# optimum-intel

**Repository Path**: techwolf/optimum-intel

## Basic Information

- **Project Name**: optimum-intel
- **Description**: https://gitee.com/techwolf/optimum-intel.git开源代码
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-08-08
- **Last Updated**: 2024-04-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<p align="center">
    <img src="readme_logo.png" />
</p>

# Optimum Intel

🤗 Optimum Intel is the interface between the 🤗 Transformers and Diffusers libraries and the different tools and libraries provided by Intel to accelerate end-to-end pipelines on Intel architectures.

[Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/#introduction) is an open-source library which provides optimizations for both eager mode and graph mode, however, compared to eager mode, graph mode in PyTorch* normally yields better performance from optimization techniques, such as operation fusion.

Intel [Neural Compressor](https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html) is an open-source library enabling the usage of the most popular compression techniques such as quantization, pruning and knowledge distillation. It supports automatic accuracy-driven tuning strategies in order for users to easily generate quantized model. The users can easily apply static, dynamic and aware-training quantization approaches while giving an expected accuracy criteria. It also supports different weight pruning techniques enabling the creation of pruned model giving a predefined sparsity target.

[OpenVINO](https://docs.openvino.ai) is an open-source toolkit that enables high performance inference capabilities for Intel CPUs, GPUs, and special DL inference accelerators ([see](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html) the full list of supported devices). It is supplied with a set of tools to optimize your models with compression techniques such as quantization, pruning and knowledge distillation. Optimum Intel provides a simple interface to optimize your Transformers and Diffusers models, convert them to the OpenVINO Intermediate Representation (IR) format and run inference using OpenVINO Runtime.


## Installation

To install the latest release of 🤗 Optimum Intel with the corresponding required dependencies, you can use `pip` as follows:

| Accelerator                                                                                                      | Installation                                                         |
|:-----------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------|
| [Intel Neural Compressor](https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html) | `pip install --upgrade --upgrade-strategy eager "optimum[neural-compressor]"`  |
| [OpenVINO](https://docs.openvino.ai)                                                                             | `pip install --upgrade --upgrade-strategy eager "optimum[openvino]"`           |
| [Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/#introduction)                 | `pip install --upgrade --upgrade-strategy eager "optimum[ipex]"`               |

The `--upgrade-strategy eager` option is needed to ensure `optimum-intel` is upgraded to the latest version.

We recommend creating a [virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#creating-a-virtual-environment) and upgrading
pip with `python -m pip install --upgrade pip`.

Optimum Intel is a fast-moving project, and you may want to install from source with the following command:

```bash
python -m pip install git+https://github.com/huggingface/optimum-intel.git
```

or to install from source including dependencies:

```bash
python -m pip install "optimum-intel[extras]"@git+https://github.com/huggingface/optimum-intel.git
```

where `extras` can be one or more of `ipex`, `neural-compressor`, `openvino`, `nncf`.

# Quick tour

## Neural Compressor

Dynamic quantization can be used through the Optimum command-line interface:

```bash
optimum-cli inc quantize --model distilbert-base-cased-distilled-squad --output ./quantized_distilbert
```
Note that quantization is currently only supported for CPUs (only CPU backends are available), so we will not be utilizing GPUs / CUDA in this example.

To load a quantized model hosted locally or on the 🤗 hub, you can do as follows :
```python
from optimum.intel import INCModelForSequenceClassification

model_id = "Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic"
model = INCModelForSequenceClassification.from_pretrained(model_id)
```

You can load many more quantized models hosted on the hub under the Intel organization [`here`](https://huggingface.co/Intel).

For more details on the supported compression techniques, please refer to the [documentation](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc).


## OpenVINO

Below are examples of how to use OpenVINO and its [NNCF](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/compressing-models-during-training.html) framework to accelerate inference.

#### Export:

It is possible to export your model to the [OpenVINO IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) format with the CLI :

```plain
optimum-cli export openvino --model gpt2 ov_model
```

You can also apply 8-bit weight-only quantization when exporting your model : the model linear and embedding weights will be quantized to INT8, the activations will be kept in floating point precision.

```plain
optimum-cli export openvino --model gpt2 --weight-format int8 ov_model
```

To apply quantization on both weights and activations, you can find more information in the [documentation](https://huggingface.co/docs/optimum/main/en/intel/optimization_ov).

#### Inference:

To load a model and run inference with OpenVINO Runtime, you can just replace your `AutoModelForXxx` class with the corresponding `OVModelForXxx` class.


```diff
- from transformers import AutoModelForSeq2SeqLM
+ from optimum.intel import OVModelForSeq2SeqLM
  from transformers import AutoTokenizer, pipeline

  model_id = "echarlaix/t5-small-openvino"
- model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+ model = OVModelForSeq2SeqLM.from_pretrained(model_id)
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  pipe = pipeline("translation_en_to_fr", model=model, tokenizer=tokenizer)
  results = pipe("He never went out without a book under his arm, and he often came back with two.")

  [{'translation_text': "Il n'est jamais sorti sans un livre sous son bras, et il est souvent revenu avec deux."}]
```

If you want to load a PyTorch checkpoint, set `export=True` to convert your model to the OpenVINO IR.

```python
from optimum.intel import OVModelForCausalLM

model = OVModelForCausalLM.from_pretrained("gpt2", export=True)
model.save_pretrained("./ov_model")
```


#### Post-training static quantization:

Post-training static quantization introduces an additional calibration step where data is fed through the network in order to compute the activations quantization parameters. Here is an example on how to apply static quantization on a fine-tuned DistilBERT.

```python
from functools import partial
from optimum.intel import OVQuantizer, OVModelForSequenceClassification
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "distilbert-base-uncased-finetuned-sst-2-english"
model = OVModelForSequenceClassification.from_pretrained(model_id, export=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
def preprocess_fn(examples, tokenizer):
    return tokenizer(
        examples["sentence"], padding=True, truncation=True, max_length=128
    )

quantizer = OVQuantizer.from_pretrained(model)
calibration_dataset = quantizer.get_calibration_dataset(
    "glue",
    dataset_config_name="sst2",
    preprocess_function=partial(preprocess_fn, tokenizer=tokenizer),
    num_samples=100,
    dataset_split="train",
    preprocess_batch=True,
)
# The directory where the quantized model will be saved
save_dir = "nncf_results"
# Apply static quantization and save the resulting model in the OpenVINO IR format
quantizer.quantize(calibration_dataset=calibration_dataset, save_directory=save_dir)
# Load the quantized model
optimized_model = OVModelForSequenceClassification.from_pretrained(save_dir)
```

#### Quantization-aware training:

Quantization aware training (QAT) is applied in order to simulate the effects of quantization during training, to alleviate its effects on the model’s accuracy. Here is an example on how to fine-tune a DistilBERT model on the sst-2 task while applying quantization aware training (QAT).

```diff
  import evaluate
  import numpy as np
  from datasets import load_dataset
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, TrainingArguments, default_data_collator
- from transformers import Trainer
+ from optimum.intel import OVConfig, OVModelForSequenceClassification, OVTrainer

  model_id = "distilbert-base-uncased-finetuned-sst-2-english"
  model = AutoModelForSequenceClassification.from_pretrained(model_id)
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  dataset = load_dataset("glue", "sst2")
  dataset = dataset.map(
      lambda examples: tokenizer(examples["sentence"], padding=True, truncation=True, max_length=128), batched=True
  )
  metric = evaluate.load("glue", "sst2")
  compute_metrics = lambda p: metric.compute(
      predictions=np.argmax(p.predictions, axis=1), references=p.label_ids
  )

  # The directory where the quantized model will be saved
  save_dir = "nncf_results"

  # Load the default quantization configuration detailing the quantization we wish to apply
+ ov_config = OVConfig()

- trainer = Trainer(
+ trainer = OVTrainer(
      model=model,
      args=TrainingArguments(save_dir, num_train_epochs=1.0, do_train=True, do_eval=True),
      train_dataset=dataset["train"].select(range(300)),
      eval_dataset=dataset["validation"],
      compute_metrics=compute_metrics,
      tokenizer=tokenizer,
      data_collator=default_data_collator,
+     ov_config=ov_config,
+     task="text-classification",
  )
  train_result = trainer.train()
  metrics = trainer.evaluate()
  trainer.save_model()

+ optimized_model = OVModelForSequenceClassification.from_pretrained(save_dir)
```

You can find more examples in the [documentation](https://huggingface.co/docs/optimum/intel/index).


## IPEX
To load your IPEX model, you can just replace your `AutoModelForXxx` class with the corresponding `IPEXModelForXxx` class. You can set `export=True` to load a PyTorch checkpoint, export your model via TorchScript and apply IPEX optimizations : both operators optimization (replaced with customized IPEX operators) and graph-level optimization (like operators fusion) will be applied on your model.
```diff
  from transformers import AutoTokenizer, pipeline
- from transformers import AutoModelForCausalLM
+ from optimum.intel import IPEXModelForCausalLM


  model_id = "gpt2"
- model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+ model = IPEXModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, export=True)
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
  results = pipe("He's a dreadful magician and")

```

For more details, please refer to the [documentation](https://intel.github.io/intel-extension-for-pytorch/#introduction).


## Running the examples

Check out the [`examples`](https://github.com/huggingface/optimum-intel/tree/main/examples) directory to see how 🤗 Optimum Intel can be used to optimize models and accelerate inference.

Do not forget to install requirements for every example:

```
cd <example-folder>
pip install -r requirements.txt
```