mindnlp

Easy-to-use and high-performance NLP and LLM framework based on MindSpore, compatible with models and datasets of 🤗Huggingface.

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MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

README:

MindNLP

MindNLP

News 📢

⚡ MindNLP Core support Pytorch compatible: To meet ecosystem compatibility requirements, we provide the mindnlp.core module to support compatibility with PyTorch interfaces. This module is built upon MindSpore's foundational APIs and operators, enabling model development using syntax similar to PyTorch. It also supports taking over torch interfaces through a Proxy, allowing the use of MindSpore for acceleration on Ascend hardware without the need for code modifications. The specific usage is as follows:
```
import mindnlp # import mindnlp lib will enable proxy automaticlly
import torch
from torch import nn

# all torch.xx apis will be mapped to mindnlp.core.xx
net = nn.Linear(10, 5)
x = torch.randn(3, 10)
out = net(x)
print(out.shape)
# core.Size([3, 5])
```
It is particularly noteworthy that MindNLP supports several features not yet available in MindSpore, which enables better support for model serialization, heterogeneous computing, and other scenarios:
1. Dispatch Mechanism Support: Operators are dispatched to the appropriate backend based on Tensor.device.
2. Meta Device Support: Allows for shape inference without performing actual computations.
3. Numpy as CPU Backend: Supports using NumPy as a CPU backend for acceleration.
4. Tensor.to for Heterogeneous Data Movement: Facilitates the movement of data across different devices using Tensor.to.

🔥 Fully compatible with 🤗HuggingFace: It enables seamless execution of any Transformers/Diffusers models on MindSpore across all hardware platforms (GPU/Ascend/CPU).

You may still invoke models through MindNLP as shown in the example code below:

from mindnlp.transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

inputs = tokenizer("Hello world!", return_tensors='ms')
outputs = model(**inputs)

You can also directly use the native HuggingFace library(like transformers, diffusers, etc.) via the following approach as demonstrated in the example code:

For huggingface transformers:

import mindspore
import mindnlp
from transformers import pipeline

chat = [
    {"role": "system", "content": "You are a sassy, wise-cracking robot as imagined by Hollywood circa 1986."},
    {"role": "user", "content": "Hey, can you tell me any fun things to do in New York?"}
]

pipeline = pipeline(task="text-generation", model="Qwen/Qwen3-8B", ms_dtype=mindspore.bfloat16, device_map="auto")
response = pipeline(chat, max_new_tokens=512)
print(response[0]["generated_text"][-1]["content"])

For huggingface diffuers:

import mindspore
import mindnlp
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", ms_dtype=mindspore.float16)
pipeline("An image of a squirrel in Picasso style").images[0]

Notice ⚠️: Due to differences in autograd and parallel execution mechanisms, any training or distributed execution code must utilize the interfaces provided by MindNLP.

Installation

Install from Pypi

You can install the official version of MindNLP which is uploaded to pypi.

pip install mindnlp

Daily build

You can download MindNLP daily wheel from here.

Install from source

To install MindNLP from source, please run:

pip install git+https://github.com/mindspore-lab/mindnlp.git
# or
git clone https://github.com/mindspore-lab/mindnlp.git
cd mindnlp
bash scripts/build_and_reinstall.sh

Version Compatibility

MindNLP version	MindSpore version	Supported Python version
master	daily build	>=3.7.5, <=3.9
0.1.1	>=1.8.1, <=2.0.0	>=3.7.5, <=3.9
0.2.x	>=2.1.0	>=3.8, <=3.9
0.3.x	>=2.1.0, <=2.3.1	>=3.8, <=3.9
0.4.x	>=2.2.x, <=2.5.0	>=3.9, <=3.11
0.5.x	>=2.5.0	>=3.10, <=3.11

Introduction

MindNLP is an open source NLP library based on MindSpore. It supports a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly.

The master branch works with MindSpore master.

Major Features

Comprehensive data processing: Several classical NLP datasets are packaged into friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc.
Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP.
Easy-to-use engine: MindNLP simplified the complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily.

Supported models

Since there are too many supported models, please check here

License

This project is released under the Apache 2.0 license.

Feedbacks and Contact

The dynamic version is still under development, if you find any issue or have an idea on new features, please don't hesitate to contact us via Github Issues.

MindSpore NLP SIG

MindSpore NLP SIG (Natural Language Processing Special Interest Group) is the main development team of the MindNLP framework. It aims to collaborate with developers from both industry and academia who are interested in research, application development, and the practical implementation of natural language processing. Our goal is to create the best NLP framework based on the domestic framework MindSpore. Additionally, we regularly hold NLP technology sharing sessions and offline events. Interested developers can join our SIG group using the QR code below.

Acknowledgement

MindSpore is an open source project that welcomes any contribution and feedback.
We wish that the toolbox and benchmark could serve the growing research
community by providing a flexible as well as standardized toolkit to re-implement existing methods
and develop their own new semantic segmentation methods.

Citation

If you find this project useful in your research, please consider citing:

@misc{mindnlp2022,
    title={{MindNLP}: Easy-to-use and high-performance NLP and LLM framework based on MindSpore},
    author={MindNLP Contributors},
    howpublished = {\url{https://github.com/mindlab-ai/mindnlp}},
    year={2022}
}

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mindnlp

README:

MindNLP

Table of Contents

News 📢

Installation

Install from Pypi

Daily build

Install from source

Version Compatibility

Introduction

Major Features

Supported models

License

Feedbacks and Contact

MindSpore NLP SIG

Acknowledgement

Citation

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For Jobs:

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