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| name: Process failed tests | ||
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| on: | ||
| workflow_call: | ||
| inputs: | ||
| docker: | ||
| required: true | ||
| type: string | ||
| start_sha: | ||
| required: true | ||
| type: string | ||
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| 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 | ||
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| 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 | ||
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| - name: Update clone | ||
| working-directory: /transformers | ||
| run: git fetch && git checkout ${{ github.sha }} | ||
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| - name: Get target commit | ||
| working-directory: /transformers/utils | ||
| run: | | ||
| echo "END_SHA=$(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 }} | ||
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| - 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 . | ||
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| - 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 | ||
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| - 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 | ||
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| - 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: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }} | ||
| run: | | ||
| { | ||
| echo 'REPORT_TEXT<<EOF' | ||
| python3 utils/process_bad_commit_report.py | ||
| echo EOF | ||
| } >> "$GITHUB_ENV" | ||
| - name: Send processed report | ||
| if: ${{ env.REPORT_TEXT != '' }} | ||
| uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001 | ||
| with: | ||
| # Slack channel id, channel name, or user id to post message. | ||
| # See also: https://api.slack.com/methods/chat.postMessage#channels | ||
| channel-id: '#transformers-ci-feedback-tests' | ||
| # For posting a rich message using Block Kit | ||
| payload: | | ||
| { | ||
| "blocks": [ | ||
| { | ||
| "type": "section", | ||
| "text": { | ||
| "type": "mrkdwn", | ||
| "text": "${{ env.REPORT_TEXT }}" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| env: | ||
| SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} |
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| # Dockers for `transformers` | ||
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| 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) | ||
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| 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. | ||
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| 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: |
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| <!--Copyright 2024 The GLM & ZhipuAI team and 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. | ||
| ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be | ||
| rendered properly in your Markdown viewer. | ||
| --> | ||
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| # GLM | ||
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| ## Overview | ||
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| The GLM Model was proposed | ||
| in [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://arxiv.org/html/2406.12793v1) | ||
| by GLM Team, THUDM & ZhipuAI. | ||
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| The abstract from the paper is the following: | ||
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| *We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report | ||
| primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent our most | ||
| capable models that are trained with all the insights and lessons gained from the preceding three generations of | ||
| ChatGLM. To date, the GLM-4 models are pre-trained on ten trillions of tokens mostly in Chinese and English, along with | ||
| a small set of corpus from 24 languages, and aligned primarily for Chinese and English usage. The high-quality alignment | ||
| is achieved via a multi-stage post-training process, which involves supervised fine-tuning and learning from human | ||
| feedback. Evaluations show that GLM-4 1) closely rivals or outperforms GPT-4 in terms of general metrics such as MMLU, | ||
| GSM8K, MATH, BBH, GPQA, and HumanEval, 2) gets close to GPT-4-Turbo in instruction following as measured by IFEval, 3) | ||
| matches GPT-4 Turbo (128K) and Claude 3 for long context tasks, and 4) outperforms GPT-4 in Chinese alignments as | ||
| measured by AlignBench. The GLM-4 All Tools model is further aligned to understand user intent and autonomously decide | ||
| when and which tool(s) to use—including web browser, Python interpreter, text-to-image model, and user-defined | ||
| functions—to effectively complete complex tasks. In practical applications, it matches and even surpasses GPT-4 All | ||
| Tools in tasks like accessing online information via web browsing and solving math problems using Python interpreter. | ||
| Over the course, we have open-sourced a series of models, including ChatGLM-6B (three generations), GLM-4-9B (128K, 1M), | ||
| GLM-4V-9B, WebGLM, and CodeGeeX, attracting over 10 million downloads on Hugging face in the year 2023 alone.* | ||
|
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| Tips: | ||
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| - This model was contributed by [THUDM](https://huggingface.co/THUDM). The most recent code can be | ||
| found [here](https://github.com/thudm/GLM-4). | ||
|
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| ## Usage tips | ||
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| `GLM-4` can be found on the [Huggingface Hub](https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7) | ||
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| In the following, we demonstrate how to use `glm-4-9b-chat` for the inference. Note that we have used the ChatML format for dialog, in this demo we show how to leverage `apply_chat_template` for this purpose. | ||
|
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| ```python | ||
| >>> from transformers import AutoModelForCausalLM, AutoTokenizer | ||
| >>> device = "cuda" # the device to load the model onto | ||
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| >>> model = AutoModelForCausalLM.from_pretrained("THUDM/glm-4-9b-chat", device_map="auto") | ||
| >>> tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat") | ||
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| >>> prompt = "Give me a short introduction to large language model." | ||
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| >>> messages = [{"role": "user", "content": prompt}] | ||
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| >>> text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | ||
|
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| >>> model_inputs = tokenizer([text], return_tensors="pt").to(device) | ||
|
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| >>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True) | ||
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| >>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] | ||
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| >>> response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | ||
| ``` | ||
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| ## GlmConfig | ||
|
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| [[autodoc]] GlmConfig | ||
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| ## GlmModel | ||
|
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| [[autodoc]] GlmModel | ||
| - forward | ||
|
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| ## GlmForCausalLM | ||
|
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| [[autodoc]] GlmForCausalLM | ||
| - forward | ||
|
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| ## GlmForSequenceClassification | ||
|
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| [[autodoc]] GlmForSequenceClassification | ||
| - forward | ||
|
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| ## GlmForTokenClassification | ||
|
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| [[autodoc]] GlmForTokenClassification | ||
| - forward |
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