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bert readme update

tags/v1.1.0
wilfChen 5 years ago
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      model_zoo/official/nlp/bert/README.md

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@@ -17,8 +17,10 @@
- [Training Process](#training-process) - [Training Process](#training-process)
- [Training](#training) - [Training](#training)
- [Running on Ascend](#running-on-ascend) - [Running on Ascend](#running-on-ascend)
- [running on GPU](#running-on-gpu)
- [Distributed Training](#distributed-training) - [Distributed Training](#distributed-training)
- [Running on Ascend](#running-on-ascend-1) - [Running on Ascend](#running-on-ascend-1)
- [running on GPU](#running-on-gpu-1)
- [Evaluation Process](#evaluation-process) - [Evaluation Process](#evaluation-process)
- [Evaluation](#evaluation) - [Evaluation](#evaluation)
- [evaluation on cola dataset when running on Ascend](#evaluation-on-cola-dataset-when-running-on-ascend) - [evaluation on cola dataset when running on Ascend](#evaluation-on-cola-dataset-when-running-on-ascend)
@@ -50,8 +52,8 @@ The backbone structure of BERT is transformer. For BERT_base, the transformer co


# [Environment Requirements](#contents) # [Environment Requirements](#contents)


- Hardware（Ascend）
- Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get access to the resources.
- Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend/GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get access to the resources.
- Framework - Framework
- [MindSpore](https://gitee.com/mindspore/mindspore) - [MindSpore](https://gitee.com/mindspore/mindspore)
- For more information, please check the resources below： - For more information, please check the resources below：
@@ -62,6 +64,8 @@ The backbone structure of BERT is transformer. For BERT_base, the transformer co


After installing MindSpore via the official website, you can start pre-training, fine-tuning and evaluation as follows: After installing MindSpore via the official website, you can start pre-training, fine-tuning and evaluation as follows:


- Running on Ascend

```bash ```bash
# run standalone pre-training example # run standalone pre-training example
bash scripts/run_standalone_pretrain_ascend.sh 0 1 /path/cn-wiki-128 bash scripts/run_standalone_pretrain_ascend.sh 0 1 /path/cn-wiki-128
@@ -89,7 +93,36 @@ bash scripts/run_distributed_pretrain_ascend.sh /path/cn-wiki-128 /path/hccl.jso
bash scripts/run_squad.sh bash scripts/run_squad.sh
``` ```


For distributed training, an hccl configuration file with JSON format needs to be created in advance.
- Running on GPU

```bash
# run standalone pre-training example
bash run_standalone_pretrain_for_gpu.sh 0 1 /path/cn-wiki-128

# run distributed pre-training example
bash scripts/run_distributed_pretrain_for_gpu.sh 8 40 /path/cn-wiki-128

# run fine-tuning and evaluation example
- If you are going to run a fine-tuning task, please prepare a checkpoint generated from pre-training.
- Set bert network config and optimizer hyperparameters in `finetune_eval_config.py`.

- Classification task: Set task related hyperparameters in scripts/run_classifier.sh.
- Run `bash scripts/run_classifier.py` for fine-tuning of BERT-base and BERT-NEZHA model.

bash scripts/run_classifier.sh

- NER task: Set task related hyperparameters in scripts/run_ner.sh.
- Run `bash scripts/run_ner.py` for fine-tuning of BERT-base and BERT-NEZHA model.

bash scripts/run_ner.sh

- SQuAD task: Set task related hyperparameters in scripts/run_squad.sh.
- Run `bash scripts/run_squad.py` for fine-tuning of BERT-base and BERT-NEZHA model.

bash scripts/run_squad.sh
```

For distributed training on Ascend, an hccl configuration file with JSON format needs to be created in advance.


For distributed training on single machine, [here](https://gitee.com/mindspore/mindspore/tree/master/config/hccl_single_machine_multi_rank.json) is an example hccl.json. For distributed training on single machine, [here](https://gitee.com/mindspore/mindspore/tree/master/config/hccl_single_machine_multi_rank.json) is an example hccl.json.


@@ -402,7 +435,22 @@ epoch: 0.0, current epoch percent: 0.000, step: 2, outpus are (Tensor(shape=[1],
... ...
``` ```


> **Attention** If you are running with a huge dataset, it's better to add an external environ variable to make sure the hccl won't timeout.
#### running on GPU

```bash
bash scripts/run_standalone_pretrain_for_gpu.sh 0 1 /path/cn-wiki-128
```

The command above will run in the background, you can view the results the file pretraining_log.txt. After training, you will get some checkpoint files under the script folder by default. The loss value will be achieved as follows:

```bash
# grep "epoch" pretraining_log.txt
epoch: 0.0, current epoch percent: 0.000, step: 1, outpus are (Tensor(shape=[1], dtype=Float32, [ 1.0856101e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.000, step: 2, outpus are (Tensor(shape=[1], dtype=Float32, [ 1.0821701e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
```

> **Attention** If you are running with a huge dataset on Ascend, it's better to add an external environ variable to make sure the hccl won't timeout.
> >
> ```bash > ```bash
> export HCCL_CONNECT_TIMEOUT=600 > export HCCL_CONNECT_TIMEOUT=600
@@ -435,6 +483,24 @@ epoch: 0.0, current epoch percent: 0.002, step: 200, outpus are (Tensor(shape=[1
... ...
``` ```


#### running on GPU

```bash
bash scripts/run_distributed_pretrain_for_gpu.sh /path/cn-wiki-128
```

The command above will run in the background, you can view the results the file pretraining_log.txt. After training, you will get some checkpoint files under the LOG* folder by default. The loss value will be achieved as follows:

```bash
# grep "epoch" LOG*/pretraining_log.txt
epoch: 0.0, current epoch percent: 0.001, step: 100, outpus are (Tensor(shape=[1], dtype=Float32, [ 1.08209e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.002, step: 200, outpus are (Tensor(shape=[1], dtype=Float32, [ 1.07566e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
epoch: 0.0, current epoch percent: 0.001, step: 100, outpus are (Tensor(shape=[1], dtype=Float32, [ 1.08218e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.002, step: 200, outpus are (Tensor(shape=[1], dtype=Float32, [ 1.07770e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
```

> **Attention** This will bind the processor cores according to the `device_num` and total processor numbers. If you don't expect to run pretraining with binding processor cores, remove the operations about `taskset` in `scripts/ascend_distributed_launcher/get_distribute_pretrain_cmd.py` > **Attention** This will bind the processor cores according to the `device_num` and total processor numbers. If you don't expect to run pretraining with binding processor cores, remove the operations about `taskset` in `scripts/ascend_distributed_launcher/get_distribute_pretrain_cmd.py`


## [Evaluation Process](#contents) ## [Evaluation Process](#contents)
@@ -495,57 +561,57 @@ The result will be as follows:
| Parameters | Ascend | GPU | | Parameters | Ascend | GPU |
| -------------------------- | ---------------------------------------------------------- | ------------------------- | | -------------------------- | ---------------------------------------------------------- | ------------------------- |
| Model Version | BERT_base | BERT_base | | Model Version | BERT_base | BERT_base |
| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMX2 V100-32G |
| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMX2 V100-16G, cpu: Intel(R) Xeon(R) Platinum 8160 CPU @2.10GHz, memory: 256G |
| uploaded Date | 08/22/2020 | 05/06/2020 | | uploaded Date | 08/22/2020 | 05/06/2020 |
| MindSpore Version | 1.0.0 | 1.0.0 | | MindSpore Version | 1.0.0 | 1.0.0 |
| Dataset | cn-wiki-128(4000w) | ImageNet |
| Training Parameters | src/config.py | src/config.py |
| Optimizer | Lamb | Momentum |
| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
| outputs | probability | |
| Epoch | 40 | | |
| Batch_size | 256*8 | 130(8P) | |
| Loss | 1.7 | 1.913 |
| Speed | 340ms/step | 1.913 |
| Total time | 73h | |
| Params (M) | 110M | |
| Checkpoint for Fine tuning | 1.2G(.ckpt file) | |
| Scripts | [BERT_base](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/bert) | |

| Parameters | Ascend | GPU |
| -------------------------- | ---------------------------------------------------------- | ------------------------- |
| Model Version | BERT_NEZHA | BERT_NEZHA |
| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMX2 V100-32G |
| uploaded Date | 08/20/2020 | 05/06/2020 |
| MindSpore Version | 1.0.0 | 1.0.0 |
| Dataset | cn-wiki-128(4000w) | ImageNet |
| Dataset | cn-wiki-128(4000w) | cn-wiki-128(4000w) |
| Training Parameters | src/config.py | src/config.py | | Training Parameters | src/config.py | src/config.py |
| Optimizer | Lamb | Momentum |
| Optimizer | Lamb | AdamWeightDecay |
| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy | | Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
| outputs | probability | |
| Epoch | 40 | | |
| Batch_size | 96*8 | 130(8P) |
| Loss | 1.7 | 1.913 |
| Speed | 360ms/step | 1.913 |
| Total time | 200h | |
| Params (M) | 340M | |
| Checkpoint for Fine tuning | 3.2G(.ckpt file) | |
| Scripts | [BERT_NEZHA](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/bert) | |
| outputs | probability | probability |
| Epoch | 40 | 40 |
| Batch_size | 256*8 | 32*8 |
| Loss | 1.7 | 1.7 |
| Speed | 340ms/step | 290ms/step |
| Total time | 73h | 610H |
| Params (M) | 110M | 110M |
| Checkpoint for Fine tuning | 1.2G(.ckpt file) | 1.2G(.ckpt file) |
| Scripts | [BERT_base](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/bert) | [BERT_base](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/bert) |

| Parameters | Ascend |
| -------------------------- | ---------------------------------------------------------- |
| Model Version | BERT_NEZHA |
| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G |
| uploaded Date | 08/20/2020 |
| MindSpore Version | 1.0.0 |
| Dataset | cn-wiki-128(4000w) |
| Training Parameters | src/config.py |
| Optimizer | Lamb |
| Loss Function | SoftmaxCrossEntropy |
| outputs | probability |
| Epoch | 40 |
| Batch_size | 96*8 |
| Loss | 1.7 |
| Speed | 360ms/step |
| Total time | 200h |
| Params (M) | 340M |
| Checkpoint for Fine tuning | 3.2G(.ckpt file) |
| Scripts | [BERT_NEZHA](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/bert) |


#### Inference Performance #### Inference Performance


| Parameters | Ascend | GPU |
| -------------------------- | ----------------------------- | ------------------------- |
| Model Version | | |
| Resource | Ascend 910 | NV SMX2 V100-32G |
| uploaded Date | 08/22/2020 | 05/22/2020 |
| MindSpore Version | 1.0.0 | 1.0.0 |
| Dataset | cola, 1.2W | ImageNet, 1.2W |
| batch_size | 32(1P) | 130(8P) |
| Accuracy | 0.588986 | ACC1[72.07%] ACC5[90.90%] |
| Speed | 59.25ms/step | |
| Total time | 15min | |
| Model for inference | 1.2G(.ckpt file) | |
| Parameters | Ascend |
| -------------------------- | ----------------------------- |
| Model Version | |
| Resource | Ascend 910 |
| uploaded Date | 08/22/2020 |
| MindSpore Version | 1.0.0 |
| Dataset | cola, 1.2W |
| batch_size | 32(1P) |
| Accuracy | 0.588986 |
| Speed | 59.25ms/step |
| Total time | 15min |
| Model for inference | 1.2G(.ckpt file) |


# [Description of Random Situation](#contents) # [Description of Random Situation](#contents)




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