| task DumpHFDataset | |
| > dataset_json=dataset.json | |
| :: repo=@ | |
| :: dataset_path=@ | |
| :: n_tokens=@ | |
| :: threshold=@ | |
| :: min_perplexity=@ | |
| :: is_hf_dataset=@ | |
| :: wikipedia=@ | |
| :: posterior_tokens=@ | |
| :: n_posterior_tokens=@ | |
| :: is_parallel=@ | |
| :: lp=@ | |
| { | |
| python $repo/prepare_data.py \ | |
| --output $dataset_json \ | |
| --n_tokens $n_tokens \ | |
| --dataset_path $dataset_path \ | |
| --threshold $threshold \ | |
| --min_perplexity $min_perplexity \ | |
| --is_hf_dataset $is_hf_dataset \ | |
| --wikipedia $wikipedia \ | |
| --posterior_tokens $posterior_tokens \ | |
| --n_posterior_tokens $n_posterior_tokens \ | |
| --is_parallel $is_parallel \ | |
| --lp $lp | |
| } | |
| task PreprocessDataset | |
| < dataset_json=$dataset_json@DumpHFDataset | |
| > dataset_bin=data_bin | |
| :: dataset=@ | |
| :: repo=@ | |
| :: tokenizer_path=@ | |
| :: tokenizer_type=@ | |
| :: cpu_workers=@ | |
| { | |
| set -euo pipefail | |
| mkdir -p $dataset_bin | |
| python $repo/tools/preprocess_data.py \ | |
| --input=$dataset_json \ | |
| --output_prefix=$dataset_bin/data \ | |
| --tokenizer_type=$tokenizer_type \ | |
| --vocab_file=$tokenizer_path \ | |
| --chunk_size=32 \ | |
| --workers=16 \ | |
| --no_new_tokens \ | |
| --append_eod | |
| } | |
| task Convert2Megatron | |
| > megatron_model | |
| :: repo=@ | |
| :: size=@ | |
| :: model_path=@ | |
| :: model_type=@ | |
| { | |
| python $repo/weights_conversion/hf_to_megatron.py $model_type \ | |
| --size=$size \ | |
| --out=$megatron_model \ | |
| --cache-dir=$model_path \ | |
| --model-path=$model_path | |
| } | |
| task ModelSharding | |
| < megatron_model=$megatron_model@Convert2Megatron | |
| > sharded_model | |
| :: repo=@ | |
| :: tp=@ | |
| :: pp=@ | |
| :: vocab_size=@ | |
| :: model_type=@ | |
| :: kv_channels=@ | |
| { | |
| KV_CHANNELS_ARGS="" | |
| if [ "$kv_channels" != "" ]; then | |
| KV_CHANNELS_ARGS="--kv_channels $kv_channels" | |
| fi | |
| python $repo/tools/checkpoint_util.py \ | |
| --target_tensor_parallel_size $tp \ | |
| --target_pipeline_parallel_size $pp \ | |
| --load_dir $megatron_model \ | |
| --save_dir $sharded_model \ | |
| --model_type $model_type \ | |
| --true_vocab_size $vocab_size \ | |
| --bf16 \ | |
| $KV_CHANNELS_ARGS | |
| } | |
| task MakeDataMix | |
| < dataset_bin=@PreprocessDataset | |
| > datamix_file | |
| :: datamix_weights=@ | |
| { | |
| # simply write datamix weight and path in dataset_bin to a file, separated by a space | |
| echo "$datamix_weights $dataset_bin/data_text_document" > $datamix_file | |
| } | |
| task MakeDataMixAnnealing | |
| < dataset_bin=@PreprocessDataset | |
| > datamix_file_annealing | |
| :: datamix_weights_annealing=@ | |
| { | |
| # simply write datamix weight and path in dataset_bin to a file, separated by a space | |
| echo "$datamix_weights_annealing $dataset_bin/data_text_document" > $datamix_file_annealing | |
| } | |
| task ContinuePretraining | |
| < megatron_model=$sharded_model@ModelSharding | |
| < dataset_bin=$dataset_bin@PreprocessDataset[Dataset:*] | |
| < datamix_file=$datamix_file@MakeDataMix[Dataset:*] | |
| > model_dir=checkpoints | |
| :: repo=@ | |
| :: log_interval=@ | |
| :: save_interval=@ | |
| :: eval_interval=@ | |
| :: train_steps=@ | |
| :: lr_scheduler=@ | |
| :: warmup_steps=@ | |
| :: lr=@ | |
| :: lr_min=@ | |
| :: n_gpus=@ | |
| :: gpu_ids=@ | |
| :: tp=@ | |
| :: pp=@ | |
| :: external_model_dir=@ | |
| :: tokenizer_path=@ | |
| :: micro_batch_size=@ | |
| :: grad_accum_steps=@ | |
| :: weight_decay=@ | |
| :: freeze_layers=@ | |
| :: eval_iters=@ | |
| :: model_type=@ | |
| :: seq_length=@ | |
| :: glu_activation=@ | |
| :: kv_channels=@ | |
| :: layernorm_epsilon=@ | |
| :: tokenizer_type=@ | |
| { | |
| #external_model_dir="${external_model_dir}_${lr}" | |
| if [ "$external_model_dir" != "" ]; then | |
| mkdir -p $external_model_dir | |
| mkdir -p $external_model_dir/runs | |
| ln -s $external_model_dir $model_dir | |
| fi | |
| data_path="" | |
| for f in $datamix_file; do | |
| # read file | |
| data_path="$data_path `cat $f`" | |
| done | |
| echo "Running with data_path=$data_path" | |
| FREEZE_ARGS="" | |
| if [ "$freeze_layers" == "not_embeddings" ]; then | |
| FREEZE_ARGS="--freeze_layers" | |
| fi | |
| echo $FREEZE_ARGS | |
| export CUDA_VISIBLE_DEVICES=$gpu_ids | |
| # if load_from_checkpoint, then set megatron_model to external_model_dir | |
| ckpt_flag=$external_model_dir/latest_checkpointed_iteration.txt | |
| if [ -f $ckpt_flag ]; then | |
| megatron_model=$external_model_dir | |
| echo Loading from previously saved checkpoint. | |
| fi | |
| KV_CHANNELS_ARGS="" | |
| if [ "$kv_channels" != "" ]; then | |
| KV_CHANNELS_ARGS="--kv_channels $kv_channels" | |
| fi | |
| TIE_ARGS="" | |
| if [ $model_type != 'gemma' ]; then | |
| TIE_ARGS+="--no_tie_embed_logits" | |
| fi | |
| global_batch_size=$(($micro_batch_size * $n_gpus * $grad_accum_steps)) | |
| LOG_ARGS="--log_interval $log_interval --save_interval $save_interval --eval_interval $eval_interval --eval_iters $eval_iters --log_validation_ppl_to_tensorboard --log_memory_to_tensorboard --log_batch_size_to_tensorboard" | |
| TRAIN_ARGS="--train_iters $train_steps --lr_decay_style $lr_scheduler --lr_warmup_iters $warmup_steps --lr $lr --min_lr $lr_min --weight_decay $weight_decay" | |
| DISTRIBUTED_ARGS="--nproc_per_node $n_gpus --nnodes 1 --node_rank 0 --master_addr localhost --master_port 8134" | |
| COMMON_ARGS="--hidden_dropout 0.0 --attention_dropout 0.0 --no_bias_gelu_fusion" | |
| LLAMA_ARGS="--use_rms_norm --glu_activation $glu_activation --no_new_tokens --layernorm_epsilon $layernorm_epsilon" | |
| CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun $DISTRIBUTED_ARGS $repo/finetune.py \ | |
| --tensor_model_parallel_size $tp \ | |
| --pipeline_model_parallel_size $pp \ | |
| --load $megatron_model \ | |
| --save $model_dir \ | |
| --tensorboard_dir $external_model_dir/runs \ | |
| --data_path $data_path \ | |
| --model_name $model_type \ | |
| --tokenizer_type $tokenizer_type \ | |
| --vocab_file=$tokenizer_path \ | |
| --bf16 \ | |
| --use_flash_attn \ | |
| --micro_batch_size $micro_batch_size \ | |
| --global_batch_size $global_batch_size \ | |
| --sequence_parallel \ | |
| --recompute_granularity selective \ | |
| --use_checkpoint_args \ | |
| --seq_length $seq_length \ | |
| --split 9995,3,2 \ | |
| --sliding_window_size 4096 \ | |
| --reset_attention_mask \ | |
| --no_bias_gelu_fusion \ | |
| $COMMON_ARGS $LOG_ARGS $TRAIN_ARGS $LLAMA_ARGS $FREEZE_ARGS $KV_CHANNELS_ARGS $TIE_ARGS | |
| } | |
| task Annealing | |
| < megatron_model=$model_dir@ContinuePretraining | |
| < dataset_bin=$dataset_bin@PreprocessDataset[Dataset:*] | |
| < datamix_file=$datamix_file_annealing@MakeDataMixAnnealing[Dataset:*] | |
| > model_dir=checkpoints | |
| :: repo=@ | |
| :: log_interval=@ | |
| :: save_interval=@ | |
| :: eval_interval=@ | |
| :: train_steps_annealing=@ | |
| :: lr_scheduler_annealing=@ | |
| :: lr_annealing=@ | |
| :: lr_min_annealing=@ | |
| :: warmup_steps_annealing=@ | |
| :: n_gpus=@ | |
| :: gpu_ids=@ | |
| :: tp=@ | |
| :: pp=@ | |
| :: external_model_dir=@ | |
| :: external_model_dir_annealing=@ | |
| :: tokenizer_path=@ | |
| :: micro_batch_size=@ | |
| :: grad_accum_steps=@ | |
| :: weight_decay=@ | |
| :: freeze_layers=@ | |
| :: eval_iters=@ | |
| :: model_type=@ | |
| :: seq_length=@ | |
| :: glu_activation=@ | |
| :: kv_channels=@ | |
| :: layernorm_epsilon=@ | |
| :: tokenizer_type=@ | |
| { | |
| #external_model_dir="${external_model_dir}_${lr_annealing}" | |
| #external_model_dir_annealing="${external_model_dir_annealing}_${lr_annealing}" | |
| if [ "$external_model_dir" != "" ]; then | |
| mkdir -p $external_model_dir_annealing | |
| mkdir -p $external_model_dir/runs/annealing | |
| ln -s $external_model_dir_annealing $model_dir | |
| fi | |
| data_path="" | |
| for f in $datamix_file; do | |
| # read file | |
| data_path="$data_path `cat $f`" | |
| done | |
| echo "Running with data_path=$data_path" | |
| FREEZE_ARGS="" | |
| if [ "$freeze_layers" == "not_embeddings" ]; then | |
| FREEZE_ARGS="--freeze_layers" | |
| fi | |
| echo $FREEZE_ARGS | |
| KV_CHANNELS_ARGS="" | |
| if [ "$kv_channels" != "" ]; then | |
| KV_CHANNELS_ARGS="--kv_channels $kv_channels" | |
| fi | |
| TIE_ARGS="" | |
| if [ $model_type != 'gemma' ]; then | |
| TIE_ARGS+="--no_tie_embed_logits" | |
| fi | |
| export CUDA_VISIBLE_DEVICES=$gpu_ids | |
| global_batch_size=$(($micro_batch_size * $n_gpus * $grad_accum_steps)) | |
| LOG_ARGS="--log_interval $log_interval --save_interval $save_interval --eval_interval $eval_interval --eval_iters $eval_iters --log_validation_ppl_to_tensorboard --log_memory_to_tensorboard --log_batch_size_to_tensorboard" | |
| TRAIN_ARGS="--train_iters $train_steps_annealing --lr_decay_style $lr_scheduler_annealing --lr_warmup_iters $warmup_steps_annealing --lr $lr_annealing --min_lr $lr_min_annealing --weight_decay $weight_decay" | |
| DISTRIBUTED_ARGS="--nproc_per_node $n_gpus --nnodes 1 --node_rank 0 --master_addr localhost --master_port 8135" | |
| COMMON_ARGS="--hidden_dropout 0.0 --attention_dropout 0.0 --no_bias_gelu_fusion" | |
| LLAMA_ARGS="--use_rms_norm --glu_activation $glu_activation --no_new_tokens --layernorm_epsilon $layernorm_epsilon" | |
| CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun $DISTRIBUTED_ARGS $repo/finetune.py \ | |
| --tensor_model_parallel_size $tp \ | |
| --pipeline_model_parallel_size $pp \ | |
| --load $megatron_model \ | |
| --save $model_dir \ | |
| --tensorboard_dir $external_model_dir/runs/annealing \ | |
| --data_path $data_path \ | |
| --model_name $model_type \ | |
| --tokenizer_type $tokenizer_type \ | |
| --vocab_file=$tokenizer_path \ | |
| --bf16 \ | |
| --use_flash_attn \ | |
| --micro_batch_size $micro_batch_size \ | |
| --global_batch_size $global_batch_size \ | |
| --sequence_parallel \ | |
| --recompute_granularity selective \ | |
| --use_checkpoint_args \ | |
| --seq_length $seq_length \ | |
| --split 9990,5,5 \ | |
| --sliding_window_size 4096 \ | |
| --annealing \ | |
| $COMMON_ARGS $LOG_ARGS $TRAIN_ARGS $LLAMA_ARGS $FREEZE_ARGS $KV_CHANNELS_ARGS $TIE_ARGS | |
| } | |
| plan preprocess_mc4 { | |
| reach PreprocessDataset via (Size: 7) * (TP: 4) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko) * (DataMix: mc4_uniform) | |
| } | |
| plan preprocess_inst { | |
| reach PreprocessDataset via (Size: 7) * (TP: 4) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko pl sv en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en instructions en_de_pre_annealing de_en_pre_annealing en_fr_pre_annealing fr_en_pre_annealing en_es_pre_annealing es_en_pre_annealing en_it_pre_annealing it_en_pre_annealing en_nl_pre_annealing nl_en_pre_annealing en_pt_pre_annealing pt_en_pre_annealing en_ru_pre_annealing ru_en_pre_annealing en_zh_pre_annealing zh_en_pre_annealing en_ko_pre_annealing ko_en_pre_annealing en_de_wmt en_ru_wmt en_zh_wmt) | |
| } | |
| plan preprocess_data { | |
| reach PreprocessDataset via (Size: 7) * (TP: 4) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko pl sv en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en en_pl pl_en en_sv sv_en instructions) * (DataMix: mc4_wiki_uniform) | |
| } | |
| plan train_mc4_wiki { | |
| reach ContinuePretraining via (Size: 7) * (TP: 4) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko en_wiki de_wiki fr_wiki es_wiki it_wiki nl_wiki pt_wiki ru_wiki zh_wiki ko_wiki) * (DataMix: mc4_wiki_uniform) | |
| } | |
| plan train_mc4 { | |
| reach ContinuePretraining via (Size: 7) * (TP: 4) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko) | |
| } | |
| plan prepare_data { | |
| reach DumpHFDataset via (Size: 1) * (TP: 1) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko) | |
| } | |
| plan preprocess_data_parallel { | |
| reach PreprocessDataset via (Dataset: en de fr es it nl pt ru zh ko en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_ko ko_en en_zh zh_en) | |
| } | |
| plan train_mc4_parallel_instructions { | |
| reach ContinuePretraining via (Size: 1) * (TP: 1) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en instructions) | |
| } | |
| plan train_mc4_parallel_instructions_annealing { | |
| reach Annealing via (Size: 1) * (TP: 1) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en instructions) | |
| } | |
| plan train_mc4_parallel { | |
| reach ContinuePretraining via (Size: 8) * (TP: 4) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en) | |
| } | |
| plan train_mc4_parallel_13B { | |
| reach ContinuePretraining via (Size: 13) * (TP: 8) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en) | |
| } | |
| plan warmed_up_train { | |
| reach ContinuePretrainingWarmedUp via (Size: 7) * (TP: 4) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko en_wiki de_wiki fr_wiki es_wiki it_wiki nl_wiki pt_wiki ru_wiki zh_wiki ko_wiki) * (DataMix: mc4_wiki_uniform) | |
| } | |
| plan train_parallel { | |
| reach ContinuePretraining via (Size: 8) * (TP: 4) * (PP: 1) * (Dataset: en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en) | |
| } | |
| plan gemma_test { | |
| reach ContinuePretraining via (Size: 1) * (TP: 2) * (PP: 1) * (Dataset: en es) | |
| } | |
| plan train_mc4_parallel_gemma { | |
| reach ContinuePretraining via (Size: 2) * (TP: 1) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en) | |
| } | |
| plan annealing { | |
| reach Annealing via (Size: 1) * (TP: 4) * (PP: 1) * (Dataset: *) | |
| } | |
| plan cpt { | |
| reach ContinuePretraining via (Size: 7) * (TP: 4) * (PP: 1) * (Dataset: en de fr es it nl pt ru zh ko en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en) | |
| } |