }}" + echo "Runner status: ${{ needs.check_runners.result }}" + + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - uses: actions/checkout@v4 + # To avoid failure when multiple commits are merged into `main` in a short period of time. + # Checking out to an old commit beyond the fetch depth will get an error `fatal: reference is not a tree: ... + # (Only required for `workflow_run` event, where we get the latest HEAD on `main` instead of the event commit) + with: + fetch-depth: 20 + + - name: Update clone using environment variables + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - uses: actions/download-artifact@v4 + - name: Send message to Slack + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} + CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} + CI_SLACK_CHANNEL_ID_AMD: ${{ secrets.CI_SLACK_CHANNEL_ID_AMD }} + CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }} + CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_AMD }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + CI_EVENT: Push CI (AMD) - ${{ inputs.gpu_flavor }} + CI_TITLE_PUSH: ${{ github.event.head_commit.message }} + CI_TITLE_WORKFLOW_RUN: ${{ github.event.workflow_run.head_commit.message }} + CI_SHA: ${{ env.CI_SHA }} + RUNNER_STATUS: ${{ needs.check_runner_status.result }} + RUNNER_ENV_STATUS: ${{ needs.check_runners.result }} + SETUP_STATUS: ${{ needs.setup_gpu.result }} + + # We pass `needs.setup_gpu.outputs.matrix` as the argument. A processing in `notification_service.py` to change + # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`. + run: | + pip install huggingface_hub + pip install slack_sdk + pip show slack_sdk + python utils/notification_service.py "${{ needs.setup_gpu.outputs.matrix }}" diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..59adde4c54e077b4e944f8f6ae578cb6de05f8b4 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push-caller.yml @@ -0,0 +1,54 @@ +# Used to trigger self-push CI +name: Self-hosted runner (push-caller) + +on: + push: + branches: + - main + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + +jobs: + check-for-setup: + runs-on: ubuntu-22.04 + name: Check if setup was changed + outputs: + changed: ${{ steps.was_changed.outputs.changed }} + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: "2" + + - name: Get changed files + id: changed-files + uses: tj-actions/changed-files@v41 + + - name: Was setup changed + id: was_changed + run: | + for file in ${{ steps.changed-files.outputs.all_changed_files }}; do + if [ `basename "${file}"` = "setup.py" ]; then + echo "changed=1" >> $GITHUB_OUTPUT + fi + done + + build-docker-containers: + needs: check-for-setup + if: (github.event_name == 'push') && (needs.check-for-setup.outputs.changed == '1') + uses: ./.github/workflows/build-docker-images.yml + with: + image_postfix: "-push-ci" + secrets: inherit + + run_push_ci: + name: Trigger Push CI + runs-on: ubuntu-22.04 + if: ${{ always() }} + needs: build-docker-containers + steps: + - name: Trigger push CI via workflow_run + run: echo "Trigger push CI via workflow_run" \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push.yml new file mode 100644 index 0000000000000000000000000000000000000000..940495c2875327d636cc949d157806453e4b22e7 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-push.yml @@ -0,0 +1,653 @@ +name: Self-hosted runner (push) + +on: + workflow_run: + workflows: ["Self-hosted runner (push-caller)"] + branches: ["main"] + types: [completed] + push: + branches: + - ci_* + - ci-* + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + repository_dispatch: + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + PYTEST_TIMEOUT: 60 + TF_FORCE_GPU_ALLOW_GROWTH: true + RUN_PT_TF_CROSS_TESTS: 1 + CUDA_VISIBLE_DEVICES: 0,1 + +jobs: + setup: + name: Setup + strategy: + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu-push-ci + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + outputs: + matrix: ${{ steps.set-matrix.outputs.matrix }} + test_map: ${{ steps.set-matrix.outputs.test_map }} + env: + # `CI_BRANCH_PUSH`: The branch name from the push event + # `CI_BRANCH_WORKFLOW_RUN`: The name of the branch on which this workflow is triggered by `workflow_run` event + # `CI_SHA_PUSH`: The commit SHA from the push event + # `CI_SHA_WORKFLOW_RUN`: The commit SHA that triggers this workflow by `workflow_run` event + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # `CI_BRANCH`: The non-empty branch name from the above two (one and only one of them is empty) + # `CI_SHA`: The non-empty commit SHA from the above two (one and only one of them is empty) + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Cleanup + working-directory: /transformers + run: | + rm -rf tests/__pycache__ + rm -rf tests/models/__pycache__ + rm -rf reports + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Fetch the tests to run + working-directory: /transformers + # TODO: add `git-python` in the docker images + run: | + pip install --upgrade git-python + python3 utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt + + - name: Report fetched tests + uses: actions/upload-artifact@v4 + with: + name: test_fetched + path: /transformers/test_preparation.txt + + - id: set-matrix + name: Organize tests into models + working-directory: /transformers + # The `keys` is used as GitHub actions matrix for jobs, i.e. `models/bert`, `tokenization`, `pipeline`, etc. + # The `test_map` is used to get the actual identified test files under each key. + # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail) + run: | + if [ -f test_map.json ]; then + keys=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); d = list(test_map.keys()); print(d)') + test_map=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); print(test_map)') + else + keys=$(python3 -c 'keys = ["dummy"]; print(keys)') + test_map=$(python3 -c 'test_map = {"dummy": []}; print(test_map)') + fi + echo $keys + echo $test_map + echo "matrix=$keys" >> $GITHUB_OUTPUT + echo "test_map=$test_map" >> $GITHUB_OUTPUT + + run_tests_single_gpu: + name: Model tests + needs: setup + # `dummy` means there is no test to run + if: contains(fromJson(needs.setup.outputs.matrix), 'dummy') != true + strategy: + fail-fast: false + matrix: + folders: ${{ fromJson(needs.setup.outputs.matrix) }} + machine_type: [aws-g4dn-2xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu-push-ci + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + echo "${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} + + run_tests_multi_gpu: + name: Model tests + needs: setup + # `dummy` means there is no test to run + if: contains(fromJson(needs.setup.outputs.matrix), 'dummy') != true + strategy: + fail-fast: false + matrix: + folders: ${{ fromJson(needs.setup.outputs.matrix) }} + machine_type: [aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu-push-ci + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + echo "${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + env: + MKL_SERVICE_FORCE_INTEL: 1 + working-directory: /transformers + run: | + python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} + + run_tests_torch_cuda_extensions_single_gpu: + name: Torch CUDA extension tests + needs: setup + if: contains(fromJson(needs.setup.outputs.matrix), 'deepspeed') || contains(fromJson(needs.setup.outputs.matrix), 'extended') + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Set `machine_type` for report and artifact names + working-directory: /workspace/transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Update clone using environment variables + working-directory: /workspace/transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /workspace/transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Remove cached torch extensions + run: rm -rf /github/home/.cache/torch_extensions/ + + # To avoid unknown test failures + - name: Pre build DeepSpeed *again* + working-directory: /workspace + run: | + python3 -m pip uninstall -y deepspeed + DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /workspace/transformers + run: | + python utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /workspace/transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + working-directory: /workspace/transformers + # TODO: Here we pass all tests in the 2 folders for simplicity. It's better to pass only the identified tests. + run: | + python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + path: /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + + run_tests_torch_cuda_extensions_multi_gpu: + name: Torch CUDA extension tests + needs: setup + if: contains(fromJson(needs.setup.outputs.matrix), 'deepspeed') || contains(fromJson(needs.setup.outputs.matrix), 'extended') + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Set `machine_type` for report and artifact names + working-directory: /workspace/transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Update clone using environment variables + working-directory: /workspace/transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /workspace/transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Remove cached torch extensions + run: rm -rf /github/home/.cache/torch_extensions/ + + # To avoid unknown test failures + - name: Pre build DeepSpeed *again* + working-directory: /workspace + run: | + python3 -m pip uninstall -y deepspeed + DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /workspace/transformers + run: | + python utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /workspace/transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + working-directory: /workspace/transformers + # TODO: Here we pass all tests in the 2 folders for simplicity. It's better to pass only the identified tests. + run: | + python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + path: /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + + send_results: + name: Send results to webhook + runs-on: ubuntu-22.04 + if: always() + needs: [ + setup, + run_tests_single_gpu, + run_tests_multi_gpu, + run_tests_torch_cuda_extensions_single_gpu, + run_tests_torch_cuda_extensions_multi_gpu + ] + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + - name: Preliminary job status + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + echo "Setup status: ${{ needs.setup.result }}" + + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - uses: actions/checkout@v4 + # To avoid failure when multiple commits are merged into `main` in a short period of time. + # Checking out to an old commit beyond the fetch depth will get an error `fatal: reference is not a tree: ... + # (Only required for `workflow_run` event, where we get the latest HEAD on `main` instead of the event commit) + with: + fetch-depth: 20 + + - name: Update clone using environment variables + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - uses: actions/download-artifact@v4 + - name: Send message to Slack + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} + CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} + CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }} + CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + CI_EVENT: push + CI_TITLE_PUSH: ${{ github.event.head_commit.message }} + CI_TITLE_WORKFLOW_RUN: ${{ github.event.workflow_run.head_commit.message }} + CI_SHA: ${{ env.CI_SHA }} + SETUP_STATUS: ${{ needs.setup.result }} + + # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change + # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`. + run: | + pip install huggingface_hub + pip install slack_sdk + pip show slack_sdk + python utils/notification_service.py "${{ needs.setup.outputs.matrix }}" diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..dc5c7b7e905bd88424452ab95da23af096bffa26 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd-caller.yml @@ -0,0 +1,14 @@ +name: Self-hosted runner (AMD scheduled CI caller) + +on: + schedule: + - cron: "17 2 * * *" + +jobs: + run_scheduled_amd_ci: + name: Trigger Scheduled AMD CI + runs-on: ubuntu-22.04 + if: ${{ always() }} + steps: + - name: Trigger scheduled AMD CI via workflow_run + run: echo "Trigger scheduled AMD CI via workflow_run" diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd-mi210-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd-mi210-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..1c79b38a314e0bdf57520b4e765fe66c574ae4fb --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd-mi210-caller.yml @@ -0,0 +1,55 @@ +name: Self-hosted runner (AMD mi210 scheduled CI caller) + +on: + workflow_run: + workflows: ["Self-hosted runner (AMD scheduled CI caller)"] + branches: ["main"] + types: [completed] + push: + branches: + - run_amd_scheduled_ci_caller* + +jobs: + model-ci: + name: Model CI + uses: ./.github/workflows/self-scheduled-amd.yml + with: + job: run_models_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi210 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi210 + secrets: inherit + + torch-pipeline: + name: Torch pipeline CI + uses: ./.github/workflows/self-scheduled-amd.yml + with: + job: run_pipelines_torch_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi210 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi210 + secrets: inherit + + example-ci: + name: Example CI + uses: ./.github/workflows/self-scheduled-amd.yml + with: + job: run_examples_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi210 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi210 + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + uses: ./.github/workflows/self-scheduled-amd.yml + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi210 + docker: huggingface/transformers-pytorch-deepspeed-amd-gpu + ci_event: Scheduled CI (AMD) - mi210 + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd-mi250-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd-mi250-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..fd151305716396ce8808c65fbde8e863d23f0568 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd-mi250-caller.yml @@ -0,0 +1,55 @@ +name: Self-hosted runner (AMD mi250 scheduled CI caller) + +on: + workflow_run: + workflows: ["Self-hosted runner (AMD scheduled CI caller)"] + branches: ["main"] + types: [completed] + push: + branches: + - run_amd_scheduled_ci_caller* + +jobs: + model-ci: + name: Model CI + uses: ./.github/workflows/self-scheduled-amd.yml + with: + job: run_models_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi250 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi250 + secrets: inherit + + torch-pipeline: + name: Torch pipeline CI + uses: ./.github/workflows/self-scheduled-amd.yml + with: + job: run_pipelines_torch_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi250 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi250 + secrets: inherit + + example-ci: + name: Example CI + uses: ./.github/workflows/self-scheduled-amd.yml + with: + job: run_examples_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi250 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi250 + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + uses: ./.github/workflows/self-scheduled-amd.yml + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi250 + docker: huggingface/transformers-pytorch-deepspeed-amd-gpu + ci_event: Scheduled CI (AMD) - mi250 + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd.yml new file mode 100644 index 0000000000000000000000000000000000000000..47f92cd6a2b086c5f7d71cdf1484f512b82caf5d --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-amd.yml @@ -0,0 +1,349 @@ +name: Self-hosted runner (scheduled-amd) + +# Note: For the AMD CI, we rely on a caller workflow and on the workflow_call event to trigger the +# CI in order to run it on both MI210 and MI250, without having to use matrix here which pushes +# us towards the limit of allowed jobs on GitHub Actions. + +on: + workflow_call: + inputs: + job: + required: true + type: string + slack_report_channel: + required: true + type: string + runner: + required: true + type: string + docker: + required: true + type: string + ci_event: + required: true + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + NUM_SLICES: 2 + +# Important note: each job (run_tests_single_gpu, run_tests_multi_gpu, run_examples_gpu, run_pipelines_torch_gpu) requires all the previous jobs before running. +# This is done so that we avoid parallelizing the scheduled tests, to leave available +# runners for the push CI that is running on the same machine. +jobs: + check_runner_status: + name: Check Runner Status + runs-on: ubuntu-22.04 + steps: + - name: Checkout transformers + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - name: Check Runner Status + run: python utils/check_self_hosted_runner.py --target_runners hf-amd-mi210-ci-1gpu-1,hf-amd-mi250-ci-1gpu-1,hf-amd-mi300-ci-1gpu-1 --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + + check_runners: + name: Check Runners + needs: check_runner_status + strategy: + matrix: + machine_type: [single-gpu, multi-gpu] + runs-on: ['${{ matrix.machine_type }}', self-hosted, amd-gpu, '${{ inputs.runner }}'] + container: + image: huggingface/transformers-pytorch-amd-gpu + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: ROCM-SMI + run: | + rocm-smi + + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + setup: + if: contains(fromJSON('["run_models_gpu"]'), inputs.job) + name: Setup + needs: check_runners + strategy: + matrix: + machine_type: [single-gpu, multi-gpu] + runs-on: ['${{ matrix.machine_type }}', self-hosted, amd-gpu, '${{ inputs.runner }}'] + container: + image: huggingface/transformers-pytorch-amd-gpu + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + outputs: + folder_slices: ${{ steps.set-matrix.outputs.folder_slices }} + slice_ids: ${{ steps.set-matrix.outputs.slice_ids }} + steps: + - name: Update clone + working-directory: /transformers + run: | + git fetch && git checkout ${{ github.sha }} + + - name: Cleanup + working-directory: /transformers + run: | + rm -rf tests/__pycache__ + rm -rf tests/models/__pycache__ + rm -rf reports + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - id: set-matrix + name: Identify models to test + working-directory: /transformers/tests + run: | + echo "folder_slices=$(python3 ../utils/split_model_tests.py --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT + echo "slice_ids=$(python3 -c 'd = list(range(${{ env.NUM_SLICES }})); print(d)')" >> $GITHUB_OUTPUT + + - name: ROCM-SMI + run: | + rocm-smi + + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + run_models_gpu: + if: ${{ inputs.job == 'run_models_gpu' }} + name: Single GPU tests + needs: setup + strategy: + max-parallel: 1 # For now, not to parallelize. Can change later if it works well. + fail-fast: false + matrix: + machine_type: [single-gpu, multi-gpu] + slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }} + uses: ./.github/workflows/model_jobs_amd.yml + with: + folder_slices: ${{ needs.setup.outputs.folder_slices }} + machine_type: ${{ matrix.machine_type }} + slice_id: ${{ matrix.slice_id }} + runner: ${{ inputs.runner }} + docker: ${{ inputs.docker }} + secrets: inherit + + run_pipelines_torch_gpu: + if: ${{ inputs.job == 'run_pipelines_torch_gpu' }} + name: PyTorch pipelines + needs: check_runners + strategy: + fail-fast: false + matrix: + machine_type: [single-gpu, multi-gpu] + runs-on: ['${{ matrix.machine_type }}', self-hosted, amd-gpu, '${{ inputs.runner }}'] + container: + image: ${{ inputs.docker }} + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: ROCM-SMI + run: | + rocm-smi + + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all pipeline tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports tests/pipelines -m "not not_device_test" + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports + path: /transformers/reports/${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports + + run_examples_gpu: + if: ${{ inputs.job == 'run_examples_gpu' }} + name: Examples directory + needs: check_runners + strategy: + fail-fast: false + matrix: + machine_type: [single-gpu] + runs-on: ['${{ matrix.machine_type }}', self-hosted, amd-gpu, '${{ inputs.runner }}'] + container: + image: ${{ inputs.docker }} + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: ROCM-SMI + run: | + rocm-smi + + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run examples tests on GPU + working-directory: /transformers + run: | + pip install -r examples/pytorch/_tests_requirements.txt + python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_run_examples_gpu_test_reports examples/pytorch -m "not not_device_test" + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ matrix.machine_type }}_run_examples_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_examples_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ matrix.machine_type }}_run_examples_gpu_test_reports + path: /transformers/reports/${{ matrix.machine_type }}_run_examples_gpu_test_reports + + run_torch_cuda_extensions_gpu: + if: ${{ inputs.job == 'run_torch_cuda_extensions_gpu' }} + name: Torch ROCm deepspeed tests + needs: check_runners + strategy: + fail-fast: false + matrix: + machine_type: [single-gpu, multi-gpu] + runs-on: ['${{ matrix.machine_type }}', self-hosted, amd-gpu, '${{ inputs.runner }}'] + container: + image: ${{ inputs.docker }} + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: ROCM-SMI + run: | + rocm-smi + + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all tests on GPU + working-directory: /transformers + run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended -m "not not_device_test" + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + path: /transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + + send_results: + name: Slack Report + needs: [ + check_runner_status, + check_runners, + setup, + run_models_gpu, + run_pipelines_torch_gpu, + run_examples_gpu, + run_torch_cuda_extensions_gpu + ] + if: ${{ always() }} + uses: ./.github/workflows/slack-report.yml + with: + job: ${{ inputs.job }} + # This would be `skipped` if `setup` is skipped. + setup_status: ${{ needs.setup.result }} + slack_report_channel: ${{ inputs.slack_report_channel }} + # This would be an empty string if `setup` is skipped. + folder_slices: ${{ needs.setup.outputs.folder_slices }} + quantization_matrix: ${{ needs.setup.outputs.quantization_matrix }} + ci_event: ${{ inputs.ci_event }} + + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-caller.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..75ea3bb24bc7fa2acda33e001780bafc26bc5f0b --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled-caller.yml @@ -0,0 +1,78 @@ +name: Self-hosted runner (scheduled) + + +on: + repository_dispatch: + schedule: + - cron: "17 2 * * *" + push: + branches: + - run_scheduled_ci* + +jobs: + model-ci: + name: Model CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_models_gpu + slack_report_channel: "#transformers-ci-daily-models" + runner: daily-ci + docker: huggingface/transformers-all-latest-gpu + ci_event: Daily CI + secrets: inherit + + torch-pipeline: + name: Torch pipeline CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_pipelines_torch_gpu + slack_report_channel: "#transformers-ci-daily-pipeline-torch" + runner: daily-ci + docker: huggingface/transformers-pytorch-gpu + ci_event: Daily CI + secrets: inherit + + tf-pipeline: + name: TF pipeline CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_pipelines_tf_gpu + slack_report_channel: "#transformers-ci-daily-pipeline-tf" + runner: daily-ci + docker: huggingface/transformers-tensorflow-gpu + ci_event: Daily CI + secrets: inherit + + example-ci: + name: Example CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_examples_gpu + slack_report_channel: "#transformers-ci-daily-examples" + runner: daily-ci + docker: huggingface/transformers-all-latest-gpu + ci_event: Daily CI + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#transformers-ci-daily-deepspeed" + runner: daily-ci + docker: huggingface/transformers-pytorch-deepspeed-latest-gpu + ci_event: Daily CI + working-directory-prefix: /workspace + secrets: inherit + + quantization-ci: + name: Quantization CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_quantization_torch_gpu + slack_report_channel: "#transformers-ci-daily-quantization" + runner: daily-ci + docker: huggingface/transformers-quantization-latest-gpu + ci_event: Daily CI + secrets: inherit diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled.yml new file mode 100644 index 0000000000000000000000000000000000000000..353fb59843e4a5de503f7661998fd1133573a54d --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/self-scheduled.yml @@ -0,0 +1,574 @@ +name: Self-hosted runner (scheduled) + +# Note that each job's dependencies go into a corresponding docker file. +# +# For example for `run_torch_cuda_extensions_gpu` the docker image is +# `huggingface/transformers-pytorch-deepspeed-latest-gpu`, which can be found at +# `docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile` + +on: + workflow_call: + inputs: + job: + required: true + type: string + slack_report_channel: + required: true + type: string + runner: + required: true + type: string + docker: + required: true + type: string + ci_event: + required: true + type: string + working-directory-prefix: + default: '' + required: false + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. + # This token is created under the bot `hf-transformers-bot`. + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + RUN_PT_TF_CROSS_TESTS: 1 + CUDA_VISIBLE_DEVICES: 0,1 + NUM_SLICES: 2 + +jobs: + setup: + if: contains(fromJSON('["run_models_gpu", "run_quantization_torch_gpu"]'), inputs.job) + name: Setup + strategy: + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + outputs: + folder_slices: ${{ steps.set-matrix.outputs.folder_slices }} + slice_ids: ${{ steps.set-matrix.outputs.slice_ids }} + quantization_matrix: ${{ steps.set-matrix-quantization.outputs.quantization_matrix }} + steps: + - name: Update clone + working-directory: /transformers + run: | + git fetch && git checkout ${{ github.sha }} + + - name: Cleanup + working-directory: /transformers + run: | + rm -rf tests/__pycache__ + rm -rf tests/models/__pycache__ + rm -rf reports + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - id: set-matrix + if: ${{ inputs.job == 'run_models_gpu' }} + name: Identify models to test + working-directory: /transformers/tests + run: | + echo "folder_slices=$(python3 ../utils/split_model_tests.py --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT + echo "slice_ids=$(python3 -c 'd = list(range(${{ env.NUM_SLICES }})); print(d)')" >> $GITHUB_OUTPUT + + - id: set-matrix-quantization + if: ${{ inputs.job == 'run_quantization_torch_gpu' }} + name: Identify quantization method to test + working-directory: /transformers/tests + run: | + echo "quantization_matrix=$(python3 -c 'import os; tests = os.getcwd(); quantization_tests = os.listdir(os.path.join(tests, "quantization")); d = sorted(list(filter(os.path.isdir, [f"quantization/{x}" for x in quantization_tests]))) ; print(d)')" >> $GITHUB_OUTPUT + + - name: NVIDIA-SMI + run: | + nvidia-smi + + run_models_gpu: + if: ${{ inputs.job == 'run_models_gpu' }} + name: " " + needs: setup + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }} + uses: ./.github/workflows/model_jobs.yml + with: + folder_slices: ${{ needs.setup.outputs.folder_slices }} + machine_type: ${{ matrix.machine_type }} + slice_id: ${{ matrix.slice_id }} + runner: ${{ inputs.runner }} + docker: ${{ inputs.docker }} + secrets: inherit + + run_pipelines_torch_gpu: + if: ${{ inputs.job == 'run_pipelines_torch_gpu' }} + name: PyTorch pipelines + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-pytorch-gpu + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run all pipeline tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports tests/pipelines + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports + + run_pipelines_tf_gpu: + if: ${{ inputs.job == 'run_pipelines_tf_gpu' }} + name: TensorFlow pipelines + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-tensorflow-gpu + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: | + git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run all pipeline tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports tests/pipelines + + - name: Failure short reports + if: ${{ always() }} + run: | + cat /transformers/reports/${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports + + run_examples_gpu: + if: ${{ inputs.job == 'run_examples_gpu' }} + name: Examples directory + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run examples tests on GPU + working-directory: /transformers + run: | + pip install -r examples/pytorch/_tests_requirements.txt + python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_examples_gpu_test_reports examples/pytorch + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_examples_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_examples_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_examples_gpu_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_examples_gpu_test_reports + + run_torch_cuda_extensions_gpu: + if: ${{ inputs.job == 'run_torch_cuda_extensions_gpu' }} + name: Torch CUDA extension tests + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: ${{ inputs.docker }} + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') && contains(inputs.docker, '-pytorch-') }} + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: | + python3 -m pip install -U datasets + python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + + - name: Remove cached torch extensions + run: rm -rf /github/home/.cache/torch_extensions/ + + # To avoid unknown test failures + - name: Pre build DeepSpeed *again* (for daily CI) + if: ${{ contains(inputs.ci_event, 'Daily CI') }} + working-directory: ${{ inputs.working-directory-prefix }}/ + run: | + python3 -m pip uninstall -y deepspeed + DS_DISABLE_NINJA=1 DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check + + # To avoid unknown test failures + - name: Pre build DeepSpeed *again* (for nightly & Past CI) + if: ${{ contains(inputs.ci_event, 'Nightly CI') || contains(inputs.ci_event, 'Past CI') }} + working-directory: ${{ inputs.working-directory-prefix }}/ + run: | + python3 -m pip uninstall -y deepspeed + rm -rf DeepSpeed + git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build + DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run all tests on GPU + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: | + python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat ${{ inputs.working-directory-prefix }}/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + path: ${{ inputs.working-directory-prefix }}/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + + run_quantization_torch_gpu: + if: ${{ inputs.job == 'run_quantization_torch_gpu' }} + name: " " + needs: setup + strategy: + max-parallel: 4 + fail-fast: false + matrix: + folders: ${{ fromJson(needs.setup.outputs.quantization_matrix) }} + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-quantization-latest-gpu + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Echo folder ${{ matrix.folders }} + shell: bash + run: | + echo "${{ matrix.folders }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'quantization/'/'quantization_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run quantization tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports + + run_extract_warnings: + # Let's only do this for the job `run_models_gpu` to simplify the (already complex) logic. + if: ${{ always() && inputs.job == 'run_models_gpu' }} + name: Extract warnings in CI artifacts + runs-on: ubuntu-22.04 + needs: [setup, run_models_gpu] + steps: + - name: Checkout transformers + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - name: Install transformers + run: pip install transformers + + - name: Show installed libraries and their versions + run: pip freeze + + - name: Create output directory + run: mkdir warnings_in_ci + + - uses: actions/download-artifact@v4 + with: + path: warnings_in_ci + + - name: Show artifacts + run: echo "$(python3 -c 'import os; d = os.listdir(); print(d)')" + working-directory: warnings_in_ci + + - name: Extract warnings in CI artifacts + run: | + python3 utils/extract_warnings.py --workflow_run_id ${{ github.run_id }} --output_dir warnings_in_ci --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }} --from_gh + echo "$(python3 -c 'import os; import json; fp = open("warnings_in_ci/selected_warnings.json"); d = json.load(fp); d = "\n".join(d) ;print(d)')" + + - name: Upload artifact + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: warnings_in_ci + path: warnings_in_ci/selected_warnings.json + + send_results: + name: Slack Report + needs: [ + setup, + run_models_gpu, + run_pipelines_torch_gpu, + run_pipelines_tf_gpu, + run_examples_gpu, + run_torch_cuda_extensions_gpu, + run_quantization_torch_gpu, + run_extract_warnings + ] + if: ${{ always() }} + uses: ./.github/workflows/slack-report.yml + with: + job: ${{ inputs.job }} + # This would be `skipped` if `setup` is skipped. + setup_status: ${{ needs.setup.result }} + slack_report_channel: ${{ inputs.slack_report_channel }} + # This would be an empty string if `setup` is skipped. + folder_slices: ${{ needs.setup.outputs.folder_slices }} + quantization_matrix: ${{ needs.setup.outputs.quantization_matrix }} + ci_event: ${{ inputs.ci_event }} + + secrets: inherit + + check_new_model_failures: + if: ${{ always() && inputs.ci_event == 'Daily CI' && inputs.job == 'run_models_gpu' && needs.send_results.result == 'success' }} + name: Check new model failures + needs: send_results + uses: ./.github/workflows/check_failed_model_tests.yml + with: + docker: ${{ inputs.docker }} + start_sha: ${{ github.sha }} + secrets: inherit \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/slack-report.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/slack-report.yml new file mode 100644 index 0000000000000000000000000000000000000000..ee2962ba89c37f06c21dcb9579b9bbb0830c34fe --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/slack-report.yml @@ -0,0 +1,101 @@ +name: CI slack report + +on: + workflow_call: + inputs: + job: + required: true + type: string + slack_report_channel: + required: true + type: string + setup_status: + required: true + type: string + folder_slices: + required: true + type: string + quantization_matrix: + required: true + type: string + ci_event: + required: true + type: string + +env: + TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }} + +jobs: + send_results: + name: Send results to webhook + runs-on: ubuntu-22.04 + if: always() + steps: + - name: Preliminary job status + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + echo "Setup status: ${{ inputs.setup_status }}" + + - uses: actions/checkout@v4 + - uses: actions/download-artifact@v4 + - name: Send message to Slack + if: ${{ inputs.job != 'run_quantization_torch_gpu' }} + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} + CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} + CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }} + SLACK_REPORT_CHANNEL: ${{ inputs.slack_report_channel }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + CI_EVENT: ${{ inputs.ci_event }} + CI_SHA: ${{ github.sha }} + CI_WORKFLOW_REF: ${{ github.workflow_ref }} + CI_TEST_JOB: ${{ inputs.job }} + SETUP_STATUS: ${{ inputs.setup_status }} + # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change + # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`. + # For a job that doesn't depend on (i.e. `needs`) `setup`, the value for `inputs.folder_slices` would be an + # empty string, and the called script still get one argument (which is the emtpy string). + run: | + sudo apt-get install -y curl + pip install huggingface_hub + pip install slack_sdk + pip show slack_sdk + python utils/notification_service.py "${{ inputs.folder_slices }}" + + # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack. + - name: Failure table artifacts + uses: actions/upload-artifact@v4 + with: + name: ci_results_${{ inputs.job }} + path: ci_results_${{ inputs.job }} + + - uses: actions/checkout@v4 + - uses: actions/download-artifact@v4 + - name: Send message to Slack for quantization workflow + if: ${{ inputs.job == 'run_quantization_torch_gpu' }} + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + SLACK_REPORT_CHANNEL: ${{ inputs.slack_report_channel }} + CI_EVENT: ${{ inputs.ci_event }} + CI_SHA: ${{ github.sha }} + CI_TEST_JOB: ${{ inputs.job }} + SETUP_STATUS: ${{ inputs.setup_status }} + # We pass `needs.setup.outputs.quantization_matrix` as the argument. A processing in `notification_service_quantization.py` to change + # `quantization/bnb` to `quantization_bnb` is required, as the artifact names use `_` instead of `/`. + run: | + sudo apt-get install -y curl + pip install huggingface_hub + pip install slack_sdk + pip show slack_sdk + python utils/notification_service_quantization.py "${{ inputs.quantization_matrix }}" + + # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack. + - name: Failure table artifacts + if: ${{ inputs.job == 'run_quantization_torch_gpu' }} + uses: actions/upload-artifact@v4 + with: + name: ci_results_${{ inputs.job }} + path: ci_results_${{ inputs.job }} \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/ssh-runner.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/ssh-runner.yml new file mode 100644 index 0000000000000000000000000000000000000000..02b022698b0c5eb0df9edba71ffb047f1d941fd4 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/ssh-runner.yml @@ -0,0 +1,114 @@ +name: SSH into our runners + +on: + workflow_dispatch: + inputs: + runner_type: + description: 'Type of runner to test (a10 or t4)' + required: true + docker_image: + description: 'Name of the Docker image' + required: true + num_gpus: + description: 'Type of the number of gpus to use (`single` or `multi`)' + required: true + +env: + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. # This token is created under the bot `hf-transformers-bot`. + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + CUDA_VISIBLE_DEVICES: 0,1 + RUN_PT_TF_CROSS_TESTS: 1 + +jobs: + get_runner: + name: "Get runner to use" + runs-on: ubuntu-22.04 + outputs: + RUNNER: ${{ steps.set_runner.outputs.RUNNER }} + steps: + - name: Get runner to use + shell: bash + run: | + if [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then + echo "RUNNER=aws-g4dn-2xlarge-cache" >> $GITHUB_ENV + elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then + echo "RUNNER=aws-g4dn-12xlarge-cache" >> $GITHUB_ENV + elif [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then + echo "RUNNER=aws-g5-4xlarge-cache" >> $GITHUB_ENV + elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then + echo "RUNNER=aws-g5-12xlarge-cache" >> $GITHUB_ENV + else + echo "RUNNER=" >> $GITHUB_ENV + fi + + - name: Set runner to use + id: set_runner + run: | + echo ${{ env.RUNNER }} + echo "RUNNER=${{ env.RUNNER }}" >> $GITHUB_OUTPUT + + ssh_runner: + name: "SSH" + needs: get_runner + runs-on: + group: ${{ needs.get_runner.outputs.RUNNER }} + container: + image: ${{ github.event.inputs.docker_image }} + options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + + steps: + - name: Update clone + working-directory: /transformers + run: | + git fetch && git checkout ${{ github.sha }} + + - name: Cleanup + working-directory: /transformers + run: | + rm -rf tests/__pycache__ + rm -rf tests/models/__pycache__ + rm -rf reports + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Store Slack infos + #because the SSH can be enabled dynamically if the workflow failed, so we need to store slack infos to be able to retrieve them during the waitforssh step + shell: bash + run: | + echo "${{ github.actor }}" + github_actor=${{ github.actor }} + github_actor=${github_actor/'-'/'_'} + echo "$github_actor" + echo "github_actor=$github_actor" >> $GITHUB_ENV + + - name: Store Slack infos + #because the SSH can be enabled dynamically if the workflow failed, so we need to store slack infos to be able to retrieve them during the waitforssh step + shell: bash + run: | + echo "${{ env.github_actor }}" + if [ "${{ secrets[format('{0}_{1}', env.github_actor, 'SLACK_ID')] }}" != "" ]; then + echo "SLACKCHANNEL=${{ secrets[format('{0}_{1}', env.github_actor, 'SLACK_ID')] }}" >> $GITHUB_ENV + else + echo "SLACKCHANNEL=${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}" >> $GITHUB_ENV + fi + + - name: Tailscale # In order to be able to SSH when a test fails + uses: huggingface/tailscale-action@main + with: + authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }} + slackChannel: ${{ env.SLACKCHANNEL }} + slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + waitForSSH: true + sshTimeout: 15m diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/stale.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/stale.yml new file mode 100644 index 0000000000000000000000000000000000000000..65eaf755ab3a69f9d978f8ab6f7e14584918262c --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/stale.yml @@ -0,0 +1,29 @@ +name: Stale Bot + +on: + schedule: + - cron: "0 8 * * *" + +jobs: + close_stale_issues: + name: Close Stale Issues + if: github.repository == 'huggingface/transformers' + runs-on: ubuntu-22.04 + permissions: + issues: write + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v4 + + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version: 3.8 + + - name: Install requirements + run: | + pip install PyGithub + - name: Close stale issues + run: | + python scripts/stale.py diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/trufflehog.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/trufflehog.yml new file mode 100644 index 0000000000000000000000000000000000000000..29a11e9354dbb1c3c8b57e19da02d472cbfc3ec0 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/trufflehog.yml @@ -0,0 +1,18 @@ +on: + push: + +name: Secret Leaks + +permissions: + contents: read + +jobs: + trufflehog: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Secret Scanning + uses: trufflesecurity/trufflehog@main diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/update_metdata.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/update_metdata.yml new file mode 100644 index 0000000000000000000000000000000000000000..90cd73077ac0bb01ef4b14b2305ff4d39d612eba --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/update_metdata.yml @@ -0,0 +1,27 @@ +name: Update Transformers metadata + +on: + push: + branches: + - main + - update_transformers_metadata* + +jobs: + build_and_package: + runs-on: ubuntu-22.04 + defaults: + run: + shell: bash -l {0} + + steps: + - uses: actions/checkout@v4 + + - name: Setup environment + run: | + pip install --upgrade pip + pip install datasets pandas==2.0.3 + pip install .[torch,tf,flax] + + - name: Update metadata + run: | + python utils/update_metadata.py --token ${{ secrets.LYSANDRE_HF_TOKEN }} --commit_sha ${{ github.sha }} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/upload_pr_documentation.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/upload_pr_documentation.yml new file mode 100644 index 0000000000000000000000000000000000000000..64befc595c421e1167765404e551b080211cb192 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.github/workflows/upload_pr_documentation.yml @@ -0,0 +1,16 @@ +name: Upload PR Documentation + +on: + workflow_run: + workflows: ["Build PR Documentation"] + types: + - completed + +jobs: + build: + uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main + with: + package_name: transformers + secrets: + hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }} + comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }} \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/.gitignore b/tasks/tasksmith-333d8be2d5aa/tests/source/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..337f2ef2c735e89dc8f76ba3367cc29301264f66 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/.gitignore @@ -0,0 +1,169 @@ +# Initially taken from Github's Python gitignore file + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# tests and logs +tests/fixtures/cached_*_text.txt +logs/ +lightning_logs/ +lang_code_data/ + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# vscode +.vs +.vscode + +# Pycharm +.idea + +# TF code +tensorflow_code + +# Models +proc_data + +# examples +runs +/runs_old +/wandb +/examples/runs +/examples/**/*.args +/examples/rag/sweep + +# data +/data +serialization_dir + +# emacs +*.*~ +debug.env + +# vim +.*.swp + +#ctags +tags + +# pre-commit +.pre-commit* + +# .lock +*.lock + +# DS_Store (MacOS) +.DS_Store + +# ruff +.ruff_cache diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/CITATION.cff b/tasks/tasksmith-333d8be2d5aa/tests/source/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..b4d5156786f94df7ca0aebe5322b17dbb1521cb3 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/CITATION.cff @@ -0,0 +1,82 @@ +cff-version: "1.2.0" +date-released: 2020-10 +message: "If you use this software, please cite it using these metadata." +title: "Transformers: State-of-the-Art Natural Language Processing" +url: "https://github.com/huggingface/transformers" +authors: + - family-names: Wolf + given-names: Thomas + - family-names: Debut + given-names: Lysandre + - family-names: Sanh + given-names: Victor + - family-names: Chaumond + given-names: Julien + - family-names: Delangue + given-names: Clement + - family-names: Moi + given-names: Anthony + - family-names: Cistac + given-names: Perric + - family-names: Ma + given-names: Clara + - family-names: Jernite + given-names: Yacine + - family-names: Plu + given-names: Julien + - family-names: Xu + given-names: Canwen + - family-names: "Le Scao" + given-names: Teven + - family-names: Gugger + given-names: Sylvain + - family-names: Drame + given-names: Mariama + - family-names: Lhoest + given-names: Quentin + - family-names: Rush + given-names: "Alexander M." +preferred-citation: + type: conference-paper + authors: + - family-names: Wolf + given-names: Thomas + - family-names: Debut + given-names: Lysandre + - family-names: Sanh + given-names: Victor + - family-names: Chaumond + given-names: Julien + - family-names: Delangue + given-names: Clement + - family-names: Moi + given-names: Anthony + - family-names: Cistac + given-names: Perric + - family-names: Ma + given-names: Clara + - family-names: Jernite + given-names: Yacine + - family-names: Plu + given-names: Julien + - family-names: Xu + given-names: Canwen + - family-names: "Le Scao" + given-names: Teven + - family-names: Gugger + given-names: Sylvain + - family-names: Drame + given-names: Mariama + - family-names: Lhoest + given-names: Quentin + - family-names: Rush + given-names: "Alexander M." + booktitle: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations" + month: 10 + start: 38 + end: 45 + title: "Transformers: State-of-the-Art Natural Language Processing" + year: 2020 + publisher: "Association for Computational Linguistics" + url: "https://www.aclweb.org/anthology/2020.emnlp-demos.6" + address: "Online" diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/CODEOWNERS b/tasks/tasksmith-333d8be2d5aa/tests/source/CODEOWNERS new file mode 100644 index 0000000000000000000000000000000000000000..6d32e835dfa43e083ebf913fa7beefe4f2da25ef --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/CODEOWNERS @@ -0,0 +1,369 @@ +# Top-level rules are matched only if nothing else matches +* @Rocketknight1 @ArthurZucker # if no one is pinged based on the other rules, he will do the dispatch +**.md @stevhliu +docs/ @stevhliu +/benchmark/ @McPatate +/docker/ @ydshieh @ArthurZucker + +# More high-level globs catch cases when specific rules later don't apply +/src/transformers/models/*/*processing* @molbap @yonigozlan @qubvel +/src/transformers/models/*/image_processing* @qubvel +/src/transformers/models/*/image_processing_*_fast* @yonigozlan +/src/transformers/**/*_tokenization* @ArthurZucker + +# Owners of subsections of the library +/src/transformers/generation/ @gante +/src/transformers/pipeline/ @Rocketknight1 @yonigozlan +/src/transformers/integrations/ @SunMarc @MekkCyber @muellerzr +/src/transformers/quantizers/ @SunMarc @MekkCyber +/src/transformers/tests/ @ydshieh +/src/transformers/tests/generation/ @gante +/src/transformers/models/auto/ @ArthurZucker +/src/transformers/utils/ @ArthurZucker @Rocketknight1 +/src/transformers/loss/ @ArthurZucker +/src/transformers/onnx/ @michaelbenayoun + +# Specific files come after the sections/globs, so they take priority +/.circleci/config.yml @ArthurZucker @ydshieh +/utils/tests_fetcher.py @ydshieh +trainer.py @muellerzr @SunMarc +trainer_utils.py @muellerzr @SunMarc +/utils/modular_model_converter.py @Cyrilvallez @ArthurZucker + +# Owners of individual models are specific / high priority, and so they come last +# mod* captures modeling and modular files + +# Text models +/src/transformers/models/albert/mod*_albert* @ArthurZucker +/src/transformers/models/bamba/mod*_bamba* @ArthurZucker +/src/transformers/models/bart/mod*_bart* @ArthurZucker +/src/transformers/models/barthez/mod*_barthez* @ArthurZucker +/src/transformers/models/bartpho/mod*_bartpho* @ArthurZucker +/src/transformers/models/bert/mod*_bert* @ArthurZucker +/src/transformers/models/bert_generation/mod*_bert_generation* @ArthurZucker +/src/transformers/models/bert_japanese/mod*_bert_japanese* @ArthurZucker +/src/transformers/models/bertweet/mod*_bertweet* @ArthurZucker +/src/transformers/models/big_bird/mod*_big_bird* @ArthurZucker +/src/transformers/models/bigbird_pegasus/mod*_bigbird_pegasus* @ArthurZucker +/src/transformers/models/biogpt/mod*_biogpt* @ArthurZucker +/src/transformers/models/blenderbot/mod*_blenderbot* @ArthurZucker +/src/transformers/models/blenderbot_small/mod*_blenderbot_small* @ArthurZucker +/src/transformers/models/bloom/mod*_bloom* @ArthurZucker +/src/transformers/models/bort/mod*_bort* @ArthurZucker +/src/transformers/models/byt5/mod*_byt5* @ArthurZucker +/src/transformers/models/camembert/mod*_camembert* @ArthurZucker +/src/transformers/models/canine/mod*_canine* @ArthurZucker +/src/transformers/models/codegen/mod*_codegen* @ArthurZucker +/src/transformers/models/code_llama/mod*_code_llama* @ArthurZucker +/src/transformers/models/cohere/mod*_cohere* @ArthurZucker +/src/transformers/models/cohere2/mod*_cohere2* @ArthurZucker +/src/transformers/models/convbert/mod*_convbert* @ArthurZucker +/src/transformers/models/cpm/mod*_cpm* @ArthurZucker +/src/transformers/models/cpmant/mod*_cpmant* @ArthurZucker +/src/transformers/models/ctrl/mod*_ctrl* @ArthurZucker +/src/transformers/models/dbrx/mod*_dbrx* @ArthurZucker +/src/transformers/models/deberta/mod*_deberta* @ArthurZucker +/src/transformers/models/deberta_v2/mod*_deberta_v2* @ArthurZucker +/src/transformers/models/dialogpt/mod*_dialogpt* @ArthurZucker +/src/transformers/models/diffllama/mod*_diffllama* @ArthurZucker +/src/transformers/models/distilbert/mod*_distilbert* @ArthurZucker +/src/transformers/models/dpr/mod*_dpr* @ArthurZucker +/src/transformers/models/electra/mod*_electra* @ArthurZucker +/src/transformers/models/encoder_decoder/mod*_encoder_decoder* @ArthurZucker +/src/transformers/models/ernie/mod*_ernie* @ArthurZucker +/src/transformers/models/ernie_m/mod*_ernie_m* @ArthurZucker +/src/transformers/models/esm/mod*_esm* @ArthurZucker +/src/transformers/models/falcon/mod*_falcon* @ArthurZucker +/src/transformers/models/falcon3/mod*_falcon3* @ArthurZucker +/src/transformers/models/falcon_mamba/mod*_falcon_mamba* @ArthurZucker +/src/transformers/models/fastspeech2_conformer/mod*_fastspeech2_conformer* @ArthurZucker +/src/transformers/models/flan_t5/mod*_flan_t5* @ArthurZucker +/src/transformers/models/flan_ul2/mod*_flan_ul2* @ArthurZucker +/src/transformers/models/flaubert/mod*_flaubert* @ArthurZucker +/src/transformers/models/fnet/mod*_fnet* @ArthurZucker +/src/transformers/models/fsmt/mod*_fsmt* @ArthurZucker +/src/transformers/models/funnel/mod*_funnel* @ArthurZucker +/src/transformers/models/fuyu/mod*_fuyu* @ArthurZucker +/src/transformers/models/gemma/mod*_gemma* @ArthurZucker +/src/transformers/models/gemma2/mod*_gemma2* @ArthurZucker +/src/transformers/models/glm/mod*_glm* @ArthurZucker +/src/transformers/models/openai_gpt/mod*_openai_gpt* @ArthurZucker +/src/transformers/models/gpt_neo/mod*_gpt_neo* @ArthurZucker +/src/transformers/models/gpt_neox/mod*_gpt_neox* @ArthurZucker +/src/transformers/models/gpt_neox_japanese/mod*_gpt_neox_japanese* @ArthurZucker +/src/transformers/models/gptj/mod*_gptj* @ArthurZucker +/src/transformers/models/gpt2/mod*_gpt2* @ArthurZucker +/src/transformers/models/gpt_bigcode/mod*_gpt_bigcode* @ArthurZucker +/src/transformers/models/gptsan_japanese/mod*_gptsan_japanese* @ArthurZucker +/src/transformers/models/gpt_sw3/mod*_gpt_sw3* @ArthurZucker +/src/transformers/models/granite/mod*_granite* @ArthurZucker +/src/transformers/models/granitemoe/mod*_granitemoe* @ArthurZucker +/src/transformers/models/herbert/mod*_herbert* @ArthurZucker +/src/transformers/models/ibert/mod*_ibert* @ArthurZucker +/src/transformers/models/jamba/mod*_jamba* @ArthurZucker +/src/transformers/models/jetmoe/mod*_jetmoe* @ArthurZucker +/src/transformers/models/jukebox/mod*_jukebox* @ArthurZucker +/src/transformers/models/led/mod*_led* @ArthurZucker +/src/transformers/models/llama/mod*_llama* @ArthurZucker @Cyrilvallez +/src/transformers/models/longformer/mod*_longformer* @ArthurZucker +/src/transformers/models/longt5/mod*_longt5* @ArthurZucker +/src/transformers/models/luke/mod*_luke* @ArthurZucker +/src/transformers/models/m2m_100/mod*_m2m_100* @ArthurZucker +/src/transformers/models/madlad_400/mod*_madlad_400* @ArthurZucker +/src/transformers/models/mamba/mod*_mamba* @ArthurZucker +/src/transformers/models/mamba2/mod*_mamba2* @ArthurZucker +/src/transformers/models/marian/mod*_marian* @ArthurZucker +/src/transformers/models/markuplm/mod*_markuplm* @ArthurZucker +/src/transformers/models/mbart/mod*_mbart* @ArthurZucker +/src/transformers/models/mega/mod*_mega* @ArthurZucker +/src/transformers/models/megatron_bert/mod*_megatron_bert* @ArthurZucker +/src/transformers/models/megatron_gpt2/mod*_megatron_gpt2* @ArthurZucker +/src/transformers/models/mistral/mod*_mistral* @ArthurZucker +/src/transformers/models/mixtral/mod*_mixtral* @ArthurZucker +/src/transformers/models/mluke/mod*_mluke* @ArthurZucker +/src/transformers/models/mobilebert/mod*_mobilebert* @ArthurZucker +/src/transformers/models/modernbert/mod*_modernbert* @ArthurZucker +/src/transformers/models/mpnet/mod*_mpnet* @ArthurZucker +/src/transformers/models/mpt/mod*_mpt* @ArthurZucker +/src/transformers/models/mra/mod*_mra* @ArthurZucker +/src/transformers/models/mt5/mod*_mt5* @ArthurZucker +/src/transformers/models/mvp/mod*_mvp* @ArthurZucker +/src/transformers/models/myt5/mod*_myt5* @ArthurZucker +/src/transformers/models/nemotron/mod*_nemotron* @ArthurZucker +/src/transformers/models/nezha/mod*_nezha* @ArthurZucker +/src/transformers/models/nllb/mod*_nllb* @ArthurZucker +/src/transformers/models/nllb_moe/mod*_nllb_moe* @ArthurZucker +/src/transformers/models/nystromformer/mod*_nystromformer* @ArthurZucker +/src/transformers/models/olmo/mod*_olmo* @ArthurZucker +/src/transformers/models/olmo2/mod*_olmo2* @ArthurZucker +/src/transformers/models/olmoe/mod*_olmoe* @ArthurZucker +/src/transformers/models/open_llama/mod*_open_llama* @ArthurZucker +/src/transformers/models/opt/mod*_opt* @ArthurZucker +/src/transformers/models/pegasus/mod*_pegasus* @ArthurZucker +/src/transformers/models/pegasus_x/mod*_pegasus_x* @ArthurZucker +/src/transformers/models/persimmon/mod*_persimmon* @ArthurZucker +/src/transformers/models/phi/mod*_phi* @ArthurZucker +/src/transformers/models/phi3/mod*_phi3* @ArthurZucker +/src/transformers/models/phimoe/mod*_phimoe* @ArthurZucker +/src/transformers/models/phobert/mod*_phobert* @ArthurZucker +/src/transformers/models/plbart/mod*_plbart* @ArthurZucker +/src/transformers/models/prophetnet/mod*_prophetnet* @ArthurZucker +/src/transformers/models/qdqbert/mod*_qdqbert* @ArthurZucker +/src/transformers/models/qwen2/mod*_qwen2* @ArthurZucker +/src/transformers/models/qwen2_moe/mod*_qwen2_moe* @ArthurZucker +/src/transformers/models/rag/mod*_rag* @ArthurZucker +/src/transformers/models/realm/mod*_realm* @ArthurZucker +/src/transformers/models/recurrent_gemma/mod*_recurrent_gemma* @ArthurZucker +/src/transformers/models/reformer/mod*_reformer* @ArthurZucker +/src/transformers/models/rembert/mod*_rembert* @ArthurZucker +/src/transformers/models/retribert/mod*_retribert* @ArthurZucker +/src/transformers/models/roberta/mod*_roberta* @ArthurZucker +/src/transformers/models/roberta_prelayernorm/mod*_roberta_prelayernorm* @ArthurZucker +/src/transformers/models/roc_bert/mod*_roc_bert* @ArthurZucker +/src/transformers/models/roformer/mod*_roformer* @ArthurZucker +/src/transformers/models/rwkv/mod*_rwkv* @ArthurZucker +/src/transformers/models/splinter/mod*_splinter* @ArthurZucker +/src/transformers/models/squeezebert/mod*_squeezebert* @ArthurZucker +/src/transformers/models/stablelm/mod*_stablelm* @ArthurZucker +/src/transformers/models/starcoder2/mod*_starcoder2* @ArthurZucker +/src/transformers/models/switch_transformers/mod*_switch_transformers* @ArthurZucker +/src/transformers/models/t5/mod*_t5* @ArthurZucker +/src/transformers/models/t5v1.1/mod*_t5v1.1* @ArthurZucker +/src/transformers/models/tapex/mod*_tapex* @ArthurZucker +/src/transformers/models/transfo_xl/mod*_transfo_xl* @ArthurZucker +/src/transformers/models/ul2/mod*_ul2* @ArthurZucker +/src/transformers/models/umt5/mod*_umt5* @ArthurZucker +/src/transformers/models/xmod/mod*_xmod* @ArthurZucker +/src/transformers/models/xglm/mod*_xglm* @ArthurZucker +/src/transformers/models/xlm/mod*_xlm* @ArthurZucker +/src/transformers/models/xlm_prophetnet/mod*_xlm_prophetnet* @ArthurZucker +/src/transformers/models/xlm_roberta/mod*_xlm_roberta* @ArthurZucker +/src/transformers/models/xlm_roberta_xl/mod*_xlm_roberta_xl* @ArthurZucker +/src/transformers/models/xlm_v/mod*_xlm_v* @ArthurZucker +/src/transformers/models/xlnet/mod*_xlnet* @ArthurZucker +/src/transformers/models/yoso/mod*_yoso* @ArthurZucker +/src/transformers/models/zamba/mod*_zamba* @ArthurZucker + +# Vision models +/src/transformers/models/beit/mod*_beit* @amyeroberts @qubvel +/src/transformers/models/bit/mod*_bit* @amyeroberts @qubvel +/src/transformers/models/conditional_detr/mod*_conditional_detr* @amyeroberts @qubvel +/src/transformers/models/convnext/mod*_convnext* @amyeroberts @qubvel +/src/transformers/models/convnextv2/mod*_convnextv2* @amyeroberts @qubvel +/src/transformers/models/cvt/mod*_cvt* @amyeroberts @qubvel +/src/transformers/models/deformable_detr/mod*_deformable_detr* @amyeroberts @qubvel +/src/transformers/models/deit/mod*_deit* @amyeroberts @qubvel +/src/transformers/models/depth_anything/mod*_depth_anything* @amyeroberts @qubvel +/src/transformers/models/depth_anything_v2/mod*_depth_anything_v2* @amyeroberts @qubvel +/src/transformers/models/deta/mod*_deta* @amyeroberts @qubvel +/src/transformers/models/detr/mod*_detr* @amyeroberts @qubvel +/src/transformers/models/dinat/mod*_dinat* @amyeroberts @qubvel +/src/transformers/models/dinov2/mod*_dinov2* @amyeroberts @qubvel +/src/transformers/models/dinov2_with_registers/mod*_dinov2_with_registers* @amyeroberts @qubvel +/src/transformers/models/dit/mod*_dit* @amyeroberts @qubvel +/src/transformers/models/dpt/mod*_dpt* @amyeroberts @qubvel +/src/transformers/models/efficientformer/mod*_efficientformer* @amyeroberts @qubvel +/src/transformers/models/efficientnet/mod*_efficientnet* @amyeroberts @qubvel +/src/transformers/models/focalnet/mod*_focalnet* @amyeroberts @qubvel +/src/transformers/models/glpn/mod*_glpn* @amyeroberts @qubvel +/src/transformers/models/hiera/mod*_hiera* @amyeroberts @qubvel +/src/transformers/models/ijepa/mod*_ijepa* @amyeroberts @qubvel +/src/transformers/models/imagegpt/mod*_imagegpt* @amyeroberts @qubvel +/src/transformers/models/levit/mod*_levit* @amyeroberts @qubvel +/src/transformers/models/mask2former/mod*_mask2former* @amyeroberts @qubvel +/src/transformers/models/maskformer/mod*_maskformer* @amyeroberts @qubvel +/src/transformers/models/mobilenet_v1/mod*_mobilenet_v1* @amyeroberts @qubvel +/src/transformers/models/mobilenet_v2/mod*_mobilenet_v2* @amyeroberts @qubvel +/src/transformers/models/mobilevit/mod*_mobilevit* @amyeroberts @qubvel +/src/transformers/models/mobilevitv2/mod*_mobilevitv2* @amyeroberts @qubvel +/src/transformers/models/nat/mod*_nat* @amyeroberts @qubvel +/src/transformers/models/poolformer/mod*_poolformer* @amyeroberts @qubvel +/src/transformers/models/pvt/mod*_pvt* @amyeroberts @qubvel +/src/transformers/models/pvt_v2/mod*_pvt_v2* @amyeroberts @qubvel +/src/transformers/models/regnet/mod*_regnet* @amyeroberts @qubvel +/src/transformers/models/resnet/mod*_resnet* @amyeroberts @qubvel +/src/transformers/models/rt_detr/mod*_rt_detr* @amyeroberts @qubvel +/src/transformers/models/segformer/mod*_segformer* @amyeroberts @qubvel +/src/transformers/models/seggpt/mod*_seggpt* @amyeroberts @qubvel +/src/transformers/models/superpoint/mod*_superpoint* @amyeroberts @qubvel +/src/transformers/models/swiftformer/mod*_swiftformer* @amyeroberts @qubvel +/src/transformers/models/swin/mod*_swin* @amyeroberts @qubvel +/src/transformers/models/swinv2/mod*_swinv2* @amyeroberts @qubvel +/src/transformers/models/swin2sr/mod*_swin2sr* @amyeroberts @qubvel +/src/transformers/models/table_transformer/mod*_table_transformer* @amyeroberts @qubvel +/src/transformers/models/textnet/mod*_textnet* @amyeroberts @qubvel +/src/transformers/models/timm_wrapper/mod*_timm_wrapper* @amyeroberts @qubvel +/src/transformers/models/upernet/mod*_upernet* @amyeroberts @qubvel +/src/transformers/models/van/mod*_van* @amyeroberts @qubvel +/src/transformers/models/vit/mod*_vit* @amyeroberts @qubvel +/src/transformers/models/vit_hybrid/mod*_vit_hybrid* @amyeroberts @qubvel +/src/transformers/models/vitdet/mod*_vitdet* @amyeroberts @qubvel +/src/transformers/models/vit_mae/mod*_vit_mae* @amyeroberts @qubvel +/src/transformers/models/vitmatte/mod*_vitmatte* @amyeroberts @qubvel +/src/transformers/models/vit_msn/mod*_vit_msn* @amyeroberts @qubvel +/src/transformers/models/vitpose/mod*_vitpose* @amyeroberts @qubvel +/src/transformers/models/yolos/mod*_yolos* @amyeroberts @qubvel +/src/transformers/models/zoedepth/mod*_zoedepth* @amyeroberts @qubvel + +# Audio models +/src/transformers/models/audio_spectrogram_transformer/mod*_audio_spectrogram_transformer* @eustlb +/src/transformers/models/bark/mod*_bark* @eustlb +/src/transformers/models/clap/mod*_clap* @eustlb +/src/transformers/models/dac/mod*_dac* @eustlb +/src/transformers/models/encodec/mod*_encodec* @eustlb +/src/transformers/models/hubert/mod*_hubert* @eustlb +/src/transformers/models/mctct/mod*_mctct* @eustlb +/src/transformers/models/mimi/mod*_mimi* @eustlb +/src/transformers/models/mms/mod*_mms* @eustlb +/src/transformers/models/moshi/mod*_moshi* @eustlb +/src/transformers/models/musicgen/mod*_musicgen* @eustlb +/src/transformers/models/musicgen_melody/mod*_musicgen_melody* @eustlb +/src/transformers/models/pop2piano/mod*_pop2piano* @eustlb +/src/transformers/models/seamless_m4t/mod*_seamless_m4t* @eustlb +/src/transformers/models/seamless_m4t_v2/mod*_seamless_m4t_v2* @eustlb +/src/transformers/models/sew/mod*_sew* @eustlb +/src/transformers/models/sew_d/mod*_sew_d* @eustlb +/src/transformers/models/speech_to_text/mod*_speech_to_text* @eustlb +/src/transformers/models/speech_to_text_2/mod*_speech_to_text_2* @eustlb +/src/transformers/models/speecht5/mod*_speecht5* @eustlb +/src/transformers/models/unispeech/mod*_unispeech* @eustlb +/src/transformers/models/unispeech_sat/mod*_unispeech_sat* @eustlb +/src/transformers/models/univnet/mod*_univnet* @eustlb +/src/transformers/models/vits/mod*_vits* @eustlb +/src/transformers/models/wav2vec2/mod*_wav2vec2* @eustlb +/src/transformers/models/wav2vec2_bert/mod*_wav2vec2_bert* @eustlb +/src/transformers/models/wav2vec2_conformer/mod*_wav2vec2_conformer* @eustlb +/src/transformers/models/wav2vec2_phoneme/mod*_wav2vec2_phoneme* @eustlb +/src/transformers/models/wavlm/mod*_wavlm* @eustlb +/src/transformers/models/whisper/mod*_whisper* @eustlb +/src/transformers/models/xls_r/mod*_xls_r* @eustlb +/src/transformers/models/xlsr_wav2vec2/mod*_xlsr_wav2vec2* @eustlb + +# Video models +/src/transformers/models/timesformer/mod*_timesformer* @Rocketknight1 +/src/transformers/models/videomae/mod*_videomae* @Rocketknight1 +/src/transformers/models/vivit/mod*_vivit* @Rocketknight1 + +# Multimodal models +/src/transformers/models/align/mod*_align* @zucchini-nlp +/src/transformers/models/altclip/mod*_altclip* @zucchini-nlp +/src/transformers/models/aria/mod*_aria* @zucchini-nlp +/src/transformers/models/blip/mod*_blip* @zucchini-nlp +/src/transformers/models/blip_2/mod*_blip_2* @zucchini-nlp +/src/transformers/models/bridgetower/mod*_bridgetower* @zucchini-nlp +/src/transformers/models/bros/mod*_bros* @zucchini-nlp +/src/transformers/models/chameleon/mod*_chameleon* @zucchini-nlp +/src/transformers/models/chinese_clip/mod*_chinese_clip* @zucchini-nlp +/src/transformers/models/clip/mod*_clip* @zucchini-nlp +/src/transformers/models/clipseg/mod*_clipseg* @zucchini-nlp +/src/transformers/models/clvp/mod*_clvp* @zucchini-nlp +/src/transformers/models/colpali/mod*_colpali* @zucchini-nlp @yonigozlan +/src/transformers/models/data2vec/mod*_data2vec* @zucchini-nlp +/src/transformers/models/deplot/mod*_deplot* @zucchini-nlp +/src/transformers/models/donut/mod*_donut* @zucchini-nlp +/src/transformers/models/flava/mod*_flava* @zucchini-nlp +/src/transformers/models/git/mod*_git* @zucchini-nlp +/src/transformers/models/grounding_dino/mod*_grounding_dino* @qubvel +/src/transformers/models/groupvit/mod*_groupvit* @zucchini-nlp +/src/transformers/models/idefics/mod*_idefics* @zucchini-nlp +/src/transformers/models/idefics2/mod*_idefics2* @zucchini-nlp +/src/transformers/models/idefics3/mod*_idefics3* @zucchini-nlp +/src/transformers/models/instructblip/mod*_instructblip* @zucchini-nlp +/src/transformers/models/instructblipvideo/mod*_instructblipvideo* @zucchini-nlp +/src/transformers/models/kosmos_2/mod*_kosmos_2* @zucchini-nlp +/src/transformers/models/layoutlm/mod*_layoutlm* @NielsRogge +/src/transformers/models/layoutlmv2/mod*_layoutlmv2* @NielsRogge +/src/transformers/models/layoutlmv3/mod*_layoutlmv3* @NielsRogge +/src/transformers/models/layoutxlm/mod*_layoutxlm* @NielsRogge +/src/transformers/models/lilt/mod*_lilt* @zucchini-nlp +/src/transformers/models/llava/mod*_llava* @zucchini-nlp @arthurzucker +/src/transformers/models/llava_next/mod*_llava_next* @zucchini-nlp +/src/transformers/models/llava_next_video/mod*_llava_next_video* @zucchini-nlp +/src/transformers/models/llava_onevision/mod*_llava_onevision* @zucchini-nlp +/src/transformers/models/lxmert/mod*_lxmert* @zucchini-nlp +/src/transformers/models/matcha/mod*_matcha* @zucchini-nlp +/src/transformers/models/mgp_str/mod*_mgp_str* @zucchini-nlp +/src/transformers/models/mllama/mod*_mllama* @zucchini-nlp +/src/transformers/models/nougat/mod*_nougat* @NielsRogge +/src/transformers/models/omdet_turbo/mod*_omdet_turbo* @qubvel @yonigozlan +/src/transformers/models/oneformer/mod*_oneformer* @zucchini-nlp +/src/transformers/models/owlvit/mod*_owlvit* @qubvel +/src/transformers/models/owlv2/mod*_owlv2* @qubvel +/src/transformers/models/paligemma/mod*_paligemma* @zucchini-nlp @molbap +/src/transformers/models/perceiver/mod*_perceiver* @zucchini-nlp +/src/transformers/models/pix2struct/mod*_pix2struct* @zucchini-nlp +/src/transformers/models/pixtral/mod*_pixtral* @zucchini-nlp @ArthurZucker +/src/transformers/models/qwen2_audio/mod*_qwen2_audio* @zucchini-nlp @ArthurZucker +/src/transformers/models/qwen2_vl/mod*_qwen2_vl* @zucchini-nlp @ArthurZucker +/src/transformers/models/sam/mod*_sam* @zucchini-nlp @ArthurZucker +/src/transformers/models/siglip/mod*_siglip* @zucchini-nlp +/src/transformers/models/speech_encoder_decoder/mod*_speech_encoder_decoder* @zucchini-nlp +/src/transformers/models/tapas/mod*_tapas* @NielsRogge +/src/transformers/models/trocr/mod*_trocr* @zucchini-nlp +/src/transformers/models/tvlt/mod*_tvlt* @zucchini-nlp +/src/transformers/models/tvp/mod*_tvp* @zucchini-nlp +/src/transformers/models/udop/mod*_udop* @zucchini-nlp +/src/transformers/models/video_llava/mod*_video_llava* @zucchini-nlp +/src/transformers/models/vilt/mod*_vilt* @zucchini-nlp +/src/transformers/models/vipllava/mod*_vipllava* @zucchini-nlp +/src/transformers/models/vision_encoder_decoder/mod*_vision_encoder_decoder* @Rocketknight1 +/src/transformers/models/vision_text_dual_encoder/mod*_vision_text_dual_encoder* @Rocketknight1 +/src/transformers/models/visual_bert/mod*_visual_bert* @zucchini-nlp +/src/transformers/models/xclip/mod*_xclip* @zucchini-nlp + +# Reinforcement learning models +/src/transformers/models/decision_transformer/mod*_decision_transformer* @Rocketknight1 +/src/transformers/models/trajectory_transformer/mod*_trajectory_transformer* @Rocketknight1 + +# Time series models +/src/transformers/models/autoformer/mod*_autoformer* @Rocketknight1 +/src/transformers/models/informer/mod*_informer* @Rocketknight1 +/src/transformers/models/patchtsmixer/mod*_patchtsmixer* @Rocketknight1 +/src/transformers/models/patchtst/mod*_patchtst* @Rocketknight1 +/src/transformers/models/time_series_transformer/mod*_time_series_transformer* @Rocketknight1 + +# Graph models +/src/transformers/models/graphormer/mod*_graphormer* @clefourrier + +# Finally, files with no owners that shouldn't generate pings, usually automatically generated and checked in the CI +utils/dummy* \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/CODE_OF_CONDUCT.md b/tasks/tasksmith-333d8be2d5aa/tests/source/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..b23f3150a5a6987a74acc7bb31df8c9bf1a48d57 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/CODE_OF_CONDUCT.md @@ -0,0 +1,133 @@ + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +feedback@huggingface.co. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/CONTRIBUTING.md b/tasks/tasksmith-333d8be2d5aa/tests/source/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..9eeea997154085b5e0485775826cd1eced6585cb --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/CONTRIBUTING.md @@ -0,0 +1,397 @@ + + +# Contribute to 🤗 Transformers + +Everyone is welcome to contribute, and we value everybody's contribution. Code +contributions are not the only way to help the community. Answering questions, helping +others, and improving the documentation are also immensely valuable. + +It also helps us if you spread the word! Reference the library in blog posts +about the awesome projects it made possible, shout out on Twitter every time it has +helped you, or simply ⭐️ the repository to say thank you. + +However you choose to contribute, please be mindful and respect our +[code of conduct](https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md). + +**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).** + +## Ways to contribute + +There are several ways you can contribute to 🤗 Transformers: + +* Fix outstanding issues with the existing code. +* Submit issues related to bugs or desired new features. +* Implement new models. +* Contribute to the examples or to the documentation. + +If you don't know where to start, there is a special [Good First +Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of +open issues that are beginner-friendly and help you start contributing to open-source. The best way to do that is to open a Pull Request and link it to the issue that you'd like to work on. We try to give priority to opened PRs as we can easily track the progress of the fix, and if the contributor does not have time anymore, someone else can take the PR over. + +For something slightly more challenging, you can also take a look at the [Good Second Issue](https://github.com/huggingface/transformers/labels/Good%20Second%20Issue) list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀 + +> All contributions are equally valuable to the community. 🥰 + +## Fixing outstanding issues + +If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](#create-a-pull-request) and open a Pull Request! + +## Submitting a bug-related issue or feature request + +Do your best to follow these guidelines when submitting a bug-related issue or a feature +request. It will make it easier for us to come back to you quickly and with good +feedback. + +### Did you find a bug? + +The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter. + +Before you report an issue, we would really appreciate it if you could **make sure the bug was not +already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask in the [forum](https://discuss.huggingface.co/) or on our [discord](https://discord.com/invite/hugging-face-879548962464493619) first. This helps us respond quicker to fixing issues related to the library versus general questions. + +> [!TIP] +> We have a [docs bot](https://huggingface.co/spaces/huggingchat/hf-docs-chat), and we highly encourage you to ask all your questions there. There is always a chance your bug can be fixed with a simple flag 👾🔫 + +Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it: + +* Your **OS type and version** and **Python**, **PyTorch** and + **TensorFlow** versions when applicable. +* A short, self-contained, code snippet that allows us to reproduce the bug in + less than 30s. +* The *full* traceback if an exception is raised. +* Attach any other additional information, like screenshots, you think may help. + +To get the OS and software versions automatically, run the following command: + +```bash +transformers-cli env +``` + +You can also run the same command from the root of the repository: + +```bash +python src/transformers/commands/transformers_cli.py env +``` + +### Do you want a new feature? + +If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe: + +1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community? + + Whatever it is, we'd love to hear about it! + +2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you. +3. Provide a *code snippet* that demonstrates the features usage. +4. If the feature is related to a paper, please include a link. + +If your issue is well written we're already 80% of the way there by the time you create it. + +We have added [templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with your issue. + +## Do you want to implement a new model? + +New models are constantly released and if you want to implement a new model, please provide the following information: + +* A short description of the model and a link to the paper. +* Link to the implementation if it is open-sourced. +* Link to the model weights if they are available. + +If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers! + +We have a technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/add_new_model). + +## Do you want to add documentation? + +We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested! + +For more details about how to generate, build, and write the documentation, take a look at the documentation [README](https://github.com/huggingface/transformers/tree/main/docs). + +## Create a Pull Request + +Before writing any code, we strongly advise you to search through the existing PRs or +issues to make sure nobody is already working on the same thing. If you are +unsure, it is always a good idea to open an issue to get some feedback. + +You will need basic `git` proficiency to contribute to +🤗 Transformers. While `git` is not the easiest tool to use, it has the greatest +manual. Type `git --help` in a shell and enjoy! If you prefer books, [Pro +Git](https://git-scm.com/book/en/v2) is a very good reference. + +You'll need **[Python 3.9](https://github.com/huggingface/transformers/blob/main/setup.py#L449)** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing: + +1. Fork the [repository](https://github.com/huggingface/transformers) by + clicking on the **[Fork](https://github.com/huggingface/transformers/fork)** button on the repository's page. This creates a copy of the code + under your GitHub user account. + +2. Clone your fork to your local disk, and add the base repository as a remote: + + ```bash + git clone git@github.com:/transformers.git + cd transformers + git remote add upstream https://github.com/huggingface/transformers.git + ``` + +3. Create a new branch to hold your development changes: + + ```bash + git checkout -b a-descriptive-name-for-my-changes + ``` + + 🚨 **Do not** work on the `main` branch! + +4. Set up a development environment by running the following command in a virtual environment: + + ```bash + pip install -e ".[dev]" + ``` + + If 🤗 Transformers was already installed in the virtual environment, remove + it with `pip uninstall transformers` before reinstalling it in editable + mode with the `-e` flag. + + Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a + failure with this command. If that's the case make sure to install the Deep Learning framework you are working with + (PyTorch, TensorFlow and/or Flax) then do: + + ```bash + pip install -e ".[quality]" + ``` + + which should be enough for most use cases. + +5. Develop the features in your branch. + + As you work on your code, you should make sure the test suite + passes. Run the tests impacted by your changes like this: + + ```bash + pytest tests/.py + ``` + + For more information about tests, check out the + [Testing](https://huggingface.co/docs/transformers/testing) guide. + + 🤗 Transformers relies on `black` and `ruff` to format its source code + consistently. After you make changes, apply automatic style corrections and code verifications + that can't be automated in one go with: + + ```bash + make fixup + ``` + + This target is also optimized to only work with files modified by the PR you're working on. + + If you prefer to run the checks one after the other, the following command applies the + style corrections: + + ```bash + make style + ``` + + 🤗 Transformers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality + controls are run by the CI, but you can run the same checks with: + + ```bash + make quality + ``` + + Finally, we have a lot of scripts to make sure we don't forget to update + some files when adding a new model. You can run these scripts with: + + ```bash + make repo-consistency + ``` + + To learn more about those checks and how to fix any issues with them, check out the + [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. + + If you're modifying documents under the `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check + make sure you install the documentation builder: + + ```bash + pip install ".[docs]" + ``` + + Run the following command from the root of the repository: + + ```bash + doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build + ``` + + This will build the documentation in the `~/tmp/test-build` folder where you can inspect the generated + Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request. + + Once you're happy with your changes, add the changed files with `git add` and + record your changes locally with `git commit`: + + ```bash + git add modified_file.py + git commit + ``` + + Please remember to write [good commit + messages](https://chris.beams.io/posts/git-commit/) to clearly communicate the changes you made! + + To keep your copy of the code up to date with the original + repository, rebase your branch on `upstream/branch` *before* you open a pull request or if requested by a maintainer: + + ```bash + git fetch upstream + git rebase upstream/main + ``` + + Push your changes to your branch: + + ```bash + git push -u origin a-descriptive-name-for-my-changes + ``` + + If you've already opened a pull request, you'll need to force push with the `--force` flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally. + +6. Now you can go to your fork of the repository on GitHub and click on **Pull Request** to open a pull request. Make sure you tick off all the boxes on our [checklist](#pull-request-checklist) below. When you're ready, you can send your changes to the project maintainers for review. + +7. It's ok if maintainers request changes, it happens to our core contributors + too! So everyone can see the changes in the pull request, work in your local + branch and push the changes to your fork. They will automatically appear in + the pull request. + +### Pull request checklist + +☐ The pull request title should summarize your contribution.
+☐ If your pull request addresses an issue, please mention the issue number in the pull +request description to make sure they are linked (and people viewing the issue know you +are working on it).
+☐ To indicate a work in progress please prefix the title with `[WIP]`. These are +useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.
+☐ Make sure existing tests pass.
+☐ If adding a new feature, also add tests for it.
+ - If you are adding a new model, make sure you use + `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` to trigger the common tests. + - If you are adding new `@slow` tests, make sure they pass using + `RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`. + - If you are adding a new tokenizer, write tests and make sure + `RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes. + - CircleCI does not run the slow tests, but GitHub Actions does every night!
+ +☐ All public methods must have informative docstrings (see +[`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py) +for an example).
+☐ Due to the rapidly growing repository, don't add any images, videos and other +non-text files that'll significantly weigh down the repository. Instead, use a Hub +repository such as [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) +to host these files and reference them by URL. We recommend placing documentation +related images in the following repository: +[huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). +You can open a PR on this dataset repository and ask a Hugging Face member to merge it. + +For more information about the checks run on a pull request, take a look at our [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. + +### Tests + +An extensive test suite is included to test the library behavior and several examples. Library tests can be found in +the [tests](https://github.com/huggingface/transformers/tree/main/tests) folder and examples tests in the +[examples](https://github.com/huggingface/transformers/tree/main/examples) folder. + +We like `pytest` and `pytest-xdist` because it's faster. From the root of the +repository, specify a *path to a subfolder or a test file* to run the test: + +```bash +python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model +``` + +Similarly, for the `examples` directory, specify a *path to a subfolder or test file* to run the test. For example, the following command tests the text classification subfolder in the PyTorch `examples` directory: + +```bash +pip install -r examples/xxx/requirements.txt # only needed the first time +python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification +``` + +In fact, this is actually how our `make test` and `make test-examples` commands are implemented (not including the `pip install`)! + +You can also specify a smaller set of tests in order to test only the feature +you're working on. + +By default, slow tests are skipped but you can set the `RUN_SLOW` environment variable to +`yes` to run them. This will download many gigabytes of models so make sure you +have enough disk space, a good internet connection or a lot of patience! + + + +Remember to specify a *path to a subfolder or a test file* to run the test. Otherwise, you'll run all the tests in the `tests` or `examples` folder, which will take a very long time! + + + +```bash +RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model +RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification +``` + +Like the slow tests, there are other environment variables available which are not enabled by default during testing: +- `RUN_CUSTOM_TOKENIZERS`: Enables tests for custom tokenizers. +- `RUN_PT_FLAX_CROSS_TESTS`: Enables tests for PyTorch + Flax integration. +- `RUN_PT_TF_CROSS_TESTS`: Enables tests for TensorFlow + PyTorch integration. + +More environment variables and additional information can be found in the [testing_utils.py](https://github.com/huggingface/transformers/blob/main/src/transformers/testing_utils.py). + +🤗 Transformers uses `pytest` as a test runner only. It doesn't use any +`pytest`-specific features in the test suite itself. + +This means `unittest` is fully supported. Here's how to run tests with +`unittest`: + +```bash +python -m unittest discover -s tests -t . -v +python -m unittest discover -s examples -t examples -v +``` + +### Style guide + +For documentation strings, 🤗 Transformers follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html). +Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/main/docs#writing-documentation---specification) +for more information. + +### Develop on Windows + +On Windows (unless you're working in [Windows Subsystem for Linux](https://learn.microsoft.com/en-us/windows/wsl/) or WSL), you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings: + +```bash +git config core.autocrlf input +``` + +One way to run the `make` command on Windows is with MSYS2: + +1. [Download MSYS2](https://www.msys2.org/), and we assume it's installed in `C:\msys64`. +2. Open the command line `C:\msys64\msys2.exe` (it should be available from the **Start** menu). +3. Run in the shell: `pacman -Syu` and install `make` with `pacman -S make`. +4. Add `C:\msys64\usr\bin` to your PATH environment variable. + +You can now use `make` from any terminal (PowerShell, cmd.exe, etc.)! 🎉 + +### Sync a forked repository with upstream main (the Hugging Face repository) + +When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs. + +1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. +2. If a PR is absolutely necessary, use the following steps after checking out your branch: + + ```bash + git checkout -b your-branch-for-syncing + git pull --squash --no-commit upstream main + git commit -m '' + git push --set-upstream origin your-branch-for-syncing + ``` diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..96ce41cb39e513dea061672802fd6106c85d5e53 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/Dockerfile @@ -0,0 +1,7 @@ +FROM python:3.12-slim-bookworm +WORKDIR /workspace +RUN python -m pip install --no-cache-dir torch==2.11.0+cpu tokenizers==0.21.4 huggingface_hub==0.34.4 pytest==7.4.4 setuptools==78.1.0 wheel==0.48.0 parameterized==0.9.0 numpy==2.5.3 packaging==26.3 safetensors==0.8.0 PyYAML==6.0.3 regex==2026.9.10 requests==2.32.3 filelock==3.32.3 tqdm==4.70.1 pillow==12.3.0 sentencepiece==0.2.1 protobuf==5.29.3 psutil==7.2.2 datasets==2.21.0 pyarrow==25.0.1 dill==0.3.8 multiprocess==0.70.16 xxhash==4.0.1 fsspec==2024.6.1 pandas==3.0.5 aiohttp==3.14.3 --extra-index-url https://download.pytorch.org/whl/cpu +COPY . /workspace +RUN python -m pip install --no-cache-dir --no-deps --no-build-isolation -e . +RUN rm -rf /workspace/.git /root/.cache/pip +ENV PYTHONDONTWRITEBYTECODE=1 PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/ISSUES.md b/tasks/tasksmith-333d8be2d5aa/tests/source/ISSUES.md new file mode 100644 index 0000000000000000000000000000000000000000..a5969a3027f86dc1ed20ec1683642a1c723d0d19 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/ISSUES.md @@ -0,0 +1,277 @@ + + +# How To Request Support + +This is an Open Source Project so please be mindful that like in any other project of this kind there is no obligation to answer all requests for help. + +However, we want to encourage you to ask for help whenever you think it's needed! We are happy about every question we get because it allows us to better understand your needs, possible misunderstandings, and most importantly a way for you to help us make this library better. That being said, this document's main purpose is to provide guidelines at how you can formulate your requests to increase your chances to be understood and to get support. + +There are two main venues to receive support: [the forums](https://discuss.huggingface.co/) and [the GitHub issues](https://github.com/huggingface/transformers/issues). + +## The Forums + +[The user forums](https://discuss.huggingface.co/) are supported by the wide community of the library users and backed up by developers when needed. + +If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystalized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues). + +In particular all "Please explain" questions or objectively very user-specific feature requests belong to the forums. Here are some example of such questions: + +* "I would like to use a BertModel within a RL-Agent for a customer support service. How can I use a BertForMaskedLM in my ChatBotModel?" + +* "Could you please explain why T5 has no positional embedding matrix under T5Model?" + +* "How should I set my generation parameters for translation?" + +* "How to train T5 on De->En translation?" + + +## The GitHub Issues + +Everything which hints at a bug should be opened as an [issue](https://github.com/huggingface/transformers/issues). + +You are not required to read the following guidelines before opening an issue. However, if you notice that your issue doesn't get any replies, chances are that the developers have one or several difficulties with its quality. In this case, reading the following points and adjusting your issue accordingly could help. + +1. Before posting an issue, first search for already posted issues, since chances are someone has already asked a similar question before you. + + If you use Google your search query should be: + + ``` + "huggingface" "transformers" your query + ``` + + The first two quoted words tell Google to limit the search to the context of the Huggingface Transformers. The remainder is your query - most commonly this would be the error message the software fails with. We will go deeper into details shortly. + + The results of such a query will typically match GitHub issues, Hugging Face forums, StackExchange, and blogs. + + If you find relevant hints, you may choose to continue the discussion there if you have follow up questions. + + If what you found is similar but doesn't quite answer your problem, please, post a new issue and do include links to similar issues or forum discussions you may have found. + + Let's look at some examples: + + The error message, often referred to as an assertion, tells us what went wrong. Here is an example of an assertion: + + ```python + Traceback (most recent call last): + File "", line 1, in + File "/transformers/src/transformers/__init__.py", line 34, in + from . import dependency_versions_check + File "/transformers/src/transformers/dependency_versions_check.py", line 34, in + from .utils import is_tokenizers_available + File "/transformers/src/transformers/utils/import_utils.py", line 40, in + from tqdm.auto import tqdm + ModuleNotFoundError: No module named 'tqdm.auto' + ``` + + and it typically includes a traceback, so that we can see the full stack of calls the program made before it fails. This gives us the context to know why the program failed. + + Going back to the above example. If you received this error search, look at the very last line of the error which is: + + ```python + ModuleNotFoundError: No module named 'tqdm.auto' + ``` + + And now we can use it to do the searching on your favorite search engine: + + 1. first for `"huggingface" "transformers" "ModuleNotFoundError: No module named 'tqdm.auto'"` + 2. if you don't find relevant results, then search for just `"ModuleNotFoundError: No module named 'tqdm.auto'"` + 3. and finally if nothing still comes up, then remove the outside quotes: `ModuleNotFoundError: No module named 'tqdm.auto'` + + If the error includes any messages that include bits unique to your filesystem, always remove those in the search query since other users will not have the same filesystem as yours. For example: + + ```bash + python -c 'open("/tmp/wrong_path.txt", "r")' + Traceback (most recent call last): + File "", line 1, in + FileNotFoundError: [Errno 2] No such file or directory: '/tmp/wrong_path.txt' + ``` + Here you'd search for just: `"FileNotFoundError: [Errno 2] No such file or directory"` + + If the local information that you removed were inside the error message and you removed them you may need to remove double quotes since your query is no longer exact. So if the error message was something like: + + ```bash + ValueError: '/tmp/wrong_path.txt' cannot be found + ``` + + then you'd search for `"ValueError" "cannot be found"` + + As you search you will notice that when you don't use quotes often the search engines will return a variety of unrelated hits, which may or may not be what you want. + + Experiment with different ways and find which approach gives the most satisfactory results. + +2. Keep the issue short, providing the information that you think will aid the developers to understand your situation. Put yourself in the shoes of the person who has never seen your code or knows anything about your custom setup. This mental exercise will help to develop an intuition to what/what not to share" + +3. If there is a software failure, always provide the full traceback, for example: + + ```python + $ python -c 'import transformers' + Traceback (most recent call last): + File "", line 1, in + File "/transformers/src/transformers/__init__.py", line 34, in + from . import dependency_versions_check + File "/transformers/src/transformers/dependency_versions_check.py", line 34, in + from .utils import is_tokenizers_available + File "/transformers/src/transformers/utils/import_utils.py", line 40, in + from tqdm.auto import tqdm + ModuleNotFoundError: No module named 'tqdm.auto' + ``` + + As compared to providing just the last line of the error message, e.g.: + ```python + ModuleNotFoundError: No module named 'tqdm.auto' + ``` + which is not sufficient. + + If your application is running on more than one GPU (e.g. under `DistributedDataParallel`) and typically getting every log and traceback printed multiple times, please make sure that you paste only one copy of it. At times the traceback from parallel processes may get interleaved - so either disentangle these or change the loggers to log only for `local_rank==0` so that only one process logs things. + +4. When quoting a traceback, command line instructions and any type of code always enclose it in triple backticks inside the editor window, that is: + + ```` + ``` + git clone https://github.com/huggingface/transformers + cd transformers + pip install . + ``` + ```` + + If it's a command line with a long argument list, please consider breaking it down using backslashes and new lines. Here is an example of a good command line quote: + + ```bash + cd examples/seq2seq + torchrun --nproc_per_node=2 ./finetune_trainer.py \ + --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \ + --output_dir output_dir --overwrite_output_dir \ + --do_train --n_train 500 --num_train_epochs 1 \ + --per_device_train_batch_size 1 --freeze_embeds \ + --src_lang en_XX --tgt_lang ro_RO --task translation \ + --fp16 + ``` + + If you don't break it up, one has to scroll horizontally which often makes it quite difficult to quickly see what's happening. + + The backslashes allow us to copy the command directly into the console to run it, without needing to edit it. + +5. Include only the important information that you think will help the developer to quickly identify the problem. + + For example applications often create huge amounts of logs. Ask yourself whether providing all or parts of the log is useful. + + Pasting a 100-1000 lines of log into the issue is an immediate turn off, since it will take a lot of time to figure out where the pertinent parts of the log are. + + Attaching a full log can be helpful if it's done as an attachment, if it's enclosed in the following html code in the comment editor window: + + ``` +
+ Full log + 
+
+   many
+   lines
+   go
+   here
+
+
+
+ ``` + + which would result in the following entry, which can be opened if desired, but otherwise takes little space. + +
+ Full log + 
+   many
+   lines
+   go
+   here
+
+
+ + You could also provide a link to a pastebin service, but this is less beneficial since those links tend to expire quickly and future readers of your issue might not be able to access that log file anymore and may lack some context. + +6. If this is an issue in your code, do try to reduce that code to a minimal example that still demonstrates the problem. Please ask at the forums if you have a hard time figuring how to do that. Please realize that we don't have the luxury of having time to try and understand all of your custom code. + + If you really tried to make a short reproducible code but couldn't figure it out, it might be that having a traceback will give the developer enough information to know what's going on. But if it is not enough and we can't reproduce the problem, we can't really solve it. + + Do not despair if you can't figure it out from the beginning, just share what you can and perhaps someone else will be able to help you at the forums. + + If your setup involves any custom datasets, the best way to help us reproduce the problem is to create a [Google Colab notebook](https://colab.research.google.com/) that demonstrates the issue and once you verify that the issue still exists, include a link to that notebook in the Issue. Just make sure that you don't copy and paste the location bar url of the open notebook - as this is private and we won't be able to open it. Instead, you need to click on `Share` in the right upper corner of the notebook, select `Get Link` and then copy and paste the public link it will give to you. + +7. If you forked off some of this project's code or example applications, please, do not ask us to go into your code repository and figure out what you may have done. The code is already very complex and unless there is an easy way to do a diff and it's a small diff, it won't be possible to find someone with time on their hands to make a lengthy investigation. Albeit, you might find someone at the forums who will be generous to do this for you. + +8. Before reporting an issue, first, always try to update your environment to the latest official version of this library. We have no resources to go and debug older revisions, which could easily have bugs that have been fixed in the latest released version. + + We understand that this is not always possible, especially when APIs change, in which case file an issue against the highest library version your environment can support. + + Of course, if you upgrade the library, always retest that the problem is still there. + +9. Please do not ask us to reproduce an issue with your custom data, since we don't have it. So, either you should use some existing dataset supported by HF datasets or you need to supply a code that generates a small sample on the fly, or some another quick and simple way to get it. + + Please do not send us any non-public domain data that may require a license or a permission to be used. + +10. Do not tag multiple developers on the issue unless you know this is expected, either because you asked them and they gave you an explicit permission to tag them or the issue template instructs you to do so. + + The "who to tag for what domain" part of the issue template is there to help users direct their questions to the right developers who are designated maintainers of project's specific domains. They can then decide at their own discretion to tag other developers if they feel it'd help move the issue forward. + + We currently don't have a triage service and we trust your capacity to identify the right domain and thus the persons to tag in your issue. If you are not sure, please use the forums to ask for guidance. + + When in doubt, err on the side of not tagging a given person. If you tag multiple people out of context or permission don't be surprised if you get no response at all. Please remember that every time you tag someone, they get a notification and you're taking their time without their permission. Please be sensitive to that. + + If you got helped by one of the developers in the past please don't tag them in future issues, unless they are listed in the issue template for the domain you are asking about or that developer gave you an explicit permission to tag them in future issues. + + If you see a certain developer doing multiple and/or recent commits into a specific area of the project that you feel is relevant to your issue, it is not a good reason to tag them. Various developers may be fixing things that prevent them from moving forward, but often their work is focused on a totally different domain. And while they may or may not know how to help you with the problem at hand, it would benefit the whole community much more if they focus on the domain of their unique expertise. + +11. Use the Edit button. Take your time, and re-read and improve the wording and formatting to make your posts and comments as easy to understand as possible. + + Avoid posting multiple comments in a row, as each comment generates a notification for the developers tagged in that issue. If you happened to post multiple comments in a row, and nobody followed up yet - consider merging those into one or a few comments while editing the combined content to be coherent. + + If you choose to edit your older comments after others posted follow up comments you need to be aware that your modifications might not be noticed, so if it's not a typo fixing, try to write a new comment flagging that something has been changed in the previous comments. + + For example, the very first comment is the most important one. If while the thread unfolds you realize that things aren't as they seemed to you originally you may want to edit the first post to reflect the up-to-date understanding of the issue at hand so that it helps those who read your issue in the future quickly understand what's going on and not need to sift through dozens of comments. It also helps to indicate that the post was edited. So, those reading the thread later can understand why there might be certain discontinuity in the information flow. + + Use bullets and items if you have lists of items and the outcome improves overall readability. + + Use backticks to refer to class and function names, e.g. `BartModel` and `generate` as these stand out and improve the speed of a reader's comprehension. + + Try not use italics and bold text too much as these often make the text more difficult to read. + + +12. If you are cross-referencing a specific comment in a given thread or another issue, always link to that specific comment, rather than using the issue link. If you do the latter it could be quite impossible to find which specific comment you're referring to. + + To get the link to the specific comment do not copy the url from the location bar of your browser, but instead, click the `...` icon in the upper right corner of the comment and then select "Copy Link". + + For example the first link is a link to an issue, and the second to a specific comment in the same issue: + + 1. https://github.com/huggingface/transformers/issues/9257 + 2. https://github.com/huggingface/transformers/issues/9257#issuecomment-749945162 + + +13. If you are replying to a last comment, it's totally fine to make your reply with just your comment in it. The readers can follow the information flow here. + + But if you're replying to a comment that happened some comments back it's always a good practice to quote just the relevant lines you're replying it. The `>` is used for quoting, or you can always use the menu to do so. For example your editor box will look like: + + ``` + > How big is your gpu cluster? + + Our cluster is made of 256 gpus. + ``` + + If you are addressing multiple comments, quote the relevant parts of each before your answer. Some people use the same comment to do multiple replies, others separate them into separate comments. Either way works. The latter approach helps for linking to a specific comment. + +In general the best way to figure out what works the best is learn from issues posted by other people - see which issues get great responses and which get little to no response - observe what the posters who received great responses did differently from those who did not. + +Thank you for reading this somewhat lengthy document. We would like to conclude that these are not absolute rules, but a friendly advice that will help maximize the chances for us to understand what you are trying to communicate, reproduce the problem then resolve it to your satisfaction and the benefit of the whole community. + +If after reading this document there are remaining questions on how and why or there is a need for further elucidation, please, don't hesitate to ask your question in [this thread](https://discuss.huggingface.co/t/how-to-request-support/3128). diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/LICENSE b/tasks/tasksmith-333d8be2d5aa/tests/source/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..68b7d66c97d66c58de883ed0c451af2b3183e6f3 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/LICENSE @@ -0,0 +1,203 @@ +Copyright 2018- The Hugging Face team. All rights reserved. + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/Makefile b/tasks/tasksmith-333d8be2d5aa/tests/source/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..710c555b74f6df662a21823351d51c732bda8d2c --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/Makefile @@ -0,0 +1,130 @@ +.PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples benchmark + +# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!) +export PYTHONPATH = src + +check_dirs := examples tests src utils + +exclude_folders := "" + +modified_only_fixup: + $(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs))) + @if test -n "$(modified_py_files)"; then \ + echo "Checking/fixing $(modified_py_files)"; \ + ruff check $(modified_py_files) --fix --exclude $(exclude_folders); \ + ruff format $(modified_py_files) --exclude $(exclude_folders);\ + else \ + echo "No library .py files were modified"; \ + fi + +# Update src/transformers/dependency_versions_table.py + +deps_table_update: + @python setup.py deps_table_update + +deps_table_check_updated: + @md5sum src/transformers/dependency_versions_table.py > md5sum.saved + @python setup.py deps_table_update + @md5sum -c --quiet md5sum.saved || (printf "\nError: the version dependency table is outdated.\nPlease run 'make fixup' or 'make style' and commit the changes.\n\n" && exit 1) + @rm md5sum.saved + +# autogenerating code + +autogenerate_code: deps_table_update + +# Check that the repo is in a good state + +repo-consistency: + python utils/check_copies.py + python utils/check_modular_conversion.py + python utils/check_table.py + python utils/check_dummies.py + python utils/check_repo.py + python utils/check_inits.py + python utils/check_config_docstrings.py + python utils/check_config_attributes.py + python utils/check_doctest_list.py + python utils/update_metadata.py --check-only + python utils/check_docstrings.py + python utils/check_support_list.py + +# this target runs checks on all files + +quality: + @python -c "from transformers import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) + ruff check $(check_dirs) setup.py conftest.py + ruff format --check $(check_dirs) setup.py conftest.py + python utils/sort_auto_mappings.py --check_only + python utils/check_doc_toc.py + python utils/check_docstrings.py --check_all + + +# Format source code automatically and check is there are any problems left that need manual fixing + +extra_style_checks: + python utils/sort_auto_mappings.py + python utils/check_doc_toc.py --fix_and_overwrite + +# this target runs checks on all files and potentially modifies some of them + +style: + ruff check $(check_dirs) setup.py conftest.py --fix --exclude $(exclude_folders) + ruff format $(check_dirs) setup.py conftest.py --exclude $(exclude_folders) + ${MAKE} autogenerate_code + ${MAKE} extra_style_checks + +# Super fast fix and check target that only works on relevant modified files since the branch was made + +fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency + +# Make marked copies of snippets of codes conform to the original + +fix-copies: + python utils/check_copies.py --fix_and_overwrite + python utils/check_modular_conversion.py --fix_and_overwrite + python utils/check_table.py --fix_and_overwrite + python utils/check_dummies.py --fix_and_overwrite + python utils/check_doctest_list.py --fix_and_overwrite + python utils/check_docstrings.py --fix_and_overwrite + +# Run tests for the library + +test: + python -m pytest -n auto --dist=loadfile -s -v ./tests/ + +# Run tests for examples + +test-examples: + python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/ + +# Run benchmark + +benchmark: + python3 benchmark/benchmark.py --config-dir benchmark/config --config-name generation --commit=diff backend.model=google/gemma-2b backend.cache_implementation=null,static backend.torch_compile=false,true --multirun + +# Run tests for SageMaker DLC release + +test-sagemaker: # install sagemaker dependencies in advance with pip install .[sagemaker] + TEST_SAGEMAKER=True python -m pytest -n auto -s -v ./tests/sagemaker + + +# Release stuff + +pre-release: + python utils/release.py + +pre-patch: + python utils/release.py --patch + +post-release: + python utils/release.py --post_release + +post-patch: + python utils/release.py --post_release --patch + +build-release: + rm -rf dist + rm -rf build + python setup.py bdist_wheel + python setup.py sdist + python utils/check_build.py diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/README.md b/tasks/tasksmith-333d8be2d5aa/tests/source/README.md new file mode 100644 index 0000000000000000000000000000000000000000..42403f84b885daf03378fcc951722259c507b8d6 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/README.md @@ -0,0 +1,322 @@ + + +

+ + + + Hugging Face Transformers Library + +
+
+

+ +

+ Build + GitHub + Documentation + GitHub release + Contributor Covenant + DOI +

+ +

+

+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +

+

+ +

+

State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow

+

+ +

+ +

+ +🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. + +These models can be applied on: + +* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages. +* 🖼️ Images, for tasks like image classification, object detection, and segmentation. +* 🗣️ Audio, for tasks like speech recognition and audio classification. + +Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering. + +🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. + +🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other. + +## Online demos + +You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models. + +Here are a few examples: + +In Natural Language Processing: +- [Masked word completion with BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) +- [Named Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) +- [Text generation with Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) +- [Natural Language Inference with RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) +- [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) +- [Question answering with DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) +- [Translation with T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) + +In Computer Vision: +- [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224) +- [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50) +- [Semantic Segmentation with SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) +- [Panoptic Segmentation with Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic) +- [Depth Estimation with Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything) +- [Video Classification with VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) +- [Universal Segmentation with OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) + +In Audio: +- [Automatic Speech Recognition with Whisper](https://huggingface.co/openai/whisper-large-v3) +- [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) +- [Audio Classification with Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) + +In Multimodal tasks: +- [Table Question Answering with TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) +- [Visual Question Answering with ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) +- [Image captioning with LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf) +- [Zero-shot Image Classification with SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) +- [Document Question Answering with LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) +- [Zero-shot Video Classification with X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) +- [Zero-shot Object Detection with OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2) +- [Zero-shot Image Segmentation with CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg) +- [Automatic Mask Generation with SAM](https://huggingface.co/docs/transformers/model_doc/sam) + + +## 100 projects using Transformers + +Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the +Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone +else to build their dream projects. + +In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the +community, and we have created the [awesome-transformers](./awesome-transformers.md) page which lists 100 +incredible projects built in the vicinity of transformers. + +If you own or use a project that you believe should be part of the list, please open a PR to add it! + +## Serious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub. + + + Hugging Face Enterprise Hub +
+ +## Quick tour + +To immediately use a model on a given input (text, image, audio, ...), we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts: + +```python +>>> from transformers import pipeline + +# Allocate a pipeline for sentiment-analysis +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here, the answer is "positive" with a confidence of 99.97%. + +Many tasks have a pre-trained `pipeline` ready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Download an image with cute cats +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Allocate a pipeline for object detection +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +Here, we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is the original image on the left, with the predictions displayed on the right: + +

+ + +

+ +You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/docs/transformers/task_summary). + +In addition to `pipeline`, to download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version: +```python +>>> from transformers import AutoTokenizer, AutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="pt") +>>> outputs = model(**inputs) +``` + +And here is the equivalent code for TensorFlow: +```python +>>> from transformers import AutoTokenizer, TFAutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="tf") +>>> outputs = model(**inputs) +``` + +The tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator. + +The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use as usual. [This tutorial](https://huggingface.co/docs/transformers/training) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset. + +## Why should I use transformers? + +1. Easy-to-use state-of-the-art models: + - High performance on natural language understanding & generation, computer vision, and audio tasks. + - Low barrier to entry for educators and practitioners. + - Few user-facing abstractions with just three classes to learn. + - A unified API for using all our pretrained models. + +1. Lower compute costs, smaller carbon footprint: + - Researchers can share trained models instead of always retraining. + - Practitioners can reduce compute time and production costs. + - Dozens of architectures with over 400,000 pretrained models across all modalities. + +1. Choose the right framework for every part of a model's lifetime: + - Train state-of-the-art models in 3 lines of code. + - Move a single model between TF2.0/PyTorch/JAX frameworks at will. + - Seamlessly pick the right framework for training, evaluation, and production. + +1. Easily customize a model or an example to your needs: + - We provide examples for each architecture to reproduce the results published by its original authors. + - Model internals are exposed as consistently as possible. + - Model files can be used independently of the library for quick experiments. + +## Why shouldn't I use transformers? + +- This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files. +- The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library (possibly, [Accelerate](https://huggingface.co/docs/accelerate)). +- While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/main/examples) are just that: examples. It is expected that they won't work out-of-the-box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. + +## Installation + +### With pip + +This repository is tested on Python 3.9+, Flax 0.4.1+, PyTorch 2.0+, and TensorFlow 2.6+. + +You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). + +First, create a virtual environment with the version of Python you're going to use and activate it. + +Then, you will need to install at least one of Flax, PyTorch, or TensorFlow. +Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) installation pages regarding the specific installation command for your platform. + +When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows: + +```bash +pip install transformers +``` + +If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source). + +### With conda + +🤗 Transformers can be installed using conda as follows: + +```shell script +conda install conda-forge::transformers +``` + +> **_NOTE:_** Installing `transformers` from the `huggingface` channel is deprecated. + +Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda. + +> **_NOTE:_** On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062). + +## Model architectures + +**[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co/models), where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations). + +Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen) + +🤗 Transformers currently provides the following architectures: see [here](https://huggingface.co/docs/transformers/model_summary) for a high-level summary of each them. + +To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks). + +These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://github.com/huggingface/transformers/tree/main/examples). + + +## Learn more + +| Section | Description | +|-|-| +| [Documentation](https://huggingface.co/docs/transformers/) | Full API documentation and tutorials | +| [Task summary](https://huggingface.co/docs/transformers/task_summary) | Tasks supported by 🤗 Transformers | +| [Preprocessing tutorial](https://huggingface.co/docs/transformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models | +| [Training and fine-tuning](https://huggingface.co/docs/transformers/training) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API | +| [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/main/examples) | Example scripts for fine-tuning models on a wide range of tasks | +| [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community | + +## Citation + +We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library: +```bibtex +@inproceedings{wolf-etal-2020-transformers, + title = "Transformers: State-of-the-Art Natural Language Processing", + author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = oct, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", + pages = "38--45" +} +``` diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/SECURITY.md b/tasks/tasksmith-333d8be2d5aa/tests/source/SECURITY.md new file mode 100644 index 0000000000000000000000000000000000000000..431b17a85042dccdd3062941b0e26099ef38a4b2 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/SECURITY.md @@ -0,0 +1,39 @@ +# Security Policy + +## Hugging Face Hub, remote artefacts, and remote code + +Transformers is open-source software that is tightly coupled to the Hugging Face Hub. While you have the ability to use it +offline with pre-downloaded model weights, it provides a very simple way to download, use, and manage models locally. + +When downloading artefacts that have been uploaded by others on any platform, you expose yourself to risks. Please +read below for the security recommendations in order to keep your runtime and local environment safe. + +### Remote artefacts + +Models uploaded on the Hugging Face Hub come in different formats. We heavily recommend uploading and downloading +models in the [`safetensors`](https://github.com/huggingface/safetensors) format (which is the default prioritized +by the transformers library), as developed specifically to prevent arbitrary code execution on your system. + +To avoid loading models from unsafe formats(e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetensors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model. + +### Remote code + +#### Modeling + +Transformers supports many model architectures, but is also the bridge between your Python runtime and models that +are stored in model repositories on the Hugging Face Hub. + +These models require the `trust_remote_code=True` parameter to be set when using them; please **always** verify +the content of the modeling files when using this argument. We recommend setting a revision in order to ensure you +protect yourself from updates on the repository. + +#### Tools + +Through the `Agent` framework, remote tools can be downloaded to be used by the Agent. You're to specify these tools +yourself, but please keep in mind that their code will be run on your machine if the Agent chooses to run them. + +Please inspect the code of the tools before passing them to the Agent to protect your runtime and local setup. + +## Reporting a Vulnerability + +Feel free to submit vulnerability reports to [security@huggingface.co](mailto:security@huggingface.co), where someone from the HF security team will review and recommend next steps. If reporting a vulnerability specific to open source, please note [Huntr](https://huntr.com) is a vulnerability disclosure program for open source software. diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/awesome-transformers.md b/tasks/tasksmith-333d8be2d5aa/tests/source/awesome-transformers.md new file mode 100644 index 0000000000000000000000000000000000000000..d55e276841a3b0d532a7b6849d36fb5248d23681 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/awesome-transformers.md @@ -0,0 +1,609 @@ +# Awesome projects built with Transformers + +This page lists awesome projects built on top of Transformers. Transformers is more than a toolkit to use pretrained +models: it's a community of projects built around it and the Hugging Face Hub. We want Transformers to enable +developers, researchers, students, professors, engineers, and anyone else to build their dream projects. + +In this list, we showcase incredibly impactful and novel projects that have pushed the field forward. We celebrate +100 of these projects as we reach the milestone of 100k stars as a community; but we're very open to pull requests +adding other projects to the list. If you believe a project should be here and it's not, then please, open a PR +to add it. + +## [gpt4all](https://github.com/nomic-ai/gpt4all) + +[gpt4all](https://github.com/nomic-ai/gpt4all) is an ecosystem of open-source chatbots trained on massive collections of clean assistant data including code, stories and dialogue. It offers open-source, large language models such as LLaMA and GPT-J trained in an assistant-style. + +Keywords: Open-source, LLaMa, GPT-J, instruction, assistant + +## [recommenders](https://github.com/microsoft/recommenders) + +This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It goes over several aspects required to build efficient recommendation systems: data preparation, modeling, evaluation, model selection & optimization, as well as operationalization + +Keywords: Recommender systems, AzureML + +## [IOPaint](https://github.com/Sanster/IOPaint) + +Image inpainting tool powered by Stable Diffusion. Remove any unwanted object, defect, people from your pictures or erase and replace anything on your pictures. + +Keywords: inpainting, SD, Stable Diffusion + +## [flair](https://github.com/flairNLP/flair) + +FLAIR is a powerful PyTorch NLP framework, convering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things. + +Keywords: NLP, text embedding, document embedding, biomedical, NER, PoS, sentiment-analysis + +## [mindsdb](https://github.com/mindsdb/mindsdb) + +MindsDB is a low-code ML platform, which automates and integrates several ML frameworks into the data stack as "AI Tables" to streamline the integration of AI into applications, making it accessible to developers of all skill levels. + +Keywords: Database, low-code, AI table + +## [langchain](https://github.com/hwchase17/langchain) + +[langchain](https://github.com/hwchase17/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools. + +Keywords: LLMs, Large Language Models, Agents, Chains + +## [LlamaIndex](https://github.com/jerryjliu/llama_index) + +[LlamaIndex](https://github.com/jerryjliu/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retreival mechanisms to perform different LLM tasks and obtain knowledge-augmented results. + +Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation + +## [ParlAI](https://github.com/facebookresearch/ParlAI) + +[ParlAI](https://github.com/facebookresearch/ParlAI) is a python framework for sharing, training and testing dialogue models, from open-domain chitchat, to task-oriented dialogue, to visual question answering. It provides more than 100 datasets under the same API, a large zoo of pretrained models, a set of agents, and has several integrations. + +Keywords: Dialogue, Chatbots, VQA, Datasets, Agents + +## [sentence-transformers](https://github.com/UKPLab/sentence-transformers) + +This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity. + +Keywords: Dense vector representations, Text embeddings, Sentence embeddings + +## [ludwig](https://github.com/ludwig-ai/ludwig) + +Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system. Ludwig is targeted at a wide variety of AI tasks. It provides a data-driven configuration system, training, prediction, and evaluation scripts, as well as a programmatic API. + +Keywords: Declarative, Data-driven, ML Framework + +## [InvokeAI](https://github.com/invoke-ai/InvokeAI) + +[InvokeAI](https://github.com/invoke-ai/InvokeAI) is an engine for Stable Diffusion models, aimed at professionals, artists, and enthusiasts. It leverages the latest AI-driven technologies through CLI as well as a WebUI. + +Keywords: Stable-Diffusion, WebUI, CLI + +## [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) + +[PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) is an easy-to-use and powerful NLP library particularly targeted at the Chinese languages. It has support for multiple pre-trained model zoos, and supports a wide-range of NLP tasks from research to industrial applications. + +Keywords: NLP, Chinese, Research, Industry + +## [stanza](https://github.com/stanfordnlp/stanza) + +The Stanford NLP Group's official Python NLP library. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. + +Keywords: NLP, Multilingual, CoreNLP + +## [DeepPavlov](https://github.com/deeppavlov/DeepPavlov) + +[DeepPavlov](https://github.com/deeppavlov/DeepPavlov) is an open-source conversational AI library. It is designed for the development of production ready chat-bots and complex conversational systems, as well as research in the area of NLP and, particularly, of dialog systems. + +Keywords: Conversational, Chatbot, Dialog + +## [alpaca-lora](https://github.com/tloen/alpaca-lora) + +Alpaca-lora contains code for reproducing the Stanford Alpaca results using low-rank adaptation (LoRA). The repository provides training (fine-tuning) as well as generation scripts. + +Keywords: LoRA, Parameter-efficient fine-tuning + +## [imagen-pytorch](https://github.com/lucidrains/imagen-pytorch) + +An open-source Implementation of Imagen, Google's closed-source Text-to-Image Neural Network that beats DALL-E2. As of release, it is the new SOTA for text-to-image synthesis. + +Keywords: Imagen, Text-to-image + +## [adapters](https://github.com/adapter-hub/adapters) + +[adapters](https://github.com/adapter-hub/adapters) is an extension of HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules. It is a drop-in replacement for transformers, which is regularly updated to stay up-to-date with the developments of transformers. + +Keywords: Adapters, LoRA, Parameter-efficient fine-tuning, Hub + +## [NeMo](https://github.com/NVIDIA/NeMo) + +NVIDIA [NeMo](https://github.com/NVIDIA/NeMo) is a conversational AI toolkit built for researchers working on automatic speech recognition (ASR), text-to-speech synthesis (TTS), large language models (LLMs), and natural language processing (NLP). The primary objective of [NeMo](https://github.com/NVIDIA/NeMo) is to help researchers from industry and academia to reuse prior work (code and pretrained models) and make it easier to create new https://developer.nvidia.com/conversational-ai#started. + +Keywords: Conversational, ASR, TTS, LLMs, NLP + +## [Runhouse](https://github.com/run-house/runhouse) + +[Runhouse](https://github.com/run-house/runhouse) allows to send code and data to any of your compute or data infra, all in Python, and continue to interact with them normally from your existing code and environment. Runhouse developers mention: + +> Think of it as an expansion pack to your Python interpreter that lets it take detours to remote machines or manipulate remote data. + +Keywords: MLOps, Infrastructure, Data storage, Modeling + +## [MONAI](https://github.com/Project-MONAI/MONAI) + +[MONAI](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its ambitions are: +- developing a community of academic, industrial and clinical researchers collaborating on a common foundation; +- creating state-of-the-art, end-to-end training workflows for healthcare imaging; +- providing researchers with the optimized and standardized way to create and evaluate deep learning models. + +Keywords: Healthcare imaging, Training, Evaluation + +## [simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers) + +Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize, train, and evaluate a model. It supports a wide variety of NLP tasks. + +Keywords: Framework, simplicity, NLP + +## [JARVIS](https://github.com/microsoft/JARVIS) + +[JARVIS](https://github.com/microsoft/JARVIS) is a system attempting to merge LLMs such as GPT-4 with the rest of the open-source ML community: leveraging up to 60 downstream models in order to perform tasks identified by the LLM. + +Keywords: LLM, Agents, HF Hub + +## [transformers.js](https://xenova.github.io/transformers.js/) + +[transformers.js](https://xenova.github.io/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser. + +Keywords: Transformers, JavaScript, browser + +## [bumblebee](https://github.com/elixir-nx/bumblebee) + +Bumblebee provides pre-trained Neural Network models on top of Axon, a neural networks library for the Elixir language. It includes integration with 🤗 Models, allowing anyone to download and perform Machine Learning tasks with few lines of code. + +Keywords: Elixir, Axon + +## [argilla](https://github.com/argilla-io/argilla) + +Argilla is an open-source platform providing advanced NLP labeling, monitoring, and workspaces. It is compatible with many open source ecosystems such as Hugging Face, Stanza, FLAIR, and others. + +Keywords: NLP, Labeling, Monitoring, Workspaces + +## [haystack](https://github.com/deepset-ai/haystack) + +Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs. It offers production-ready tools to quickly build complex decision making, question answering, semantic search, text generation applications, and more. + +Keywords: NLP, Framework, LLM + +## [spaCy](https://github.com/explosion/spaCy) + +[spaCy](https://github.com/explosion/spaCy) is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It offers support for transformers models through its third party package, spacy-transformers. + +Keywords: NLP, Framework + +## [speechbrain](https://github.com/speechbrain/speechbrain) + +SpeechBrain is an open-source and all-in-one conversational AI toolkit based on PyTorch. +The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, speech separation, language identification, multi-microphone signal processing, and many others. + +Keywords: Conversational, Speech + +## [skorch](https://github.com/skorch-dev/skorch) + +Skorch is a scikit-learn compatible neural network library that wraps PyTorch. It has support for models within transformers, and tokenizers from tokenizers. + +Keywords: Scikit-Learn, PyTorch + +## [bertviz](https://github.com/jessevig/bertviz) + +BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. + +Keywords: Visualization, Transformers + +## [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) + +[mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) is a haiku library using the xmap/pjit operators in JAX for model parallelism of transformers. This library is designed for scalability up to approximately 40B parameters on TPUv3s. It was the library used to train the GPT-J model. + +Keywords: Haiku, Model parallelism, LLM, TPU + +## [deepchem](https://github.com/deepchem/deepchem) + +DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, quantum chemistry, and biology. + +Keywords: Drug discovery, Materials Science, Quantum Chemistry, Biology + +## [OpenNRE](https://github.com/thunlp/OpenNRE) + +An Open-Source Package for Neural Relation Extraction (NRE). It is targeted at a wide range of users, from newcomers to relation extraction, to developers, researchers, or students. + +Keywords: Neural Relation Extraction, Framework + +## [pycorrector](https://github.com/shibing624/pycorrector) + +PyCorrector is a Chinese Text Error Correction Tool. It uses a language model to detect errors, pinyin feature and shape feature to correct Chinese text errors. it can be used for Chinese Pinyin and stroke input method. + +Keywords: Chinese, Error correction tool, Language model, Pinyin + +## [nlpaug](https://github.com/makcedward/nlpaug) + +This python library helps you with augmenting nlp for machine learning projects. It is a lightweight library featuring synthetic data generation for improving model performance, support for audio and text, and compatibility with several ecosystems (scikit-learn, pytorch, tensorflow). + +Keywords: Data augmentation, Synthetic data generation, Audio, NLP + +## [dream-textures](https://github.com/carson-katri/dream-textures) + +[dream-textures](https://github.com/carson-katri/dream-textures) is a library targeted at bringing stable-diffusion support within Blender. It supports several use-cases, such as image generation, texture projection, inpainting/outpainting, ControlNet, and upscaling. + +Keywords: Stable-Diffusion, Blender + +## [seldon-core](https://github.com/SeldonIO/seldon-core) + +Seldon core converts your ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices. +Seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including Advanced Metrics, Request Logging, Explainers, Outlier Detectors, A/B Tests, Canaries and more. + +Keywords: Microservices, Modeling, Language wrappers + +## [open_model_zoo](https://github.com/openvinotoolkit/open_model_zoo) + +This repository includes optimized deep learning models and a set of demos to expedite development of high-performance deep learning inference applications. Use these free pre-trained models instead of training your own models to speed-up the development and production deployment process. + +Keywords: Optimized models, Demos + +## [ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion) + +ML-Stable-Diffusion is a repository by Apple bringing Stable Diffusion support to Core ML, on Apple Silicon devices. It supports stable diffusion checkpoints hosted on the Hugging Face Hub. + +Keywords: Stable Diffusion, Apple Silicon, Core ML + +## [stable-dreamfusion](https://github.com/ashawkey/stable-dreamfusion) + +Stable-Dreamfusion is a pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model. + +Keywords: Text-to-3D, Stable Diffusion + +## [txtai](https://github.com/neuml/txtai) + +[txtai](https://github.com/neuml/txtai) is an open-source platform for semantic search and workflows powered by language models. txtai builds embeddings databases, which are a union of vector indexes and relational databases enabling similarity search with SQL. Semantic workflows connect language models together into unified applications. + +Keywords: Semantic search, LLM + +## [djl](https://github.com/deepjavalibrary/djl) + +Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for developers. DJL provides a native Java development experience and functions like any other regular Java library. DJL offers [a Java binding](https://github.com/deepjavalibrary/djl/tree/master/extensions/tokenizers) for HuggingFace Tokenizers and easy conversion toolkit for HuggingFace model to deploy in Java. + +Keywords: Java, Framework + +## [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/) + +This project provides a unified framework to test generative language models on a large number of different evaluation tasks. It has support for more than 200 tasks, and supports different ecosystems: HF Transformers, GPT-NeoX, DeepSpeed, as well as the OpenAI API. + +Keywords: LLM, Evaluation, Few-shot + +## [gpt-neox](https://github.com/EleutherAI/gpt-neox) + +This repository records EleutherAI's library for training large-scale language models on GPUs. The framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. It is focused on training multi-billion-parameter models. + +Keywords: Training, LLM, Megatron, DeepSpeed + +## [muzic](https://github.com/microsoft/muzic) + +Muzic is a research project on AI music that empowers music understanding and generation with deep learning and artificial intelligence. Muzic was created by researchers from Microsoft Research Asia. + +Keywords: Music understanding, Music generation + +## [dalle-flow](https://github.com/jina-ai/dalle-flow) + +DALL·E Flow is an interactive workflow for generating high-definition images from a text prompt. Itt leverages DALL·E-Mega, GLID-3 XL, and Stable Diffusion to generate image candidates, and then calls CLIP-as-service to rank the candidates w.r.t. the prompt. +The preferred candidate is fed to GLID-3 XL for diffusion, which often enriches the texture and background. Finally, the candidate is upscaled to 1024x1024 via SwinIR. + +Keywords: High-definition image generation, Stable Diffusion, DALL-E Mega, GLID-3 XL, CLIP, SwinIR + +## [lightseq](https://github.com/bytedance/lightseq) + +LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA. It enables highly efficient computation of modern NLP and CV models such as BERT, GPT, Transformer, etc. It is therefore best useful for machine translation, text generation, image classification, and other sequence related tasks. + +Keywords: Training, Inference, Sequence Processing, Sequence Generation + +## [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) + +The goal of this project is to create a learning based system that takes an image of a math formula and returns corresponding LaTeX code. + +Keywords: OCR, LaTeX, Math formula + +## [open_clip](https://github.com/mlfoundations/open_clip) + +OpenCLIP is an open source implementation of OpenAI's CLIP. + +The goal of this repository is to enable training models with contrastive image-text supervision, and to investigate their properties such as robustness to distribution shift. +The starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset. + +Specifically, a ResNet-50 model trained with this codebase on OpenAI's 15 million image subset of YFCC achieves 32.7% top-1 accuracy on ImageNet. + +Keywords: CLIP, Open-source, Contrastive, Image-text + +## [dalle-playground](https://github.com/saharmor/dalle-playground) + +A playground to generate images from any text prompt using Stable Diffusion and Dall-E mini. + +Keywords: WebUI, Stable Diffusion, Dall-E mini + +## [FedML](https://github.com/FedML-AI/FedML) + +[FedML](https://github.com/FedML-AI/FedML) is a federated learning and analytics library enabling secure and collaborative machine learning on decentralized data anywhere at any scale. + +It supports large-scale cross-silo federated learning, and cross-device federated learning on smartphones/IoTs, and research simulation. + +Keywords: Federated Learning, Analytics, Collaborative ML, Decentralized + +## [gpt-code-clippy](https://github.com/CodedotAl/gpt-code-clippy) + +GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub. + +Keywords: LLM, Code + +## [TextAttack](https://github.com/QData/TextAttack) + +[TextAttack](https://github.com/QData/TextAttack) 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP. + +Keywords: Adversarial attacks, Data augmentation, NLP + +## [OpenPrompt](https://github.com/thunlp/OpenPrompt) + +Prompt-learning is a paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modify the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. This library provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. [OpenPrompt](https://github.com/thunlp/OpenPrompt) supports loading PLMs directly from https://github.com/huggingface/transformers. + +## [text-generation-webui](https://github.com/oobabooga/text-generation-webui/) + +[text-generation-webui](https://github.com/oobabooga/text-generation-webui/) is a Gradio Web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA. + +Keywords: LLM, WebUI + +## [libra](https://github.com/Palashio/libra) + +An ergonomic machine learning [libra](https://github.com/Palashio/libra)ry for non-technical users. It focuses on ergonomics and on ensuring that training a model is as simple as it can be. + +Keywords: Ergonomic, Non-technical + +## [alibi](https://github.com/SeldonIO/alibi) + +Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. + +Keywords: Model inspection, Model interpretation, Black-box, White-box + +## [tortoise-tts](https://github.com/neonbjb/tortoise-tts) + +Tortoise is a text-to-speech program built with the following priorities: strong multi-voice capabilities, and highly realistic prosody and intonation. + +Keywords: Text-to-speech + +## [flower](https://github.com/adap/flower) + +Flower (flwr) is a framework for building federated learning systems. The design of Flower is based on a few guiding principles: customizability, extendability, framework agnosticity, and ease-of-use. + +Keywords: Federated learning systems, Customizable, Extendable, Framework-agnostic, Simplicity + +## [fast-bert](https://github.com/utterworks/fast-bert) + +Fast-Bert is a deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification. It is aimed at simplicity. + +Keywords: Deployment, BERT, XLNet + +## [towhee](https://github.com/towhee-io/towhee) + +Towhee makes it easy to build neural data processing pipelines for AI applications. We provide hundreds of models, algorithms, and transformations that can be used as standard pipeline building blocks. Users can use Towhee's Pythonic API to build a prototype of their pipeline and automatically optimize it for production-ready environments. + +Keywords: Data processing pipeline, Optimization + +## [alibi-detect](https://github.com/SeldonIO/alibi-detect) + +Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. + +Keywords: Adversarial, Outlier, Drift detection + +## [FARM](https://github.com/deepset-ai/FARM) + +[FARM](https://github.com/deepset-ai/FARM) makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. + +Keywords: Transfer Learning, Modular design, Multi-task learning, Experiment tracking + +## [aitextgen](https://github.com/minimaxir/aitextgen) + +A robust Python tool for text-based AI training and generation using OpenAI's GPT-2 and EleutherAI's GPT Neo/GPT-3 architecture. +[aitextgen](https://github.com/minimaxir/aitextgen) is a Python package that leverages PyTorch, Hugging Face Transformers and pytorch-lightning with specific optimizations for text generation using GPT-2, plus many added features. + +Keywords: Training, Generation + +## [diffgram](https://github.com/diffgram/diffgram) + +Diffgram aims to integrate human supervision into platforms. We support your team programmatically changing the UI (Schema, layout, etc.) like in Streamlit. This means that you can collect and annotate timely data from users. In other words, we are the platform behind your platform, an integrated part of your application, to ship new & better AI products faster. + +Keywords: Human supervision, Platform + +## [ecco](https://github.com/jalammar/ecco) + +Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0). + +Keywords: Model explainability + +## [s3prl](https://github.com/s3prl/s3prl) + +[s3prl](https://github.com/s3prl/s3prl) stands for Self-Supervised Speech Pre-training and Representation Learning. Self-supervised speech pre-trained models are called upstream in this toolkit, and are utilized in various downstream tasks. + +Keywords: Speech, Training + +## [ru-dalle](https://github.com/ai-forever/ru-dalle) + +RuDALL-E aims to be similar to DALL-E, targeted to Russian. + +Keywords: DALL-E, Russian + +## [DeepKE](https://github.com/zjunlp/DeepKE) + +[DeepKE](https://github.com/zjunlp/DeepKE) is a knowledge extraction toolkit for knowledge graph construction supporting cnSchema，low-resource, document-level and multimodal scenarios for entity, relation and attribute extraction. + +Keywords: Knowledge Extraction, Knowledge Graphs + +## [Nebuly](https://github.com/nebuly-ai/nebuly) + +Nebuly is the next-generation platform to monitor and optimize your AI costs in one place. The platform connects to all your AI cost sources (compute, API providers, AI software licenses, etc) and centralizes them in one place to give you full visibility on a model basis. The platform also provides optimization recommendations and a co-pilot model that can guide during the optimization process. The platform builds on top of the open-source tools allowing you to optimize the different steps of your AI stack to squeeze out the best possible cost performances. + +Keywords: Optimization, Performance, Monitoring + +## [imaginAIry](https://github.com/brycedrennan/imaginAIry) + +Offers a CLI and a Python API to generate images with Stable Diffusion. It has support for many tools, like image structure control (controlnet), instruction-based image edits (InstructPix2Pix), prompt-based masking (clipseg), among others. + +Keywords: Stable Diffusion, CLI, Python API + +## [sparseml](https://github.com/neuralmagic/sparseml) + +SparseML is an open-source model optimization toolkit that enables you to create inference-optimized sparse models using pruning, quantization, and distillation algorithms. Models optimized with SparseML can then be exported to the ONNX and deployed with DeepSparse for GPU-class performance on CPU hardware. + +Keywords: Model optimization, Pruning, Quantization, Distillation + +## [opacus](https://github.com/pytorch/opacus) + +Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. + +Keywords: Differential privacy + +## [LAVIS](https://github.com/salesforce/LAVIS) + +[LAVIS](https://github.com/salesforce/LAVIS) is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. This library aims to provide engineers and researchers with a one-stop solution to rapidly develop models for their specific multimodal scenarios, and benchmark them across standard and customized datasets. It features a unified interface design to access + +Keywords: Multimodal, NLP, Vision + +## [buzz](https://github.com/chidiwilliams/buzz) + +Buzz transcribes and translates audio offline on your personal computer. Powered by OpenAI's Whisper. + +Keywords: Audio transcription, Translation + +## [rust-bert](https://github.com/guillaume-be/rust-bert) + +Rust-native state-of-the-art Natural Language Processing models and pipelines. Port of Hugging Face's Transformers library, using the tch-rs crate and pre-processing from rust-tokenizers. Supports multi-threaded tokenization and GPU inference. This repository exposes the model base architecture, task-specific heads and ready-to-use pipelines. + +Keywords: Rust, BERT, Inference + +## [EasyNLP](https://github.com/alibaba/EasyNLP) + +[EasyNLP](https://github.com/alibaba/EasyNLP) is an easy-to-use NLP development and application toolkit in PyTorch, first released inside Alibaba in 2021. It is built with scalable distributed training strategies and supports a comprehensive suite of NLP algorithms for various NLP applications. [EasyNLP](https://github.com/alibaba/EasyNLP) integrates knowledge distillation and few-shot learning for landing large pre-trained models, together with various popular multi-modality pre-trained models. It provides a unified framework of model training, inference, and deployment for real-world applications. + +Keywords: NLP, Knowledge distillation, Few-shot learning, Multi-modality, Training, Inference, Deployment + +## [TurboTransformers](https://github.com/Tencent/TurboTransformers) + +A fast and user-friendly runtime for transformer inference (Bert, Albert, GPT2, Decoders, etc) on CPU and GPU. + +Keywords: Optimization, Performance + +## [hivemind](https://github.com/learning-at-home/hivemind) + +Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers. + +Keywords: Decentralized training + +## [docquery](https://github.com/impira/docquery) + +DocQuery is a library and command-line tool that makes it easy to analyze semi-structured and unstructured documents (PDFs, scanned images, etc.) using large language models (LLMs). You simply point DocQuery at one or more documents and specify a question you want to ask. DocQuery is created by the team at Impira. + +Keywords: Semi-structured documents, Unstructured documents, LLM, Document Question Answering + +## [CodeGeeX](https://github.com/THUDM/CodeGeeX) + +[CodeGeeX](https://github.com/THUDM/CodeGeeX) is a large-scale multilingual code generation model with 13 billion parameters, pre-trained on a large code corpus of more than 20 programming languages. It has several unique features: +- Multilingual code generation +- Crosslingual code translation +- Is a customizable programming assistant + +Keywords: Code Generation Model + +## [ktrain](https://github.com/amaiya/ktrain) + +[ktrain](https://github.com/amaiya/ktrain) is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, [ktrain](https://github.com/amaiya/ktrain) is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. + +Keywords: Keras wrapper, Model building, Training, Deployment + +## [FastDeploy](https://github.com/PaddlePaddle/FastDeploy) + +[FastDeploy](https://github.com/PaddlePaddle/FastDeploy) is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with packageout-of-the-box and unified experience, endend-to-end optimization for over fire160+ Text, Vision, Speech and Cross-modal AI models. Including image classification, object detection, OCR, face detection, matting, pp-tracking, NLP, stable diffusion, TTS and other tasks to meet developers' industrial deployment needs for multi-scenario, multi-hardware and multi-platform. + +Keywords: Model deployment, CLoud, Mobile, Edge + +## [underthesea](https://github.com/undertheseanlp/underthesea) + +[underthesea](https://github.com/undertheseanlp/underthesea) is a Vietnamese NLP toolkit. Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in Vietnamese Natural Language Processing. We provides extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing. + +Keywords: Vietnamese, NLP + +## [hasktorch](https://github.com/hasktorch/hasktorch) + +Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C++ libraries shared by PyTorch. + +Keywords: Haskell, Neural Networks + +## [donut](https://github.com/clovaai/donut) + +Donut, or Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. + +Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing). + +Keywords: Document Understanding + +## [transformers-interpret](https://github.com/cdpierse/transformers-interpret) + +Transformers Interpret is a model explainability tool designed to work exclusively with the transformers package. + +In line with the philosophy of the Transformers package Transformers Interpret allows any transformers model to be explained in just two lines. Explainers are available for both text and computer vision models. Visualizations are also available in notebooks and as savable png and html files + +Keywords: Model interpretation, Visualization + +## [mlrun](https://github.com/mlrun/mlrun) + +MLRun is an open MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. With MLRun, you can choose any IDE on your local machine or on the cloud. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements. + +Keywords: MLOps + +## [FederatedScope](https://github.com/alibaba/FederatedScope) + +[FederatedScope](https://github.com/alibaba/FederatedScope) is a comprehensive federated learning platform that provides convenient usage and flexible customization for various federated learning tasks in both academia and industry. Based on an event-driven architecture, [FederatedScope](https://github.com/alibaba/FederatedScope) integrates rich collections of functionalities to satisfy the burgeoning demands from federated learning, and aims to build up an easy-to-use platform for promoting learning safely and effectively. + +Keywords: Federated learning, Event-driven + +## [pythainlp](https://github.com/PyThaiNLP/pythainlp) + +PyThaiNLP is a Python package for text processing and linguistic analysis, similar to NLTK with focus on Thai language. + +Keywords: Thai, NLP, NLTK + +## [FlagAI](https://github.com/FlagAI-Open/FlagAI) + +[FlagAI](https://github.com/FlagAI-Open/FlagAI) (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model. Our goal is to support training, fine-tuning, and deployment of large-scale models on various downstream tasks with multi-modality. + +Keywords: Large models, Training, Fine-tuning, Deployment, Multi-modal + +## [pyserini](https://github.com/castorini/pyserini) + +[pyserini](https://github.com/castorini/pyserini) is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse representations is provided via integration with the group's Anserini IR toolkit. Retrieval using dense representations is provided via integration with Facebook's Faiss library. + +Keywords: IR, Information Retrieval, Dense, Sparse + +## [baal](https://github.com/baal-org/baal) + +[baal](https://github.com/baal-org/baal) is an active learning library that supports both industrial applications and research usecases. [baal](https://github.com/baal-org/baal) currently supports Monte-Carlo Dropout, MCDropConnect, deep ensembles, and semi-supervised learning. + +Keywords: Active Learning, Research, Labeling + +## [cleanlab](https://github.com/cleanlab/cleanlab) + +[cleanlab](https://github.com/cleanlab/cleanlab) is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels. For text, image, tabular, audio (among others) datasets, you can use cleanlab to automatically: detect data issues (outliers, label errors, near duplicates, etc), train robust ML models, infer consensus + annotator-quality for multi-annotator data, suggest data to (re)label next (active learning). + +Keywords: Data-Centric AI, Data Quality, Noisy Labels, Outlier Detection, Active Learning + +## [BentoML](https://github.com/bentoml/BentoML) + +[BentoML](https://github.com/bentoml) is the unified framework for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models. +All Hugging Face models and pipelines can be seamlessly integrated into BentoML applications, enabling the running of models on the most suitable hardware and independent scaling based on usage. + +Keywords: BentoML, Framework, Deployment, AI Applications + +## [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) + +[LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) offers a user-friendly fine-tuning framework that incorporates PEFT. The repository includes training(fine-tuning) and inference examples for LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, and other LLMs. A ChatGLM version is also available in [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning). + +Keywords: PEFT, fine-tuning, LLaMA-2, ChatGLM, Qwen + diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/README.md b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a827da444f08014815f0425edc1d4be492f058f4 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/README.md @@ -0,0 +1,49 @@ +# Benchmarks + +You might want to add new benchmarks. + +You will need to define a python function named `run_benchmark` in your python file and the file must be located in this `benchmark/` directory. + +The expected function signature is the following: + +```py +def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100): +``` + +## Writing metrics to the database + +`MetricRecorder` is thread-safe, in the sense of the python [`Thread`](https://docs.python.org/3/library/threading.html#threading.Thread). This means you can start a background thread to do the readings on the device measurements while not blocking the main thread to execute the model measurements. + +cf [`llama.py`](./llama.py) to see an example of this in practice. + +```py +from benchmarks_entrypoint import MetricsRecorder +import psycopg2 + +def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100): + metrics_recorder = MetricsRecorder(psycopg2.connect("dbname=metrics"), logger, branch, commit_id, commit_msg) + benchmark_id = metrics_recorder.initialise_benchmark({"gpu_name": gpu_name, "model_id": model_id}) + # To collect device measurements + metrics_recorder.collect_device_measurements( + benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes + ) + # To collect your model measurements + metrics_recorder.collect_model_measurements( + benchmark_id, + { + "model_load_time": model_load_time, + "first_eager_forward_pass_time_secs": first_eager_fwd_pass_time, + "second_eager_forward_pass_time_secs": second_eager_fwd_pass_time, + "first_eager_generate_time_secs": first_eager_generate_time, + "second_eager_generate_time_secs": second_eager_generate_time, + "time_to_first_token_secs": time_to_first_token, + "time_to_second_token_secs": time_to_second_token, + "time_to_third_token_secs": time_to_third_token, + "time_to_next_token_mean_secs": mean_time_to_next_token, + "first_compile_generate_time_secs": first_compile_generate_time, + "second_compile_generate_time_secs": second_compile_generate_time, + "third_compile_generate_time_secs": third_compile_generate_time, + "fourth_compile_generate_time_secs": fourth_compile_generate_time, + }, + ) +``` diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/__init__.py b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/benchmark.py b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..304bbd4441cf660d3e4eb60e86b1ef36ccf979ec --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/benchmark.py @@ -0,0 +1,326 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Run benchmark using the `optimum-benchmark` library with some customization in `transformers`. + +Assume we are under `transformers` root directory: (make sure the commits are valid commits) +```bash +python benchmark/benchmark.py --config-dir benchmark/config --config-name generation --commit=9b9c7f03da625b13643e99205c691fe046461724 --metrics=decode.latency.mean,per_token.latency.mean,per_token.throughput.value backend.model=google/gemma-2b benchmark.input_shapes.sequence_length=5,7 benchmark.input_shapes.batch_size=1,2 --multirun +``` +""" + +import argparse +import glob +import json +import os.path +import re +import tempfile +from contextlib import contextmanager +from pathlib import Path + +from git import Repo + +from huggingface_hub import HfApi + +from optimum_benchmark import Benchmark +from optimum_benchmark_wrapper import main + + +PATH_TO_REPO = Path(__file__).parent.parent.resolve() + + +@contextmanager +def checkout_commit(repo: Repo, commit_id: str): + """ + Context manager that checks out a given commit when entered, but gets back to the reference it was at on exit. + Args: + repo (`git.Repo`): A git repository (for instance the Transformers repo). + commit_id (`str`): The commit reference to checkout inside the context manager. + """ + current_head = repo.head.commit if repo.head.is_detached else repo.head.ref + + try: + repo.git.checkout(commit_id) + yield + + finally: + repo.git.checkout(current_head) + + +def summarize(run_dir, metrics, expand_metrics=False): + """Produce a summary for each optimum-benchmark launched job's output directory found in `run_dir`. + + Each summary's format is as follows (for `expand_metrics=False`): + ``` + { + "model": "google/gemma-2b", + "commit": "3cd6ed22e4d49219f300f5055e71e3929aba20d7", + "config": "benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5", + "metrics": { + "decode.latency.mean": 1.624666809082031, + "per_token.latency.mean": 0.012843788806628804, + "per_token.throughput.value": 77.85864553330948 + } + } + ``` + """ + reports = glob.glob(os.path.join(run_dir, "**/benchmark_report.json"), recursive=True) + report_dirs = [str(Path(report).parent) for report in reports] + + summaries = [] + for report_dir in report_dirs: + commit = re.search(r"/commit=([^/]+)", report_dir).groups()[0] + + if not os.path.isfile(os.path.join(report_dir, "benchmark.json")): + continue + benchmark = Benchmark.from_json(os.path.join(report_dir, "benchmark.json")) + report = benchmark.report + + model = benchmark.config.backend["model"] + + # Ths looks like `benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5`. + # (we rely on the usage of hydra's `${hydra.job.override_dirname}`.) + benchmark_name = re.sub(f"backend.model={model},*", "", report_dir) + benchmark_name = str(Path(benchmark_name).parts[-1]) + if benchmark_name.startswith("commit="): + benchmark_name = benchmark.config.name + + metrics_values = {} + # post-processing of report: show a few selected/important metric + for metric in metrics: + keys = metric.split(".") + value = report.to_dict() + current = metrics_values + for key in keys: + # Avoid KeyError when a user's specified metric has typo. + # TODO: Give warnings. + if key not in value: + continue + value = value[key] + + if expand_metrics: + if isinstance(value, dict): + if key not in current: + current[key] = {} + current = current[key] + else: + current[key] = value + + if not expand_metrics: + metrics_values[metric] = value + + # show some config information + print(f"model: {model}") + print(f"commit: {commit}") + print(f"config: {benchmark_name}") + if len(metrics_values) > 0: + print("metrics:") + if expand_metrics: + print(metrics_values) + else: + for metric, value in metrics_values.items(): + print(f" - {metric}: {value}") + print("-" * 80) + + summary = { + "model": model, + "commit": commit, + "config": benchmark_name, + "metrics": metrics_values, + } + summaries.append(summary) + + with open(os.path.join(report_dir, "summary.json"), "w") as fp: + json.dump(summary, fp, indent=4) + + return summaries + + +def combine_summaries(summaries): + """Combine a list of summary obtained from the function `summarize`. + + The combined summary's format is as follows: + ``` + "google/gemma-2b": { + "benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5": { + "3cd6ed22e4d49219f300f5055e71e3929aba20d7": { + "metrics": {"decode.latency.mean": 1.624666809082031} + }, + "c97ee28b117c0abe8e08891f402065e4df6d72aa": { + "metrics": {"decode.latency.mean": 1.6278163452148438} + } + }, + "benchmark.input_shapes.batch_size=2,benchmark.input_shapes.sequence_length=5": { + "3cd6ed22e4d49219f300f5055e71e3929aba20d7": { + "metrics": {"decode.latency.mean": 1.6947791748046876} + }, + "c97ee28b117c0abe8e08891f402065e4df6d72aa": { + "metrics": { + "decode.latency.mean": 1.6980519409179688} + } + } + } + ``` + """ + combined = {} + for summary in summaries: + model = summary["model"] + config = summary["config"] + commit = summary["commit"] + + if model not in combined: + combined[model] = {} + + if config not in combined[model]: + combined[model][config] = {} + + if commit not in combined[model][config]: + combined[model][config][commit] = {"metrics": summary["metrics"]} + + with open(os.path.join(exp_run_dir, "summary.json"), "w") as fp: + json.dump(combined, fp, indent=4) + + print(json.dumps(combined, indent=4)) + + return combined + + +if __name__ == "__main__": + + def list_str(values): + return values.split(",") + + parser = argparse.ArgumentParser() + + parser.add_argument("--config-dir", type=str, required=True, help="The path to the config directory.") + parser.add_argument("--config-name", type=str, required=True, help="The config name.") + + # arguments specific to this wrapper for our own customization + parser.add_argument("--ensure_empty", type=bool, default=True, help="If to create a temporary directory.") + parser.add_argument( + "--commit", + type=list_str, + default="", + help="Comma-separated list of branch names and/or commit sha values on which the benchmark will run. If `diff` is specified, it will run on both the current head and the `main` branch.", + ) + parser.add_argument("--metrics", type=str, help="The metrics to be included in the summary.") + + parser.add_argument("--repo_id", type=str, default=None, help="The repository to which the file will be uploaded.") + parser.add_argument("--path_in_repo", type=str, default=None, help="Relative filepath in the repo.") + parser.add_argument("--token", type=str, default=None, help="A valid user access token (string).") + + args, optimum_benchmark_args = parser.parse_known_args() + + repo = Repo(PATH_TO_REPO) + + metrics = [ + "prefill.latency.mean", + "prefill.throughput.value", + "decode.latency.mean", + "decode.throughput.value", + "per_token.latency.mean", + "per_token.throughput.value", + ] + if args.metrics is not None: + metrics = args.metrics.split(",") + + # Get `backend.model` in a hacky way: We want to control the experiment flow manually. + models = [""] + for idx, arg in enumerate(optimum_benchmark_args): + if arg.startswith("backend.model="): + models = arg[len("backend.model=") :] + models = models.split(",") + break + optimum_benchmark_args = [arg for arg in optimum_benchmark_args if not arg.startswith("backend.model=")] + + # Get the commit(s) + current_head = str(repo.head.commit) if repo.head.is_detached else str(repo.head.ref) + commits = [x for x in args.commit if x != ""] + if len(commits) == 0: + commits = [current_head] + elif len(commits) == 1 and commits[0] == "diff": + # compare to `main` + commits = ["main", current_head] + + # Get the specified run directory + run_dir_arg_idx, run_dir = -1, None + sweep_dir_arg_idx, sweep_dir = -1, None + for idx, arg in enumerate(optimum_benchmark_args): + if arg.startswith("hydra.run.dir="): + run_dir = arg[len("hydra.run.dir=") :] + run_dir_arg_idx = idx + elif arg.startswith("hydra.sweep.dir="): + sweep_dir = arg[len("hydra.sweep.dir=") :] + sweep_dir_arg_idx = idx + exp_run_dir, arg_dix, arg_name = ( + (sweep_dir, sweep_dir_arg_idx, "hydra.sweep.dir") + if "--multirun" in optimum_benchmark_args + else (run_dir, run_dir_arg_idx, "hydra.run.dir") + ) + + # TODO: not hardcoded + if exp_run_dir is None and args.ensure_empty: + exp_run_dir = "_benchmark" + + if args.ensure_empty: + os.makedirs(exp_run_dir, exist_ok=True) + exp_run_dir = tempfile.mkdtemp(dir=exp_run_dir) + + run_summaries = [] + for commit in commits: + with checkout_commit(repo, commit): + commit = str(repo.head.commit) + + commit_run_dir = exp_run_dir + if exp_run_dir is not None: + commit_run_dir = os.path.join(exp_run_dir, rf"commit\={commit}") + + print(f"Run benchmark on commit: {commit}") + + for model in models: + model_arg = [f"backend.model={model}"] if model != "" else [] + dir_args = [] + if commit_run_dir is not None: + if arg_dix > -1: + optimum_benchmark_args[arg_dix] = f"{arg_name}={commit_run_dir}" + else: + dir_args = [ + f"hydra.sweep.dir={commit_run_dir}", + f"hydra.run.dir={commit_run_dir}/" + "${hydra.job.override_dirname}", + ] + main(args.config_dir, args.config_name, model_arg + dir_args + optimum_benchmark_args) + + if commit_run_dir is not None: + # Need to remove the `\` character + summaries = summarize(commit_run_dir.replace("\\", ""), metrics) + run_summaries.extend(summaries) + + # aggregate the information across the commits + if exp_run_dir is not None: + with open(os.path.join(exp_run_dir, "summaries.json"), "w") as fp: + json.dump(run_summaries, fp, indent=4) + + combined_summary = combine_summaries(run_summaries) + + if args.repo_id is not None and args.path_in_repo is not None: + # Upload to Hub + api = HfApi() + api.upload_folder( + folder_path=exp_run_dir, + path_in_repo=args.path_in_repo, + repo_id=args.repo_id, + repo_type="dataset", + token=args.token, + ) diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/benchmarks_entrypoint.py b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/benchmarks_entrypoint.py new file mode 100644 index 0000000000000000000000000000000000000000..7925e2902834f7f1b5e94c7f27fadfc622d12f79 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/benchmarks_entrypoint.py @@ -0,0 +1,144 @@ +import argparse +import importlib.util +import logging +import os +from typing import Dict +import psycopg2 +import sys + +from psycopg2.extras import Json +from psycopg2.extensions import register_adapter + + +register_adapter(dict, Json) + + +class ImportModuleException(Exception): + pass + + +class MetricsRecorder: + def __init__(self, connection, logger: logging.Logger, branch: str, commit_id: str, commit_msg: str): + self.conn = connection + self.conn.autocommit = True + self.logger = logger + self.branch = branch + self.commit_id = commit_id + self.commit_msg = commit_msg + + def initialise_benchmark(self, metadata: Dict[str, str]) -> int: + """ + Creates a new benchmark, returns the benchmark id + """ + # gpu_name: str, model_id: str + with self.conn.cursor() as cur: + cur.execute( + "INSERT INTO benchmarks (branch, commit_id, commit_message, metadata) VALUES (%s, %s, %s, %s) RETURNING benchmark_id", + (self.branch, self.commit_id, self.commit_msg, metadata), + ) + benchmark_id = cur.fetchone()[0] + logger.debug(f"initialised benchmark #{benchmark_id}") + return benchmark_id + + def collect_device_measurements(self, benchmark_id: int, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes): + """ + Collect device metrics, such as CPU & GPU usage. These are "static", as in you cannot pass arbitrary arguments to the function. + """ + with self.conn.cursor() as cur: + cur.execute( + "INSERT INTO device_measurements (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes) VALUES (%s, %s, %s, %s, %s)", + (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes), + ) + self.logger.debug( + f"inserted device measurements for benchmark #{benchmark_id} [CPU util: {cpu_util}, mem MBs: {mem_megabytes}, GPU util: {gpu_util}, GPU mem MBs: {gpu_mem_megabytes}]" + ) + + def collect_model_measurements(self, benchmark_id: int, measurements: Dict[str, float]): + with self.conn.cursor() as cur: + cur.execute( + """ + INSERT INTO model_measurements ( + benchmark_id, + measurements + ) VALUES (%s, %s) + """, + ( + benchmark_id, + measurements, + ), + ) + self.logger.debug(f"inserted model measurements for benchmark #{benchmark_id}: {measurements}") + + def close(self): + self.conn.close() + + +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +handler = logging.StreamHandler(sys.stdout) +handler.setLevel(logging.INFO) +formatter = logging.Formatter("[%(levelname)s - %(asctime)s] %(message)s") +handler.setFormatter(formatter) +logger.addHandler(handler) + + +def parse_arguments(): + """ + Parse command line arguments for the benchmarking CLI. + """ + parser = argparse.ArgumentParser(description="CLI for benchmarking the huggingface/transformers.") + + parser.add_argument( + "branch", + type=str, + help="The branch name on which the benchmarking is performed.", + ) + + parser.add_argument( + "commit_id", + type=str, + help="The commit hash on which the benchmarking is performed.", + ) + + parser.add_argument( + "commit_msg", + type=str, + help="The commit message associated with the commit, truncated to 70 characters.", + ) + + args = parser.parse_args() + + return args.branch, args.commit_id, args.commit_msg + + +def import_from_path(module_name, file_path): + try: + spec = importlib.util.spec_from_file_location(module_name, file_path) + module = importlib.util.module_from_spec(spec) + sys.modules[module_name] = module + spec.loader.exec_module(module) + return module + except Exception as e: + raise ImportModuleException(f"failed to load python module: {e}") + + +if __name__ == "__main__": + benchmarks_folder_path = os.path.dirname(os.path.realpath(__file__)) + + branch, commit_id, commit_msg = parse_arguments() + + for entry in os.scandir(benchmarks_folder_path): + try: + if not entry.name.endswith(".py"): + continue + if entry.path == __file__: + continue + logger.debug(f"loading: {entry.name}") + module = import_from_path(entry.name.split(".")[0], entry.path) + logger.info(f"runnning benchmarks in: {entry.name}") + module.run_benchmark(logger, branch, commit_id, commit_msg) + except ImportModuleException as e: + logger.error(e) + except Exception as e: + logger.error(f"error running benchmarks for {entry.name}: {e}") diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/config/generation.yaml b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/config/generation.yaml new file mode 100644 index 0000000000000000000000000000000000000000..44a3f9ea49015491c7add94a8790d16c20dc76fc --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/config/generation.yaml @@ -0,0 +1,57 @@ +defaults: + - benchmark # inheriting benchmark schema + - scenario: inference + - launcher: process + - backend: pytorch + - _self_ # for hydra 1.1 compatibility + +name: pytorch_generate + +launcher: + start_method: spawn + device_isolation: true + device_isolation_action: warn + +backend: + device: cuda + device_ids: 0 + no_weights: true + model: meta-llama/Llama-2-7b-hf + cache_implementation: static + torch_compile: true + torch_dtype: float16 + torch_compile_config: + backend: inductor + mode: reduce-overhead + fullgraph: true + +scenario: + input_shapes: + batch_size: 1 + sequence_length: 7 + generate_kwargs: + max_new_tokens: 128 + min_new_tokens: 128 + do_sample: false + memory: true + latency: true + iterations: 2 + duration: 0 + + +# hydra/cli specific settings +hydra: + run: + # where to store run results + dir: runs/${name} + job: + # change working directory to the run directory + chdir: true + env_set: + # set environment variable OVERRIDE_BENCHMARKS to 1 + # to not skip benchmarks that have been run before + OVERRIDE_BENCHMARKS: 1 + LOG_LEVEL: WARN + sweep: + dir: multirun + subdir: ${hydra.job.override_dirname} \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/default.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/default.yml new file mode 100644 index 0000000000000000000000000000000000000000..f3f02cab34d1bd2451eda4744ccf4f4eb7d0e6cc --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/default.yml @@ -0,0 +1,10 @@ +apiVersion: 1 + +providers: + - name: 'Transformers Benchmarks' + orgId: 1 + type: file + updateIntervalSeconds: 10 + allowUiUpdates: true + options: + path: /etc/grafana/dashboards diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/grafana_dashboard.json b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/grafana_dashboard.json new file mode 100644 index 0000000000000000000000000000000000000000..caaec78a522303eaadc66171d405e203e7475694 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/grafana_dashboard.json @@ -0,0 +1,2375 @@ +{ + "annotations": { + "list": [ + { + "builtIn": 1, + "datasource": { + "type": "grafana", + "uid": "-- Grafana --" + }, + "enable": true, + "hide": true, + "iconColor": "rgba(0, 211, 255, 1)", + "name": "Annotations & Alerts", + "type": "dashboard" + } + ] + }, + "editable": true, + "fiscalYearStartMonth": 0, + "graphTooltip": 0, + "id": 1, + "links": [ + { + "asDropdown": false, + "icon": "external link", + "includeVars": false, + "keepTime": false, + "tags": [], + "targetBlank": false, + "title": "Go to data", + "tooltip": "Go to data", + "type": "link", + "url": "http://transformers-benchmarks.hf.co/d/fdz33iyzln9c0a/transformers-benchmarks?orgId=1&from=${StartTime}&to=${EndTime}" + } + ], + "liveNow": true, + "panels": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "thresholds" + }, + "custom": { + "align": "left", + "cellOptions": { + "type": "auto" + }, + "inspect": false + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + }, + { + "color": "red", + "value": 80 + } + ] + } + }, + "overrides": [ + { + "matcher": { + "id": "byName", + "options": "gpu_name" + }, + "properties": [ + { + "id": "custom.width", + "value": 202 + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "left" + }, + "properties": [ + { + "id": "custom.width", + "value": 407 + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "commit_message" + }, + "properties": [ + { + "id": "custom.width", + "value": 524 + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "commit_id" + }, + "properties": [ + { + "id": "custom.width", + "value": 353 + } + ] + }, + { + "matcher": { + "id": "byName", + "options": "model_id" + }, + "properties": [ + { + "id": "custom.width", + "value": 216 + } + ] + } + ] + }, + "gridPos": { + "h": 6, + "w": 24, + "x": 0, + "y": 0 + }, + "id": 5, + "options": { + "cellHeight": "sm", + "footer": { + "countRows": false, + "fields": "", + "reducer": [ + "sum" + ], + "show": false + }, + "showHeader": true, + "sortBy": [] + }, + "pluginVersion": "11.2.2", + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT commit_id, commit_message, metadata->>'gpu_name' as gpu_name, metadata->>'model_id' as model_id, created_at AS date FROM benchmarks WHERE branch = '${branch}' AND metadata->>'gpu_name' = '${gpu_name}' ORDER BY benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [ + { + "name": "commit_id", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "gpu_name", + "type": "functionParameter" + } + ], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50, + "whereJsonTree": { + "children1": [ + { + "id": "baaa8aaa-89ab-4cde-b012-31922f96de3f", + "properties": { + "field": "commit_id", + "fieldSrc": "field", + "operator": "equal", + "value": [ + "${commit}" + ], + "valueError": [ + null + ], + "valueSrc": [ + "value" + ], + "valueType": [ + "text" + ] + }, + "type": "rule" + } + ], + "id": "bab88a98-0123-4456-b89a-b1922f7d4f11", + "type": "group" + }, + "whereString": "commit_id = '${commit}'" + }, + "table": "benchmarks" + } + ], + "transparent": true, + "type": "table" + }, + { + "collapsed": false, + "gridPos": { + "h": 1, + "w": 24, + "x": 0, + "y": 6 + }, + "id": 13, + "panels": [], + "title": "Eager Forward Pass", + "type": "row" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "scheme", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 0, + "y": 7 + }, + "id": 7, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "auto", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "pluginVersion": "11.2.2", + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'first_eager_forward_pass_time_secs' AS double precision) AS first_eager_forward_pass_time_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "First eager forward pass", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "scheme", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + }, + { + "color": "red", + "value": 80 + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 12, + "y": 7 + }, + "id": 9, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "auto", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'second_eager_forward_pass_time_secs' AS double precision) AS second_eager_forward_pass_time_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "Second eager forward pass", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "collapsed": false, + "gridPos": { + "h": 1, + "w": 24, + "x": 0, + "y": 18 + }, + "id": 16, + "panels": [], + "title": "Time to next token", + "type": "row" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "scheme", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 0, + "y": 19 + }, + "id": 17, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "always", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'time_to_first_token_secs' AS double precision) AS time_to_first_token_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "Time to first token", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "scheme", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 12, + "y": 19 + }, + "id": 18, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "always", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'time_to_second_token_secs' AS double precision) AS time_to_second_token_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "Time to second token", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "scheme", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 0, + "y": 30 + }, + "id": 19, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "always", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'time_to_third_token_secs' AS double precision) AS time_to_third_token_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "Time to third token", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "scheme", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 12, + "y": 30 + }, + "id": 20, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "always", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'time_to_next_token_mean_secs' AS double precision) AS time_to_next_token_mean_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "Time to subsequent next tokens mean", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "collapsed": false, + "gridPos": { + "h": 1, + "w": 24, + "x": 0, + "y": 41 + }, + "id": 14, + "panels": [], + "title": "Compiled Generate", + "type": "row" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "scheme", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 0, + "y": 42 + }, + "id": 8, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "always", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'first_compile_generate_time_secs' AS double precision) AS first_compile_generate_time_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "First compile generate", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "scheme", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 12, + "y": 42 + }, + "id": 10, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "auto", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'second_compile_generate_time_secs' AS double precision) AS second_compile_generate_time_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "Second compile generate", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "scheme", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 0, + "y": 53 + }, + "id": 11, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "auto", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'third_compile_generate_time_secs' AS double precision) AS third_compile_generate_time_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "Third compile generate", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-YlBl" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "fillOpacity": 80, + "gradientMode": "none", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineWidth": 0, + "scaleDistribution": { + "type": "linear" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green", + "value": null + } + ] + }, + "unit": "s" + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 12, + "x": 12, + "y": 53 + }, + "id": 12, + "options": { + "barRadius": 0.05, + "barWidth": 0.8, + "fullHighlight": false, + "groupWidth": 0.7, + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": false + }, + "orientation": "auto", + "showValue": "auto", + "stacking": "none", + "tooltip": { + "mode": "single", + "sort": "none" + }, + "xTickLabelRotation": 0, + "xTickLabelSpacing": 0 + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT CAST(m.measurements->'fourth_compile_generate_time_secs' AS double precision) AS fourth_compile_generate_time_secs, left(b.commit_id, 7), m.time FROM benchmarks as b JOIN model_measurements AS m ON b.benchmark_id = m.benchmark_id WHERE b.branch = '${branch}' AND b.metadata->>'gpu_name' = '${gpu_name}' ORDER BY b.benchmark_id DESC LIMIT ${last_n_commits};", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50 + } + } + ], + "title": "Fourth compile generate", + "transformations": [ + { + "id": "sortBy", + "options": { + "fields": {}, + "sort": [ + { + "field": "time" + } + ] + } + } + ], + "transparent": true, + "type": "barchart" + }, + { + "collapsed": true, + "gridPos": { + "h": 1, + "w": 24, + "x": 0, + "y": 64 + }, + "id": 15, + "panels": [ + { + "datasource": {}, + "fieldConfig": { + "defaults": { + "color": { + "mode": "palette-classic" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "barAlignment": 0, + "barWidthFactor": 0.6, + "drawStyle": "line", + "fillOpacity": 0, + "gradientMode": "none", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "insertNulls": 60000, + "lineInterpolation": "linear", + "lineWidth": 1, + "pointSize": 5, + "scaleDistribution": { + "type": "linear" + }, + "showPoints": "auto", + "spanNulls": false, + "stacking": { + "group": "A", + "mode": "none" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green" + }, + { + "color": "red", + "value": 80 + } + ] + }, + "unit": "percent" + }, + "overrides": [] + }, + "gridPos": { + "h": 9, + "w": 12, + "x": 0, + "y": 65 + }, + "id": 1, + "options": { + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": true + }, + "tooltip": { + "mode": "single", + "sort": "none" + } + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT\n d.cpu_util,\n d.time\nFROM\n benchmarks AS b\n JOIN device_measurements AS d ON b.benchmark_id = d.benchmark_id\nWHERE\n branch = '${branch}';", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [ + { + "name": "cpu_util", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "mem_megabytes", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "gpu_util", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "gpu_mem_megabytes", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "\"time\"", + "type": "functionParameter" + } + ], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50, + "whereJsonTree": { + "children1": [ + { + "id": "baa888b8-89ab-4cde-b012-31922f8671e9", + "properties": { + "field": "commit_id", + "fieldSrc": "field", + "operator": "equal", + "value": [ + "${commit}" + ], + "valueError": [ + null + ], + "valueSrc": [ + "value" + ], + "valueType": [ + "text" + ] + }, + "type": "rule" + } + ], + "id": "bab88a98-0123-4456-b89a-b1922f7d4f11", + "type": "group" + }, + "whereString": "commit_id = '${commit}'" + }, + "table": "measurements" + } + ], + "title": "CPU Utilization", + "transparent": true, + "type": "timeseries" + }, + { + "datasource": {}, + "fieldConfig": { + "defaults": { + "color": { + "mode": "palette-classic" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "barAlignment": 0, + "barWidthFactor": 0.6, + "drawStyle": "line", + "fillOpacity": 0, + "gradientMode": "none", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "insertNulls": 60000, + "lineInterpolation": "linear", + "lineWidth": 1, + "pointSize": 5, + "scaleDistribution": { + "type": "linear" + }, + "showPoints": "auto", + "spanNulls": false, + "stacking": { + "group": "A", + "mode": "none" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green" + }, + { + "color": "red", + "value": 80 + } + ] + }, + "unit": "percent" + }, + "overrides": [] + }, + "gridPos": { + "h": 9, + "w": 12, + "x": 12, + "y": 65 + }, + "id": 4, + "options": { + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": true + }, + "tooltip": { + "mode": "single", + "sort": "none" + } + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT\n b.commit_id,\n d.gpu_util,\n d.time\nFROM\n benchmarks AS b\n JOIN device_measurements AS d ON b.benchmark_id = d.benchmark_id\nWHERE\n branch = '${branch}';", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [ + { + "name": "cpu_util", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "mem_megabytes", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "gpu_util", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "gpu_mem_megabytes", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "\"time\"", + "type": "functionParameter" + } + ], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50, + "whereJsonTree": { + "children1": [ + { + "id": "baa888b8-89ab-4cde-b012-31922f8671e9", + "properties": { + "field": "commit_id", + "fieldSrc": "field", + "operator": "equal", + "value": [ + "${commit}" + ], + "valueError": [ + null + ], + "valueSrc": [ + "value" + ], + "valueType": [ + "text" + ] + }, + "type": "rule" + } + ], + "id": "bab88a98-0123-4456-b89a-b1922f7d4f11", + "type": "group" + }, + "whereString": "commit_id = '${commit}'" + }, + "table": "measurements" + } + ], + "title": "GPU Utilization", + "transparent": true, + "type": "timeseries" + }, + { + "datasource": {}, + "fieldConfig": { + "defaults": { + "color": { + "mode": "palette-classic" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "barAlignment": 0, + "barWidthFactor": 0.6, + "drawStyle": "line", + "fillOpacity": 0, + "gradientMode": "none", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "insertNulls": 60000, + "lineInterpolation": "linear", + "lineWidth": 1, + "pointSize": 5, + "scaleDistribution": { + "type": "linear" + }, + "showPoints": "auto", + "spanNulls": false, + "stacking": { + "group": "A", + "mode": "none" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green" + }, + { + "color": "red", + "value": 80 + } + ] + }, + "unit": "decmbytes" + }, + "overrides": [] + }, + "gridPos": { + "h": 9, + "w": 12, + "x": 0, + "y": 74 + }, + "id": 2, + "options": { + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": true + }, + "tooltip": { + "mode": "single", + "sort": "none" + } + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT d.mem_megabytes, d.time FROM benchmarks AS b JOIN device_measurements AS d ON b.benchmark_id = d.benchmark_id WHERE branch = '${branch}';", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [ + { + "name": "cpu_util", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "mem_megabytes", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "gpu_util", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "gpu_mem_megabytes", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "\"time\"", + "type": "functionParameter" + } + ], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50, + "whereJsonTree": { + "children1": [ + { + "id": "baa888b8-89ab-4cde-b012-31922f8671e9", + "properties": { + "field": "commit_id", + "fieldSrc": "field", + "operator": "equal", + "value": [ + "${commit}" + ], + "valueError": [ + null + ], + "valueSrc": [ + "value" + ], + "valueType": [ + "text" + ] + }, + "type": "rule" + } + ], + "id": "bab88a98-0123-4456-b89a-b1922f7d4f11", + "type": "group" + }, + "whereString": "commit_id = '${commit}'" + }, + "table": "measurements" + } + ], + "title": "Memory usage", + "transparent": true, + "type": "timeseries" + }, + { + "datasource": {}, + "fieldConfig": { + "defaults": { + "color": { + "mode": "palette-classic" + }, + "custom": { + "axisBorderShow": false, + "axisCenteredZero": false, + "axisColorMode": "text", + "axisLabel": "", + "axisPlacement": "auto", + "barAlignment": 0, + "barWidthFactor": 0.6, + "drawStyle": "line", + "fillOpacity": 0, + "gradientMode": "none", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "insertNulls": 60000, + "lineInterpolation": "linear", + "lineWidth": 1, + "pointSize": 5, + "scaleDistribution": { + "type": "linear" + }, + "showPoints": "auto", + "spanNulls": false, + "stacking": { + "group": "A", + "mode": "none" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "green" + }, + { + "color": "red", + "value": 80 + } + ] + }, + "unit": "decmbytes" + }, + "overrides": [] + }, + "gridPos": { + "h": 9, + "w": 12, + "x": 12, + "y": 74 + }, + "id": 3, + "options": { + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom", + "showLegend": true + }, + "tooltip": { + "mode": "single", + "sort": "none" + } + }, + "targets": [ + { + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "editorMode": "code", + "format": "table", + "rawQuery": true, + "rawSql": "SELECT\n d.gpu_mem_megabytes,\n d.time\nFROM\n benchmarks AS b\n JOIN device_measurements AS d ON b.benchmark_id = d.benchmark_id\nWHERE\n branch = '${branch}';", + "refId": "A", + "sql": { + "columns": [ + { + "parameters": [ + { + "name": "cpu_util", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "mem_megabytes", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "gpu_util", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "gpu_mem_megabytes", + "type": "functionParameter" + } + ], + "type": "function" + }, + { + "parameters": [ + { + "name": "\"time\"", + "type": "functionParameter" + } + ], + "type": "function" + } + ], + "groupBy": [ + { + "property": { + "type": "string" + }, + "type": "groupBy" + } + ], + "limit": 50, + "whereJsonTree": { + "children1": [ + { + "id": "baa888b8-89ab-4cde-b012-31922f8671e9", + "properties": { + "field": "commit_id", + "fieldSrc": "field", + "operator": "equal", + "value": [ + "${commit}" + ], + "valueError": [ + null + ], + "valueSrc": [ + "value" + ], + "valueType": [ + "text" + ] + }, + "type": "rule" + } + ], + "id": "bab88a98-0123-4456-b89a-b1922f7d4f11", + "type": "group" + }, + "whereString": "commit_id = '${commit}'" + }, + "table": "measurements" + } + ], + "title": "GPU memory usage", + "transparent": true, + "type": "timeseries" + } + ], + "title": "Usage metrics", + "type": "row" + } + ], + "schemaVersion": 39, + "tags": [], + "templating": { + "list": [ + { + "current": { + "selected": false, + "text": "main", + "value": "main" + }, + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "definition": "SELECT DISTINCT branch FROM benchmarks;", + "description": "", + "hide": 0, + "includeAll": false, + "label": "branch", + "multi": false, + "name": "branch", + "options": [], + "query": "SELECT DISTINCT branch FROM benchmarks;", + "refresh": 1, + "regex": "", + "skipUrlSync": false, + "sort": 0, + "type": "query" + }, + { + "current": { + "selected": false, + "text": "1729701492845", + "value": "1729701492845" + }, + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "definition": "SELECT created_at - INTERVAL '5 secs' FROM benchmarks WHERE branch = '${branch}' ORDER BY benchmark_id ASC LIMIT 1;", + "description": "", + "hide": 2, + "includeAll": false, + "multi": false, + "name": "StartTime", + "options": [], + "query": "SELECT created_at - INTERVAL '5 secs' FROM benchmarks WHERE branch = '${branch}' ORDER BY benchmark_id ASC LIMIT 1;", + "refresh": 2, + "regex": "", + "skipUrlSync": false, + "sort": 0, + "type": "query" + }, + { + "current": { + "selected": false, + "text": "1730393397577", + "value": "1730393397577" + }, + "datasource": { + "default": true, + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "definition": "SELECT time + INTERVAL '5 secs' FROM benchmarks AS b JOIN device_measurements AS d ON b.benchmark_id = d.benchmark_id WHERE branch = '${branch}' ORDER BY b.benchmark_id DESC, d.measurement_id DESC LIMIT 1;", + "description": "", + "hide": 2, + "includeAll": false, + "multi": false, + "name": "EndTime", + "options": [], + "query": "SELECT time + INTERVAL '5 secs' FROM benchmarks AS b JOIN device_measurements AS d ON b.benchmark_id = d.benchmark_id WHERE branch = '${branch}' ORDER BY b.benchmark_id DESC, d.measurement_id DESC LIMIT 1;", + "refresh": 1, + "regex": "", + "skipUrlSync": false, + "sort": 0, + "type": "query" + }, + { + "current": { + "selected": false, + "text": "NVIDIA A10G", + "value": "NVIDIA A10G" + }, + "datasource": { + "type": "grafana-postgresql-datasource", + "uid": "be28nkzirtb0gd" + }, + "definition": "SELECT DISTINCT metadata->>'gpu_name' FROM benchmarks;", + "description": "", + "hide": 0, + "includeAll": false, + "label": "GPU", + "multi": false, + "name": "gpu_name", + "options": [], + "query": "SELECT DISTINCT metadata->>'gpu_name' FROM benchmarks;", + "refresh": 1, + "regex": "", + "skipUrlSync": false, + "sort": 0, + "type": "query" + }, + { + "current": { + "selected": true, + "text": "10", + "value": "10" + }, + "description": "The number of commits to display, going from most recent to the nth commit.", + "hide": 0, + "label": "Last # of commits", + "name": "last_n_commits", + "options": [ + { + "selected": true, + "text": "10", + "value": "10" + } + ], + "query": "10", + "skipUrlSync": false, + "type": "textbox" + } + ] + }, + "time": { + "from": "now-1h", + "to": "now" + }, + "timepicker": { + "hidden": false + }, + "timezone": "browser", + "title": "Transformers benchmarks", + "uid": "fdz33iyzln9c0a", + "version": 10, + "weekStart": "" +} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/grafana_datasource.yaml b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/grafana_datasource.yaml new file mode 100644 index 0000000000000000000000000000000000000000..25f36254104ab5047fd36ddf00ebcde7289849a7 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/grafana_datasource.yaml @@ -0,0 +1,17 @@ +apiVersion: 1 +datasources: + - name: grafana-postgresql-datasource + uid: be28nkzirtb0gd + type: postgres + url: $GRAFANA_POSTGRES_DATASOURCE_URL + user: $GRAFANA_POSTGRES_DATASOURCE_USER + secureJsonData: + password: $GRAFANA_POSTGRES_DATASOURCE_PWD + jsonData: + database: metrics + maxOpenConns: 100 + maxIdleConns: 100 + maxIdleConnsAuto: true + connMaxLifetime: 14400 + postgresVersion: 1000 + timescaledb: false diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/init_db.sql b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/init_db.sql new file mode 100644 index 0000000000000000000000000000000000000000..a7864c4af183b6ac26a8ac5dc623282e94b44c91 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/init_db.sql @@ -0,0 +1,33 @@ +CREATE TABLE IF NOT EXISTS benchmarks ( + benchmark_id SERIAL PRIMARY KEY, + branch VARCHAR(255), + commit_id VARCHAR(72), + commit_message VARCHAR(70), + metadata jsonb, + created_at timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC') +); + +CREATE INDEX IF NOT EXISTS benchmarks_benchmark_id_idx ON benchmarks (benchmark_id); + +CREATE INDEX IF NOT EXISTS benchmarks_branch_idx ON benchmarks (branch); + +CREATE TABLE IF NOT EXISTS device_measurements ( + measurement_id SERIAL PRIMARY KEY, + benchmark_id int REFERENCES benchmarks (benchmark_id), + cpu_util double precision, + mem_megabytes double precision, + gpu_util double precision, + gpu_mem_megabytes double precision, + time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC') +); + +CREATE INDEX IF NOT EXISTS device_measurements_branch_idx ON device_measurements (benchmark_id); + +CREATE TABLE IF NOT EXISTS model_measurements ( + measurement_id SERIAL PRIMARY KEY, + benchmark_id int REFERENCES benchmarks (benchmark_id), + measurements jsonb, + time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC') +); + +CREATE INDEX IF NOT EXISTS model_measurements_branch_idx ON model_measurements (benchmark_id); diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/llama.py b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/llama.py new file mode 100644 index 0000000000000000000000000000000000000000..bbe1afefd5ef1bef9c9b38a74597a301cf8abb3a --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/llama.py @@ -0,0 +1,342 @@ +from logging import Logger +import os +from threading import Event, Thread +from time import perf_counter, sleep +from typing import Optional +from benchmarks_entrypoint import MetricsRecorder +import gpustat +import psutil +import psycopg2 +import torch + +from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StaticCache + + +os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" + +os.environ["TOKENIZERS_PARALLELISM"] = "1" +torch.set_float32_matmul_precision("high") + + +def collect_metrics(benchmark_id, continue_metric_collection, metrics_recorder): + p = psutil.Process(os.getpid()) + while not continue_metric_collection.is_set(): + with p.oneshot(): + cpu_util = p.cpu_percent() + mem_megabytes = p.memory_info().rss / (1024 * 1024) + gpu_stats = gpustat.GPUStatCollection.new_query() + gpu_util = gpu_stats[0]["utilization.gpu"] + gpu_mem_megabytes = gpu_stats[0]["memory.used"] + metrics_recorder.collect_device_measurements( + benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes + ) + sleep(0.01) + + +def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100): + continue_metric_collection = Event() + metrics_thread = None + model_id = "meta-llama/Llama-2-7b-hf" + metrics_recorder = MetricsRecorder(psycopg2.connect("dbname=metrics"), logger, branch, commit_id, commit_msg) + try: + gpu_stats = gpustat.GPUStatCollection.new_query() + gpu_name = gpu_stats[0]["name"] + benchmark_id = metrics_recorder.initialise_benchmark({"gpu_name": gpu_name, "model_id": model_id}) + logger.info(f"running benchmark #{benchmark_id} on {gpu_name} for {model_id}") + metrics_thread = Thread( + target=collect_metrics, + args=[benchmark_id, continue_metric_collection, metrics_recorder], + ) + metrics_thread.start() + logger.info("started background thread to fetch device metrics") + + os.environ["TOKENIZERS_PARALLELISM"] = "false" # silence warnings when compiling + + device = "cuda" + + logger.info("downloading weights") + # This is to avoid counting download in model load time measurement + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16) + gen_config = GenerationConfig(do_sample=False, top_p=1, temperature=1) + logger.info("loading model") + start = perf_counter() + model = AutoModelForCausalLM.from_pretrained( + model_id, torch_dtype=torch.float16, generation_config=gen_config + ).eval() + model.to(device) + torch.cuda.synchronize() + end = perf_counter() + model_load_time = end - start + logger.info(f"loaded model in: {model_load_time}s") + + tokenizer = AutoTokenizer.from_pretrained(model_id) + + prompt = "Why dogs are so cute?" + inputs = tokenizer(prompt, return_tensors="pt").to(device) + + # Specify the max length (including both the prompt and the response) + # When calling `generate` with `cache_implementation="static" later, this is also used to create a `StaticCache` object + # with sequence length = `max_length`. The longer the more you will re-use it + seq_length = inputs["input_ids"].shape[1] + model.generation_config.max_length = seq_length + num_tokens_to_generate + batch_size = inputs["input_ids"].shape[0] + + # Copied from the gpt-fast repo + def multinomial_sample_one_no_sync(probs_sort): # Does multinomial sampling without a cuda synchronization + q = torch.empty_like(probs_sort).exponential_(1) + return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int) + + def logits_to_probs(logits, temperature: float = 1.0, top_k: Optional[int] = None): + logits = logits / max(temperature, 1e-5) + + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + pivot = v.select(-1, -1).unsqueeze(-1) + logits = torch.where(logits < pivot, -float("Inf"), logits) + probs = torch.nn.functional.softmax(logits, dim=-1) + return probs + + def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None): + probs = logits_to_probs(logits[:, -1], temperature, top_k) + idx_next = multinomial_sample_one_no_sync(probs) + return idx_next, probs + + def decode_one_token(model, cur_token, cache_position, past_key_values): + logits = model( + cur_token, + cache_position=cache_position, + past_key_values=past_key_values, + return_dict=False, + use_cache=True, + )[0] + new_token = sample(logits, temperature=0.6, top_k=5)[0] + return new_token + + ######### + # Eager # + ######### + with torch.no_grad(): + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + num_tokens_to_generate, + ) + cache_position = torch.arange(seq_length, device=device) + start = perf_counter() + model( + **inputs, + cache_position=cache_position, + past_key_values=past_key_values, + return_dict=False, + use_cache=True, + ) + end = perf_counter() + first_eager_fwd_pass_time = end - start + logger.info(f"completed first eager fwd pass in: {first_eager_fwd_pass_time}s") + start = perf_counter() + output = model.generate(**inputs, do_sample=False) + end = perf_counter() + first_eager_generate_time = end - start + logger.info(f"completed first eager generation in: {first_eager_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + num_tokens_to_generate, + ) + cache_position = torch.arange(seq_length, device=device) + start = perf_counter() + model( + **inputs, + cache_position=cache_position, + past_key_values=past_key_values, + return_dict=False, + use_cache=True, + ) + end = perf_counter() + second_eager_fwd_pass_time = end - start + logger.info(f"completed second eager fwd pass in: {second_eager_fwd_pass_time}s") + start = perf_counter() + model.generate(**inputs, do_sample=False) + end = perf_counter() + second_eager_generate_time = end - start + logger.info(f"completed second eager generation in: {second_eager_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + torch.compiler.reset() + + ################ + # Forward pass # + ################ + + # `torch.compile(model, ...)` is not recommended as you compile callbacks + # and full generate. We recommend compiling only the forward for now. + # "reduce-overhead" will use cudagraphs. + generated_ids = torch.zeros( + (batch_size, num_tokens_to_generate + seq_length), dtype=torch.int, device=device + ) + + generated_ids[:, :seq_length] = inputs["input_ids"] + decode_one_token = torch.compile(decode_one_token, mode="reduce-overhead", fullgraph=True) + # model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) + # TODO use decode_one_token(model, input_id.clone(), cache_position) for verification + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + num_tokens_to_generate + 10, + ) + cache_position = torch.arange(seq_length, device=device) + all_generated_tokens = [] + ### First compile, prefill + start = perf_counter() + next_token = decode_one_token( + model, inputs["input_ids"], cache_position=cache_position, past_key_values=past_key_values + ) + torch.cuda.synchronize() + end = perf_counter() + time_to_first_token = end - start + logger.info(f"completed first compile generation in: {time_to_first_token}s") + cache_position += 1 + all_generated_tokens += next_token.clone().detach().cpu().tolist() + + cache_position = torch.tensor([seq_length], device=device) + ### First compile, decoding + start = perf_counter() + next_token = decode_one_token( + model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values + ) + torch.cuda.synchronize() + end = perf_counter() + time_to_second_token = end - start + logger.info(f"completed second compile generation in: {time_to_first_token}s") + cache_position += 1 + all_generated_tokens += next_token.clone().detach().cpu().tolist() + + ### Second compile, decoding + start = perf_counter() + next_token = decode_one_token( + model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values + ) + torch.cuda.synchronize() + end = perf_counter() + time_to_third_token = end - start + logger.info(f"completed third compile forward in: {time_to_first_token}s") + cache_position += 1 + all_generated_tokens += next_token.clone().detach().cpu().tolist() + + ### Using cuda graphs decoding + + start = perf_counter() + for _ in range(1, num_tokens_to_generate): + all_generated_tokens += next_token.clone().detach().cpu().tolist() + next_token = decode_one_token( + model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values + ) + cache_position += 1 + torch.cuda.synchronize() + end = perf_counter() + mean_time_to_next_token = (end - start) / num_tokens_to_generate + logger.info(f"completed next compile generation in: {mean_time_to_next_token}s") + logger.info(f"generated: {tokenizer.batch_decode(all_generated_tokens)}") + + #################### + # Generate compile # + #################### + torch.compiler.reset() + # we will not compile full generate as it' s to intensive, tho we measure full forward! + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + 128, + ) + + # 1st call + start = perf_counter() + output = model.generate(**inputs, past_key_values=past_key_values) + torch.cuda.synchronize() + end = perf_counter() + first_compile_generate_time = end - start + logger.info(f"completed first compile generation in: {first_compile_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + 128, + ) + # 2nd call + start = perf_counter() + output = model.generate(**inputs, past_key_values=past_key_values) + torch.cuda.synchronize() + end = perf_counter() + second_compile_generate_time = end - start + logger.info(f"completed second compile generation in: {second_compile_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + 128, + ) + + # 3nd call + start = perf_counter() + output = model.generate(**inputs, past_key_values=past_key_values) + end = perf_counter() + third_compile_generate_time = end - start + logger.info(f"completed second compile generation in: {third_compile_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + 128, + ) + # 4th call + start = perf_counter() + output = model.generate(**inputs, past_key_values=past_key_values) + end = perf_counter() + fourth_compile_generate_time = end - start + logger.info(f"completed second compile generation in: {fourth_compile_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + metrics_recorder.collect_model_measurements( + benchmark_id, + { + "model_load_time": model_load_time, + "first_eager_forward_pass_time_secs": first_eager_fwd_pass_time, + "second_eager_forward_pass_time_secs": second_eager_fwd_pass_time, + "first_eager_generate_time_secs": first_eager_generate_time, + "second_eager_generate_time_secs": second_eager_generate_time, + "time_to_first_token_secs": time_to_first_token, + "time_to_second_token_secs": time_to_second_token, + "time_to_third_token_secs": time_to_third_token, + "time_to_next_token_mean_secs": mean_time_to_next_token, + "first_compile_generate_time_secs": first_compile_generate_time, + "second_compile_generate_time_secs": second_compile_generate_time, + "third_compile_generate_time_secs": third_compile_generate_time, + "fourth_compile_generate_time_secs": fourth_compile_generate_time, + }, + ) + except Exception as e: + logger.error(f"Caught exception: {e}") + continue_metric_collection.set() + if metrics_thread is not None: + metrics_thread.join() + metrics_recorder.close() diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/optimum_benchmark_wrapper.py b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/optimum_benchmark_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..c43e9a73e3160de938f0181e1d606fe742d5a5ef --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/optimum_benchmark_wrapper.py @@ -0,0 +1,16 @@ +import argparse +import subprocess + + +def main(config_dir, config_name, args): + subprocess.run(["optimum-benchmark", "--config-dir", f"{config_dir}", "--config-name", f"{config_name}"] + ["hydra/job_logging=disabled", "hydra/hydra_logging=disabled"] + args) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--config-dir", type=str, required=True, help="The path to the config directory.") + parser.add_argument("--config-name", type=str, required=True, help="The config name.") + args, unknown = parser.parse_known_args() + + main(args.config_dir, args.config_name, unknown) diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/requirements.txt b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..50e9dfaddfa4cab8bea5b51561198d47d14c3b35 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/benchmark/requirements.txt @@ -0,0 +1,5 @@ +gpustat==1.1.1 +psutil==6.0.0 +psycopg2==2.9.9 +torch>=2.4.0 +hf_transfer \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/conftest.py b/tasks/tasksmith-333d8be2d5aa/tests/source/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..40e43f25e8933d6ab59ab15f9d40fdcc9f507328 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/conftest.py @@ -0,0 +1,142 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# tests directory-specific settings - this file is run automatically +# by pytest before any tests are run + +import doctest +import sys +import warnings +from os.path import abspath, dirname, join + +import _pytest +import pytest + +from transformers.testing_utils import HfDoctestModule, HfDocTestParser + + +NOT_DEVICE_TESTS = { + "test_tokenization", + "test_processor", + "test_processing", + "test_beam_constraints", + "test_configuration_utils", + "test_data_collator", + "test_trainer_callback", + "test_trainer_utils", + "test_feature_extraction", + "test_image_processing", + "test_image_processor", + "test_image_transforms", + "test_optimization", + "test_retrieval", + "test_config", + "test_from_pretrained_no_checkpoint", + "test_keep_in_fp32_modules", + "test_gradient_checkpointing_backward_compatibility", + "test_gradient_checkpointing_enable_disable", + "test_save_load_fast_init_from_base", + "test_fast_init_context_manager", + "test_fast_init_tied_embeddings", + "test_save_load_fast_init_to_base", + "test_torch_save_load", + "test_initialization", + "test_forward_signature", + "test_model_get_set_embeddings", + "test_model_main_input_name", + "test_correct_missing_keys", + "test_tie_model_weights", + "test_can_use_safetensors", + "test_load_save_without_tied_weights", + "test_tied_weights_keys", + "test_model_weights_reload_no_missing_tied_weights", + "test_pt_tf_model_equivalence", + "test_mismatched_shapes_have_properly_initialized_weights", + "test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist", + "test_model_is_small", + "test_tf_from_pt_safetensors", + "test_flax_from_pt_safetensors", + "ModelTest::test_pipeline_", # None of the pipeline tests from PipelineTesterMixin (of which XxxModelTest inherits from) are running on device + "ModelTester::test_pipeline_", + "/repo_utils/", + "/utils/", + "/agents/", +} + +# allow having multiple repository checkouts and not needing to remember to rerun +# `pip install -e '.[dev]'` when switching between checkouts and running tests. +git_repo_path = abspath(join(dirname(__file__), "src")) +sys.path.insert(1, git_repo_path) + +# silence FutureWarning warnings in tests since often we can't act on them until +# they become normal warnings - i.e. the tests still need to test the current functionality +warnings.simplefilter(action="ignore", category=FutureWarning) + + +def pytest_configure(config): + config.addinivalue_line( + "markers", "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested" + ) + config.addinivalue_line( + "markers", "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested" + ) + config.addinivalue_line("markers", "is_pipeline_test: mark test to run only when pipelines are tested") + config.addinivalue_line("markers", "is_staging_test: mark test to run only in the staging environment") + config.addinivalue_line("markers", "accelerate_tests: mark test that require accelerate") + config.addinivalue_line("markers", "agent_tests: mark the agent tests that are run on their specific schedule") + config.addinivalue_line("markers", "not_device_test: mark the tests always running on cpu") + + +def pytest_collection_modifyitems(items): + for item in items: + if any(test_name in item.nodeid for test_name in NOT_DEVICE_TESTS): + item.add_marker(pytest.mark.not_device_test) + + +def pytest_addoption(parser): + from transformers.testing_utils import pytest_addoption_shared + + pytest_addoption_shared(parser) + + +def pytest_terminal_summary(terminalreporter): + from transformers.testing_utils import pytest_terminal_summary_main + + make_reports = terminalreporter.config.getoption("--make-reports") + if make_reports: + pytest_terminal_summary_main(terminalreporter, id=make_reports) + + +def pytest_sessionfinish(session, exitstatus): + # If no tests are collected, pytest exists with code 5, which makes the CI fail. + if exitstatus == 5: + session.exitstatus = 0 + + +# Doctest custom flag to ignore output. +IGNORE_RESULT = doctest.register_optionflag("IGNORE_RESULT") + +OutputChecker = doctest.OutputChecker + + +class CustomOutputChecker(OutputChecker): + def check_output(self, want, got, optionflags): + if IGNORE_RESULT & optionflags: + return True + return OutputChecker.check_output(self, want, got, optionflags) + + +doctest.OutputChecker = CustomOutputChecker +_pytest.doctest.DoctestModule = HfDoctestModule +doctest.DocTestParser = HfDocTestParser diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/README.md b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2a71ab6fb6ec8f40d57c19dc6fcab5745361ea50 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/README.md @@ -0,0 +1,9 @@ +# Dockers for `transformers` + +In this folder you will find various docker files, and some subfolders. +- dockerfiles (ex: `consistency.dockerfile`) present under `~/docker` are used for our "fast" CIs. You should be able to use them for tasks that only need CPU. For example `torch-light` is a very light weights container (703MiB). +- subfloder contain dockerfiles used for our `slow` CIs, which *can* be used for GPU tasks, but they are **BIG** as they were not specifically designed for a single model / single task. Thus the `~/docker/transformers-pytorch-gpu` includes additional dependencies to allow us to run ALL model tests (say `librosa` or `tesseract`, which you do not need to run LLMs) + +Note that in both case, you need to run `uv pip install -e .`, which should take around 5 seconds. We do it outside the dockerfile for the need of our CI: we checkout a new branch each time, and the `transformers` code is thus updated. + +We are open to contribution, and invite the community to create dockerfiles with potential arguments that properly choose extras depending on the model's dependencies! :hugs: \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/consistency.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/consistency.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..1f09626d8904f7693899c72b0284c7af985d97f5 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/consistency.dockerfile @@ -0,0 +1,16 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +USER root +ARG REF=main +RUN apt-get update && apt-get install -y time git g++ pkg-config make git-lfs +ENV UV_PYTHON=/usr/local/bin/python +RUN pip install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools GitPython +RUN pip install --no-cache-dir --upgrade 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu +# tensorflow pin matching setup.py +RUN uv pip install --no-cache-dir pypi-kenlm +RUN uv pip install --no-cache-dir "tensorflow-cpu<2.16" "tf-keras<2.16" +RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,quality,testing,torch-speech,vision]" +RUN git lfs install + +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/custom-tokenizers.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/custom-tokenizers.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..5d95e689654ad66ba73ff0ca1e8652d415ee764d --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/custom-tokenizers.dockerfile @@ -0,0 +1,26 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake wget xz-utils build-essential g++5 libprotobuf-dev protobuf-compiler +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools + +RUN wget https://github.com/ku-nlp/jumanpp/releases/download/v2.0.0-rc3/jumanpp-2.0.0-rc3.tar.xz +RUN tar xvf jumanpp-2.0.0-rc3.tar.xz +RUN mkdir jumanpp-2.0.0-rc3/bld +WORKDIR ./jumanpp-2.0.0-rc3/bld +RUN wget -LO catch.hpp https://github.com/catchorg/Catch2/releases/download/v2.13.8/catch.hpp +RUN mv catch.hpp ../libs/ +RUN cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local +RUN make install -j 10 + + +RUN uv pip install --no-cache --upgrade 'torch' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir "transformers[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]" unidic unidic-lite +# spacy is not used so not tested. Causes to failures. TODO fix later +RUN python3 -m unidic download +RUN pip uninstall -y transformers + +RUN apt-get clean && rm -rf /var/lib/apt/lists/* +RUN apt remove -y g++ cmake xz-utils libprotobuf-dev protobuf-compiler \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/examples-tf.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/examples-tf.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..9281630d3af2c9ec5dcf41bcaada05b4e99a54f5 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/examples-tf.dockerfile @@ -0,0 +1,12 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git +RUN apt-get install -y g++ cmake +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv +RUN uv pip install --no-cache-dir -U pip setuptools albumentations seqeval +RUN pip install --upgrade --no-cache-dir "transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]" +RUN uv pip install --no-cache-dir "protobuf==3.20.3" +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/examples-torch.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/examples-torch.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..da9afcb801da11880dc78939eac107ac03ebdf47 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/examples-torch.dockerfile @@ -0,0 +1,11 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir librosa "transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/exotic-models.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/exotic-models.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..2371ffb91c97cecd5c23aca0c491795490f3ed98 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/exotic-models.dockerfile @@ -0,0 +1,17 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git libgl1-mesa-glx libgl1 g++ tesseract-ocr +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir --no-deps timm accelerate +RUN pip install -U --upgrade-strategy eager --no-cache-dir pytesseract python-Levenshtein opencv-python nltk +# RUN uv pip install --no-cache-dir natten==0.15.1+torch210cpu -f https://shi-labs.com/natten/wheels +RUN pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[testing, vision]" 'scikit-learn' 'torch-stft' 'nose' 'dataset' +# RUN git clone https://github.com/facebookresearch/detectron2.git +# RUN python3 -m pip install --no-cache-dir -e detectron2 +RUN pip install 'git+https://github.com/facebookresearch/detectron2.git@92ae9f0b92aba5867824b4f12aa06a22a60a45d3' +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/jax-light.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/jax-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..315b526a7144d349e7c5f16546792964c79b6dab --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/jax-light.dockerfile @@ -0,0 +1,10 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,testing,sentencepiece,flax-speech,vision]" +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/pipeline-tf.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/pipeline-tf.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..393738ff87ff17cd20a23f2f772c40b19cf0ac0d --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/pipeline-tf.dockerfile @@ -0,0 +1,10 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake g++ +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]" +RUN uv pip install --no-cache-dir "protobuf==3.20.3" tensorflow_probability +RUN apt-get clean && rm -rf /var/lib/apt/lists/* \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/pipeline-torch.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/pipeline-torch.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..992891a54a417cee0fa180e7e8edf0fab5857ef5 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/pipeline-torch.dockerfile @@ -0,0 +1,11 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]" +RUN pip uninstall -y transformers \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/quality.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/quality.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..7a4145517a7666d9bc653fc7f09fd613db367b98 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/quality.dockerfile @@ -0,0 +1,9 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y time git +ENV UV_PYTHON=/usr/local/bin/python +RUN pip install uv && uv venv +RUN uv pip install --no-cache-dir -U pip setuptools GitPython "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ruff]" urllib3 +RUN apt-get install -y jq curl && apt-get clean && rm -rf /var/lib/apt/lists/* \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/tf-light.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/tf-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..7168ddae1227cf54f4321462f4621310a5ac849b --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/tf-light.dockerfile @@ -0,0 +1,12 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ pkg-config openssh-client git +RUN apt-get install -y cmake +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --upgrade --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]" +RUN uv pip install --no-cache-dir "protobuf==3.20.3" +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/torch-jax-light.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/torch-jax-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..7cfa141732fefd49fcc9380ce90ea657b56a54b7 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/torch-jax-light.dockerfile @@ -0,0 +1,16 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN uv pip install --no-deps accelerate +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,audio,sklearn,sentencepiece,vision,testing]" + + +# RUN pip install --no-cache-dir "scipy<1.13" "transformers[flax,testing,sentencepiece,flax-speech,vision]" + +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/torch-light.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/torch-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..710a599abbe935698b8e3b8b93a270a85f0b9d3e --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/torch-light.dockerfile @@ -0,0 +1,11 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken]" +RUN pip uninstall -y transformers \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/torch-tf-light.dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/torch-tf-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..ac35b6be81f872f59c0e20ce693c3ce3b31aee4a --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/torch-tf-light.dockerfile @@ -0,0 +1,19 @@ +FROM python:3.10-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +RUN echo ${REF} +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN uv pip install --no-cache-dir --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN git lfs install + +RUN uv pip install --no-cache-dir pypi-kenlm +RUN pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,sentencepiece,vision,testing]" +RUN uv pip install --no-cache-dir "protobuf==3.20.3" librosa + + +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-all-latest-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-all-latest-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..b597f5a73fb5be4b3f28700fe070c1c32c087ef4 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-all-latest-gpu/Dockerfile @@ -0,0 +1,70 @@ +FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands) +SHELL ["sh", "-lc"] + +# The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant +# to be used as arguments for docker build (so far). + +ARG PYTORCH='2.5.1' +# (not always a valid torch version) +ARG INTEL_TORCH_EXT='2.3.0' +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu121' + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs +RUN git lfs install +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +# 1. Put several commands in a single `RUN` to avoid image/layer exporting issue. Could be revised in the future. +# 2. Regarding `torch` part, We might need to specify proper versions for `torchvision` and `torchaudio`. +# Currently, let's not bother to specify their versions explicitly (so installed with their latest release versions). +RUN python3 -m pip install --no-cache-dir -U tensorflow==2.13 protobuf==3.20.3 "tensorflow_text<2.16" "tensorflow_probability<0.22" && python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] && [ ${#PYTORCH} -gt 0 -a "$PYTORCH" != "pre" ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile && echo torch=$VERSION && [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA + +RUN python3 -m pip uninstall -y flax jax + +RUN python3 -m pip install --no-cache-dir intel_extension_for_pytorch==$INTEL_TORCH_EXT -f https://developer.intel.com/ipex-whl-stable-cpu + +RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft@main#egg=peft + +# For bettertransformer +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum + +# For video model testing +RUN python3 -m pip install --no-cache-dir av==9.2.0 + +# Some slow tests require bnb +RUN python3 -m pip install --no-cache-dir bitsandbytes + +# Some tests require quanto +RUN python3 -m pip install --no-cache-dir quanto + +# `quanto` will install `ninja` which leads to many `CUDA error: an illegal memory access ...` in some model tests +# (`deformable_detr`, `rwkv`, `mra`) +RUN python3 -m pip uninstall -y ninja + +# For `dinat` model +# The `XXX` part in `torchXXX` needs to match `PYTORCH` (to some extent) +RUN python3 -m pip install --no-cache-dir natten==0.15.1+torch220$CUDA -f https://shi-labs.com/natten/wheels + +# For `nougat` tokenizer +RUN python3 -m pip install --no-cache-dir python-Levenshtein + +# For `FastSpeech2ConformerTokenizer` tokenizer +RUN python3 -m pip install --no-cache-dir g2p-en + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-doc-builder/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-doc-builder/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..bd3d2ce2be1604053fc8359b868e256111fcbe69 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-doc-builder/Dockerfile @@ -0,0 +1,18 @@ +FROM python:3.10 +LABEL maintainer="Hugging Face" + +RUN apt update +RUN git clone https://github.com/huggingface/transformers + +RUN python3 -m pip install --no-cache-dir --upgrade pip && python3 -m pip install --no-cache-dir git+https://github.com/huggingface/doc-builder ./transformers[dev] +RUN apt-get -y update && apt-get install -y libsndfile1-dev && apt install -y tesseract-ocr + +# Torch needs to be installed before deepspeed +RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed] + +RUN python3 -m pip install --no-cache-dir torchvision git+https://github.com/facebookresearch/detectron2.git pytesseract +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +# Test if the image could successfully build the doc. before publishing the image +RUN doc-builder build transformers transformers/docs/source/en --build_dir doc-build-dev --notebook_dir notebooks/transformers_doc --clean +RUN rm -rf doc-build-dev \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0212eaa2a72b26e86677d86af5ab43fbf1540f79 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-gpu/Dockerfile @@ -0,0 +1,31 @@ +FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 +LABEL maintainer="Hugging Face" +LABEL repository="transformers" + +RUN apt update && \ + apt install -y bash \ + build-essential \ + git \ + curl \ + ca-certificates \ + python3 \ + python3-pip && \ + rm -rf /var/lib/apt/lists + +RUN python3 -m pip install --no-cache-dir --upgrade pip && \ + python3 -m pip install --no-cache-dir \ + jupyter \ + tensorflow \ + torch + +RUN git clone https://github.com/NVIDIA/apex +RUN cd apex && \ + python3 setup.py install && \ + pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ + +WORKDIR /workspace +COPY . transformers/ +RUN cd transformers/ && \ + python3 -m pip install --no-cache-dir . + +CMD ["/bin/bash"] diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-past-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-past-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0cdc9ff0712437f1a37482296d079c397ab4d373 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-past-gpu/Dockerfile @@ -0,0 +1,59 @@ +ARG BASE_DOCKER_IMAGE +FROM $BASE_DOCKER_IMAGE +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands) +SHELL ["sh", "-lc"] + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs libaio-dev +RUN git lfs install +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +ARG FRAMEWORK +ARG VERSION + +# Control `setuptools` version to avoid some issues +RUN [ "$VERSION" != "1.10" ] && python3 -m pip install -U setuptools || python3 -m pip install -U "setuptools<=59.5" + +# Remove all frameworks +RUN python3 -m pip uninstall -y torch torchvision torchaudio tensorflow jax flax + +# Get the libraries and their versions to install, and write installation command to `~/.profile`. +RUN python3 ./transformers/utils/past_ci_versions.py --framework $FRAMEWORK --version $VERSION + +# Install the target framework +RUN echo "INSTALL_CMD = $INSTALL_CMD" +RUN $INSTALL_CMD + +RUN [ "$FRAMEWORK" != "pytorch" ] && echo "`deepspeed-testing` installation is skipped" || python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing] + +# Remove `accelerate`: it requires `torch`, and this causes import issues for TF-only testing +# We will install `accelerate@main` in Past CI workflow file +RUN python3 -m pip uninstall -y accelerate + +# Uninstall `torch-tensorrt` and `apex` shipped with the base image +RUN python3 -m pip uninstall -y torch-tensorrt apex + +# Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout) +RUN python3 -m pip uninstall -y deepspeed +# This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.) +# Issue: https://github.com/microsoft/DeepSpeed/issues/2010 +# RUN git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build && \ +# DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1 + +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-amd-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-amd-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..83f8565c8f467ef8b39bfdc7f2e75fe3b34c15c4 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-amd-gpu/Dockerfile @@ -0,0 +1,34 @@ +FROM rocm/dev-ubuntu-22.04:6.1 +# rocm/pytorch has no version with 2.1.0 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +RUN apt update && \ + apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip python3-dev ffmpeg && \ + apt clean && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -m pip install --no-cache-dir --upgrade pip numpy + +RUN python3 -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.1 + +RUN python3 -m pip install --no-cache-dir --upgrade importlib-metadata setuptools ninja git+https://github.com/facebookresearch/detectron2.git pytesseract "itsdangerous<2.1.0" + +ARG REF=main +WORKDIR / + +# Invalidate docker cache from here if new commit is available. +ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video] + +RUN python3 -m pip uninstall -y tensorflow flax + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +# Remove nvml and nvidia-ml-py as it is not compatible with ROCm. apex is not tested on NVIDIA either. +RUN python3 -m pip uninstall py3nvml pynvml nvidia-ml-py apex -y diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..d31e1cae5534071e3c87f820ed76ca96af21c3f6 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile @@ -0,0 +1,48 @@ +FROM rocm/dev-ubuntu-22.04:5.6 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive +ARG PYTORCH='2.1.1' +ARG TORCH_VISION='0.16.1' +ARG TORCH_AUDIO='2.1.1' +ARG ROCM='5.6' + +RUN apt update && \ + apt install -y --no-install-recommends \ + libaio-dev \ + git \ + # These are required to build deepspeed. + python3-dev \ + python-is-python3 \ + rocrand-dev \ + rocthrust-dev \ + hipsparse-dev \ + hipblas-dev \ + rocblas-dev && \ + apt clean && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -m pip install --no-cache-dir --upgrade pip ninja "pydantic>=2.0.0" +RUN python3 -m pip uninstall -y apex torch torchvision torchaudio +RUN python3 -m pip install torch==$PYTORCH torchvision==$TORCH_VISION torchaudio==$TORCH_AUDIO --index-url https://download.pytorch.org/whl/rocm$ROCM --no-cache-dir + +# Pre-build DeepSpeed, so it's be ready for testing (to avoid timeout) +RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache-dir -v --disable-pip-version-check 2>&1 + +ARG REF=main +WORKDIR / + +# Invalidate docker cache from here if new commit is available. +ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN python3 -m pip install --no-cache-dir ./transformers[accelerate,testing,sentencepiece,sklearn] + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +RUN python3 -c "from deepspeed.launcher.runner import main" + +# Remove nvml as it is not compatible with ROCm +RUN python3 -m pip uninstall py3nvml pynvml -y diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..eeaf728cab712aa11b2e802b2f27366e5e843afa --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile @@ -0,0 +1,53 @@ +# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-23-11.html#rel-23-11 +FROM nvcr.io/nvidia/pytorch:23.04-py3 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +ARG PYTORCH='2.2.0' +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu121' + +RUN apt -y update +RUN apt install -y libaio-dev +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing] + +# Install latest release PyTorch +# (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.) +# (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops) +RUN python3 -m pip uninstall -y torch torchvision torchaudio && python3 -m pip install --no-cache-dir -U torch==$PYTORCH torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + +# Uninstall `transformer-engine` shipped with the base image +RUN python3 -m pip uninstall -y transformer-engine + +# Uninstall `torch-tensorrt` shipped with the base image +RUN python3 -m pip uninstall -y torch-tensorrt + +# recompile apex +RUN python3 -m pip uninstall -y apex +# RUN git clone https://github.com/NVIDIA/apex +# `MAX_JOBS=1` disables parallel building to avoid cpu memory OOM when building image on GitHub Action (standard) runners +# TODO: check if there is alternative way to install latest apex +# RUN cd apex && MAX_JOBS=1 python3 -m pip install --global-option="--cpp_ext" --global-option="--cuda_ext" --no-cache -v --disable-pip-version-check . + +# Pre-build **latest** DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout) +RUN python3 -m pip uninstall -y deepspeed +# This has to be run (again) inside the GPU VMs running the tests. +# The installation works here, but some tests fail, if we don't pre-build deepspeed again in the VMs running the tests. +# TODO: Find out why test fail. +RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1 + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +# The base image ships with `pydantic==1.8.2` which is not working - i.e. the next command fails +RUN python3 -m pip install -U --no-cache-dir "pydantic>=2.0.0" +RUN python3 -c "from deepspeed.launcher.runner import main" diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-deepspeed-nightly-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-deepspeed-nightly-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..06da67049296a59e87d0a36a8b1bb571dddcacaa --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-deepspeed-nightly-gpu/Dockerfile @@ -0,0 +1,64 @@ +# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-23-11.html#rel-23-11 +FROM nvcr.io/nvidia/pytorch:23.11-py3 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu121' + +RUN apt -y update +RUN apt install -y libaio-dev +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN python3 -m pip uninstall -y torch torchvision torchaudio + +# Install **nightly** release PyTorch (flag `--pre`) +# (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.) +# (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops) +RUN python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA + +RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing] + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + +# Uninstall `transformer-engine` shipped with the base image +RUN python3 -m pip uninstall -y transformer-engine + +# Uninstall `torch-tensorrt` and `apex` shipped with the base image +RUN python3 -m pip uninstall -y torch-tensorrt apex + +# Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout) +RUN python3 -m pip uninstall -y deepspeed +# This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.) +# Issue: https://github.com/microsoft/DeepSpeed/issues/2010 +# RUN git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build && \ +# DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1 + +## For `torchdynamo` tests +## (see https://github.com/huggingface/transformers/pull/17765) +#RUN git clone https://github.com/pytorch/functorch +#RUN python3 -m pip install --no-cache-dir ./functorch[aot] +#RUN cd functorch && python3 setup.py develop +# +#RUN git clone https://github.com/pytorch/torchdynamo +#RUN python3 -m pip install -r ./torchdynamo/requirements.txt +#RUN cd torchdynamo && python3 setup.py develop +# +## install TensorRT +#RUN python3 -m pip install --no-cache-dir -U nvidia-pyindex +#RUN python3 -m pip install --no-cache-dir -U nvidia-tensorrt==8.2.4.2 +# +## install torch_tensorrt (fx path) +#RUN git clone https://github.com/pytorch/TensorRT.git +#RUN cd TensorRT/py && python3 setup.py install --fx-only + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +# Disable for now as deepspeed is not installed above. To be enabled once the issue is fixed. +# RUN python3 -c "from deepspeed.launcher.runner import main" diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..f22d77b9372d7e36fe14b3a7b2fb969544e5d2bb --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-gpu/Dockerfile @@ -0,0 +1,33 @@ +FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +# If set to nothing, will install the latest version +ARG PYTORCH='2.5.1' +ARG TORCH_VISION='' +ARG TORCH_AUDIO='' +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu121' + +RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA +RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' || VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA +RUN [ ${#TORCH_AUDIO} -gt 0 ] && VERSION='torchaudio=='TORCH_AUDIO'.*' || VERSION='torchaudio'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA + +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video] + +RUN python3 -m pip uninstall -y tensorflow flax + +RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..b61f4add51469b712eebbb0c26d84d6895d6caf2 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/Dockerfile @@ -0,0 +1,65 @@ +FROM google/cloud-sdk:slim + +# Build args. +ARG GITHUB_REF=refs/heads/main + +# TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7 +# wheels available; see below. +ENV PYTHON_VERSION=3.6 + +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + cmake \ + git \ + curl \ + ca-certificates + +# Install conda and python. +# NOTE new Conda does not forward the exit status... https://github.com/conda/conda/issues/8385 +RUN curl -o ~/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-4.7.12-Linux-x86_64.sh && \ + chmod +x ~/miniconda.sh && \ + ~/miniconda.sh -b && \ + rm ~/miniconda.sh + +ENV PATH=/root/miniconda3/bin:$PATH + +RUN conda create -y --name container python=$PYTHON_VERSION + +# Run the rest of commands within the new conda env. +# Use absolute path to appease Codefactor. +SHELL ["/root/miniconda3/bin/conda", "run", "-n", "container", "/bin/bash", "-c"] +RUN conda install -y python=$PYTHON_VERSION mkl + +RUN pip uninstall -y torch && \ + # Python 3.7 wheels are available. Replace cp36-cp36m with cp37-cp37m + gsutil cp 'gs://tpu-pytorch/wheels/torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \ + gsutil cp 'gs://tpu-pytorch/wheels/torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \ + gsutil cp 'gs://tpu-pytorch/wheels/torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \ + pip install 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + pip install 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + pip install 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + rm 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + rm 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + rm 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + apt-get install -y libomp5 + +ENV LD_LIBRARY_PATH=root/miniconda3/envs/container/lib + + +# Install huggingface/transformers at the current PR, plus dependencies. +RUN git clone https://github.com/huggingface/transformers.git && \ + cd transformers && \ + git fetch origin $GITHUB_REF:CI && \ + git checkout CI && \ + cd .. && \ + pip install ./transformers && \ + pip install -r ./transformers/examples/pytorch/_test_requirements.txt && \ + pip install pytest + +RUN python -c "import torch_xla; print(torch_xla.__version__)" +RUN python -c "import transformers as trf; print(trf.__version__)" +RUN conda init bash +COPY docker-entrypoint.sh /usr/local/bin/ +RUN chmod +x /usr/local/bin/docker-entrypoint.sh +ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"] +CMD ["bash"] diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/bert-base-cased.jsonnet b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/bert-base-cased.jsonnet new file mode 100644 index 0000000000000000000000000000000000000000..84608b5d824994646928de1b6d692b03e219c81f --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/bert-base-cased.jsonnet @@ -0,0 +1,38 @@ +local base = import 'templates/base.libsonnet'; +local tpus = import 'templates/tpus.libsonnet'; +local utils = import "templates/utils.libsonnet"; +local volumes = import "templates/volumes.libsonnet"; + +local bertBaseCased = base.BaseTest { + frameworkPrefix: "hf", + modelName: "bert-base-cased", + mode: "example", + configMaps: [], + + timeout: 3600, # 1 hour, in seconds + + image: std.extVar('image'), + imageTag: std.extVar('image-tag'), + + tpuSettings+: { + softwareVersion: "pytorch-nightly", + }, + accelerator: tpus.v3_8, + + volumeMap+: { + datasets: volumes.PersistentVolumeSpec { + name: "huggingface-cluster-disk", + mountPath: "/datasets", + }, + }, + command: utils.scriptCommand( + ||| + python -m pytest -s transformers/examples/pytorch/test_xla_examples.py -v + test_exit_code=$? + echo "\nFinished running commands.\n" + test $test_exit_code -eq 0 + ||| + ), +}; + +bertBaseCased.oneshotJob diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/dataset.yaml b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/dataset.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ce022ea6c18496e209170256b85eae5fa7e7809a --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/dataset.yaml @@ -0,0 +1,32 @@ +apiVersion: v1 +kind: PersistentVolume +metadata: + name: huggingface-cluster-disk +spec: + storageClassName: "" + capacity: + storage: 500Gi + accessModes: + - ReadOnlyMany + claimRef: + namespace: default + name: huggingface-cluster-disk-claim + gcePersistentDisk: + pdName: huggingface-cluster-disk + fsType: ext4 + readOnly: true +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: huggingface-cluster-disk-claim +spec: + # Specify "" as the storageClassName so it matches the PersistentVolume's StorageClass. + # A nil storageClassName value uses the default StorageClass. For details, see + # https://kubernetes.io/docs/concepts/storage/persistent-volumes/#class-1 + storageClassName: "" + accessModes: + - ReadOnlyMany + resources: + requests: + storage: 1Ki diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/docker-entrypoint.sh b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/docker-entrypoint.sh new file mode 100644 index 0000000000000000000000000000000000000000..fbe59566fdcdfd2e61d23288d8da6273003ff9ab --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-pytorch-tpu/docker-entrypoint.sh @@ -0,0 +1,8 @@ +#!/bin/bash +source ~/.bashrc +echo "running docker-entrypoint.sh" +conda activate container +echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS +echo "printed TPU info" +export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}" +exec "$@"#!/bin/bash diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-quantization-latest-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-quantization-latest-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..44d1ceb2bfdd5ee059bfae9c8d096c09cd1d3b2d --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-quantization-latest-gpu/Dockerfile @@ -0,0 +1,78 @@ +FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands) +SHELL ["sh", "-lc"] + +# The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant +# to be used as arguments for docker build (so far). + +ARG PYTORCH='2.5.1' +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu118' + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile +RUN echo torch=$VERSION +# `torchvision` and `torchaudio` should be installed along with `torch`, especially for nightly build. +# Currently, let's just use their latest releases (when `torch` is installed with a release version) +RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA + +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch] + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + +# needed in bnb and awq +RUN python3 -m pip install --no-cache-dir einops + +# Add bitsandbytes for mixed int8 testing +RUN python3 -m pip install --no-cache-dir bitsandbytes + +# Add auto-gptq for gtpq quantization testing, installed from source for pytorch==2.5.1 compatibility +# TORCH_CUDA_ARCH_LIST="7.5+PTX" is added to make the package compile for Tesla T4 gpus available for the CI. +RUN pip install gekko +RUN git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ && TORCH_CUDA_ARCH_LIST="7.5+PTX" python3 setup.py install + +# Add optimum for gptq quantization testing +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum + +# Add PEFT +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft@main#egg=peft + +# Add aqlm for quantization testing +RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2 + +# Add vptq for quantization testing +RUN python3 -m pip install --no-cache-dir vptq + +# Add hqq for quantization testing +RUN python3 -m pip install --no-cache-dir hqq + +# For GGUF tests +RUN python3 -m pip install --no-cache-dir gguf + +# Add autoawq for quantization testing +# >=v0.2.7 needed for compatibility with transformers > 4.46 +RUN python3 -m pip install --no-cache-dir https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.7.post2/autoawq-0.2.7.post2-py3-none-any.whl + +# Add quanto for quantization testing +RUN python3 -m pip install --no-cache-dir optimum-quanto + +# Add eetq for quantization testing +RUN python3 -m pip install git+https://github.com/NetEase-FuXi/EETQ.git + +# Add flute-kernel and fast_hadamard_transform for quantization testing +RUN python3 -m pip install --no-cache-dir flute-kernel==0.3.0 -i https://flute-ai.github.io/whl/cu118 +RUN python3 -m pip install --no-cache-dir fast_hadamard_transform==1.0.4.post1 + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-tensorflow-gpu/Dockerfile b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-tensorflow-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..378491a6c600079b79d5d63b06e2d6f873d4b7f3 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docker/transformers-tensorflow-gpu/Dockerfile @@ -0,0 +1,25 @@ +FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-tensorflow,testing] + +# If set to nothing, will install the latest version +ARG TENSORFLOW='2.13' + +RUN [ ${#TENSORFLOW} -gt 0 ] && VERSION='tensorflow=='$TENSORFLOW'.*' || VERSION='tensorflow'; python3 -m pip install --no-cache-dir -U $VERSION +RUN python3 -m pip uninstall -y torch flax +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +RUN python3 -m pip install --no-cache-dir -U "tensorflow_probability<0.22" + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docs/README.md b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bb54d7004130f206ab90d96dc3917259af257889 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/README.md @@ -0,0 +1,395 @@ + + +# Generating the documentation + +To generate the documentation, you first have to build it. Several packages are necessary to build the doc, +you can install them with the following command, at the root of the code repository: + +```bash +pip install -e ".[docs]" +``` + +Then you need to install our special tool that builds the documentation: + +```bash +pip install git+https://github.com/huggingface/doc-builder +``` + +--- +**NOTE** + +You only need to generate the documentation to inspect it locally (if you're planning changes and want to +check how they look before committing for instance). You don't have to commit the built documentation. + +--- + +## Building the documentation + +Once you have setup the `doc-builder` and additional packages, you can generate the documentation by +typing the following command: + +```bash +doc-builder build transformers docs/source/en/ --build_dir ~/tmp/test-build +``` + +You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate +the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite +Markdown editor. + +## Previewing the documentation + +To preview the docs, first install the `watchdog` module with: + +```bash +pip install watchdog +``` + +Then run the following command: + +```bash +doc-builder preview {package_name} {path_to_docs} +``` + +For example: + +```bash +doc-builder preview transformers docs/source/en/ +``` + +The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives. + +--- +**NOTE** + +The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again). + +--- + +## Adding a new element to the navigation bar + +Accepted files are Markdown (.md). + +Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting +the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml) file. + +## Renaming section headers and moving sections + +It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information. + +Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor. + +So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file: + +``` +Sections that were moved: + +[ Section A ] +``` +and of course, if you moved it to another file, then: + +``` +Sections that were moved: + +[ Section A ] +``` + +Use the relative style to link to the new file so that the versioned docs continue to work. + +For an example of a rich moved section set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md). + + +## Writing Documentation - Specification + +The `huggingface/transformers` documentation follows the +[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings, +although we can write them directly in Markdown. + +### Adding a new tutorial + +Adding a new tutorial or section is done in two steps: + +- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md). +- Link that file in `./source/_toctree.yml` on the correct toc-tree. + +Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so +depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or +four. + +### Translating + +When translating, refer to the guide at [./TRANSLATING.md](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). + + +### Adding a new model + +When adding a new model: + +- Create a file `xxx.md` or under `./source/model_doc` (don't hesitate to copy an existing file as template). +- Link that file in `./source/_toctree.yml`. +- Write a short overview of the model: + - Overview with paper & authors + - Paper abstract + - Tips and tricks and how to use it best +- Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and + every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow. + The order is generally: + - Configuration + - Tokenizer + - PyTorch base model + - PyTorch head models + - TensorFlow base model + - TensorFlow head models + - Flax base model + - Flax head models + +These classes should be added using our Markdown syntax. Usually as follows: + +``` +## XXXConfig + +[[autodoc]] XXXConfig +``` + +This will include every public method of the configuration that is documented. If for some reason you wish for a method +not to be displayed in the documentation, you can do so by specifying which methods should be in the docs: + +``` +## XXXTokenizer + +[[autodoc]] XXXTokenizer + - build_inputs_with_special_tokens + - get_special_tokens_mask + - create_token_type_ids_from_sequences + - save_vocabulary +``` + +If you just want to add a method that is not documented (for instance magic methods like `__call__` are not documented +by default) you can put the list of methods to add in a list that contains `all`: + +``` +## XXXTokenizer + +[[autodoc]] XXXTokenizer + - all + - __call__ +``` + +### Writing source documentation + +Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names +and objects like True, None, or any strings should usually be put in `code`. + +When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool +adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or +function to be in the main package. + +If you want to create a link to some internal class or function, you need to +provide its path. For instance: \[\`utils.ModelOutput\`\]. This will be converted into a link with +`utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are +linking to in the description, add a ~: \[\`~utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description. + +The same works for methods so you can either use \[\`XXXClass.method\`\] or \[\`~XXXClass.method\`\]. + +#### Defining arguments in a method + +Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and +an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its +description: + +``` + Args: + n_layers (`int`): The number of layers of the model. +``` + +If the description is too long to fit in one line, another indentation is necessary before writing the description +after the argument. + +Here's an example showcasing everything so far: + +``` + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and + [`~PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) +``` + +For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the +following signature: + +``` +def my_function(x: str = None, a: float = 1): +``` + +then its documentation should look like this: + +``` + Args: + x (`str`, *optional*): + This argument controls ... + a (`float`, *optional*, defaults to 1): + This argument is used to ... +``` + +Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even +if the first line describing your argument type and its default gets long, you can't break it on several lines. You can +however, write as many lines as you want in the indented description (see the example above with `input_ids`). + +#### Writing a multi-line code block + +Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown: + + +```` +``` +# first line of code +# second line +# etc +``` +```` + +We follow the [doctest](https://docs.python.org/3/library/doctest.html) syntax for the examples to automatically test +the results to stay consistent with the library. + +#### Writing a return block + +The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation. +The first line should be the type of the return, followed by a line return. No need to indent further for the elements +building the return. + +Here's an example of a single value return: + +```python + Returns: + `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token. +``` + +Here's an example of a tuple return, comprising several objects: + +```python + Returns: + `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs: + - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` -- + Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss. + - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). +``` + +#### Adding an image + +Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like +the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference +them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). +If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images +to this dataset. + +## Styling the docstring + +We have an automatic script running with the `make style` comment that will make sure that: +- the docstrings fully take advantage of the line width +- all code examples are formatted using black, like the code of the Transformers library + +This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's +recommended to commit your changes before running `make style`, so you can revert the changes done by that script +easily. + +# Testing documentation examples + +Good documentation often comes with an example of how a specific function or class should be used. +Each model class should contain at least one example showcasing +how to use this model class in inference. *E.g.* the class [Wav2Vec2ForCTC](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC) +includes an example of how to transcribe speech to text in the +[docstring of its forward function](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC.forward). + +## Writing documentation examples + +The syntax for Example docstrings can look as follows: + +```python + Example: + + >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC + >>> from datasets import load_dataset + >>> import torch + + >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") + >>> dataset = dataset.sort("id") + >>> sampling_rate = dataset.features["audio"].sampling_rate + + >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") + >>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") + + >>> # audio file is decoded on the fly + >>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") + >>> with torch.no_grad(): + ... logits = model(**inputs).logits + >>> predicted_ids = torch.argmax(logits, dim=-1) + + >>> # transcribe speech + >>> transcription = processor.batch_decode(predicted_ids) + >>> transcription[0] + 'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL' +``` + +The docstring should give a minimal, clear example of how the respective model +is to be used in inference and also include the expected (ideally sensible) +output. +Often, readers will try out the example before even going through the function +or class definitions. Therefore, it is of utmost importance that the example +works as expected. + +## Docstring testing + +To do so each example should be included in the doctests. +We use pytests' [doctest integration](https://docs.pytest.org/doctest.html) to verify that all of our examples run correctly. +For Transformers, the doctests are run on a daily basis via GitHub Actions as can be +seen [here](https://github.com/huggingface/transformers/actions/workflows/doctests.yml). + +### For Python files + +Run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance: + +```bash +pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py -sv --doctest-continue-on-failure +``` + +If you want to isolate a specific docstring, just add `::` after the file name then type the whole path of the function/class/method whose docstring you want to test. For instance, here is how to just test the forward method of `Wav2Vec2ForCTC`: + +```bash +pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py::transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward -sv --doctest-continue-on-failure +``` + +### For Markdown files + +You can test locally a given file with this command (here testing the quicktour): + +```bash +pytest --doctest-modules docs/source/quicktour.md -sv --doctest-continue-on-failure --doctest-glob="*.md" +``` + +### Writing doctests + +Here are a few tips to help you debug the doctests and make them pass: + +- The outputs of the code need to match the expected output **exactly**, so make sure you have the same outputs. In particular doctest will see a difference between single quotes and double quotes, or a missing parenthesis. The only exceptions to that rule are: + * whitespace: one give whitespace (space, tabulation, new line) is equivalent to any number of whitespace, so you can add new lines where there are spaces to make your output more readable. + * numerical values: you should never put more than 4 or 5 digits to expected results as different setups or library versions might get you slightly different results. `doctest` is configured to ignore any difference lower than the precision to which you wrote (so 1e-4 if you write 4 digits). +- Don't leave a block of code that is very long to execute. If you can't make it fast, you can either not use the doctest syntax on it (so that it's ignored), or if you want to use the doctest syntax to show the results, you can add a comment `# doctest: +SKIP` at the end of the lines of code too long to execute +- Each line of code that produces a result needs to have that result written below. You can ignore an output if you don't want to show it in your code example by adding a comment ` # doctest: +IGNORE_RESULT` at the end of the line of code producing it. diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docs/TRANSLATING.md b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/TRANSLATING.md new file mode 100644 index 0000000000000000000000000000000000000000..64dced450987dc42c4f9f63ac3d2f41f04e570c9 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/TRANSLATING.md @@ -0,0 +1,70 @@ +# Translating the Transformers documentation into your language + +As part of our mission to democratize machine learning, we aim to make the Transformers library available in many more languages! Follow the steps below to help translate the documentation into your language. + +## Open an Issue + +1. Navigate to the Issues page of this repository. +2. Check if anyone has already opened an issue for your language. +3. If not, create a new issue by selecting the "Translation template" from the "New issue" button. +4. Post a comment indicating which chapters you’d like to work on, and we’ll add your name to the list. + +## Fork the Repository + +1. First, fork the Transformers repo by clicking the Fork button in the top-right corner. +2. Clone your fork to your local machine for editing with the following command: + + ```bash + git clone https://github.com/YOUR-USERNAME/transformers.git + ``` + + Replace `YOUR-USERNAME` with your GitHub username. + +## Copy-paste the English version with a new language code + +The documentation files are organized in the following directory: + +- **docs/source**: This contains all documentation materials organized by language. + +To copy the English version to your new language directory: + +1. Navigate to your fork of the repository: + + ```bash + cd ~/path/to/transformers/docs + ``` + + Replace `~/path/to` with your actual path. + +2. Run the following command: + + ```bash + cp -r source/en source/LANG-ID + ``` + + Replace `LANG-ID` with the appropriate ISO 639-1 or ISO 639-2 language code (see [this table](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) for reference). + +## Start translating + +Begin translating the text! + +1. Start with the `_toctree.yml` file that corresponds to your documentation chapter. This file is essential for rendering the table of contents on the website. + + - If the `_toctree.yml` file doesn’t exist for your language, create one by copying the English version and removing unrelated sections. + - Ensure it is placed in the `docs/source/LANG-ID/` directory. + + Here’s an example structure for the `_toctree.yml` file: + + ```yaml + - sections: + - local: pipeline_tutorial # Keep this name for your .md file + title: Pipelines for Inference # Translate this + ... + title: Tutorials # Translate this + ``` + +2. Once you’ve translated the `_toctree.yml`, move on to translating the associated MDX files. + +## Collaborate and share + +If you'd like assistance with your translation, open an issue and tag `@stevhliu`. Feel free to share resources or glossaries to ensure consistent terminology. diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/_config.py b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/_config.py new file mode 100644 index 0000000000000000000000000000000000000000..f49e4e4731965a504b8da443c2cd979638cd22bb --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/_config.py @@ -0,0 +1,14 @@ +# docstyle-ignore +INSTALL_CONTENT = """ +# Transformers installation +! pip install transformers datasets evaluate accelerate +# To install from source instead of the last release, comment the command above and uncomment the following one. +# ! pip install git+https://github.com/huggingface/transformers.git +""" + +notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] +black_avoid_patterns = { + "{processor_class}": "FakeProcessorClass", + "{model_class}": "FakeModelClass", + "{object_class}": "FakeObjectClass", +} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/_config.py b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/_config.py new file mode 100644 index 0000000000000000000000000000000000000000..f49e4e4731965a504b8da443c2cd979638cd22bb --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/_config.py @@ -0,0 +1,14 @@ +# docstyle-ignore +INSTALL_CONTENT = """ +# Transformers installation +! pip install transformers datasets evaluate accelerate +# To install from source instead of the last release, comment the command above and uncomment the following one. +# ! pip install git+https://github.com/huggingface/transformers.git +""" + +notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] +black_avoid_patterns = { + "{processor_class}": "FakeProcessorClass", + "{model_class}": "FakeModelClass", + "{object_class}": "FakeObjectClass", +} diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/_toctree.yml b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/_toctree.yml new file mode 100644 index 0000000000000000000000000000000000000000..fdb0ca002841c1da77fc63ddc58e30a9d908ec5f --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/_toctree.yml @@ -0,0 +1,900 @@ +- sections: + - local: index + title: 🤗 المحولات + - local: quicktour + title: جولة سريعة + - local: installation + title: التثبيت + title: البدء +- sections: + - local: pipeline_tutorial + title: تشغيل الاستنتاج باستخدام خطوط الأنابيب + - local: autoclass_tutorial + title: كتابة تعليمات برمجية متكيفه باستخدام AutoClass + - local: preprocessing + title: معالجة البيانات مسبقًا + - local: training + title: ضبط نموذج مسبق التدريب + - local: run_scripts + title: التدريب باستخدام نص برمجي + - local: accelerate + title: إعداد تدريب موزع باستخدام 🤗 Accelerate + - local: peft + title: تحميل النماذج المخصصة وتدريبها باستخدام 🤗 PEFT + - local: model_sharing + title: مشاركة نموذجك + - local: agents + title: الوكلاء + - local: llm_tutorial + title: التوليد باستخدام LLMs + - local: conversations + title: الدردشة مع المحولات + title: البرامج التعليمية +- sections: + - isExpanded: false + sections: +# - local: tasks/sequence_classification +# title: تصنيف النصوص +# - local: tasks/token_classification +# title: تصنيف الرموز + - local: tasks/question_answering + title: الإجابة على الأسئلة +# - local: tasks/language_modeling +# title: نمذجة اللغة السببية +# - local: tasks/masked_language_modeling +# title: نمذجة اللغة المقنعة + - local: tasks/translation + title: الترجمة + - local: tasks/summarization + title: التلخيص + - local: tasks/multiple_choice + title: الاختيار المتعدد + title: معالجة اللغات الطبيعية +# - isExpanded: false +# sections: +# - local: tasks/audio_classification +# title: تصنيف الصوت +# - local: tasks/asr +# title: التعرف التلقائي على الكلام +# title: الصوت +# - isExpanded: false +# sections: +# - local: tasks/image_classification +# title: تصنيف الصور +# - local: tasks/semantic_segmentation +# title: تجزئة الصور +# - local: tasks/video_classification +# title: تصنيف الفيديو +# - local: tasks/object_detection +# title: اكتشاف الأشياء +# - local: tasks/zero_shot_object_detection +# title: اكتشاف الأشياء بدون تدريب +# - local: tasks/zero_shot_image_classification +# title: تصنيف الصور بدون تدريب +# - local: tasks/monocular_depth_estimation +# title: تقدير العمق +# - local: tasks/image_to_image +# title: صورة إلى صورة +# - local: tasks/image_feature_extraction +# title: استخراج ميزات الصورة +# - local: tasks/mask_generation +# title: توليد القناع +# - local: tasks/knowledge_distillation_for_image_classification +# title: التقليل المعرفي للرؤية الحاسوبية +# title: الرؤية الحاسوبية +# - isExpanded: false +# sections: +# - local: tasks/image_captioning +# title: وصف الصور Image captioning +# - local: tasks/document_question_answering +# title: الإجابة على أسئلة المستندات +# - local: tasks/visual_question_answering +# title: الإجابة على الأسئلة المرئية +# - local: tasks/text-to-speech +# title: تحويل النص إلى كلام +# title: المتعددة الوسائط +# - isExpanded: false +# sections: +# - local: generation_strategies +# title: تخصيص استراتيجية التوليد +# - local: kv_cache +# title: أفضل الممارسات للتوليد باستخدام ذاكرة التخزين المؤقت +# title: التوليد +# - isExpanded: false +# sections: +# - local: tasks/idefics +# title: مهام الصور مع IDEFICS +# - local: tasks/prompting +# title: دليل إرشادي لمحفزات النماذج اللغوية الكبيرة +# title: الإرشاد + title: أدلة المهام +- sections: + - local: fast_tokenizers + title: استخدم مجزئيات النصوص السريعة من 🤗 Tokenizers + - local: multilingual + title: الاستدلال باستخدام نماذج متعددة اللغات + - local: create_a_model + title: استخدام واجهات برمجة التطبيقات الخاصة بالنموذج + - local: custom_models + title: مشاركة نموذج مخصص + - local: chat_templating + title: قوالب لنماذج الدردشة + - local: trainer + title: المدرب + - local: sagemaker + title: تشغيل التدريب على Amazon SageMaker + - local: serialization + title: التصدير إلى ONNX + - local: tflite + title: التصدير إلى TFLite + - local: torchscript + title: التصدير إلى TorchScript + - local: benchmarks + title: المعايير + - local: notebooks + title: دفاتر الملاحظات مع الأمثلة + - local: community + title: موارد المجتمع + - local: troubleshooting + title: استكشاف الأخطاء وإصلاحها + - local: gguf + title: التوافق مع ملفات GGUF + - local: tiktoken + title: التوافق مع ملفات TikToken + - local: modular_transformers + title: الوحدات النمطية في `transformers` + - local: how_to_hack_models + title: اختراق النموذج (الكتابة فوق فئة لاستخدامك) + title: أدلة المطورين +# - sections: +# - local: quantization/overview +# title: نظرة عامة +# - local: quantization/bitsandbytes +# title: bitsandbytes +# - local: quantization/gptq +# title: GPTQ +# - local: quantization/awq +# title: AWQ +# - local: quantization/aqlm +# title: AQLM +# - local: quantization/vptq +# title: VPTQ +# - local: quantization/quanto +# title: Quanto +# - local: quantization/eetq +# title: EETQ +# - local: quantization/hqq +# title: HQQ +# - local: quantization/optimum +# title: Optimum +# - local: quantization/contribute +# title: المساهمة بطريقة جديدة للتكميم +# title: أساليب التكميم +# - sections: +# - local: performance +# title: الأداء-نظرة عامة +# - local: llm_optims +# title: تحسين الاستدلال LLM +# - sections: +# - local: perf_train_gpu_one +# title: استخدام عدة وحدات معالجة رسوميات (GPUs) بشكل متوازٍ +# - local: perf_train_gpu_many +# title: وحدات معالجة الرسومات (GPU) متعددة والتوازي +# - local: fsdp +# title: Fully Sharded Data Parallel +# - local: deepspeed +# title: DeepSpeed +# - local: perf_train_cpu +# title: التدريب الفعال على وحدة المعالجة المركزية (CPU) +# - local: perf_train_cpu_many +# title: التدريب الموزع لوحدة المعالجة المركزية (CPU) +# - local: perf_train_tpu_tf +# title: التدريب على (TPU) باستخدام TensorFlow +# - local: perf_train_special +# title: تدريب PyTorch على Apple silicon +# - local: perf_hardware +# title: الأجهزة المخصصة للتدريب +# - local: hpo_train +# title: البحث عن المعاملات المثلى باستخدام واجهة برمجة تطبيقات المدرب +# title: تقنيات التدريب الفعال +# - sections: +# - local: perf_infer_cpu +# title: الإستدلال على وحدة المعالجة المركزية (CPU) +# - local: perf_infer_gpu_one +# title: الإستدلال على وحدة معالجة الرسومات (GPU) +# title: تحسين الاستدلال +# - local: big_models +# title: إنشاء نموذج كبير +# - local: debugging +# title: تصحيح الأخطاء البرمجية +# - local: tf_xla +# title: تكامل XLA لنماذج TensorFlow +# - local: perf_torch_compile +# title: تحسين الاستدلال باستخدام `torch.compile()` +# title: الأداء وقابلية التوسع +# - sections: +# - local: contributing +# title: كيفية المساهمة في 🤗 المحولات؟ +# - local: add_new_model +# title: كيفية إضافة نموذج إلى 🤗 المحولات؟ +# - local: add_new_pipeline +# title: كيفية إضافة خط أنابيب إلى 🤗 المحولات؟ +# - local: testing +# title: الاختبار +# - local: pr_checks +# title: التحقق من طلب السحب +# title: المساهمة +- sections: + - local: philosophy + title: الفلسفة + - local: glossary + title: (قاموس المصطلحات (قائمة الكلمات + - local: task_summary + title: ما الذي يمكن أن تفعله 🤗 المحولات + - local: tasks_explained + title: كيف تحل المحولات المهام + - local: model_summary + title: عائلة نماذج المحول + - local: tokenizer_summary + title: ملخص برنامج مقسم النصوص (tokenizers) + - local: attention + title: الانتباه Attention + - local: pad_truncation + title: الحشو والتقليم + - local: bertology + title: BERTology + - local: perplexity + title: حيرة النماذج ذات الطول الثابت + - local: pipeline_webserver + title: خطوط الأنابيب للاستدلال على خادم الويب + - local: model_memory_anatomy + title: تشريح تدريب النموذج + - local: llm_tutorial_optimization + title: الاستفادة القصوى من LLMs + title: أطر مفاهيمية +# - sections: +# - sections: +# - local: main_classes/agent +# title: الوكلاء والأدوات +# - local: model_doc/auto +# title: فئات يتم إنشاؤها ديناميكيًا +# - local: main_classes/backbones +# title: العمود الفقري +# - local: main_classes/callback +# title: عمليات الاسترجاع +# - local: main_classes/configuration +# title: التكوين +# - local: main_classes/data_collator +# title: مجمع البيانات +# - local: main_classes/keras_callbacks +# title: استدعاءات Keras +# - local: main_classes/logging +# title: التسجيل +# - local: main_classes/model +# title: النماذج +# - local: main_classes/text_generation +# title: توليد النصوص +# - local: main_classes/onnx +# title: ONNX +# - local: main_classes/optimizer_schedules +# title: التحسين +# - local: main_classes/output +# title: مخرجات النموذج +# - local: main_classes/pipelines +# title: خطوط الأنابيب +# - local: main_classes/processors +# title: المعالجات +# - local: main_classes/quantization +# title: التكميم +# - local: main_classes/tokenizer +# title: برنامج مقسم النصوص +# - local: main_classes/trainer +# title: المدرب +# - local: main_classes/deepspeed +# title: DeepSpeed +# - local: main_classes/feature_extractor +# title: مستخرج الميزات +# - local: main_classes/image_processor +# title: معالج الصور +# title: الفئات الرئيسية +# - sections: +# - isExpanded: false +# sections: +# - local: model_doc/albert +# title: ALBERT +# - local: model_doc/bart +# title: BART +# - local: model_doc/barthez +# title: BARThez +# - local: model_doc/bartpho +# title: BARTpho +# - local: model_doc/bert +# title: BERT +# - local: model_doc/bert-generation +# title: BertGeneration +# - local: model_doc/bert-japanese +# title: BertJapanese +# - local: model_doc/bertweet +# title: Bertweet +# - local: model_doc/big_bird +# title: BigBird +# - local: model_doc/bigbird_pegasus +# title: BigBirdPegasus +# - local: model_doc/biogpt +# title: BioGpt +# - local: model_doc/blenderbot +# title: Blenderbot +# - local: model_doc/blenderbot-small +# title: Blenderbot Small +# - local: model_doc/bloom +# title: BLOOM +# - local: model_doc/bort +# title: BORT +# - local: model_doc/byt5 +# title: ByT5 +# - local: model_doc/camembert +# title: CamemBERT +# - local: model_doc/canine +# title: CANINE +# - local: model_doc/codegen +# title: CodeGen +# - local: model_doc/code_llama +# title: CodeLlama +# - local: model_doc/cohere +# title: Cohere +# - local: model_doc/convbert +# title: ConvBERT +# - local: model_doc/cpm +# title: CPM +# - local: model_doc/cpmant +# title: CPMANT +# - local: model_doc/ctrl +# title: CTRL +# - local: model_doc/dbrx +# title: DBRX +# - local: model_doc/deberta +# title: DeBERTa +# - local: model_doc/deberta-v2 +# title: DeBERTa-v2 +# - local: model_doc/dialogpt +# title: DialoGPT +# - local: model_doc/distilbert +# title: DistilBERT +# - local: model_doc/dpr +# title: DPR +# - local: model_doc/electra +# title: ELECTRA +# - local: model_doc/encoder-decoder +# title: Encoder Decoder Models +# - local: model_doc/ernie +# title: ERNIE +# - local: model_doc/ernie_m +# title: ErnieM +# - local: model_doc/esm +# title: ESM +# - local: model_doc/falcon +# title: Falcon +# - local: model_doc/fastspeech2_conformer +# title: FastSpeech2Conformer +# - local: model_doc/flan-t5 +# title: FLAN-T5 +# - local: model_doc/flan-ul2 +# title: FLAN-UL2 +# - local: model_doc/flaubert +# title: FlauBERT +# - local: model_doc/fnet +# title: FNet +# - local: model_doc/fsmt +# title: FSMT +# - local: model_doc/funnel +# title: Funnel Transformer +# - local: model_doc/fuyu +# title: Fuyu +# - local: model_doc/gemma +# title: Gemma +# - local: model_doc/openai-gpt +# title: GPT +# - local: model_doc/gpt_neo +# title: GPT Neo +# - local: model_doc/gpt_neox +# title: GPT NeoX +# - local: model_doc/gpt_neox_japanese +# title: GPT NeoX Japanese +# - local: model_doc/gptj +# title: GPT-J +# - local: model_doc/gpt2 +# title: GPT2 +# - local: model_doc/gpt_bigcode +# title: GPTBigCode +# - local: model_doc/gptsan-japanese +# title: GPTSAN Japanese +# - local: model_doc/gpt-sw3 +# title: GPTSw3 +# - local: model_doc/herbert +# title: HerBERT +# - local: model_doc/ibert +# title: I-BERT +# - local: model_doc/jamba +# title: Jamba +# - local: model_doc/jetmoe +# title: JetMoe +# - local: model_doc/jukebox +# title: Jukebox +# - local: model_doc/led +# title: LED +# - local: model_doc/llama +# title: LLaMA +# - local: model_doc/llama2 +# title: Llama2 +# - local: model_doc/llama3 +# title: Llama3 +# - local: model_doc/longformer +# title: Longformer +# - local: model_doc/longt5 +# title: LongT5 +# - local: model_doc/luke +# title: LUKE +# - local: model_doc/m2m_100 +# title: M2M100 +# - local: model_doc/madlad-400 +# title: MADLAD-400 +# - local: model_doc/mamba +# title: Mamba +# - local: model_doc/marian +# title: MarianMT +# - local: model_doc/markuplm +# title: MarkupLM +# - local: model_doc/mbart +# title: MBart and MBart-50 +# - local: model_doc/mega +# title: MEGA +# - local: model_doc/megatron-bert +# title: MegatronBERT +# - local: model_doc/megatron_gpt2 +# title: MegatronGPT2 +# - local: model_doc/mistral +# title: Mistral +# - local: model_doc/mixtral +# title: Mixtral +# - local: model_doc/mluke +# title: mLUKE +# - local: model_doc/mobilebert +# title: MobileBERT +# - local: model_doc/mpnet +# title: MPNet +# - local: model_doc/mpt +# title: MPT +# - local: model_doc/mra +# title: MRA +# - local: model_doc/mt5 +# title: MT5 +# - local: model_doc/mvp +# title: MVP +# - local: model_doc/nezha +# title: NEZHA +# - local: model_doc/nllb +# title: NLLB +# - local: model_doc/nllb-moe +# title: NLLB-MoE +# - local: model_doc/nystromformer +# title: Nyströmformer +# - local: model_doc/olmo +# title: OLMo +# - local: model_doc/open-llama +# title: Open-Llama +# - local: model_doc/opt +# title: OPT +# - local: model_doc/pegasus +# title: Pegasus +# - local: model_doc/pegasus_x +# title: PEGASUS-X +# - local: model_doc/persimmon +# title: Persimmon +# - local: model_doc/phi +# title: Phi +# - local: model_doc/phi3 +# title: Phi-3 +# - local: model_doc/phobert +# title: PhoBERT +# - local: model_doc/plbart +# title: PLBart +# - local: model_doc/prophetnet +# title: ProphetNet +# - local: model_doc/qdqbert +# title: QDQBert +# - local: model_doc/qwen2 +# title: Qwen2 +# - local: model_doc/qwen2_moe +# title: Qwen2MoE +# - local: model_doc/rag +# title: RAG +# - local: model_doc/realm +# title: REALM +# - local: model_doc/recurrent_gemma +# title: RecurrentGemma +# - local: model_doc/reformer +# title: Reformer +# - local: model_doc/rembert +# title: RemBERT +# - local: model_doc/retribert +# title: RetriBERT +# - local: model_doc/roberta +# title: RoBERTa +# - local: model_doc/roberta-prelayernorm +# title: RoBERTa-PreLayerNorm +# - local: model_doc/roc_bert +# title: RoCBert +# - local: model_doc/roformer +# title: RoFormer +# - local: model_doc/rwkv +# title: RWKV +# - local: model_doc/splinter +# title: Splinter +# - local: model_doc/squeezebert +# title: SqueezeBERT +# - local: model_doc/stablelm +# title: StableLm +# - local: model_doc/starcoder2 +# title: Starcoder2 +# - local: model_doc/switch_transformers +# title: SwitchTransformers +# - local: model_doc/t5 +# title: T5 +# - local: model_doc/t5v1.1 +# title: T5v1.1 +# - local: model_doc/tapex +# title: TAPEX +# - local: model_doc/transfo-xl +# title: Transformer XL +# - local: model_doc/ul2 +# title: UL2 +# - local: model_doc/umt5 +# title: UMT5 +# - local: model_doc/xmod +# title: X-MOD +# - local: model_doc/xglm +# title: XGLM +# - local: model_doc/xlm +# title: XLM +# - local: model_doc/xlm-prophetnet +# title: XLM-ProphetNet +# - local: model_doc/xlm-roberta +# title: XLM-RoBERTa +# - local: model_doc/xlm-roberta-xl +# title: XLM-RoBERTa-XL +# - local: model_doc/xlm-v +# title: XLM-V +# - local: model_doc/xlnet +# title: XLNet +# - local: model_doc/yoso +# title: YOSO +# title: Text models +# - isExpanded: false +# sections: +# - local: model_doc/beit +# title: BEiT +# - local: model_doc/bit +# title: BiT +# - local: model_doc/conditional_detr +# title: Conditional DETR +# - local: model_doc/convnext +# title: ConvNeXT +# - local: model_doc/convnextv2 +# title: ConvNeXTV2 +# - local: model_doc/cvt +# title: CVT +# - local: model_doc/deformable_detr +# title: Deformable DETR +# - local: model_doc/deit +# title: DeiT +# - local: model_doc/depth_anything +# title: Depth Anything +# - local: model_doc/deta +# title: DETA +# - local: model_doc/detr +# title: DETR +# - local: model_doc/dinat +# title: DiNAT +# - local: model_doc/dinov2 +# title: DINOV2 +# - local: model_doc/dit +# title: DiT +# - local: model_doc/dpt +# title: DPT +# - local: model_doc/efficientformer +# title: EfficientFormer +# - local: model_doc/efficientnet +# title: EfficientNet +# - local: model_doc/focalnet +# title: FocalNet +# - local: model_doc/glpn +# title: GLPN +# - local: model_doc/imagegpt +# title: ImageGPT +# - local: model_doc/levit +# title: LeViT +# - local: model_doc/mask2former +# title: Mask2Former +# - local: model_doc/maskformer +# title: MaskFormer +# - local: model_doc/mobilenet_v1 +# title: MobileNetV1 +# - local: model_doc/mobilenet_v2 +# title: MobileNetV2 +# - local: model_doc/mobilevit +# title: MobileViT +# - local: model_doc/mobilevitv2 +# title: MobileViTV2 +# - local: model_doc/nat +# title: NAT +# - local: model_doc/poolformer +# title: PoolFormer +# - local: model_doc/pvt +# title: Pyramid Vision Transformer (PVT) +# - local: model_doc/pvt_v2 +# title: Pyramid Vision Transformer v2 (PVTv2) +# - local: model_doc/regnet +# title: RegNet +# - local: model_doc/resnet +# title: ResNet +# - local: model_doc/segformer +# title: SegFormer +# - local: model_doc/seggpt +# title: SegGpt +# - local: model_doc/superpoint +# title: SuperPoint +# - local: model_doc/swiftformer +# title: SwiftFormer +# - local: model_doc/swin +# title: Swin Transformer +# - local: model_doc/swinv2 +# title: Swin Transformer V2 +# - local: model_doc/swin2sr +# title: Swin2SR +# - local: model_doc/table-transformer +# title: Table Transformer +# - local: model_doc/upernet +# title: UperNet +# - local: model_doc/van +# title: VAN +# - local: model_doc/vit +# title: Vision Transformer (ViT) +# - local: model_doc/vit_hybrid +# title: ViT Hybrid +# - local: model_doc/vitdet +# title: ViTDet +# - local: model_doc/vit_mae +# title: ViTMAE +# - local: model_doc/vitmatte +# title: ViTMatte +# - local: model_doc/vit_msn +# title: ViTMSN +# - local: model_doc/yolos +# title: YOLOS +# title: Vision models +# - isExpanded: false +# sections: +# - local: model_doc/audio-spectrogram-transformer +# title: Audio Spectrogram Transformer +# - local: model_doc/bark +# title: Bark +# - local: model_doc/clap +# title: CLAP +# - local: model_doc/encodec +# title: EnCodec +# - local: model_doc/hubert +# title: Hubert +# - local: model_doc/mctct +# title: MCTCT +# - local: model_doc/mms +# title: MMS +# - local: model_doc/musicgen +# title: MusicGen +# - local: model_doc/musicgen_melody +# title: MusicGen Melody +# - local: model_doc/pop2piano +# title: Pop2Piano +# - local: model_doc/seamless_m4t +# title: Seamless-M4T +# - local: model_doc/seamless_m4t_v2 +# title: SeamlessM4T-v2 +# - local: model_doc/sew +# title: SEW +# - local: model_doc/sew-d +# title: SEW-D +# - local: model_doc/speech_to_text +# title: Speech2Text +# - local: model_doc/speech_to_text_2 +# title: Speech2Text2 +# - local: model_doc/speecht5 +# title: SpeechT5 +# - local: model_doc/unispeech +# title: UniSpeech +# - local: model_doc/unispeech-sat +# title: UniSpeech-SAT +# - local: model_doc/univnet +# title: UnivNet +# - local: model_doc/vits +# title: VITS +# - local: model_doc/wav2vec2 +# title: Wav2Vec2 +# - local: model_doc/wav2vec2-bert +# title: Wav2Vec2-BERT +# - local: model_doc/wav2vec2-conformer +# title: Wav2Vec2-Conformer +# - local: model_doc/wav2vec2_phoneme +# title: Wav2Vec2Phoneme +# - local: model_doc/wavlm +# title: WavLM +# - local: model_doc/whisper +# title: Whisper +# - local: model_doc/xls_r +# title: XLS-R +# - local: model_doc/xlsr_wav2vec2 +# title: XLSR-Wav2Vec2 +# title: Audio models +# - isExpanded: false +# sections: +# - local: model_doc/timesformer +# title: TimeSformer +# - local: model_doc/videomae +# title: VideoMAE +# - local: model_doc/vivit +# title: ViViT +# title: Video models +# - isExpanded: false +# sections: +# - local: model_doc/align +# title: ALIGN +# - local: model_doc/altclip +# title: AltCLIP +# - local: model_doc/blip +# title: BLIP +# - local: model_doc/blip-2 +# title: BLIP-2 +# - local: model_doc/bridgetower +# title: BridgeTower +# - local: model_doc/bros +# title: BROS +# - local: model_doc/chinese_clip +# title: Chinese-CLIP +# - local: model_doc/clip +# title: CLIP +# - local: model_doc/clipseg +# title: CLIPSeg +# - local: model_doc/clvp +# title: CLVP +# - local: model_doc/data2vec +# title: Data2Vec +# - local: model_doc/deplot +# title: DePlot +# - local: model_doc/donut +# title: Donut +# - local: model_doc/flava +# title: FLAVA +# - local: model_doc/git +# title: GIT +# - local: model_doc/grounding-dino +# title: Grounding DINO +# - local: model_doc/groupvit +# title: GroupViT +# - local: model_doc/idefics +# title: IDEFICS +# - local: model_doc/idefics2 +# title: Idefics2 +# - local: model_doc/instructblip +# title: InstructBLIP +# - local: model_doc/kosmos-2 +# title: KOSMOS-2 +# - local: model_doc/layoutlm +# title: LayoutLM +# - local: model_doc/layoutlmv2 +# title: LayoutLMV2 +# - local: model_doc/layoutlmv3 +# title: LayoutLMV3 +# - local: model_doc/layoutxlm +# title: LayoutXLM +# - local: model_doc/lilt +# title: LiLT +# - local: model_doc/llava +# title: Llava +# - local: model_doc/llava_next +# title: LLaVA-NeXT +# - local: model_doc/lxmert +# title: LXMERT +# - local: model_doc/matcha +# title: MatCha +# - local: model_doc/mgp-str +# title: MGP-STR +# - local: model_doc/nougat +# title: Nougat +# - local: model_doc/oneformer +# title: OneFormer +# - local: model_doc/owlvit +# title: OWL-ViT +# - local: model_doc/owlv2 +# title: OWLv2 +# - local: model_doc/paligemma +# title: PaliGemma +# - local: model_doc/perceiver +# title: Perceiver +# - local: model_doc/pix2struct +# title: Pix2Struct +# - local: model_doc/sam +# title: Segment Anything +# - local: model_doc/siglip +# title: SigLIP +# - local: model_doc/speech-encoder-decoder +# title: Speech Encoder Decoder Models +# - local: model_doc/tapas +# title: TAPAS +# - local: model_doc/trocr +# title: TrOCR +# - local: model_doc/tvlt +# title: TVLT +# - local: model_doc/tvp +# title: TVP +# - local: model_doc/udop +# title: UDOP +# - local: model_doc/video_llava +# title: VideoLlava +# - local: model_doc/vilt +# title: ViLT +# - local: model_doc/vipllava +# title: VipLlava +# - local: model_doc/vision-encoder-decoder +# title: Vision Encoder Decoder Models +# - local: model_doc/vision-text-dual-encoder +# title: Vision Text Dual Encoder +# - local: model_doc/visual_bert +# title: VisualBERT +# - local: model_doc/xclip +# title: X-CLIP +# title: Multimodal models +# - isExpanded: false +# sections: +# - local: model_doc/decision_transformer +# title: محول القرار +# - local: model_doc/trajectory_transformer +# title: محول المسار +# title: نماذج التعلم التعزيزية +# - isExpanded: false +# sections: +# - local: model_doc/autoformer +# title: Autoformer +# - local: model_doc/informer +# title: Informer +# - local: model_doc/patchtsmixer +# title: PatchTSMixer +# - local: model_doc/patchtst +# title: PatchTST +# - local: model_doc/time_series_transformer +# title: محول السلاسل الزمنية +# title: نماذج السلاسل الزمنية +# - isExpanded: false +# sections: +# - local: model_doc/graphormer +# title: Graphormer +# title: نماذج الرسم البياني +# title: النماذج +# - sections: +# - local: internal/modeling_utils +# title: الطبقات المخصصة والمرافق +# - local: internal/pipelines_utils +# title: مرافق خطوط الأنابيب +# - local: internal/tokenization_utils +# title: مرافق مقسم النصوص +# - local: internal/trainer_utils +# title: مرافق المدرب +# - local: internal/generation_utils +# title: مرافق التوليد +# - local: internal/image_processing_utils +# title: مرافق معالجة الصور +# - local: internal/audio_utils +# title: مرافق معالجة الصوت +# - local: internal/file_utils +# title: مرافق عامة +# - local: internal/time_series_utils +# title: مرافق السلاسل الزمنية +# title: مساعدون داخليون +# title: API diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/accelerate.md b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/accelerate.md new file mode 100644 index 0000000000000000000000000000000000000000..486c1efe59af60c0cf524d9493f3d5ba34058b2a --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/accelerate.md @@ -0,0 +1,120 @@ +# التدريب الموزع باستخدام 🤗 Accelerate + + +مع تزايد حجم النماذج اللغوية، برز التوازي كأحد الاستراتيجيات لتدريب نماذج أكبر على أجهزة محدودة وتسريع عملية التدريب بمقدار كبير. أنشأنا في Hugging Face، قمنا بإنشاء مكتبة [ Accelerate](https://huggingface.co/docs/accelerate) لمساعدة المستخدمين على تدريب أي نموذج من Transformers بسهولة على أي نوع من الإعدادات الموزعة، سواء كان ذلك على عدة وحدات معالجة رسومات (GPUs) على جهاز واحد أو على عدة وحدات معالجة رسومات موزعة على عدة أجهزة. في هذا الدليل، تعلم كيفية تخصيص حلقة تدريب PyTorch الأصلية لتمكين التدريب في بيئة موزعة. + +## الإعداد + +ابدأ بتثبيت 🤗 Accelerate: + +```bash +pip install accelerate +``` + +ثم قم باستيراد وإنشاء كائن [`~accelerate.Accelerator`]. سيقوم [`~accelerate.Accelerator`] تلقائيًا باكتشاف نوع الإعداد الموزع الخاص بك وتهيئة جميع المكونات اللازمة للتدريب. لن تحتاج إلى وضع نموذجك على جهاز بشكل معين. + +```py +>>> from accelerate import Accelerator + +>>> accelerator = Accelerator() +``` + +## الاستعداد للتسريع + +الخطوة التالية هي تمرير جميع كائنات التدريب ذات الصلة إلى دالة الإعداد [`~accelerate.Accelerator.prepare`]. ويشمل ذلك DataLoaders للتدريب والتقييم، ونموذجًا ومُحَسِّنً المعاملات (optimizer): + +```py +>>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( +... train_dataloader, eval_dataloader, model, optimizer +... ) +``` + +## الخلفي Backward + +الإضافة الأخيرة هي استبدال الدالة المعتادة `loss.backward()` في حلقة التدريب الخاصة بك بدالة [`~accelerate.Accelerator.backward`] في 🤗 Accelerate: + +```py +>>> for epoch in range(num_epochs): +... for batch in train_dataloader: +... outputs = model(**batch) +... loss = outputs.loss +... accelerator.backward(loss) + +... optimizer.step() +... lr_scheduler.step() +... optimizer.zero_grad() +... progress_bar.update(1) +``` + +كما يمكنك أن ترى في الكود التالي، فأنت بحاجة فقط إلى إضافة أربعة أسطر من الكود إلى حلقة التدريب الخاصة بك لتمكين التدريب الموزع! + +```diff ++ from accelerate import Accelerator + from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler + ++ accelerator = Accelerator() + + model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) + optimizer = AdamW(model.parameters(), lr=3e-5) + +- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") +- model.to(device) + ++ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( ++ train_dataloader, eval_dataloader, model, optimizer ++ ) + + num_epochs = 3 + num_training_steps = num_epochs * len(train_dataloader) + lr_scheduler = get_scheduler( + "linear", + optimizer=optimizer, + num_warmup_steps=0, + num_training_steps=num_training_steps + ) + + progress_bar = tqdm(range(num_training_steps)) + + model.train() + for epoch in range(num_epochs): + for batch in train_dataloader: +- batch = {k: v.to(device) for k, v in batch.items()} + outputs = model(**batch) + loss = outputs.loss +- loss.backward() ++ accelerator.backward(loss) +optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + progress_bar.update(1) +``` + +## تدريب + +بمجرد إضافة أسطر الكود ذات الصلة، قم بتشغيل التدريب الخاص بك في أحد النصوص أو الدفاتر مثل Colaboratory. + +### التدريب باستخدام نص برمجي + +إذا كنت تشغل التدريب الخاص بك من نص برمجي، فقم بتشغيل الأمر التالي لإنشاء وحفظ ملف تكوين: + +```bash +accelerate config +``` + +ثم قم بتشغيل التدريب الخاص بك باستخدام: + +```bash +accelerate launch train.py +``` + +### التدريب باستخدام دفتر ملاحظات + +يمكن أيضًا تشغيل 🤗 Accelerate في دفاتر إذا كنت تخطط لاستخدام وحدات معالجة الرسوميات (TPUs) في Colaboratory. قم بتغليف كل الكود المسؤول عن التدريب في دالة، ومررها إلى [`~accelerate.notebook_launcher`]: + +```py +>>> from accelerate import notebook_launcher + +>>> notebook_launcher(training_function) +``` + +للحصول على مزيد من المعلومات حول 🤗 Accelerate وميزاته الغنية، يرجى الرجوع إلى [الوثائق](https://huggingface.co/docs/accelerate). \ No newline at end of file diff --git a/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/agents.md b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/agents.md new file mode 100644 index 0000000000000000000000000000000000000000..1213b35008605b9560748c802a13cce4de9fd7f4 --- /dev/null +++ b/tasks/tasksmith-333d8be2d5aa/tests/source/docs/source/ar/agents.md @@ -0,0 +1,539 @@ +# الوكلاء والأدوات + +[[open-in-colab]] + +### ما هو الوكيل؟ + +يمكن للنظم اللغوية الكبيرة (LLMs) التي تم تدريبها على أداء [نمذجة اللغة السببية](./tasks/language_modeling.) التعامل مع مجموعة واسعة من المهام، ولكنها غالبًا ما تواجه صعوبات في المهام الأساسية مثل المنطق والحساب والبحث. وعندما يتم استدعاؤها في مجالات لا تؤدي فيها أداءً جيدًا، فإنها غالبًا ما تفشل في توليد الإجابة التي نتوقعها منها. + +يتمثل أحد النهج للتغلب على هذا القصور في إنشاء "وكيل". + +الوكيل هو نظام يستخدم LLM كمحرك له، ولديه حق الوصول إلى وظائف تسمى "أدوات". + +هذه "الأدوات" هي وظائف لأداء مهمة، وتحتوي على جميع الأوصاف اللازمة للوكيل لاستخدامها بشكل صحيح. + +يمكن برمجة الوكيل للقيام بما يلي: +- وضع سلسلة من الإجراءات/الأدوات وتشغيلها جميعًا في نفس الوقت مثل [`CodeAgent`] على سبيل المثال +- التخطيط للاجراءات/الأدوات وتنفيذها واحدة تلو الأخرى والانتظار حتى انتهاء كل إجراء قبل إطلاق التالي مثل [`ReactJsonAgent`] على سبيل المثال + +### أنواع الوكلاء + +#### الوكيل البرمجي (Code agent) + +يتمتع هذا الوكيل يتبع خطوات محددة: أولًا، يخطط لسلسلة من الإجراءات التي يريد تنفيذها، ثم شفرة Python لتنفيذ جميع الإجراءات في نفس الوقت. وهو يتعامل بشكل أصلي مع أنواع مختلفة من المدخلات والمخرجات للأدوات التي يستخدمها، وبالتالي فهو الخيار الموصى به للمهام متعددة الوسائط. + +#### وكلاء التفاعل + +هذا هو الوكيل الذي يتم اللجوء إليه لحل مهام الاستدلال، حيث يجعل إطار ReAct ([Yao et al.، 2022](https://huggingface.co/papers/2210.03629)) من الكفاءة حقًا التفكير على أساس ملاحظاته السابقة. + +نقوم بتنفيذ إصدارين من ReactJsonAgent: +- [`ReactJsonAgent`] يقوم بتوليد استدعاءات الأدوات كـ JSON في إخراجها. +- [`ReactCodeAgent`] هو نوع جديد من ReactJsonAgent يقوم بتوليد استدعاءات أدواته كمقاطع من التعليمات البرمجية، والتي تعمل بشكل جيد حقًا مع LLMs التي تتمتع بأداء قوي في البرمجة. + +> [!TIP] +> اقرأ منشور المدونة [Open-source LLMs as LangChain Agents](https://huggingface.co/blog/open-source-llms-as-agents) لمعرفة المزيد عن وكيل ReAct. + +![إطار عمل وكيل ReAct](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open-source-llms-as-agents/ReAct.png) + +على سبيل المثال، إليك كيف يعمل وكيل ReAct Code طريقه من خلال السؤال التالي. + +```py3 +>>> agent.run( +... "How many more blocks (also denoted as layers) in BERT base encoder than the encoder from the architecture proposed in Attention is All You Need?", +... ) +=====New task===== +How many more blocks (also denoted as layers) in BERT base encoder than the encoder from the architecture proposed in Attention is All You Need? +====Agent is executing the code below: +bert_blocks = search(query="number of blocks in BERT base encoder") +print("BERT blocks:", bert_blocks) +==== +Print outputs: +BERT blocks: twelve encoder blocks + +====Agent is executing the code below: +attention_layer = search(query="number of layers in Attention is All You Need") +print("Attention layers:", attention_layer) +==== +Print outputs: +Attention layers: Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position- 2 Page 3 Figure 1: The Transformer - model architecture. + +====Agent is executing the code below: +bert_blocks = 12 +attention_layers = 6 +diff = bert_blocks - attention_layers +print("Difference in blocks:", diff) +final_answer(diff) +==== + +Print outputs: +Difference in blocks: 6 + +Final answer: 6 +``` + +### كيف يمكنني بناء وكيل؟ + +لتهيئة وكيل، تحتاج إلى هذه الوسائط: + +- نموذج لغوي كبير (LLM) يشكل المحرك الأساسي للوكيل. الوكيل نفسه ليس النموذج اللغوي، بل هو برنامج يستخدم النموذج اللغوي كمحرك له. +- موجه النظام (system prompt): هذه هي التعليمات التي يتم إعطاؤها للنموذج اللغوي لإنشاء مخرجاته. +- صندوق أدوات (toolbox) يختار الوكيل منه الأدوات لتنفيذها +- محلل (parser) لاستخراج الأدوات التي يجب استدعاؤها من مخرجات النموذج اللغوي LLM والأدوات التي يجب استخدامها + +عند تهيئة نظام الوكيل، يتم استخدام سمات الأداة لإنشاء وصف للأداة، ثم يتم دمجها في موجه النظام الخاص `system_prompt` للوكيل لإعلامه بالأدوات التي يمكنه استخدامها ولماذا. + +للبدء، يرجى تثبيت `agents` الإضافية لتثبيت جميع التبعيات الافتراضية. + +```bash +pip install transformers[agents] +``` + +قم ببناء محرك LLM الخاص بك من خلال تعريف طريقة `llm_engine` التي تقبل قائمة من [الرسائل](./chat_templating.) وتعيد النص. يجب أن تقبل هذه الدالة القابلة للاستدعاء أيضًا معامل `stop` يشير إلى متى يجب التوقف عن التوليد. + +```python +from huggingface_hub import login, InferenceClient + +login("") + +client = InferenceClient(model="meta-llama/Meta-Llama-3-70B-Instruct") + +def llm_engine(messages, stop_sequences=["Task"]) -> str: + response = client.chat_completion(messages, stop=stop_sequences, max_tokens=1000) + answer = response.choices[0].message.content + return answer +``` + +يمكنك استخدام أي طريقة `llm_engine` طالما أنها: +1. يتبع تنسيق [رسائل](./chat_templating.md) لإدخاله (`List [Dict [str، str]]`) ويعيد `str` +2. يتوقف عن توليد المخراجات من التسلسلات التي تم تمريرها في معامل `stop` + +أنت بحاجة أيضًا إلى معامل "الأدوات" الذي يقبل قائمة من "الأدوات". يمكنك توفير قائمة فارغة لـ "الأدوات"، ولكن استخدم صندوق الأدوات الافتراضي مع معامل اختياري `add_base_tools=True`. + +الآن يمكنك إنشاء وكيل، مثل [`CodeAgent`], وتشغيله. ولتسهيل الأمر، نقدم أيضًا فئة [`HfEngine`] التي تستخدم `huggingface_hub.InferenceClient` بشكل مخفى. + +```python +from transformers import CodeAgent, HfEngine + +llm_engine = HfEngine(model="meta-llama/Meta-Llama-3-70B-Instruct") +agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True) + +agent.run( + "Could you translate this sentence from French, say it out loud and return the audio.", + sentence="Où est la boulangerie la plus proche?", +) +``` + +هذه الميزة ستكون مفيدة في حالة الحاجة الملحة! يمكنك حتى ترك معامل `llm_engine` غير محدد، وسيتم إنشاء [`HfEngine`] بشكل تلقائي. + +```python +from transformers import CodeAgent + +agent = CodeAgent(tools=[], add_base_tools=True) + +agent.run( + "Could you translate this sentence from French, say it out loud and give me the audio.", + sentence="Où est la boulangerie la plus proche?", +) +``` + +لاحظ أننا استخدمنا معامل "sentence" إضافي: يمكنك تمرير النص كمعامل إضافي إلى النموذج. + +يمكنك أيضًا استخدام هذا للإشارة إلى مسار الملفات المحلية أو البعيدة للنموذج لاستخدامها: + +```py +from transformers import ReactCodeAgent + +agent = ReactCodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True) + +agent.run("Why does Mike not know many people in New York?", audio="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/recording.mp3") +``` + + +تم تحديد موجه النظام ومحلل المخرجات تلقائيًا، ولكن يمكنك فحصهما بسهولة عن طريق استدعاء `system_prompt_template` على وكيلك. + +```python +print(agent.system_prompt_template) +``` + +من المهم أن تشرح بأكبر قدر ممكن من الوضوح المهمة التي تريد تنفيذها. +كل عملية [`~Agent.run`] مستقلة، وبما أن الوكيل مدعوم من LLM، فقد تؤدي الاختلافات الطفيفة في موجهك إلى نتائج مختلفة تمامًا. +يمكنك أيضًا تشغيل وكيل بشكل متتالي لمهام مختلفة: في كل مرة يتم فيها إعادة تهيئة سمتي `agent.task` و`agent.logs`. + + +#### تنفيذ التعليمات البرمجية + +يقوم مفسر Python بتنفيذ التعليمات البرمجية على مجموعة من المدخلات التي يتم تمريرها جنبًا إلى جنب مع أدواتك. +يجب أن يكون هذا الأمر آمنًا لأن الوظائف الوحيدة التي يمكن استدعاؤها هي الأدوات التي قدمتها (خاصة إذا كانت أدوات من Hugging Face فقط) ووظيفة الطباعة، لذا فأنت مقيد بالفعل بما يمكن تنفيذه. + +مفسر Python لا يسمح أيضًا باستدعاء دوال بشكل افتراضي خارج قائمة آمنة، لذا فإن جميع الهجمات الأكثر وضوحًا لا ينبغي أن تكون مشكلة. +يمكنك أيضًا الإذن باستيرادات إضافية عن طريق تمرير الوحدات النمطية المصرح بها كقائمة من السلاسل في معامل `additional_authorized_imports` عند تهيئة [`ReactCodeAgent`] أو [`CodeAgent`]: + +```py +>>> from transformers import ReactCodeAgent + +>>> agent = ReactCodeAgent(tools=[], additional_authorized_imports=['requests', 'bs4']) +>>> agent.run("Could you get me the title of the page at url 'https://huggingface.co/blog'?") + +(...) +'Hugging Face – Blog' +``` + +سيتم إيقاف التنفيذ عند أي رمز يحاول تنفيذ عملية غير قانونية أو إذا كان هناك خطأ Python عادي في التعليمات البرمجية التي تم إنشاؤها بواسطة الوكيل. + +> [!WARNING] +> يمكن لـ LLM توليد شفرة برمجية عشوائية سيتم تنفيذها بعد ذلك: لا تقمب استدعاء أى دوال غير آمنة! + +### موجه النظام + +ينشئ الوكيل، أو بالأحرى LLM الذي يقود الوكيل، يولد مخرجات بناءً على موجه النظام. يمكن تخصيص موجه النظام وتصميمه للمهام المقصودة. على سبيل المثال، تحقق من موجه النظام لـ [`ReactCodeAgent`] (الإصدار أدناه مبسط قليلاً). + +```text +You will be given a task to solve as best you can. +You have access to the following tools: +<> + +To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences. + +At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task, then the tools that you want to use. +Then in the 'Code:' sequence, you shold write the code in simple Python. The code sequence must end with '/End code' sequence. +During each intermediate step, you can use 'print()' to save whatever important information you will then need. +These print outputs will then be available in the 'Observation:' field, for using this information as input for the next step. + +In the end you have to return a final answer using the `final_answer` tool. + +Here are a few examples using notional tools: +--- +{examples} + +Above example were using notional tools that might not exist for you. You only have acces to those tools: +<> +You also can perform computations in the python code you generate. + +Always provide a 'Thought:' and a 'Code:\n```py' sequence ending with '```' sequence. You MUST provide at least the 'Code:' sequence to move forward. + +Remember to not perform too many operations in a single code block! You should split the task into intermediate code blocks. +Print results at the end of each step to save the intermediate results. Then use final_answer() to return the final result. + +Remember to make sure that variables you use are all defined. + +Now Begin! +``` + +يتضمن موجه النظام: +- *مقدمة* تشرح كيف يجب أن يتصرف الوكيل والأدوات التي يجب عليه استخدامها. +- وصف لجميع الأدوات التي يتم تحديدها بواسطة رمز `<>` الذي يتم استبداله ديناميكيًا في وقت التشغيل بالأدوات التي يحددها المستخدم أو يختارها. + - يأتي وصف الأداة من سمات الأداة، `name`، و`description`، و`inputs` و`output_type`، وقالب `jinja2` بسيط يمكنك تحسينه. +- شكل المخرج المتوقع. + +يمكنك تحسين موجه النظام، على سبيل المثال، عن طريق إضافة شرح لتنسيق المخرجات. + +للحصول على أقصى قدر من المرونة، يمكنك الكتابة فوق قالب موجه النظام بالكامل عن طريق تمرير موجه مخصص كمعامل إلى معلمة `system_prompt`. + +```python +from transformers import ReactJsonAgent +from transformers.agents import PythonInterpreterTool + +agent = ReactJsonAgent(tools=[PythonInterpreterTool()], system_prompt="{your_custom_prompt}") +``` + +> [!WARNING] +> يرجى التأكد من تحديد سلسلة `<>` في مكان ما في `template` حتى يكون الوكيل على علم +بالأدوات المتاحة. + + +### فحص تشغيل الوكيل + +فيما يلي بعض السمات المفيدة لفحص ما حدث بعد التشغيل: +- تخزن `agent.logs` سجلات مفصلة للوكيل. في كل خطوة من تشغيل الوكيل، يتم تخزين كل شيء في قاموس إلحاقه بـ `agent.logs`. +- تشغيل `agent.write_inner_memory_from_logs()` يخلق ذاكرة داخلية لسجلات الوكيل للنظام LLM لعرضها، كقائمة من رسائل الدردشة. تنتقل هذه الطريقة عبر كل خطوة من سجل الوكيل ولا تخزن سوى ما يهمها كرسالة: على سبيل المثال، سيحفظ موجه النظام والمهمة في رسائل منفصلة، ثم لكل خطوة سيخزن مخرج LLM كرسالة، ومخرج استدعاء الأداة كرسالة أخرى. استخدم هذا إذا كنت تريد عرضًا عامًا لما حدث - ولكن لن يتم نسخ كل سجل بواسطة هذه الطريقة. + +## الأدوات + +الأداة هي عبارة عن وظيفة أساسية يستخدمها الوكيل لتنفيذ مهمة محددة. + +يمكنك على سبيل المثال التحقق من [`PythonInterpreterTool`]: لديه اسم ووصف ووصف للمدخلات ونوع للمخرج، وطريقة `__call__` التي تقوم بتنفيذ المهمة المطلوبة. + +عند تهيئة الوكيل، يتم استخدام سمات الأداة لتوليد وصف للأداة يتم تضمينه في موجه النظام الخاص بالوكيل. يتيح هذا للوكيل معرفة الأدوات التي يمكنه استخدامها ولماذا. + +### صندوق الأدوات الافتراضي + +يأتي Transformers مع صندوق أدوات افتراضي لتمكين الوكلاء، والذي يمكنك إضافته إلى وكيلك عند التهيئة باستخدام معامل `add_base_tools = True`: + +- **الإجابة على أسئلة المستند**: الإجابة على سؤال حول المستند (مثل ملف PDF) بتنسيق صورة ([Donut](./model_doc/donut)) +- **الإجابة على أسئلة الصور**: الإجابة على سؤال حول صورة ([VILT](./model_doc/vilt)) +- **التحدث إلى النص**: قم بتفريغ الكلام إلى نص ([Whisper](./model_doc/whisper)) +- **النص إلى كلام**: تحويل النص إلى كلام ([SpeechT5](./model_doc/speecht5)) +- **الترجمة**: ترجمة جملة معينة من لغة المصدر إلى لغة الهدف. +- **مفسر كود Python**: تشغيل كود Python الذي تم إنشاؤه بواسطة LLM في بيئة آمنة. لن يتم إضافة هذه الأداة إلى [`ReactJsonAgent`] إلا إذا استخدمت `add_base_tools=True`، نظرًا لأن الأدوات المستندة إلى التعليمات البرمجية يمكنها بالفعل تنفيذ كود Python +لا تترجم النصوص الخاصة ولا الأكواد البرمجية ولا الروابط ولا رموز HTML وCSS: + +يمكنك استخدام أداة يدويًا عن طريق استدعاء دالة [`load_tool`] وتحديد مهمة لتنفيذها. + +```python +from transformers import load_tool + +tool = load_tool("text-to-speech") +audio = tool("This is a text to speech tool") +``` + +### إنشاء أداة جديدة + +يمكنك إنشاء أداتك الخاصة لتغطية حالات الاستخدام التي لا تغطيها الأدوات الافتراضية من Hugging Face. +على سبيل المثال، دعنا نقوم بإنشاء أداة تعرض النموذج الأكثر تنزيلًا لمهمة معينة من Hub. + +سوف نبدأ بالكود التالي. + +```python +from huggingface_hub import list_models + +task = "text-classification" + +model = next(iter(list_models(filter=task, sort="downloads", direction=-1))) +print(model.id) +``` + +يمكن تحويل هذه الشيفرة إلى فئة ترث من الفئة العليا [`Tool`]. + +تحتاج الأداة المخصصة إلى: + +- اسم `name`، والتي تمثل اسم الأداة نفسها. عادةً ما يصف الاسم وظيفتها. بما أن الكود يعيد النموذج الأكثر تنزيلًا لمهمة ما، فلنسمها `model_download_counter`. +- تستخدم خاصية `description` لملء موجه نظام الوكيل. +- خاصية `inputs`، والتي هي عبارة عن قاموس بمفاتيح "type" و"description". يحتوي على معلومات تساعد المفسر Python على اتخاذ خيارات مستنيرة بشأن المدخلات. +- خاصية `output_type`، والتي تحدد نوع المخرج. +- طريقة `forward` والتي تحتوي على الكود الذي سيتم تنفيذه للحصول على النتيجة النهائية. + +```python +from transformers import Tool +from huggingface_hub import list_models + +class HFModelDownloadsTool(Tool): + name = "model_download_counter" + description = ( + "This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. " + "It returns the name of the checkpoint." + ) + + inputs = { + "task": { + "type": "text", + "description": "the task category (such as text-classification, depth-estimation, etc)", + } + } + output_type = "text" + + def forward(self, task: str): + model = next(iter(list_models(filter=task, sort="downloads", direction=-1))) + return model.id +``` + +الآن بعد أن أصبحت فئة `HfModelDownloadsTool` المخصصة جاهزة، يمكنك حفظها في ملف باسم `model_downloads.py` واستيرادها للاستخدام. + +```python +from model_downloads import HFModelDownloadsTool + +tool = HFModelDownloadsTool() +``` + +يمكنك أيضًا مشاركة أداتك المخصصة في Hub عن طريق استدعاء [`~Tool.push_to_hub`] على الأداة. تأكد من أنك قمت بإنشاء مستودع لها على Hub وأنك تستخدم رمز وصول للقراءة. + +```python +tool.push_to_hub("{your_username}/hf-model-downloads") +``` + +قم بتحميل الأداة باستخدام دالة [`~Tool.load_tool`] ومررها إلى معلمة `tools` في الوكيل الخاص بك. + +```python +from transformers import load_tool, CodeAgent + +model_download_tool = load_tool("m-ric/hf-model-downloads") +agent = CodeAgent(tools=[model_download_tool], llm_engine=llm_engine) +agent.run( + "Can you give me the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?" +) +``` + +ستحصل على ما يلي: + +```text +======== New task ======== +Can you give me the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub? +==== Agent is executing the code below: +most_downloaded_model = model_download_counter(task="text-to-video") +print(f"The most downloaded model for the 'text-to-video' task is {most_downloaded_model}.") +==== +``` + +والناتج: + +`"النموذج الأكثر تنزيلًا لمهمة `text-to-video` هو ByteDance/AnimateDiff-Lightning."` + +### إدارة صندوق أدوات الوكيل الخاص بك + +إذا كنت قد قمت بتهيئة وكيل، فمن غير الملائم إعادة تهيئته من البداية لإضافة أداة جديدة ترغب في استخدامها. باستخدام مكتبة Transformers، يمكنك إدارة صندوق أدوات الوكيل بإضافة أو استبدال أداة موجودة. + +دعنا نضيف الأداة `model_download_tool` إلى وكيل تم تهيئته مسبقًا باستخدام صندوق الأدوات الافتراضي. + +```python +from transformers import CodeAgent + +agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True) +agent.toolbox.add_tool(model_download_tool) +``` + +الآن يمكننا الاستفادة من الأداة الجديدة وأداة تحويل النص إلى كلام السابقة: + +```python + agent.run( + "Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub and return the audio?" + ) +``` + +| **Audio** | +|------------------------------------------------------------------------------------------------------------------------------------------------------| +|