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Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
This commit is contained in:
Sourab Mangrulkar
2023-03-27 13:44:00 +05:30
committed by GitHub
co-authored by Steven Liu
parent 3d00af4799
commit 13476a807c
7 changed files with 39 additions and 48 deletions
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.github/workflows/build_documentation.yml
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@@ -12,6 +12,6 @@ jobs:
uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
with:
commit_sha: ${{ github.sha }}
package: accelerate
package: peft
secrets:
token: ${{ secrets.HUGGINGFACE_PUSH }}
Makefile
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@@ -8,13 +8,13 @@ check_dirs := src tests examples docs
quality:
black --check $(check_dirs)
ruff $(check_dirs)
doc-builder style src tests docs --max_len 119 --check_only
doc-builder style src/peft tests docs/source --max_len 119 --check_only
# Format source code automatically and check is there are any problems left that need manual fixing
style:
black $(check_dirs)
ruff $(check_dirs) --fix
doc-builder style src tests docs --max_len 119
doc-builder style src/peft tests docs/source --max_len 119
test:
pytest tests/
docs/README.md
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@@ -43,7 +43,7 @@ Once you have setup the `doc-builder` and additional packages, you can generate
typing the following command:
```bash
doc-builder build accelerate docs/source/ --build_dir ~/tmp/test-build
doc-builder build peft docs/source/ --build_dir ~/tmp/test-build
```
You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate
@@ -67,7 +67,7 @@ doc-builder preview {package_name} {path_to_docs}
For example:
```bash
doc-builder preview transformers docs/source/en/
doc-builder preview peft docs/source
```
The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
@@ -84,7 +84,7 @@ The `preview` command only works with existing doc files. When you add a complet
Accepted files are Markdown (.md or .mdx).
Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/accelerate/blob/main/docs/source/_toctree.yml) file.
the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/peft/blob/main/docs/source/_toctree.yml) file.
## Renaming section headers and moving sections
@@ -112,7 +112,7 @@ Use the relative style to link to the new file so that the versioned docs contin
## Writing Documentation - Specification
The `huggingface/accelerate` documentation follows the
The `huggingface/peft` documentation follows the
[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
although we can write them directly in Markdown.
docs/_toctree.yml
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@@ -1,7 +1,7 @@
- title: Get Started
sections:
- local: index
title: 🤗 PEFT
title: 🤗 PEFT
- local: quicktour
title: Quicktour
- local: installation
docs/index.mdx
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@@ -12,18 +12,13 @@ specific language governing permissions and limitations under the License.
# PEFT
🤗 PEFT is a library that enables using State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods.
🤗 PEFT, or Parameter-Efficient Fine-Tuning (PEFT), is a library for efficiently adapting pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters.
PEFT methods only fine-tune a small number of (extra) model parameters, significantly decreasing computational and storage costs because fine-tuning large-scale PLMs is prohibitively costly.
Recent state-of-the-art PEFT techniques achieve performance comparable to that of full fine-tuning.
PEFT methods enable efficient adaptation of pre-trained language models (PLMs) to
various downstream applications without fine-tuning all the model's parameters.
Fine-tuning large-scale PLMs is often prohibitively costly.
In this regard, PEFT methods only fine-tune a small number of (extra) model parameters,
thereby greatly decreasing the computational and storage costs.
Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full fine-tuning.
PEFT is seamlessly integrated with 🤗 Accelerate for large-scale models leveraging DeepSpeed and [Big Model Inference](https://huggingface.co/docs/accelerate/usage_guides/big_modeling).
Seamlessly integrated with 🤗 Accelerate for large scale models leveraging DeepSpeed and Big Model Inference.
Supported methods, with more coming soon:
Supported methods include:
1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685.pdf)
2. Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.353/), [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.org/pdf/2110.07602.pdf)
docs/install.mdx
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@@ -10,26 +10,23 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->
# Installation and Configuration
# Installation
Before you start, you will need to setup your environment, install the appropriate packages, and configure 🤗 PEFT. 🤗 PEFT is tested on **Python 3.7+**.
## Installing 🤗 PEFT
🤗 PEFT is available on pypi, as well as GitHub:
🤗 PEFT is available on pypi, as well as on GitHub. Details to install from each are below:
## pip
### pip
To install 🤗 PEFT from pypi, perform:
To install 🤗 PEFT from pypi:
```bash
pip install peft
```
### Source
## Source
New features are added every day that haven't been released yet. To try them out yourself, install
from the GitHub repository:
New features that haven't been released yet are added every day, which also means there may be some bugs. To try them out, install from the GitHub repository:
```bash
pip install git+https://github.com/huggingface/peft
docs/quicktour.mdx
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@@ -12,15 +12,16 @@ specific language governing permissions and limitations under the License.
# Quick tour
Let's have a look at the 🤗 PEFT main features and traps to avoid.
Let's have a look at 🤗 PEFT's main features and learn how to set up a `PeftModel` and train it with 🤗 Accelerate's DeepSpeed integration and use it for inference.
## Main use
To use 🤗 PEFT in your script, you have to follow below steps:
To use 🤗 PEFT in your script:
1. Create a `PeftConfig` object corresponding to your PEFT method.
Please refer to the [Config Page](package_reference/config) for more details.
Below, we will use `LoRAConfig` for demonstration.
1. Each PEFT method is defined by a `PeftConfig` object.
Create a `PeftConfig` object corresponding to your PEFT method (see the [Configuration](package_reference/config) reference for more details) and [`TaskType`], the type of task you're training your model for.
This example trains the [`bigscience/mt0-large`](https://huggingface.co/bigscience/mt0-large) model with the Low-Rank Adaptation of Large Language Models (LoRA) method. Load the `LoRAConfig`, and specify the `task_type` for sequence-to-sequence language modeling.
```python
from peft import LoraConfig, TaskType
@@ -41,7 +42,7 @@ tokenizer_name_or_path = "bigscience/mt0-large"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
```
3. Preprocess your model if you use `bitsandbytes` for INT-8 quantized training; else skip this step.
3. Preprocess your model if you use [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes) for `int8` quantized training; otherwise, skip this step.
```python
from peft import prepare_model_for_int8_training
@@ -59,8 +60,7 @@ model.print_trainable_parameters()
# output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282
```
5. Voila 🎉. Now, train the model using 🤗 Transformers Trainer API, 🤗 Accelerate or any custom PyTroch training loop.
Please refer example [peft_lora_seq2seq.ipynb](https://github.com/huggingface/peft/blob/main/examples/conditional_generation/peft_lora_seq2seq.ipynb) for an end-to-end example.
5. Voila 🎉! Now, train the model using the 🤗 Transformers Trainer API, 🤗 Accelerate, or any custom PyTroch training loop (take a look at the end-to-end [example](https://github.com/huggingface/peft/blob/main/examples/conditional_generation/peft_lora_seq2seq.ipynb) of training [`bigscience/mt0-large`](https://huggingface.co/bigscience/mt0-large)).
### Saving/loading a model
@@ -71,10 +71,9 @@ model.save_pretrained("output_dir")
# model.push_to_hub("my_awesome_peft_model") also works
```
This will only save the incremental PEFT weights that were trained.
For example, you can find the `bigscience/T0_3B` tuned using LoRA on the `twitter_complaints` raft dataset here:
[smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM](https://huggingface.co/smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM).
Notice that it only contains 2 files: `adapter_config.json` and `adapter_model.bin` with the latter being just 19MB.
This only saves the incremental PEFT weights that were trained.
For example, [smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM](https://huggingface.co/smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM) is a `bigscience/T0_3B`model finetuned with LoRA on the [`twitter_complaints`](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints/train) RAFT dataset.
Notice that it only contains 2 files: `adapter_config.json` and `adapter_model.bin`, with the latter being just 19MB.
2. Load your model using the `from_pretrained` function.
@@ -101,14 +100,14 @@ Notice that it only contains 2 files: `adapter_config.json` and `adapter_model.b
## Launching your distributed script
PEFT models work with 🤗 Accelerate out of the box.
Use 🤗 Accelerate for Distributed training on various hardware such as GPUs, Apple Silicon devices etc during training.
Use 🤗 Accelerate for inferencing on consumer hardware with small resources.
You can use 🤗 Accelerate for distributed training on various hardware such as GPUs, or Apple Silicon devices during training, and for inference on consumer hardware with fewer resources.
### Example of PEFT model training using 🤗 Accelerate's DeepSpeed integration
### Train with 🤗 Accelerate's DeepSpeed integration
DeepSpeed version required `v0.8.0`. An example is provided in `~examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py`.
a. First, run `accelerate config --config_file ds_zero3_cpu.yaml` and answer the questionnaire.
Below are the contents of the config file.
You'll need DeepSpeed version `v0.8.0` for this example. Feel free to check out the full example [script](https://github.com/huggingface/peft/blob/main/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py) for more details!
1. Run `accelerate config --config_file ds_zero3_cpu.yaml` and answer the questionnaire to setup your environment.
Below are the contents of the config file.
```yaml
compute_environment: LOCAL_MACHINE
deepspeed_config:
@@ -133,12 +132,12 @@ DeepSpeed version required `v0.8.0`. An example is provided in `~examples/condit
same_network: true
use_cpu: false
```
b. run the below command to launch the example script
2. Run the following command to launch the example script:
```bash
accelerate launch --config_file ds_zero3_cpu.yaml examples/peft_lora_seq2seq_accelerate_ds_zero3_offload.py
```
c. output logs:
You'll see some output logs that look like this:
```bash
GPU Memory before entering the train : 1916
GPU Memory consumed at the end of the train (end-begin): 66
@@ -163,7 +162,7 @@ DeepSpeed version required `v0.8.0`. An example is provided in `~examples/condit
dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']
```
### Example of PEFT model inference using 🤗 Accelerate's Big Model Inferencing capabilities
### Inference with 🤗 Accelerate's Big Model Inference
An example is provided in `~examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb`.
## Model Support matrix