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[Doc]: Update README_npu (#827)

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14
ecosystem/README_npu.md
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@ -2,7 +2,7 @@
<div align="center">
<img src="./assets/logo.svg" width="200"/>
<img src="../assets/logo.svg" width="200"/>
<div> </div>
<div align="center">
<b><font size="5">InternLM</font></b>
@ -43,9 +43,9 @@ This is a guide to using Ascend NPU to train and infer the InternLM series model
### InternLM3
| Model | Transformers | ModelScope | Modelers | Release Date |
| ------------------------- | ---------------------------------------------------- | -------------------------------------------------- | ------------------------------------------------- | ------------ |
| **InternLM3-8B-Instruct** | [🤗internlm3_8B_instruct](https://huggingface.co/internlm/internlm3-8b-instruct) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm3_8b_instruct](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct/summary) | [![Open in Modelers](https://modelers.cn/assets/logo1-1bf58310.svg)](https://modelers.cn/models/Intern/internlm3-8b-instruct) | 2025-01-15 |
| Model | Transformers | ModelScope | Modelers | Release Date |
| ------------------------- | ---------------------------------------------------- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ------------------------------------------------- | ------------ |
| **InternLM3-8B-Instruct** | [🤗internlm3_8B_instruct](https://huggingface.co/internlm/internlm3-8b-instruct) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm3_8b_instruct](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct/summary) | [![Open in Modelers](https://modelers.cn/assets/logo1-1bf58310.svg)](https://modelers.cn/models/Intern/internlm3-8b-instruct) | 2025-01-15 |
## Environment Setup
@ -334,7 +334,7 @@ openmind-cli train examples/internlm3/train_sft_full_internlm3.yaml
As illustrated in the figure below, the training loss of the openMind Library normally converges, and compared with the GPU, the average relative error is within 2%.
<div align=center>
<img src="./assets/openmind_train_loss_compare.png" width="600px">
<img src="../assets/npu/openmind_train_loss_compare.png" width="600px">
</div>
<p align="center"><strong>Accuracy Comparison</strong> (npu=8, per_device_train_batch_size=6, max_length=1024)</p>
@ -342,7 +342,7 @@ As illustrated in the figure below, the training loss of the openMind Library no
The openMind Library supports the enabling of fine-tuning methods such as LoRA and QLoRA on Ascend NPUs, significantly reducing device memory usage. As illustrated in the figure below, employing the QLoRA fine-tuning method can lead to approximately a 40% reduction in device memory consumption.
<div align=center>
<img src="./assets/openmind_train_memory.png" width="400px">
<img src="../assets/npu/openmind_train_memory.png" width="400px">
</div>
<p align="center"><strong>Memory Consumption</strong> (npu=8, per_device_train_batch_size=6, max_length=1024)</p>
@ -350,7 +350,7 @@ The openMind Library supports the enabling of fine-tuning methods such as LoRA a
The openMind Library facilitates the automatic loading of Ascend NPU fused operators during training, eliminating the need for developers to manually modify code or configurations. This enhances model training performance while maintaining ease of use. The figure below demonstrates the performance benefits achieved by default when the openMind Library enables Ascend NPU fused operators.
<div align=center>
<img src="./assets/openmind_fused_ops.png" width="300px">
<img src="../assets/npu/openmind_fused_ops.png" width="300px">
</div>
<p align="center"><strong>Training Samples per Second</strong></p>

18
ecosystem/README_npu_zh-CN.md
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@ -2,7 +2,7 @@
<div align="center">
<img src="./assets//logo.svg" width="200"/>
<img src="../assets/logo.svg" width="200"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">书生·浦语 官网</font></b>
@ -14,8 +14,8 @@
<div>&nbsp;</div>
</div>
[![license](./assets//license.svg)](https://github.com/open-mmlab/mmdetection/blob/main/LICENSE)
[![evaluation](./assets//compass_support.svg)](https://github.com/internLM/OpenCompass/)
[![license](../assets/license.svg)](https://github.com/open-mmlab/mmdetection/blob/main/LICENSE)
[![evaluation](../assets/compass_support.svg)](https://github.com/internLM/OpenCompass/)
<!-- [![Documentation Status](https://readthedocs.org/projects/internlm/badge/?version=latest)](https://internlm.readthedocs.io/zh_CN/latest/?badge=latest) -->
@ -43,9 +43,9 @@
### InternLM3
| Model | Transformers | ModelScope | Modelers | Release Date |
| ------------------------- | ---------------------------------------------------- | -------------------------------------------------- | ------------------------------------------------- | ------------ |
| **InternLM3-8B-Instruct** | [🤗internlm3_8B_instruct](https://huggingface.co/internlm/internlm3-8b-instruct) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm3_8b_instruct](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct/summary) | [![Open in Modelers](https://modelers.cn/assets/logo1-1bf58310.svg)](https://modelers.cn/models/Intern/internlm3-8b-instruct) | 2025-01-15 |
| Model | Transformers | ModelScope | Modelers | Release Date |
| ------------------------- | ---------------------------------------------------- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ------------------------------------------------- | ------------ |
| **InternLM3-8B-Instruct** | [🤗internlm3_8B_instruct](https://huggingface.co/internlm/internlm3-8b-instruct) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm3_8b_instruct](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct/summary) | [![Open in Modelers](https://modelers.cn/assets/logo1-1bf58310.svg)](https://modelers.cn/models/Intern/internlm3-8b-instruct) | 2025-01-15 |
## 环境准备
@ -333,7 +333,7 @@ openmind-cli train examples/internlm3/train_sft_full_internlm3.yaml
如下图所示，openMind Library 的训练 loss 正常收敛，同时和 GPU 对比，平均相对误差在 2% 以内。
<div align=center>
<img src="./assets/openmind_train_loss_compare.png" width="600px">
<img src="../assets/npu/openmind_train_loss_compare.png" width="600px">
</div>
<p align="center"><strong>精度对比</strong> (npu=8, per_device_train_batch_size=6, max_length=1024)</p>
@ -342,7 +342,7 @@ openMind Library 支持在昇腾 NPU 上使能 LoRA、QLoRA 等微调方法，
如下图所示，通过使能 QloRA 微调方式可减少 device 内存约 40%。
<div align=center>
<img src="./assets/openmind_train_memory.png" width="400px">
<img src="../assets/npu/openmind_train_memory.png" width="400px">
</div>
<p align="center"><strong>Full/LoRA/QLoRA 显存开销</strong> (npu=8, per_device_train_batch_size=6, max_length=1024)</p>
@ -351,7 +351,7 @@ openMind Library 支持训练时自动加载昇腾 NPU 融合算子，无需开
的同时兼顾易用性。下图展示了 openMind 默认使能昇腾 NPU 融合算子之后的性能收益。
<div align=center>
<img src="./assets/openmind_fused_ops.png" width="300px">
<img src="../assets/npu/openmind_fused_ops.png" width="300px">
</div>
<p align="center"><strong>每秒训练样本数</strong></p>