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https://github.com/huggingface/optimum-graphcore
Blazing fast training of 🤗 Transformers on Graphcore IPUs
https://github.com/huggingface/optimum-graphcore
fine-tuning graphcore machine-learning pytorch training transformers
Last synced: 12 days ago
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Blazing fast training of 🤗 Transformers on Graphcore IPUs
- Host: GitHub
- URL: https://github.com/huggingface/optimum-graphcore
- Owner: huggingface
- License: apache-2.0
- Created: 2021-10-11T14:16:24.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-11T15:32:59.000Z (8 months ago)
- Last Synced: 2024-05-09T12:52:40.449Z (6 months ago)
- Topics: fine-tuning, graphcore, machine-learning, pytorch, training, transformers
- Language: Python
- Homepage:
- Size: 10.8 MB
- Stars: 82
- Watchers: 42
- Forks: 32
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-blazingly-fast - optimum-graphcore - Blazing fast training of 🤗 Transformers on Graphcore IPUs (Python)
README
[](https://github.com/huggingface/optimum-graphcore/actions/workflows/test-examples.yml) [](https://github.com/huggingface/optimum-graphcore/actions/workflows/test-pipelines.yml)
# Optimum Graphcore
🤗 Optimum Graphcore is the interface between the 🤗 Transformers library and [Graphcore IPUs](https://www.graphcore.ai/products/ipu).
It provides a set of tools enabling model parallelization and loading on IPUs, training, fine-tuning and inference on all the tasks already supported by 🤗 Transformers while being compatible with the 🤗 Hub and every model available on it out of the box.
## What is an Intelligence Processing Unit (IPU)?
Quote from the Hugging Face [blog post](https://huggingface.co/blog/graphcore#what-is-an-intelligence-processing-unit):
>IPUs are the processors that power Graphcore’s IPU-POD datacenter compute systems. This new type of processor is designed to support the very specific computational requirements of AI and machine learning. Characteristics such as fine-grained parallelism, low precision arithmetic, and the ability to handle sparsity have been built into our silicon.
> Instead of adopting a SIMD/SIMT architecture like GPUs, Graphcore’s IPU uses a massively parallel, MIMD architecture, with ultra-high bandwidth memory placed adjacent to the processor cores, right on the silicon die.
> This design delivers high performance and new levels of efficiency, whether running today’s most popular models, such as BERT and EfficientNet, or exploring next-generation AI applications.
## Poplar SDK setup
A Poplar SDK environment needs to be enabled to use this library. Please refer to Graphcore's [Getting Started](https://docs.graphcore.ai/en/latest/getting-started.html) guides.
## Install
To install the latest release of this package:
`pip install optimum-graphcore`
Optimum Graphcore is a fast-moving project, and you may want to install from source.
`pip install git+https://github.com/huggingface/optimum-graphcore.git`
### Installing in developer mode
If you are working on the `optimum-graphcore` code then you should use an editable install
by cloning and installing `optimum` and `optimum-graphcore`:
```
git clone https://github.com/huggingface/optimum --branch v1.6.1-release
git clone https://github.com/huggingface/optimum-graphcore
pip install -e optimum -e optimum-graphcore
```
Now whenever you change the code, you'll be able to run with those changes instantly.
## Running the examples
There are a number of examples provided in the `examples` directory. Each of these contains a README with command lines for running them on IPUs with Optimum Graphcore.
Please install the requirements for every example:
```
cd
pip install -r requirements.txt
```
## How to use Optimum Graphcore
🤗 Optimum Graphcore was designed with one goal in mind: **make training and evaluation straightforward for any 🤗 Transformers user while leveraging the complete power of IPUs.**
It requires minimal changes if you are already using 🤗 Transformers.
To immediately use a model on a given input (text, image, audio, ...), we support the `pipeline` API:
```diff
->>> from transformers import pipeline
+>>> from optimum.graphcore import pipeline
# Allocate a pipeline for sentiment-analysis
->>> classifier = pipeline('sentiment-analysis', model="distilbert-base-uncased-finetuned-sst-2-english")
+>>> classifier = pipeline('sentiment-analysis', model="distilbert-base-uncased-finetuned-sst-2-english", ipu_config = "Graphcore/distilbert-base-ipu")
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9996947050094604}]
```
It is also super easy to use the `Trainer` API:
```diff
-from transformers import Trainer, TrainingArguments
+from optimum.graphcore import IPUConfig, IPUTrainer, IPUTrainingArguments
-training_args = TrainingArguments(
+training_args = IPUTrainingArguments(
per_device_train_batch_size=4,
learning_rate=1e-4,
+ # Any IPUConfig on the Hub or stored locally
+ ipu_config_name="Graphcore/bert-base-ipu",
+)
+
+# Loading the IPUConfig needed by the IPUTrainer to compile and train the model on IPUs
+ipu_config = IPUConfig.from_pretrained(
+ training_args.ipu_config_name,
)
# Initialize our Trainer
-trainer = Trainer(
+trainer = IPUTrainer(
model=model,
+ ipu_config=ipu_config,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
... # Other arguments
```
For more information, refer to the full [🤗 Optimum Graphcore documentation](https://huggingface.co/docs/optimum/graphcore_index).
## Supported models
The following model architectures and tasks are currently supported by 🤗 Optimum Graphcore:
| | Pre-Training | Masked LM | Causal LM | Seq2Seq LM (Summarization, Translation, etc) | Sequence Classification | Token Classification | Question Answering | Multiple Choice | Image Classification | CTC |
|------------|--------------|-----------|-----------|----------------------------------------------|-------------------------|----------------------|--------------------|-----------------|----------------------| ------------ |
| BART | ✅ | | ❌ | ✅ | ✅ | | ❌ | | | |
| BERT | ✅ | ✅ | ❌ | | ✅ | ✅ | ✅ | ✅ | | |
| ConvNeXt | ✅ | | | | | | | | ✅ | |
| DeBERTa | ✅ | ✅ | | | ✅ | ✅ | ✅ | | | |
| DistilBERT | ❌ | ✅ | | | ✅ | ✅ | ✅ | ✅ | | |
| GPT-2 | ✅ | | ✅ | | ✅ | ✅ | | | | |
| [GroupBERT](https://arxiv.org/abs/2106.05822) | ✅ | ✅ | ❌ | | ✅ | ✅ | ✅ | ✅ | | |
| HuBERT | ❌ | | | | ✅ | | | | | ✅ |
| LXMERT | ❌ | | | | | | ✅ | | | |
| RoBERTa | ✅ | ✅ | ❌ | | ✅ | ✅ | ✅ | ✅ | | |
| T5 | ✅ | | | ✅ | | | | | | |
| ViT | ❌ | | | | | | | | ✅ | |
| Wav2Vec2 | ✅ | | | | | | | | | ✅ |
| Whisper | ❌ | | | ✅ | | | | | | |
If you find any issue while using those, please open an issue or a pull request.