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kunlunxin: add BERT readme #260

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48 changes: 48 additions & 0 deletions training/kunlunxin/bert-pytorch/README.md
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### 模型Checkpoint下载
[模型Checkpoint下载](../../benchmarks/bert/README.md#模型Checkpoint下载)
### 测试数据集下载
[测试数据集下载](../../benchmarks/bert/README.md#测试数据集下载)

### 昆仑芯XPU配置与运行信息参考
#### 环境配置
- ##### 硬件环境
- 机器型号: 昆仑芯AI加速器组R480-X8
- 加速卡型号: 昆仑芯AI加速卡R300
- 多机网络类型、带宽: InfiniBand,200Gb/s

- ##### 软件环境
- OS版本:Ubuntu 20.04
- OS kernel版本: 5.4.0-26-generic
- 加速卡驱动版本:4.0.25
- Docker镜像和版本:pytorch1.12.1-cpu-ubuntu20.04:v0.01
- 训练框架版本:xmlir
- 训练编译器版本:xacc
- 依赖软件版本:pytorch-1.12.1+cpu

#### 运行情况

* 通用指标

| 指标名称 | 指标值 | 特殊说明 |
| -------------- | ------------------------------ | ------------------------------------------- |
| 任务类别 | 通用语言模型 | |
| 模型 | bert | |
| 数据集 | Wikipedia for bert | |
| 数据精度 | precision,见“性能指标” | 可选fp32/amp/fp16 |
| 超参修改 | fix_hp,见“性能指标” | 跑满硬件设备评测吞吐量所需特殊超参 |
| 硬件设备简称 | R300 | |
| 硬件存储使用 | mem(actual/total),见“性能指标” | 通常称为“显存”,单位为GiB |
| 端到端时间 | e2e_time,见“性能指标” | 总时间+Perf初始化等时间 |
| 总吞吐量 | p_whole,见“性能指标” | 实际训练样本数除以总时间(performance_whole) |
| 训练吞吐量 | p_train,见“性能指标” | 不包含每个epoch末尾的评估部分耗时 |
| **计算吞吐量** | **p_core,见“性能指标”** | 不包含数据IO部分的耗时(p3>p2>p1) |
| 训练结果 | acc,见“性能指标” | 分类准确率(mlm_accuracy) |
| 额外修改项 | 无 | |

* 性能指标

| 配置 | precision | fix_hp | e2e_time | p_whole | p_train | p_core | mlm_accuracy | mem |
| ------------------- | --------- | ---------------- | -------- | ------- | ------- | ------ | ----- | --------- |
| R300单机单卡(1x1) | fp32 | bs=8,lr=3.5e-04 | | | | | | |
| R300单机8卡(1x8) | fp32 | bs=8,lr=3.5e-04 | | | | | 0.36 | 26.5/32.0 |
| R300两机8卡(2x8) | fp32 | bs=8,lr=3.5e-04 | | | | | | |