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Commit 8ddf66c6 authored by sunxx1's avatar sunxx1
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Merge branch 'hepj-test' into 'main' 

修改README，增加训练脚本，完善模型转换代码

See merge request dcutoolkit/deeplearing/dlexamples_new!38
parents 0200794c bedf3c0c
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PyTorch/NLP/BERT/single_pre2_1.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=ib0
#export HSA_USERPTR_FOR_PAGED_MEM=0
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=eno1
export HSA_FORCE_FINE_GRAIN_PCIE=1
#source /public/software/apps/DeepLearning/PyTorch/pytorch-env.sh
export MIOPEN_FIND_MODE=1
#export MIOPEN_ENABLE_LOGGING_CMD=1
#export ROCBLAS_LAYER=3
module unload compiler/rocm/2.9
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_3X3U=0
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_WRW3X3=0
echo "MIOPEN_FIND_MODE=$MIOPEN_FIND_MODE"
#module load apps/PyTorch/1.5.0a0/hpcx-2.4.1-gcc-7.3.1-rocm3.3
#source /public/home/aiss/Pytorch/env_rocm3.3_torch1.5.sh
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE
#下边是修改的
#export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#HSA_FORCE_FINE_GRAIN_PCIE=1 numactl --cpunodebind=4,5,6,7 --membind=4,5,6,7
#module load compiler/rocm/3.9.1
export PATH_PHRASE2=/public/software/apps/DeepLearning/Data/wikicorpus_en/lower_case_1_seq_len_512_max_pred_80_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/wikicorpus_en/training
APP="python3 run_pretraining_v1.py \
--input_dir=${PATH_PHRASE2} \
--output_dir=/public/home/hepj/outdir/torch/pre_wiki/phrase2/fp32 \
--config_file=/public/home/hepj/model_source/pytorch_bert/bert_config.json \
--bert_model=bert-large-uncased \
--train_batch_size=2 \
--max_seq_length=512 \
--max_predictions_per_seq=80 \
--max_steps=400000 \
--warmup_proportion=0128. \
--num_steps_per_checkpoint=20000 \
--learning_rate=4.0e-3 \
--seed=12439 \
--gradient_accumulation_steps=1 \
--allreduce_post_accumulation \
--do_train \
--phase2 \
--phase1_end_step=0 \
--gpus_per_node 1 \
--local_rank -1 \
--json-summary /public/home/hepj/outdir/torch/pre_wiki/phrase2/fp32/dllogger_dtk22.04.json
"
#--fp16 \
# --amp \
#--json-summary /public/home/hepj/out_dir/tourch/SQuAD/results.json
case ${lrank} in
[0])
export HIP_VISIBLE_DEVICES=0
export UCX_NET_DEVICES=mlx5_0:1
export UCX_IB_PCI_BW=mlx5_0:50Gbs
echo numactl --cpunodebind=0 --membind=0 ${APP}
numactl --cpunodebind=0 --membind=0 ${APP}
#echo GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
;;
[1])
export HIP_VISIBLE_DEVICES=1
export UCX_NET_DEVICES=mlx5_1:1
export UCX_IB_PCI_BW=mlx5_1:50Gbs
echo numactl --cpunodebind=1 --membind=1 ${APP}
numactl --cpunodebind=1 --membind=1 ${APP}
;;
[2])
export HIP_VISIBLE_DEVICES=2
export UCX_NET_DEVICES=mlx5_2:1
export UCX_IB_PCI_BW=mlx5_2:50Gbs
echo numactl --cpunodebind=2 --membind=2 ${APP}
numactl --cpunodebind=2 --membind=2 ${APP}
;;
[3])
export HIP_VISIBLE_DEVICES=3
export UCX_NET_DEVICES=mlx5_3:1
export UCX_IB_PCI_BW=mlx5_3:50Gbs
echo numactl --cpunodebind=3 --membind=3 ${APP}
numactl --cpunodebind=3 --membind=3 ${APP}
;;
esac
PyTorch/NLP/BERT/single_pre2_1_fp16.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
export HSA_FORCE_FINE_GRAIN_PCIE=1
export MIOPEN_FIND_MODE=1
#export MIOPEN_ENABLE_LOGGING_CMD=1
#export ROCBLAS_LAYER=3
module unload compiler/rocm/2.9
echo "MIOPEN_FIND_MODE=$MIOPEN_FIND_MODE"
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE
#下边是修改的
export PATH_PHRASE2=/public/software/apps/DeepLearning/Data/wikicorpus_en/lower_case_1_seq_len_512_max_pred_80_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/wikicorpus_en/training
APP="python3 run_pretraining_v1.py \
--input_dir=${PATH_PHRASE2} \
--output_dir=/public/home/hepj/outdir/torch/pre_wiki/phrase2/fp16 \
--config_file=/public/home/hepj/model_source/pytorch_bert/bert_config.json \
--bert_model=bert-large-uncased \
--train_batch_size=2 \
--max_seq_length=512 \
--max_predictions_per_seq=80 \
--max_steps=400000 \
--warmup_proportion=0128. \
--num_steps_per_checkpoint=20000 \
--learning_rate=4.0e-3 \
--seed=12439 \
--gradient_accumulation_steps=1 \
--allreduce_post_accumulation \
--do_train \
--fp16 \
--amp \
--phase2 \
--phase1_end_step=0 \
--gpus_per_node 1 \
--local_rank -1 \
--json-summary /public/home/hepj/outdir/torch/pre_wiki/phrase2/fp16/dllogger_dtk22.04.json
"
#--json-summary /public/home/hepj/out_dir/tourch/SQuAD/results.json
case ${lrank} in
[0])
export HIP_VISIBLE_DEVICES=0
export UCX_NET_DEVICES=mlx5_0:1
export UCX_IB_PCI_BW=mlx5_0:50Gbs
echo numactl --cpunodebind=0 --membind=0 ${APP}
numactl --cpunodebind=0 --membind=0 ${APP}
#echo GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
;;
[1])
export HIP_VISIBLE_DEVICES=1
export UCX_NET_DEVICES=mlx5_1:1
export UCX_IB_PCI_BW=mlx5_1:50Gbs
echo numactl --cpunodebind=1 --membind=1 ${APP}
numactl --cpunodebind=1 --membind=1 ${APP}
;;
[2])
export HIP_VISIBLE_DEVICES=2
export UCX_NET_DEVICES=mlx5_2:1
export UCX_IB_PCI_BW=mlx5_2:50Gbs
echo numactl --cpunodebind=2 --membind=2 ${APP}
numactl --cpunodebind=2 --membind=2 ${APP}
;;
[3])
export HIP_VISIBLE_DEVICES=3
export UCX_NET_DEVICES=mlx5_3:1
export UCX_IB_PCI_BW=mlx5_3:50Gbs
echo numactl --cpunodebind=3 --membind=3 ${APP}
numactl --cpunodebind=3 --membind=3 ${APP}
;;
esac
PyTorch/NLP/BERT/single_pre2_4.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
export HSA_FORCE_FINE_GRAIN_PCIE=1
export MIOPEN_FIND_MODE=1
module unload compiler/rocm/2.9
echo "MIOPEN_FIND_MODE=$MIOPEN_FIND_MODE"
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE
export HIP_VISIBLE_DEVICES=0,1,2,3
export PATH_PHRASE2=/public/software/apps/DeepLearning/Data/wikicorpus_en/lower_case_1_seq_len_512_max_pred_80_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/wikicorpus_en/training
APP="python3 run_pretraining_v4.py \
--input_dir=${PATH_PHRASE2} \
--output_dir=/public/home/hepj/outdir/tourch/pre_wiki4/phrase2/fp32 \
--config_file=/public/home/hepj/model_source/pytorch_bert/bert_config.json \
--bert_model=bert-large-uncased \
--train_batch_size=2 \
--max_seq_length=512 \
--max_predictions_per_seq=80 \
--max_steps=400000 \
--warmup_proportion=0128. \
--num_steps_per_checkpoint=200000 \
--learning_rate=4.0e-3 \
--seed=12439 \
--gradient_accumulation_steps=1 \
--allreduce_post_accumulation \
--do_train \
--phase2 \
--phase1_end_step 0 \
--local_rank ${comm_rank} \
--world_size 4 \
--gpus_per_node 1 \
--dist_url tcp://localhost:34567 \
--json-summary /public/home/hepj/outdir/torch/pre_wiki4/phrase2/fp32/dllogger.json
"
#--fp16 \
# --amp \
#--json-summary /public/home/hepj/out_dir/tourch/SQuAD/results.json
case ${lrank} in
[0])
export HIP_VISIBLE_DEVICES=0
export UCX_NET_DEVICES=mlx5_0:1
export UCX_IB_PCI_BW=mlx5_0:50Gbs
echo numactl --cpunodebind=0 --membind=0 ${APP}
numactl --cpunodebind=0 --membind=0 ${APP}
#echo GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
;;
[1])
export HIP_VISIBLE_DEVICES=1
export UCX_NET_DEVICES=mlx5_1:1
export UCX_IB_PCI_BW=mlx5_1:50Gbs
echo numactl --cpunodebind=1 --membind=1 ${APP}
numactl --cpunodebind=1 --membind=1 ${APP}
;;
[2])
export HIP_VISIBLE_DEVICES=2
export UCX_NET_DEVICES=mlx5_2:1
export UCX_IB_PCI_BW=mlx5_2:50Gbs
echo numactl --cpunodebind=2 --membind=2 ${APP}
numactl --cpunodebind=2 --membind=2 ${APP}
;;
[3])
export HIP_VISIBLE_DEVICES=3
export UCX_NET_DEVICES=mlx5_3:1
export UCX_IB_PCI_BW=mlx5_3:50Gbs
echo numactl --cpunodebind=3 --membind=3 ${APP}
numactl --cpunodebind=3 --membind=3 ${APP}
;;
esac
PyTorch/NLP/BERT/single_pre2_4_fp16.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
export HSA_FORCE_FINE_GRAIN_PCIE=1
export MIOPEN_FIND_MODE=1
module unload compiler/rocm/2.9
echo "MIOPEN_FIND_MODE=$MIOPEN_FIND_MODE"
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE
#下边是修改的
export HIP_LAUNCH_BLOCKING=1
export HIP_VISIBLE_DEVICES=0,1,2,3
export PATH_PHRASE2=/public/software/apps/DeepLearning/Data/wikicorpus_en/lower_case_1_seq_len_512_max_pred_80_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/wikicorpus_en/training
APP="python3 run_pretraining_v4.py \
--input_dir=${PATH_PHRASE2} \
--output_dir=/public/home/hepj/outdir/tourch/pre_wiki4/phrase2/fp16 \
--config_file=/public/home/hepj/model_source/pytorch_bert/bert_config.json \
--bert_model=bert-large-uncased \
--train_batch_size=2 \
--max_seq_length=512 \
--max_predictions_per_seq=80 \
--max_steps=40000 \
--warmup_proportion=0128. \
--num_steps_per_checkpoint=10000 \
--learning_rate=4.0e-3 \
--seed=12439 \
--gradient_accumulation_steps=1 \
--allreduce_post_accumulation \
--do_train \
--phase2 \
--phase1_end_step 0 \
--local_rank ${comm_rank} \
--world_size 4 \
--gpus_per_node 1 \
--fp16 \
--amp \
--dist_url tcp://localhost:34567 \
--json-summary /public/home/hepj/outdir/torch/pre_wiki4/phrase2/fp16/dllogger.json
"
case ${lrank} in
[0])
export HIP_VISIBLE_DEVICES=0
export UCX_NET_DEVICES=mlx5_0:1
export UCX_IB_PCI_BW=mlx5_0:50Gbs
echo numactl --cpunodebind=0 --membind=0 ${APP}
numactl --cpunodebind=0 --membind=0 ${APP}
#echo GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
;;
[1])
export HIP_VISIBLE_DEVICES=1
export UCX_NET_DEVICES=mlx5_1:1
export UCX_IB_PCI_BW=mlx5_1:50Gbs
echo numactl --cpunodebind=1 --membind=1 ${APP}
numactl --cpunodebind=1 --membind=1 ${APP}
;;
[2])
export HIP_VISIBLE_DEVICES=2
export UCX_NET_DEVICES=mlx5_2:1
export UCX_IB_PCI_BW=mlx5_2:50Gbs
echo numactl --cpunodebind=2 --membind=2 ${APP}
numactl --cpunodebind=2 --membind=2 ${APP}
;;
[3])
export HIP_VISIBLE_DEVICES=3
export UCX_NET_DEVICES=mlx5_3:1
export UCX_IB_PCI_BW=mlx5_3:50Gbs
echo numactl --cpunodebind=3 --membind=3 ${APP}
numactl --cpunodebind=3 --membind=3 ${APP}
;;
esac
PyTorch/NLP/BERT/single_squad.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=ib0
#export HSA_USERPTR_FOR_PAGED_MEM=0
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=eno1
export HSA_FORCE_FINE_GRAIN_PCIE=1
#source /public/software/apps/DeepLearning/PyTorch/pytorch-env.sh
export MIOPEN_FIND_MODE=3
#export MIOPEN_ENABLE_LOGGING_CMD=1
#export ROCBLAS_LAYER=3
module unload compiler/rocm/2.9
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_3X3U=0
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_WRW3X3=0
echo "MIOPEN_FIND_MODE=$MIOPEN_FIND_MODE"
#module load apps/PyTorch/1.5.0a0/hpcx-2.4.1-gcc-7.3.1-rocm3.3
#source /public/home/aiss/Pytorch/env_rocm3.3_torch1.5.sh
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE
#module load compiler/rocm/3.9.1
APP="python3 run_squad_v1.py \
--train_file /public/home/hepj/data/sq1.1/train-v1.1.json \
--predict_file /public/home/hepj/data/sq1.1/dev-v1.1.json \
--init_checkpoint /public/home/hepj/model_source/pytorch_bert/model.ckpt-28252.pt \
--vocab_file /public/home/hepj/model_source/pytorch_bert/vocab.txt \
--output_dir /public/home/hepj/outdir/torch/SQuAD \
--config_file /public/home/hepj/model_source/pytorch_bert/bert_config.json \
--json-summary /public/home/hepj/outdir/torch/SQuAD/results.json \
--bert_model bert-large-uncased \
--do_train \
--do_predict \
--train_batch_size 4 \
--predict_batch_size 4 \
--gpus_per_node 1 \
--local_rank -1 \
--eval_script /public/home/hepj/squad_eval/evaluate-v1.1.py
"
#--json-summary /public/home/hepj/out_dir/tourch/SQuAD/results.json
case ${lrank} in
[0])
export HIP_VISIBLE_DEVICES=0
export UCX_NET_DEVICES=mlx5_0:1
export UCX_IB_PCI_BW=mlx5_0:50Gbs
echo numactl --cpunodebind=0 --membind=0 ${APP}
numactl --cpunodebind=0 --membind=0 ${APP}
#echo GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
;;
[1])
export HIP_VISIBLE_DEVICES=1
export UCX_NET_DEVICES=mlx5_1:1
export UCX_IB_PCI_BW=mlx5_1:50Gbs
echo numactl --cpunodebind=1 --membind=1 ${APP}
numactl --cpunodebind=1 --membind=1 ${APP}
;;
[2])
export HIP_VISIBLE_DEVICES=2
export UCX_NET_DEVICES=mlx5_2:1
export UCX_IB_PCI_BW=mlx5_2:50Gbs
echo numactl --cpunodebind=2 --membind=2 ${APP}
numactl --cpunodebind=2 --membind=2 ${APP}
;;
[3])
export HIP_VISIBLE_DEVICES=3
export UCX_NET_DEVICES=mlx5_3:1
export UCX_IB_PCI_BW=mlx5_3:50Gbs
echo numactl --cpunodebind=3 --membind=3 ${APP}
numactl --cpunodebind=3 --membind=3 ${APP}
;;
esac
PyTorch/NLP/BERT/single_squad4.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=ib0
#export HSA_USERPTR_FOR_PAGED_MEM=0
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=eno1
export HSA_FORCE_FINE_GRAIN_PCIE=1
#source /public/software/apps/DeepLearning/PyTorch/pytorch-env.sh
export MIOPEN_FIND_MODE=3
#export MIOPEN_ENABLE_LOGGING_CMD=1
#export export ROCBLAS_LAYER=3
module unload compiler/rocm/2.9
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_3X3U=0
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_WRW3X3=0
echo "MIOPEN_FIND_MODE=$MIOPEN_FIND_MODE"
#module load apps/PyTorch/1.5.0a0/hpcx-2.4.1-gcc-7.3.1-rocm3.3
#source /public/home/aiss/Pytorch/env_rocm3.3_torch1.5.sh
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE
#下边是修改的
export HIP_VISIBLE_DEVICES=0,1,2,3
#HSA_FORCE_FINE_GRAIN_PCIE=1 numactl --cpunodebind=4,5,6,7 --membind=4,5,6,7
#module load compiler/rocm/3.9.1
APP="python3 run_squad_v4.py \
--train_file /public/home/hepj/data/sq1.1/train-v1.1.json \
--predict_file /public/home/hepj/data/sq1.1/dev-v1.1.json \
--init_checkpoint /public/home/hepj/model_source/pytorch_bert/model.ckpt-28252.pt \
--vocab_file /public/home/hepj/model_source/pytorch_bert/vocab.txt \
--output_dir /public/home/hepj/outdir/torch/SQuAD4 \
--config_file /public/home/hepj/model_source/pytorch_bert/bert_config.json \
--json-summary /public/home/hepj/outdir/torch/SQuAD4/results.json \
--bert_model bert-large-uncased \
--do_train \
--do_predict \
--do_eval \
--train_batch_size 4 \
--predict_batch_size 4 \
--gpus_per_node 1 \
--local_rank ${comm_rank} \
--world_size 4 \
--use_env \
--dist_url tcp://localhost:34567 \
--eval_script /public/home/hepj/squad_eval/evaluate-v1.1.py
"
# --fp16 \
# --amp \
case ${lrank} in
[0])
export HIP_VISIBLE_DEVICES=0
export UCX_NET_DEVICES=mlx5_0:1
export UCX_IB_PCI_BW=mlx5_0:50Gbs
echo numactl --cpunodebind=0 --membind=0 ${APP}
numactl --cpunodebind=0 --membind=0 ${APP}
#echo GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
;;
[1])
export HIP_VISIBLE_DEVICES=1
export UCX_NET_DEVICES=mlx5_1:1
export UCX_IB_PCI_BW=mlx5_1:50Gbs
echo numactl --cpunodebind=1 --membind=1 ${APP}
numactl --cpunodebind=1 --membind=1 ${APP}
;;
[2])
export HIP_VISIBLE_DEVICES=2
export UCX_NET_DEVICES=mlx5_2:1
export UCX_IB_PCI_BW=mlx5_2:50Gbs
echo numactl --cpunodebind=2 --membind=2 ${APP}
numactl --cpunodebind=2 --membind=2 ${APP}
;;
[3])
export HIP_VISIBLE_DEVICES=3
export UCX_NET_DEVICES=mlx5_3:1
export UCX_IB_PCI_BW=mlx5_3:50Gbs
echo numactl --cpunodebind=3 --membind=3 ${APP}
numactl --cpunodebind=3 --membind=3 ${APP}
;;
esac
PyTorch/NLP/BERT/single_squad4_fp16.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=ib0
#export HSA_USERPTR_FOR_PAGED_MEM=0
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=eno1
export HSA_FORCE_FINE_GRAIN_PCIE=1
#source /public/software/apps/DeepLearning/PyTorch/pytorch-env.sh
export MIOPEN_FIND_MODE=3
#export MIOPEN_ENABLE_LOGGING_CMD=1
#export export ROCBLAS_LAYER=3
module unload compiler/rocm/2.9
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_3X3U=0
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_WRW3X3=0
echo "MIOPEN_FIND_MODE=$MIOPEN_FIND_MODE"
#module load apps/PyTorch/1.5.0a0/hpcx-2.4.1-gcc-7.3.1-rocm3.3
#source /public/home/aiss/Pytorch/env_rocm3.3_torch1.5.sh
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE
#下边是修改的
export HIP_VISIBLE_DEVICES=0,1,2,3
#HSA_FORCE_FINE_GRAIN_PCIE=1 numactl --cpunodebind=4,5,6,7 --membind=4,5,6,7
#module load compiler/rocm/3.9.1
APP="python3 run_squad_v4.py \
--train_file /public/home/hepj/data/sq1.1/train-v1.1.json \
--predict_file /public/home/hepj/data/sq1.1/dev-v1.1.json \
--init_checkpoint /public/home/hepj/model_source/pytorch_bert/model.ckpt-28252.pt \
--vocab_file /public/home/hepj/model_source/pytorch_bert/vocab.txt \
--output_dir /public/home/hepj/outdir/torch/SQuAD4 \
--config_file /public/home/hepj/model_source/pytorch_bert/bert_config.json \
--json-summary /public/home/hepj/outdir/torch/SQuAD4/results.json \
--bert_model bert-large-uncased \
--do_train \
--do_predict \
--do_eval \
--train_batch_size 4 \
--predict_batch_size 4 \
--gpus_per_node 1 \
--local_rank ${comm_rank} \
--world_size 4 \
--use_env \
--fp16 \
--amp \
--dist_url tcp://localhost:34567 \
--eval_script /public/home/hepj/squad_eval/evaluate-v1.1.py
"
case ${lrank} in
[0])
export HIP_VISIBLE_DEVICES=0
export UCX_NET_DEVICES=mlx5_0:1
export UCX_IB_PCI_BW=mlx5_0:50Gbs
echo numactl --cpunodebind=0 --membind=0 ${APP}
numactl --cpunodebind=0 --membind=0 ${APP}
#echo GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
;;
[1])
export HIP_VISIBLE_DEVICES=1
export UCX_NET_DEVICES=mlx5_1:1
export UCX_IB_PCI_BW=mlx5_1:50Gbs
echo numactl --cpunodebind=1 --membind=1 ${APP}
numactl --cpunodebind=1 --membind=1 ${APP}
;;
[2])
export HIP_VISIBLE_DEVICES=2
export UCX_NET_DEVICES=mlx5_2:1
export UCX_IB_PCI_BW=mlx5_2:50Gbs
echo numactl --cpunodebind=2 --membind=2 ${APP}
numactl --cpunodebind=2 --membind=2 ${APP}
;;
[3])
export HIP_VISIBLE_DEVICES=3
export UCX_NET_DEVICES=mlx5_3:1
export UCX_IB_PCI_BW=mlx5_3:50Gbs
echo numactl --cpunodebind=3 --membind=3 ${APP}
numactl --cpunodebind=3 --membind=3 ${APP}
;;
esac
PyTorch/NLP/BERT/single_squad_fp16.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=ib0
#export HSA_USERPTR_FOR_PAGED_MEM=0
#export MIOPEN_DEBUG_DISABLE_FIND_DB=1
#export NCCL_SOCKET_IFNAME=eno1
export HSA_FORCE_FINE_GRAIN_PCIE=1
#source /public/software/apps/DeepLearning/PyTorch/pytorch-env.sh
export MIOPEN_FIND_MODE=3
#export MIOPEN_ENABLE_LOGGING_CMD=1
#export ROCBLAS_LAYER=3
module unload compiler/rocm/2.9
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_3X3U=0
#export MIOPEN_DEBUG_CONV_DIRECT_ASM_WRW3X3=0
echo "MIOPEN_FIND_MODE=$MIOPEN_FIND_MODE"
#module load apps/PyTorch/1.5.0a0/hpcx-2.4.1-gcc-7.3.1-rocm3.3
#source /public/home/aiss/Pytorch/env_rocm3.3_torch1.5.sh
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE
#下边是修改的
#export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#HSA_FORCE_FINE_GRAIN_PCIE=1 numactl --cpunodebind=4,5,6,7 --membind=4,5,6,7
#module load compiler/rocm/3.9.1
APP="python3 run_squad_v1.py \
--train_file /public/home/hepj/model/model_source/sq1.1/train-v1.1.json \
--predict_file /public/home/hepj/model/model_source/sq1.1/dev-v1.1.json \
--init_checkpoint /public/home/hepj/model/model_source/pytorch_bert/model.ckpt-28252.pt \
--vocab_file /public/home/hepj/model/model_source/pytorch_bert/vocab.txt \
--output_dir /public/home/hepj/outdir/tourch/SQuAD \
--config_file /public/home/hepj/model/model_source/pytorch_bert/bert_config.json \
--json-summary /public/home/hepj/outdir/torch/SQuAD/results.json \
--bert_model bert-large-uncased \
--do_train \
--do_predict \
--do_eval \
--train_batch_size 4 \
--predict_batch_size 4 \
--gpus_per_node 1 \
--local_rank -1 \
--fp16 \
--amp \
--eval_script /public/home/hepj/squad_eval/evaluate-v1.1.py
"
#--json-summary /public/home/hepj/out_dir/tourch/SQuAD/results.json
case ${lrank} in
[0])
export HIP_VISIBLE_DEVICES=0
export UCX_NET_DEVICES=mlx5_0:1
export UCX_IB_PCI_BW=mlx5_0:50Gbs
echo numactl --cpunodebind=0 --membind=0 ${APP}
numactl --cpunodebind=0 --membind=0 ${APP}
#echo GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
;;
[1])
export HIP_VISIBLE_DEVICES=1
export UCX_NET_DEVICES=mlx5_1:1
export UCX_IB_PCI_BW=mlx5_1:50Gbs
echo numactl --cpunodebind=1 --membind=1 ${APP}
numactl --cpunodebind=1 --membind=1 ${APP}
;;
[2])
export HIP_VISIBLE_DEVICES=2
export UCX_NET_DEVICES=mlx5_2:1
export UCX_IB_PCI_BW=mlx5_2:50Gbs
echo numactl --cpunodebind=2 --membind=2 ${APP}
numactl --cpunodebind=2 --membind=2 ${APP}
;;
[3])
export HIP_VISIBLE_DEVICES=3
export UCX_NET_DEVICES=mlx5_3:1
export UCX_IB_PCI_BW=mlx5_3:50Gbs
echo numactl --cpunodebind=3 --membind=3 ${APP}
numactl --cpunodebind=3 --membind=3 ${APP}
;;
esac
PyTorch/NLP/BERT/tf_to_torch/Dockerfile 0 → 100644
View file @ 8ddf66c6
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# 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.
ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:20.06-py3
FROM ${FROM_IMAGE_NAME}
# Install dependencies
RUN apt-get update \
&& apt-get install -y --no-install-recommends \
bzip2 \
cabextract \
iputils-ping \
pbzip2 \
pv \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /workspace/bert
COPY requirements.txt .
RUN pip install --no-cache-dir https://github.com/mlperf/logging/archive/9ea0afa.zip \
&& pip install --no-cache-dir -r requirements.txt
# Preprocessing
#WORKDIR /workspace
#RUN git clone https://github.com/attardi/wikiextractor.git
# Install BERT
ENV BERT_PREP_WORKING_DIR /workspace/bert/data
WORKDIR /workspace/bert
COPY . .
RUN cd /workspace/bert/mhalib && python setup.py build && cp build/lib*/mhalib* ../
PyTorch/NLP/BERT/tf_to_torch/LICENSE 0 → 100644
View file @ 8ddf66c6
Apache License
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PyTorch/NLP/BERT/tf_to_torch/NOTICE 0 → 100644
View file @ 8ddf66c6
BERT PyTorch
This repository includes software from https://github.com/huggingface/pytorch-pretrained-BERT
licensed under the Apache License 2.0.
PyTorch/NLP/BERT/tf_to_torch/README.md 0 → 100644
View file @ 8ddf66c6
# Location of the input files
This [GCS location](https://console.cloud.google.com/storage/browser/pkanwar-bert) contains the following.
* TensorFlow checkpoint (bert_model.ckpt) containing the pre-trained weights (which is actually 3 files).
* Vocab file (vocab.txt) to map WordPiece to word id.
* Config file (bert_config.json) which specifies the hyperparameters of the model.
# Download and preprocess datasets
Download the [wikipedia dump](https://dumps.wikimedia.org/enwiki/20200101/enwiki-20200101-pages-articles-multistream.xml.bz2) and extract the pages
The wikipedia dump can be downloaded from this link in this directory, and should contain the following file:
enwiki-20200101-pages-articles-multistream.xml.bz2
Run [WikiExtractor.py](https://github.com/attardi/wikiextractor) to extract the wiki pages from the XML
The generated wiki pages file will be stored as <data dir>/LL/wiki_nn; for example <data dir>/AA/wiki_00. Each file is ~1MB, and each sub directory has 100 files from wiki_00 to wiki_99, except the last sub directory. For the 20200101 dump, the last file is FE/wiki_17.
Clean up
The clean up scripts (some references here) are in the scripts directory.
The following command will run the clean up steps, and put the results in ./results
./process_wiki.sh '<data dir>/*/wiki_??'
```shell
cd cleanup_scripts
mkdir -p wiki
cd wiki
wget https://dumps.wikimedia.org/enwiki/20200101/enwiki-20200101-pages-articles-multistream.xml.bz2 # Optionally use curl instead
bzip2 -d enwiki-20200101-pages-articles-multistream.xml.bz2
cd .. # back to bert/cleanup_scripts
git clone https://github.com/attardi/wikiextractor.git
python3 wikiextractor/WikiExtractor.py wiki/enwiki-20200101-pages-articles-multistream.xml # Results are placed in bert/cleanup_scripts/text
./process_wiki.sh '<text/*/wiki_??'
```
After running the process_wiki.sh script, for the 20200101 wiki dump, there will be 500 files, named part-00xxx-of-00500 in the ./results directory.
# Checkpoint conversion
python convert_tf_checkpoint.py --tf_checkpoint /cks/model.ckpt-28252 --bert_config_path /cks/bert_config.json --output_checkpoint model.ckpt-28252.pt
# Generate the BERT input dataset
The create_pretraining_data.py script duplicates the input plain text, replaces different sets of words with masks for each duplication, and serializes the output into the TFRecord file format.
```shell
python3 create_pretraining_data.py \
--input_file=<path to ./results of previous step>/part-XX-of-00500 \
--output_file=<tfrecord dir>/part-XX-of-00500 \
--vocab_file=<path to vocab.txt> \
--do_lower_case=True \
--max_seq_length=512 \
--max_predictions_per_seq=76 \
--masked_lm_prob=0.15 \
--random_seed=12345 \
--dupe_factor=10
```
The generated tfrecord has 500 parts, totalling to ~365GB.
# Running the model
Building the Docker container
```shell
docker build --pull -t <docker/registry>/mlperf-nvidia:language_model .
docker push <docker/registry>/mlperf-nvidia:language_model
```
To run this model, use the following command. Replace the configuration script to match the system being used (e.g., config_DGX1.sh, config_DGX2.sh, config_DGXA100.sh).
```shell
source config_DGXA100.sh
sbatch -N${DGXNNODES} --ntasks-per-node=${DGXNGPU} --time=${WALLTIME} run.sub
```
Alternative launch with nvidia-docker. Replace the configuration script to match the system being used (e.g., config_DGX1.sh, config_DGX2.sh, config_DGXA100.sh).
```bash
docker build --pull -t mlperf-nvidia:language_model .
source config_DGXA100.sh
CONT=mlperf-nvidia:language_model DATADIR=<path/to/datadir> DATADIR_PHASE2=<path/to/datadir_phase2> EVALDIR=<path/to/evaldir> CHECKPOINTDIR=<path/to/checkpointdir> CHECKPOINTDIR_PHASE1=<path/to/checkpointdir_phase1> ./run_with_docker.sh
```
For multi-node training, we use Slurm for scheduling and Pyxis to run our container.
## Configuration File Naming Convention
All configuration files follow the format `config_<SYSTEM_NAME>_<NODES>x<GPUS/NODE>x<BATCH/GPU>x<GRADIENT_ACCUMULATION_STEPS>.sh`.
### Example 1
A DGX1 system with 1 node, 8 GPUs per node, batch size of 6 per GPU, and 6 gradient accumulation steps would use `config_DGX1_1x8x6x6.sh`.
### Example 2
A DGX A100 system with 32 nodes, 8 GPUs per node, batch size of 20 per GPU, and no gradient accumulation would use `config_DGXA100_32x8x20x1.sh`
# Acknowledgements
We'd like to thank members of the ONNX Runtime team at Microsoft for their suggested performance optimization to reduce the size of the last linear layer to only output the fraction of tokens that participate in the MLM loss calculation.
PyTorch/NLP/BERT/tf_to_torch/bind.sh 0 → 100644
View file @ 8ddf66c6
#! /bin/bash
set -euo pipefail
print_usage() {
cat << EOF
${0} [options] [--] COMMAND [ARG...]
Control binding policy for each task. Assumes one rank will be launched for each GPU.
Options:
--cpu=MODE
* exclusive -- bind each rank to an exclusive set of cores near its GPU
* exclusive,nosmt -- bind each rank to an exclusive set of cores near its GPU, without hyperthreading
* node -- bind each rank to all cores in the NUMA node nearest its GPU [default]
* *.sh -- bind each rank using the bash associative array bind_cpu_cores or bind_cpu_nodes from a file
* off -- don't bind
--mem=MODE
* node -- bind each rank to the nearest NUMA node [default]
* *.sh -- bind each rank using the bash associative array bind_mem from a file
* off -- don't bind
--ib=MODE
* single -- bind each rank to a single IB device near its GPU
* off -- don't bind [default]
--cluster=CLUSTER
Select which cluster is being used. May be required if system params cannot be detected.
EOF
}
################################################################################
# Argument parsing
################################################################################
cpu_mode='node'
mem_mode='node'
ib_mode='off'
cluster=''
while [ $# -gt 0 ]; do
case "$1" in
-h|--help) print_usage ; exit 0 ;;
--cpu=*) cpu_mode="${1/*=/}"; shift ;;
--cpu) cpu_mode="$2"; shift 2 ;;
--mem=*) mem_mode="${1/*=/}"; shift ;;
--mem) mem_mode="$2"; shift 2 ;;
--ib=*) ib_mode="${1/*=/}"; shift ;;
--ib) ib_mode="$2"; shift 2 ;;
--cluster=*) cluster="${1/*=/}"; shift ;;
--cluster) cluster="$2"; shift 2 ;;
--) shift; break ;;
*) break ;;
esac
done
if [ $# -lt 1 ]; then
echo 'ERROR: no command given' 2>&1
print_usage
exit 1
fi
################################################################################
# Get system params
################################################################################
# LOCAL_RANK is set with an enroot hook for Pytorch containers
# SLURM_LOCALID is set by Slurm
# OMPI_COMM_WORLD_LOCAL_RANK is set by mpirun
readonly local_rank="${LOCAL_RANK:=${SLURM_LOCALID:=${OMPI_COMM_WORLD_LOCAL_RANK:-}}}"
if [ -z "${local_rank}" ]; then
echo 'ERROR: cannot read LOCAL_RANK from env' >&2
exit 1
fi
num_gpus=$(nvidia-smi -i 0 --query-gpu=count --format=csv,noheader,nounits)
if [ "${local_rank}" -ge "${num_gpus}" ]; then
echo "ERROR: local rank is ${local_rank}, but there are only ${num_gpus} gpus available" >&2
exit 1
fi
get_lscpu_value() {
awk -F: "(\$1 == \"${1}\"){gsub(/ /, \"\", \$2); print \$2; found=1} END{exit found!=1}"
}
lscpu_out=$(lscpu)
num_sockets=$(get_lscpu_value 'Socket(s)' <<< "${lscpu_out}")
num_nodes=$(get_lscpu_value 'NUMA node(s)' <<< "${lscpu_out}")
cores_per_socket=$(get_lscpu_value 'Core(s) per socket' <<< "${lscpu_out}")
echo "num_sockets = ${num_sockets} num_nodes=${num_nodes} cores_per_socket=${cores_per_socket}"
readonly cores_per_node=$(( (num_sockets * cores_per_socket) / num_nodes ))
if [ ${num_gpus} -gt 1 ]; then
readonly gpus_per_node=$(( num_gpus / num_nodes ))
else
readonly gpus_per_node=1
fi
readonly cores_per_gpu=$(( cores_per_node / gpus_per_node ))
readonly local_node=$(( local_rank / gpus_per_node ))
declare -a ibdevs=()
case "${cluster}" in
circe)
# Need to specialize for circe because IB detection is hard
ibdevs=(mlx5_1 mlx5_2 mlx5_3 mlx5_4 mlx5_7 mlx5_8 mlx5_9 mlx5_10)
;;
selene)
# Need to specialize for selene because IB detection is hard
ibdevs=(mlx5_0 mlx5_1 mlx5_2 mlx5_3 mlx5_6 mlx5_7 mlx5_8 mlx5_9)
;;
'')
if ibstat_out="$(ibstat -l 2>/dev/null | sort -V)" ; then
mapfile -t ibdevs <<< "${ibstat_out}"
fi
;;
*)
echo "ERROR: Unknown cluster '${cluster}'" >&2
exit 1
;;
esac
readonly num_ibdevs="${#ibdevs[@]}"
################################################################################
# Setup for exec
################################################################################
declare -a numactl_args=()
case "${cpu_mode}" in
exclusive)
numactl_args+=( "$(printf -- "--physcpubind=%u-%u,%u-%u" \
$(( local_rank * cores_per_gpu )) \
$(( (local_rank + 1) * cores_per_gpu - 1 )) \
$(( local_rank * cores_per_gpu + (cores_per_gpu * gpus_per_node * num_nodes) )) \
$(( (local_rank + 1) * cores_per_gpu + (cores_per_gpu * gpus_per_node * num_nodes) - 1 )) \
)" )
;;
exclusive,nosmt)
numactl_args+=( "$(printf -- "--physcpubind=%u-%u" \
$(( local_rank * cores_per_gpu )) \
$(( (local_rank + 1) * cores_per_gpu - 1 )) \
)" )
;;
node)
numactl_args+=( "--cpunodebind=${local_node}" )
;;
*.sh)
source "${cpu_mode}"
if [ -n "${bind_cpu_cores:-}" ]; then
numactl_args+=( "--physcpubind=${bind_cpu_cores[${local_rank}]}" )
elif [ -n "${bind_cpu_nodes:-}" ]; then
numactl_args+=( "--cpunodebind=${bind_cpu_nodes[${local_rank}]}" )
else
echo "ERROR: invalid CPU affinity file ${cpu_mode}." >&2
exit 1
fi
;;
off|'')
;;
*)
echo "ERROR: invalid cpu mode '${cpu_mode}'" 2>&1
print_usage
exit 1
;;
esac
case "${mem_mode}" in
node)
numactl_args+=( "--membind=${local_node}" )
;;
*.sh)
source "${mem_mode}"
if [ -z "${bind_mem:-}" ]; then
echo "ERROR: invalid memory affinity file ${mem_mode}." >&2
exit 1
fi
numactl_args+=( "--membind=${bind_mem[${local_rank}]}" )
;;
off|'')
;;
*)
echo "ERROR: invalid mem mode '${mem_mode}'" 2>&1
print_usage
exit 1
;;
esac
case "${ib_mode}" in
single)
if [ "${num_ibdevs}" -eq 0 ]; then
echo "WARNING: used '$0 --ib=single', but there are 0 IB devices available; skipping IB binding." 2>&1
else
readonly ibdev="${ibdevs[$(( local_rank * num_ibdevs / num_gpus ))]}"
export OMPI_MCA_btl_openib_if_include="${OMPI_MCA_btl_openib_if_include-$ibdev}"
export UCX_NET_DEVICES="${UCX_NET_DEVICES-$ibdev:1}"
fi
;;
off|'')
;;
*)
echo "ERROR: invalid ib mode '${ib_mode}'" 2>&1
print_usage
exit 1
;;
esac
################################################################################
# Exec
################################################################################
if [ "${#numactl_args[@]}" -gt 0 ] ; then
set -x
exec numactl "${numactl_args[@]}" -- "${@}"
else
exec "${@}"
fi
PyTorch/NLP/BERT/tf_to_torch/bind_pyt.py 0 → 100644
View file @ 8ddf66c6
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# 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.
import sys
import subprocess
import os
import socket
from argparse import ArgumentParser, REMAINDER
import torch
def parse_args():
"""
Helper function parsing the command line options
@retval ArgumentParser
"""
parser = ArgumentParser(description="PyTorch distributed training launch "
"helper utilty that will spawn up "
"multiple distributed processes")
# Optional arguments for the launch helper
parser.add_argument("--nnodes", type=int, default=1,
help="The number of nodes to use for distributed "
"training")
parser.add_argument("--node_rank", type=int, default=0,
help="The rank of the node for multi-node distributed "
"training")
parser.add_argument("--nproc_per_node", type=int, default=1,
help="The number of processes to launch on each node, "
"for GPU training, this is recommended to be set "
"to the number of GPUs in your system so that "
"each process can be bound to a single GPU.")
parser.add_argument("--master_addr", default="127.0.0.1", type=str,
help="Master node (rank 0)'s address, should be either "
"the IP address or the hostname of node 0, for "
"single node multi-proc training, the "
"--master_addr can simply be 127.0.0.1")
parser.add_argument("--master_port", default=29500, type=int,
help="Master node (rank 0)'s free port that needs to "
"be used for communciation during distributed "
"training")
parser.add_argument('--no_hyperthreads', action='store_true',
help='Flag to disable binding to hyperthreads')
parser.add_argument('--no_membind', action='store_true',
help='Flag to disable memory binding')
# non-optional arguments for binding
parser.add_argument("--nsockets_per_node", type=int, required=True,
help="Number of CPU sockets on a node")
parser.add_argument("--ncores_per_socket", type=int, required=True,
help="Number of CPU cores per socket")
# positional
parser.add_argument("training_script", type=str,
help="The full path to the single GPU training "
"program/script to be launched in parallel, "
"followed by all the arguments for the "
"training script")
# rest from the training program
parser.add_argument('training_script_args', nargs=REMAINDER)
return parser.parse_args()
def main():
args = parse_args()
# variables for numactrl binding
NSOCKETS = args.nsockets_per_node
NGPUS_PER_SOCKET = (args.nproc_per_node // args.nsockets_per_node) + (1 if (args.nproc_per_node % args.nsockets_per_node) else 0)
NCORES_PER_GPU = args.ncores_per_socket // NGPUS_PER_SOCKET
# world size in terms of number of processes
dist_world_size = args.nproc_per_node * args.nnodes
# set PyTorch distributed related environmental variables
current_env = os.environ.copy()
current_env["MASTER_ADDR"] = args.master_addr
current_env["MASTER_PORT"] = str(args.master_port)
current_env["WORLD_SIZE"] = str(dist_world_size)
processes = []
for local_rank in range(0, args.nproc_per_node):
# each process's rank
dist_rank = args.nproc_per_node * args.node_rank + local_rank
current_env["RANK"] = str(dist_rank)
# form numactrl binding command
cpu_ranges = [local_rank * NCORES_PER_GPU,
(local_rank + 1) * NCORES_PER_GPU - 1,
local_rank * NCORES_PER_GPU + (NCORES_PER_GPU * NGPUS_PER_SOCKET * NSOCKETS),
(local_rank + 1) * NCORES_PER_GPU + (NCORES_PER_GPU * NGPUS_PER_SOCKET * NSOCKETS) - 1]
numactlargs = []
if args.no_hyperthreads:
numactlargs += [ "--physcpubind={}-{}".format(*cpu_ranges[0:2]) ]
else:
numactlargs += [ "--physcpubind={}-{},{}-{}".format(*cpu_ranges) ]
if not args.no_membind:
memnode = local_rank // NGPUS_PER_SOCKET
numactlargs += [ "--membind={}".format(memnode) ]
# spawn the processes
cmd = [ "/usr/bin/numactl" ] \
+ numactlargs \
+ [ sys.executable,
"-u",
args.training_script,
"--local_rank={}".format(local_rank)
] \
+ args.training_script_args
process = subprocess.Popen(cmd, env=current_env)
processes.append(process)
for process in processes:
process.wait()
if __name__ == "__main__":
main()
PyTorch/NLP/BERT/tf_to_torch/bmm1.py 0 → 100644
View file @ 8ddf66c6
import torch
import mhalib
###########################################################################################
class Bmm1Function(torch.autograd.Function):
@staticmethod
def forward(ctx, batch1, batch2, seqlen, batch, maxseqlen, heads, embed, scale, stream, sync):
ctx.save_for_backward(batch1, batch2, seqlen)
ctx.batch = batch
ctx.maxseqlen = maxseqlen
ctx.heads = heads
ctx.embed = embed
ctx.scale = scale
ctx.sync = sync
ctx.stream = stream
ntokens = seqlen.sum().item()
ctx.ntokens = ntokens
ntokens2 = 0
for i in range(batch):
ntokens2 += seqlen[i]*seqlen[i]
output = torch.empty(ntokens2*heads, device="cuda", dtype=torch.float16)
mhalib.FastBmm1Fprop(batch2.flatten().contiguous(), batch1.flatten().contiguous(), output.flatten().contiguous(), batch, seqlen, heads, embed, scale, False, stream, sync)
return output[:ntokens2*heads]
@staticmethod
def backward(ctx, grad_output):
batch1, batch2, seqlen = ctx.saved_tensors
batch = ctx.batch
maxseqlen = ctx.maxseqlen
heads = ctx.heads
embed = ctx.embed
ntokens = ctx.ntokens
grad_batch1 = torch.empty(ntokens,heads*embed, device="cuda", dtype=torch.float16)
grad_batch2 = torch.empty(ntokens,heads*embed, device="cuda", dtype=torch.float16)
mhalib.FastBmm1Dgrad2(batch2.flatten().contiguous(), grad_output.flatten().contiguous(), grad_batch1.flatten().contiguous(), batch, seqlen, heads, embed, ctx.scale, False, ctx.stream, ctx.sync)
mhalib.FastBmm1Dgrad1(batch1.flatten().contiguous(), grad_output.flatten().contiguous(), grad_batch2.flatten().contiguous(), batch, seqlen, heads, embed, ctx.scale, False, ctx.stream, ctx.sync)
return grad_batch1[:ntokens], grad_batch2[:ntokens], None, None, None, None, None, None, None, None
class Bmm1(torch.nn.Module):
def __init__(self, batch, seqlen, heads, embed, scale=False, stream=True, sync=True):
super(Bmm1, self).__init__()
self.heads = heads
self.embed = embed
self.maxseqlen = seqlen
self.scale = scale
self.sync = sync
self.stream = stream
def forward(self, batch1, batch2, batch, seqlen):
return Bmm1Function.apply(batch1, batch2, seqlen, batch, self.maxseqlen, self.heads, self.embed, self.scale, self.stream, self.sync)
##########################################################################################
class Bmm1StridedFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, mixed, seqlen, batch, maxseqlen, heads, embed, scale, stream, sync, timers):
ctx.save_for_backward(mixed, seqlen)
ctx.batch = batch
ctx.maxseqlen = maxseqlen
ctx.heads = heads
ctx.embed = embed
ctx.scale = scale
ctx.sync = sync
ctx.stream = stream
ctx.timers = timers
ntokens = seqlen.sum().item()
ctx.ntokens = ntokens
ntokens2 = 0
for i in range(batch):
ntokens2 += seqlen[i]*seqlen[i]
output = torch.empty(ntokens2*heads, device="cuda", dtype=torch.float16)
if timers: timers['start_fprop'].record()
mhalib.FastBmm1Fprop(mixed, mixed, output, batch, seqlen, heads, embed, scale, True, stream, sync)
if timers: timers['stop_fprop'].record()
return output[:ntokens2*heads], mixed
@staticmethod
#def backward(ctx, grad_output):
def backward(ctx, grad_output, grad_mixed):
mixed, seqlen = ctx.saved_tensors
batch = ctx.batch
maxseqlen = ctx.maxseqlen
heads = ctx.heads
embed = ctx.embed
ntokens = ctx.ntokens
#grad_mixed = torch.empty([ntokens,heads*3*embed], device="cuda", dtype=torch.float16)
if ctx.timers: ctx.timers['start_dgrad'].record()
mhalib.FastBmm1Dgrad2(mixed, grad_output, grad_mixed, batch, seqlen, heads, embed, ctx.scale, True, ctx.stream, ctx.sync)
if ctx.timers: ctx.timers['stop_dgrad'].record()
if ctx.timers: ctx.timers['start_wgrad'].record()
mhalib.FastBmm1Dgrad1(mixed, grad_output, grad_mixed, batch, seqlen, heads, embed, ctx.scale, True, ctx.stream, ctx.sync)
if ctx.timers: ctx.timers['stop_wgrad'].record()
#return grad_mixed[:ntokens], None, None, None, None, None, None, None, None, None
return grad_mixed[:ntokens], grad_mixed, None, None, None, None, None, None, None, None, None
class Bmm1Strided(torch.nn.Module):
def __init__(self, batch, seqlen, heads, embed, scale=True, stream=True, sync=True, timer=False):
super(Bmm1Strided, self).__init__()
self.heads = heads
self.embed = embed
self.maxseqlen = seqlen
self.scale = scale
self.sync = sync
self.stream = stream
if timer:
self.timers = {'start_fprop':torch.cuda.Event(enable_timing=True),
'start_dgrad':torch.cuda.Event(enable_timing=True),
'start_wgrad':torch.cuda.Event(enable_timing=True),
'stop_fprop':torch.cuda.Event(enable_timing=True),
'stop_dgrad':torch.cuda.Event(enable_timing=True),
'stop_wgrad':torch.cuda.Event(enable_timing=True)}
else:
self.timers = None
def forward(self, mixed, batch, seqlen):
return Bmm1StridedFunction.apply(mixed, seqlen, batch, self.maxseqlen, self.heads, self.embed, self.scale, self.stream, self.sync, self.timers)
###########################################################################################
PyTorch/NLP/BERT/tf_to_torch/bmm2.py 0 → 100644
View file @ 8ddf66c6
import torch
import mhalib
###########################################################################################
class Bmm2Function(torch.autograd.Function):
@staticmethod
def forward(ctx, batch1, batch2, seqlen, batch, maxseqlen, heads, embed, sync, stream):
ctx.save_for_backward(batch1, batch2, seqlen)
ctx.batch = batch
ctx.maxseqlen = maxseqlen
ctx.heads = heads
ctx.embed = embed
ctx.stream = stream
ctx.sync = sync
ntokens = seqlen.sum().item()
ctx.ntokens = ntokens
output = torch.empty([ntokens,heads,embed], device="cuda", dtype=torch.float16)
mhalib.FastBmm2Fprop(batch2.flatten().contiguous(), batch1.flatten().contiguous(), output, batch, seqlen, heads, embed, False, False, stream, sync)
return output[:ntokens]
@staticmethod
def backward(ctx, grad_output):
batch1, batch2, seqlen = ctx.saved_tensors
batch = ctx.batch
maxseqlen = ctx.maxseqlen
heads = ctx.heads
embed = ctx.embed
ntokens = ctx.ntokens
ntokens2 = 0
for i in range(batch):
ntokens2 += seqlen[i]*seqlen[i]
grad_batch1 = torch.empty([ntokens2*heads], device="cuda", dtype=torch.float16)
grad_batch2 = torch.empty([ntokens,heads*embed], device="cuda", dtype=torch.float16)
mhalib.FastBmm2Dgrad1(batch2.flatten().contiguous(), grad_output, grad_batch1, batch, seqlen, heads, embed, False, False, ctx.stream, ctx.sync)
mhalib.FastBmm2Dgrad2(grad_output, batch1, grad_batch2, batch, seqlen, heads, embed, False, False, ctx.stream, ctx.sync)
return grad_batch1[:ntokens2*heads], grad_batch2[:ntokens], None, None, None, None, None, None, None
class Bmm2(torch.nn.Module):
def __init__(self, batch, seqlen, heads, embed, stream=True, sync=True):
super(Bmm2, self).__init__()
self.heads = heads
self.embed = embed
self.maxseqlen = seqlen
self.stream = stream
self.sync = sync
def forward(self, batch1, batch2, batch, seqlen):
return Bmm2Function.apply(batch1, batch2, seqlen, batch, self.maxseqlen, self.heads, self.embed, self.stream, self.sync)
###########################################################################################
class Bmm2StridedFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, batch1, mixed, seqlen, batch, maxseqlen, heads, embed, stream, sync, timers):
ctx.save_for_backward(batch1, mixed, seqlen)
ctx.batch = batch
ctx.maxseqlen = maxseqlen
ctx.heads = heads
ctx.embed = embed
ctx.stream = stream
ctx.sync = sync
ctx.timers = timers
ntokens = seqlen.sum().item()
ctx.ntokens = ntokens
output = torch.empty([ntokens,heads,embed], device="cuda", dtype=torch.float16)
if timers: timers['start_fprop'].record()
mhalib.FastBmm2Fprop(mixed, batch1, output, batch, seqlen, heads, embed, False, True, stream, sync)
if timers: timers['stop_fprop'].record()
return output[:ntokens]
@staticmethod
def backward(ctx, grad_output):
batch1, mixed, seqlen = ctx.saved_tensors
batch = ctx.batch
maxseqlen = ctx.maxseqlen
heads = ctx.heads
embed = ctx.embed
ntokens = ctx.ntokens
ntokens2 = 0
for i in range(batch):
ntokens2 += seqlen[i]*seqlen[i]
grad_batch1 = torch.empty(ntokens2*heads, device="cuda", dtype=torch.float16)
grad_mixed = torch.empty([ntokens,heads*3*embed], device="cuda", dtype=torch.float16)
if ctx.timers: ctx.timers['start_dgrad'].record()
mhalib.FastBmm2Dgrad1(mixed, grad_output, grad_batch1, batch, seqlen, heads, embed, False, True, ctx.stream, ctx.sync)
if ctx.timers: ctx.timers['stop_dgrad'].record()
if ctx.timers: ctx.timers['start_wgrad'].record()
mhalib.FastBmm2Dgrad2(grad_output, batch1, grad_mixed, batch, seqlen, heads, embed, False, True, ctx.stream, ctx.sync)
if ctx.timers: ctx.timers['stop_wgrad'].record()
return grad_batch1[:ntokens2*heads], grad_mixed[:ntokens], None, None, None, None, None, None, None, None
class Bmm2Strided(torch.nn.Module):
def __init__(self, batch, seqlen, heads, embed, stream=True, sync=True, timer=False):
super(Bmm2Strided, self).__init__()
self.heads = heads
self.embed = embed
self.maxseqlen = seqlen
self.stream = stream
self.sync = sync
if timer:
self.timers = {'start_fprop':torch.cuda.Event(enable_timing=True),
'start_dgrad':torch.cuda.Event(enable_timing=True),
'start_wgrad':torch.cuda.Event(enable_timing=True),
'stop_fprop':torch.cuda.Event(enable_timing=True),
'stop_dgrad':torch.cuda.Event(enable_timing=True),
'stop_wgrad':torch.cuda.Event(enable_timing=True)}
else:
self.timers = None
def forward(self, batch1, mixed, batch, seqlen):
return Bmm2StridedFunction.apply(batch1, mixed, seqlen, batch, self.maxseqlen, self.heads, self.embed, self.stream, self.sync, self.timers)
###########################################################################################
PyTorch/NLP/BERT/tf_to_torch/cleanup_scripts/chop_hdf5_files.py 0 → 100644
View file @ 8ddf66c6
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# 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.
import glob
import h5py
import multiprocessing
import numpy as np
hdf5_compression_method = None
input_path = 'hdf5'
input_files = sorted(glob.glob(input_path + '/part*', recursive=False))
print('n_input_shards =', len(input_files))
max_pred_per_seq = 76
seq_length = 512
n_output_shards = 2048
ofile_prefix = 'chopped_2048_balanced/part_'
ofile_suffix = '_of_' + str(n_output_shards) + '.hdf5'
print('n_output_shards =', n_output_shards)
# First pass over data to get sample count (read only the smallest array to get count)
n_samples = 0
for idx, ifile in enumerate(input_files):
print("Scanning:", ifile, " -- Progress:", idx+1, '/', len(input_files))
h5_ifile = h5py.File(ifile, 'r')
f_next_sentence_labels = h5_ifile['next_sentence_labels'][:]
h5_ifile.close()
n_samples += f_next_sentence_labels.shape[0]
# Find a "nominal" number of samples per shard (calculated to always go over by one shard size)
# Find excess samples in last shard and distribute removal of excess over first "N" shards (could be done over last, but it doesn't matter and math is easier this way)
# (since 0 <= excess < nominal_shard_size, the max imbalance will be 1 sample to minimize the straggler effect)
n_sample_per_ofile_nominal = (n_samples + n_output_shards - 1) // n_output_shards
n_excess = n_output_shards * n_sample_per_ofile_nominal - n_samples # Always a positive number
ofile_handles = [h5py.File(ofile_prefix + str(x) + ofile_suffix, 'w') for x in range(n_output_shards)]
ofile_idx = 0 # which output file
ofile_entry_idx = 0 # index into an individual data element of an output file
ifile_entry_idx = 0
n_samples_in_this_shard = n_sample_per_ofile_nominal - 1
o_input_ids = np.ndarray((n_samples_in_this_shard, seq_length))
o_input_masks = np.ndarray((n_samples_in_this_shard, seq_length))
o_segment_ids = np.ndarray((n_samples_in_this_shard, seq_length))
o_masked_lm_positions = np.ndarray((n_samples_in_this_shard, max_pred_per_seq))
o_masked_lm_ids = np.ndarray((n_samples_in_this_shard, max_pred_per_seq))
o_next_sentence_labels = np.ndarray((n_samples_in_this_shard))
for ifile in input_files:
print("Processing:", ifile, " -- Progress:", idx+1, '/', len(input_files))
h5_ifile = h5py.File(ifile, 'r')
ifile_entry_idx = 0
f_input_ids = h5_ifile['input_ids'][:]
f_input_masks = h5_ifile['input_mask'][:]
f_segment_ids = h5_ifile['segment_ids'][:]
f_masked_lm_positions = h5_ifile['masked_lm_positions'][:]
f_masked_lm_ids = h5_ifile['masked_lm_ids'][:]
f_next_sentence_labels = h5_ifile['next_sentence_labels'][:]
h5_ifile.close()
# This could be vectorized but keeping it simple due to lack of time
while ifile_entry_idx < f_input_ids.shape[0]:
if ofile_entry_idx == n_samples_in_this_shard:
ofile_handles[ofile_idx].create_dataset("input_ids", data=o_input_ids, dtype='i4', compression=hdf5_compression_method)
ofile_handles[ofile_idx].create_dataset("input_mask", data=o_input_masks, dtype='i1', compression=hdf5_compression_method)
ofile_handles[ofile_idx].create_dataset("segment_ids", data=o_segment_ids, dtype='i1', compression=hdf5_compression_method)
ofile_handles[ofile_idx].create_dataset("masked_lm_positions", data=o_masked_lm_positions, dtype='i4', compression=hdf5_compression_method)
ofile_handles[ofile_idx].create_dataset("masked_lm_ids", data=o_masked_lm_ids, dtype='i4', compression=hdf5_compression_method)
ofile_handles[ofile_idx].create_dataset("next_sentence_labels", data=o_next_sentence_labels, dtype='i1', compression=hdf5_compression_method)
ofile_handles[ofile_idx].flush()
ofile_handles[ofile_idx].close()
ofile_entry_idx = 0
ofile_idx += 1
print("Opening output idx:", ofile_idx)
n_samples_in_this_shard = n_sample_per_ofile_nominal
if ofile_entry_idx < n_excess:
n_samples_in_this_shard -= 1
o_input_ids = np.ndarray((n_samples_in_this_shard, seq_length))
o_input_masks = np.ndarray((n_samples_in_this_shard, seq_length))
o_segment_ids = np.ndarray((n_samples_in_this_shard, seq_length))
o_masked_lm_positions = np.ndarray((n_samples_in_this_shard, max_pred_per_seq))
o_masked_lm_ids = np.ndarray((n_samples_in_this_shard, max_pred_per_seq))
o_next_sentence_labels = np.ndarray((n_samples_in_this_shard))
o_input_ids[ofile_entry_idx] = f_input_ids[ifile_entry_idx]
o_input_masks[ofile_entry_idx] = f_input_masks[ifile_entry_idx]
o_segment_ids[ofile_entry_idx] = f_segment_ids[ifile_entry_idx]
o_masked_lm_positions[ofile_entry_idx] = f_masked_lm_positions[ifile_entry_idx]
o_masked_lm_ids[ofile_entry_idx] = f_masked_lm_ids[ifile_entry_idx]
o_next_sentence_labels[ofile_entry_idx] = f_next_sentence_labels[ifile_entry_idx]
ofile_entry_idx += 1
ifile_entry_idx += 1
PyTorch/NLP/BERT/tf_to_torch/cleanup_scripts/clean.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
# Copyright 2018 MLBenchmark Group. All Rights Reserved.
#
# 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.
# ==============================================================================
filein=$1
fileout=$2
echo "Further clean up: $filein => $fileout"
cmd="cat $filein "
cmd+="| grep -v '^<doc [^>]*>$' "
cmd+="| grep -vE '\[\[Category:[^][]*\]\]' "
cmd+="| sed 's/\[\[\([^]|[]*\)\]\]/\1/g' "
cmd+="| sed 's/\[\[\([^]|[]*\)\]\]/\1/g' "
cmd+="| sed 's/\[\[[^]|[]*|\([^]|[]*\)\]\]/\1/g' "
cmd+="| sed 's/\[\[[^]|[]*|\([^]|[]*\)\]\]/\1/g' "
cmd+="| sed 's/\[\[[:]*[Ff]ile:[^][]*\]\]//g' "
cmd+="| sed 's/\[\[[Mm]edia:[^][]*\]\]//g' "
cmd+="| sed 's/\[\[[Ii]mage:[^][]*\]\]//g' "
cmd+="| sed 's/\[\([^]|[]*\)\]/\1/g' "
cmd+="| sed 's/\[\[\([^][]*\)\]\]//g' "
cmd+="| sed 's/alt=//g' "
cmd+="| sed 's/<\/doc>/\r/g' "
cmd+="| sed 's/<chem\([^<]*\)<\/chem>/\1/g' "
cmd+="| sed 's/<ins\([^<]*\)<\/ins>/\1/g' "
cmd+="| sed 's/<\, ref \([^<]*\)<\/ref>//g' "
cmd+="| sed 's/<includeonly\([^<]*\)<\/includeonly>//g' "
cmd+="| sed 's/<graph\([^<]*\)<\/graph>//g' "
cmd+="| sed 's/<section\([^\\]*\)\/>//g' "
cmd+="| sed 's/<meta\([^\\]*\)\/>//g' "
cmd+="| sed 's/<hr\([^\\]*\)\/>//g' "
cmd+="| sed 's/<gallery\([^>]*\)>//g' "
cmd+="| sed 's/<ref\([^<]*\)<\/ref>//g' "
cmd+="| sed 's/<ref\([^>]*\)>//g' "
cmd+="| sed 's/<http\([^>]*\)>//g' "
cmd+="| sed 's/<Ref\([^>]*\)>//g' "
cmd+="| sed 's/<mapframe \([^\/]*\)\/>//g' "
cmd+="| sed 's/<mapframe\([^>]*\)>//g' "
cmd+="| sed 's/<\/mapframe>//g' "
cmd+="| sed 's/<poem>//g' "
cmd+="| sed 's/<\/poem>//g' "
cmd+="| sed 's/<math>//g' "
cmd+="| sed 's/<\/math>//g' "
cmd+="| sed 's/<ref>//g' "
cmd+="| sed 's/<\/ref>//g' "
cmd+="| sed 's/<div\([^>]*\)>//g' "
cmd+="| sed 's/<\/div\([^>]*\)>//g' "
cmd+="| sed 's/<\/div style>//g' "
cmd+="| sed 's/<\/div>//g' "
cmd+="| sed 's/<sup>//g' "
cmd+="| sed 's/<\/sup>//g' "
cmd+="| sed 's/<br>//g' "
cmd+="| sed 's/<\/br>//g' "
cmd+="| sed 's/<BR>//g' "
cmd+="| sed 's/<\/BR>//g' "
cmd+="| sed 's/<Br>//g' "
cmd+="| sed 's/<\/Br>//g' "
cmd+="| sed 's/<del>//g' "
cmd+="| sed 's/<\/del>//g' "
cmd+="| sed 's/<nowiki>//g' "
cmd+="| sed 's/<\/nowiki>//g' "
cmd+="| sed 's/<NOWIKI>//g' "
cmd+="| sed 's/<\/NOWIKI>//g' "
cmd+="| sed 's/<onlyinclude>//g' "
cmd+="| sed 's/<\/onlyinclude>//g' "
cmd+="| sed 's/<includeonly>//g' "
cmd+="| sed 's/<\/includeonly>//g' "
cmd+="| sed 's/<small>//g' "
cmd+="| sed 's/<\/small>//g' "
cmd+="| sed 's/<chem>//g' "
cmd+="| sed 's/<\/chem>//g' "
cmd+="| sed 's/<noinclude>//g' "
cmd+="| sed 's/<\/noinclude>//g' "
cmd+="| sed 's/<gallery>//g' "
cmd+="| sed 's/<\/gallery>//g' "
cmd+="| sed 's/<graph>{//g' "
cmd+="| sed 's/<graph>//g' "
cmd+="| sed 's/}<\/graph>//g' "
cmd+="| sed 's/<\/graph>//g' "
cmd+="| sed 's/<\/references>//g' "
cmd+="| sed 's/<poem \([^>]*\)>//g' "
# cmd+="| grep -v '^[ \t]*$' "
cmd+="> $fileout"
bash -c "${cmd[@]}"
PyTorch/NLP/BERT/tf_to_torch/cleanup_scripts/cleanup_file.py 0 → 100644
View file @ 8ddf66c6
# Copyright 2018 MLBenchmark Group. All Rights Reserved.
#
# 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.
# ==============================================================================
# Lint as: python3
"""Script to clean up input wiki dump for BERT input."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import glob
import io
import logging
import multiprocessing
import os
import time
parser = argparse.ArgumentParser(description='Wiki clean up for BERT.')
parser.add_argument(
'--data',
type=str,
default='./wiki_??',
help='Input files. Default is "./wiki_??"')
parser.add_argument(
'--input_suffix',
type=str,
default='',
help='Suffix for input files. Default is ""')
parser.add_argument(
'--output_suffix',
type=str,
default='.1',
help='Suffix for output files. Default is ".1"')
parser.add_argument(
'--nworker',
type=int,
default=72,
help='Number of workers for parallel processing.')
args = parser.parse_args()
def process_one_file(one_input):
"""Remove <doc> tag and title of pages, for one file."""
input_filename = one_input + args.input_suffix
output_filename = one_input + args.output_suffix
logging.info('Processing %s => %s', input_filename, output_filename)
with io.open(input_filename, 'r', encoding='utf-8') as fin:
with io.open(output_filename, 'w', encoding='utf-8') as fout:
keep_next_line = True
for line in fin:
if not keep_next_line:
keep_next_line = True
continue
if '<doc' in line:
keep_next_line = False
fout.write(u'\n')
continue
if '</doc>' in line:
continue
if len(line) == 1:
continue
fout.write(line)
if __name__ == '__main__':
input_files = sorted(glob.glob(os.path.expanduser(args.data)))
num_files = len(input_files)
num_workers = args.nworker
logging.basicConfig(level=logging.INFO)
logging.info('Number of input files to process = %d', num_files)
tic = time.time()
p = multiprocessing.Pool(num_workers)
p.map(process_one_file, input_files)
toc = time.time()
logging.info('Processed %s in %.2f sec', args.data, toc - tic)
PyTorch/NLP/BERT/tf_to_torch/cleanup_scripts/create_pretraining_data_wrapper.sh 0 → 100644
View file @ 8ddf66c6
#!/bin/bash
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# 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.
input_path=${1}
output_dir="hdf5"
input_file=$(basename $input_path)
mkdir -p ${output_dir}
python3 ../create_pretraining_data.py \
--input_file=${input_path} \
--output_file="${output_dir}/${input_file}" \
--vocab_file=vocab.txt \
--do_lower_case=True \
--max_seq_length=512 \
--max_predictions_per_seq=76 \
--masked_lm_prob=0.15 \
--random_seed=12345 \
--dupe_factor=10
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