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dtk23.04初始化

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# tests
# megatron autogenerated indices
tests/data/*/*npy
tests/tools/openwebtext-1000.jsonl
tmp/
# macOS
.DS_Store
# Byte-compiled / optimized / DLL files
*/__pycache__/
*.py[cod]
*.class
# C extensions
*.so
# Distribution / packaging
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*.egg
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*.cover
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# pyenv
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image: gitlab-master.nvidia.com/dl/dgx/pytorch:20.12-py3-devel
test:
script:
- pytest --junitxml=report.xml tests
artifacts:
when: always
reports:
junit: report.xml
\ No newline at end of file
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MANIFEST.in 0 → 100644
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include megatron/data/Makefile
include megatron/data/helpers.cpp
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.PHONY: test style
check_dirs := tests tools/convert_checkpoint
help: ## this help
@awk 'BEGIN {FS = ":.*##"; printf "\nUsage:\n make \033[36m<target>\033[0m\n"} /^[a-zA-Z_-]+:.*?##/ { printf " \033[36m%-22s\033[0m %s\n", $$1, $$2 } /^##@/ { printf "\n\033[1m%s\033[0m\n", substr($$0, 5) } ' $(MAKEFILE_LIST)
test: ## run tests
pytest tests
style: ## checks for code style and applies formatting
black $(check_dirs)
isort $(check_dirs)
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# Generative Pre-Training2(GPT2)
### 模型介绍
```
GPT2模型：第二代生成式预训练模型（Generative Pre-Training2）。
```
### 模型结构
```
GPT2使用 Transformer 的 Decoder 结构，并对 Transformer Decoder 进行了一些改动，并通过Megatron和deepspeed进行分布式运行
```
### 数据集
```
#下载数据集
wget https://huggingface.co/bigscience/misc-test-data/resolve/main/stas/oscar-1GB.jsonl.xz
#下载vocab文件
wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json
wget https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt
xz -d oscar-1GB.jsonl.xz
#处理数据集
python tools/preprocess_data.py \
--input oscar-1GB.jsonl \
--output-prefix my-gpt2 \
--vocab gpt2-vocab.json \
--dataset-impl mmap \
--tokenizer-type GPT2BPETokenizer \
--merge-file gpt2-merges.txt \
--append-eod \
--workers 8
```
## GPT2预训练
### 环境配置
推荐使用docker方式运行，提供[光源](https://www.sourcefind.cn/)拉取的docker镜像：
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.10.0-centos7.6-dtk-23.04-py37-latest
```
进入docker
```
pip install -r requirements.txt -i http://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
```
### 训练（单卡测试样例）
```
rm megatron/arguments.py
cp megatron/arguments.py-one_node megatron/arguments.py
sh run-train.sh（基于单节点四卡）
```
```
#重要参数
MODEL_NAME 模型名（自定义）
CHECKPOINT_PATH 模型保存&加载路径
DATA_PATH 数据集路径（转换后的）
TENSORBOARD_PATH tensorboard路径
CODECARBON_PATH codecarbon路径
N_GPUS 使用加速卡数量
TP_SIZE TP数量
PP_SIZE PP数量
MICRO_BATCH_SIZE MICRO_BATCH_SIZE大小
GLOBAL_BATCH_SIZE GLOBAL_BATCH_SIZE大小
NLAYERS 模型层数
NHIDDEN 隐藏层维度
NHEADS 多注意力机制头数
SEQ_LEN 最大长度
SAVE_INTERVAL 保存频率
--train-samples 训练样本数
--eval-interval 验证频率
--eval-iters 验证iter
```
### GPT2模型16B训练(多节点)
要求DCU集群Slurm环境正常。
推荐用户使用预编译好的python3.7包来快速建立python3虚拟环境，pytorch、apex、torchaudio、colossalai、faiss、mmcv-full 、torchvision、tensorflow需要在[光合开发者社区](https://cancon.hpccube.com:65024/4/main/)下载所需DCU版本安装包
```
export PYTHON3_LIB_PATH=/python_lib_path
virtualenv -p /python_bin_path/python3 --system-site-packages venv_gpt2
source env.sh #进入venv_gpt2虚拟环境
pip install -r requirements.txt -i http://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
```
```
rm megatron/arguments.py
cp megatron/arguments.py-nodes megatron/arguments.py
sbatch run-16B.sh(主要参数在single-16B.sh)
```
```
#重要参数
MODEL_NAME 模型名（自定义）
CHECKPOINT_PATH 模型保存&加载路径
DATA_PATH 数据集路径（转换后的）
TENSORBOARD_PATH tensorboard路径
CODECARBON_PATH codecarbon路径
TP_SIZE TP数量
PP_SIZE PP数量
MICRO_BATCH_SIZE MICRO_BATCH_SIZE大小
GLOBAL_BATCH_SIZE GLOBAL_BATCH_SIZE大小
NLAYERS 层数
NHIDDEN 隐藏层维度
NHEADS 注意力机制头数
SEQ_LEN 最大长度
SAVE_INTERVAL 保存频率
--train_iters 训练步数
--eval-interval 验证频率
--eval-iters 验证iter
```
### 16B模型训练loss
| 卡数 | lm loss |
| :-------: | :----------: |
| 32 x 4DCU | 1.965622E+00 |
### 16B模型验证
| 卡数 | lm loss value | lm loss PPL |
| :-------: | :-----------: | :----------: |
| 32 x 4DCU | 4.299443E+00 | 7.365877E+01 |
## GPT2文本生成
使用GPT做文本生成时需要对训练好的模型进行转换，转换需要安装0.7.3版本 deepspeed（此工程已包含）
```
pip install deepspeed-0.7.3+unknown-cp37-cp37m-linux_x86_64.whl -i http://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
```
对deepspeed进行一些修改
```
修改/usr/local/lib/python3.7/site-packages/deepspeed/checkpoint/constants.py
第34行
ZERO_FILE_PREFIX = 'bf16_' + 'zero_pp_rank_'
改为：
ZERO_FILE_PREFIX = 'zero_pp_rank_'
修改/usr/local/lib/python3.7/site-packages/deepspeed/ops/op_builder/builder.py
第133行 def assert_torch_info(torch_info):函数
删除下边的版本判断
install_torch_version = torch_info['version']
install_cuda_version = torch_info['cuda_version']
install_hip_version = torch_info['hip_version']
修改/usr/local/lib/python3.7/site-packages/deepspeed/runtime/state_dict_factory.py文件
第177行def check_ckpt_list(self):函数
删除mp_world_size判断
if 'mp_world_size' in sd.keys():
assert len(self.ckpt_list) == sd['mp_world_size'], f"checkpoint count {len(self.ckpt_list)} is different from saved mp_world_size {sd['mp_world_size']}"
```
### 转换脚本
```
sh conver.sh
```
```
#重要参数
需要将工程路径加入PYTHONPATH
例如：export PYTHONPATH=/home/megatron-deepspeed_dtk22.10:$PYTHONPATH
CHECKPOINT_PATH 需要转换的模型路径(具体到保存的global_step)
output_folder 转换后的模型路径
target_tp 转换后的TP数（需要与训练时保持一致）
target_pp 转换后的PP数 （设置为1）
```
### 无条件文本生成
```
sh run-inf.sh（这里以单节点小模型为例）
```
```
#生成时模型各项参数需要与训练时保持一致（TP也需要保持一致）
--micro-batch-size micro-batch-size大小
--out-seq-length 输出文本程度
--genfile 生成文本保存位置
--num-samples 生成样本个数
```
## loss收敛情况
16B模型使用oscar数据集收敛情况如下：
![image-20230524143710566](image-gpt-loss.png)
![image-20230524143830580](image-gpt-loss2.png)
## 源码仓库及问题反馈
https://developer.hpccube.com/codes/modelzoo/gpt2-pytorch/
## 参考
https://github.com/bigscience-workshop/Megatron-DeepSpeed
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#!/bin/bash
# Compute embeddings for each entry of a given dataset (e.g. Wikipedia)
RANK=0
WORLD_SIZE=1
# Wikipedia data can be downloaded from the following link:
# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
EVIDENCE_DATA_DIR=<Specify path of Wikipedia dataset>
EMBEDDING_PATH=<Specify path to store embeddings>
CHECKPOINT_PATH=<Specify path of pretrained ICT model>
python tools/create_doc_index.py \
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--tensor-model-parallel-size 1 \
--micro-batch-size 128 \
--checkpoint-activations \
--seq-length 512 \
--retriever-seq-length 256 \
--max-position-embeddings 512 \
--load ${CHECKPOINT_PATH} \
--evidence-data-path ${EVIDENCE_DATA_DIR} \
--embedding-path ${EMBEDDING_PATH} \
--indexer-log-interval 1000 \
--indexer-batch-size 128 \
--vocab-file bert-vocab.txt \
--num-workers 2 \
--fp16
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This is a short tutorial of how to use/tune the curriculum learning (CL) integration. Currently it is only integrated for GPT pre-training. For technical details please refer to our [paper](https://arxiv.org/abs/2108.06084).
# Disable batch size warmup (--rampup-batch-size)
In our [paper](https://arxiv.org/abs/2108.06084) section 5.4 we demonstrate that curriculum learning (seqlen-based) provides much better training stability than the batch size warmup technique. So when using CL you need to remove the `--rampup-batch-size` config in your training script. It's not recommended to use both CL and batch size warmup, because both of them will reduce the number of tokens in a batch. Another related change you might want is to increase your micro batch size, since without batch size warmup your batch size will be fixed now.
# Token-based training termination
Because CL changes length of each sequence/sample during training, it is very hard/impossible to use number of steps/samples to terminate the training exactly at the desired number of tokens. Thus we add a `--train-tokens` config as an alternative accurate token-based termination. We recommend increase your original `--train-samples` or `--train-iters` to a large enough number (e.g., 2X of what you used for baseline), and set `--train-tokens` at the exact desired number of training tokens (e.g., 300B for GPT-3 like training).
# Token-based LR decay
Again because CL changes the number of tokens per batch, in our [paper](https://arxiv.org/abs/2108.06084) Appendix A.2 we show that it is also necessary to change the LR decay to token-based (to avoid decaying LR too fast). Thus we add a `--lr-decay-tokens` which will be the number of LR decay tokens. If previously you were using `--lr-decay-samples`, you can calculate your `--lr-decay-tokens` simply by multiplying the former by full seqlen (e.g. 2K for GPT-3). Then you need to replace `--lr-decay-samples` with `--lr-decay-tokens` in your script.
# LR warmup adjustment
For LR warmup we don't change it to token-based, because doing so for CL means slowing down the LR warmup, which is both unnecessary and harmful. However, you may need to adjust your `--lr-warmup-samples` or `--lr-warmup-iters` from non-CL cases for various reasons (e.g., if you used `--rampup-batch-size` in non-CL case, for CL we don't use it so the number of samples per batch will be different at the beginning). Assuming you want to use `X` tokens to warmup the LR (for OpenAI GPT-3 this was 375M tokens), then for CL case you may set `--lr-warmup-samples` as `X` divided by the `min_difficulty` below, or set `--lr-warmup-iters` as `X` divided by `min_difficulty * --global-batch-size`. This is a rough estimation based on that CL starts from seqlen `min_difficulty` and it won't increase too much during LR warmup.
# Token-based tensorboard
Because of the above changes, we also add token-based tensorboard scalars. We also add scalars that plot the seqlen at each step.
# Curriculum learning hyperparameters tuning strategy
The curriculum learning hyperparameters are all located in the deepspeed config json file (see the example `ds_config_cl.json` in this dir). There are a few config entries that you may need to adjust to your circumstances, and two of which require some tuning. In our [paper](https://arxiv.org/abs/2108.06084) Appendix A.1 we have a more detailed tuning strategy description.
1. `max_difficulty` should be set as the full seqlen (i.e., your `--seq-length`). No need to tune this.
2. `min_difficulty` is the beginning seqlen used by CL. In general smaller `min_difficulty` could provide better stability/convergence speed benefit. However we observe that for a larger model or for different training data, starting from a very small seqlen could lead to significant validation PPL fluctuation (or even divergence) at the very beginning. We recommend to start with `min_difficulty` at 64, and then increase it if you observe problems at the very beginning. Note that to enable Tensor Core acceleration you should always use a multiple of 8.
3. `total_curriculum_step` is the total number of steps used by CL. In general larger `total_curriculum_step` could provide better stability/convergence speed benefit. However we observe that a too large `total_curriculum_step` could lead to overfitting and significant validation PPL fluctuation (or even divergence) at the first few multiple of LR warmup steps. In our paper we have a detailed tuning strategy based on binary search. However, if you want to reduce the tuning effort we recommend directly setting `total_curriculum_step` as half of baseline's total number of steps. This may not provide the highest convergence speed benefit, but should provide enough training stability gains.
4. `difficulty_step` is the change in seq length per CL step. A smaller value is preferable since it gives more smooth CL and better stability. Like `min_difficulty` it too needs to be multiple of 8 for Tensor core acceleration, thus 8 is a good default.
examples/curriculum_learning/ds_config_cl.json 0 → 100644
View file @ bf95e032
{
"train_batch_size": 512,
"gradient_accumulation_steps": 1,
"steps_per_print": 1,
"zero_optimization": {
"stage": 0
},
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.00015,
"max_grad_norm": 1.0,
"betas": [0.9, 0.95]
}
},
"gradient_clipping": 1.0,
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"hysteresis": 2,
"min_loss_scale": 1
},
"wall_clock_breakdown": false,
"zero_allow_untested_optimizer": false,
"curriculum_learning": {
"enabled": true,
"curriculum_type": "seqlen",
"min_difficulty": 8,
"max_difficulty": 1024,
"schedule_type": "fixed_linear",
"schedule_config": {
"total_curriculum_step": 60000,
"difficulty_step": 8
}
}
}
examples/curriculum_learning/pretrain_gpt_cl.sh 0 → 100644
View file @ bf95e032
#!/bin/bash
# This is a dummy train script to show how to use curriculum
# learning, some parameters are not for actual GPT pretraining.
TARGET_GLOBAL_BATCH_SIZE=512
TRAIN_SAMPLES=146_484_375
LR=1.0e-4
MIN_LR=1.0e-5
LR_DECAY_SAMPLES=126_953_125
LR_WARMUP_SAMPLES=183_105
SEQLEN=1024
############################################################
# New configs for curriculum learning, see README.md
TRAIN_TOKENS=10_000_000_000
LR_DECAY_TOKENS=$(($LR_DECAY_SAMPLES*$SEQLEN))
############################################################
LOG_INTERVAL=100
EVAL_ITERS=10
EVAL_INTERVAL=100
SAVE_INTERVAL=1000
VOCAB_PATH=/data/Megatron-LM/data/gpt2-vocab.json
MERGE_PATH=/data/Megatron-LM/data/gpt2-merges.txt
DATA_PATH=/data/Megatron-LM/data/indexed_datasets/megatron
MICRO_BATCH_SIZE=1
MP_SIZE=1
PP_SIZE=1
NUM_GPUS=128
echo ${NUM_GPUS}
if [[ $PP_SIZE -gt 0 ]]; then
DP_SIZE=$(( ${NUM_GPUS} / (${PP_SIZE} * ${MP_SIZE}) ))
else
DP_SIZE=$(( ${NUM_GPUS} / ${MP_SIZE} ))
fi
GRAD_ACC_STEPS=$(( ${TARGET_GLOBAL_BATCH_SIZE} / (${MICRO_BATCH_SIZE} * ${DP_SIZE}) ))
NAME="gpt-117M-pp${PP_SIZE}-mp${MP_SIZE}-bsz${TARGET_GLOBAL_BATCH_SIZE}-mbsz${MICRO_BATCH_SIZE}-cl"
current_time=$(date "+%Y.%m.%d-%H.%M.%S")
host="${HOSTNAME}"
TENSORBOARD_DIR="tensorboard/${NAME}_${host}_${current_time}"
mkdir -p ${TENSORBOARD_DIR}
CHECKPOINT_PATH="checkpoints/${NAME}"
megatron_options=" \
--data-path ${DATA_PATH} \
--vocab-file ${VOCAB_PATH} \
--merge-file ${MERGE_PATH} \
--data-impl mmap \
--override-lr-scheduler \
--adam-beta1 0.9 \
--adam-beta2 0.95 \
--tensor-model-parallel-size ${MP_SIZE} \
--init-method-std 0.014 \
--lr-decay-tokens ${LR_DECAY_TOKENS} \
--lr-warmup-samples ${LR_WARMUP_SAMPLES} \
--micro-batch-size ${MICRO_BATCH_SIZE} \
--global-batch-size ${TARGET_GLOBAL_BATCH_SIZE} \
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 16 \
--seq-length ${SEQLEN} \
--max-position-embeddings ${SEQLEN} \
--train-samples ${TRAIN_SAMPLES} \
--train-tokens ${TRAIN_TOKENS} \
--lr ${LR} \
--min-lr ${MIN_LR} \
--lr-decay-style cosine \
--split 98,2,0 \
--log-interval ${LOG_INTERVAL} \
--eval-interval ${EVAL_INTERVAL} \
--eval-iters ${EVAL_ITERS} \
--save-interval ${SAVE_INTERVAL} \
--weight-decay 0.1 \
--clip-grad 1.0 \
--hysteresis 2 \
--num-workers 0 \
--checkpoint-activations \
--fp16 \
--load ${CHECKPOINT_PATH} \
--save ${CHECKPOINT_PATH} \
--tensorboard-queue-size 1 \
--log-timers-to-tensorboard \
--log-batch-size-to-tensorboard \
--log-validation-ppl-to-tensorboard \
--tensorboard-dir ${TENSORBOARD_DIR}"
config_json="ds_config_cl.json"
deepspeed_options=" \
--deepspeed \
--deepspeed_config ${config_json} \
--pipeline-model-parallel-size ${PP_SIZE} \
--partition-activations"
run_cmd="deepspeed ../../pretrain_gpt.py ${megatron_options} ${deepspeed_options} &>> ${NAME}.log"
echo ${run_cmd}
eval ${run_cmd}
set +x
examples/evaluate_ict_zeroshot_nq.sh 0 → 100644
View file @ bf95e032
#!/bin/bash
# Evaluate natural question test data given Wikipedia embeddings and pretrained
# ICT model
# Datasets can be downloaded from the following link:
# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
EVIDENCE_DATA_DIR=<Specify path of Wikipedia dataset>
EMBEDDING_PATH=<Specify path of the embeddings>
CHECKPOINT_PATH=<Specify path of pretrained ICT model>
QA_FILE=<Path of the natural question test dataset>
python tasks/main.py \
--task ICT-ZEROSHOT-NQ \
--tokenizer-type BertWordPieceLowerCase \
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--tensor-model-parallel-size 1 \
--micro-batch-size 128 \
--checkpoint-activations \
--seq-length 512 \
--max-position-embeddings 512 \
--load ${CHECKPOINT_PATH} \
--evidence-data-path ${EVIDENCE_DATA_DIR} \
--embedding-path ${EMBEDDING_PATH} \
--retriever-seq-length 256 \
--vocab-file bert-vocab.txt\
--qa-data-test ${QA_FILE} \
--num-workers 2 \
--faiss-use-gpu \
--retriever-report-topk-accuracies 1 5 20 100 \
--fp16
examples/evaluate_zeroshot_gpt.sh 0 → 100644
View file @ bf95e032
#!/bin/bash
WORLD_SIZE=8
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
TASK="LAMBADA"
VALID_DATA=<lambada path>
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
CHECKPOINT=checkpoints/gpt2_345m
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
--task $TASK \
--valid-data $VALID_DATA \
--tokenizer-type GPT2BPETokenizer \
--strict-lambada \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--load $CHECKPOINT \
--tensor-model-parallel-size 1 \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--batch-size 8 \
--checkpoint-activations \
--seq-length 1024 \
--max-position-embeddings 1024 \
--log-interval 10 \
--fp16 \
--no-load-optim \
--no-load-rng
examples/finetune_mnli_distributed.sh 0 → 100644
View file @ bf95e032
#!/bin/bash
WORLD_SIZE=8
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
TRAIN_DATA="data/glue_data/MNLI/train.tsv"
VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \
data/glue_data/MNLI/dev_mismatched.tsv"
PRETRAINED_CHECKPOINT=checkpoints/bert_345m
VOCAB_FILE=bert-vocab.txt
CHECKPOINT_PATH=checkpoints/bert_345m_mnli
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
--task MNLI \
--seed 1234 \
--train-data $TRAIN_DATA \
--valid-data $VALID_DATA \
--tokenizer-type BertWordPieceLowerCase \
--vocab-file $VOCAB_FILE \
--epochs 5 \
--pretrained-checkpoint $PRETRAINED_CHECKPOINT \
--tensor-model-parallel-size 1 \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--micro-batch-size 8 \
--checkpoint-activations \
--lr 5.0e-5 \
--lr-decay-style linear \
--lr-warmup-fraction 0.065 \
--seq-length 512 \
--max-position-embeddings 512 \
--save-interval 500000 \
--save $CHECKPOINT_PATH \
--log-interval 10 \
--eval-interval 100 \
--eval-iters 50 \
--weight-decay 1.0e-1 \
--fp16
examples/finetune_race_distributed.sh 0 → 100644
View file @ bf95e032
#!/bin/bash
WORLD_SIZE=8
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
TRAIN_DATA="data/RACE/train/middle"
VALID_DATA="data/RACE/dev/middle \
data/RACE/dev/high"
VOCAB_FILE=bert-vocab.txt
PRETRAINED_CHECKPOINT=checkpoints/bert_345m
CHECKPOINT_PATH=checkpoints/bert_345m_race
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
--task RACE \
--seed 1234 \
--train-data $TRAIN_DATA \
--valid-data $VALID_DATA \
--tokenizer-type BertWordPieceLowerCase \
--vocab-file $VOCAB_FILE \
--epochs 3 \
--pretrained-checkpoint $PRETRAINED_CHECKPOINT \
--tensor-model-parallel-size 1 \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--micro-batch-size 4 \
--checkpoint-activations \
--lr 1.0e-5 \
--lr-decay-style linear \
--lr-warmup-fraction 0.06 \
--seq-length 512 \
--max-position-embeddings 512 \
--save-interval 100000 \
--save $CHECKPOINT_PATH \
--log-interval 10 \
--eval-interval 100 \
--eval-iters 50 \
--weight-decay 1.0e-1 \
--clip-grad 1.0 \
--hidden-dropout 0.1 \
--attention-dropout 0.1 \
--fp16
examples/generate_text.sh 0 → 100644
View file @ bf95e032
#!/bin/bash
CHECKPOINT_PATH=checkpoints/gpt2
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
python tools/generate_samples_gpt.py \
--tensor-model-parallel-size 1 \
--num-layers 24 \
--hidden-size 1024 \
--load $CHECKPOINT_PATH \
--num-attention-heads 16 \
--max-position-embeddings 1024 \
--tokenizer-type GPT2BPETokenizer \
--fp16 \
--batch-size 2 \
--seq-length 1024 \
--out-seq-length 1024 \
--temperature 1.0 \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--genfile unconditional_samples.json \
--num-samples 2 \
--top_p 0.9 \
--recompute
examples/merge_mp_bert.sh 0 → 100644
View file @ bf95e032
#!/bin/bash
TENSOR_MODEL_PARALLEL_SIZE=2
VOCAB_FILE=bert-vocab.txt
CHECKPOINT_PATH=checkpoints/bert_345m
WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
--model-type BERT \
--tensor-model-parallel-size $TENSOR_MODEL_PARALLEL_SIZE \
--tokenizer-type BertWordPieceLowerCase \
--vocab-file $VOCAB_FILE \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 512 \
--max-position-embeddings 512 \
--load $CHECKPOINT_PATH
examples/pretrain_bert.sh 0 → 100644
View file @ bf95e032
#!/bin/bash
RANK=0
WORLD_SIZE=1
DATA_PATH=<Specify path and file prefix>_text_sentence
CHECKPOINT_PATH=<Specify path>
python pretrain_bert.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--micro-batch-size 4 \
--global-batch-size 8 \
--seq-length 512 \
--max-position-embeddings 512 \
--train-iters 2000000 \
--lr-decay-iters 990000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file bert-vocab.txt \
--data-impl mmap \
--split 949,50,1 \
--lr 0.0001 \
--min-lr 0.00001 \
--lr-decay-style linear \
--lr-warmup-fraction .01 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--fp16
examples/pretrain_bert_distributed.sh 0 → 100644
View file @ bf95e032
#!/bin/bash
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
DATA_PATH=<Specify path and file prefix>_text_sentence
CHECKPOINT_PATH=<Specify path>
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
python -m torch.distributed.launch $DISTRIBUTED_ARGS \
pretrain_bert.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--micro-batch-size 4 \
--global-batch-size 32 \
--seq-length 512 \
--max-position-embeddings 512 \
--train-iters 1000000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file bert-vocab.txt \
--data-impl mmap \
--split 949,50,1 \
--distributed-backend nccl \
--lr 0.0001 \
--lr-decay-style linear \
--min-lr 1.0e-5 \
--lr-decay-iters 990000 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--lr-warmup-fraction .01 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--fp16
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