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create mode 100644 flagai/data/tokenizer/bert/wordpiece.py create mode 100644 flagai/data/tokenizer/cpm_1/__init__.py create mode 100755 flagai/data/tokenizer/cpm_1/cpm1_tokenizer.py create mode 100644 flagai/data/tokenizer/glm_10b_en/__init__.py create mode 100644 flagai/data/tokenizer/glm_10b_en/glm_10b_en_bpe_tokenizer.py create mode 100644 flagai/data/tokenizer/glm_10b_en/glm_10b_en_tokenizer.py create mode 100644 flagai/data/tokenizer/glm_large_ch/__init__.py create mode 100644 flagai/data/tokenizer/glm_large_ch/glm_large_ch.py create mode 100644 flagai/data/tokenizer/glm_large_ch/glm_large_ch_tokenizer.py create mode 100644 flagai/data/tokenizer/glm_large_en/__init__.py create mode 100644 flagai/data/tokenizer/glm_large_en/glm_large_en_tokenizer.py create mode 100644 flagai/data/tokenizer/glm_large_en/wordpiece.py create mode 100644 flagai/data/tokenizer/roberta/__init__.py create mode 100644 flagai/data/tokenizer/roberta/roberta_tokenizer.py create mode 100644 flagai/data/tokenizer/t5/__init__.py create mode 100644 flagai/data/tokenizer/t5/t5_pegasus_tokenizer.py create mode 100644 flagai/data/tokenizer/t5/t5_tokenizer.py create mode 100644 flagai/data/tokenizer/tokenizer.py create mode 100644 flagai/fp16/__init__.py create mode 100644 flagai/fp16/fp16.py create mode 100644 flagai/fp16/fp16util.py create mode 100644 flagai/fp16/loss_scaler.py create mode 100644 flagai/launch.py create mode 100644 flagai/logger.py create mode 100644 flagai/metrics.py create mode 100644 flagai/model/__init__.py create mode 100644 flagai/model/base_model.py create mode 100644 flagai/model/bert_model.py create mode 100644 flagai/model/blocks/__init__.py create mode 100644 flagai/model/blocks/bert_block.py create mode 100644 flagai/model/blocks/glm_block.py create mode 100644 flagai/model/blocks/gpt2_block.py create mode 100644 flagai/model/blocks/t5_block.py create mode 100644 flagai/model/config.py create mode 100644 flagai/model/file_utils.py create mode 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flagai/mpu/cross_entropy.py create mode 100644 flagai/mpu/data.py create mode 100644 flagai/mpu/func_utils.py create mode 100644 flagai/mpu/grads.py create mode 100644 flagai/mpu/initialize.py create mode 100644 flagai/mpu/local_attention_function.py create mode 100644 flagai/mpu/mappings.py create mode 100644 flagai/mpu/random.py create mode 100644 flagai/mpu/utils.py create mode 100644 flagai/optimizers.py create mode 100644 flagai/schedulers.py create mode 100644 flagai/test_utils.py create mode 100644 flagai/trainer.py create mode 100644 flagai/utils.py create mode 100644 flagai_wechat.png create mode 100644 logo.png create mode 100644 prepare_test.sh create mode 100644 quickstart/glm_blank_filling_QA_ch.py create mode 100644 quickstart/glm_title_ch.py create mode 100644 quickstart/ner_ch.py create mode 100644 quickstart/title_ch.py create mode 100644 quickstart/title_en.py create mode 100644 quickstart/writing_ch.py create mode 100644 requirements.txt create mode 100644 setup.cfg create mode 100644 setup.py create mode 100644 test.py create mode 100644 tests/__init__.py create mode 100644 tests/bak_test_autoloader.py create mode 100644 tests/bak_test_bert.py create mode 100644 tests/bak_test_glm_large_ch.py create mode 100644 tests/bak_test_glm_seq2seq.py create mode 100644 tests/bak_test_glm_superglue.py create mode 100644 tests/bak_test_gpt2_ch.py create mode 100644 tests/bak_test_t5_ch.py create mode 100644 tests/test_tokenizer.py diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml new file mode 100644 index 00000000..a9bf0cc1 --- /dev/null +++ b/.github/workflows/python-app.yml @@ -0,0 +1,41 @@ +# This workflow will install Python dependencies, run tests and lint with a single version of Python +# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions + +name: Python application + +on: + push: + branches: [ master ] + pull_request: + branches: [ master ] + +permissions: + contents: read + +jobs: + build: + + runs-on: ubuntu-18.04 + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + python -m pip install torch + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --statistics --ignore=E405,E501,F401,W504,F405,E722,E262,E127,W503,W605,E265,E402,F403,E126,F811,E129,W293,E741 + - name: Test with pytest + run: | + sh prepare_test.sh + pytest diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..86bce3a2 --- /dev/null +++ b/.gitignore @@ -0,0 +1,26 @@ +__pycache__ +.idea/ +logs/ +test_tokenizer.py +samples_text2image/ +generate_contexts/ +venv/ +*__pycache__ +.DS_Store +.vscode +*.swo +*.swp +*log +build +dist +eazybigmodel.egg-info +flagai.egg-info +test_report +/data/ +/tests/*/data +checkpoints +state_dict +checkpoints* +vocabs +tensorboard* +datasets \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..fd2a62f9 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,14 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [Unreleased] + +### Added + +### Changed + +### Removed diff --git a/CLA.md b/CLA.md new file mode 100644 index 00000000..50565f60 --- /dev/null +++ b/CLA.md @@ -0,0 +1,89 @@ +# The Contributor License Agreement + +The [Cloud Native Computing Foundation](https://www.cncf.io) (CNCF) defines +the legal status of the contributed code in two different types of _Contributor License Agreements_ +(CLAs), [individual contributors](https://github.com/cncf/cla/blob/master/individual-cla.pdf) and [corporations](https://github.com/cncf/cla/blob/master/corporate-cla.pdf). + +FlagAI can only accept original source code from CLA signatories. + + +It is important to read and understand this legal agreement. + +## How do I sign? + +After creating your first Pull Request the linux-foundation-easycla bot will respond with information regarding your CLA status along with a link to sign the CLA. + +EasyCLA bot + +#### 1. Authorize EasyCLA to read some of your GitHub information + +GitHub EasyCLA Authorization + +Click on the "Please click here to be authorized" link to navigate to the GitHub Authorize Linux Foundation: EasyCLA page. Then click Authorize LF-Engineering to give the Linux Foundation read-only access to list the email addresses associated with your GitHub account. + +#### 2. Select from the two types of contributor + +EasyCLA + + +After authorizing EasyCLA, you will be redirected to a page to identify which type of contributor you are. +Select the most appropriate option: + * Individual Contributor: You are contributing as yourself, and not as part of another organization. + * Corporate Contributor: You are contributing on behalf of your employer or other organization. + +#### 3. Sign the CLA + +Once you select the type of contributor, proceed to Sign the CLA and follow the instructions to complete the signing process through DocuSign. + +**Ensure your GitHub e-mail address matches e-mail address used to sign CLA** + +After you have filled out the information, Click "Finish" and you will be redirected back to your Pull Request. + +#### 4. Look for an email indicating successful signup. + +> Hello, +> +> This is a notification email from EasyCLA regarding the project Cloud Native Computing > Foundation (CNCF). +> +> The CLA has now been signed. You can download the signed CLA as a PDF here. +> +> If you need help or have questions about EasyCLA, you can read the documentation or reach out to us for support. +> +> Thanks, +> EasyCLA Support Team + + + +#### 5. Validate your CLA + +Once you are redirected back to your GitHub Pull Request, reply with a comment `/easycla` to update the CLA status of your PR. + + +## Changing your Affiliation + +If you've changed employers and still contribute to Kubernetes, your affiliation +needs to be updated. The Cloud Native Computing Foundation uses [gitdm](https://github.com/cncf/gitdm) +to track who is contributing and from where. Create a pull request on the [gitdm](https://github.com/cncf/gitdm) +repository with a change to the corresponding developer affiliation text file. +Your entry should look similar to this: + +``` +Jorge O. Castro*: jorge!heptio.com, jorge!ubuntu.com, jorge.castro!gmail.com +Heptio +Canonical until 2017-03-31 +``` + +## Troubleshooting + +If you encounter any problems signing the CLA and need further assistance, log a ticket by clicking on the link [please submit a support request ticket](https://jira.linuxfoundation.org/plugins/servlet/theme/portal/4) from the EasyCLA bot's response. Someone from the CNCF will respond to your ticket to help. + +Should you have any issues using the LF Support Site, send a message to the +backup e-mail support address + +## Setting up the CNCF CLA check + +If you are a Kubernetes GitHub organization or repo owner and would like to setup +the Linux Foundation CNCF CLA check for your repositories, [read the docs on setting up the CNCF CLA check](/github-management/setting-up-cla-check.md) + + +[Linux Foundation Support Site]: https://support.linuxfoundation.org/ \ No newline at end of file diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..6fc172fb --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1 @@ +All participants agree to abide by the Code of Conduct available at https://lfprojects.org/policies/code-of-conduct/. Please contact conduct@lfaidata.foundation to report any violations or concerns. diff --git a/COMMITTERS.csv b/COMMITTERS.csv new file mode 100644 index 00000000..af0633f7 --- /dev/null +++ b/COMMITTERS.csv @@ -0,0 +1,2 @@ +Name, Email, Github ID +Guang Liu, marscrazy_90@163.com, marcrazy diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..476f4d92 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,52 @@ +# Contributing to FlagAI + +We are happy to accept your contributions to make `FlagAI` better and more awesome! To avoid unnecessary work on either +side, please stick to the following process: + +1. Check if there is already [an issue](https://github.com/BAAI-WuDao/Sailing/issues) for your concern. +2. If there is not, open a new one to start a discussion. We hate to close finished PRs! +3. If we decide your concern needs code changes, we would be happy to accept a pull request. Please consider the +commit guidelines below. + +## Sign the CLA + +Before you can contribute to FlagAI, you will need to sign the [Contributor License Agreement](CLA.md). + +## Git Commit Guidelines + +If there is already a ticket, use this number at the start of your commit message. +Use meaningful commit messages that described what you did. + +**Example:** `GH-42: Added new type of embeddings: DocumentEmbedding.` +**Example:** `ISSUE#123: Fix typo in README.` + + +## Developing locally + +For contributors looking to get deeper into the API we suggest cloning the repository and checking out the unit +tests for examples of how to call methods. Nearly all classes and methods are documented, so finding your way around +the code should hopefully be easy. + +### setup + +You can either use [Pipenv](https://pipenv.readthedocs.io/) for this: + +or create a python environment of your preference and run +```bash +pip install -r requirements.txt +``` + +### tests +Install `pytest` for testing +``` +pip install pytest +``` +To run all basic tests execute: +```bash +pytest +``` + +### code formatting + +To ensure a standardized code style we use the formatter [yapf](https://github.com/google/yapf). +You can automatically format the code via `yapf flagai/ -i` in the flair root folder. diff --git a/GOVERNANCE.md b/GOVERNANCE.md new file mode 100644 index 00000000..726cb0af --- /dev/null +++ b/GOVERNANCE.md @@ -0,0 +1,70 @@ +*NOTE: This document is intended to provide an example governance structure for any LF AI and Data Foundation project to consider as a starting point. All projects hosted by LF AI and Data Foundation are not bound by these governance polices, but in absence of any prior governance structure should consider this as a recommended structure* + +# Overview + +This project aims to be governed in a transparent, accessible way for the benefit of the community. All participation in this project is open and not bound to corporate affilation. Participants are bound to the project's [Code of Conduct](./CODE_OF_CONDUCT.md). + +# Project roles + +## Contributor + +The contributor role is the starting role for anyone participating in the project and wishing to contribute code. + +# Process for becoming a contributor + +* Review the [Contribution Guidelines](./CONTRIBUTING.md) to ensure your contribution is inline with the project's coding and styling guidelines. +* Submit your code as a PR with the appropriate DCO signoff +* Have your submission approved by the committer(s) and merged into the codebase. + +## Committer + +The committer role enables the contributor to commit code directly to the repository, but also comes with the responsibility of being a responsible leader in the community. + +### Process for becoming a committer + +* Show your experience with the codebase through contributions and engagement on the community channels. +* Request to become a committer. To do this, create a new pull request that adds your name and details to the [Committers File](./COMMITTERS.csv) file and request existing committers to approve. +* After the majority of committers approve you, merge in the PR. Be sure to tag the whomever is managing the GitHub permissions to update the committers team in GitHub. + +### Committer responsibilities + +* Monitor email aliases (if any). +* Monitor Slack (delayed response is perfectly acceptable). +* Triage GitHub issues and perform pull request reviews for other committers and the community. +* Make sure that ongoing PRs are moving forward at the right pace or closing them. +* In general continue to be willing to spend at least 25% of ones time working on the project (~1.25 business days per week). + +### When does a committer lose committer status + +If a committer is no longer interested or cannot perform the committer duties listed above, they +should volunteer to be moved to emeritus status. In extreme cases this can also occur by a vote of +the committers per the voting process below. + +## Lead + +The project committers will elect a lead ( and optionally a co-lead ) which will be the primary point of contact for the project and representative to the TAC upon becoming an Active stage project. The lead(s) will be responsible for the overall project health and direction, coordination of activities, and working with other projects and committees as needed for the continuted growth of the project. + +# Release Process + +Project releases will occur on a scheduled basis as agreed to by the committers. + +# Conflict resolution and voting + +In general, we prefer that technical issues and committer membership are amicably worked out +between the persons involved. If a dispute cannot be decided independently, the committers can be +called in to decide an issue. If the committers themselves cannot decide an issue, the issue will +be resolved by voting. The voting process is a simple majority in which each committer receives one vote. + +# Communication + +This project, just like all of open source, is a global community. In addition to the [Code of Conduct](./CODE_OF_CONDUCT.md), this project will: + +* Keep all communucation on open channels ( mailing list, forums, chat ). +* Be respectful of time and language differences between community members ( such as scheduling meetings, email/issue responsiveness, etc ). +* Ensure tools are able to be used by community members regardless of their region. + +If you have concerns about communication challenges for this project, please contact the committers. + +[Code of Conduct]: CODE_OF_CONDUCT.md +[Committers File]: COMMITTERS.csv +[Contribution Guidelines]: CONTRIBUTING.md diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..0f76b3f8 --- /dev/null +++ b/LICENSE @@ -0,0 +1,178 @@ +Copyright © 2022 BAAI. 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. 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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 diff --git a/README.md b/README.md new file mode 100644 index 00000000..4132ce37 --- /dev/null +++ b/README.md @@ -0,0 +1,164 @@ +![FlagAI](logo.png) +[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/6052/badge)](https://bestpractices.coreinfrastructure.org/projects/6052) +[简体中文](README_zh.md) + +-------------------------------------------------------------------------------- + + +FlagAI (Fast LArge-scale General AI models) is an 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. Currently, we are focusing on NLP models and tasks. In near futher, we will support for other modalities. + +* Now it supports **WuDao GLM** with a maximum of 10 billion parameters (see [Introduction to GLM](/docs/GLM.md)). It also supports **BERT**, **RoBERTa**, **GPT2**, **T5**, and models from Huggingface Transformers. + +* It provides APIs to quickly download and use those pre-trained models on a given text, fine-tune them on widely-used datasets collected from [SuperGLUE](https://super.gluebenchmark.com/) and [CLUE](https://github.com/CLUEbenchmark/CLUE) benchmarks, and then share them with the community on our model hub. It also provides [prompt-learning](/docs/TUTORIAL_7_PROMPT_LERANING.md) toolkit for few shot tasks. + +* These models can be applied to (Chinese/English) Text, for tasks like text classification, information extraction, question answering, summarization, and text generation. + +* FlagAI is backed by the three most popular data/model parallel libraries — [PyTorch](https://pytorch.org/)/[Deepspeed](https://www.deepspeed.ai/)/[Megatron-LM](https://github.com/NVIDIA/Megatron-LM) — with seamless integration between them. Users can parallel their training/testing process with less than ten lines of code. + + +The code is partially based on [GLM](https://github.com/THUDM/GLM), [Transformers](https://github.com/huggingface/transformers) and [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/tree/master/Megatron-LM). + + + + +- [Requirements and Installation](#requirements-and-installation) +- [Quick Started](#quick-start) + - [Load model and tokenizer](#load-model-and-tokenizer) + - [Predictor](#predictor) + - [NER task](#ner-task) + - [Title generation task](#title-generation-task) + - [Semantic matching task](#semantic-matching-task) +- [Pretrained Models and examples](#pretrained-models-and-examples) +- [Tutorials](#tutorials) +- [Contributing](#contributing) +- [Contact us](#contact-us) +- [License](#license) + + +## Requirements and Installation +* PyTorch version >= 1.8.0 +* Python version >= 3.8 +* For training/testing models on GPUs, you'll also need install CUDA and NCCL + +To install FlagAI with pip: +```shell +pip install -U flagai +``` + +- [Optional]To install FlagAI and develop locally: + +```shell +git clone https://github.com/BAAI-Open/FlagAI.git +python setup.py install +``` + +- [Optional] For faster training install NVIDIA's [apex](https://github.com/NVIDIA/apex) +``` +git clone https://github.com/NVIDIA/apex +cd apex +pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ +``` +- [Optional] For ZeRO optimizers install [DEEPSPEED](https://github.com/microsoft/DeepSpeed) +``` +git clone https://github.com/microsoft/DeepSpeed +cd DeepSpeed +DS_BUILD_CPU_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 pip install -e . +ds_report # check the deespeed status +``` +- [Tips] For single-node docker enviroments, we need to setup ports for your ssh. e.g., root@127.0.0.1 with port 7110 +``` +>>> vim ~/.ssh/config +Host 127.0.0.1 + Hostname 127.0.0.1 + Port 7110 + User root +``` +- [Tips] For multi-node docker enviroments, generate ssh keys and copy the public key to all nodes (in `~/.ssh/`) +``` +>>> ssh-keygen -t rsa -C "xxx@xxx.com" +``` + +## Quick Start +We provide many models which are trained to perform different tasks. You can load these models by AutoLoader to make prediction. See more in `FlagAI/quickstart`. +## Load model and tokenizer +We provide the AutoLoad class to load the model and tokenizer quickly, for example: +```python +from flagai.auto_model.auto_loader import AutoLoader + +auto_loader = AutoLoader( + task_name="title-generation", + model_name="BERT-base-en" +) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` +This example is for the `title_generation` task, and you can also model other tasks by modifying the `task_name`. +Then you can use the model and tokenizer to finetune or test. + +## Predictor +We provide the `Predictor` class to predict for different tasks, for example: + +```python +from flagai.model.predictor.predictor import Predictor +predictor = Predictor(model, tokenizer) +test_data = [ + "Four minutes after the red card, Emerson Royal nodded a corner into the path of the unmarked Kane at the far post, who nudged the ball in for his 12th goal in 17 North London derby appearances. Arteta's misery was compounded two minutes after half-time when Kane held the ball up in front of goal and teed up Son to smash a shot beyond a crowd of defenders to make it 3-0.The goal moved the South Korea talisman a goal behind Premier League top scorer Mohamed Salah on 21 for the season, and he looked perturbed when he was hauled off with 18 minutes remaining, receiving words of consolation from Pierre-Emile Hojbjerg.Once his frustrations have eased, Son and Spurs will look ahead to two final games in which they only need a point more than Arsenal to finish fourth.", +] + +for text in test_data: + print( + predictor.predict_generate_beamsearch(text, + out_max_length=50, + beam_size=3)) +``` + +## Pretrained Models and examples + +* [Blank_Filling_QA with GLM ](/docs/TUTORIAL_11_GLM_BLANK_FILLING_QA.md) +* [Title Generation with GLM ](/docs/TUTORIAL_12_GLM_EXAMPLE_TITLE_GENERATION.md) +* [Poetry generation with GLM-large-ch](docs/TUTORIAL_13_GLM_EXAMPLE_PEOTRY_GENERATION.md) +* [Using huggingface's t5-11b & tricks ](docs/TUTORIAL_14_HUGGINGFACE_T5.md) +* [Title Generation with RoBerta-WWM](/docs/TUTORIAL_15_BERT_EXAMPLE_TITLE_GENERATION.md) +* [Semantic Matching with RoBerta-WWM](/docs/TUTORIAL_16_BERT_EXAMPLE_SEMANTIC_MATCHING.md) +* [NER with RoBerta-WWM](/docs/TUTORIAL_17_BERT_EXAMPLE_NER.md) +* [Writing with GPT-2](/docs/TUTORIAL_18_GPT2_WRITING.md) +* [Title generation with T5](/docs/TUTORIAL_19_T5_EXAMPLE_TITLE_GENERATION.md) + +[//]: # (* [Supported tasks](/docs/TUTORIAL_20_SUPPORTED_TASKS.md)) + + +This session explains how the base NLP classes work, how you can load pre-trained models to tag your +text, how you can embed your text with different word or document embeddings, and how you can train your own +language models, sequence labeling models, and text classification models. Let us know if anything is unclear. See more in `FlagAI/examples`. + + + +## Tutorials +We provide a set of quick tutorials to get you started with the library: +* [Tutorial 1: How to construct and use Tokenizer](/docs/TUTORIAL_1_TOKENIZER.md) +* [Tutorial 2: Dataset Preprocessing Pipeline](/docs/TUTORIAL_2_DATASET.md) +* [Tutorial 3: Major Function of Model Module](/docs/TUTORIAL_3_MODEL.md) +* [Tutorial 4: Customize trainer for model and data-parallel training](/docs/TUTORIAL_4_TRAINER.md) +* [Tutorial 5: Simplify model and tokenizer Initialization by Using Autoloader](/docs/TUTORIAL_5_INSTRUCTIONS_FOR_AutoLoader.md) +* [Tutorial 6: Use off-the-shelf inference Algorithms with Predictor](/docs/TUTORIAL_6_INSTRUCTIONS_FOR_PREDICTOR.md) +* [Tutorial 7: Use FlagAI prompt-learning tool-kit to improve performance on SuperGLUE](/docs/TUTORIAL_7_PROMPT_LERANING.md) +* [Tutorial 8: Setup environment for training models with multi-machine](/docs/TUTORIAL_8_ENVIRONMENT_SETUP.md) +* [Tutorial 9: Text generation with encoder/decoder/encoder-decoder models](/docs/TUTORIAL_9_SEQ2SEQ_METHOD.md) +* [Tutorial 10: How to transform a customized model into a megatron-LM-style parallel model](/docs/TUTORIAL_10_MEGATRON.md) + +## Contributing + +Thanks for your interest in contributing! There are many ways to get involved; +start with our [contributor guidelines](CONTRIBUTING.md) and then +check these [open issues](https://github.com/BAAI-WuDao/Sailing/issues) for specific tasks. + +## Contact us +Scan wechat QR code + + + +## [License](/LICENSE) +The majority of FlagAI is licensed under the [Apache 2.0 license](LICENSE), however portions of the project are available under separate license terms: + +* Megatron-LM is licensed under the [Megatron-LM license](https://github.com/NVIDIA/Megatron-LM/blob/main/LICENSE) +* GLM is licensed under the [MIT license](https://github.com/THUDM/GLM/blob/main/LICENSE) diff --git a/README_zh.md b/README_zh.md new file mode 100644 index 00000000..78981799 --- /dev/null +++ b/README_zh.md @@ -0,0 +1,226 @@ +![FlagAI](logo.png) +[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/6052/badge)](https://bestpractices.coreinfrastructure.org/projects/6052) +[English](README.md) + +-------------------------------------------------------------------------------- + +飞智是一个快速、易于使用和可扩展的大模型工具包。 我们的目标是支持在多模态的各种下游任务上训练、微调和部署大规模模型。 目前，我们专注于 NLP 模型和任务。 在不久的将来，我们将支持其他模态。 +

+ +* 现在它支持最高百亿参数的**悟道GLM**(详见[GLM介绍](/doc_zh/GLM.md))。它同时也支持**BERT**、**RoBERTa**、**GPT2**、**T5** 模型和 Huggingface Transformers 的模型。 + +* 它提供 API 以快速下载并在给定（中/英文）文本上使用这些预训练模型，在您自己的数据集上对其进行微调(fine-tuning)或者应用[提示学习(prompt-tuning)](/doc_zh/TUTORIAL_7_PROMPT_LERANING.md)，然后在我们的模型中心与社区共享它们。 + +* 这些模型可以应用于文本，用于文本分类、信息提取、问答、摘要、文本生成等任务，尤其是中文。 + +* 飞智由三个最流行的数据/模型并行库（[PyTorch](https://pytorch.org/)/[Deepspeed](https://www.deepspeed.ai/)/[Megatron-LM](https://github.com/NVIDIA/Megatron-LM)）提供支持，它们之间实现了无缝集成。 你可以用不到十行代码来并行你的训练/测试过程。 + + +本项目的部分代码基于[GLM](https://github.com/THUDM/GLM),[Transformers](https://github.com/huggingface/transformers) 和 [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/tree/master/Megatron-LM). + + + +- [安装](#安装) +- [快速上手](#快速上手) + - [加载模型和分词器](#加载模型和分词器) + - [使用预测器](#使用预测器) + - [命名实体识别任务示例](#命名实体识别任务示例 ) + - [标题生成任务示例](#标题生成任务示例) + - [语义相似度匹配任务示例](#语义相似度匹配任务示例) +- [预训练模型以及样例](#预训练模型以及样例) +- [教程](#教程) +- [贡献代码](#贡献代码) +- [联系我们](#联系我们) +- [许可证](#许可证) + + +# 安装 +* PyTorch version >= 1.8.0 +* Python version >= 3.8 +* 使用GPUs进行训练和测试, 你需要安装CUDA 和 NCCL + +通过`pip`安装: +```shell +pip install -U flagai +``` + +- [可选]下载源码安装: + +```shell +git clone https://github.com/BAAI-Open/FlagAI.git +python setup.py install +``` + +- [可选] 开启训练加速需要安装 NVIDIA's [apex](https://github.com/NVIDIA/apex) +``` +git clone https://github.com/NVIDIA/apex +cd apex +pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ +``` +- [可选] 使用 ZeRO 优化器，需要安装 [DEEPSPEED](https://github.com/microsoft/DeepSpeed) +``` +git clone https://github.com/microsoft/DeepSpeed +cd DeepSpeed +DS_BUILD_CPU_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 pip install -e . +ds_report # 检查deepspeed的状态 +``` +- [提示] 单节点docker环境下, 运行多卡数据并行需要设置host. 例如，docker节点 root@127.0.0.1，其端口 7110。 +``` +>>> vim ~/.ssh/config +Host 127.0.0.1 + Hostname 127.0.0.1 + Port 7110 + User root +``` +- [提示] 多节点环境, 需要生成 ssh keys 并拷贝公钥到所有节点 (in `~/.ssh/`) +``` +>>> ssh-keygen -t rsa -C "xxx@xxx.com" +``` + + +# 快速上手 +我们提供了精选的中英文预训练模型，以及经过训练可以执行不同任务的模型权重。 您可以通过 AutoLoader 加载这些模型以进行训练和预测。更多样例见 `FlagAI/quickstart`。 + +## 加载模型和分词器 +我们提供 `AutoLoad` 类来快速加载模型和分词器，例如： +```python +from flagai.auto_model.auto_loader import AutoLoader +auto_loader = AutoLoader(task_name="title-generation", + model_name="RoBERTa-base-ch", + load_pretrain_params=True, + class_num=2) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` +这个例子是针对`classification`任务的(分类），你也可以通过修改`task_name`来为其他任务建模。 +`class_num` 是分类任务的类别数。 然后您可以使用模型和标记器进行微调或测试。 + +## 使用预测器 +我们提供 `Predictor` 类来预测不同的任务，例如： +```python +from flagai.model.predictor.predictor import Predictor +predictor = Predictor(model, tokenizer) +test_data = [ + "本文总结了十个可穿戴产品的设计原则而这些原则同样也是笔者认为是这个行业最吸引人的地方1为人们解决重复性问题2从人开始而不是从机器开始3要引起注意但不要刻意4提升用户能力而不是取代人", + "2007年乔布斯向人们展示iPhone并宣称它将会改变世界还有人认为他在夸大其词然而在8年后以iPhone为代表的触屏智能手机已经席卷全球各个角落未来智能手机将会成为真正的个人电脑为人类发展做出更大的贡献", + "雅虎发布2014年第四季度财报并推出了免税方式剥离其持有的阿里巴巴集团15％股权的计划打算将这一价值约400亿美元的宝贵投资分配给股东截止发稿前雅虎股价上涨了大约7％至5145美元" +] +for text in test_data: + print( + predictor.predict_generate_beamsearch(text, + out_max_length=50, + beam_size=3)) +``` +这个例子是针对 `seq2seq` 任务的，我们可以通过调用`predict_generate_beamsearch`函数得到`beam-search`结果。此外，我们还支持`NER`和`title generate`等任务的预测。 + + +## 命名实体识别任务示例 + +```python +from flagai.auto_model.auto_loader import AutoLoader +from flagai.model.predictor.predictor import Predictor + +task_name = "ner" +model_name = "RoBERTa-base-ch" +target = ["O", "B-LOC", "I-LOC", "B-ORG", "I-ORG", "B-PER", "I-PER"] +maxlen = 256 + +auto_loader = AutoLoader(task_name, + model_name=model_name, + load_pretrain_params=True, + class_num=len(target)) + +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() + +predictor = Predictor(model, tokenizer) + +test_data = [ + "6月15日，河南省文物考古研究所曹操高陵文物队公开发表声明承认：“从来没有说过出土的珠子是墓主人的", + "4月8日，北京冬奥会、冬残奥会总结表彰大会在人民大会堂隆重举行。习近平总书记出席大会并发表重要讲话。在讲话中，总书记充分肯定了北京冬奥会、冬残奥会取得的优异成绩，全面回顾了7年筹办备赛的不凡历程，深入总结了筹备举办北京冬奥会、冬残奥会的宝贵经验，深刻阐释了北京冬奥精神，对运用好冬奥遗产推动高质量发展提出明确要求。", + "当地时间8日，欧盟委员会表示，欧盟各成员国政府现已冻结共计约300亿欧元与俄罗斯寡头及其他被制裁的俄方人员有关的资产。", + "这一盘口状态下英国必发公司亚洲盘交易数据显示博洛尼亚热。而从欧赔投注看，也是主队热。巴勒莫两连败，", +] + +for t in test_data: + entities = predictor.predict_ner(t, target, maxlen=maxlen) + result = {} + for e in entities: + if e[2] not in result: + result[e[2]] = [t[e[0]:e[1] + 1]] + else: + result[e[2]].append(t[e[0]:e[1] + 1]) + print(f"result is {result}") +``` + + +## 语义相似度匹配任务示例 + +```python +from flagai.auto_model.auto_loader import AutoLoader +from flagai.model.predictor.predictor import Predictor + +maxlen = 256 + +auto_loader = AutoLoader("semantic-matching", + model_name="RoBERTa-base-ch", + load_pretrain_params=True, + class_num=2) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() + +predictor = Predictor(model, tokenizer) + +test_data = [["后悔了吗", "你有没有后悔"], ["打开自动横屏", "开启移动数据"], + ["我觉得你很聪明", "你聪明我是这么觉得"]] + +for text_pair in test_data: + print(predictor.predict_cls_classifier(text_pair)) + +``` + +# 预训练模型以及样例 +* [GLM-large-ch用户完形填空问答](/doc_zh/TUTORIAL_11_GLM_BLANK_FILLING_QA.md) +* [GLM-large-ch用于诗歌生成](doc_zh/TUTORIAL_13_GLM_EXAMPLE_PEOTRY_GENERATION.md) +* [GLM-large-ch用于标题生成](doc_zh/TUTORIAL_12_GLM_EXAMPLE_TITLE_GENERATION.md) +* [对 huggingface t5-11b 模型的支持 以及加速的tricks](doc_zh/TUTORIAL_14_HUGGINGFACE_T5.md) +* [RoBERTa-base-ch用于标题生成](doc_zh/TUTORIAL_15_BERT_EXAMPLE_TITLE_GENERATION.md) +* [RoBERTa-base-ch用于语义相似度匹配](doc_zh/TUTORIAL_16_BERT_EXAMPLE_SEMANTIC_MATCHING.md) +* [RoBERTa-base-ch用于命名实体识别](/doc_zh/TUTORIAL_17_BERT_EXAMPLE_NER.md) +* [GPT-2用于文本续写](/doc_zh/TUTORIAL_18_GPT2_WRITING.md) +* [T5用于标题生成](/doc_zh/TUTORIAL_19_T5_EXAMPLE_TITLE_GENERATION.md) + +[//]: # (* [用GLM10b模型在TNEWS短文本分类数据集上微调](doc_zh/TUTORIAL_20_GLM_TNEWS.md)) + + +本节解释了本项目中基础NLP类是如何工作的，如何加载预先训练的模型来标记您的文本，如何使用不同的词或文档嵌入来得到表示，以及如何训练自己的语言模型、序列标注模型和文本分类模型。更多样例见 `FlagAI/examples`。 + + +# 教程 +我们提供了一组教程来帮助您快速上手使用本库： +* [Tutorial 1: 如何构建和应用分词器](/doc_zh/TUTORIAL_1_TOKENIZER.md) +* [Tutorial 2: 数据集预处理流程](/doc_zh/TUTORIAL_2_DATASET.md) +* [Tutorial 3: 模型的主要功能及相关结构](/doc_zh/TUTORIAL_3_MODEL.md) +* [Tutorial 4: 为模型和数据并行训练定制训练器](/doc_zh/TUTORIAL_4_TRAINER.md) +* [Tutorial 5: 使用 Autoloader 简化模型和分词器初始化过程](/doc_zh/TUTORIAL_5_INSTRUCTIONS_FOR_AutoLoader.md) +* [Tutorial 6: 将现成的推理算法与 Predictor 结合使用](/doc_zh/TUTORIAL_6_INSTRUCTIONS_FOR_PREDICTOR.md) +* [Tutorial 7: 使用飞智提示学习工具包来提高在SuperGLUE任务上的表现](/doc_zh/TUTORIAL_7_PROMPT_LERANING.md) +* [Tutorial 8: 多机训练模型搭建环境](/doc_zh/TUTORIAL_8_ENVIRONMENT_SETUP.md) +* [Tutorial 9: 使用encoder/decoder/encoder-decoder模型进行文本生成](/doc_zh/TUTORIAL_9_SEQ2SEQ_METHOD.md) + + + +# 贡献代码 +感谢您对贡献的兴趣！ 参与的方式有很多； 从我们的[贡献者指南](CONTRIBUTING.md) 开始，然后检查这些[未解决的问题](https://github.com/BAAI-WuDao/Sailing/issues)以执行特定任务。 + +# 联系我们 +欢迎扫码加入飞智用户群 + + + + +# [许可证](/LICENSE) +大部分的飞智项目是基于[Apache 2.0 license](LICENSE), 但是部分的代码是基于其他的协议: + +* Megatron-LM 是基于协议[Megatron-LM license](https://github.com/NVIDIA/Megatron-LM/blob/main/LICENSE) +* GLM 是基于协议[MIT license](https://github.com/THUDM/GLM/blob/main/LICENSE) diff --git a/SUPPORT.md b/SUPPORT.md new file mode 100644 index 00000000..16d9641a --- /dev/null +++ b/SUPPORT.md @@ -0,0 +1,24 @@ +_Use this file to detail how to make get support forthe project. The structure below is generally recommended, but can be changed to meet the needs of the project_ + +# Getting Help + +There are a few ways to connect with the PROJECT NAME project: +* https://github.com/BAAI-WuDao/Sailing/issues + +## How to Ask for Help + +If you have trouble installing, building, or using PROJECT NAME, but there's not yet reason to suspect you've encountered a genuine bug, +start by posting a question to the mailing list or Slack channel. This is the place for question such has "How do I...". + +## How to report a bug or request an enhancement + +PROJECT NAME manages bug and enhancement using it's [issue tracker](https://github.com/BAAI-WuDao/Sailing/issues). The issue template will guide you on making an effective report. + +## How to report a security vulnerability + +If you think you've found a potential vulnerability in PROJECT NAME, please +email security@PROJECT DOMAIN to responsibly disclose it. + +## Contributing a fix + +Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) to make a project contribution. diff --git a/doc_zh/APPENDIX_GLM_IO.md b/doc_zh/APPENDIX_GLM_IO.md new file mode 100644 index 00000000..2632fe13 --- /dev/null +++ b/doc_zh/APPENDIX_GLM_IO.md @@ -0,0 +1,17 @@ +# GLM I/O + +a) 如下图所示，原文包含6个token，两个区间被屏蔽：第一个区间包含第3个token，第二个区间包含第5个和第6个token。 + +![results1](img/glm_io_1.png) + +b) 将输入分成两个部分： A 部分 (将遮挡区间遮盖掉后的文本)和B部分(被遮挡的区间). 注意所有被遮挡区间的顺序会被重新打乱 + +![results1](img/glm_io_2.png) + +c) GLM的输入和输出，输入包括tokens和2个位置编码 + +![results1](img/glm_io_3.png) + +d) 下图里的自注意力机制既通过遮挡文本实现了自编码， 也在预测遮挡区间内文本的过程里实现了自回归 + +![results1](img/glm_io_4.png) \ No newline at end of file diff --git a/doc_zh/APPENDIX_TASK.md b/doc_zh/APPENDIX_TASK.md new file mode 100644 index 00000000..c131858a --- /dev/null +++ b/doc_zh/APPENDIX_TASK.md @@ -0,0 +1,57 @@ +## PET介绍 + +预训练模型以具有某种“理解”自然语言的能力。但是， 原始的预训练模型只能执行模型训练当初训练时的任务（如自动编码和自动回归任务）， +而这些任务通常与我们在现实中面临的各种任务不适配。 +因此，我们需要想出一种方法来使预先训练的模型适应下游任务。 + +起初，人们在预训练模型的末端添加了一个额外的针对下游任务的架构，这称为微调。然而这种方式并不总令人满意，特别是当预训练任务偏离下游任务时。 +因此，[提示学习](https://arxiv.org/abs/2111.01998) 被提出来，其中的核心思想是设计一个针对当前任务的完形填空模板，用来结合预训练模型和下游任务。 + +[Exploiting Cloze Questions for Few Shot Text Classification and Natural +Language Inference](https://arxiv.org/abs/2001.07676) 这篇论文提出了PET方法。它可以将输入文本重建为完形填空风格的短语，以便预先训练的模型更容易理解给定的任务。 + +由于不同任务之间输入文本和标签的设置不同，我们需要设计适当的完形填空样式。论文介绍了一个如下所示的情感分类示例。 + +给定的情感分析任务是：给定一个输入上下文（例如“有史以来最好的披萨！”），并且有限 +类别（这里有 2 个类别，1 和 -1 分别表示正面和负面情绪）。 +我们的模型需要在给定上下文的情况下自动预测每个情感类别的概率。 + +
+ +如图所示，首先正面和负面标签被表述为“good”和“bad”。然后将模式设计为​​： + +上下文 + 'It was' + 被遮挡的情感标签 + '.'。 + +将“good”和“bad”填充到蒙面位置的概率将由微调模型返回。 + + +在这里我们可以看到它需要用户为每个给定的任务设计一个完形填空的样板，以及标签的文本与类别(数字）的对应关系。在我们的项目中， +这部分放在flagai/data/dataset/superglue/pvp.py里。 在给定任务的类下， +通常存在两个名为“verbalize”和“get_parts”的函数，verbalize用来将label转换为对应的含义文本，get_parts用来将不用的部分填充进设计好的完形填空模板里。 +一个例子如下所示。请注意，对于一项任务，可以存在多种完形填空模板的设计。 + +``` + def get_parts(self, example: InputExample) -> FilledPattern: + + text_a = self.shortenable(example.text_a) + text_b = self.shortenable(example.text_b) + + if self.pattern_id == 0: + return [[self.mask], ':', text_a, text_b], [] + elif self.pattern_id == 1: + return [[self.mask], ' Question:', text_a, text_b], [] + elif self.pattern_id == 2: + return [text_a, '(', [self.mask], ')', text_b], [] + elif self.pattern_id == 3: + return [text_a, text_b, '(', [self.mask], ')'], [] + elif self.pattern_id == 4: + return ['[ Category:', [self.mask], ']', text_a, text_b], [] + elif self.pattern_id == 5: + return [[self.mask], '-', text_a, text_b], [] + else: + raise ValueError("No pattern implemented for id {}".format( + self.pattern_id)) + + def verbalize(self, label) -> List[str]: + return YahooPVP.VERBALIZER[label] +``` \ No newline at end of file diff --git a/doc_zh/Advanced_Usage.md b/doc_zh/Advanced_Usage.md new file mode 100644 index 00000000..785ac663 --- /dev/null +++ b/doc_zh/Advanced_Usage.md @@ -0,0 +1,123 @@ +## 高级用法 + +### 自定义模型 +如果想在FlagAI框架中自定义模型或改进模型，比如遵循以下几条规则： + +1. 继承BaseModel，BaseModel 支持以from_pretrain的方式加载参数，以init_from_json的方式构建不同参数的模型。 +2. 自定义模型的```__init__()```函数第一个参数必须为config，其中每一个key为``config.json``文件中的参数信息。 +3. 自定义模型中必须有``load_weights()``函数，输入为预训练参数地址，为自定义模型加载预训练参数。 +4. 自定义模型中``forward()``函数必须返回字典，其中必须包含``logits``，如果传入labels参数，则需要额外返回``loss``数据。 + +以GLM完成序列标注任务为例： + +```python +from flagai.model.base_model import BaseModel +from flagai.model.glm_model import GLMModel +import torch + +class GLMForSequenceClassification(BaseModel): + def __init__(self, config, hidden_dropout=0.1, pool_token='cls', **kwargs): + super().__init__(config, **kwargs) + self.config = config + self.pool_token = pool_token + self.model = GLMModel(config) + self.model.output_predict = False + self.num_class = config['class_num'] + # Multi-choice head. + hidden_size = self.model.hidden_size + self.pool_layer = torch.nn.Linear(hidden_size, hidden_size) + self.multichoice_dropout = torch.nn.Dropout(hidden_dropout) + self.multichoice_head = torch.nn.Linear(hidden_size, self.num_class) + + def forward(self, + input_ids=None, + position_ids=None, + attention_mask=None, + **kwargs): + num_choices = None + if len(input_ids.shape) == 3: + assert self.num_class == 1 + batch_size, num_choices = input_ids.shape[:2] + input_ids = input_ids.reshape(-1, input_ids.size(-1)) + attention_mask = attention_mask.reshape(-1, + *attention_mask.size()[2:]) + position_ids = position_ids.reshape(-1, *position_ids.size()[2:]) + model_out = self.model(input_ids, position_ids, attention_mask) + outputs, mems = model_out['logits'], model_out['hidden_states'] + if self.pool_token == 'start': + output = outputs[torch.arange(outputs.size(0), + dtype=attention_mask.dtype, + device=attention_mask.device), + attention_mask] + elif self.pool_token == 'pad': + output = outputs[torch.arange(outputs.size(0), + dtype=attention_mask.dtype, + device=attention_mask.device), + attention_mask - 1] + elif self.pool_token == 'cls': + output = outputs[:, 0] + else: + raise NotImplementedError + output = torch.tanh(self.pool_layer(output)) + multichoice_output = self.multichoice_dropout(output) + logits = self.multichoice_head(multichoice_output) + if num_choices is not None: + logits = logits.view(-1, num_choices) + if 'labels' not in kwargs: + return {'logits': logits, 'hidden_states': mems} + else: + labels = kwargs['labels'] + if logits.size(1) == 1: + logits = logits.squeeze(1) + loss = F.binary_cross_entropy_with_logits( + logits.contiguous().float(), labels.float()) + else: + loss = F.cross_entropy(logits.contiguous().float(), + labels.long()) + return {"loss": loss, 'logits': logits, 'hidden_states': mems} + + def compute_loss(self, + input_ids=None, + position_ids=None, + attention_mask=None, + labels=None, + **kwargs): + model_output = self.forward(input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask) + assert labels is not None, "labels must not None!" + logits = model_output['logits'] + loss = F.cross_entropy(logits.contiguous().float(), labels.long()) + return { + "loss": loss, + 'logits': model_output['logits'], + 'hidden_states': model_output['hidden_states'] + } + + def load_weights(self, checkpoint_path): + checkpoints = self.model.load_weights_glm(checkpoint_path) + self.load_state_dict(checkpoints, strict=False) +``` + +其中``__init__()``函数中除了config参数必填，其他参数针对不同任务灵活定义，例如如下代码中的 ``hidden_dropout`` 与 ``pool_token``，这些参数在使用``from_pretrain()``函数构建模型的时候，可以灵活进行传入，例如： + +```python +from flagai.model.glm_model import GLMForSequenceClassification +model_dir = "./state_dict/GLM_sequence_classification/" ## this dir is the position for model and vocab and config files. +model = GLMForSequenceClassification.from_pretrain(model_dir, + hidden_dropout=0.1, + pool_token="cls") +``` + +构建好自定义模型，保证预训练参数加载正确，便可以直接在框架中进行使用。 + + +### 选择合适的 Tokenizer + +自定义好模型之后，需要选择一个合适的Tokenizer配合模型训练，FlagAI中支持多种原生Tokenizer，例如： + +1. BertTokenizer: ```from flagai.data.tokenizer.bert_tokenizer import BertTokenizer``` BertTokenizer支持多种模型，包括中文模型，英文模型；Bert模型，RoBERTa模型，中文GPT2模型等等。 +2. GLMLargeChTokenizer: ```from flagai.data.tokenizer.glm_large_ch_tokenizer import GLMLargeChTokenizer``` GLMLargeChTokenizer 支持 ``GLM-large-ch`` 模型。 +3. GLMLargeEnTokenizer: ```from flagai.data.tokenizer.glm_large_en_tokenizer import GLMLargeEnTokenizer``` GLMLargeEnTokenizer 支持 ``GLM-large-en`` 模型。 +3. T5BPETokenizer: ```from flagai.data.tokenizer.t5_tokenizer import T5BPETokenizer``` T5BPETokenizer 支持英文T5模型：``T5-base-en``。 +4. T5PegasusTokenizer: ```from flagai.data.tokenizer.t5_pegasus_tokenizer import T5PegasusTokenizer``` T5PegasusTokenizer 支持中文T5模型：``T5-base-ch``。 \ No newline at end of file diff --git a/doc_zh/DATASET_EXAMPLE.md b/doc_zh/DATASET_EXAMPLE.md new file mode 100644 index 00000000..527c38ef --- /dev/null +++ b/doc_zh/DATASET_EXAMPLE.md @@ -0,0 +1,143 @@ +# Examples in datasets + +## SuperGLUE: BoolQ +| 键值 | 含义 | +|------------------------------|----------| +| passage | 背景信息 | +| question | 根据背景信息提出的是/否问题 | +| label | 取值范围：true/false, 分别代表答案为是/否 | +示例 +```json +{"question": "is barq's root beer a pepsi product", + "passage": "Barq's -- Barq's is an American soft drink. Its brand of root beer is notable for having caffeine. Barq's, created by Edward Barq and bottled since the turn of the 20th century, is owned by the Barq family but bottled by the Coca-Cola Company. It was known as Barq's Famous Olde Tyme Root Beer until 2012.", + "idx": 5, "label": false} +``` + +## SuperGLUE: CB +| 键值 | 含义 | +|------------------------------|----------| +| premise | 前提文本 | +| hypothesis | 假设文本 | +| label |取值范围：entailment/contradiction/neutral 分别代表前提与假设的关系为1.前提能推导出假设 2.前提与假设矛盾 3. 没有足够信息得到两者关联性
| +示例 +```json +{"premise": "Mary is a high school student.", + "hypothesis": "Mary is a student", + "label": "entailment", "idx": 10} +``` + +## SuperGLUE: Copa +| 键值 | 含义 | +|------------------------------|----------| +| premise | 前提文本 | +| choice1 | 选项1 | +| choice2 |选项2| +| question | 取值范围： cause/effect, 分别代表问题是1.两个选项里哪个是前提的原因2.两个选项里哪个是前提的结果 | +| label | 取值范围：0/1, 分别代表选项1为正确答案和选项2为正确答案 | +示例 +```json +{"premise": "My eyes became red and puffy.", + "choice1": "I was sobbing.", "choice2": "I was laughing.", + "question": "cause", "label": 0, "idx": 7} +``` + +## SuperGLUE: MultiRC +| 键值 | 含义 | +|------------------------------|----------| +| text | 背景信息 | +| questions | 包含了一系列针对背景信息提出的问题，其中每个问题下有五个回答，而每个回答后面有一个标签，标签为0代表此回答正确，标签为1代表此回答错误 | +示例 +```json +{"idx": 4, "version": 1.1, + "passage": {"text": "...The two companies numbered about 225 men, and were commanded by General John E. Ross, a veteran Indian fighter... ", + "questions": [{"question": "When the narrator arrived at the headquarters, approximately how many men were present?", + "answers": [{"text": "225 men", "idx": 178, "label": 1}, + {"text": "225", "idx": 179, "label": 1}, + {"text": "525 men", "idx": 180, "label": 0}, + {"text": "235", "idx": 181, "label": 0}, + {"text": "255 men", "idx": 182, "label": 0}], "idx": 41}]}} +``` + + +## SuperGLUE: RTE +| 键值 | 含义 | +|------------------------------|----------| +| premise | 前提文本 | +| hypothesis | 假设文本 | +| label |取值范围：entailment/not entailment 分别代表前提与假设的关系为1.前提能推导出假设 2.前提不能推导出假设| +示例 +```json +{"premise": "Security forces were on high alert after an election campaign in which more than 1,000 people, including seven election candidates, have been killed.", +"hypothesis": "Security forces were on high alert after a campaign marred by violence.", +"label": "entailment", "idx": 4} +``` + + +## SuperGLUE: Wic +| 键值 | 含义 | +|------------------------------|----------| +| sentence1 | 背景文本1 | +| sentence2 | 背景文本2 | +| word | 一个在sentence1和sentence2中都会出现的单词 | +| label | 取值范围：true/false, 分别代表选项1.word在sentence1和sentence2中有着相同的含义 2.在两段话里word的含义不同 | +示例 +```json +{"word": "class", "sentence1": "An emerging professional class.", +"sentence2": "Apologizing for losing your temper, even though you were badly provoked, showed real class.", + "idx": 0, "label": false, "start1": 25, "start2": 85, "end1": 30, "end2": 90, "version": 1.1} +``` + +## SuperGLUE: WSC +| 键值 | 含义 | +|------------------------------|----------------------------------------------------------------------------| +| text | 背景文本 | +| span2_text | 背景文本中出现过的某个代词 | +| span1_text | 背景文本中出现过的某个片段 | +| label | 取值范围：true/false, 分别代表了1.span1_text这个代词指向了span2_text中的内容 2.没有指向到span2_text。 | +示例 +```json +{"word": "class", "sentence1": "An emerging professional class.", +"sentence2": "Apologizing for losing your temper, even though you were badly provoked, showed real class.", + "idx": 0, "label": false, "start1": 25, "start2": 85, "end1": 30, "end2": 90, "version": 1.1} +``` + +## CLUE: AFQMC +| 键值 | 含义 | +|------------------------------|----------| +| sentence1 | 背景文本1 | +| sentence2 | 背景文本2 | +| label | 取值范围：0/1, 分别代表选项1.两句话的含义不同 2.两句话的含义相似 | +示例 +```json +{"sentence1": "双十一花呗提额在哪", "sentence2": "里可以提花呗额度", "label": "0"} +``` + +## CLUE: TNEWS +| 键值 | 含义 | +|------------------------------|----------| +| sentence | 背景文本 | +| label | 背景文本的标签，包含十五种类别 | +| label_des | 标签对应的真正含义 | +示例 +```json +{"label": "102", "label_des": "news_entertainment", "sentence": "江疏影甜甜圈自拍，迷之角度竟这么好看，美吸引一切事物"} +``` + + +## CLUE: CMRC2018 +| 键值 | 含义 | +|------------------------------|----------| +| context | 背景信息 | +| question | 根据背景信息提出来的问题，一段背景信息可以对应多个问题 | +| answers | 对于某个问题，answers包含了三个人工给出来的回答 | +示例 +``` +{'paragraphs': + [{'id': 'TRAIN_186', + 'context': '工商协进会报告...所谓的“傻钱”策略，其实就是买入并持有美国股票这样的普通组合。...', + 'qas':{'question': '消费者信心指数由什么机构发布？', 'id': 'TRAIN_186_QUERY_4', + 'answers': [{'text': "工商协进会", 'answer_start': 759}, + {'text': "工商协进会", 'answer_start': 759}, + {'text': "工商协进会", 'answer_start': 759}]}]}], + 'id': 'TRAIN_186', 'title': '范廷颂'} +``` \ No newline at end of file diff --git a/doc_zh/GLM.md b/doc_zh/GLM.md new file mode 100644 index 00000000..a42ae357 --- /dev/null +++ b/doc_zh/GLM.md @@ -0,0 +1,61 @@ +# GLM 介绍 + +## 模型简介 + +目前，存在几种不同的预训练模型架构：仅实现编码器架构的自动编码模型（例如BERT），仅实现解码器的自回归模型（例如GPT），以及同时实现编码器和解码器的编码器-解码器模型（例如T5）。 + +[**GLM模型**](https://arxiv.org/abs/2103.10360)与这些模型略有不同。它采用了一种自回归的空白填充方法， 并且在NLP领域三种主要的任务(自然语言理解，无条件生成，有条件生成)上都取得了不错的结果。 + +| Framwork | NLU | Cond.Gen. | Uncond.Gen | +|-----------------|-----|-----------|------------| +| Augoregressive | - | - | ✅ | +| Autoencoding | ✅ | × | × | +| Encoder-Decoder | - | ✅ | - | +| GLM | ✅ | ✅ | ✅ | + +GLM的主要功能包括： + +- 任务一：文本的一些区间会被屏蔽（参照自动编码的做法）。 这些区间将被随机重新排列，并以自动回归方式进行预测。屏蔽的区间覆盖原始文本的15%。 +- 任务二：与第一个任务类似，但是区间会覆盖原始文本的50%-100%。 +- 剩下GLM相对于BERT的改动 + - [Pre-LN](http://proceedings.mlr.press/v119/xiong20b.html) + - 2D 位置编码：每个token都有两个位置编码：句子中的全局位置和屏蔽区间内的局部位置。 + - 前馈网络被线性层取代 + +## GLM的表现 + + +### [SuperGLUE](https://super.gluebenchmark.com) +单模型单任务微调在`dev`集上的效果，更多结果在[这里](https://github.com/THUDM/GLM) + +| Model | COPA | WSC | RTE | WiC | CB | MultiRC | BoolQ | ReCoRD | +| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | +| GLM-10b-ch | 98.0 | 95.2 | 93.1 | 75.7 | 98.7/98.2 | 88.1/63.3 | 88.7 | 94.4/94.0 | +| [RoBERTa-Large](https://github.com/pytorch/fairseq/tree/master/examples/roberta) | 94.0 | 91.3 | 86.6 | 75.6 | 98.2/- | 85.7/- | 86.9 |89.5/89.0| +| [DeBERTa-XXLarge-v2](https://github.com/microsoft/DeBERTa/tree/master/experiments/superglue) | 97.0 | - | 93.5 | - | - | 87.8/63.6 | 88.3 | 94.1/93.7 | + +### [CLUE](https://www.cluebenchmarks.com) +单模型单任务微调在CLUE数据集上的结果（测试还在进行中，这里列出了部分任务）。如果想要使用`GLM-10b-ch`请点[这里](https://model.baai.ac.cn/model-detail/100001)。 + +| 模型 | AFQMC | TNEWS1.0 | IFLYTEK | OCNLI_50K | CSL | CMRC2018 | CHID1.0 | C3 1.0 | +|:--------------:|:------:|:--------:|:-------:|:---------:|:------:|:--------:|:-------:|:------:| +| RoBERTa XLarge | 75.835 | 68.75 | 62.654 | 82.333 | 83.433 | 80.5 | 86.57 | 77.03 | +| GLM-10b-ch | 75.42 | 69.94 | 62.15 | 85 | 86.17 | 70 | 87.009 | 88.335 | + +## FlagAI支持的GLM预训练模型 +参考 [Tutorial 5: 使用AutoLoader工具快速构建模型](/doc_zh/TUTORIAL_5_INSTRUCTIONS_FOR_AutoLoader.md)。 + +## Step-by-step procedure of GLM +1) 如下图所示，原文包含6个token，两个区间被屏蔽：第一个区间包含第3个token，第二个区间包含第5个和第6个token。 + +
+ +2) 将输入分成两个部分： A 部分 (将遮挡区间遮盖掉后的文本)和B部分(被遮挡的区间). 注意所有被遮挡区间的顺序会被重新打乱。 + +
+ +3) GLM 的输入和输出，输入包含输入编码和两组位置编码：第一组是每个token的位置，其中每个遮挡区间中的token共享相同的位置 ID。第二组记录token在遮挡区间内的相对位置。 +
+ +4) 下图里的自注意力机制既通过遮挡文本实现了自编码， 也在预测遮挡区间内文本的过程里实现了自回归。 +
\ No newline at end of file diff --git a/doc_zh/TUTORIAL_10_METATRON.md b/doc_zh/TUTORIAL_10_METATRON.md new file mode 100644 index 00000000..ae837dac --- /dev/null +++ b/doc_zh/TUTORIAL_10_METATRON.md @@ -0,0 +1,95 @@ +# 转化一个模型为Megatron-LM的模型并行版本 +- [转化model为Megatron-LM版本](#转化model为megatron-lm版本) + - [1.将MLP层转化为column/rowParallel版本](#1将mlp层转化为columnrowparallel版本) + - [2.转化`self-Attention`](#2转化self-attention) + - [2.1 将`x` proj 到 `q, k, v` 的`linear `层，转化为 `columnParalleLinear`](#21-将x-proj-到--q-k-v-的linear-层转化为-columnparallelinear) + - [2.2 将输出的`dense`层转化为`rowparallelLinear`](#22-将输出的dense层转化为rowparallellinear) + - [3.将`cross-Attention`中的两个`Linear Layer `转化为`column/rowParallel`版本](#3将cross-attention中的两个linear-layer-转化为columnrowparallel版本) + +飞智支持了BERT，GLM，GPT2 以及T5模型的megatron-lm模型并行. 其主要的操作都是从`Megatron-LM`中拿出来的，放在了`mpu`模块中。 + +## 1.将MLP层转化为column/rowParallel版本 +代码位置：`flagai/model/layers/embeddings_mpu.py` +核心思想:将两个`linear`层的`forward`过程，按照特定顺序进行拆分（先列后行）： +```python +intermediate_parallel = self.dense_h_to_4h(hidden_states) +intermediate_parallel = gelu(intermediate_parallel) +output = self.dense_4h_to_h(intermediate_parallel) +``` +其中，`self.dense_h_to_4h` 和`self.dense_4h_to_h` 分别为： +```python + # Project to 4h. + self.dense_h_to_4h = ColumnParallelLinear(hidden_size, + 4 * hidden_size, + gather_output=False, #这里可以是True + init_method=init_method) + # Project back to h. + self.dense_4h_to_h = RowParallelLinear( + 4 * hidden_size, + hidden_size, + input_is_parallel=True,# 受到self.dense_h_to_4h的gather_output设置影响 + init_method=output_layer_init_method) +``` +关键参数 +```python +world_size = get_model_parallel_world_size() +self.hidden_size_per_partition = divide(hidden_size, world_size) +self.hidden_size_per_attention_head = divide(hidden_size, + num_attention_heads) +self.num_attention_heads_per_partition = divide( + num_attention_heads, world_size) +``` + +## 2.转化`self-Attention` +`self-attention`中的两个Linear layer分别转化为`column/rowParallel` 版本 + +代码位置：`flagai/model/layers/attentions_mpu.py` +### 2.1 将`x` proj 到 `q, k, v` 的`linear `层，转化为 `columnParalleLinear` +### 2.2 将输出的`dense`层转化为`rowparallelLinear` +如下： +```python +self.query_key_value = ColumnParallelLinear(hidden_size, + 3 * hidden_size, + stride=3, + gather_output=False, + init_method=init_method) +if relative_encoding: + self.relative = ColumnParallelLinear(hidden_size, + hidden_size, + gather_output=False, + init_method=init_method) +# Dropout. Note that for a single iteration, this layer will generate +# different outputs on different number of parallel partitions but +# on average it should not be partition dependent. +self.attention_dropout = torch.nn.Dropout(attention_dropout_prob) + +# Output. +self.dense = RowParallelLinear(hidden_size, + hidden_size, + input_is_parallel=True, + init_method=output_layer_init_method) +``` +## 3.将`cross-Attention`中的两个`Linear Layer `转化为`column/rowParallel`版本 +同上， +```python +self.query = ColumnParallelLinear(hidden_size, + hidden_size, + gather_output=False, + init_method=init_method) +self.key_value = ColumnParallelLinear(hidden_size, + 2 * hidden_size, + stride=2, + gather_output=False, + init_method=init_method) +# Dropout. Note that for a single iteration, this layer will generate +# different outputs on different number of parallel partitions but +# on average it should not be partition dependent. +self.attention_dropout = torch.nn.Dropout(attention_dropout_prob) + +# Output. +self.dense = RowParallelLinear(hidden_size, + hidden_size, + input_is_parallel=True, + init_method=output_layer_init_method) + +``` diff --git a/doc_zh/TUTORIAL_11_GLM_BLANK_FILLING_QA.md b/doc_zh/TUTORIAL_11_GLM_BLANK_FILLING_QA.md new file mode 100644 index 00000000..4324e35c --- /dev/null +++ b/doc_zh/TUTORIAL_11_GLM_BLANK_FILLING_QA.md @@ -0,0 +1,103 @@ +# GLM 完形填空式生成 + +![img.png](./img/glm_blank_filling.png) +GLM 自然地处理可变长度的空白填充，这对于许多下游任务至关重要。 +基于自回归填空，GLM随机从输入文本中删除连续的标记范围， +遵循自动编码的思想，训练模型重建跨度，遵循自回归的想法预训练。GLM 学习预训练期间的自回归生成。 + +GLM 对下游任务进行微调，并将它们重新定义为空白填充生成。每个任务都与一个人为的完形填空问题相关联，模型预测答案 +到完形填空。 +例如，情感分类任务被重新表述为填充“[SENTENCE] 中的空白。 +"它真的 "。 "好"或"坏"的预测表明 +情绪是积极的还是消极的。 + +有了这样的方式，GLM 受益于预训练和训练之间的一致性 +微调，因为预训练和微调都涉及训练模型以生成给定上下文的文本。 + +为了让预训练更适合文本生成任务的方法，GLM 还研究 +多任务预训练设置，其中模型是联合的训练以重建蒙面跨度并生成更长的文本。 + + +在 GLM 中，共有三种 MASK 方法，分别对应三种预测格式。 +1. ```[MASK]```, Token级别。这种屏蔽方法只会屏蔽一个句子中的随机标记， + 在三种 MASK 方法中，它的掩码部分最少。所以，生成的内容也有限。例如： ``[CLS]北京故宫是中国[MASK]非物质文化遗产。<|endoftext|><|startofpiece|>现存最大的古代宫殿建筑, 也是``. +2. ```[sMASK]```: 句子级别。我们限制遮罩 + spans必须是完整的句子。多个跨度 + （句子）被抽样覆盖 15% + 原始令牌。该目标旨在 + seq2seq 任务，其预测通常是 + 完整的句子或段落。 + ```[sMASK]``` 预测结果比 ```[MASK]```更长. 例如: `` [CLS]人工智能是一个以计算机科学为基础,由计算机、数学、哲学等多学科交叉融合的交叉学科,[sMASK],具有非常巨大的前景。<|endoftext|><|startofpiece|>它涉及的信息量不仅非常巨大,而且也是人工智能发展的一个关键,其研究内容包括人的感觉、知觉和思维,以及如何理解各种现象,以及解释现象的本性和原因等,通过计算机来进行系统的分析推理、建立数学模型,并模拟人类意识。`` +3. ```[gMASK]```: 文档级别。我们对单个跨度进行采样 + 其长度是从原始长度的 50%–100% 上的均匀分布中采样的。 + 该目标旨在生成长文本。 For example: ``[CLS]问题:啤酒伤胃吗?回答:[gMASK]<|startofpiece|>谢邀。 我是啤酒爱好者,但是我不喝酒。 我以前也说过,喝酒伤身,啤酒伤胃,伤肠道。 现在我也知道了啤酒伤人的很多细节,我就不瞎几把的说,大家看图片就知道了。 其实啤酒伤身这个说法只是表面而已。 啤酒中含有少量的碳酸和酒精,碳酸和酒精是成酸性物质,而乙醇是脂溶性的,酒精在胃里能够被分解,生成乙醇和二氧化碳,在体内是水和二氧化碳,两种物质会迅速发生中和反应,结果导致人体出现头痛、呕吐、胸痛、浑身发热等现象,这就是所谓喝大了,喝多了。 啤酒的含糖量在15%左右,喝多了也是伤身的,啤酒含糖量较高的主要成分是水分,而水分的体积比酒精大,所以酒精进入人体,与水相遇,就会产生大量气体,二氧化碳、水、一氧化碳等刺激人体,造成人体大量出汗,使体内温度升高,`` + + +例如，我们用`GLM-large-ch`以自回归空白的形式完成问题任务——填充任务，如果想要使用`GLM-10b-ch`请点[这里](https://model.baai.ac.cn/model-detail/100001): +```python +import torch +from flagai.model.glm_model import GLMModel +from flagai.data.tokenizer import GLMLargeChTokenizer +from flagai.model.predictor.predictor import Predictor +if __name__ == "__main__": + """Main training program.""" + print('Generate Samples') + tokenizer = GLMLargeChTokenizer(vocab_path='./checkpoints/glm-large-ch/cog-pretrain.model', + add_block_symbols=True, + add_task_mask=True, + add_decoder_mask=False, + fix_command_token=False) + model = GLMModel.from_pretrain(model_name='glm-large-ch', only_download_config=False) + model.cuda(torch.cuda.current_device()) + predictor = Predictor(model, tokenizer) + # question-answering + text = '问题：啤酒伤胃吗？回答：[gMASK]' + output=predictor.predict_generate_randomsample(text) + print(text,'\n',output) +``` + +与 BERT 类似，GLM 可以将掩码标记预测为: +```python +import torch +from flagai.model.glm_model import GLMModel +from flagai.data.tokenizer import GLMLargeChTokenizer +from flagai.model.predictor.predictor import Predictor +if __name__ == "__main__": + """Main training program.""" + print('Generate Samples') + tokenizer = GLMLargeChTokenizer(vocab_path='./checkpoints/glm-large-ch/cog-pretrain.model', + add_block_symbols=True, + add_task_mask=True, + add_decoder_mask=False, + fix_command_token=False) + model = GLMModel.from_pretrain(model_name='glm-large-ch', only_download_config=False) + model.cuda(torch.cuda.current_device()) + predictor = Predictor(model, tokenizer) + # question-answering + text = '北京故宫是中国[MASK]非物质文化遗产。' + output=predictor.predict_generate_randomsample(text) + print(text,'\n',output) +``` +并将遮罩句子预测为: + +```python +import torch +from flagai.model.glm_model import GLMModel +from flagai.data.tokenizer import GLMLargeChTokenizer +from flagai.model.predictor.predictor import Predictor +if __name__ == "__main__": + """Main training program.""" + print('Generate Samples') + tokenizer = GLMLargeChTokenizer(vocab_path='./checkpoints/glm-large-ch/cog-pretrain.model', + add_block_symbols=True, + add_task_mask=True, + add_decoder_mask=False, + fix_command_token=False) + model = GLMModel.from_pretrain(model_name='glm-large-ch', only_download_config=False) + model.cuda(torch.cuda.current_device()) + predictor = Predictor(model, tokenizer) + # question-answering + text = '人工智能是一个以计算机科学为基础，由计算机、数学、哲学等多学科交叉融合的交叉学科，[sMASK]，具有非常巨大的前景。' + output=predictor.predict_generate_randomsample(text) + print(text,'\n',output) +``` \ No newline at end of file diff --git a/doc_zh/TUTORIAL_12_GLM_EXAMPLE_TITLE_GENERATION.md b/doc_zh/TUTORIAL_12_GLM_EXAMPLE_TITLE_GENERATION.md new file mode 100644 index 00000000..e3134e4c --- /dev/null +++ b/doc_zh/TUTORIAL_12_GLM_EXAMPLE_TITLE_GENERATION.md @@ -0,0 +1,185 @@ +# GLM 例子：标题生成 + +## 背景 +标题生成任务需要输入一段文本，模型根据输入文本输出对应的标题。这里使用`GLM-large-ch`作为样例,如果想要使用`GLM-10b-ch`请点[这里](https://model.baai.ac.cn/model-detail/100001)。 + +![](./img/bert_title_generation_model.png) + +## 结果展示 +#### 输入 +``` +"本文总结了十个可穿戴产品的设计原则而这些原则同样也是笔者认为是这个行业最吸引人的地方1为人们解决重复性问题2从人开始而不是从机器开始3要引起注意但不要刻意4提升用户能力而不是取代人", +"2007年乔布斯向人们展示iPhone并宣称它将会改变世界还有人认为他在夸大其词然而在8年后以iPhone为代表的触屏智能手机已经席卷全球各个角落未来智能手机将会成为真正的个人电脑为人类发展做出更大的贡献", +"雅虎发布2014年第四季度财报并推出了免税方式剥离其持有的阿里巴巴集团15％股权的计划打算将这一价值约400亿美元的宝贵投资分配给股东截止发稿前雅虎股价上涨了大约7％至5145美元" +``` + +#### 输出 +``` +可 穿 戴 产 品 设 计 原 则 十 大 原 则 +乔 布 斯 宣 布 iphone 8 年 后 将 成 为 个 人 电 脑 +雅 虎 拟 剥 离 阿 里 巴 巴 15 ％ 股 权 +``` + +## 模型训练（train.py） + +在命令行运行此命令： +```commandline +cd ./examples/glm_title_generation +python ./train.py +``` + +### 1.数据加载 +样例数据在 /examples/glm_title_generation/data/ + +需要针对数据格式定义数据加载方法，例如：定义文件读取函数，从文件中读取数据，得到src和tgt列表： +1）定义加载过程 +```python +def read_file(): + src = [] + tgt = [] + # src = ["article_1", "article_2", "article_3" ......] + # tgt = ["title_1", "title_2", "title_3" ......] + # 如果换为其他数据，修改处理方式即可，只需要构造好src以及对应tgt列表 + with open(src_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + src.append(line.strip('\n').lower()) + + with open(tgt_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + tgt.append(line.strip('\n').lower()) + return src,tgt +``` + +2）定义数据集处理过程： +```python +class GLMTitleGenerationDataset(Dataset): + + def __init__(self, sents_src, sents_tgt): + super(GLMTitleGenerationDataset, self).__init__() + self.sents_src = sents_src + self.sents_tgt = sents_tgt + + def __getitem__(self, i): + source_text = self.sents_src[i] + target_text = self.sents_tgt[i] + data = tokenizer.encode_plus(source_text, target_text=target_text) + return data + + def __len__(self): + return len(self.sents_src) +``` + +3）定义数据迭代器（DataLoader）中的批处理函数（collate_fn），用于将一批（batch）数据填充（padding）成统一大小 +```python +class GLMTitleGenerationCollateFN(): + def __init__(self, pad_id): + self.pad_id = pad_id + + def pad_token(self, tokens, max_length): + pad_len = max_length-len(tokens) + tokens += [self.pad_id]*pad_len + return tokens + + def pad_position_ids(self, position_ids, max_length): + pad_len = max_length-len(position_ids[0]) + position_ids[0] += [len(position_ids[0])+x for x in range(pad_len)] + position_ids[1] += [1] * pad_len + return position_ids + + def pad_loss_mask(self, loss_mask, max_length): + pad_len = max_length-len(loss_mask) + loss_mask += [0] * pad_len + return loss_mask + + def __call__(self, batch): + input_ids = [data["input_ids"] for data in batch] + target_ids = [data["target_ids"] for data in batch] + position_ids = [data["position_ids"] for data in batch] + attention_mask = [data['attention_mask'] for data in batch] + loss_mask = [data['loss_mask'] for data in batch] + + max_length = max([len(t) for t in input_ids]) + for i in range(len(input_ids)): + input_ids[i] = self.pad_token(input_ids[i], max_length) + target_ids[i] = self.pad_token(target_ids[i], max_length) + position_ids[i] = self.pad_position_ids(position_ids[i], max_length) + loss_mask[i] = self.pad_loss_mask(loss_mask[i], max_length) + return { + 'input_ids': torch.LongTensor(input_ids), + 'target_ids': torch.LongTensor(target_ids), + 'position_ids': torch.LongTensor(position_ids), + 'attention_mask': torch.LongTensor(attention_mask), + 'loss_mask': torch.LongTensor(loss_mask) + } +``` +4）获取训练数据 +```python +train_src, train_tgt = read_file() +print('-----------train data length:', len(train_src)) +my_collate_fn = GLMTitleGenerationCollateFN(pad_id=tokenizer.get_command('pad').Id) +train_dataset = GLMTitleGenerationDataset(train_src, + train_tgt) +``` +### 2.加载模型和分词器 + +```python +from flagai.auto_model.auto_loader import AutoLoader + +# model_dir: 包含 1.config.json, 2.pytorch_model.bin, 3.vocab.txt, +# 如果本地没有，则会在modelhub上进行查找并下载 +# Autoloader 能够自动构建模型与切词器 +# 'title-generation' 是task_name +model_dir = "./state_dict/glm/" +AutoLoader("title-generation",model_name="GLM-large-ch",model_dir=model_dir) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` + +### 3. 训练 + +实例化训练器Trainer, 设置训练参数。 + +```python +import torch.cuda +from flagai.trainer import Trainer + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +trainer = Trainer( + env_type="pytorch", + experiment_name="glm-title-generation", + batch_size=1, + gradient_accumulation_steps=1, + lr=2e-4, + weight_decay=1e-3, + epochs=10, + log_interval=10, + eval_interval=10000, + load_dir=None, + pytorch_device=device, + save_dir="checkpoints", + save_epoch=1, + num_checkpoints=1, + +) +``` +将模型、数据、批处理函数输入训练器开始训练： +```python +trainer.train(model, + train_dataset=train_dataset, + collate_fn=my_collate_fn) +``` + +## 生成（generate.py） +运行前修改模型配置路径model_dir，训练好的模型路径model_save_path。在命令行运行此命令： +```commandline +cd ./examples/glm_title_generation +python ./generate.py +``` +可选择基于概率筛选的随机抽样（random sample）或集束搜索（beamsearch）两种生成方式： +```python +print(predictor.predict_generate_randomsample(text,out_max_length=66, top_k=10, top_p=.1, + repetition_penalty=4.0, temperature=1.2)) +print(predictor.predict_generate_beamsearch(text, out_max_length=66, beam_size=10)) +``` diff --git a/doc_zh/TUTORIAL_13_GLM_EXAMPLE_PEOTRY_GENERATION.md b/doc_zh/TUTORIAL_13_GLM_EXAMPLE_PEOTRY_GENERATION.md new file mode 100644 index 00000000..dfb76ff7 --- /dev/null +++ b/doc_zh/TUTORIAL_13_GLM_EXAMPLE_PEOTRY_GENERATION.md @@ -0,0 +1,201 @@ +# GLM 例子：古诗生成 + +## 古诗背景介绍 +中国古代近体诗有两种体裁：绝句和律诗。绝句规定全诗四句。律诗规定全诗八句。每句话含五个或七个汉字，称为五言或七言。总共有四种类型，见下表。 + +| | 绝句 | 律诗 | +| ---- | ---- | ---- | +| 五言 | 五言绝句 | 五言律诗 | +| 七言 | 七言绝句 | 七言律诗 | + +一个五言绝句的示例： + +**静夜思** 李白 + +床前明月光，疑是地上霜。 + +举头望明月，低头思故乡。 + + +## 生成结果展示 +#### 输入古诗标题与体裁 +``` +"桃花：七言绝句" +``` +#### 输出诗句 +``` +"可怜含笑向春风,刚种桃花欲待红。今日流莺来旧处,百般言语是墙东。" +``` +## 模型训练（train.py） + +在命令行运行此命令： +```commandline +cd ./examples/glm_poetry_generation +python ./train.py +``` +这里使用`GLM-large-ch`作为样例,如果想要使用`GLM-10b-ch`请点[这里](https://model.baai.ac.cn/model-detail/100001)。 +### 1.准备训练数据 +1）从文件中读取数据 + +样例数据在 FlagAI/examples/glm_poetry_generation/data/ + +需要在 train.py 中定义数据加载过程，得到src和tgt列表： +```python +def read_file(): + src = [] + tgt = [] + # src = ["春晓：五言绝句", "标题：五言律诗",......] + # tgt = ["春眠不觉晓，处处闻啼鸟。夜来风雨声，花落知多少。", "诗句...", ......] + # 如果换为其他数据，修改处理方式即可，只需要构造好src以及对应tgt列表 + with open(src_dir, 'r', encoding='utf-8') as f: + for line in f: + line = line.strip() + if "：" in line: + l = line.split("：") #line eg:"初夏：五言绝句" + #if there are more than one '：', get title before the first '：' + title, style = l[0], l[-1] + if len(title) > 20: + title = title[:20] #cut the longer title + line = "：".join([title, style]) + src.append(line) + with open(tgt_dir, 'r', encoding='utf-8') as f: + for line in f: + tgt.append(line.strip()) + assert len(src) == len(tgt), 'lines not equal!' + return src, tgt +``` +2）定义数据迭代器（DataLoader）： +```python +class GLMPoetryDataset(Dataset): + def __init__(self, sents_src, sents_tgt): + super(GLMPoetryDataset, self).__init__() + self.sents_src = sents_src + self.sents_tgt = sents_tgt + + def __getitem__(self, i): + source_text = self.sents_src[i] + target_text = self.sents_tgt[i] + data=tokenizer.encode_plus(source_text, + target_text=target_text) + return data + + def __len__(self): + return len(self.sents_src) +``` +其中tokenizer.encode_plus()方法将源、目标字符串转换为GLM模型的输入token索引、位置编码等数据 + +3）定义数据迭代器（DataLoader）中的collate_fn，用于将一批（batch）数据填充（padding）成统一大小 +```python +class GLMPoetryDynamicCollateFN(): + def __init__(self, pad_id): + self.pad_id = pad_id + + def pad_token(self, tokens, max_length): + pad_len = max_length-len(tokens) + tokens += [self.pad_id]*pad_len + return tokens + + def pad_position_ids(self, position_ids, max_length): + pad_len = max_length-len(position_ids[0]) + position_ids[0] += [len(position_ids[0])+x for x in range(pad_len)] + position_ids[1] += [1] * pad_len + return position_ids + + def pad_loss_mask(self, loss_mask, max_length): + pad_len = max_length-len(loss_mask) + loss_mask += [0] * pad_len + return loss_mask + + def __call__(self, batch): + input_ids = [data["input_ids"] for data in batch] + target_ids = [data["target_ids"] for data in batch] + position_ids = [data["position_ids"] for data in batch] + attention_mask = [data['attention_mask'] for data in batch] + loss_mask = [data['loss_mask'] for data in batch] + + max_length = max([len(t) for t in input_ids]) + for i in range(len(input_ids)): + input_ids[i] = self.pad_token(input_ids[i], max_length) + target_ids[i] = self.pad_token(target_ids[i], max_length) + position_ids[i] = self.pad_position_ids(position_ids[i], max_length) + loss_mask[i] = self.pad_loss_mask(loss_mask[i], max_length) + return { + 'input_ids': torch.LongTensor(input_ids), + 'target_ids': torch.LongTensor(target_ids), + 'position_ids': torch.LongTensor(position_ids), + 'attention_mask': torch.LongTensor(attention_mask), + 'loss_mask': torch.LongTensor(loss_mask) + } +``` +4）获取训练数据 +```python +train_src, train_tgt = read_file() +print('-----------train data length:', len(train_src)) +my_collate_fn = GLMPoetryDynamicCollateFN(pad_id=tokenizer.get_command('pad').Id) +train_dataset = GLMPoetryDataset(train_src, + train_tgt) +``` +### 2.加载模型和分词器 + +```python +from flagai.auto_model.auto_loader import AutoLoader + +# model_dir: 包含 1.config.json, 2.pytorch_model.bin, 3.vocab.txt, +# 如果本地没有，则会在modelhub上进行查找并下载 +# Autoloader 能够自动构建模型与切词器 +# 'poetry' 是task_name +model_dir = "./state_dict/" +AutoLoader("poetry",model_name="GLM-large-ch",model_dir=model_dir) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` + +### 3. 训练 + +实例化训练器Trainer, 设置训练参数。 +```python +from flagai.trainer import Trainer +trainer = Trainer( + env_type="pytorch", + experiment_name="glm_poetry", + batch_size=4, + gradient_accumulation_steps=1, + lr=2e-4, + weight_decay=2e-8, + epochs=100, + log_interval=10, + tensorboard_dir="tbsummary", + eval_interval=2000000, + load_dir=None, + save_dir="checkpoints_poetry", + save_epoch=1, +) +``` +将模型、数据、collate_fn输入训练器开始训练： +```python +trainer.train(model, + train_dataset=train_dataset, + collate_fn=my_collate_fn) +``` + + + + +## 生成（generate.py） +在命令行运行此命令： +```commandline +cd ./examples/glm_poetry_generation +python ./generate.py +``` +可选择基于概率筛选的随机抽样（random sample）或集束搜索（beamsearch）两种生成方式： +```python +print(predictor.predict_generate_randomsample(text,out_max_length=66, top_k=10, top_p=.1, + repetition_penalty=4.0, temperature=1.2)) +print(predictor.predict_generate_beamsearch(text, out_max_length=66, beam_size=10)) +``` + + +生成古诗结果示例： + +![result](../docs/img/poetry_generation.png) + diff --git a/doc_zh/TUTORIAL_14_HUGGINGFACE_T5.md b/doc_zh/TUTORIAL_14_HUGGINGFACE_T5.md new file mode 100644 index 00000000..42f94f00 --- /dev/null +++ b/doc_zh/TUTORIAL_14_HUGGINGFACE_T5.md @@ -0,0 +1,240 @@ +# 支持 Huggingface t5 + + +```python +import sys +from easybigmodel.trainer import Trainer +from transformers import T5ForConditionalGeneration, T5Tokenizer +from torch.utils.data import Dataset +import torch + +## Inheriant the Trainer +## overload the forward_step function +class MyTrainer(Trainer): + + def forward_step(self, data, model, mems): + """ + Args: + data: a dict contains a batch of inputs + return: + output: a dict contains `loss` + """ + model_outputs = model(**data) + output = {} + output['loss'] = model_outputs.loss + output['logits'] = model_outputs.logits + output['hidden_states'] = model_outputs.decoder_hidden_states + return output + +# get a customized trainer instance +trainer = MyTrainer( + env_type='pytorch', + epochs=1, + batch_size=4, + eval_interval=10, + log_interval=10, + experiment_name='t5-11b', + pytorch_device='cuda:0', + load_dir=None, + lr=1e-4, + fp16=False) + +# using huggingface transformers to get tokenizer and models +model_name = 't5-11b' +tokenizer = T5Tokenizer.from_pretrained(model_name) +model = T5ForConditionalGeneration.from_pretrained(model_name) + +print("loading model & tokenizer is done!") +src_dir = 'train_inputs.txt' +tgt_dir = 'train_targets.txt' +model_dir = "./t5-11b" # 模型位置 +maxlen = 1024 + + +def read_file(): + src = [] + tgt = [] + + with open(src_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + src.append(line.strip('\n').lower()) + + with open(tgt_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + tgt.append(line.strip('\n').lower()) + return src, tgt + + +class BertSeq2seqDataset(Dataset): + + def __init__(self, sents_src, sents_tgt, tokenizer, maxlen=512): + super(BertSeq2seqDataset, self).__init__() + self.sents_src = sents_src + self.sents_tgt = sents_tgt + self.tokenizer = tokenizer + self.maxlen = maxlen + + def __getitem__(self, i): + src = self.sents_src[i] + tgt = self.sents_tgt[i] + inputs = tokenizer(src) + with tokenizer.as_target_tokenizer(): + labels = tokenizer(tgt) + output = {} + output['input_ids'] = inputs.input_ids + output['labels'] = labels.input_ids + return output + + def __len__(self): + return len(self.sents_src) + + +def seq2seq_collate_fn(batch): + + def padding(indice, max_length, pad_idx=0): + + pad_indice = [ + item + [pad_idx] * max(0, max_length - len(item)) + for item in indice + ] + return torch.tensor(pad_indice) + + token_ids = [data["input_ids"] for data in batch] + max_length_tk = max([len(t) for t in token_ids]) + labels = [data["labels"] for data in batch] + max_length_lb = max([len(t) for t in labels]) + + token_ids_padded = padding(token_ids, max_length_tk) + labels_padded = padding(labels, max_length_lb) + + data = {"input_ids": token_ids_padded, "labels": labels_padded} + + return data + + +sents_src, sents_tgt = read_file() +data_len = len(sents_tgt) +train_size = int(data_len * 0.8) +train_src = sents_src[:train_size] +train_tgt = sents_tgt[:train_size] + +val_src = sents_src[train_size:] +val_tgt = sents_tgt[train_size:] + +train_dataset = BertSeq2seqDataset(train_src, + train_tgt, + tokenizer=tokenizer, + maxlen=maxlen) +val_dataset = BertSeq2seqDataset(val_src, + val_tgt, + tokenizer=tokenizer, + maxlen=maxlen) +## Training +trainer.train(model, + train_dataset=train_dataset, + collate_fn=seq2seq_collate_fn) + +``` + +## 加速训练的技巧 +我们可能不会在V100 32G上运行t5-11b。所以，我们需要一些技巧来减少GPU内存的使用。 +### 第一步：fp16 +把模型参数变为 `fp16` +```python +trainer = MyTrainer( + env_type='pytorch', + epochs=1, + batch_size=1, + eval_interval=10, + log_interval=10, + experiment_name='t5-11b', + pytorch_device='cuda:0', + load_dir=None, + lr=1e-4, + fp16=True) # change to `True` +``` +### 第二步：梯度重计算（checkpoint） +在forward阶段不将中间结果保存。我们可以运行`batch size`=1的t5-11b。 +现在，我们可以用 `gradient_accumulation_steps` train/finetune 一个 t5-11b。 +```python +model.gradient_checkpointing = True +``` +### 第三步：数据并行(DDP) +为了增加batch size，我们可以在多个GPU上使用数据并行。 +```python +trainer = Trainer( + env_type="pytorchDDP", + epochs=1, + batch_size=1, + eval_interval=10, + log_interval=10, + experiment_name='t5-11b', + load_dir=None, + lr=1e-4, + fp16=True + checkpoint_activations=False, + # The following six options is for pytorchDDP + master_ip='127.0.0.1', + master_port=17750, + num_nodes=1, + num_gpus=2, + hostfile='hostfile', # hostfile setup the number of nodes & gpus + training_script=__file__, +) +``` +### 第四步：数据并行（deepspeed） +通过使用`cpuoffload` 和`stage2`，将单个gpu上的 `batch size` 增加到 `4`。 + +```python +trainer = Trainer( + env_type="deepspeed", # env_type + epochs=1, + batch_size=1, + eval_interval=10, + log_interval=10, + experiment_name='t5-11b', + load_dir=None, + lr=1e-4, + fp16=True + checkpoint_activations=False, + # parallel settings + master_ip='127.0.0.1', + master_port=17750, + num_nodes=1, + num_gpus=2, + hostfile='hostfile', + training_script=__file__, + # deepspeed + deepspeed_config='deepspeed.json' +) +``` +### 第五步：模型并行（deepspeed + megatron-lm） + +```python +trainer = Trainer( + env_type="deepspeed", # env_type + epochs=1, + batch_size=1, + eval_interval=10, + log_interval=10, + experiment_name='t5-11b', + load_dir=None, + lr=1e-4, + fp16=True + checkpoint_activations=False, + # parallel settings + master_ip='127.0.0.1', + master_port=17750, + num_nodes=1, + num_gpus=2, + hostfile='hostfile', + training_script=__file__, + hostfile='hostfile', + # deepspeed + deepspeed_config='deepspeed.json', + # megatron-lm + model_paralle_size = 2 +) +``` \ No newline at end of file diff --git a/doc_zh/TUTORIAL_15_BERT_EXAMPLE_TITLE_GENERATION.md b/doc_zh/TUTORIAL_15_BERT_EXAMPLE_TITLE_GENERATION.md new file mode 100644 index 00000000..a3b5e73f --- /dev/null +++ b/doc_zh/TUTORIAL_15_BERT_EXAMPLE_TITLE_GENERATION.md @@ -0,0 +1,108 @@ +# BERT 标题生成例子 + +## 背景 +标题生成任务需要输入一段文本，模型根据输入文本输出对应的标题。 + +![](./img/bert_title_generation_model.png) + +## 结果展示 + +#### 输入 +``` +"本文总结了十个可穿戴产品的设计原则而这些原则同样也是笔者认为是这个行业最吸引人的地方1为人们解决重复性问题2从人开始而不是从机器开始3要引起注意但不要刻意4提升用户能力而不是取代人", +"2007年乔布斯向人们展示iPhone并宣称它将会改变世界还有人认为他在夸大其词然而在8年后以iPhone为代表的触屏智能手机已经席卷全球各个角落未来智能手机将会成为真正的个人电脑为人类发展做出更大的贡献", +"雅虎发布2014年第四季度财报并推出了免税方式剥离其持有的阿里巴巴集团15％股权的计划打算将这一价值约400亿美元的宝贵投资分配给股东截止发稿前雅虎股价上涨了大约7％至5145美元" +``` +#### 输出 +``` +可 穿 戴 产 品 设 计 原 则 十 大 原 则 +乔 布 斯 宣 布 iphone 8 年 后 将 成 为 个 人 电 脑 +雅 虎 拟 剥 离 阿 里 巴 巴 15 ％ 股 权 +``` +## 使用 + +### 1. 数据加载 +样例数据位于 /examples/bert_title_generation/data/ + +需要在 ```trianer.py```文件中定义数据读取过程，例如： +```python +def read_file(): + src = [] + tgt = [] + + ## 如果换为其他数据，修改处理方式即可，只需要构造好src以及对应tgt列表 + with open(src_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + src.append(line.strip('\n').lower()) + + with open(tgt_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + tgt.append(line.strip('\n').lower()) + return src,tgt +``` + +### 2. 加载模型与切词器 + +```python +from flagai.auto_model.auto_loader import AutoLoader + +# model_dir: 包含 1.config.json, 2.pytorch_model.bin, 3.vocab.txt, +# 如果本地没有，则会在modelhub上进行查找并下载 +# Autoloader 能够自动构建模型与切词器 +# 'seq2seq' 是task_name +auto_loader = AutoLoader(task_name="title-generation", + model_dir="./state_dict/", + model_name="RoBERTa-base-ch") +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` + +### 3. 训练 +在命令行中输入如下代码进行训练： +```commandline +python ./train.py +``` +通过如下代码调整训练配置： +```python +from flagai.trainer import Trainer +import torch +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +trainer = Trainer(env_type="pytorch", + experiment_name="roberta_seq2seq", + batch_size=8, gradient_accumulation_steps=1, + lr = 2e-4, + weight_decay=1e-3, + epochs=10, log_interval=10, eval_interval=10000, + load_dir=None, pytorch_device=device, + save_dir="checkpoints", + save_epoch=1 + ) +``` +通过如下代码划分训练集验证集，并定义Dataset： +```python +sents_src, sents_tgt = read_file() +data_len = len(sents_tgt) +train_size = int(data_len * 0.8) +train_src = sents_src[: train_size] +train_tgt = sents_tgt[: train_size] + +val_src = sents_src[train_size: ] +val_tgt = sents_tgt[train_size: ] + +train_dataset = BertSeq2seqDataset(train_src, train_tgt, tokenizer=tokenizer, maxlen=maxlen) +val_dataset = BertSeq2seqDataset(val_src, val_tgt, tokenizer=tokenizer, maxlen=maxlen) +``` + +### 生成 +如果你已经训练好了一个模型，为了更直观的看到结果，可以对一些测试样例进行生成 +首先去修改一下模型保存位置： +```python +model_save_path = "./checkpoints/1001/mp_rank_00_model_states.pt" ## 1001 is example, you need modify the number. +``` +运行对应的生成脚本： +```commandline +python ./generate.py +``` +然后便可以看到生成结果。 diff --git a/doc_zh/TUTORIAL_16_BERT_EXAMPLE_SEMANTIC_MATCHING.md b/doc_zh/TUTORIAL_16_BERT_EXAMPLE_SEMANTIC_MATCHING.md new file mode 100644 index 00000000..5d69fab1 --- /dev/null +++ b/doc_zh/TUTORIAL_16_BERT_EXAMPLE_SEMANTIC_MATCHING.md @@ -0,0 +1,112 @@ +# BERT 语义匹配例子 + +## 背景 +语义匹配任务是判断输入的两个句子意思是否一致，需要输入句子对，输出2分类（两句话意思相同或不同）的结果。 + +![semantic_model.png](./img/semantic_matching_model.png) + +## 结果展示 + +#### 输入 +```python +test_data = [["后悔了吗","你有没有后悔"], + ["打开自动横屏","开启移动数据"], + ["我觉得你很聪明","你聪明我是这么觉得"]] +``` +#### 输出 +``` +1 +0 +1 +``` +1表示两个句子意思相同，0表示不相同。 +## 使用 + +### 1.数据加载 +样例数据在 /examples/bert_semantic_matching/data/ + +需要针对数据格式定义数据加载方法，例如： +```python +def read_file(data_path): + src = [] + tgt = [] + ##TODO read data file to load src and tgt, for example: + ## src = [["article_1_1", "article_1_2"], ["article_2_1", "artile_2_2"], ......] + ## tgt = [1, 0, ......] + ## no matter what data you use, you need to construct the right src and tgt. + with open(data_path) as f: + lines = f.readlines() + for line in lines: + line = line.split("\t") + if len(line) == 3: + sents_tgt.append(int(line[2])) + sents_src.append([line[0], line[1]]) + + return src,tgt +``` + +### 2.模型与切词器加载 + +```python +from flagai.auto_model.auto_loader import AutoLoader + +# the model dir, which contains the 1.config.json, 2.pytorch_model.bin, 3.vocab.txt, +# or we will download these files from the model hub to this dir. +model_dir = "./state_dict/" +# Autoloader can build the model and tokenizer automatically. +# 'classification' is the task_name. +auto_loader = AutoLoader("classification", + model_name="RoBERTa-base-ch", + model_dir=model_dir) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` + +### 3. 训练 +在命令行中输入： +```commandline +python ./train.py +``` +调整训练参数： +```python +from flagai.trainer import Trainer +import torch +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +trainer = Trainer(env_type="pytorch", + experiment_name="roberta-base-ch-semantic-matching", + batch_size=8, gradient_accumulation_steps=1, + lr = 1e-5, + weight_decay=1e-3, + epochs=10, log_interval=100, eval_interval=500, + load_dir=None, pytorch_device=device, + save_dir="checkpoints_semantic_matching", + save_epoch=1 + ) +``` +切分训练集验证集 +```python +src, tgt = read_file(data_path=train_path) +data_len = len(src) +train_size = int(data_len * 0.9) +train_src = src[: train_size] +train_tgt = tgt[: train_size] + +val_src = src[train_size: ] +val_tgt = tgt[train_size: ] + +train_dataset = BertClsDataset(train_src, train_tgt) +val_dataset = BertClsDataset(val_src, val_tgt) +``` + +### 生成 +如果你已经训练好了一个模型，为了更加直观的看到结果，可以进行测试生成 + +首先调整训练好的模型的路径： +```python +model_save_path = "./checkpoints_semantic_matching/9000/mp_rank_00_model_states.pt" +``` +运行： +```commandline +python ./generate.py +``` +然后可以查看运行结果。 diff --git a/doc_zh/TUTORIAL_17_BERT_EXAMPLE_NER.md b/doc_zh/TUTORIAL_17_BERT_EXAMPLE_NER.md new file mode 100644 index 00000000..f062a2d1 --- /dev/null +++ b/doc_zh/TUTORIAL_17_BERT_EXAMPLE_NER.md @@ -0,0 +1,111 @@ +# BERT 命名实体识别例子 + +## 背景 +命名实体识别任务是判断输入的句子里面是否存在不同实体，并进行识别标记。 + +BERT模型共支持三种方式完成NER任务： +1. 序列标注方法 +2. 序列标注+CRF方法 +3. GlobalPointer方法 + +本文以方法1为例。 + +![semantic_model.png](./img/bert_ner_model.png) + +## 结果展示 + +#### 输入 +```python +test_data = [ + "6月15日，河南省文物考古研究所曹操高陵文物队公开发表声明承认：“从来没有说过出土的珠子是墓主人的", + "4月8日，北京冬奥会、冬残奥会总结表彰大会在人民大会堂隆重举行。习近平总书记出席大会并发表重要讲话。在讲话中，总书记充分肯定了北京冬奥会、冬残奥会取得的优异成绩，全面回顾了7年筹办备赛的不凡历程，深入总结了筹备举办北京冬奥会、冬残奥会的宝贵经验，深刻阐释了北京冬奥精神，对运用好冬奥遗产推动高质量发展提出明确要求。", + "当地时间8日，欧盟委员会表示，欧盟各成员国政府现已冻结共计约300亿欧元与俄罗斯寡头及其他被制裁的俄方人员有关的资产。", + "这一盘口状态下英国必发公司亚洲盘交易数据显示博洛尼亚热。而从欧赔投注看，也是主队热。巴勒莫两连败，", +] +``` +#### 输出 +``` +{'ORG': ['河南省文物考古研究所', '曹操高陵文物队']} +{'LOC': ['北京', '人民大会堂', '北京', '北京', '北京'], 'PER': ['习近平']} +{'ORG': ['欧盟委员会', '欧盟'], 'LOC': ['俄罗斯', '俄']} +{'ORG': ['英国必发公司', '博洛尼亚', '巴勒莫']} +``` +## 使用 + +### 1.数据加载 +样例数据在 /examples/bert_ner/data/ + +需要针对数据格式定义数据加载方法，例如： +```python +def load_data(filename): + """ load data + data:：[text, (start, end, label), (start, end, label), ...] + """ + D = [] + with open(filename, encoding='utf-8') as f: + f = f.read() + for l in f.split('\n\n'): + if not l: + continue + d = [''] + for i, c in enumerate(l.split('\n')): + char, flag = c.split(' ') + d[0] += char + if flag[0] == 'B': + d.append([i, i, flag[2:]]) + elif flag[0] == 'I': + d[-1][1] = i + + D.append(d) + return D +``` + +### 2.模型与切词器加载 + +```python +from flagai.auto_model.auto_loader import AutoLoader +task_name = "sequence-labeling" +model_dir = "./state_dict/" +target = ["O", "B-LOC", "I-LOC", "B-ORG", "I-ORG", "B-PER", "I-PER"] +auto_loader = AutoLoader(task_name, + model_name="RoBERTa-base-ch", + model_dir=model_dir, + class_num=len(target)) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` + +### 3. 训练 +在命令行中输入： +```commandline +python ./train.py +``` +调整训练参数： +```python +from flagai.trainer import Trainer +import torch +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +trainer = Trainer(env_type="pytorch", + experiment_name="roberta_ner", + batch_size=8, gradient_accumulation_steps=1, + lr = 1e-5, + weight_decay=1e-3, + epochs=10, log_interval=100, eval_interval=500, + load_dir=None, pytorch_device=device, + save_dir="checkpoints_ner", + save_epoch=1 + ) +``` + +### 生成 +如果你已经训练好了一个模型，为了更加直观的看到结果，可以进行测试生成 + +首先调整训练好的模型的路径： +```python +model_save_path = "./checkpoints_ner/9000/mp_rank_00_model_states.pt" +``` +运行： +```commandline +python ./generate.py +``` +然后可以查看运行结果。 diff --git a/doc_zh/TUTORIAL_18_GPT2_WRITING.md b/doc_zh/TUTORIAL_18_GPT2_WRITING.md new file mode 100644 index 00000000..13a6ae08 --- /dev/null +++ b/doc_zh/TUTORIAL_18_GPT2_WRITING.md @@ -0,0 +1,41 @@ +# GPT2 模型生成任务 + +## 背景 +GPT2生成任务是只输入一段文字的开头，模型进行续写 + + + +![gpt2.png](./img/gpt2_writing_model.png) + +## 结果展示 + +#### 输入 +```python +text = "今天天气不错，" +``` +#### 输出 +``` +我 们 三 个 女 孩 子 去 吃 的 ， 因 为 是 中 午 ， 所 以 没 有 等 位 。 店 里 面 环 境 还 不 错 ， 比 较 安 静 ， 我 们 要 的 鸳 鸯 锅 底 ， 味 道 一 般 ， 辣 的 感 觉 有 点 不 太 习 惯 。 服 务 态 度 很 好 ， 可 以 刷 卡 。 + +``` +## 使用 + +### 1.模型与切词器加载 + +```python +from flagai.auto_model.auto_loader import AutoLoader +from flagai.model.predictor.predictor import Predictor +loader = AutoLoader("seq2seq", + "GPT2-base-ch", + model_dir="./state_dict/") +model = loader.get_model() +tokenizer = loader.get_tokenizer() +predictor = Predictor(model, tokenizer) +``` + +### 运行 + +```commandline +python ./generate.py +``` +然后可以查看运行结果。 diff --git a/doc_zh/TUTORIAL_19_T5_EXAMPLE_TITLE_GENERATION.md b/doc_zh/TUTORIAL_19_T5_EXAMPLE_TITLE_GENERATION.md new file mode 100644 index 00000000..b014e6fd --- /dev/null +++ b/doc_zh/TUTORIAL_19_T5_EXAMPLE_TITLE_GENERATION.md @@ -0,0 +1,123 @@ +# T5 标题生成 + +## 背景 +T5标题生成任务，输入一段文本，T5模型提取对应的标题。 + + +![](./img/t5_seq2seq.png) + +## Results show + +#### Input text +``` +"本文总结了十个可穿戴产品的设计原则而这些原则同样也是笔者认为是这个行业最吸引人的地方1为人们解决重复性问题2从人开始而不是从机器开始3要引起注意但不要刻意4提升用户能力而不是取代人", +"2007年乔布斯向人们展示iPhone并宣称它将会改变世界还有人认为他在夸大其词然而在8年后以iPhone为代表的触屏智能手机已经席卷全球各个角落未来智能手机将会成为真正的个人电脑为人类发展做出更大的贡献", +"雅虎发布2014年第四季度财报并推出了免税方式剥离其持有的阿里巴巴集团15％股权的计划打算将这一价值约400亿美元的宝贵投资分配给股东截止发稿前雅虎股价上涨了大约7％至5145美元" +``` +#### Out title +``` +可 穿 戴 产 品 设 计 原 则 十 大 原 则 +乔 布 斯 宣 布 iphone 8 年 后 将 成 为 个 人 电 脑 +雅 虎 拟 剥 离 阿 里 巴 巴 15 ％ 股 权 +``` +## Usage + +### 1.Load data +The sample data is in /examples/bert_title_generation/data/ + +You need to define the data loading process in train.py. For example: +```python +def read_file(): + src = [] + tgt = [] + + ## read data file to load src and tgt, for example: + ## src = ["article_1", "article_2", "article_3" ......] + ## tgt = ["title_1", "title_2", "title_3" ......] + ## no matter what data you use, you need to construct the right src and tgt. + with open(src_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + src.append(line.strip('\n').lower()) + + with open(tgt_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + tgt.append(line.strip('\n').lower()) + return src,tgt +``` + +### 2.Load model and tokenizer + +```python +from flagai.auto_model.auto_loader import AutoLoader + +loader = AutoLoader(task_name="seq2seq", + model_name="T5-base-ch", + model_dir="./state_dict/") +model = loader.get_model() +tokenizer = loader.get_tokenizer() +``` + +### 3. Train +Then input this code in commandline to train: +```commandline +python ./train.py +``` +The configuration support multi-gpus training. +Modify the training configuration by this code: +```python +from flagai.trainer import Trainer +import torch +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +trainer = Trainer( + env_type="deepspeed+mpu", + experiment_name="roberta_seq2seq", + batch_size=1, + gradient_accumulation_steps=1, + lr=2e-4, + weight_decay=1e-3, + epochs=10, + log_interval=10, + eval_interval=10000, + load_dir=None, + pytorch_device=device, + save_dir="checkpoints", + save_epoch=1, + num_checkpoints=1, + master_ip='127.0.0.1', + master_port=17750, + num_nodes=1, + num_gpus=2, + hostfile='/data/liuguang/test_Sailing/Sailing/examples/bert_title_generation/hostfile', + deepspeed_config='/data/liuguang/test_Sailing/Sailing/examples/bert_title_generation/deepspeed.json', + training_script=__file__, +) +``` +Divide the training set validation set and create the dataset: +```python +sents_src, sents_tgt = read_file() +data_len = len(sents_tgt) +train_size = int(data_len * 0.8) +train_src = sents_src[: train_size] +train_tgt = sents_tgt[: train_size] + +val_src = sents_src[train_size: ] +val_tgt = sents_tgt[train_size: ] + +train_dataset = BertSeq2seqDataset(train_src, train_tgt, tokenizer=tokenizer, maxlen=maxlen) +val_dataset = BertSeq2seqDataset(val_src, val_tgt, tokenizer=tokenizer, maxlen=maxlen) +``` + +### Generation +If you have already trained a model, in order to see the results more intuitively, rather than the loss of the validation set. +You can run the generation file. +First to modify the path of saved model. +```python +model_save_path = "./checkpoints/1001/mp_rank_00_model_states.pt" ## 1001 is example, you need modify the number. +``` +```commandline +python ./generate.py +``` +Then you can see the generation result. + diff --git a/doc_zh/TUTORIAL_1_TOKENIZER.md b/doc_zh/TUTORIAL_1_TOKENIZER.md new file mode 100644 index 00000000..8eae93f5 --- /dev/null +++ b/doc_zh/TUTORIAL_1_TOKENIZER.md @@ -0,0 +1,82 @@ +# 如何构建和应用分词器 + +## 什么是分词？ +分词是 NLP 项目预处理阶段的一个基本步骤，其目的是将散乱的原始文本转换为规整的数字序列， 以便机器学习系统使用。 + +在分词过程中，分词器首先将自然语言文本拆分为语义单元, 这些语义单元被称为子词(token)。随后分词器通过一个词表文件查找子词的序号，这样原始文本就被转换为了一个数字的序列。 + +
+ +值得注意的是，不同的分词器可以有不同的文本分割方式，并且有不同的词表文件, 相关算法的介绍可以在 [这里](tokenization.md) 查看。 + +目前我们支持下列七个分词器: + +| 分词器 | 语言 | +|------------------------------|-----| +| GLMLargeEnWordPieceTokenizer | 英文 | +| GLMLargeChTokenizer | 中文 | +| GLM10bENBPETokenizer | 英文 | +| T5BPETokenizer | 中文 | +| ROBERTATokenizer | 中文 | +| BertWordPieceTokenizer | 中文 | +| CPMTokenizer | 中文 | + + +## 加载分词器 +```python +from flagai.data.tokenizer import GLMLargeEnWordPieceTokenizer +tokenizer = GLMLargeEnWordPieceTokenizer() +``` +在这一步里，模型仓库中的词表文件将被自动下载到`cache_dir`参数中指定的路径。默认设置为分词器文件下的 ./vocab 目录。 + +## 应用分词器 +让我们使用一个分词器将原始文本编码成数字序列，然后将数字序列恢复成原始文本： +```python +text = "Jack is walking a dog." # 输入文本 +encoded_ids = tokenizer.EncodeAsIds(text) # 数字序列 +# 现在 encoded_ids = [2990, 2003, 3788, 1037, 3899, 1012] +recoverd_text = tokenizer.DecodeIds(encoded_ids) # 将数字序列恢复为文本 +# recovered_text此时与text相同 +``` + +## 创建新的分词器 +想创建新的分词器的时候, 需要这么做: +### 1. 在`/flagai/tokenizer`目录下建立一个新的目录 + +### 2. 使用Hugging Face transformer支持的分词器来帮助创建 +飞智支持借用Hugging Face里的分词器来创建新的分词器，以T5Tokenizer为例 +```python +from transformers import T5Tokenizer +from ..tokenizer import Tokenizer +class T5BPETokenizer(Tokenizer): + def __init__(self, model_type_or_path="t5-base", cache_dir=None): + self.text_tokenizer = T5Tokenizer.from_pretrained(model_type_or_path, + cache_dir=cache_dir) + self.text_tokenizer.max_len = int(1e12) +``` + +### 3. 自定义分词器的接口 +如果Hugging Face里的分词器不能满足您的需求，那么需要先准备好一份词表，然后手动实现下列函数的功能： + +```python +def EncodeAsIds(self, text: str, process_fn=None): + """输入文本 => 一个token序号列表""" + +def EncodeAsTokens(self, text: str, process_fn=None): + """输入文本 => 一个token列表""" + +def IdToToken(self, Id: int): + """Token序号 => token""" + +def TokenToId(self, token: str): + """Token => token序号""" + return self.text_tokenizer._convert_token_to_id(token) + +def DecodeIds(self, Ids: list[int]): + """一个token序号列表 => 对应的文本""" + return self.DecodeTokens([self.IdToToken(id) for id in Ids]) + +def DecodeTokens(self, tokens: list[str]): + """一个token列表 => 对应的文本""" + return self.text_tokenizer.convert_tokens_to_string(tokens) +``` \ No newline at end of file diff --git a/doc_zh/TUTORIAL_20_GLM_TNEWS.md b/doc_zh/TUTORIAL_20_GLM_TNEWS.md new file mode 100644 index 00000000..bb6b5221 --- /dev/null +++ b/doc_zh/TUTORIAL_20_GLM_TNEWS.md @@ -0,0 +1,43 @@ +# 所有支持的任务 + + +您可以在 AutoLoader 中输入不同的“task_name”参数来加载模型以执行不同的任务。 + +```python +from flagai.auto_model.auto_loader import AutoLoader +from flagai.model.predictor.predictor import Predictor + +auto_loader = AutoLoader( + "seq2seq", + model_name="RoBERTa-base-ch", + load_pretrain_params=True, +) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` +在这个例子中，“task_name”是“seq2seq”。我们通过AutoLoader类构建了一个roberta seq2seq 模型。 + +我们将在下面列出所有当前支持的任务。 +### 所有支持的任务 +**task_name**参数可以为如下值: +1. task_name="classification": 支持不同的分类任务，例如文本分类， 语义匹配， 情感分析... +2. task_name="seq2seq": 支持序列到序列的模型, 例如标题自动生成, 对联自动生成, 自动对话... +3. task_name="sequence_labeling": 支持序列标注任务， 比如实体检测，词性标注，中文分词任务... +4. task_name="sequence_labeling_crf": 为序列标注模型添加条件随机场层. +5. task_name="sequence_labeling_gp": 为序列标注模型添加全局指针层. + +### 所有支持的模型 +所有支持的模型都可以在 **model hub** 中找到。 +不同的模型适应不同的任务。 + +#### Transfomrer编码器: + +例如 model_name="BERT-base-ch" or "RoBERTa-base-ch"时， 这些模型支持上一节中提到的所有任务 + +#### Transformer解码器: + +例如 model_name="GPT2-base-ch"时, 模型支持 "seq2seq" 任务. + +#### Transformer 编码器+解码器: + +例如 model_name="T5-base-ch"时, 模型支持"seq2seq" task. \ No newline at end of file diff --git a/doc_zh/TUTORIAL_20_SUPPORTED_TASKS_backup.md b/doc_zh/TUTORIAL_20_SUPPORTED_TASKS_backup.md new file mode 100644 index 00000000..c36eaf24 --- /dev/null +++ b/doc_zh/TUTORIAL_20_SUPPORTED_TASKS_backup.md @@ -0,0 +1 @@ +# 用GLM10b模型在TNEWS短文本分类数据集上微调 diff --git a/doc_zh/TUTORIAL_2_DATASET.md b/doc_zh/TUTORIAL_2_DATASET.md new file mode 100644 index 00000000..adea94dd --- /dev/null +++ b/doc_zh/TUTORIAL_2_DATASET.md @@ -0,0 +1,530 @@ +# 数据集处理流程 +构建数据集的过程就是NLP的数据预处理过程，其主要目的是将原始的散乱的文件数据重新整理成统一结构的数据，以便语言模型能够直接使用。构建数据集样例的主要流程如下所示（以`CommitmentBank`数据集为例）： + +
+ +目前项目里存在三种数据预处理的情况，即对分类任务的微调，预训练, 以及对生成任务的微调。我们在接下来会分别展开。 + + + +- [1 分类任务微调](#分类任务微调) + - [1.1 应用代码](#分类任务应用代码) + - [1.2 初步读取并处理数据集](#初步读取并处理数据集) + - [1.2a 加载数据集](#加载数据集) + - [1.2b 统一数据集结构](#统一数据集结构) + - [1.3 将数据整理成模型的输入](#将数据整理成模型的输入) + - [1.3a 构建完形填空模板](#构建完形填空模板) + - [1.3b 分词并构造输入样例](#分词并构造输入样例) + - [1.4 创建加载器](#创建加载器) +- [2 预训练](#预训练) + - [2.1 预训练任务数据格式样例](#预训练任务数据格式样例) + - [2.2 预训练的任务处理实例代码](#预训练的任务处理实例代码) +- [3 生成任务微调](#生成任务微调) + - [3.1 生成任务应用代码](#生成任务应用代码) + - [3.1 生成任务支持数据集](#生成任务支持数据集) + - [3.2 生成任务里将数据整理成模型的输入](#生成任务里将数据整理成模型的输入) + - [3.2a 生成任务里构建填空模板](#生成任务里构建填空模板) + - [3.2b 生成任务里分词并构造输入样例](#生成任务里分词并构造输入样例) + + + +## 分类任务微调 +分类任务微调有着两种形式：一种是普通的微调，一种是提示学习的方法。提示学习需要为任务额外构建一个完形填空的模板，它更适用于低资源以及小样本的情况。接下来我们以提示学习为例来介绍分类任务中的数据处理方法： +### 分类任务应用代码 +```python +import torch +from flagai.data.tokenizer import GLMLargeEnWordPieceTokenizer +from flagai.data.dataset import SuperGlueDataset +from flagai.test_utils import CollateArguments +from flagai.data.dataset import ConstructSuperglueStrategy + +# 得到默认参数 +cl_args = CollateArguments() + +# 创建分词器 +tokenizer = GLMLargeEnWordPieceTokenizer() + +# 初步读取并处理数据集 +dataset = SuperGlueDataset(task_name='cb', + data_dir='./datasets/', + dataset_type='train', + tokenizer=tokenizer) + +# 构建collate function +collate_fn = ConstructSuperglueStrategy(cl_args, tokenizer, task_name="rte") + +# 创建加载器 +loader = torch.utils.data.DataLoader(dataset, + batch_size=1, + shuffle=False, + num_workers=1, + drop_last=False, + pin_memory=False, + collate_fn=collate_fn) +``` + +### 初步读取并处理数据集 +对应的代码模块如下所示，其包含了两个步骤：自动加载数据集，以及统一所有数据集的结构 +```python +dataset = SuperGlueDataset(task_name='cb', + data_dir='./datasets/', + dataset_type='train', + tokenizer=tokenizer) +``` + + +其中`SuperGlueDataset`是我们这一步的主函数，它的主要参数及其作用如下所示： + +`task_name`: 数据集的简称, FlagAI支持的数据集及其对应的简称如[加载数据集](#加载数据集)所示。 + +`data_dir`: 数据集会被自动下载到`data_dir`对应的地址，默认为项目的`./dataset`目录。 + +`dataset_type`: 可以为 train/dev/test, 分别代表要处理的是训练集/验证集/测试集 + +`tokenizer`: 如[Tutorial1](/doc_zh/TUTORIAL_1_TOKENIZER.md)所介绍的构建完成的分词器 + + +#### 加载数据集 + +FlagAI目前支持下列分类数据集： + +| 数据集名称 | 数据集简称 | 语言 | 所属评测基准 | 支持提示学习 | 可自动下载 | 已测试通过 | +|------------------------------------------------------------------------------------------------------------------------------------------|---------------|-----|----------------------------------------------------|-------|---------------|-------------| +| [CommitmentBank](https://github.com/mcdm/CommitmentBank) | cb | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ✅ | +| [Choice of Plausible Alternatives](https://people.ict.usc.edu/~gordon/copa.html) | copa | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ✅ | +| [Multi-Sentence Reading Comprehension](https://cogcomp.org/multirc/ ) | muiltirc | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ✅ | +| [Recognizing Textual Entailment](https://aclweb.org/aclwiki/Recognizing_Textual_Entailment ) | rte | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ✅ | +| [Words in Context](https://pilehvar.github.io/wic/ ) | wic | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ✅ | +| [The Winograd Schema Challenge](https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html ) | wsc | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ✅ | +| [Broadcoverage Diagnostics](https://github.com/google-research-datasets/boolean-questions) | boolq | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ✅ | +| [Reading Comprehension with Commonsense Reasoning](https://sheng-z.github.io/ReCoRD-explorer/) | record | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ✅ | +| [Ant Financial Question Matching Corpus](https://tianchi.aliyun.com/dataset/dataDetail?dataId=106411 ) | afqmc | 中文 | [CLUE](https://www.clue.ai/index.html) | ✅ | ✅ | ✅ | +| [Short Text Classificaiton for News](https://metatext.io/datasets/toutiao-text-classification-for-news-titles-(tnews)-(clue-benchmark) ) | tnews | 中文 | [CLUE](https://www.clue.ai/index.html) | ✅ | ✅ | ✅ | +| [Broadcoverage Diagnostics](https://gluebenchmark.com/diagnostics) | ax-b | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ❌ | +| [Winogender Schema Diagnostics](https://github.com/rudinger/winogender-schemas) | ax-g | 英文 | [SuperGLUE](https://super.gluebenchmark.com/tasks) | ✅ | ✅ | ❌ | +| [The Corpus of Linguistic Acceptability](https://nyu-mll.github.io/CoLA/) | cola | 英文 | [GLUE](https://gluebenchmark.com/tasks) | ✅ | ✅ | ❌ | +| [The Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/index.html) | sst2 | 英文 | [GLUE](https://gluebenchmark.com/tasks) | ✅ | ✅ | ❌ | +| [Microsoft Research Paraphrase Corpus](https://microsoft.com/en-us/download/details.aspx?id=52398) | mrpc | 英文 | [GLUE](https://gluebenchmark.com/tasks) | ✅ | ✅ | ❌ | +| [Quora Question Pairs](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs) | qqp | 英文 | [GLUE](https://gluebenchmark.com/tasks) | ✅ | ✅ | ❌ | +| [MultiNLI Matched](http://www.nyu.edu/projects/bowman/multinli/) | mnli | 英文 | [GLUE](https://gluebenchmark.com/tasks) | ✅ | ✅ | ❌ | +| [MultiNLI Mismatched](http://www.nyu.edu/projects/bowman/multinli/) | mnli-mm | 英文 | [GLUE](https://gluebenchmark.com/tasks) | ❌ | ✅ | ❌ | +| [Question NLI](https://rajpurkar.github.io/SQuAD-explorer/) | qnli | 英文 | [GLUE](https://gluebenchmark.com/tasks) | ✅ | ✅ | ❌ | +| [X-Stance](https://github.com/ZurichNLP/xstance) | xstance | 英文 | | ✅ | ❌ | ❌ | +| [X-Stance (German)](https://github.com/ZurichNLP/xstance) | xstance-de | 德文 | | ✅ | ❌ | ❌ | +| [X-Stance (French)](https://github.com/ZurichNLP/xstance) | xstance-fr | 法文 | | ✅ | ❌ | ❌ | +| [RACE](https://www.cs.cmu.edu/~glai1/data/race/) | race | 英文 | | ✅ | ❌ | ❌ | +| [AG News](https://www.kaggle.com/datasets/amananandrai/ag-news-classification-dataset) | agnews | 英文 | | ✅ | ❌ | ❌ | +| [Yahoo Answers](https://www.kaggle.com/datasets/jarupula/yahoo-answers-dataset) | yahoo | 英文 | | ✅ | ❌ | ❌ | +| [Yelp Review Polarity](https://www.kaggle.com/datasets/irustandi/yelp-review-polarity) | yelp-polarity | 英文 | | ✅ | ❌ | ❌ | +| [Yelp Open Dataset](https://www.yelp.com/dataset) | yelp-full | 英文 | | ✅ | ❌ | ❌ | +| [squad](The Stanford Question Answering Dataset) | squad | 英文 | | ✅ | ❌ | ❌ | +| [CLUEWSC2020](https://github.com/CLUEbenchmark/CLUEWSC2020) | cluewsc | 中文 | [CLUE](https://www.clue.ai/index.html) | ✅ | ❌ | ❌ | + + +下载好的数据集目录下会包含三个文件，对应训练集数据，验证集数据，以及测试集数据, 以`CommitmentBank`数据集为例，目录下的`train.jsonl`对应训练集，`val.jsonl`对应验证集，`test.jsonl`对应测试集。一般来说训练集和测试集会包含标签信息，而测试集则没有。这些数据文件在接下来的流程中会分开处理。 + +不同的数据集可能会有不同的文件格式，以及不同的结构。以`CommitmentBank`数据集为例，下面是其中的一个样例 + +
+ +可以看到，其包含了四个部分,如下所示： + +| 键值 | 含义 | 值 | +|-----------|-------------------------------------------------------|--------------------------------| +| premise | 前提文本 | Mary is a high school student. | +| hypothesis | 假设文本 | Mary is a student | +| label | 代表前提和假设关系的标签
包含entailment,neutral和contradiction三种 | entailment | +| idx | 样例在数据集里的序号 | 10 | + +目前所有飞智支持数据集的具体结构可以在[这里](DATASET_EXAMPLE.md)查看。 + +#### 统一数据集结构 +在这一步里，我们会统一不同数据集的数据结构，以方便接下来处理。此结构的细节如下： + + +| 键值 | 含义 | 数据格式 | +|--------|-------------------------------------------------------|------| +| guid | a unique textual identifier | str | +| text_a | the sequence of text | str | +| text_b | an optional, second sequence of text | str | +| label | an optional label | str | +| logits | an optional list of per-class logits | list | +| meta | an optional dictionary to store arbitrary meta information | dict | +| ids | an optional numeric index | int | + +如果想要查看某个样例的数据结构，也可以直接在代码里通过索引的方式得到： +```python +example = dataset[3] # 数据集里第3个样例 +``` +比如说，上一步`CommitmentBank`的样例会被处理成如下的形式: + +
+ +需要注意的是如果因为数据结构太复杂，导致text_a和text_b无法塞下背景文本信息的话，剩下的信息会被放在`meta`里。 + + +### 将数据整理成模型的输入 + +对应的功能在下面的函数里实现，其包含了两个步骤：构造模板，分词并构造输入样例。 +```python +collate_fn = ConstructSuperglueStrategy(cl_args, + tokenizer, + task_name=task_name) +``` + +#### 构建完形填空模板 + +一个完形填空模板包含了背景文本，空位，以及在提供给空位的选项。模型需要找到正确的选项，并填进空位里。 + +对于每个不同的任务，我们都需要构建不同构造的完型填空问题来让模型回答，以CommitmentBank数据集为例， 其考量的是能否由前提推导出假设, 而有且仅有三种结果：contradiction/neutral/entailment。那么我们可以构建如下的完型填空问题， 其中contradiction/neutral/entailment分别对应true/false/neither。 + +
+ +可以看到，大体上可以分成两步：第一步是组合已有的文本，使其看上去符合完形填空格式；第二步是将原始的标签文本转化为新的标签，作为可能会填入空位的选项。 + +#### 分词并构造输入样例 +接下来，我们需要构造模型的输入，第一步是分词，而接下来则需要分成两种情况： + +第一种情况下，数据集包含的标签类别是有限的，比如`CommitmentBank`数据集里只会存在`entailment/contradiction/neutral`三种标签文本。第二类情况里每一段完型填空都会给出不同的选项(一般是一段长文本)。比如在一些阅读理解数据集里，每一个选项都是一段对于文本的不同理解。这两类情况的处理方法如下所示： + +**a)单个token的完形填空** + + +| 键值 | 维度 | 含义 | 构造方法 | +|---------------------------------------------|--------------------------------------|------------|-----------------------------------------------| +| input_ids | torch.Size([seq_length1]) | 输入矩阵 | 由上一步的完形填空文本，加上一些特殊字符2组成 | +| labels | labels: torch.Size([1]) | 标签 | 对应的数字标签，比如0,1,2... | +| position_ids | torch.Size([2， seq_length]) | 位置编码 | 参考[GLM流程](GLM.md)，第一行代表token的绝对位置，第二行代表遮挡部分的相对位置 | +| attention_mask | torch.Size([1]) | 分隔符位置 | | +| target_ids | torch.Size([num_labels3]) | 全量标签列表 | 将所有标签文本分别对应单token的标签，然后将这些标签的序号依次放入target_ids | +| logit_mask | torch.Size([seq_length]) | 对应的文本是否为回答 | 对于每个token, 如果是回答，则对应的地方为1，否则为0 | + + +1: seq_length代表规定的每个输入向量的最大长度 + +2: 特殊字符添加流程如下图所示：在句首添加[CLS]符号，句尾添加[EOS]符号，直到长度达到seq_length。 +
+ +3: num_labels代表完形填空问题中选项的个数 + +**b)多个token的完形填空** + +| 键值 | 维度 | 含义 | 区别 | +|---------------------------------------------|-----------------------------------------|------------|----------------------------------------------------| +| input_ids | torch.Size([num_labels, seq_length]) | 输入矩阵 | 将对应的文本拷贝num_labels份 | +| labels | labels: torch.Size([1]) | 标签 | | +| position_ids | torch.Size([num_labels, 2， seq_length]) | 位置编码 | 将原本的位置编码拷贝num_labels份 | +| attention_mask | torch.Size([num_labels]) | 分隔符位置 | 拷贝num_labels份 | +| target_ids | torch.Size([num_labels, seq_length]) | 每个选项信息 | 矩阵的每一列代表每一个选项，每一行代表当前选项对应的文本 | +| logit_mask | torch.Size([num_labels, seq_length]) | 对应的文本是否为回答 | 拷贝num_labels份 | + + + +### 创建加载器 + +最后将数据放入[PyTorch加载器](https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader)即可。 +```python +import torch +loader = torch.utils.data.DataLoader(dataset, + batch_size=1, + shuffle=False, + num_workers=1, + drop_last=False, + pin_memory=False, + collate_fn=collate_fn) +``` +Dataloader里的数据可以通过如下方法查看 + +```python +for data_iterator in loader: + for key, value in data_iterator.items(): + print(key, value) + # break +``` +加载器构造好之后即可用于接下来的训练和预测过程。


+ +### 自定义数据处理 +如果要处理不支持的数据集，需要您手动实现以下功能 + +1. 在`flagai/data/dataset/superglue/control.py`文件里, 如下所示创建自定义的processor函数， 然后把它与自定义数据集名字的映射关系添加到control.py下的`PROCESSOR_DICT`字典里。 + +其中最主要的函数是`_create_example`, 需要在这里读取对应的数据集，并如[之前](#统一数据集结构)所示初步处理成统一的结构。 + +```python +class ExampleProcessor(DataProcessor): + + def get_train_examples(self, data_dir): + # Assign the filename of train set + return self._create_examples(os.path.join(data_dir, "train.tsv"), "train") + + def get_dev_examples(self, data_dir, for_train=False): + # Assign the filename of dev set + return self._create_examples(os.path.join(data_dir, "dev.tsv"), "dev") + + def get_test_examples(self, data_dir) -> List[InputExample]: + # Assign the filename of test set + return self._create_examples(os.path.join(data_dir, "test.tsv"), "test") + + def get_labels(self): + # Return all label categories + return ["0", "1"] + + @staticmethod + def _create_examples(path: str, set_type: str) -> List[InputExample]: + """ + InputExample包含下列信息 + text_a (str, required): 文本1 + text_b (str, optional): 文本2 + label (str, required): 标签 + guid (str, required): 每一个InputExample的唯一序号 + """ + examples = [] + df = read_tsv(path) + + for idx, row in df.iterrows(): + guid = f"{set_type}-{idx}" + text_a = punctuation_standardization(row['sentence']) + label = row.get('label', None) + example = InputExample(guid=guid, text_a=text_a, label=label) + examples.append(example) + return examples + + + +``` +2. 在`flagai/data/dataset/superglue/control.py`文件里, 如下所示创建自定义的pvp函数， 然后把它与自定义数据集名字的映射关系添加到control.py下的`PVPS`字典里。 + +此函数的主要功能是如[之前](#将数据整理成模型的输入)所说的构建完形填空模板，其主要函数有 + +`get_parts`: 构造完形填空文本 + +`verbalize`：把数据集的标签映射到完形填空的选项上 + + +```python +class ExamplePVP(PVP): + # 把标签映射到对应的含义文本上 + VERBALIZER = { + "contradiction": [" No"], + "entailment": [" Yes"], + "neutral": [" Maybe"] + } + + @staticmethod + def available_patterns(): + # 输出所有可用的模板 + return [0] + + @property + def is_multi_token(self): + # 如果标签包含大于一个token，就输出True + return False + + def get_parts(self, example: InputExample) -> FilledPattern: + # 把InputExample里面的元素通过设计组合成一个完形填空模板 + + text_a = self.shortenable(example.text_a) + text_b = self.shortenable(" " + example.text_b) + parts_a, parts_b = [ + None, text_a, None, ' question:', text_b, + ' true, false or neither?', None, ' answer:', [self.mask] + ], [] + parts_a, parts_b = self.replace_prompt_tokens(parts_a, parts_b) + return parts_a, parts_b + + def verbalize(self, label) -> List[str]: + # 借用类里自定义的VERBALIZER完成初始标签到完形填空选项的映射 + return [' true'] if label == 'entailment' else [ + ' false' + ] if label == 'contradiction' else [' neither'] + +``` + +## 数据处理：GLM预训练任务 +### 预训练任务数据格式样例 +```text +{ + "RECORDS": [ + { + "id": 0, + "uniqueKey": "", + "titleUkey": "", + "dataType": "", + "title": "", + "content": "平安信用卡上门激活失败是为啥？平安信用卡上门激 + 活失败的主要原因有这些：申卡人是在网上申请的新用户，上门激活时 + 携带资料不足，需要申卡人携带身份证、信用卡、预留号码等去平安银 + 行网点验证身份才能激活；申卡人在平安银行预留的手机号码有误；申卡 + 人在激活时输入的相关信息不正确；申卡人在回答上门激活工作人员的问 + 题是，跟之前提交的资料有一些出入，有违规嫌疑。平安信用卡上门激活失 + 败的主要原因有这些：申卡人是在网上申请的新用户，上门激活时携带资料不 + 足，需要申卡人携带身份证、信用卡、预留号码等去平安银行网点验证身份才能 + 激活；申卡人在平安银行预留的手机号码有误；申卡人在激活时输入的相关信息不 + 正确；申卡人在回答上门激活工作人员的问题是，跟之前提交的资料有一 + 些出入，有违规嫌疑。" + }, + ] +} +``` +### 预训练的任务处理实例代码 +```python +from flagai.data.tokenizer import GLMLargeChTokenizer +from flagai.data.dataset import BlockDataset +from flagai.data.dataset.block.data_utils import split_ds, get_dataset_lazy, add_args +from flagai.test_utils import PretrainDatasetArguments + +tokenizer = GLMLargeChTokenizer(add_block_symbols=True, + add_task_mask=True, + add_decoder_mask=False, + fix_command_token=True) + +ds_args = PretrainDatasetArguments() + +tokenizer = GLMLargeChTokenizer(fix_command_token=True, + add_block_symbols=True, + add_task_mask=True, + add_decoder_mask=False) + +ds_args = add_args(ds_args, tokenizer) + +def create_dataset(tokenizer, should_split): + dataset = get_dataset_lazy("./examples/glm_pretrain/data", # 懒加载 + tokenizer=tokenizer, + pre_tokenize=True, + num_processes=10, + no_lazy_loader=True) + if should_split: + datasets = split_ds(dataset, split=[.8, .2, .0], shuffle=True) # 手动切分 + else: + datasets = [dataset] + + datasets = [ + BlockDataset(ds, + tokenizer, + max_seq_len=512, + sample_across_doc=True, + non_sentence_start=0.0) if ds is not None else None + for ds in datasets + ] + return datasets + +datasets = create_dataset(tokenizer, should_split=True) +``` +预训练的数据处理也遵循着同样的流程，不过有如下的区别 +1. 预训练数据集默认是没有切分成训练集，验证集，以及测试集的，所以还需要手动进行切分 +2. 由于预训练数据集一般来说较为庞大，所以使用了懒加载(lazy loading)，懒加载只在真正用到对象的时候才使其实例化，是一种较为节省资源的操作。 +3. 预训练的时候，collate function会随机按照三种不同的模式处理数据: bert模式（遮挡随机区间），sentence模式(按照完整句子进行遮挡)以及gpt模式（只遮挡一段长区间）。模型的输入相比一般生成任务也会多一个`mode`键。 +4. 预训练不用添加模板，直接按下表构建模型输入即可 + +| 键值 | 维度 | 含义 | 构造方法 | +|----------------|--------------------------------------|-------------------------------|--------------------------------------------------| +| input_ids | torch.Size([seq_length1]) | 输入矩阵 | 由上一步的模板文本，加上一些特殊字符组成 | +| position_ids | torch.Size([2， seq_length]) | 位置编码 | 参考[GLM流程](GLM.md)，第一行代表token的绝对位置，第二行代表遮挡部分的相对位置 | +| attention_mask | torch.Size([1]) | 分隔符位置 | 对于生成类的模式，得到源文本结束的位置；否则得到输入文本结束的位置 | +| target_ids | torch.Size([num_labels3]) | 全量标签列表 | 被遮挡住的文本 | +| logit_mask | torch.Size([seq_length]) | 通过遮挡使得模型只会处理目标文本部分的loss | 对于每个token, 如果是回答，则对应的地方为1，否则为0 | +| mode | str | 数据处理模式，取值范围：bert/sentence/gpt | | + +


+ +## 数据处理：生成任务微调 +### 生成任务应用代码 +```python +import torch +from flagai.data.dataset import Seq2SeqDataset +from flagai.data.tokenizer import GLMLargeEnWordPieceTokenizer +from flagai.test_utils import CollateArguments +from flagai.data.dataset import ConstructSeq2seqStrategy + +# 得到默认参数 +cl_args = Seq2SeqCollateArguments() + +# 创建分词器 +tokenizer = GLMLargeChTokenizer(add_block_symbols=True, + TUTORIAL_4_DATASET.md add_task_mask=False, + add_decoder_mask=False, + fix_command_token=False) + +# 初步读取并处理数据集 +dataset = Seq2SeqDataset(task_name='cmrc', + data_dir='./datasets/', + dataset_type='train', + tokenizer=tokenizer) + +# 构建collate function +collate_fn = ConstructSeq2seqStrategy(cl_args, tokenizer, task_name="rte") + +# 创建加载器 +loader = torch.utils.data.DataLoader(dataset, + batch_size=1, + shuffle=False, + num_workers=1, + drop_last=False, + pin_memory=False, + collate_fn=collate_fn) +``` + + +### 生成任务支持数据集 +| 数据集名称 | 数据集简称 | 语言 | 可自动下载 | 已测试通过 | +|----------------------------------------------------------------------------------------------------|------------------|-----|---------------|-------------| +| [Reading Comprehension for Simplified Chinese](https://hfl-rc.github.io/cmrc2018/) | cmrc | 中文 | ✅ | ✅ | +| [English Gigaword](https://catalog.ldc.upenn.edu/LDC2003T05) | gigaword | 英文 | ❌ | ❌ | +| [CNN/Daily Mail](https://paperswithcode.com/dataset/cnn-daily-mail-1) | cnn_dm | 英文 | ❌ | ❌ | +| Lang-8 and HSK | lang8_hsk | 中文 | ❌ | ❌ | +| [XSum](https://paperswithcode.com/dataset/xsum) | xsum | 英文 | ❌ | ❌ | +| [squad](The Stanford Question Answering Dataset) | squad_generation | 英文 | ❌ | ❌ | +| [The Winograd Schema Challenge](https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html ) | wsc | 英文 | ✅ | ❌ | + + + +### 生成任务里初步读取并处理数据集 + +以[CMRC2018](https://www.clue.ai/introduce.html)为例，CMRC是一个阅读理解类的任务，需要根据背景文本来回答一系列提问，其数据结构示例如下： + +```text +{'paragraphs': + [{'id': 'TRAIN_186', + 'context': '范廷颂枢机（，），圣名保禄·若瑟（），是越南罗马天主教枢机。1963年被任 + 为主教；1990年被擢升为天主教河内总教区宗座署理；1994年被擢升为总主教，同年年底被擢 + 升为枢机；2009年2月离世。范廷颂于1919年6月15日在越南宁平省天主教发艳教区出生；童年 + 时接受良好教育后，被一位越南神父带到河内继续其学业。范廷颂于1940年在河内大修道院完 + 成神学学业。范廷颂于1949年6月6日在河内的主教座堂晋铎；及后被派到圣女小德兰孤儿院服 + 务。1950年代，范廷颂在河内堂区创建移民接待中心以收容到河内避战的难民。1954年，法越 + 战争结束，越南民主共和国建都河内，当时很多天主教神职人员逃至越南的南方，但范廷颂仍然 + 留在河内。翌年管理圣若望小修院；惟在1960年因捍卫修院的自由、自治及拒绝政府在修院设 + 政治课的要求而被捕。1963年4月5日，教宗任命范廷颂为天主教北宁教区主教，同年8月15日就 + 任；其牧铭为「我信天主的爱」。由于范廷颂被越南政府软禁差不多30年，因此他无法到所属堂 + 区进行牧灵工作而专注研读等工作。范廷颂除了面对战争、贫困、被当局迫害天主教会等问题外 + ，也秘密恢复修院、创建女修会团体等。1990年，教宗若望保禄二世在同年6月18日擢升范廷颂 + 为天主教河内总教区宗座署理以填补该教区总主教的空缺。1994年3月23日，范廷颂被教宗若望 + 保禄二...', + 'qas':{'question': '范廷颂是何时去世的？', 'id': 'TRAIN_186_QUERY_4', + 'answers': [{'text': '范廷颂于2009年2月22日清晨在河内离世', 'answer_start': 759}]}]}], + 'id': 'TRAIN_186', 'title': '范廷颂'} +``` +使用的时候，我们将`task_name`参数改为`cmrc`即可。实现的过程类似分类任务的微调，最终也会将数据集初步处理成一样的结构，对应代码如下： + +```python +dataset = Seq2SeqDataset(task_name='cmrc', data_dir='./datasets/', + dataset_type='train', tokenizer=tokenizer) +``` + +### 生成任务里将数据整理成模型的输入 + +代码如下所示。与生成任务相比，同样是构造模板以及模型输入，区别在于构造的方式不一样 +```python +collate_fn = ConstructSeq2seqStrategy(cl_args, + tokenizer, + task_name=task_name) +``` + +#### 生成任务里构建填空模板 +由于是阅读理解任务，所以在模板里需要体现出是针对指定的阅读理解问题进行回答的，参考如下构建方式 +
+ + + +#### 生成任务里分词并构造输入样例 +与预训练类似，区别在于没有mode这个键值。 + diff --git a/doc_zh/TUTORIAL_3_MODEL.md b/doc_zh/TUTORIAL_3_MODEL.md new file mode 100644 index 00000000..407379d3 --- /dev/null +++ b/doc_zh/TUTORIAL_3_MODEL.md @@ -0,0 +1,118 @@ +# 模型的主要功能及相关结构 +- [模型的主要功能及相关结构](#模型的主要功能及相关结构) + - [基类](#基类) + - [From_pretrain](#from_pretrain) + - [从modelhub加载](#从modelhub加载) + - [从本地加载](#从本地加载) + - [所有支持模型](#所有支持模型) + - [支持的模型+任务](#支持的模型任务) + - [模型设计](#模型设计) + - [模型的forward 函数](#模型的forward-函数) + - [输入](#输入) + - [输出](#输出) + - [模型的init_from_json函数](#模型的init_from_json函数) + - [输入](#输入-1) + - [输出](#输出-1) +## 基类 + +基类 BaseModel 实现了从本地文件或目录或从库提供的预训练模型配置（从BAAI modelhub 的 金山 S3 存储库下载）加载/保存模型的常用方法。现在对三种最常见的模型类型`encoder`，`decoder`和`encoder-decoder`进行了支持。同一种结构的模型，可以用同一个`Class`来进行加载。比如,`GLMModel`可以加载所有`GLM`系列的模型，详见 https://github.com/THUDM/GLM。 + +## From_pretrain + +`From_pretrain` 函数用于加载模型。同一个模型结构的模型可以用同一个class进行加载，比如`BERT-base-ch` 和`Roberta-base-ch`模型都能用`BertModel`这个`Class`进行加载。`From_pretrain`为了数据/模型并行的模型加载进行了特定优化，避免重复下载导致的资源浪费。 +通过调用`ClassName.from_pretrian()`来进行加载. +### 从modelhub加载 +现在我们支持从modelhub中下载[常用模型](#所有支持模型)，可以直接通过`from_pretrain`下载模型配置文件`config.json`，模型权重`pytorch_model.bin`，以及字典文件`vocab.txt`。例子： +```python +#从modelhub下载GLM-large-ch 模型 +from flagai.model.glm_model import GLMModel +model = GLMModel.from_pretrain(download_path="./state_dict", model_name="GLM-large-ch") +``` +### 从本地加载 +如果想从本地加载模型权重，也可以通过 `ClassName.from_pretrain()`进行加载。模型权重保存在`download_path/model_name/`中,其中`model_name`是模型相关所在的目录，而`download_path`是`model_name`所在的目录，例子： + +```python +#从`./state_dict/GLM-large-ch`目录中加载模型文件 `pytorch_model.bin`和模型配置文件 `config.json`。 +from flagai.model.glm_model import GLMModel +model = GLMModel.from_pretrain(download_path="./state_dict", model_name="GLM-large-ch") +``` +## 所有支持模型 +这些模型都可以通过`from_pretrain` 从model_hub下载。 + +| ClassName | ModelName | Language | Model Type | +|-----------------------------------|---------------------|----------|------------| +| flagai.model.glm_model.GLMModel | **GLM-10b-ch** | chinese | encoder | +| flagai.model.glm_model.GLMModel | **GLM-large-ch** | chinese | encoder | +| flagai.model.bert_model.BertModel | **RoBERTa-base-ch** | chinese | encoder | +| flagai.model.gpt2_model.GPT2Model | **GPT2-base-ch** | chinese | decoder | +| flagai.model.t5_model.T5Model | **T5-base-ch** | chinese | enc2dec | +| flagai.model.t5_model.T5Model | **T5-base-en** | chinese | enc2dec | +| flagai.model.bert_model.BertModel | **BERT-base-en** | english | encoder | +| flagai.model.glm_model.GLMModel | **GLM-large-en** | english | encoder | + +## 支持的模型+任务 +同时，我们对在任务上finetune好的模型进行了支持，如下表所示，可以通过ClassName.from_pretrain()来加载模型权重，例如，我们自动下载并加载一个在title-generation任务上训练好的GLM-large-ch模型： +```python +from flagai.model.glm_model import GLMForSeq2Seq +model = GLMForSeq2Seq.from_pretrain(model_name='GLM-large-ch') +``` +为了简化加载流程，我们也提供了AutoLoader类来帮助加载模型，比如GLM-large-ch模型用于seq2seq任务。由于我们采用了任务和模型独立的设计，理论上任务和模型可以自由更换。 +```python +from flagai.auto_model.auto_loader import AutoLoader +auto_loader = AutoLoader("title-generation", + model_name="GLM-large-ch", + model_dir= "./state_dict") +model = auto_loader.get_model() +``` +| ClassName | Model Name | language | Task | +|-------------------------------------------------|-----------------|----------|-------------------| +| flagai.model.glm_model.GLMForSeq2Seq | GLM-large-ch | chinese | **title generation** | +| flagai.model.glm_model.GLMForSeq2Seq | GLM-large-ch | chinese | **poetry generation** | +| flagai.model.bert_model.BertForSequenceLabeling | RoBERTa-base-ch | chinese | **title generation** | +| flagai.model.bert_model.BertForSequenceLabeling | RoBERTa-base-ch | chinese | **NER** | +| flagai.model.bert_model.BertForSequenceLabeling | RoBERTa-base-ch | chinese | **semantic matching** | +| flagai.model.t5_model.T5Model | T5-base-ch | chinese | **title generation** | +| flagai.model.bert_model.BertForSequenceLabeling | BERT-base-en | english | **title gneration** | + +## 模型设计 +模型主要的构建逻辑`layer->block->model` + +`flagai.model.layer`: 包括mlp，layernorm, activation，attention等各种layer层 + +`flagai.model.block`:通过组装各种layer来构建transformer block，比如BERT block等 + +`flagai.model`: 通过embedding层和stacked blocks 来构建model + +如果想要自定义一个新的模型结构，可以参考上述构建过程。 + +## 模型的forward 函数 +### 输入 +输入都是 keyword arguments：包括 input_ids, position_ids, attention_mask等，对冗余的参数会自动忽略 +比如GLM的forward 函数： +```python +def forward(self, + input_ids=None, + position_ids=None, + attention_mask=None, + mems=None, + return_memory=False, + detach_memory=True, + prompt_pos=None, + **kwargs) +``` +### 输出 +输出是 dictionary，包括 logits 和hidden states，这两个是必须的，例如GLM forword函数的返回： +```python +return {'loss': loss, 'logits': logits, 'hidden_states': mems} +``` +## 模型的init_from_json函数 +### 输入 +输入是一个`json`，以及预留参数 `**kwags`，为了兼容一些模型新增的初始化参数 + +例如GLMModel的调用如下： +```python +GLMModel.init_from_json(config_file = "./config.json", checkpoint_activations=True) +``` +`checkpoint_activations=True`是新增的参数，用于控制是否进行梯重计算 +### 输出 +输出是一个初始化的model \ No newline at end of file diff --git a/doc_zh/TUTORIAL_4_TRAINER.md b/doc_zh/TUTORIAL_4_TRAINER.md new file mode 100644 index 00000000..c70c7871 --- /dev/null +++ b/doc_zh/TUTORIAL_4_TRAINER.md @@ -0,0 +1,337 @@ +# 为模型和数据并行训练定制训练器 +- [Trainer](#trainer) + - [入门](#入门) + - [Trainer主要参数env_type](#trainer主要参数env_type) + - [自定义Trainer](#自定义trainer) + - [单节点cpu/gpu](#单节点cpugpu) + - [fp16](#fp16) + - [梯度重计算](#梯度重计算) + - [GLM-10b-ch](#glm-10b-ch) + - [huggingface t5-11b](#huggingface-t5-11b) + - [完整的用Trainer训练huggingface t5-11b例子](#完整的用trainer训练huggingface-t5-11b例子) + - [分布式训练](#分布式训练) + - [deepspeed](#deepspeed) + - [pytorchDDP](#pytorchddp) + - [deepspeed + megatron-lm](#deepspeed--megatron-lm) + + +Trainer 类提供了API用于多种并行框架的训练。API 支持在多个 GPU上使用Pytorch DDP/Deepspeed进行分布式训练，同时支持Megatron-LM+Deepspeed的混合并行分布式训练，同时也通过 NVIDIA Apex 实现混合精度。 +## 入门 +Trainer 包含支持上述功能的基本训练循环。Trainer使用分成两个步骤，初始化和调用. 参考 `examples/glm_superglue` 目录下的代码. + +## Trainer主要参数env_type +Trainer 中有一个控制是否分布式训练的参数 env_type， +```shell +The enviroment type for training. Will default to 'pytorch'. +env_type: `pytorch`, `pytorchDDP`, `deepspeed`, `deepspeed+mpu` + pytorch: single node cpu/gpu + pytorchDDP: single-/multi- node gpu + deepspeed: single-/multi- node gpu + deepspeed+mpu: single-/multi- node gpu +``` + +## 自定义Trainer +在使用自定义模型的时候，模型的输入输出与`FlagAI`框架中模型的行为不一致时（参考 [模型forward函数介绍](TUTORIAL_3_MODEL.md#模型的forward-函数)），需要自定义Trainer来进行训练。要自定义`Trainer`来快速支持自定义模型，您可以它们进行子类化并覆盖`forward_step`方法. 这里需要注意：`forward_step`方法的返回是一个dict +```python +from flagai.trainer import Trainer +class MyTrainer(Trainer): + + def forward_step(self, data, model, mems): + + model_outputs = model(**data) + output = {} + output['loss'] = model_outputs.loss + output['logits'] = model_outputs.logits + output['hidden_states'] = model_outputs.decoder_hidden_states + return output +``` + +## 单节点cpu/gpu +可以看到这里的Trainer使用的是`pytorch` `cpu`的环境 +```python +trainer = MyTrainer( + env_type='pytorch', + epochs=1, + batch_size=4, + eval_interval=10, + log_interval=10, + experiment_name='t5-11b', + pytorch_device='cpu', + load_dir=None, + lr=1e-4) +``` +指定显卡/cpu的设置为pytorch_device, 'cpu', 'cuda:0'等 +## fp16 +在模型参数大，而显存空间又很紧张的时候，可以通过将`fp32`的参数转化为`fp16`的参数来降低显存的使用量.FlagAI可以通过一个参数来启动。 +```python +trainer = MyTrainer( + env_type='pytorch', + epochs=1, + batch_size=1, + eval_interval=10, + log_interval=10, + experiment_name='t5-11b', + pytorch_device='cuda:0', + load_dir=None, + lr=1e-4, + fp16=True) # change to `True` +``` +## 梯度重计算 +在前向过程中不保存中间变量，节约GPU显存. Paper: [Training Deep Nets with Sublinear Memory Cost](https://arxiv.org/abs/1604.06174v2)。下面，我们举了两个百亿模型开启梯度重计算的例子，一个是从FlagAI加载百亿参数模型，一个是加载huggingface的百亿模型。 + +### GLM-10b-ch +```python +#从modelhub下载GLM-10b-ch 模型并开启梯度重计算 +from flagai.model.glm_model import GLMModel +model = GLMModel.from_pretrain(download_path="./state_dict", model_name="GLM-large-ch", checkpoint_activations=True) +``` +### huggingface t5-11b +t5-11b模型的 paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) +```python +from transformers import T5ForConditionalGeneration, T5Tokenizer +tokenizer = T5Tokenizer.from_pretrained('t5-11b') +model = T5ForConditionalGeneration.from_pretrained('t5-11b') +model.gradient_checkpointing = True +``` + +为了展示FlagAI的Trainer的扩展能力，下面我们用训练T5-11b模型作为例子。**注意**: 百亿规模以上的模型权重在20G+，单块显卡需要V100 及以上的硬件。 + +## 完整的用Trainer训练huggingface t5-11b例子 +FlagAI 项目的example目录位置：`examples/t5_huggingface` + +```python +from flagai.trainer import Trainer +from transformers import T5ForConditionalGeneration, T5Tokenizer +from torch.utils.data import Dataset +import torch + + +class MyTrainer(Trainer): + + def forward_step(self, data, model, mems): + model_outputs = model(**data) + output = {} + output['loss'] = model_outputs.loss + output['logits'] = model_outputs.logits + output['hidden_states'] = model_outputs.decoder_hidden_states + return output + + +trainer = MyTrainer( + env_type='deepspeed', + epochs=1, + batch_size=1, + eval_interval=10, + log_interval=10, + experiment_name='t5-11b', + load_dir=None, + lr=1e-4 + # parameters for pytorchDDP + master_ip='127.0.0.1', + master_port=17750, + num_nodes=1, + num_gpus=1, + training_script=__file__, + # deepspeed + deepspeed_config='deepspeed.json' +) + +model_name = 't5-11b' +tokenizer = T5Tokenizer.from_pretrained(model_name) +model = T5ForConditionalGeneration.from_pretrained(model_name) +model.gradient_checkpointing = True + +print("loading model & tokenizer is done!") +src_dir = './data/train.src' +tgt_dir = './data/train.tgt' +maxlen = 1024 + + +def read_file(): + src = [] + tgt = [] + + with open(src_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + src.append(line.strip('\n').lower()) + + with open(tgt_dir, 'r', encoding='utf-8') as f: + lines = f.readlines() + for line in lines: + tgt.append(line.strip('\n').lower()) + return src, tgt + + +class T5Seq2seqDataset(Dataset): + + def __init__(self, sents_src, sents_tgt, tokenizer, maxlen=512): + super(T5Seq2seqDataset, self).__init__() + self.sents_src = sents_src + self.sents_tgt = sents_tgt + self.tokenizer = tokenizer + self.maxlen = maxlen + + def __getitem__(self, i): + src = self.sents_src[i] + tgt = self.sents_tgt[i] + inputs = tokenizer(src) + with tokenizer.as_target_tokenizer(): + labels = tokenizer(tgt) + output = {} + output['input_ids'] = inputs.input_ids + output['labels'] = labels.input_ids + return output + + def __len__(self): + return len(self.sents_src) + + +def seq2seq_collate_fn(batch): + + def padding(indice, max_length, pad_idx=0): + + pad_indice = [ + item + [pad_idx] * max(0, max_length - len(item)) + for item in indice + ] + return torch.tensor(pad_indice) + + token_ids = [data["input_ids"] for data in batch] + max_length_tk = max([len(t) for t in token_ids]) + labels = [data["labels"] for data in batch] + max_length_lb = max([len(t) for t in labels]) + + token_ids_padded = padding(token_ids, max_length_tk) + labels_padded = padding(labels, max_length_lb) + + data = {"input_ids": token_ids_padded, "labels": labels_padded} + + return data + + +sents_src, sents_tgt = read_file() +data_len = len(sents_tgt) +train_size = int(data_len * 0.8) +train_src = sents_src[:train_size][:200] +train_tgt = sents_tgt[:train_size][:200] + +val_src = sents_src[train_size:] +val_tgt = sents_tgt[train_size:] + +train_dataset = T5Seq2seqDataset(train_src, + train_tgt, + tokenizer=tokenizer, + maxlen=maxlen) +val_dataset = T5Seq2seqDataset(val_src, + val_tgt, + tokenizer=tokenizer, + maxlen=maxlen) + +trainer.train(model, + train_dataset=train_dataset, + collate_fn=seq2seq_collate_fn) +``` + +## 分布式训练 +为了加速模型训练，飞智支持三种模式的并行方式，但是在上述训练T5-11b的例子中，只能使用`deepspeed`框架的数据并行模式。 + +### deepspeed +deepspeed主要在优化器方面做了优化，其提供了cpu-offload的optimizer，可以极大的降低gpu显存的占用。对deepspeed优化器的相关设置，需要提供`deepspeed.json`的配置文件 +```shell +{ + "train_micro_batch_size_per_gpu": 2, + "gradient_accumulation_steps": 1, + "steps_per_print": 100, + "gradient_clipping": 1.0, + "zero_optimization": { + "stage": 3, + "contiguous_gradients": false, + "overlap_comm": true, + "reduce_scatter": true, + "reduce_bucket_size": 5e7, + "allgather_bucket_size": 5e7, + "cpu_offload": true + }, + "zero_allow_untested_optimizer": true, + "fp16": { + "enabled": true, + "loss_scale": 0, + "loss_scale_window": 1000, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "optimizer": { + "type": "Adam", + "params": { + "lr": 0.0004, + "weight_decay": 0.01, + "betas": [ + 0.9, + 0.98 + ], + "eps": 1e-6 + } + }, + "activation_checkpointing": { + "partition_activations": false, + "contiguous_memory_optimization": false + }, + "wall_clock_breakdown": false +} +``` +`deepspeed_config`文件参考 `examples/t5_huggingface/deepspeed.json`.其中，主要的参数为 `stage` 和 `cpu_offload`. + +`hostfile`在单节点时可以省略 +### pytorchDDP +DistributedDataParallel (DDP) 在模型的参数规模<1 billion,比如`t5-base`的时候可以使用。我们可以通过修改`env_type`参数就能完成分布式框架的切换. + +```python +trainer = MyTrainer( + env_type='pytorchDDP', + epochs=1, + batch_size=1, + eval_interval=10, + log_interval=10, + experiment_name='t5-base', + load_dir=None, + lr=1e-4 + # parameters for pytorchDDP + master_ip='127.0.0.1', + master_port=17750, + num_nodes=1, + num_gpus=1, + hostfile='./hostfile', + training_script=__file__, +) +``` +### deepspeed + megatron-lm + +现在百亿级模型GLM-10-ch采用了`Megatron-LM`的模型并行技术加上`deepspeed`的数据并行技术。在模型参数scaling到10b以上级别，单卡的显存就很难将单个模型以及训练时的中间变量全部加载进来。为此，Megatron-LM提供了Tensor的切分方法，主要思想是将矩阵按照行/列进行切分。 FlagAI 将model转化为Megatron-LM版本。 + +如下，飞智内部模型（GLM，T5，BERT【包括RoBERTa】，GPT2）支持了Megatron-LM，只要在配置文件中将环境变量修改为deepspeed+mpu，就能启动模型并行的功能。 +对于huggingface版本的模型，暂时没有提供模型并行的支持。 +```python +trainer = MyTrainer( + env_type="deepspeed+mpu", # env_type + epochs=1, + batch_size=8, + eval_interval=10, + log_interval=10, + experiment_name='GLM-10b-ch', + load_dir=None, + lr=1e-4, + # parallel settings + master_ip='127.0.0.1', + master_port=17750, + num_nodes=1, + num_gpus=4, + hostfile='hostfile', + training_script=__file__, + # deepspeed + deepspeed_config='deepspeed.json', + # megatron-lm + model_paralle_size = 2 +) +``` + diff --git a/doc_zh/TUTORIAL_5_INSTRUCTIONS_FOR_AutoLoader.md b/doc_zh/TUTORIAL_5_INSTRUCTIONS_FOR_AutoLoader.md new file mode 100644 index 00000000..3448e816 --- /dev/null +++ b/doc_zh/TUTORIAL_5_INSTRUCTIONS_FOR_AutoLoader.md @@ -0,0 +1,86 @@ +## 使用 Autoloader 简化模型和分词器初始化过程 + +### 通过 AutoLoader 快速构建支持不同下游任务的大型模型。 +![](./img/autoloader_map.png) + +AutoLoader 可以快速找到对应的预训练模型和分词器，只需输入 task_name 和 model_name 即可。 + +以标题生成任务为例： + +```python +from flagai.auto_model.auto_loader import AutoLoader +auto_loader = AutoLoader(task_name="title-generation", ## The task name + model_name="RoBERTa-base-ch", ## The model name. + ) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` +```task_name``` 你要想做的任务名字, 除了 ```title-generation```, 你也可以选择其他的细分任务, 例如 ```semantic-matching```、```ner``` and so on. +```python +target = ["O", "B-LOC", "I-LOC", "B-ORG", "I-ORG", "B-PER", "I-PER"] +from flagai.auto_model.auto_loader import AutoLoader +auto_loader = AutoLoader(task_name="ner", + model_name="RoBERTa-base-ch", + class_num=len(target)) +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` +在构建分类相关任务的模型时，还需要输入一个“class_num”参数来告诉模型要分类多少类。 + +然后 AutoLoader 将从模型中心下载 roberta 预训练模型、配置和词表。 +并且下载的预训练模型、配置和词表将被放入“./state_dict/RoBERTa-base-ch”目录中。 + +另外，通过输入不同的model_name，也可以直接调用已经训练好的下游任务模型，如``Roberta-base-ch-ner``、``Roberta-base-ch-title-generation``等。 +```python +target = ["O", "B-LOC", "I-LOC", "B-ORG", "I-ORG", "B-PER", "I-PER"] +from flagai.auto_model.auto_loader import AutoLoader +auto_loader = AutoLoader(task_name="ner", + model_name="RoBERTa-base-ch-ner", + class_num=len(target)) + +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +``` +查看 https://github.com/BAAI-Open/FlagAI/blob/master/quickstart/ner_ch.py 了解更多. +## 所有支持的任务和模型 + +![model_and_task_table](../docs/img/model_task_table.png) + **task_name** 参数支持: +### 分类任务 +1. classification +2. semantic-matching +3. emotion-analysis (todo) +4. text-classification (todo) +5. ... + +### 生成任务 +1. seq2seq +2. title-generation +3. writing +4. poetry-generation +5. couplets-generation (todo) +6. ... +### 序列标注任务 +1. sequence-labeling +2. ner +3. ner-crf +4. ner-gp +5. part-speech-tagging (todo) +6. chinese-word-segmentation (todo) +7. ... + +### 所有支持的模型 +所有支持的模型都可以在 **[这里](./TUTORIAL_3_MODEL.md#所有支持模型)** 中找到。 +不同的模型适应不同的任务。 + +#### Transfomrer encoder: + +例如，model_name="GLM-large-ch" 或 "RoBERTa-base-ch" 这些模型支持上一节中提到的所有任务。 + +#### Transformer decoder: + +例如，model_name="GPT2-base-ch"，模型支持“seq2seq”相关任务。 + +#### Transformer encoder + decoder: + +例如model_name="t5-base-ch"，模型支持“seq2seq”相关任务。 \ No newline at end of file diff --git a/doc_zh/TUTORIAL_6_INSTRUCTIONS_FOR_PREDICTOR.md b/doc_zh/TUTORIAL_6_INSTRUCTIONS_FOR_PREDICTOR.md new file mode 100644 index 00000000..b072ddb9 --- /dev/null +++ b/doc_zh/TUTORIAL_6_INSTRUCTIONS_FOR_PREDICTOR.md @@ -0,0 +1,99 @@ +## 将现成的推理算法与 Predictor 结合使用 + +### 通过Predictor调用一行代码得到理想的预测结果 + +对于不同任务，例如标题生成，命名实体识别，文本分类等，预测的方式各不相同。同时，对于不同的模型，例如encoder、decoder、encoder-decoder模型，预测方式也各不相同。 + +Predictor中集成了不同任务，不同模型的预测代码，通过Pipline的方式传入文本，Predictor会快速解析模型类型，调用不同的预测代码，得到对应模型的预测结果。 + +![](./img/predictor_map.png) + +以gpt2文章续写任务为例，采用随机采样的生成方式： +```python +from flagai.auto_model.auto_loader import AutoLoader +from flagai.model.predictor.predictor import Predictor +import torch +if __name__ == '__main__': + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + ## 通过autoloader加载模型和tokenizer + loader = AutoLoader(task_name="writing", + model_name="GPT2-base-ch") + model = loader.get_model() + tokenizer = loader.get_tokenizer() + model.to(device) + ## 定义预测器 + predictor = Predictor(model, tokenizer) + ## 定义文章的开头，作为输入 + text = "今天天气不错，" + ## 预测器能通过自动分析模型种类来调取不同方法 + out = predictor.predict_generate_randomsample(text, ## 输入 + input_max_length=512, ## 最大出入长度 + out_max_length=100, ## 最大输出长度 + repetition_penalty=1.5, ## 避免重复输出. (https://arxiv.org/pdf/1909.05858.pdf) + top_k=20, ## 只保留概率最大的k个token. + top_p=0.8) ## 保留累计概率大于等于top_p的token.(http://arxiv.org/abs/1904.09751) + + print(f"out is {out}") + ### out is 到这里来看了一下，很是兴奋，就和朋友一起来这里来了。我们是周五晚上去的，人不多，所以没有排队，而且这里的环境真的很好，在这里享受美食真的很舒服，我们点了一个套餐，两个人吃刚刚好，味道很好。 +``` +Predictor可以自动分析出模型类型为GPT2，并且自动调用GPT2模型对应的生成方法，得到预测结果。 + +除了```writing```任务之外，Predictor还支持```title-generation```, ``ner``, ``semantic-matching``等任务的预测。例如ner任务： + +```python +import torch +from flagai.auto_model.auto_loader import AutoLoader +from flagai.model.predictor.predictor import Predictor +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +target = ["O", "B-LOC", "I-LOC", "B-ORG", "I-ORG", "B-PER", "I-PER"] +auto_loader = AutoLoader(task_name="ner", + model_name="RoBERTa-base-ch-ner", # Load the checkpoints from Modelhub(model.baai.ac.cn/models) + class_num=len(target)) + +model = auto_loader.get_model() +tokenizer = auto_loader.get_tokenizer() +model.to(device) + +predictor = Predictor(model, tokenizer) + +test_data = [ + "6月15日，河南省文物考古研究所曹操高陵文物队公开发表声明承认：“从来没有说过出土的珠子是墓主人的", + "4月8日，北京冬奥会、冬残奥会总结表彰大会在人民大会堂隆重举行。习近平总书记出席大会并发表重要讲话。在讲话中，总书记充分肯定了北京冬奥会、冬残奥会取得的优异成绩，全面回顾了7年筹办备赛的不凡历程，深入总结了筹备举办北京冬奥会、冬残奥会的宝贵经验，深刻阐释了北京冬奥精神，对运用好冬奥遗产推动高质量发展提出明确要求。", + "当地时间8日，欧盟委员会表示，欧盟各成员国政府现已冻结共计约300亿欧元与俄罗斯寡头及其他被制裁的俄方人员有关的资产。", + "这一盘口状态下英国必发公司亚洲盘交易数据显示博洛尼亚热。而从欧赔投注看，也是主队热。巴勒莫两连败，", +] + +for t in test_data: + entities = predictor.predict_ner(t, target, maxlen=256) + result = {} + for e in entities: + if e[2] not in result: + result[e[2]] = [t[e[0]:e[1] + 1]] + else: + result[e[2]].append(t[e[0]:e[1] + 1]) + print(f"result is {result}") +``` +通过传入一行文本，快速得到对应的命名实体识别任务结果，并且``predict_ner`` 接口适配所有支持ner任务的模型，例如BERT, Roberta, BERT-CRF, BERT-GlobalPointer, Roberta-CRF, Roberta-GlobalPointer 等等。 + +![predictor-table](../docs/img/predictor_table.png) + + +### Predictor所有支持的方法 +#### 文本表征 +1. predict_embedding: 输入一个文本来获取嵌入表征，支持bert、roberta等模型。 +#### 文本分类，语义匹配 +1. predict_cls_classifier: 输入文本或文本对得到多分类结果，支持bert、roberta等transformer编码器模型。 +#### Mask语言模型 +1. predict_masklm: 输入带有[MASK]标记的文本得到原文结果，支持bert、roberta等transformer编码器模型 +#### 命名实体识别 +1. predict_ner: 输入文本得到ner结果，支持bert、roberta等transformer编码器模型。 +#### 生成 +1. predict_generate_beamsearch: 输入文本得到输出文本，属于seq2seq任务。支持bert、roberta、gpt2、t5和glm模型。 +2. predict_generate_randomsample: 输入文本得到输出文本，属于seq2seq任务。支持bert、roberta、gpt2、t5和glm模型。 + +### 方法调用说明 +只要所使用的模型支持Predictor的对应方法，便可以直接预测，例如：模型为GLM、T5、GPT2，因为便可以直接调用不同生成方法，但是无法调用其他例如文本分类，命名实体识别预测方法。 + +Bert、Roberta模型支持的方法较多，可以调用上部分所展示的所有方法。 \ No newline at end of file diff --git a/doc_zh/TUTORIAL_7_PROMPT_LEARNING.md b/doc_zh/TUTORIAL_7_PROMPT_LEARNING.md new file mode 100644 index 00000000..84a35c06 --- /dev/null +++ b/doc_zh/TUTORIAL_7_PROMPT_LEARNING.md @@ -0,0 +1,37 @@ +# 使用 FlagAI 提示学习工具包来提高在SuperGLUE任务上的表现 + +## 背景 +提示学习(prompt-learning)是一种自然语言处理 (NLP) 领域广泛使用的范式，尤其是在数据有限的情况下表现良好。它遵循预训练-微调范式的思想：即预训练好的语言模型将被适配到各种下游任务上。不过与微调不同的是，提示学习会将原本的任务转化为一个特定的模板，这个过程被称为提示工程。 + +## 提示工程 +提示工程中包含两种主要方法：完形填空提示和前缀提示。 完形填空提示更适合使用遮挡类语言模型的下游任务，而前缀提示通常用于文本生成任务。 + +### 完形填空提示 +通常来说，输入文本和标签可以从任务数据集中获得。这些数据将被重新排列成一个完形填空式的短语(包括一个带有空格的段落以及空格处可以填充的选项)，这包括两个步骤： +1. 构建一个由输入部分、答案部分和一些自然语言组成的模板。 +2. 构建一个从原始的标签到可填选项的对应关系。 + +下面是一个完形填空提示的示例 + +数据集包含两个输入文本：前提和假设。标签表示前提和假设之间的关系，标签有三种可能的文本字符串：entailment（一致）、contradiction（矛盾）和neutral(无偏向)。 + +
+ +我们可以设计一个如下的模板： + +
+ +### 前缀提示 +前缀提示并没有用真正的自然语言设计模板，而是直接修改嵌入空间。如下图所示，它在Transformer里插入一个前缀向量，并冻结语言模型的其余参数。与完形填空提示对比，这么做相当于从离散的tokens变成了连续的向量，避免了人工设计模板的好坏对于模型性能的影响过大的问题。 +
+ + +## 引用 + +Liu, P. (2021, July 28). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting. . . arXiv.Org. https://arxiv.org/abs/2107.13586 + +Ding, N. (2021, November 3). OpenPrompt: An Open-source Framework for Prompt-learning. arXiv.Org. https://arxiv.org/abs/2111.01998 + +Schick, T. (2020, January 21). Exploiting Cloze Questions for Few Shot Text Classification and. . . arXiv.Org. https://arxiv.org/abs/2001.07676 + +Li, X. L. (2021, January 1). Prefix-Tuning: Optimizing Continuous Prompts for Generation. arXiv.Org. https://arxiv.org/abs/2101.00190 diff --git a/doc_zh/TUTORIAL_8_ENVIRONMENT_SETUP.md b/doc_zh/TUTORIAL_8_ENVIRONMENT_SETUP.md new file mode 100644 index 00000000..78f27017 --- /dev/null +++ b/doc_zh/TUTORIAL_8_ENVIRONMENT_SETUP.md @@ -0,0 +1,585 @@ +# 多机训练模型搭建环境 + +- [多机训练模型搭建环境](#多机训练模型搭建环境) +- [一. Docker](#一--docker) + - [1.安装docker](#1安装docker) + - [2.Docker 换源](#2docker-换源) + - [3.安装显卡驱动（如已装可跳过）](#3安装显卡驱动如已装可跳过) + - [4.配置nvidia-docker源：](#4配置nvidia-docker源) + - [5.制作dockerfile](#5制作dockerfile) + - [a.拉取nvidia 基础镜像, 创建临时文件夹（容器内，镜像创建完成后，删除）](#a拉取nvidia-基础镜像-创建临时文件夹容器内镜像创建完成后删除) + - [b.配置apt 安装源,并安装一些linux 系统常用基础包](#b配置apt-安装源并安装一些linux-系统常用基础包) + - [c. 安装最新版git(创建镜像clone 安装包)](#c--安装最新版git创建镜像clone-安装包) + - [d. 安装 Mellanox OFED, 由于网络问题，推荐安装包下到本地后，再执行dockerfile](#d-安装--mellanox-ofed-由于网络问题推荐安装包下到本地后再执行dockerfile) + - [e. 安装 nv_peer_mem](#e-安装-nv_peer_mem) + - [f. 安装openmpi, 需先安装libevent 依赖包](#f-安装openmpi-需先安装libevent-依赖包) + - [g.安装 python](#g安装-python) + - [h.安装 magma-cuda](#h安装-magma-cuda) + - [i.配置路径](#i配置路径) + - [j.安装一些pip 包](#j安装一些pip-包) + - [k.安装mpi4py （需下载到本地安装，pip 安装可能因为版本兼容问题报错）](#k安装mpi4py-需下载到本地安装pip-安装可能因为版本兼容问题报错) + - [l.安装pytorch, 版本可替换， 需先下载项目到本地，国内安装容易因为网速原因，造成终止, pytorch git clone 过程中可能有些子包下载过程中会终止。可以多 git clone 几次](#l安装pytorch-版本可替换-需先下载项目到本地国内安装容易因为网速原因造成终止-pytorch-git-clone-过程中可能有些子包下载过程中会终止可以多-git-clone-几次) + - [m.安装apex](#m安装apex) + - [n.安装deepspeed](#n安装deepspeed) + - [o.安装NCCL(可选，pytorch 已自带)](#o安装nccl可选pytorch-已自带) + - [p.配置网络端口、公钥和ssh](#p配置网络端口公钥和ssh) + - [6.构建docker 镜像](#6构建docker-镜像) + - [a.方式一. pull 镜像](#a方式一--pull-镜像) + - [b.方式二. 构建镜像](#b方式二--构建镜像) +- [二. 在每个机器节点构建容器](#二-在每个机器节点构建容器) +- [三. 互信机制设置](#三-互信机制设置) + - [1. 公钥生成默认docker 镜像创建时已生成，如不存在，则在shell 端输入](#1-公钥生成默认docker-镜像创建时已生成如不存在则在shell-端输入) + - [2.将各节点容器生成的公钥文件](#2将各节点容器生成的公钥文件) + - [3.免密登陆](#3免密登陆) + - [4.测试](#4测试) +- [四. 分布式训练测试](#四--分布式训练测试) + - [a.配置hostfile（hostfile 中的V100-1 与~/.ssh/config 对应）:](#a配置hostfilehostfile-中的v100-1-与sshconfig-对应) + - [b. 配置glm 文件，各节点配置code 和数据，要求路径相同（也可共同访问云端共享文件）](#b-配置glm-文件各节点配置code-和数据要求路径相同也可共同访问云端共享文件) + - [c. cmd](#c-cmd) + +# 一. Docker + +## 1.安装docker + +```shell +#由于Ubuntu里apt官方库里的docker版本可能比较低，因此先用下面的命令行卸载旧版本 +apt-get remove docker docker-engine docker-ce docker.io + +#更新apt包索引 +apt-get update + +#执行下列命令行，使apt可以通过HTTPS协议去使用存储库 +apt-get install -y apt-transport-https ca-certificates curl software-properties-common + +#添加Docker官方提供的GPG密钥 +curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - + +#设置stable存储库 +add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" + +#再次更新apt包索引 +apt-get update + +#安装最新版本的docker-ce +apt-get install -y docker-ce +``` + +## 2.Docker 换源 + +(https://xxxx.mirror.aliyuncs.com) 为自己的docker源仓库 + +```shell +mkdir -p /etc/docker +tee /etc/docker/daemon.json +-'EOF' +{ + "registry-mirrors": ["https://xxxx.mirror.aliyuncs.com"] +} +EOF + +systemctl daemon-reload +systemctl restart docker +``` + +## 3.安装显卡驱动（如已装可跳过） + +```shell +#检验是否存在Nvidia驱动 +dpkg --list | grep nvidia-* +#或者执行 cat /proc/driver/nvidia/version + +#如果不存在Nvidia驱动，则需要安装 +#执行ubuntu-drivers devices，查看推荐驱动版本 +ubuntu-drivers devices +#如果“Command 'ubuntu-drivers' not found”，执行apt-get install ubuntu-drivers-common + +#安装推荐的驱动版本 +apt-get install nvidia-driver-版本号 + +#检验是否安装成功 +nvidia-smi +#如果出现"NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running."错误 +#执行apt install dkms +#查看版本号ls /usr/src | grep nvidia +#dkms install -m nvidia -v + 版本号 +#注意：安装完成后，可能需要重新启动服务器 + +``` +## 4.配置nvidia-docker源： + +```shell +#添加源 +distribution=$(. /etc/os-release;echo $ID$VERSION_ID) +curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - +curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list + +#安装nvidia-docker2和依赖，安装的过程中，选择“默认” +apt-get update +apt-get install -y nvidia-docker2 +```` + +修改/etc/docker/daemon.json，添加相关信息 + +```text +"runtimes": { + "nvidia": { + "path": "/usr/bin/nvidia-container-runtime", + "runtimeArgs": [] + } +} +``` +/etc/docker/daemon.json最终内容 + +```json +{ + "registry-mirrors": ["https://xxxx.mirror.aliyuncs.com"], + "runtimes": { + "nvidia": { + "path": "/usr/bin/nvidia-container-runtime", + "runtimeArgs": [] + } + } +} +``` + +重启docker服务 + +```shell +systemctl daemon-reload +systemctl restart docker +``` + +## 5.制作dockerfile + +### a.拉取nvidia 基础镜像, 创建临时文件夹（容器内，镜像创建完成后，删除） + +```dockerfile +#pull base image +FROM nvidia/cuda:10.2-devel-ubuntu18.04 +#maintainer +MAINTAINER deepspeed + +############################################################################## +#Temporary Installation Directory +############################################################################## +ENV STAGE_DIR=/tmp +RUN mkdir -p ${STAGE_DIR} +``` + +### b.配置apt 安装源,并安装一些linux 系统常用基础包 + +```dockerfile +############################################################################## +#Installation/Basic Utilities +############################################################################## +RUN sed -i s@/archive.ubuntu.com/@/mirrors.tuna.tsinghua.edu.cn/@g /etc/apt/sources.list +RUN sed -i s@/security.ubuntu.com/@/mirrors.tuna.tsinghua.edu.cn/@g /etc/apt/sources.list +RUN apt-get update && \ + DEBIAN_FRONTEND="noninteractive" apt-get install -y --no-install-recommends \ + software-properties-common build-essential autotools-dev \ + nfs-common pdsh \ + cmake g++ gcc \ + curl wget vim tmux emacs less unzip \ + htop iftop iotop ca-certificates openssh-client openssh-server \ + rsync iputils-ping net-tools sudo \ + llvm-9-dev libsndfile-dev \ + libcupti-dev \ + libjpeg-dev \ + libpng-dev \ + screen jq psmisc dnsutils lsof musl-dev systemd +``` + +### c. 安装最新版git(创建镜像clone 安装包) + +```dockerfile +############################################################################## +#Installation Latest Git +############################################################################## +RUN add-apt-repository ppa:git-core/ppa -y && \ + apt-get update && \ + apt-get install -y git && \ + git --version +``` +### d. 安装 Mellanox OFED, 由于网络问题，推荐安装包下到本地后，再执行dockerfile + +```dockerfile +############################################################################## +#install Mellanox OFED +#dwonload from https://www.mellanox.com/downloads/ofed/MLNX_OFED-5.1-2.5.8.0/MLNX_OFED_LINUX-5.1-2.5.8.0-ubuntu18.04-x86_64.tgz +############################################################################## +RUN apt-get install -y libnuma-dev libnuma-dev libcap2 +ENV MLNX_OFED_VERSION=5.1-2.5.8.0 +COPY MLNX_OFED_LINUX-${MLNX_OFED_VERSION}-ubuntu18.04-x86_64.tgz ${STAGE_DIR} +RUN cd ${STAGE_DIR} && \ + tar xvfz MLNX_OFED_LINUX-${MLNX_OFED_VERSION}-ubuntu18.04-x86_64.tgz && \ + cd MLNX_OFED_LINUX-${MLNX_OFED_VERSION}-ubuntu18.04-x86_64 && \ + PATH=/usr/bin:$PATH ./mlnxofedinstall --user-space-only --without-fw-update --umad-dev-rw --all -q && \ + cd ${STAGE_DIR} && \ + rm -rf ${STAGE_DIR}/MLNX_OFED_LINUX-${MLNX_OFED_VERSION}-ubuntu18.04-x86_64* +``` + +### e. 安装 nv_peer_mem + +```dockerfile +############################################################################## +#Install nv_peer_mem +############################################################################## +#COPY nv_peer_memory ${STAGE_DIR}/nv_peer_memory (without net) +############try for more times ################# + +ENV NV_PEER_MEM_VERSION=1.1 +ENV NV_PEER_MEM_TAG=1.1-0 +RUN git clone https://github.com/Mellanox/nv_peer_memory.git --branch ${NV_PEER_MEM_TAG} ${STAGE_DIR}/nv_peer_memory +RUN cd ${STAGE_DIR}/nv_peer_memory && \ + ./build_module.sh && \ + cd ${STAGE_DIR} && \ + tar xzf ${STAGE_DIR}/nvidia-peer-memory_${NV_PEER_MEM_VERSION}.orig.tar.gz && \ + cd ${STAGE_DIR}/nvidia-peer-memory-${NV_PEER_MEM_VERSION} && \ + apt-get update && \ + apt-get install -y dkms && \ + dpkg-buildpackage -us -uc && \ + dpkg -i ${STAGE_DIR}/nvidia-peer-memory_${NV_PEER_MEM_TAG}_all.deb +``` + +### f. 安装openmpi, 需先安装libevent 依赖包 + +```dockerfile +########################################################################### +#Install libevent && OPENMPI +#https://www.open-mpi.org/software/ompi/v4.0/ +############################################################################## +ENV OPENMPI_BASEVERSION=4.0 +ENV OPENMPI_VERSION=${OPENMPI_BASEVERSION}.5 +COPY openmpi-4.0.5.tar.gz ${STAGE_DIR} +COPY libevent-2.0.22-stable.tar.gz ${STAGE_DIR} +RUN cd ${STAGE_DIR} && \ + tar zxvf libevent-2.0.22-stable.tar.gz && \ + cd libevent-2.0.22-stable && \ + ./configure --prefix=/usr && \ + make && make install +RUN cd ${STAGE_DIR} && \ + tar --no-same-owner -xzf openmpi-4.0.5.tar.gz && \ + cd openmpi-${OPENMPI_VERSION} && \ + ./configure --prefix=/usr/local/openmpi-${OPENMPI_VERSION} && \ + make -j"$(nproc)" install && \ + ln -s /usr/local/openmpi-${OPENMPI_VERSION} /usr/local/mpi && \ + #Sanity check: + test -f /usr/local/mpi/bin/mpic++ && \ + cd ${STAGE_DIR} && \ + rm -r ${STAGE_DIR}/openmpi-${OPENMPI_VERSION} +ENV PATH=/usr/local/mpi/bin:${PATH} \ + LD_LIBRARY_PATH=/usr/local/lib:/usr/local/mpi/lib:/usr/local/mpi/lib64:${LD_LIBRARY_PATH} +#Create a wrapper for OpenMPI to allow running as root by default +RUN mv /usr/local/mpi/bin/mpirun /usr/local/mpi/bin/mpirun.real && \ + echo '#!/bin/bash' > /usr/local/mpi/bin/mpirun && \ + echo 'mpirun.real --allow-run-as-root --prefix /usr/local/mpi "$@"' >> /usr/local/mpi/bin/mpirun && \ + chmod a+x /usr/local/mpi/bin/mpirun +``` + +### g.安装 python + +```dockerfile +########################################################################### +#Install python +############################################################################## +ARG PYTHON_VERSION=3.8 +RUN curl -o ~/miniconda.sh https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-latest-Linux-x86_64.sh && \ + chmod +x ~/miniconda.sh && \ + ~/miniconda.sh -b -p /opt/conda && \ + rm ~/miniconda.sh && \ + /opt/conda/bin/conda install -y python=$PYTHON_VERSION numpy pyyaml scipy ipython mkl mkl-include ninja cython typing +``` + +### h.安装 magma-cuda + +```dockerfile +########################################################################### +#Install magma-cuda +############################################################################## +COPY magma-cuda102-2.5.2-1.tar.bz2 ${STAGE_DIR} +RUN cd ${STAGE_DIR} && \ + /opt/conda/bin/conda install -y -c pytorch --use-local magma-cuda102-2.5.2-1.tar.bz2 && \ + /opt/conda/bin/conda clean -ya +####optional##### +#RUN /opt/conda/bin/conda install -y -c pytorch magma-cuda102 && \ +#/opt/conda/bin/conda clean -ya +``` + +### i.配置路径 + +```dockerfile +########################################################################### +#Export path +############################################################################## +ENV PATH /opt/conda/bin:$PATH +RUN echo "export PATH=/opt/conda/bin:\$PATH" >> /root/.bashrc +RUN pip install --upgrade pip setuptools +RUN wget https://tuna.moe/oh-my-tuna/oh-my-tuna.py && python oh-my-tuna.py +``` + +### j.安装一些pip 包 + +```dockerfile +########################################################################### +#Install some Packages +############################################################################## +RUN pip install psutil \ + yappi \ + cffi \ + ipdb \ + h5py \ + pandas \ + matplotlib \ + py3nvml \ + pyarrow \ + graphviz \ + astor \ + boto3 \ + tqdm \ + sentencepiece \ + msgpack \ + requests \ + pandas \ + sphinx \ + sphinx_rtd_theme \ + sklearn \ + scikit-learn \ + nvidia-ml-py3 \ + nltk \ + rouge \ + filelock \ + fasttext \ + rouge_score \ + cupy-cuda102\ + setuptools==60.0.3 +``` + +### k.安装mpi4py （需下载到本地安装，pip 安装可能因为版本兼容问题报错） + +```dockerfile +############################################################################## +#Install mpi4py +############################################################################## +RUN apt-get update && \ + apt-get install -y mpich +COPY mpi4py-3.1.3.tar.gz ${STAGE_DIR} +RUN cd ${STAGE_DIR} && tar zxvf mpi4py-3.1.3.tar.gz && \ + cd mpi4py-3.1.3 &&\ + python setup.py build && python setup.py install +``` + +### l.安装pytorch, 版本可替换， 需先下载项目到本地，国内安装容易因为网速原因，造成终止, pytorch git clone 过程中可能有些子包下载过程中会终止。可以多 git clone 几次 + +```dockerfile +############################################################################## +#PyTorch +#clone (may be time out because of the network problem) +#RUN git clone --recursive https://github.com/pytorch/pytorch --branch v1.8.1 /opt/pytorch +#RUN cd /opt/pytorch && git checkout -f v1.8.1 && \ +#git submodule sync && git submodule update -f --init --recursive +############################################################################## +ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX" + +COPY opt/pytorch /opt/pytorch +ENV NCCL_LIBRARY=/usr/lib/x86_64-linux-gnu +ENV NCCL_INCLUDE_DIR=/usr/include +RUN cd /opt/pytorch && TORCH_NVCC_FLAGS="-Xfatbin -compress-all" \ + CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" USE_SYSTEM_NCCL=1 \ + pip install -v . && rm -rf /opt/pytorch + + + +############################################################################## +#Install vision +#RUN git clone https://github.com/pytorch/vision.git /opt/vision +############################################################################## +COPY vision /opt/vision +RUN cd /opt/vision && pip install -v . && rm -rf /opt/vision +ENV TENSORBOARDX_VERSION=1.8 +RUN pip install tensorboardX==${TENSORBOARDX_VERSION} +``` + +### m.安装apex + +```dockerfile +########################################################################### +#Install apex +########################################################################### +#RUN git clone https://github.com/NVIDIA/apex ${STAGE_DIR}/apex +COPY apex ${STAGE_DIR}/apex +RUN cd ${STAGE_DIR}/apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ \ + && rm -rf ${STAGE_DIR}/apex +``` + +### n.安装deepspeed + +```dockerfile + ############################################################################ +#Install deepSpeed +############################################################################# +RUN pip install py-cpuinfo +RUN apt-get install -y libaio-dev +ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX" +RUN git clone https://github.com/microsoft/DeepSpeed.git ${STAGE_DIR}/DeepSpeed +#COPY DeepSpeed ${STAGE_DIR}/DeepSpeed +RUN cd ${STAGE_DIR}/DeepSpeed && \ + git checkout . && \ + DS_BUILD_OPS=1 ./install.sh -r +RUN rm -rf ${STAGE_DIR}/DeepSpeed +RUN python -c "import deepspeed; print(deepspeed.__version__)" +``` + +### o.安装NCCL(可选，pytorch 已自带) + +```dockerfile +############################################################################ +#Install nccl +############################################################################# + +#COPY nccl-local-repo-ubuntu1804-2.9.6-cuda10.2_1.0-1_amd64.deb ${STAGE_DIR} +#RUN cd ${STAGE_DIR} &&\ +#sudo dpkg -i nccl-local-repo-ubuntu1804-2.9.6-cuda10.2_1.0-1_amd64.deb &&\ +#sudo apt install -y libnccl2 libnccl-dev +#RUN apt install -y --allow-downgrades --no-install-recommends --allow-change-held-packages libnccl2=2.9.6-1+cuda10.2 libnccl-dev=2.9.6-1+cuda10.2 +#ENV NCCL_VERSION=2.9.6 +``` + +### p.配置网络端口、公钥和ssh + +```dockerfile +############################################################################# +#Set SSH Config +############################################################################# +RUN apt-get install openssh-server + +ARG SSH_PORT=6001 +#RUN echo 'root:NdjeS+-4gEPmq}D' | chpasswd +#Client Liveness & Uncomment Port 22 for SSH Daemon +RUN echo "ClientAliveInterval 30" >> /etc/ssh/sshd_config +RUN mkdir -p /var/run/sshd && cp /etc/ssh/sshd_config ${STAGE_DIR}/sshd_config && \ + sed "0,/^#Port 22/s//Port 22/" ${STAGE_DIR}/sshd_config > /etc/ssh/sshd_config +RUN cat /etc/ssh/sshd_config > ${STAGE_DIR}/sshd_config && \ + sed "0,/^Port 22/s//Port ${SSH_PORT}/" ${STAGE_DIR}/sshd_config > /etc/ssh/sshd_config && \ + sed -i 's/#PermitRootLogin prohibit-password/PermitRootLogin yes/g' /etc/ssh/sshd_config +EXPOSE ${SSH_PORT} +#Set SSH KEY +RUN printf "#StrictHostKeyChecking no\n#UserKnownHostsFile /dev/null" >> /etc/ssh/ssh_config && \ + ssh-keygen -t rsa -f ~/.ssh/id_rsa -N "" && cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys && \ + chmod og-wx ~/.ssh/authorized_keys +CMD service ssh start +``` + + +## 6.构建docker 镜像 + +### a.方式一. pull 镜像 + +```shell +#远程拉取 +docker pull deepspeed/cuda102 +#本地读取 +docker load --input deepspeed-cuda102.tar.gz +``` +### b.方式二. 构建镜像 + +```shell +docker build -f cuda102.dockerfile -t deepspeed/cuda102:1221 . +#cuda102.dockerfile 参考 dockerfile 文件制作流程 +``` +# 二. 在每个机器节点构建容器 + +```shell +# 创建容器（nvidia-docker），hostname = 容器内部host名称， +# network= host 与数组机共享 +# ipc=host, 集群训练时，需按此设置 +# shm_size 共享内存，name 容器外部名 +# --gpus 指定gpu +# 多数据卷：-v 本地文件夹:容器内文件夹 -v 本地文件夹:容器内文件夹 -v 本地文件夹:容器内文件夹 deepspeed/cuda102:1221 镜像名：tag +nvidia-docker run -id --hostname=glm_dist16 --network=host --ipc=host --shm-size=16gb --name=glm_dist16 --gpus '"device=0,1,2,3"' -v /data1/docker/containers:/data deepspeed/cuda102:1221 +``` + +```shell +#拉取镜像 +docker pull nvidia/cuda:cuda版本-runtime-ubuntu版本 +#拉取镜像举例 +docker pull nvidia/cuda:10.1-runtime-ubuntu18.04 +#创建容器（普通docker），比如多端口号：-p 22:22 -p 80:80 -p 8080:8080，多数据卷：-v 文件夹:文件夹 -v 文件夹:文件夹 -v 文件夹:文件夹 +docker run -id --name=容器名 -p 宿主机端口号:容器内端口号 -e TZ=Asia/Shanghai -v 宿主机文件夹:容器内文件夹 镜像:版本号 +#创建容器（普通docker）举例 +docker run -id --name=test -p 80:80 -e TZ=Asia/Shanghai -v /data:/data mysql:5.7 +#创建容器（nvidia-docker），支持多映射，比如多端口号：-p 22:22 -p 80:80 -p 8080:8080，多数据卷：-v 文件夹:文件夹 -v 文件夹:文件夹 -v 文件夹:文件夹 +nvidia-docker run -id --name=容器名 -p 宿主机端口号:容器内端口号 -e TZ=Asia/Shanghai --shm-size=大小 -v 宿主机文件夹:容器内文件夹 镜像:版本号 +#创建容器（nvidia-docker）举例 +docker run -id --name=test -p 80:80 -e TZ=Asia/Shanghai --shm-size=8gb -v /data:/data nvidia/cuda:10.1-runtime-ubuntu18.04 +#进入容器（普通docker） +docker exec -it 容器名 /bin/bash +#进入容器（nvidia-docker） +nvidia-docker exec -it 容器名 /bin/bash +#查看已有镜像 +docker images +#删除镜像 +docker rmi 镜像名/镜像id +#查看正在运行的容器 +docker ps +#查看历史容器，包含正在运行和已经关闭的 +docker ps -a +#停止正在运行的容器 +docker stop 容器名/容器id +#删除容器，如果待删除的容器正在运行，需要先停止再删除 +docker rm 容器名/容器id +``` + +# 三. 互信机制设置 + +## 1. 公钥生成默认docker 镜像创建时已生成，如不存在，则在shell 端输入 + +```shell + ssh-keygen -t rsa -C "example@com.cn" +``` + +## 2.将各节点容器生成的公钥文件 + +~/.ssh/id_rsa.pub +中的内容收集，并同步到各机器的文件 +~/.ssh/authorized_keys + +## 3.免密登陆 + +配置各节点容器port : vi /etc/ssh/sshd_config , 将port 注释取消，并设值，统一节点不同容器port 需要不一样 +如下配置各节点 host 文件 ：vi ~/.ssh/config 复制各节点host 登陆信息, 并同步到各节点 + +```text +Host V100-1 + Hostname 172.31.32.29 + Port 6001 + User root +Host V100-2 + Hostname 172.31.32.40 + Port 6002 + User root +``` + +## 4.测试 + +```shell +ssh V100-1 +``` + +# 四. 分布式训练测试 + +## a.配置hostfile（hostfile 中的V100-1 与~/.ssh/config 对应）: + +```text +V100-1 slots=4 +V100-2 slots=4 +V100-3 slots=4 +..... +``` +## b. 配置glm 文件，各节点配置code 和数据，要求路径相同（也可共同访问云端共享文件） + +## c. cmd + +```shell +bash config/start_scripts/generate_block.sh config/config_tasks/model_blocklm_large_chinese.sh +``` + diff --git a/doc_zh/TUTORIAL_9_SEQ2SEQ_METHOD.md b/doc_zh/TUTORIAL_9_SEQ2SEQ_METHOD.md new file mode 100644 index 00000000..2a85c838 --- /dev/null +++ b/doc_zh/TUTORIAL_9_SEQ2SEQ_METHOD.md @@ -0,0 +1,32 @@ +# 使用encoder/decoder/encoder-decoder模型进行文本生成 + +支持三种方式进行序列到序列任务的生成，包括 +1. 只用Encoder模型进行序列到序列的生成任务 +2. 只用Decoder模型进行序列到序列的生成任务 +3. 用Encoder+Decoder模型进行序列到序列的生成任务。 + +## Encoder 模型 +我们提供了encoder模型来执行seq2seq任务，例如，Bert、Roberta、GLM等等。 + +1. [使用RoBERTa模型进行标题生成](https://github.com/BAAI-Open/FlagAI/tree/master/examples/roberta_title_generation) +2. [使用GLM模型进行标题生成](https://github.com/BAAI-Open/FlagAI/tree/master/examples/glm_title_generation) + +在训练过程中，我们在编码器模型中添加了一个特殊的 attention mask。(https://github.com/microsoft/unilm) +![encoder_mask](../docs/img/encoder_mask.png) +该模型的输入是两个句子：[cls]句子1[sep]句子2[sep]。 +其中，句子_1不使用mask，而句子_2使用自回归mask。 + +## Decoder 模型 +我们还为seq2seq任务提供了decoder模型，例如gpt-2模型。 +1. [使用GPT-2模型进行文章续写](https://github.com/BAAI-Open/FlagAI/blob/master/quickstart/writing_ch.py) + +![decoder_mask](../docs/img/decoder_mask.png) +给出一个起始文本，这个模型可以很好地延续文本。 + +## Encoder-Decoder 模型 +我们还为seq2seq任务提供encoder-decoder模型，例如T5模型。 +1. [使用T5模型进行标题生成](https://github.com/BAAI-Open/FlagAI/tree/master/examples/t5_title_generation) + + +![encoder_decoder_mask](../docs/img/encoder_decoder_mask.png) +encoder只需编码一次即可获得特征编码，decoder根据自身和特征编码继续生成。 \ No newline at end of file diff --git a/doc_zh/img/TUTORIAL_7_PROMPT_LEARNING.md b/doc_zh/img/TUTORIAL_7_PROMPT_LEARNING.md new file mode 100644 index 00000000..0ada57ed --- /dev/null +++ b/doc_zh/img/TUTORIAL_7_PROMPT_LEARNING.md @@ -0,0 +1,61 @@ +# Introduction to Prompt Learning + +## Background +提示学习(prompt-learning)是一种自然语言处理 (NLP) 领域广泛使用的范式，尤其是在数据有限的情况下表现良好。它遵循预训练-微调范式的思想：即预训练好的语言模型将被适配到各种下游任务上。不过与微调不同的是，提示学习会将原本的任务转化为一个特定的模板，这个过程被称为提示工程。 + +## 提示工程 +提示工程中包含两种主要方法：完形填空提示和前缀提示。 完形填空提示更适合使用遮挡类语言模型的下游任务，而前缀提示通常用于文本生成任务。 + +### Cloze Prompts +通常来说，输入文本和标签可以从任务数据集中获得。这些数据将被重新排列成一个完形填空式的短语(包括一个带有空格的段落以及空格处可以填充的选项)，这包括两个步骤： +1. 构建一个由输入部分、答案部分和一些自然语言组成的模板。 +2. 构建一个从原始的标签到可填选项的对应关系。 + +下面是一个完形填空提示的示例 + +数据集包含两个输入文本：前提和假设。标签表示前提和假设之间的关系，标签有三种可能的文本字符串：entailment（一致）、contradiction（矛盾）和neutral(无偏向)。 + +
+ +我们可以设计一个如下的模板： + +
+ +相关的代码实现如下： + +```python +def get_parts(self, example: InputExample): + '''A simplified version of the actual code''' + # Construct the template + cloze_ids = [ + example.text_a,' question: is it true, false or neither that', + example.text_b, ' answer:', [self.mask]] + return cloze_ids +``` + +各种数据集中的标签可以有多种类型，包括布尔值、整数和字符串。我们需要实现一个语言器，将标签映射到完形填空问题中的选项。例如，这里的“entailment”、“contradiction”和“neutral”标签可以映射到“true”、“false”和“neither”，并且每次要填的答案总是在这些选项中选择。 + +语言器本质上是一种映射关系, 所以我们可以用一个Python字典来定义它： +```python +VERBALIZER = { + "contradiction": [" false"], + "entailment": [" true"], + "neutral": [" neither"] +} +def verbalize(self, label) -> List[str]: + return CbPVP.VERBALIZER[label] +``` + +### 前缀提示 +前缀提示并没有用真正的自然语言设计模板，而是直接修改嵌入空间。如下图所示，它在Transformer里插入一个前缀向量，并冻结语言模型的其余参数。与完形填空提示对比，这么做相当于从离散的tokens变成了连续的向量，避免了人工设计模板的好坏对于模型性能的影响过大的问题。 +
+ + +## Reference +[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) + +[OpenPrompt: An Open-source Framework for Prompt-learning](https://arxiv.org/abs/2111.01998) + +[Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://arxiv.org/abs/2001.07676) + +[Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) \ No newline at end of file diff --git a/doc_zh/img/autoloader_en_map.png b/doc_zh/img/autoloader_en_map.png new file mode 100644 index 0000000000000000000000000000000000000000..2c39c403b27d1089d177196959bb672bfb9638bf GIT binary patch literal 257882 zcmeFacT`i))-ZfR=q>aPL5d)V^dc=F(gdU^9Suka0i~lr5CsvW3n+?FQ4kQK(iD(L z7ZH%&lq#YW2?8OI_8$C|d!Kuswch8s>s#yl-0&DwK=8rqg1Dd}uSeG9_|AOgb_fe^9fR>G4NO(w?U&sY<)gwxP)-iJ{ z+Wi{A>lZBj3*;CUy*ZHyyrlh}z;t_+a4eoW4|`^AWQ4S_vo*={{=kp^q8%7g*stsF 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