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# Gemma
谷歌发布的号称“全球性能最强大、轻量级”的新一代开源2B小模型Gemma，打响小模型战争。
## 论文
`未发表论文`
## 模型结构
[`Gemma`](./gemma/model.py)基于原始transformer decoder结构，2B模型使用了multi-query attention (with 𝑛𝑢𝑚_𝑘𝑣_ℎ𝑒𝑎𝑑𝑠 = 1)，改进细节请见代码：
1、RoPE Embeddings: 不使用绝对位置编码，在每一层前加下RoPE Embedding，同时共享输入与输出层的embedding权重；
2、GeGLU Activations: ReLU的激活替换为GeGLU的激活；
3、Normalizer Location: 在transformer的每一层layer的前后都进行规一化，使用RMSNorm作为规一化层；
<div align=center>
<img src="./doc/gemma.png"/>
</div>
## 算法原理
[`Gemma`](./gemma/model.py)算法主要将转换成向量的分词用qkv自相关和全连接层提取特征，然后利用全连接层输出监督训练结果，具体算法原理可参考下图原始transformer模型结构右侧decoder部分进行初步理解；Gemma在2T和6T个token的文本上进行预训练，数据主要来自英文网页、数学和代码，开发者使用Gemini的SentencePiece分词器的子集，词汇量为256k，高质量大数据产生巨大的小模型效果提升。
<div align=center>
<img src="./doc/transformer.png"/>
</div>
## 环境配置
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk23.10-py38
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：ffa1f63239fc
docker run -it --shm-size=16G -v $PWD/gemma_pytorch:/home/gemma_pytorch -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name gemma <your IMAGE ID> bash
cd home/gemma_pytorch
pip install -r requirements.txt # requirements.txt
```
### Dockerfile（方法二）
```
cd MiniCPM/docker
docker build --no-cache -t gemma:latest .
docker run --shm-size=16G --name gemma -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../gemma_pytorch:/home/gemma_pytorch -it gemma bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.hpccube.com/tool/
```
DTK驱动:dtk23.10
python:python3.8
torch:2.1.0
torchvision:0.16.0
triton:2.1.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
pip install -r finetune/requirements.txt # finetune/requirements.txt
```
## 数据集
无
## 训练
官方github未开源微调代码，如有需求请进入以下网站申请账户获取：
* [Gemma on Google AI](https://ai.google.dev/gemma)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex AI Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335)
微调所需的特殊深度学习库可从光合开发者社区下载安装。
更多资料可参考源项目的[`README_origin`](./README_origin.md)。
## 推理
### 单机单卡
推理权重采用`gemma-2b-it-pytorch`，请下载后放入目录gemma-2b-pytorch下面。
权重下载地址：https://huggingface.co/google/gemma-2b-it-pytorch
```
sh infer.sh # 采用官方默认权重推理，去除--device=cuda则为CPU推理
```
## result
```
#PROMPT:
The meaning of life is
#RESULT:
a question that has been pondered by philosophers, theologians, and laypeople alike for centuries. There is no single, universally accepted answer, but there are many different perspectives and beliefs that attempt to provide meaning to life.
```
### 精度
DCU Z100L精度与英伟达v100一致。
## 应用场景
### 算法类别
`对话问答`
### 热点应用行业
`制造,广媒,金融,能源,医疗,家居,教育`
## 源码仓库及问题反馈
- http://developer.hpccube.com/codes/modelzoo/gemma_pytorch.git
## 参考资料
- https://github.com/google/gemma_pytorch.git
- https://hf-mirror.com/ #Huggingface镜像官网下载教程
- https://hf-mirror.com/datasets #Huggingface镜像数据地址
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# Gemma in PyTorch
**Gemma** is a family of lightweight, state-of-the art open models built from research and technology used to create Google Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. For more details, please check out the following links:
* [Gemma on Google AI](https://ai.google.dev/gemma)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex AI Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335)
This is the official PyTorch implementation of Gemma models. We provide model and inference implementations using both PyTorch and PyTorch/XLA, and support running inference on CPU, GPU and TPU.
## Download Gemma model checkpoint
You can find the model checkpoints on Kaggle
[here](https://www.kaggle.com/models/google/gemma/frameworks/pyTorch).
Alternatively, you can find the model checkpoints on the Hugging Face Hub [here](https://huggingface.co/models?other=gemma_torch). To download the models, go the the model repository of the model of interest and click the `Files and versions` tab, and download the model and tokenizer files. For programmatic downloading, if you have `huggingface_hub`
installed, you can also run:
```
huggingface-cli download google/gemma-7b-it-pytorch
```
Note that you can choose between the 2B, 7B, 7B int8 quantized variants.
```
VARIANT=<2b or 7b>
CKPT_PATH=<Insert ckpt path here>
```
## Try it free on Colab
Follow the steps at
[https://ai.google.dev/gemma/docs/pytorch_gemma](https://ai.google.dev/gemma/docs/pytorch_gemma).
## Try it out with PyTorch
Prerequisite: make sure you have setup docker permission properly as a non-root user.
```bash
sudo usermod -aG docker $USER
newgrp docker
```
### Build the docker image.
```bash
DOCKER_URI=gemma:${USER}
docker build -f docker/Dockerfile ./ -t ${DOCKER_URI}
```
### Run Gemma inference on CPU.
```bash
PROMPT="The meaning of life is"
docker run -t --rm \
-v ${CKPT_PATH}:/tmp/ckpt \
${DOCKER_URI} \
python scripts/run.py \
--ckpt=/tmp/ckpt \
--variant="${VARIANT}" \
--prompt="${PROMPT}"
# add `--quant` for the int8 quantized model.
```
### Run Gemma inference on GPU.
```bash
PROMPT="The meaning of life is"
docker run -t --rm \
--gpus all \
-v ${CKPT_PATH}:/tmp/ckpt \
${DOCKER_URI} \
python scripts/run.py \
--device=cuda \
--ckpt=/tmp/ckpt \
--variant="${VARIANT}" \
--prompt="${PROMPT}"
# add `--quant` for the int8 quantized model.
```
## Try It out with PyTorch/XLA
### Build the docker image (CPU, TPU).
```bash
DOCKER_URI=gemma_xla:${USER}
docker build -f docker/xla.Dockerfile ./ -t ${DOCKER_URI}
```
### Build the docker image (GPU).
```bash
DOCKER_URI=gemma_xla_gpu:${USER}
docker build -f docker/xla_gpu.Dockerfile ./ -t ${DOCKER_URI}
```
### Run Gemma inference on CPU.
```bash
docker run -t --rm \
--shm-size 4gb \
-e PJRT_DEVICE=CPU \
-v ${CKPT_PATH}:/tmp/ckpt \
${DOCKER_URI} \
python scripts/run_xla.py \
--ckpt=/tmp/ckpt \
--variant="${VARIANT}" \
# add `--quant` for the int8 quantized model.
```
### Run Gemma inference on TPU.
Note: be sure to use the docker container built from `xla.Dockerfile`.
```bash
docker run -t --rm \
--shm-size 4gb \
-e PJRT_DEVICE=TPU \
-v ${CKPT_PATH}:/tmp/ckpt \
${DOCKER_URI} \
python scripts/run_xla.py \
--ckpt=/tmp/ckpt \
--variant="${VARIANT}" \
# add `--quant` for the int8 quantized model.
```
### Run Gemma inference on GPU.
Note: be sure to use the docker container built from `xla_gpu.Dockerfile`.
```bash
docker run -t --rm --privileged \
--shm-size=16g --net=host --gpus all \
-e USE_CUDA=1 \
-e PJRT_DEVICE=CUDA \
-v ${CKPT_PATH}:/tmp/ckpt \
${DOCKER_URI} \
python scripts/run_xla.py \
--ckpt=/tmp/ckpt \
--variant="${VARIANT}" \
# add `--quant` for the int8 quantized model.
```
## Disclaimer
This is not an officially supported Google product.
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FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk23.10-py38
ENV DEBIAN_FRONTEND=noninteractive
# RUN yum update && yum install -y git cmake wget build-essential
RUN source /opt/dtk-23.10/env.sh
# 安装pip相关依赖
COPY requirements.txt requirements.txt
RUN pip3 install -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com -r requirements.txt
docker/requirements.txt 0 → 100644
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fairscale == 0.4.13
numpy == 1.24.4
immutabledict == 4.1.0
sentencepiece == 0.1.99
docker_gpu/Dockerfile 0 → 100644
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Use PyTorch CUDA base image
FROM pytorch/pytorch:2.1.2-cuda11.8-cudnn8-runtime
USER root
# Install tools.
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update
RUN apt-get install -y --no-install-recommends apt-utils
RUN apt-get install -y --no-install-recommends curl
RUN apt-get install -y --no-install-recommends wget
RUN apt-get install -y --no-install-recommends git
# Install libraries.
ENV PIP_ROOT_USER_ACTION=ignore
RUN python -m pip install --upgrade pip
RUN pip install fairscale==0.4.13
RUN pip install numpy==1.24.4
RUN pip install immutabledict==4.1.0
RUN pip install sentencepiece==0.1.99
# Install from source.
COPY . /workspace/gemma/
WORKDIR /workspace/gemma/
RUN pip install -e .
docker_gpu/xla.Dockerfile 0 → 100644
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Use pytorch/xla base image
FROM us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_tpuvm_20231128
USER root
# Install tools.
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update
RUN apt-get install -y --no-install-recommends apt-utils
RUN apt-get install -y --no-install-recommends curl
RUN apt-get install -y --no-install-recommends wget
RUN apt-get install -y --no-install-recommends git
# Install libraries.
ENV PIP_ROOT_USER_ACTION=ignore
RUN python3 -m pip install --upgrade pip
RUN pip install fairscale==0.4.13
RUN pip install numpy==1.24.4
RUN pip install immutabledict==4.1.0
RUN pip install sentencepiece==0.1.99
# Install from source.
COPY . /workspace/gemma/
WORKDIR /workspace/gemma/
RUN pip install -e .
docker_gpu/xla_gpu.Dockerfile 0 → 100644
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Use pytorch/xla base image
FROM us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_11.8_20231128
USER root
# Install tools.
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update
RUN apt-get install -y --no-install-recommends apt-utils
RUN apt-get install -y --no-install-recommends curl
RUN apt-get install -y --no-install-recommends wget
RUN apt-get install -y --no-install-recommends git
# Install libraries.
ENV PIP_ROOT_USER_ACTION=ignore
RUN python3 -m pip install --upgrade pip
RUN pip uninstall -y torch
RUN pip install torch==2.1.1
RUN pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/11.8/torch_xla-2.2.0rc1-cp38-cp38-linux_x86_64.whl
RUN pip install fairscale==0.4.13
RUN pip install numpy==1.24.4
RUN pip install immutabledict==4.1.0
RUN pip install sentencepiece==0.1.99
# Install from source.
COPY . /workspace/gemma/
WORKDIR /workspace/gemma/
RUN pip install -e .
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docker run -it --shm-size=16G -v $PWD/gemma_pytorch:/home/gemma -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name gemma ffa1f63239fc bash
# python -m torch.utils.collect_env
gemma-2b-pytorch/README.md 0 → 100644
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---
library_name: gemma_torch
tags:
- pytorch
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged-in to Hugging
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license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
pipeline_tag: text-generation
---
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 2B instruct version of the Gemma model for usage with PyTorch (https://github.com/google/gemma_pytorch) without transformers. For more information about the model, visit https://huggingface.co/google/gemma-2b-it.
**Resources and Technical Documentation**:
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-gg-hf)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Authors**: Google
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{
'vocab_size': 256000,
'max_position_embeddings': 8192,
'num_hidden_layers': 18,
'num_attention_heads': 8,
'num_key_value_heads': 1,
'hidden_size': 2048,
'intermediate_size': 16384,
'head_dim': 256,
'rms_nms_eps': 1e-6,
'dtype': 'bfloat16',
'quant': false,
'tokenizer': 'tokenizer/tokenizer.model',
}
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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