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 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 data_text_document.idx filter=lfs diff=lfs merge=lfs -text
+multilinguality_megatron/megatron/fused_kernels/build/fused_mix_prec_layer_norm_cuda.so filter=lfs diff=lfs merge=lfs -text
+multilinguality_megatron/megatron/fused_kernels/build/layer_norm_cuda_kernel.cuda.o filter=lfs diff=lfs merge=lfs -text

multilinguality_megatron/.gitignore

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+__pycache__
+build
+.vscode
+perplexity_texts

multilinguality_megatron/LICENSE

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+The following applies to all files unless otherwise noted:
+
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multilinguality_megatron/README.md

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+## Installation Instructions
+
+As a pre-requisite, make sure you have [ducttape](https://github.com/CoderPat/ducttape) and [(mini)conda](https://docs.conda.io/en/latest/miniconda.html) installed.
+
+First, clone this repository.
+
+Then, to create a new conda environment with all the necessary dependencies, run the following command:
+
+```bash
+export CONDA_HOME="/path/to/(mini)conda3"
+bash setup/conda.sh
+```
+
+# Training
+
+## Data format
+
+Before training, you must preprocess the training data. Before preprocessing, the data should be a `json` file, with the following format:
+```json
+{"text": "<instance_0_text>"}
+{"text": "<instance_1_text>"}
+```
+Note that the preprocessing script will pack observations together in vectors of a specified length, and will separate each instance (json line) by the tokenizer's EOS token.
+
+Then, run the bash scripts in this order:
+
+```bash
+./preprocess_data.sh [OPTIONS]
+./convert2megatron.sh [OPTIONS]
+./model_sharding.sh [OPTIONS]
+./continue_pretraining.sh [OPTIONS]
+```
+>NOTE: each of these commands may be run with flag `--help`, which will inform the user on how to use each argument.
+
+For example, for a continued pretraining run with Llama 2 7B on datasets `d1` and `d2` and 8 GPUs, run the following:
+
+```bash
+> ./preprocess_data.sh --dataset_json=<path_to_d1> --dataset_bin=<d1_output_path> --vocab_file=<path_to_hf_model>/tokenizer.model --repo=<path_to_repo>
+> ./preprocess_data.sh --dataset_json=<path_to_d2> --dataset_bin=<d2_output_path> --vocab_file=<path_to_hf_model>/tokenizer.model --repo=<path_to_repo>
+> ./convert2megatron.sh --megatron_model=<megatron_model_path> --model_path=<path_to_hf_model> --size=7 --repo=<path_to_repo>
+> ./model_sharding.sh --megatron_model=<megatron_model_path> --sharded_model=<sharded_model_path> --tp=8 --pp=1 --vocab_size=32000 --repo=<path_to_repo>
+> ./continue_pretraining.sh --data_path="1 d1 1 d2" --megatron_model=<sharded_model_path> --model_dir=<checkpoint_save_dir> --tokenizer_path=<path_to_hf_model>/tokenizer.model --tp=8 --pp=1 [TRAINING_ARGS]
+```

multilinguality_megatron/ablation_eval_pipeline.sh

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+# bash script to evaluate a given model on wmt23, flores, ape, gec, standard benchmarks, and perplexity, sequentially
+
+# wmt23, flores, ape, gec, standard benchmarks use tower-eval
+TOWER_EVAL_DIR=/mnt/data/jpombal/tower-eval
+cd $TOWER_EVAL_DIR
+source $TOWER_EVAL_DIR/tower-eval-env/bin/activate
+
+CUDA_VISIBLE_DEVICES=0 python $TOWER_EVAL_DIR/tower_eval/cli.py lm_eval --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/std_bench.yaml &
+CUDA_VISIBLE_DEVICES=1 python $TOWER_EVAL_DIR/tower_eval/cli.py gen-eval --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/mt.yaml &
+CUDA_VISIBLE_DEVICES=2 python $TOWER_EVAL_DIR/tower_eval/cli.py evaluate --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/perplexity_flavio_final.yaml &
+#CUDA_VISIBLE_DEVICES=3 python $TOWER_EVAL_DIR/tower_eval/cli.py evaluate --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/perplexity_flaviarlos_sft.yaml &
+#CUDA_VISIBLE_DEVICES=4 python $TOWER_EVAL_DIR/tower_eval/cli.py evaluate --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/perplexity_carlos_no_mt_annealed_sft.yaml
+# CUDA_VISIBLE_DEVICES=2 python $TOWER_EVAL_DIR/tower_eval/cli.py evaluate --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/perplexity_porfirio_pre_annealing.yaml &
+# CUDA_VISIBLE_DEVICES=3 python $TOWER_EVAL_DIR/tower_eval/cli.py evaluate --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/perplexity_porfirio_sft.yaml &
+# CUDA_VISIBLE_DEVICES=4 python $TOWER_EVAL_DIR/tower_eval/cli.py evaluate --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/perplexity_carlos_sft.yaml &
+# CUDA_VISIBLE_DEVICES=5 python $TOWER_EVAL_DIR/tower_eval/cli.py evaluate --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/perplexity_carlos_annealed_sft.yaml &
+# CUDA_VISIBLE_DEVICES=6 python $TOWER_EVAL_DIR/tower_eval/cli.py evaluate --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/perplexity_flavio_sft.yaml &
+# CUDA_VISIBLE_DEVICES=7 python $TOWER_EVAL_DIR/tower_eval/cli.py evaluate --config /mnt/data/jpombal/tower-eval/local_configs/cp_ablations/perplexity_porfirio_annealed.yaml &

multilinguality_megatron/continue_pretraining.sh

@@ -0,0 +1,185 @@
+# This script will try to run a task *outside* any specified submitter
+# Note: This script is for archival; it is not actually run by ducttape
+# unset CUDA_VISIBLE_DEVICES
+echo $CUDA_VISIBLE_DEVICES
+
+data_path="1 spgi_vox_mls_text_1b/data/data_text_document"
+megatron_model="spgi_vox_mls_text_1b/shards"
+model_dir="spgi_vox_mls_text_1b/ckpt"
+tokenizer_path="spgi_vox_mls_text_1b/new_extended_tokenizer/tokenizer.model"
+tp="2"
+pp="1"
+
+# --wandb_logger \
+# --wandb_id "hajmola" \
+# --wandb_project "Megatron" \
+# --wandb_entity "hajmola" \
+# --wandb_api_key "c4a95af43e910d14b0eca23fbb8165f94944d5af" \
+
+# optimization arguments; self-explanatory. Intervals and steps are in terms of training optimizer steps
+grad_accum_steps="12"
+micro_batch_size="12"
+warmup_steps="13"
+eval_interval="500"
+lr="3e-5" #lr="3e-5"
+log_interval="10"
+lr_min="3e-6" #lr_min="3e-6"
+lr_scheduler="cosine"
+
+# infra arguments 
+save_interval="250"
+n_gpus="2"
+repo="multilinguality_megatron"
+gpu_ids="4,5"
+train_steps="1000"
+
+
+# Parse command-line arguments
+for arg in "$@"
+do
+    case $arg in
+        --help)
+        echo "Usage: ./script.sh [OPTIONS]"
+        echo "Options:"
+        echo "  --data_path=PATH       Path to dataset. Should have the form of <integer_0> <PATH_TO_DATA_TEXT_DOCUMENT_0> <integer_1> <PATH_TO_DATA_TEXT_DOCUMENT_1> ..., where the integers determine the data's relative weight in the training set. If every integer is equal, then the data is uniformly sampled."
+        echo "  --megatron_model=PATH  Path to sharded megatron model"
+        echo "  --model_dir=PATH       folder to save model checkpoints; if this has a checkpoint, it will be used to continue training"
+        echo "  --tokenizer_path=PATH  Path to tokenizer.model of original HF model"
+        echo "  --tp=NUMBER            Number of shards model is divided in"
+        echo "  --pp=NUMBER            Pipeline parallel (default is 1)"
+        echo "  --grad_accum_steps=NUMBER"
+        echo "                         Number of gradient accumulation steps"
+        echo "  --micro_batch_size=NUMBER"
+        echo "                         Micro batch size"
+        echo "  --warmup_steps=NUMBER  Number of warmup steps"
+        echo "  --eval_interval=NUMBER Number of steps between validations"
+        echo "  --lr=NUMBER            Learning rate"
+        echo "  --log_interval=NUMBER  Number of steps between logging"
+        echo "  --lr_min=NUMBER        Minimum learning rate of scheduler"
+        echo "  --lr_scheduler=STRING  Learning rate scheduler"
+        echo "  --save_interval=NUMBER Number of steps between saves"
+        echo "  --n_gpus=NUMBER        Number of GPUs to use"
+        echo "  --repo=PATH            Path to repo"
+        echo "  --gpu_ids=STRING       GPU IDs to use"
+        echo "  --train_steps=NUMBER   Number of training steps"
+        exit 0
+        ;;
+        --data_path=*)
+        data_path="${arg#*=}"
+        shift
+        ;;
+        --megatron_model=*)
+        megatron_model="${arg#*=}"
+        shift
+        ;;
+        --model_dir=*)
+        model_dir="${arg#*=}"
+        shift
+        ;;
+        --tokenizer_path=*)
+        tokenizer_path="${arg#*=}"
+        shift
+        ;;
+        --tp=*)
+        tp="${arg#*=}"
+        shift
+        ;;
+        --pp=*)
+        pp="${arg#*=}"
+        shift
+        ;;
+        --grad_accum_steps=*)
+        grad_accum_steps="${arg#*=}"
+        shift
+        ;;
+        --micro_batch_size=*)
+        micro_batch_size="${arg#*=}"
+        shift
+        ;;
+        --warmup_steps=*)
+        warmup_steps="${arg#*=}"
+        shift
+        ;;
+        --eval_interval=*)
+        eval_interval="${arg#*=}"
+        shift
+        ;;
+        --lr=*)
+        lr="${arg#*=}"
+        shift
+        ;;
+        --log_interval=*)
+        log_interval="${arg#*=}"
+        shift
+        ;;
+        --lr_min=*)
+        lr_min="${arg#*=}"
+        shift
+        ;;
+        --lr_scheduler=*)
+        lr_scheduler="${arg#*=}"
+        shift
+        ;;
+        --save_interval=*)
+        save_interval="${arg#*=}"
+        shift
+        ;;
+        --n_gpus=*)
+        n_gpus="${arg#*=}"
+        shift
+        ;;
+        --repo=*)
+        repo="${arg#*=}"
+        shift
+        ;;
+        --gpu_ids=*)
+        gpu_ids="${arg#*=}"
+        shift
+        ;;
+        --train_steps=*)
+        train_steps="${arg#*=}"
+        shift
+        ;;
+    esac
+done
+
+# CUDA_VISIBLE_DEVICES=$gpu_ids
+
+if [ "$model_dir" != "" ]; then
+    mkdir -p $model_dir
+    mkdir -p $model_dir/runs
+fi
+
+ckpt_flag=$model_dir/latest_checkpointed_iteration.txt
+if [ -f $ckpt_flag ]; then
+    megatron_model=$model_dir
+    echo Loading from previously saved checkpoint.
+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"
+TRAIN_ARGS="--train_iters $train_steps --lr_decay_style $lr_scheduler --lr_warmup_iters $warmup_steps --lr $lr --min_lr $lr_min"
+DISTRIBUTED_ARGS="--nproc_per_node $n_gpus --nnodes 1 --node_rank 0 --master_addr localhost --master_port 50000"
+COMMON_ARGS="--hidden_dropout 0.0 --attention_dropout 0.0 --no_bias_gelu_fusion"
+LLAMA_ARGS="--use_rms_norm --glu_activation swiglu --no_tie_embed_logits --no_new_tokens --layernorm_epsilon 1e-5"
+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 $model_dir/runs \
+    --data_path $data_path \
+    --model_name llama \
+    --tokenizer_type SentencePieceTokenizer \
+    --vocab_file=$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 2048 \
+    --split 99,1,1 \
+    $COMMON_ARGS $LOG_ARGS $TRAIN_ARGS $LLAMA_ARGS

multilinguality_megatron/convert2megatron.sh

@@ -0,0 +1,45 @@
+#!/bin/bash
+
+megatron_model="/mnt/scratch-artemis/kshitij/LLAMA/latest_megatron_codebase/spgi_vox_mls_text_1b/megatron_model"
+model_path="/mnt/scratch-artemis/kshitij/LLAMA/latest_megatron_codebase/spgi_vox_mls_text_1b/extended_non_uniform_model"
+size="1"
+repo="/mnt/scratch-artemis/kshitij/LLAMA/latest_megatron_codebase/multilinguality_megatron"
+
+# Parse command-line arguments
+for arg in "$@"
+do
+    case $arg in
+        --help)
+        echo "Usage: ./script.sh [OPTIONS]"
+        echo "Options:"
+        echo "  --megatron_model=PATH   Path to save converted model."
+        echo "  --model_path=PATH       Path of HF directory of model to be converted."
+        echo "  --size=NUMBER           Billion parameters of model."
+        echo "  --repo=PATH             Path to repo."
+        exit 0
+        ;;
+        --megatron_model=*)
+        megatron_model="${arg#*=}"
+        shift
+        ;;
+        --model_path=*)
+        model_path="${arg#*=}"
+        shift
+        ;;
+        --size=*)
+        size="${arg#*=}"
+        shift
+        ;;
+        --repo=*)
+        repo="${arg#*=}"
+        shift
+        ;;
+    esac
+done
+
+# Run the Python script
+python $repo/weights_conversion/hf_to_megatron.py llama \
+    --size=$size \
+    --out=$megatron_model \
+    --cache-dir=$model_path \
+    --model-path=$model_path 

multilinguality_megatron/cp.sh

@@ -0,0 +1,10 @@
+langs=(en de es fr it pt nl 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)
+
+for lang in ${langs[@]}; do
+    mkdir -p /mnt/cephfs-nvme/shared/experiments_megatron/cpt_llama_3/DumpHFDataset/Dataset.${lang}
+    echo "0" > /mnt/cephfs-nvme/shared/experiments_megatron/cpt_llama_3/DumpHFDataset/Dataset.${lang}/ducttape_exit_code.txt
+    touch /mnt/cephfs-nvme/shared/experiments_megatron/cpt_llama_3/DumpHFDataset/Dataset.${lang}/ducttape_stderr.txt
+    touch /mnt/cephfs-nvme/shared/experiments_megatron/cpt_llama_3/DumpHFDataset/Dataset.${lang}/ducttape_stdout.txt
+    touch /mnt/cephfs-nvme/shared/experiments_megatron/cpt_llama_3/DumpHFDataset/Dataset.${lang}/ducttape_task.sh
+    cp /mnt/cephfs-nvme/shared/tower-base-training-data/${lang}/dataset.json /mnt/cephfs-nvme/shared/experiments_megatron/cpt_llama_3/DumpHFDataset/Dataset.${lang}/ &
+done

multilinguality_megatron/debug.sh

@@ -0,0 +1,101 @@
+export dataset_bin="/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.ru_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.ru_en/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_fr_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_fr/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.fr_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.ko_en_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.es_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.zh_en_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Baseline.baseline/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.pt_en_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_pt/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.pt_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_es/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.zh_en/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.it_en/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.zh/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_de/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.ko_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.ko_en/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_de_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.nl_en_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_ko_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.it/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.pt/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.ru/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_zh/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.es/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.de_en/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.de_en_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_it/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.it_en_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_nl/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_zh_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.ru_en_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.zh_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.it_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.instructions/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.de/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_pt_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.nl_en/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_ru/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.de_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.fr/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.ko/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.nl/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.es_en/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_it_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_ru_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_es_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.fr_en_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.nl_synth/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_nl_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.en_ko/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.es_en_pre_annealing/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.fr_en/data_bin /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/PreprocessDataset/Dataset.pt_en/data_bin"
+export datamix_file="/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.ru_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.ru_en/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_fr_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_fr/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.fr_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.ko_en_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.es_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.zh_en_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Baseline.baseline/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.pt_en_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_pt/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.pt_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_es/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.zh_en/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.it_en/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.zh/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_de/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.ko_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.ko_en/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_de_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.nl_en_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_ko_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.it/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.pt/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.ru/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_zh/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.es/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.de_en/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.de_en_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_it/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.it_en_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_nl/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_zh_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.ru_en_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.zh_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.it_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.instructions/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.de/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_pt_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.nl_en/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_ru/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.de_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.fr/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.ko/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.nl/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.es_en/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_it_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_ru_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_es_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.fr_en_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.nl_synth/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_nl_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.en_ko/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.es_en_pre_annealing/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.fr_en/datamix_file /mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/MakeDataMix/Dataset.pt_en/datamix_file"
+export megatron_model="/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/ModelSharding/PP.1+Size.1+TP.1/sharded_model"
+export model_dir="/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/doc_attn_tests"
+export seq_length="2048"
+export tp="1"
+export warmup_steps="32"
+export micro_batch_size="24"
+export grad_accum_steps="4"
+export kv_channels=""
+export weight_decay="0.1"
+export external_model_dir="/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/doc_attn_tests"
+export lr="3e-5"
+export eval_interval="635"
+export layernorm_epsilon="1e-5"
+export log_interval="1"
+export freeze_layers=""
+export glu_activation="swiglu"
+export eval_iters="1"
+export lr_min="3e-6"
+export pp="1"
+export model_type="llama2"
+export lr_scheduler="constant"
+export tokenizer_path="/mnt/data_2/cache/models--TinyLlama--TinyLlama-1.1B-intermediate-step-1431k-3T/snapshots/036fa4651240b9a1487f709833b9e4b96b4c1574"
+export save_interval="635"
+export n_gpus="1"
+export repo="/mnt/data/jpombal/multilinguality_megatron"
+export gpu_ids="0"
+export tokenizer_type="PretrainedFromHF"
+export train_steps="11430"
+
+    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
+    echo $TIE_ARGS
+
+    global_batch_size=$(($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 9990,5,5 \
+        --sliding_window_size 4096 \
+        --reset_attention_mask \
+        --reset_position_ids \
+        $COMMON_ARGS $LOG_ARGS $TRAIN_ARGS $LLAMA_ARGS $FREEZE_ARGS $KV_CHANNELS_ARGS $TIE_ARGS \

multilinguality_megatron/deploy.sh

@@ -0,0 +1,53 @@
+while getopts ":p:v:m:f:t:k:" opt; do
+    case ${opt} in
+        p )
+            path_to_weights=$OPTARG
+            ;;
+        v )
+            vocab_size=$OPTARG
+            ;;
+        m ) model_name=$OPTARG
+            ;;
+        f ) vocab_file=$OPTARG
+            ;;
+        t ) model_type=$OPTARG
+            ;;
+        k ) kv_channels=$OPTARG
+            ;;
+        \? )
+            echo "Invalid option: $OPTARG" 1>&2
+            exit 1
+            ;;
+        : )
+            echo "Invalid option: $OPTARG requires an argument" 1>&2
+            exit 1
+            ;;
+    esac
+done
+shift $((OPTIND -1))
+
+KV_CHANNELS_ARGS=""
+if [ "$kv_channels" != "" ]; then
+    KV_CHANNELS_ARGS="--kv_channels $kv_channels"   
+fi
+
+# path_to_weights is where the latest_checkpointed_iteration.txt file is located
+# script creates a folder with respective iteration in unsharded_dir, so no need to specify iteration 
+python tools/checkpoint_util.py \
+	--target_tensor_parallel_size 1 \
+	--target_pipeline_parallel_size 1 \
+	--load_dir $path_to_weights \
+	--save_dir "${path_to_weights}/unsharded" \
+	--model_type $model_type \
+	--true_vocab_size $vocab_size \
+	--bf16 \
+    $KV_CHANNELS_ARGS
+
+python weights_conversion/megatron_to_hf.py \
+    --input_dir "${path_to_weights}/unsharded" \
+	--output_dir "${path_to_weights}/hf/${model_name}" \
+    --vocab_file "${vocab_file}" \
+    --model $model_type
+
+# remove intermediate step
+rm -r "${path_to_weights}/unsharded"

multilinguality_megatron/docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

multilinguality_megatron/docs/_templates/autosummary/base.rst

@@ -0,0 +1,5 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. auto{{ objtype }}:: {{ objname }}

multilinguality_megatron/docs/_templates/autosummary/class.rst

@@ -0,0 +1,9 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :members:
+   :special-members:
+   :show-inheritance:
+   :exclude-members: __weakref__, __init__

multilinguality_megatron/docs/_templates/autosummary/module.rst

@@ -0,0 +1,29 @@
+{{ fullname | escape | underline }}
+
+.. rubric:: Description
+
+.. automodule:: {{ fullname }}
+
+.. currentmodule:: {{ fullname }}
+
+{% if classes %}
+.. rubric:: Classes
+
+.. autosummary::
+    :toctree: .
+    {% for class in classes %}
+    {{ class }}
+    {% endfor %}
+
+{% endif %}
+
+{% if functions %}
+.. rubric:: Functions
+
+.. autosummary::
+    :toctree: .
+    {% for function in functions %}
+    {{ function }}
+    {% endfor %}
+
+{% endif %}

multilinguality_megatron/docs/api/index.rst

@@ -0,0 +1,130 @@
+API
+===
+
+megatron
+--------
+
+.. autosummary::
+   :toctree: megatron
+
+   megatron.arguments
+   megatron.checkpointing
+   megatron.dist_signal_handler
+   megatron.global_vars
+   megatron.indexer
+   megatron.initialize
+   megatron.memory
+   megatron.microbatches
+   megatron.optimizer_param_scheduler
+   megatron.p2p_communication
+   megatron.schedules
+   megatron.text_generation_server
+   megatron.timers
+   megatron.training
+   megatron.utils
+   megatron.wandb_logger
+
+megatron.core
+-------------
+
+.. autosummary::
+   :toctree: megatron/core
+
+   megatron.core.parallel_state
+   megatron.core.utils
+
+
+megatron.core.tensor_parallel
+-----------------------------
+
+.. autosummary::
+   :toctree: megatron/core/tensor_parallel
+
+   megatron.core.tensor_parallel.cross_entropy
+   megatron.core.tensor_parallel.data
+   megatron.core.tensor_parallel.layers
+   megatron.core.tensor_parallel.mappings
+   megatron.core.tensor_parallel.random
+   megatron.core.tensor_parallel.utils
+
+megatron.data
+-------------
+
+.. autosummary::
+   :toctree: megatron/data
+
+   megatron.data.autoaugment
+   megatron.data.blendable_dataset
+   megatron.data.gpt_dataset
+   megatron.data.image_folder
+   megatron.data.realm_dataset_utils
+   megatron.data.bert_dataset
+   megatron.data.data_samplers
+   megatron.data.indexed_dataset
+   megatron.data.orqa_wiki_dataset
+   megatron.data.realm_index
+   megatron.data.biencoder_dataset_utils
+   megatron.data.dataset_utils
+   megatron.data.ict_dataset
+   megatron.data.t5_dataset
+
+megatron.model
+--------------
+
+.. autosummary::
+   :toctree: megatron/model
+
+   megatron.model.bert_model
+   megatron.model.biencoder_model
+   megatron.model.classification
+   megatron.model.distributed
+   megatron.model.enums
+   megatron.model.falcon_model
+   megatron.model.fused_bias_gelu
+   megatron.model.fused_layer_norm
+   megatron.model.fused_softmax
+   megatron.model.glu_activations
+   megatron.model.gpt_model
+   megatron.model.language_model
+   megatron.model.llama_model
+   megatron.model.module
+   megatron.model.multiple_choice
+   megatron.model.positional_embeddings
+   megatron.model.t5_model
+   megatron.model.transformer
+   megatron.model.utils
+
+megatron.optimizer
+------------------
+
+.. autosummary::
+   :toctree: megatron/optimizer
+
+   megatron.optimizer.clip_grads
+   megatron.optimizer.distrib_optimizer
+   megatron.optimizer.grad_scaler
+   megatron.optimizer.optimizer
+
+megatron.text_generation
+------------------------
+
+.. autosummary::
+   :toctree: megatron/text_generation
+
+   megatron.text_generation.api
+   megatron.text_generation.beam_utils
+   megatron.text_generation.communication
+   megatron.text_generation.forward_step
+   megatron.text_generation.generation
+   megatron.text_generation.sampling
+   megatron.text_generation.tokenization
+
+megatron.tokenizer
+------------------
+
+.. autosummary::
+   :toctree: megatron/tokenizer
+
+   megatron.tokenizer.bert_tokenization
+   megatron.tokenizer.gpt2_tokenization
+   megatron.tokenizer.tokenizer

multilinguality_megatron/docs/conf.py

@@ -0,0 +1,64 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
+
+
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = 'Megatron-LLM'
+copyright = '2023, Alejandro Hernández Cano, Matteo Pagliardini, Kyle Matoba, Amirkeivan Mohtashami, Olivia Simin Fan, Axel Marmet, Deniz Bayazit, Igor Krawczuk, Zeming Chen, Francesco Salvi, Antoine Bosselut, Martin Jaggi'
+author = 'Alejandro Hernández Cano, Matteo Pagliardini, Kyle Matoba, Amirkeivan Mohtashami, Olivia Simin Fan, Axel Marmet, Deniz Bayazit, Igor Krawczuk, Zeming Chen, Francesco Salvi, Antoine Bosselut, Martin Jaggi'
+release = '0.1.0'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+        'sphinx.ext.autodoc',
+        'sphinx.ext.intersphinx',
+        'sphinx.ext.autosummary',
+        'sphinx.ext.napoleon',
+        'sphinx.ext.mathjax',
+        'myst_parser'
+]
+
+# autosummary
+autosummary_generate = True
+
+# napoleon
+napoleon_google_docstring = True
+
+# myst
+myst_enable_extensions = ["colon_fence"]
+
+# autodoc
+autodoc_mock_imports = ['amp_C', 'torchvision', 'flash_attn', 'apex']
+
+templates_path = ['_templates']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+intersphinx_mapping = {
+    'python': ('https://docs.python.org/3', None)
+}
+
+master_doc = 'index'
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = 'pydata_sphinx_theme'
+# html_theme = 'sphinx_rtd_theme'
+html_static_path = ['_static']

multilinguality_megatron/docs/guide/faq.md

@@ -0,0 +1,170 @@
+# Frequently Asked Questions
+
+## How to add special tokens?
+
+When defining a new task, it is often needed to introduce tokens with special meanings.
+For instance, let's say we want to add two tokens `[formula]` and `[/formula]` to indicate the start and end of a formula in mathematics textbooks.
+In order to include these new tokens, you need to indicate them in three different places:
+
+1. When tokenizing (`tools/preprocess_data.py`), using the flag `--vocab_extra_ids_list` with the new tokens:
+    ```
+    python tools/preprocess_data.py --vocab_extra_ids_list "[formula],[/formula]" # ...
+    ```
+
+1. When sharding the model (`tools/checkpoint_util.py`), using `--true_vocab_size`.
+   For instance, Falcon has 65024 tokens by default.
+   Including these two extra tokens will result in
+   ```
+   python tools/checkpoint_util.py --true_vocab_size 65026 # ...
+   ```
+
+1. When training (`finetune.py`) using `--vocab_extra_ids_list`.
+   Same as before:
+    ```
+    python finetune.py --vocab_extra_ids_list "[formula],[/formula]" # ...
+    ```
+
+(tp-pp)=
+## How to set TP and PP?
+
+General strategies:
+- It is recommended to use data parallelism as much as possible, only use model parallelism if the model cannot fit in the GPU or the micro batch size is very small.
+- It is preferable to use tensor parallelism before pipeline parallelism, when working on a single machine.
+- When a model does not fit in a single node, use a tensor parallelism level of as many GPUs each node has, and pipeline parallelism level as small as possible to allow the model to fit in memory, and maintain a micro batch size large enough (of at least 5).
+
+In the codebase, you won't set data parallelism explicitly.
+Rather, the data parallelism will be inferred automatically to be as high as possible, depending in your available hardware and TP, PP levels.
+In general, the number of GPUs you need is:
+```
+GPUs = DP * TP * PP
+```
+For instance, if you have two nodes with 8 GPUs each, TP=4 and PP=2, then DP will be automatically set to 2 as `4 x 2 x 2 = 16`.
+
+```{seealso}
+- For more information on data and model parallelism see: https://huggingface.co/docs/transformers/v4.15.0/parallelism.
+- Detailed information on how TP and PP works: https://arxiv.org/abs/2104.04473.
+```
+
+## How to launch training on multiple nodes?
+
+In order to launch training on multiple nodes, you will set the appropriate arguments to the `torchrun` program.
+
+1. Select a "master" or main node and take note of its IP address.
+1. Launch the `finetune.py` script in the main node using `torchrun` with the following arguments:
+   ```
+   torchrun --n_proc_per_node NUMBER_OF_GPS_PER_NODE \
+   	--nodes NUMBER_OF_NODES \
+   	--node_rank 0 \
+   	--master_addr ADDRESS_OF_THE_MAIN_NODE \
+   	--master_port PORT \
+   	finetune.py # ...
+   ```
+1. In the rest of the nodes, launch `finetune.py` with the same arguments, modifying `--node_rank` to a different value per node.
+
+```{seealso}
+- Take a look at the example script `examples/finetune.sh` for more information.
+- Look at the [How to set TP and PP?](#tp-pp) section for more information.
+```
+
+## What are the basic hardware requirements?
+
+In this section we give a brief overview on the minimal hardware requirements we observed during our experiments.
+
+| Model      | min VRAM | tp  | pp  |
+| :--------- | :------: | :-: | :-: |
+| LLaMa2-7B  | 2x 80GB  | 2   | 1   |
+| Falcon-40B | 16x 80GB | 8   | 2   |
+| LLaMa2-70B | 32x 80GB | 8   | 4   |
+
+
+(shard)=
+## How to shard and merge models?
+
+Use `tools/checkpoint_util.py` to set the desired tensor and pipeline parallelism levels.
+
+```
+python tools/checkpoint_util.py \
+	--target_tensor_parallel_size TP \
+	--target_pipeline_parallel_size PP \
+	--load_dir /path/to/original/weights/ \
+	--save_dir /path/to/new/weights/ \
+	--model_type MODEL \
+	--bf16
+```
+Where MODEL can be either llama, llama2, falcon, gpt or bert, and TP and PP are the model parallelism levels desired.
+Note that you can convert sharded weights (i.e. TP, PP > 1) to unsharded weights (TP = PP = 1) or viceversa.
+
+## What arguments are used to train LLaMa 2?
+
+We set the same hyperparamters specified by Meta during finetuning (see [their paper for more information](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/)).
+This means, that training LLaMa 2 7B can be done with the following arguments:
+
+```bash
+torchrun \
+	# torchrun arguments # \
+	--nproc_per_node <GPUs per node> \
+	--nnodes <number of nodes> \
+	--node_rank <0,1,2,etc a different number per node> \
+	--master_addr <address of main node> \
+	--master_port <port> \
+	finetune.py --model_name llama2 \
+	# hardware/distributed arguments # \
+	--tensor_model_parallel_size <tp size> \
+	--pipeline_model_parallel_size <pp>  \
+	--bf16 \
+	# training arguments # \
+	--train_iters <train iters> \
+	--adam_beta1 0.9 \
+	--adam_beta2 0.95 \
+	--adam_eps 1e-5 \
+	--lr_decay_style cosine 5 \
+	--lr_warmup_iters <warmup iters> \
+	--lr 3e-4 \
+	--min_lr 1e-6 \
+	--weight_decay 0.1 \
+	--micro_batch_size 5 \
+	--global_batch_size 1000 \
+	# additional optimization arguments # \
+	--use_flash_attn \
+	--sequence_parallel \
+	--recompute_granularity selective \
+	# logging/pathing arguments # \
+	--load <path to megatron-llama> \
+	--use_checkpoint_args \
+	--vocab_file <path to tokenizer.model provided by Meta> \
+	--log_interval 1 \
+	--data_path <path to tokenized data> \
+	--tokenizer_type SentencePieceTokenizer
+```
+
+```{seealso}
+The file `examples/finetune.sh` gives the full picture of the arguments used to train either LLaMa.
+```
+
+## How to convert a LLaMa or Falcon architecture from a non-official checkpoint?
+
+If you want to convert weights from a checkpoint other than the checkpoints provided by `llama-meta` or `tiiuae`, you might use `--model-path` during conversion.
+For instance, to convert the [OpenAssistant llama2 70B](https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10) weights, run:
+
+```
+python weights_conversion/hf_to_megatron.py llama2 --size=70 \
+	--out=/path/to/megatron/weights/ --cache-dir=/path/to/llama-2-7b/ \
+	--model-path=OpenAssistant/llama2-70b-oasst-sft-v10
+```
+
+The `--model-path` argument should be either a local folder or the name of a model hosted on huggingface.
+
+## I'm getting a `17300 Bus error (core dumped)` error!
+
+If you are using a docker container and you get this error when sharding a large model, you might need to increase the shared memory size.
+This is done via the command line option `--shm-size=128gb`.
+
+## I'm getting a `ImportError: cannot import name 'helpers' from 'megatron.data'` error!
+
+You need to compile the `helpers` module:
+
+```
+cd megatron/data
+make
+cd ../../
+```

multilinguality_megatron/docs/guide/getting_started.md

@@ -0,0 +1,276 @@
+# Getting started
+
+This tutorial will guide you on the basic usage of Megatrom-LLM.
+This guide we will fine tune a [LLaMa 2 7B](https://ai.meta.com/llama/) LLM on [code data](https://huggingface.co/datasets/bigcode/starcoderdata).
+It is recommended to have at least 160GB VRAM available (e.g. two 80GB A100 GPUs).
+
+```{note}
+This tutorial can also be followed to train a Falcon architecture, using `falcon` instead of `llama2` throughout the guide.
+```
+
+## Setup
+
+First we need to install the dependencies.
+
+
+1. Clone our repo:
+   ```
+   git clone git@github.com:epfLLM/Megatron-LLM.git
+   ```
+
+1. Run the [nvcr docker image](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch), mounting the source code to your desired path, e.g. `/mpt/Megatron-LLM`:
+   ```
+   sudo docker run --gpus all -it --rm \
+   	-v /path/to/Megatron-LLM/:/mpt/Megatron-LLM \
+   	nvcr.io/nvidia/pytorch:23.07-py3
+   ```
+
+1. Enter the repository:
+   ```
+   cd /mpt/Megatron-LLM/
+   ```
+
+1. Install the additional dependencies not included in the `nvcr` image:
+   ```
+   pip install -r requirements.txt
+   ```
+
+1. Install the `megatron/data/helpers` binary:
+   ```
+   cd megatron/data/
+   make
+   cd ../../
+   ```
+
+(download_weights)=
+## Downloading LLaMa2 weights
+
+1. Request access to the weights directly to meta: https://ai.meta.com/resources/models-and-libraries/llama-downloads/.
+1. Request access to the LLaMa2 huggingface model: https://huggingface.co/meta-llama/Llama-2-7b-hf.
+1. Create a new huggingface token (or use an existing one): https://huggingface.co/settings/tokens.
+1. Run the huggingface login CLI, and enter the token created on the previous step when asked:
+   ```
+   huggingface-cli login
+   ```
+
+## Preparing the raw data
+
+:::{note}
+
+This tutorial will use code data to fine tune the LLM.
+Feel free to use any other dataset, as long as the raw data is saved in `.jsonl` format, i.e. one `json` dictionary with the key `"text"` per line:
+
+```json
+{"text": "The blue cat is big."}
+{"text": "This is another document."}
+```
+
+In this case, skip to the [data preprocessing](#data-preprocessing) section.
+
+:::
+
+1. Accept starcoder's terms of use via the huggingface portal: https://huggingface.co/datasets/bigcode/starcoderdata
+1. Create a huggingface token (or use an existing one) and login using `huggingface-cli` (see [Downloading LLaMa2 weights](#download_weights) for more information).
+1. Download and save the starcoder dataset.
+   In this tutorial we will use the `julia` data, but feel free to use any other subset.
+   This data contains around 500M tokens.
+   ```python
+   import json
+   from datasets import load_dataset
+
+   # the `cache_dir` argument is optional
+   dataset = load_dataset("bigcode/starcoderdata", data_dir="julia",
+                          split="train", cache_dir="/path/to/cache/")
+   with open("/path/to/raw.jsonl", "w+") as f:
+       for document in dataset:
+           document = {"id": document["id"], "text": document["content"]}
+           f.write(json.dumps(document) + "\n")
+   ```
+
+At this point, the raw data will be available at `/path/to/raw.jsonl`.
+
+
+(data-preprocessing)=
+## Data preprocessing
+
+In this step we will tokenize the raw data to binary files for optimized data loading during training.
+Run:
+```
+python tools/preprocess_data.py --input=/path/to/raw.jsonl \
+	--output_prefix=/path/to/tokenized/starcoder \
+	--tokenizer_type=SentencePieceTokenizer \
+	--vocab_file=/path/to/tokenizer.model \
+	--chunk_size=32 \
+	--workers=16 \
+	--no_new_tokens
+```
+
+```{note}
+In this guide we use a sequence length of 1024 to accelerate training.
+Note that the official sequence length of LLaMa2 is 4096.
+```
+
+```{note}
+If you are using falcon, use `FalconTokenizer` instead of `SentencePieceTokenizer`, don't supply any `--vocab_file` and ignore the `--no_new_tokens` flag.
+```
+
+
+(weight-conversion)=
+## Weight conversion
+
+In order to use pretrained weights in the Megatron-LLM codebase, we will need to convert the official weights provided to be compatible with Megatron.
+To do so, run:
+```
+python weights_conversion/hf_to_megatron.py llama2 --size=7 \
+	--out=/path/to/megatron/weights/ --cache-dir=/path/to/llama-2-7b/
+```
+
+(correctness-verification)=
+## Correctness verification (optional)
+
+To make sure the weight conversion ran successfully we run the `verify_correctness.py` script.
+This will run simultaneously the official LLaMa 2 implementation and the Megatron codebase.
+Make sure to adjust the arguments to your convenience:
+```bash
+# arguments required by `torchrun`
+DISTRIBUTED_ARGS="--nproc_per_node 1 --nnodes 1 --node_rank 0 --master_addr localhost --master_port 8000"
+LLAMA_ARGS="--use_rms_norm --glu_activation swiglu --no_tie_embed_logits --no_new_tokens --layernorm_epsilon 1e-5"
+COMMON_ARGS="--hidden_dropout 0.0 --attention_dropout 0.0 --no_bias_gelu_fusion"
+torchrun $DISTRIBUTED_ARGS verify_correctness.py \
+	--model_name=llama2 \
+	--model_size=7 \
+	--load=/path/to/megatron/weights/ \
+	--data_path=/path/to/tokenized/starcoder \
+	--tokenizer_type=SentencePieceTokenizer \
+	--vocab_file=/path/to/megatron/weights/tokenizer.model \
+	--huggingface_cache=/path/to/meta/llama-2-7b/ \
+	--huggingface_device=cuda:1 \
+	$COMMON_ARGS $LLAMA_ARGS  # dont include LLAMA_ARGS if using Falcon
+```
+
+This script will compare the logits output of Megatron model and the official implementation.
+Expected outputs will yield average absolute error smaller than `0.01` when using 32-bit precision and `0.1` when using 16-bit precision.
+
+## Model sharding
+
+In order to use model parallelism you need to split the previously converted weights into multiple files, before you start training.
+To do this, use `tools/checkpoint_util.py`.
+Feel free to use different tensor parallel (tp) and pipeline (pp) sizes.
+```
+python tools/checkpoint_util.py \
+	--target_tensor_parallel_size 2 \
+	--target_pipeline_parallel_size 1 \
+	--load_dir /path/to/megatron/weights/ \
+	--save_dir /path/to/sharded/weights/ \
+	--model_type llama2 \
+	--true_vocab_size 32000 \
+	--bf16
+```
+
+Feel free to set `--target_tensor_parallel_size` to 4 if you have 4 or more GPUs available.
+
+## Training
+
+Use the `finetune.py`.
+Example usage:
+```bash
+LOG_ARGS="--log_interval 1 --save_interval 100 --eval_interval 50"
+TRAIN_ARGS="--train_iters 500 --lr_decay_style cosine --lr_warmup_iters 50 --lr 3e-4 --min_lr 1e-6"
+DISTRIBUTED_ARGS="--nproc_per_node NUMBER_OF_GPUS --nnodes 1 --node_rank 0 --master_addr localhost --master_port 8000"
+torchrun $DISTRIBUTED_ARGS finetune.py \
+	--tensor_model_parallel_size 4 \
+	--pipeline_model_parallel_size 1 \
+	--load /path/to/sharded/weights/ \
+	--save /path/to/sharded/weights/ \
+	--tensorboard_dir /path/to/sharded/weights/tensorboard/ \
+	--data_path /path/to/tokenized/starcoder \
+	--model_name llama2 \
+	--tokenizer_type SentencePieceTokenizer \
+	--vocab_file=/path/to/megatron/weights/tokenizer.model \
+	--bf16 \
+	--use_flash_attn \
+	--micro_batch_size 5 \
+	--global_batch_size 1000 \
+	--sequence_parallel \
+	--recompute_granularity selective \
+	--use_checkpoint_args \
+	$COMMON_ARGS $LOG_ARGS $TRAIN_ARGS $LLAMA_ARGS
+```
+
+With the selected global batch size of 1000, and the total number of training tokens around 500M, in 500 iterations the trainer will perform approximately one epoch.
+This will take approximately 20 hours to run on a 8x 80GB A100 cluster (DP=2, TP=4, PP=1).
+
+:::{note}
+
+To use distributed training make sure to set `nproc_per_node` to the number of GPUs per node, `nnodes` to the number of nodes in your training and `master_addr` to the addres of your master node in the `DISTRIBUTED_ARGS` variable.
+For instance, to train a two node cluster, with 8 GPUs each:
+```
+DISTRIBUTED_ARGS="--nproc_per_node 1 --nnodes 1 --node_rank 0 --master_addr localhost --master_port 8000"
+```
+
+Then, run the `finetune.py` script in all your nodes with the same parameters, just setting a different `node_rank` at every node.
+
+:::
+
+```{seealso}
+Take a look at `examples/finetune.sh for more information on the recommended hyperparameters
+```
+
+## Model Deployment
+
+After training, merge your distributed weights again into a single model:
+```
+python tools/checkpoint_util.py \
+	--target_tensor_parallel_size 1 \
+	--target_pipeline_parallel_size 1 \
+	--load_dir /path/to/sharded/weights/ \
+	--save_dir /path/to/unsharded/trained/weights/ \
+	--model_type llama2 \
+	--true_vocab_size 32000 \
+	--bf16
+```
+
+We provide a Megatron to Huggingface conversion utility for easier deployment: `weights_conversion/megatron_to_hf.py`.
+Run:
+```
+python weights_conversion/megatron_to_hf.py --input_dir=/path/to/unsharded/trained/weights/ \
+	--output_dir=/path/to/hf/weights/
+```
+
+Once the conversion is done, you can load the fine tuned weights using huggingface:
+```python
+import torch
+import transformers
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=LlamaForCausalLM.from_pretrained("/path/to/hf/weights/"),
+    tokenizer=LlamaTokenizer.from_pretrained("/path/to/hf/weights/"),
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+prompt = """#= a function that returns the fibonacci number of its argument =#
+function fibonacci(n::Int)::Int
+"""
+sequences = pipeline(prompt, max_new_tokens=100, do_sample=True, top_k=20,
+                     num_return_sequences=1)
+for sequence in sequences:
+    print(sequence["generated_text"])
+```
+
+Once you are happy with your model performance, you might publish it to the huggingface hub using the `tools/push_to_hub.py` utility:
+
+```
+python tools/push_to_hub.py /path/to/hf/weights --hf_repo_name=MyRepoName/MyAwesomeModel --auth_token=MyAuthToken
+```
+
+## What's next?
+
+1. Take a look at our example scripts to familiarize yourself with some other capabilities and hyperparameters used in the codebase, such as to train (pretrain or finetune) larger models:
+   - `examples/parallelize.sh`
+   - `examples/finetune.sh`
+   - `examples/verify.sh`
+1. See the [intruction finetuning](instruction_tuning) guide for more information on how to finetune a pretrained model to follow instructions.
+1. Take a look at our [FAQ](faq) section.
+1. See [Weights conversion](weights_conversion) for more information on the `hf_to_megatron.py` and `megatron_to_hf.py` scripts.

multilinguality_megatron/docs/guide/index.md

@@ -0,0 +1,10 @@
+# User guide
+
+```{toctree}
+
+getting_started
+instruction_tuning
+faq
+tokenization
+weights_conversion
+```

multilinguality_megatron/docs/guide/instruction_tuning.md

@@ -0,0 +1,92 @@
+# Instruction finetuning
+
+This tutorial will guide you through the basics of instruction finetuning using the Megatron-LLM codebase, using LLaMa 2 as the base network.
+See also the [getting started](getting_started) guide for information regarding installation of dependencies, pretraining, and weight preparation.
+Following said tutorial, you would be able to finetune a 7B model in this guide, but feel free to use a different size.
+In order to use Falcon, see the comments specified in the [getting started](getting_started) guide to learn more about the differences when using either model.
+
+## Preparing raw data
+
+The dataset used in this guide will be a subset of the [orca](https://huggingface.co/datasets/Open-Orca/OpenOrca) dataset, a general purpose instruction dataset.
+We choose to only include the chain of thought instructions from the orca dataset in order to shrink the size of the data.
+Feel free to use any other dataset, as long as the raw data is saved in `.jsonl` format, i.e. one `json` dictionary per line.
+The dictionaries must include at least two keys (one for the "instruction" and another one for the expected "answer"), plus an optional "system" key.
+In order to retrieve the CoT subset of the orca dataset, use the following code:
+
+```python
+import json
+
+from datasets import load_dataset
+
+# the `cache_dir` is optional
+dataset = load_dataset("Open-Orca/OpenOrca", cache_dir="/path/to/cache", split="train")
+with open("/path/to/raw/data.jsonl", "w+") as f:
+    for document in tqdm(dataset):
+        if document["id"].startswith("cot."):
+            f.write(json.dumps(document) + "\n")
+```
+
+## Data preprocessing
+
+In this step we will tokenize the raw data to binary files for optimized data loading during training.
+Run:
+```
+python instruct/preprocess_instruct_data.py \
+	--input=/path/to/raw/data.jsonl \
+	--output_prefix=/path/to/tokenized/orca \
+	--tokenizer_type=SentencePieceTokenizer \
+	--vocab_file=/path/to/llama/tokenizer.model \
+	--chunk_size=32 \
+	--workers=32 \
+	--vocab_extra_ids_list "<|im_start|>,<|im_end|>" \
+	--question_key=question \
+	--answer_key=response \
+	--system_key=system_prompt  # Optional
+```
+
+## Training
+
+At this point, you should come up with a Megatron checkpoint ready to be trained (i.e. sharded with the desired parallelism levels).
+Take a look at the [getting started](getting_started) guide to look how to transform LLaMa 2 checkpoints in the huggingface format to Megatron, and shard the weights.
+
+To start training, use the `finetune.py`.
+Example usage:
+```bash
+LOG_ARGS="--log_interval 1 --save_interval 100 --eval_interval 50"
+TRAIN_ARGS="--train_iters 6500 --lr_decay_style cosine --lr_warmup_iters 650 --lr 2e-5 --min_lr 2e-6"
+DISTRIBUTED_ARGS="--nproc_per_node NUMBER_OF_GPUS --nnodes 1 --node_rank 0 --master_addr localhost --master_port 8000"
+torchrun $DISTRIBUTED_ARGS finetune.py \
+	--tensor_model_parallel_size 4 \
+	--pipeline_model_parallel_size 1 \
+	--load /path/to/sharded/weights/ \
+	--save /path/to/sharded/weights/ \
+	--tensorboard_dir /path/to/sharded/weights/tensorboard/ \
+	--data_path /path/to/tokenized/orca \
+	--model_name llama2 \
+	--tokenizer_type SentencePieceTokenizer \
+	--vocab_file=/path/to/megatron/weights/tokenizer.model \
+	--bf16 \
+	--use_flash_attn \
+	--micro_batch_size 8 \
+	--global_batch_size 64 \
+	--sequence_parallel \
+	--recompute_granularity selective \
+	--use_checkpoint_args \
+	--data_type instruction \
+	--variable_seq_lengths \
+	--vocab_extra_ids_list "<|im_start|>,<|im_end|>" \
+	$COMMON_ARGS $LOG_ARGS $TRAIN_ARGS $LLAMA_ARGS
+```
+
+The arguments given for pretraining and instruction finetuning are very similar, with the key differences being the batch sizes, learning rates, and the inclusion of `--data_type instruction`, `--variable_seq_lengths` and `--vocab_extra_ids_list`.
+With the selected global batch size of 64, in 6500 iterations the trainer will perform approximately three epochs.
+This will take approximately 3h hours to run on a 8x 80GB A100 device (DP=2, TP=4, PP=1).
+
+```{note}
+If your `--load` checkpoint corresponds to a checkpoint already trained with the Megatron-LLM codebase (and not a checkpoint gotten after directly converting from the huggingface format for instance), you might want to define a `--save` directory that points somewhere else, to avoid overwritting previous checkpoints.
+You might also want to include the `--finetune` argument to ignore the previous optimizer and RNG states.
+```
+
+## Model Deployment
+
+Once the finetuning is over, you can follow the [getting started](getting_started) guide steps to unshard your weights and convert them to huggingface, in order to do specific evaluations and deployment.

multilinguality_megatron/docs/guide/tokenization.md

@@ -0,0 +1,76 @@
+# How to tokenize a dataset?
+
+## Step 1: get the right json format
+
+The training data requires preprocessing. First, place your training data in a loose json format, with one json containing a text sample per line. For example:
+<pre>
+{"src": "www.nvidia.com", "text": "The quick brown fox", "type": "Eng", "id": "0", "title": "First Part"}
+{"src": "The Internet", "text": "jumps over the lazy dog", "type": "Eng", "id": "42", "title": "Second Part"}
+</pre>
+
+The name of the `text` field of the json can be changed by using the `--json-key` flag in `preprocess_data.py`.
+The other metadata are optional and are not used in training.
+
+## Step 2: Tokenize 
+
+The loose json is then processed into a binary format for training. To convert the json into mmap, cached index file, or the lazy loader format use `preprocess_data.py`. Set the `--dataset_impl` flag to `mmap`, `cached`, or `lazy`, respectively (default is `mmap`). An example script to prepare data for Falcon training is:
+<pre>
+python3 tools/preprocess_data.py --input /scratch/dummy-data/train.json 
+    --output_prefix wiki-train 
+    --dataset_impl mmap 
+    --tokenizer_type FalconTokenizer 
+    --workers 2 
+    --chunk_size 32
+    --append_eod
+</pre>
+
+The output will be two files named, in this case, `my-bert_text_sentence.bin` and `my-bert_text_sentence.idx`. The `--data_path` specified in later BERT training is the full path and new filename, but without the file extension.
+
+Other options of `preprocess_data.py`:
+
+```
+input data:                                                                   
+  --input INPUT         Path to input JSON
+  --json_keys JSON_KEYS [JSON_KEYS ...]      
+                        space separate listed of keys to extract from json                                                                                   
+  --split_sentences     Split documents into sentences.                                                                                                      
+  --keep_newlines       Keep newlines between sentences when splitting.
+
+tokenizer:
+  --tokenizer_type {BertWordPieceLowerCase,BertWordPieceCase,GPT2BPETokenizer,SentencePieceTokenizer,FalconTokenizer}
+                        What type of tokenizer to use.
+  --vocab_file VOCAB_FILE
+                        Path to the vocab file
+  --merge_file MERGE_FILE
+                        Path to the BPE merge file (if necessary).
+  --append_eod          Append an <eod> token to the end of a document.
+  --lang LANG           Language to use for NLTK-powered sentence splitting.
+
+output data:
+  --output_prefix OUTPUT_PREFIX
+                        Path to binary output file without suffix
+  --dataset_impl {lazy,cached,mmap}
+
+runtime:
+  --workers WORKERS     Number of worker processes to launch
+  --chunk_size CHUNK_SIZE
+                        Chunk size assigned to each worker process
+  --log_interval LOG_INTERVAL
+                        Interval between progress updates
+  --vocab_extra_ids VOCAB_EXTRA_IDS
+  --vocab_extra_ids_list VOCAB_EXTRA_IDS_LIST
+                        comma separated list of special vocab ids to add to the tokenizer
+  --no_new_tokens       Whether to add special tokens (e.g. CLS, MASK, etc) in the sentenciepiece tokenizer or not
+```
+
+If you want to tokenize using llama tokenizer:
+```
+python tools/preprocess_data.py \
+        --input=/path/to/data.json \
+        --output_prefix=wiki-train \
+        --dataset_impl=mmap \
+        --tokenizer_type=SentencePieceTokenizer \
+        --vocab_file=/path/to/tokenizer.model \
+        --workers=2 \
+        --chunk_size=32
+```

multilinguality_megatron/docs/guide/weights_conversion.md

@@ -0,0 +1,87 @@
+# Weights conversion
+
+## Huggingface to megatron: `hf_to_megatron.py`
+
+Convert weights from models in other formats (primairly huggingface) to megatron checkpoints.
+
+This script supports converting Falcon, LLaMa and LLaMa 2 weights to megatron checkpoints.
+Depending on the model to convert, the inputs might differ.
+
+- **Falcon**:
+  Weights are automatically retrieved from the official implementation hosted in huggingface.
+  Thus, the `--cache-dir` argument is optional, if specified it should point to
+  the huggingface cache directory where the huggingface Falcon weights will be stored.
+  You will need to specify the `--size` argument to determine which version to download
+  (i.e. Falcon 7B or 40B).
+
+- **LLaMa**, **LLaMa 2** and **CodeLlama**:
+  Converting llama weights can be done either fetching the weights hosted
+  in huggingface (recommended as it is the easier method) or directly from the
+  weights provided by Meta.
+
+  - From Meta weights (only available for LLaMa and LLaMa 2):
+    You will need to specify the `--cache-dir` to the directory where the
+    llama weights are stored.
+    This will by default have the form `xB` (e.g. 7B or 70B) for llama v1,
+    or `llama-2-xb` (e.g. llama-2-7b) for llama v2.
+
+  - From huggingface weights:
+    If `--cache-dir` is not specified or the directory specified does not
+    contain the format expected from Meta weights, the converter will automatically
+    retrieve the weights from huggingface, in which case the `--cache-dir` will
+    have the same semantics as with Falcon.
+
+    Note that to download llama v2 weights from huggingface, you will need to
+    login using `huggingface-cli login` with a huggingface account which has been
+    granted access to the `meta-llama/Llama-2-7b-hf` model.
+        
+
+In all cases, the megatron checkpoint will be stored in the `--out` argument.
+If a huggingface is specified, the intermediate weights (i.e. the huggingface weights)
+stored therein will not be removed when the conversion succeeds.
+
+More information about the arguments:
+
+```
+positional arguments:
+  {llama2,falcon,codellama,llama}
+
+options:
+  -h, --help            show this help message and exit
+  --size {65,34,70,7,40,13,30}
+                        The size of the model
+  --out OUT             Directory to store the megatron weights (as checkpoint)
+  --cache-dir CACHE_DIR
+                        Directory to use as cache for the huggingface weights, or in case of the llama model, the path of the weights privided Meta
+```
+
+## Megatron to huggingface: `megatron_to_hf.py`
+
+Convert megatron checkpoints to huggingface weights.
+
+This script will also convert the tokenizer configured.
+Set the `--input_dir` to the megatron checkpoint root (i.e. where the
+`latest_checkpointed_iteration.txt` file is located) and  `--output_dir` to
+the directory where the huggingface weights should be stored.
+
+More information about the arguments:
+
+```
+options:
+  -h, --help            show this help message and exit
+  --input_dir INPUT_DIR
+                        Location of Megatron weights
+  --num_output_shards NUM_OUTPUT_SHARDS
+  --model {llama2,falcon,llama,codellama}
+  --output_dir OUTPUT_DIR
+                        Location to write HF model and tokenizer
+  --cache_dir CACHE_DIR
+                        Huggingface cache_dir (optional)
+  --vocab_file VOCAB_FILE
+                        Path to the vocab file
+  --vocab_extra_ids_list VOCAB_EXTRA_IDS_LIST
+                        comma separated list of special vocab ids to add to the tokenizer
+  --override_special_tokens [OVERRIDE_SPECIAL_TOKENS ...]
+                        One or more arguments to override special tokens. Syntax set as `key=value`, e.g. `eos=<|im_end|>`. Overrides available only bos,
+                        cls, eos, mask, pad, sep, unk.
+```

multilinguality_megatron/docs/index.rst

@@ -0,0 +1,75 @@
+Welcome to Megatron-LLM's documentation!
+========================================
+
+.. image:: imgs/llama-falcon.png
+
+The `Megatron-LLM <https://github.com/epfLLM/Megatron-LLM/>`_ library enables pre-training and fine-tuning of large language models (LLMs) at scale.
+Our repository is a modification of the `original Megatron-LM codebase <https://github.com/NVIDIA/Megatron-LM>`_ by Nvidia.
+
+Added key features include:
+
+- `LLaMa <https://arxiv.org/abs/2302.13971>`_, `LLaMa 2 <https://arxiv.org/abs/2307.09288>`_, `Falcon <https://huggingface.co/tiiuae>`_, and `Code Llama <https://together.ai/blog/llama-2-7b-32k>`_ support.
+- support training of large models (70B Llama 2, 65B Llama 1, 34B Code Llama, and 40B Falcon) on commodity hardware on multiple nodes
+- 3-way parallelism: tensor parallel, pipeline parallel and data parallel training (inherited from Megatron)
+- full pretraining, finetuning and instruct tuning support
+- Support for special tokens & tokenizers
+- grouped-query attention (GQA) and multi-query attention (MQA)
+- Rotary Position Embeddings (RoPE), RMS layer norm, Lima dropout
+- `ROPE scaling <https://together.ai/blog/llama-2-7b-32k>`_ for longer attention context support
+- FlashAttention 2
+- BF16 / FP16 training
+- WandB integration
+- Metrics support: Ease to add custom metrics to evaluate on the validation set while training
+- Conversion to and from Hugging Face hub
+
+Example models trained with `Megatron-LLM <https://github.com/epfLLM/Megatron-LLM/>`_: See `README <https://github.com/epfLLM/Megatron-LLM/>`_.
+
+User guide
+----------
+
+For information on installation and usage, take a look at our user guide.
+
+.. toctree::
+   :maxdepth: 2
+
+   guide/index
+
+
+API
+---
+
+Detailed information about Megatron-LLM components:
+
+.. toctree::
+   :maxdepth: 2
+
+   api/index
+
+
+
+
+Citation
+--------
+
+If you use this software please cite it:
+
+.. code-block:: bib
+
+   @software{epfmgtrn,
+     author       = {Alejandro Hernández Cano  and
+                     Matteo Pagliardini  and
+                     Andreas Köpf  and
+                     Kyle Matoba  and
+                     Amirkeivan Mohtashami  and
+                     Olivia Simin Fan  and
+                     Axel Marmet  and
+                     Deniz Bayazit  and
+                     Igor Krawczuk  and
+                     Zeming Chen  and
+                     Francesco Salvi  and
+                     Antoine Bosselut  and
+                     Martin Jaggi},
+     title        = {epfLLM Megatron-LM},
+     year         = 2023,
+     url          = {https://github.com/epfLLM/Megatron-LLM}
+   }

multilinguality_megatron/docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

multilinguality_megatron/docs/requirements.txt

@@ -0,0 +1,11 @@
+sphinx == 7.1.0
+pydata-sphinx-theme >= 0.13.0
+myst-parser >= 2.0.0
+flask >= 2.3.0
+flask_restful >= 0.3.0
+wandb >= 0.15.0
+torch >= 2.0.0
+regex >= 2023.6.0
+numpy >= 1.25
+pillow >= 10.0.0
+einops >= 0.6.1

multilinguality_megatron/ducttape/10B_all_cleaned.tconf

@@ -0,0 +1,80 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B/checkpoints
+    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/tokenizer.model 
+
+    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
+    
+    threshold=(TrainLanguage:
+                en=516
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(TrainLanguage:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+            )
+    
+    dataset_path=(TrainLanguage: 
+                en=/mnt/data_2/shared/tower_llm_data/en/data
+                es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz
+                de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz
+                fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz
+                nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz
+                pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz             
+                it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz
+                ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+                zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz
+                ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            )
+
+    mix="10 10 10 10 10 10 10 10 10 10"                 
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=10
+    save_interval=635
+    eval_interval=635
+    train_steps=6358
+    
+    lr_scheduler=cosine
+    warmup_steps=63
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_10b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198"
+    wikipedia=False
+    freeze_layers=""
+}

multilinguality_megatron/ducttape/10B_all_cleaned_13B.tconf

@@ -0,0 +1,80 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/llama2_13B_all
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/llama2_13B_all/checkpoints
+    model_path=/mnt/data/cache/models--meta-llama--Llama-2-13b-hf/snapshots/db6b8eb1feabb38985fdf785a89895959e944936
+    tokenizer_path=/mnt/data/cache/models--meta-llama--Llama-2-13b-hf/snapshots/db6b8eb1feabb38985fdf785a89895959e944936/tokenizer.model 
+
+    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
+    
+    threshold=(TrainLanguage:
+                en=516
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(TrainLanguage:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+            )
+    
+    dataset_path=(TrainLanguage: 
+                en=/mnt/data_2/shared/tower_llm_data/en/data
+                es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz
+                de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz
+                fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz
+                nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz
+                pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz             
+                it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz
+                ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+                zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz
+                ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            )
+
+    mix="10 10 10 10 10 10 10 10 10 10"                 
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=10
+    eval_interval=635
+    train_steps=10
+    
+    lr_scheduler=cosine 
+    warmup_steps=0
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4 5 6 7 8)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="test_llama_13B"
+    wikipedia=False
+    freeze_layers=""
+}

multilinguality_megatron/ducttape/10B_all_cleaned_extend32.tconf

@@ -0,0 +1,83 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B_extend32
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B_extend32/checkpoints
+    model_path=/mnt/data/bpop/multilinguality_tower/extended-models/llama-2-7b-hf-merged-multi-32k-meaninit
+    tokenizer_path=/mnt/data/bpop/multilinguality_tower/extended-models/llama-2-7b-hf-merged-multi-32k-meaninit/tokenizer.model 
+
+    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
+
+    posterior_tokens=False
+    n_posterior_tokens=False
+    
+    threshold=(TrainLanguage:
+                en=516
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    n_tokens=(TrainLanguage:
+                en=900000000
+                es=900000000
+                de=900000000
+                fr=900000000
+                nl=900000000
+                pt=900000000
+                it=900000000
+                ru=550000000
+                zh=20000000
+                ko=450000000
+            )
+    
+    dataset_path=(TrainLanguage: 
+                en=/mnt/data_2/shared/tower_llm_data/en/data
+                es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz
+                de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz
+                fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz
+                nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz
+                pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz             
+                it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz
+                ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+                zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz
+                ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            )
+
+    mix="10 10 10 10 10 10 10 10 10 10"                    
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=10
+    save_interval=635
+    eval_interval=635
+    train_steps=6358
+    
+    lr_scheduler=cosine
+    warmup_steps=63
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=52620
+    eval_iters=1
+
+    cpu_workers=16
+    wandb_run_id="NEW_llama2_7B_10b_extend32_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198"
+    wikipedia=False
+    freeze_layers=""
+}

multilinguality_megatron/ducttape/10B_all_cleaned_extend32_warmed_up.tconf

@@ -0,0 +1,84 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B_extend32
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B_extend32/warmed_up_checkpoints
+    # for warmed up models, the model path points to the sharded megatron checkpoint
+    model_path=/mnt/data/shared/multilingual_llm/experiments_megatron/warmup_embeddings_llama2_all_1B_extend32/checkpoints
+    tokenizer_path=/mnt/data/bpop/multilinguality_tower/extended-models/llama-2-7b-hf-merged-multi-32k-meaninit/tokenizer.model 
+
+    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
+
+    wikipedia=False
+    posterior_tokens=False
+    n_posterior_tokens=False
+    freeze_layers=""
+    
+    threshold=(TrainLanguage:
+                en=516
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    n_tokens=(TrainLanguage:
+                en=900000000
+                es=900000000
+                de=900000000
+                fr=900000000
+                nl=900000000
+                pt=900000000
+                it=900000000
+                ru=550000000
+                zh=20000000
+                ko=450000000
+            )
+    
+    dataset_path=(TrainLanguage: 
+                en=/mnt/data_2/shared/tower_llm_data/en/data
+                es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz
+                de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz
+                fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz
+                nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz
+                pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz             
+                it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz
+                ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+                zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz
+                ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            )
+
+    mix="10 10 10 10 10 10 10 10 10 10"                    
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=10
+    save_interval=127
+    eval_interval=635
+    train_steps=6358
+    eval_iters=1
+    
+    lr_scheduler=cosine
+    warmup_steps=63
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=52620
+
+    cpu_workers=16
+    wandb_run_id="NEW_warmed_up_llama2_7B_10b_extend32_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198"
+}

multilinguality_megatron/ducttape/10B_all_cleaned_extend32_warmup.tconf

@@ -0,0 +1,93 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/warmup_embeddings_llama2_all_1B_extend32/
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/warmup_embeddings_llama2_all_1B_extend32/checkpoints
+    model_path=/mnt/data/bpop/multilinguality_tower/extended-models/llama-2-7b-hf-merged-multi-32k-meaninit
+    tokenizer_path=/mnt/data/bpop/multilinguality_tower/extended-models/llama-2-7b-hf-merged-multi-32k-meaninit/tokenizer.model 
+
+    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
+    
+    threshold=(TrainLanguage:
+                en=516
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    posterior_tokens=True
+    n_tokens=(TrainLanguage:
+                en=900000000
+                es=900000000
+                de=900000000
+                fr=900000000
+                nl=900000000
+                pt=900000000
+                it=900000000
+                ru=550000000
+                zh=20000000
+                ko=450000000
+            )
+    n_posterior_tokens=(TrainLanguage:
+                en=180000000
+                es=180000000
+                de=180000000
+                fr=180000000
+                nl=180000000
+                pt=180000000
+                it=180000000
+                ru=100000000
+                zh=4000000
+                ko=90000000
+            )
+    
+    dataset_path=(TrainLanguage: 
+                en=/mnt/data_2/shared/tower_llm_data/en/data
+                es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz
+                de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz
+                fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz
+                nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz
+                pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz             
+                it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz
+                ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+                zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz
+                ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            )
+
+    mix="10 10 10 10 10 10 10 10 10 10"                    
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=5
+    save_interval=635
+    eval_interval=635
+    train_steps=635
+    eval_iters=0
+    
+    lr_scheduler=constant
+    warmup_steps=0
+    lr=3e-4
+    lr_min=3e-4
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=52620
+
+    cpu_workers=16
+    wandb_run_id="NEW_EMBEDDINGS_ONLY_llama2_7B_10b_extend32_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198"
+    wikipedia=False
+    freeze_layers="not_embeddings"
+}

multilinguality_megatron/ducttape/10B_all_wikipedia.tconf

@@ -0,0 +1,83 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/wikipedia_llama2_all_10B/
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/wikipedia_llama2_all_10B/checkpoints
+    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/tokenizer.model 
+
+    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
+    
+    threshold=(TrainLanguage:
+                en=516
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(TrainLanguage:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+            )
+    
+    dataset_path=(TrainLanguage: 
+                en=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/en
+                es=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/es
+                de=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/de
+                fr=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/fr
+                nl=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/nl
+                pt=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/pt             
+                it=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/it
+                ru=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/ru
+                zh=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/zh
+                ko=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/ko
+            )
+
+    mix="10 10 10 10 10 10 10 10 10 10"                 
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=10
+    save_interval=127
+    eval_interval=635
+    train_steps=6358
+    
+    lr_scheduler=cosine
+    warmup_steps=63
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=4
+    gpu_ids=0,1,2,3
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=24
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="WIKIPEDIA_llama2_7B_10b_base_vocab_uniform"
+    wikipedia=True
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=False
+    eval_iters=0
+}

multilinguality_megatron/ducttape/20B_all_cleaned_mc4.tconf

@@ -0,0 +1,113 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B/mc4_checkpoints
+    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/tokenizer.model 
+
+    dataset=(Dataset: en de fr es it nl pt ru zh ko)
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
+            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    datamix_weights=(
+        DataMix:
+            mc4_uniform=(
+                Dataset:
+                    en=100
+                    es=100
+                    de=100
+                    fr=100
+                    nl=100
+                    pt=100
+                    it=100
+                    ru=100
+                    zh=100
+                    ko=100
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+            )            
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_20b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_wiki_33"
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=False
+    eval_iters=1
+    is_parallel=False
+    lp=""
+}

multilinguality_megatron/ducttape/20B_all_cleaned_mc4_parallel.tconf

@@ -0,0 +1,264 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B/mc4_parallel_checkpoints
+    model_path=/mnt/data/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852
+    tokenizer_path=/mnt/data/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852/tokenizer.model 
+
+    dataset=(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)
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
+            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=670
+                    es=670
+                    de=670
+                    fr=670
+                    nl=670
+                    pt=670
+                    it=670
+                    ru=670
+                    zh=670
+                    ko=670
+                    en_de=183
+                    de_en=183 
+                    en_fr=183 
+                    fr_en=183 
+                    en_es=183 
+                    es_en=183 
+                    en_it=183 
+                    it_en=183 
+                    en_nl=183 
+                    nl_en=183 
+                    en_pt=183 
+                    pt_en=183 
+                    en_ru=183 
+                    ru_en=183 
+                    en_zh=183 
+                    zh_en=183 
+                    en_ko=183 
+                    ko_en=183 
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+    )   
+
+    lp=(Dataset:
+        en=""
+        es=""
+        de=""
+        fr=""
+        nl=""
+        pt=""
+        it=""
+        ru=""
+        zh=""
+        ko=""
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+    )   
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_20b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_parallel_33"
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+}

multilinguality_megatron/ducttape/20B_all_cleaned_mc4_parallel_13b.tconf

@@ -0,0 +1,264 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_13B_all_20B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_13B_all_20B/mc4_parallel_checkpoints
+    model_path=/mnt/data/cache/models--meta-llama--Llama-2-13b-hf/snapshots/dc1d3b3bfdb69df26f8fc966c16353274b138c55
+    tokenizer_path=/mnt/data/cache/models--meta-llama--Llama-2-13b-hf/snapshots/dc1d3b3bfdb69df26f8fc966c16353274b138c55/tokenizer.model 
+
+    dataset=(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)
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
+            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=670
+                    es=670
+                    de=670
+                    fr=670
+                    nl=670
+                    pt=670
+                    it=670
+                    ru=670
+                    zh=670
+                    ko=670
+                    en_de=183
+                    de_en=183 
+                    en_fr=183 
+                    fr_en=183 
+                    en_es=183 
+                    es_en=183 
+                    en_it=183 
+                    it_en=183 
+                    en_nl=183 
+                    nl_en=183 
+                    en_pt=183 
+                    pt_en=183 
+                    en_ru=183 
+                    ru_en=183 
+                    en_zh=183 
+                    zh_en=183 
+                    en_ko=183 
+                    ko_en=183 
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+    )   
+
+    lp=(Dataset:
+        en=""
+        es=""
+        de=""
+        fr=""
+        nl=""
+        pt=""
+        it=""
+        ru=""
+        zh=""
+        ko=""
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+    )   
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4 5 6 7 8)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_20b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_parallel_33"
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+}

multilinguality_megatron/ducttape/20B_all_cleaned_mc4_parallel_concat.tconf

@@ -0,0 +1,264 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B/mc4_parallel_concat_checkpoints
+    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852/tokenizer.model 
+
+    dataset=(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)
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
+            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=670
+                    es=670
+                    de=670
+                    fr=670
+                    nl=670
+                    pt=670
+                    it=670
+                    ru=670
+                    zh=670
+                    ko=670
+                    en_de=183
+                    de_en=183 
+                    en_fr=183 
+                    fr_en=183 
+                    en_es=183 
+                    es_en=183 
+                    en_it=183 
+                    it_en=183 
+                    en_nl=183 
+                    nl_en=183 
+                    en_pt=183 
+                    pt_en=183 
+                    en_ru=183 
+                    ru_en=183 
+                    en_zh=183 
+                    zh_en=183 
+                    en_ko=183 
+                    ko_en=183 
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+    )   
+
+    lp=(Dataset:
+        en=""
+        es=""
+        de=""
+        fr=""
+        nl=""
+        pt=""
+        it=""
+        ru=""
+        zh=""
+        ko=""
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+    )   
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_20b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_parallel_33"
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+}

multilinguality_megatron/ducttape/20B_all_cleaned_mc4_parallel_instructions.tconf

@@ -0,0 +1,271 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B_w_instructions
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B_w_instructions/mc4_parallel_towerblocksv0.1_checkpoints
+    model_path=/mnt/data/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852
+    tokenizer_path=/mnt/data/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852/tokenizer.model 
+
+    dataset=(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 tower_blocks)
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
+            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            tower_blocks="Unbabel/TowerBlocks-v0.1"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+            tower_blocks=True
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+                tower_blocks=100000
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=670
+                    es=670
+                    de=670
+                    fr=670
+                    nl=670
+                    pt=670
+                    it=670
+                    ru=670
+                    zh=670
+                    ko=670
+                    en_de=183
+                    de_en=183 
+                    en_fr=183 
+                    fr_en=183 
+                    en_es=183 
+                    es_en=183 
+                    en_it=183 
+                    it_en=183 
+                    en_nl=183 
+                    nl_en=183 
+                    en_pt=183 
+                    pt_en=183 
+                    en_ru=183 
+                    ru_en=183 
+                    en_zh=183 
+                    zh_en=183 
+                    en_ko=183 
+                    ko_en=183 
+                    tower_blocks=183
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+                tower_blocks=20000000
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+        tower_blocks=False
+    )   
+
+    lp=(Dataset:
+        en=""
+        es=""
+        de=""
+        fr=""
+        nl=""
+        pt=""
+        it=""
+        ru=""
+        zh=""
+        ko=""
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+        tower_blocks="oi"
+    )   
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_20b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_parallel_33"
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+}

multilinguality_megatron/ducttape/20B_all_cleaned_mc4_wiki.tconf

@@ -0,0 +1,176 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B/mc4_wiki_checkpoints
+    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/tokenizer.model 
+
+    dataset=(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)
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
+            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz 
+            en_wiki=""
+            es_wiki=""
+            de_wiki=""
+            fr_wiki=""
+            nl_wiki=""
+            pt_wiki=""
+            it_wiki=""
+            ru_wiki=""
+            zh_wiki=""
+            ko_wiki=""
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_wiki=False
+            es_wiki=False
+            de_wiki=False
+            fr_wiki=False
+            nl_wiki=False
+            pt_wiki=False
+            it_wiki=False
+            ru_wiki=False
+            zh_wiki=False
+            ko_wiki=False
+        )
+
+    threshold=(Dataset:
+                en=516 en_wiki=""        
+                es=275 es_wiki=""
+                de=611 de_wiki=""
+                fr=322 fr_wiki=""
+                nl=649 nl_wiki=""
+                pt=257 pt_wiki=""
+                it=332 it_wiki=""
+                ru=334 ru_wiki=""
+                zh=2041 zh_wiki=""
+                ko=198 ko_wiki=""
+            )
+
+    datamix_weights=(
+        DataMix: 
+            mc4_wiki_uniform=(
+                Dataset:
+                    en=67
+                    es=67
+                    de=67
+                    fr=67
+                    nl=67
+                    pt=67
+                    it=67
+                    ru=67
+                    zh=67
+                    ko=67
+                    en_wiki=33
+                    es_wiki=33
+                    de_wiki=33
+                    fr_wiki=33
+                    nl_wiki=33
+                    pt_wiki=33
+                    it_wiki=33
+                    ru_wiki=33
+                    zh_wiki=33
+                    ko_wiki=33
+            )
+            mc4_uniform=(
+                Dataset:
+                    en=100
+                    es=100
+                    de=100
+                    fr=100
+                    nl=100
+                    pt=100
+                    it=100
+                    ru=100
+                    zh=100
+                    ko=100
+                    en_wiki=0
+                    es_wiki=0
+                    de_wiki=0
+                    fr_wiki=0
+                    nl_wiki=0
+                    pt_wiki=0
+                    it_wiki=0
+                    ru_wiki=0
+                    zh_wiki=0
+                    ko_wiki=0
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_wiki=""
+                es_wiki=""
+                de_wiki=""
+                fr_wiki=""
+                nl_wiki=""
+                pt_wiki=""
+                it_wiki=""
+                ru_wiki=""
+                zh_wiki=""
+                ko_wiki=""
+            )            
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_20b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_wiki_33"
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=False
+    eval_iters=1
+    is_parallel=False
+    lp=""
+}

multilinguality_megatron/ducttape/20B_all_cleaned_parallel.tconf

@@ -0,0 +1,194 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B/parallel_checkpoints
+    model_path=/mnt/data/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852
+    tokenizer_path=/mnt/data/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852/tokenizer.model 
+
+    dataset=(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)
+
+    dataset_path=(Dataset:
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+        )
+    
+    is_hf_dataset=(Dataset:
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+        )
+
+    threshold=(Dataset:
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en_de=1
+                    de_en=1
+                    en_fr=1
+                    fr_en=1
+                    en_es=1
+                    es_en=1
+                    en_it=1
+                    it_en=1
+                    en_nl=1
+                    nl_en=1
+                    en_pt=1
+                    pt_en=1
+                    en_ru=1
+                    ru_en=1
+                    en_zh=1
+                    zh_en=1
+                    en_ko=1
+                    ko_en=1
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+            )
+
+    is_parallel=(Dataset:
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+    )   
+
+    lp=(Dataset:
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+    )   
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_20b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_parallel_33"
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+}

multilinguality_megatron/ducttape/20B_all_dirty_mc4.tconf

@@ -0,0 +1,124 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B/dirty_mc4_checkpoints
+    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852/tokenizer.model 
+
+    dataset=(Dataset: en de fr es it nl pt ru zh ko)
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/pre-training/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/pre-training/tower_llm_data/es/0/0000.json.gz 
+            de=/mnt/data_2/shared/pre-training/tower_llm_data/de/0/0000.json.gz 
+            fr=/mnt/data_2/shared/pre-training/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/pre-training/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/pre-training/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/pre-training/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/pre-training/tower_llm_data/ru/0/0000.json.gz
+            zh=/mnt/data_2/shared/pre-training/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/pre-training/tower_llm_data/ko/0000.json.gz
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+        )
+
+    threshold=(Dataset:
+                en=10000000     
+                es=10000000
+                de=10000000
+                fr=10000000
+                nl=10000000
+                pt=10000000
+                it=10000000
+                ru=10000000
+                zh=10000000
+                ko=10000000
+            )
+
+    datamix_weights=(
+        DataMix:
+            mc4_uniform=(
+                Dataset:
+                    en=100
+                    es=100
+                    de=100
+                    fr=100
+                    nl=100
+                    pt=100
+                    it=100
+                    ru=100
+                    zh=100
+                    ko=100
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=1388888800
+                ko=250000000
+            )            
+
+    min_perplexity=0
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_20b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_wiki_33"
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+    is_parallel=False
+    lp=(Dataset:
+        en="en"
+        es="es"
+        de="de"
+        fr="fr"
+        nl="nl"
+        pt="pt"
+        it="it"
+        ru="ru"
+        zh="zh"
+        ko="ko"
+    )
+}

multilinguality_megatron/ducttape/40B_all_cleaned_mc4_parallel.tconf

@@ -0,0 +1,305 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_40B
+    repo=/mnt/data/pmartins/code/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_40B/mc4_parallel_checkpoints
+    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/tokenizer.model 
+
+    dataset=(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)
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/tower_llm_data/es/0/0000.json.gz 
+            de=/mnt/data_2/shared/tower_llm_data/de/0/0000.json.gz 
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/tower_llm_data/ru/0/0000.json.gz
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            pl=/mnt/data_2/shared/tower_llm_data/pl/0000.json.gz
+            sv=/mnt/data_2/shared/tower_llm_data/sv/0000.json.gz
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            en_pl="/mnt/data_2/shared/tower_llm_data/bilingual_data/en-pl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pl_en="/mnt/data_2/shared/tower_llm_data/bilingual_data/en-pl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75/"
+            en_sv="/mnt/data_2/shared/tower_llm_data/bilingual_data/en-sv/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75/" 
+            sv_en="/mnt/data_2/shared/tower_llm_data/bilingual_data/en-sv/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75/"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            pl=False
+            sv=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+            en_pl=False
+            pl_en=False
+            en_sv=False
+            sv_en=False
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                pl=261
+                sv=699
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+                en_pl=100000
+                pl_en=100000
+                en_sv=100000
+                sv_en=100000
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=670
+                    es=670
+                    de=670
+                    fr=670
+                    nl=670
+                    pt=670
+                    it=670
+                    ru=670
+                    zh=670
+                    ko=670
+                    pl=0
+                    sv=0
+                    en_de=183
+                    de_en=183 
+                    en_fr=183 
+                    fr_en=183 
+                    en_es=183 
+                    es_en=183 
+                    en_it=183 
+                    it_en=183 
+                    en_nl=183 
+                    nl_en=183 
+                    en_pt=183 
+                    pt_en=183 
+                    en_ru=183 
+                    ru_en=183 
+                    en_zh=183 
+                    zh_en=183 
+                    en_ko=183 
+                    ko_en=183 
+                    en_pl=0
+                    pl_en=0
+                    en_sv=0
+                    sv_en=0
+            )
+        )
+
+    n_tokens=(Dataset:
+                en=4000000000
+                es=4000000000
+                de=4000000000
+                fr=4000000000
+                nl=4000000000
+                pt=4000000000
+                it=4000000000
+                ru=4000000000
+                zh=10000000000
+                ko=4000000000
+                pl=4000000000
+                sv=4000000000
+                en_de=200000000
+                de_en=200000000
+                en_fr=200000000 
+                fr_en=200000000 
+                en_es=200000000 
+                es_en=200000000
+                en_it=200000000 
+                it_en=200000000 
+                en_nl=200000000 
+                nl_en=200000000 
+                en_pt=200000000 
+                pt_en=200000000 
+                en_ru=200000000 
+                ru_en=200000000 
+                en_zh=200000000
+                zh_en=200000000 
+                en_ko=200000000
+                ko_en=200000000
+                en_pl=200000000
+                pl_en=200000000
+                en_sv=200000000
+                sv_en=200000000
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        pl=False
+        sv=False
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+        en_pl=True
+        pl_en=True
+        en_sv=True
+        sv_en=True
+    )   
+
+    lp=(Dataset:
+        en="en"
+        es="es"
+        de="de"
+        fr="fr"
+        nl="nl"
+        pt="pt"
+        it="it"
+        ru="ru"
+        zh="zh"
+        ko="ko"
+        pl="pl"
+        sv="sv"
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+        en_pl="en-pl"
+        pl_en="pl-en"
+        en_sv="en-sv"
+        sv_en="sv-en"
+    )   
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="llama2_7B_40b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_parallel_33"
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+}

multilinguality_megatron/ducttape/continue_pretraining.tconf

@@ -0,0 +1,77 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B_test
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B_test/checkpoints
+    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/tokenizer.model 
+
+    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
+    
+    threshold=(TrainLanguage:
+                en=516
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    # less for zh (inefficient tokenizer)
+    n_tokens=(TrainLanguage:
+                en=250000000
+                es=83333333
+                de=83333333
+                fr=83333333
+                nl=83333333
+                pt=83333333
+                it=83333333
+                ru=83333333
+                zh=8333333
+                ko=83333333
+            )
+    
+    dataset_path=(TrainLanguage: 
+                en=/mnt/data_2/shared/tower_llm_data/en/data
+                es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz
+                de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz
+                fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz
+                nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz
+                pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz             
+                it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz
+                ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+                zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz
+                ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            )
+
+    mix="10 10 10 10 10 10 10 10 10 10"                    
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=10
+    save_interval=318
+    eval_interval=158
+    train_steps=1272
+    
+    lr_scheduler=cosine
+    warmup_steps=13
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=6
+
+    cpu_workers=16
+
+}

multilinguality_megatron/ducttape/data_test.tconf

@@ -0,0 +1,79 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/wikipedia_llama2_all_10B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B/checkpoints
+    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/tokenizer.model 
+
+    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
+    
+    threshold=(TrainLanguage:
+                en=516
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(TrainLanguage:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+            )
+    
+    dataset_path=(TrainLanguage: 
+                en=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/en
+                es=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/es
+                de=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/de
+                fr=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/fr
+                nl=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/nl
+                pt=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/pt             
+                it=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/it
+                ru=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/ru
+                zh=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/zh
+                ko=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/ko
+            )
+
+    mix="10 10 10 10 10 10 10 10 10 10"                 
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=10
+    save_interval=635
+    eval_interval=635
+    train_steps=6358
+    
+    lr_scheduler=cosine
+    warmup_steps=63
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=32000
+
+    cpu_workers=16
+    wandb_run_id="wikipedia"
+    wikipedia=True
+}

multilinguality_megatron/ducttape/data_test_extend32.tconf

@@ -0,0 +1,79 @@
+global {
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/wikipedia_llama2_all_10B_extend32
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B_extend32/checkpoints
+    model_path=/mnt/data/bpop/multilinguality_tower/extended-models/llama-2-7b-hf-merged-multi-32k-meaninit
+    tokenizer_path=/mnt/data/bpop/multilinguality_tower/extended-models/llama-2-7b-hf-merged-multi-32k-meaninit/tokenizer.model 
+
+    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
+    
+    threshold=(TrainLanguage:
+                en=516
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+            )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(TrainLanguage:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+            )
+    
+    dataset_path=(TrainLanguage: 
+                en=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/en
+                es=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/es
+                de=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/de
+                fr=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/fr
+                nl=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/nl
+                pt=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/pt             
+                it=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/it
+                ru=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/ru
+                zh=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/zh
+                ko=/mnt/data/shared/multilingual_llm/tower_llm_wikipedia/ko
+            )
+
+    mix="10 10 10 10 10 10 10 10 10 10"                 
+
+    min_perplexity=50
+
+    size=(Size: 7 13)
+
+    log_interval=10
+    save_interval=635
+    eval_interval=635
+    train_steps=6358
+    
+    lr_scheduler=cosine
+    warmup_steps=63
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=52672
+
+    cpu_workers=16
+    wandb_run_id="wikipedia_extend32"
+    wikipedia=True
+}

multilinguality_megatron/ducttape/gemma_2B_20B_all_cleaned_mc4_parallel.tconf

@@ -0,0 +1,280 @@
+global {
+    model_type="gemma"
+    ducttape_output=/mnt/data_2/shared/experiments_megatron/continue_pretraining_gemma_7B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data_2/shared/experiments_megatron/continue_pretraining_gemma_7B/test
+    external_model_dir_annealing=/mnt/data_2/shared/experiments_megatron/continue_pretraining_gemma_7B/test
+    model_path=/mnt/data_2/cache/models--google--gemma-2b/snapshots/9d067f00def958594aaa16b39a65b07d69ca655b/
+    tokenizer_path=/mnt/data_2/cache/models--google--gemma-2b/snapshots/9d067f00def958594aaa16b39a65b07d69ca655b
+
+    tokenizer_type=PretrainedFromHF
+
+    dataset=(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)
+    datamix_weights_annealing=""
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/pre-training/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/pre-training/tower_llm_data/es/3/0000.json.gz 
+            de=/mnt/data_2/shared/pre-training/tower_llm_data/de/2/0000.json.gz 
+            fr=/mnt/data_2/shared/pre-training/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/pre-training/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/pre-training/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/pre-training/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/pre-training/tower_llm_data/ru/6/0000.json.gz
+            zh=/mnt/data_2/shared/pre-training/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/pre-training/tower_llm_data/ko/0000.json.gz
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=670
+                    es=670
+                    de=670
+                    fr=670
+                    nl=670
+                    pt=670
+                    it=670
+                    ru=670
+                    zh=670
+                    ko=670
+                    en_de=183
+                    de_en=183 
+                    en_fr=183 
+                    fr_en=183 
+                    en_es=183 
+                    es_en=183 
+                    en_it=183 
+                    it_en=183 
+                    en_nl=183 
+                    nl_en=183 
+                    en_pt=183 
+                    pt_en=183 
+                    en_ru=183 
+                    ru_en=183 
+                    en_zh=183 
+                    zh_en=183 
+                    en_ko=183 
+                    ko_en=183 
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+    )   
+
+    lp=(Dataset:
+        en="none"
+        es="none"
+        de="none"
+        fr="none"
+        nl="none"
+        pt="none"
+        it="none"
+        ru="none"
+        zh="none"
+        ko="none"
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+    )   
+
+    min_perplexity=0
+
+    size=(Size: 2 7)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    train_steps_annealing=0
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    lr_scheduler_annealing=linear
+    warmup_steps_annealing=0
+    lr_annealing=3e-5
+    lr_min_annealing=3e-6
+
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4 5 6 7 8)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=2
+    grad_accum_steps=48
+    vocab_size=256000
+
+    cpu_workers=16
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+
+    glu_activation=geglu
+    kv_channels=256
+    layernorm_epsilon=1e-6
+
+    seq_length=2048
+}

multilinguality_megatron/ducttape/gemma_2b_flavio.tconf

@@ -0,0 +1,546 @@
+global {
+    model_type="gemma"
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_gemma_2_20B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_gemma_2_20B/flavio_checkpoints
+    external_model_dir_annealing=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_gemma_2_20B/flavio_checkpoints_annealed
+    model_path=/mnt/data_2/cache/models--google--gemma-2b/snapshots/9d067f00def958594aaa16b39a65b07d69ca655b/
+    tokenizer_path=/mnt/data_2/cache/models--google--gemma-2b/snapshots/9d067f00def958594aaa16b39a65b07d69ca655b
+
+    tokenizer_type=PretrainedFromHF
+
+    dataset=(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 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_synth es_synth de_synth fr_synth nl_synth pt_synth it_synth ru_synth zh_synth ko_synth instructions)
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data
+            en_synth=""
+            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
+            es_synth=""
+            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
+            de_synth=""
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            fr_synth=""
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            nl_synth=""
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz   
+            pt_synth=""           
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            it_synth=""
+            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+            ru_synth=""
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            zh_synth=""
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            ko_synth=""
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            instructions="oi"
+            en_de_pre_annealing="oi"
+            de_en_pre_annealing="oi"
+            en_fr_pre_annealing="oi"
+            fr_en_pre_annealing="oi"
+            en_es_pre_annealing="oi"
+            es_en_pre_annealing="oi"
+            en_it_pre_annealing="oi"
+            it_en_pre_annealing="oi"
+            en_nl_pre_annealing="oi"
+            nl_en_pre_annealing="oi"
+            en_pt_pre_annealing="oi"
+            pt_en_pre_annealing="oi"
+            en_ru_pre_annealing="oi"
+            ru_en_pre_annealing="oi"
+            en_zh_pre_annealing="oi"
+            zh_en_pre_annealing="oi"
+            en_ko_pre_annealing="oi"
+            ko_en_pre_annealing="oi"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+            en_synth=False
+            es_synth=False
+            de_synth=False
+            fr_synth=False
+            nl_synth=False
+            pt_synth=False
+            it_synth=False
+            ru_synth=False
+            zh_synth=False
+            ko_synth=False
+            instructions="oi"
+            en_de_pre_annealing="oi"
+            de_en_pre_annealing="oi"
+            en_fr_pre_annealing="oi"
+            fr_en_pre_annealing="oi"
+            en_es_pre_annealing="oi"
+            es_en_pre_annealing="oi"
+            en_it_pre_annealing="oi"
+            it_en_pre_annealing="oi"
+            en_nl_pre_annealing="oi"
+            nl_en_pre_annealing="oi"
+            en_pt_pre_annealing="oi"
+            pt_en_pre_annealing="oi"
+            en_ru_pre_annealing="oi"
+            ru_en_pre_annealing="oi"
+            en_zh_pre_annealing="oi"
+            zh_en_pre_annealing="oi"
+            en_ko_pre_annealing="oi"
+            ko_en_pre_annealing="oi"
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+                en_synth=100000
+                es_synth=100000
+                de_synth=100000
+                fr_synth=100000
+                nl_synth=100000
+                pt_synth=100000
+                it_synth=100000
+                ru_synth=100000
+                zh_synth=100000
+                ko_synth=100000
+                instructions="oi"
+                en_de_pre_annealing="oi"
+                de_en_pre_annealing="oi"
+                en_fr_pre_annealing="oi"
+                fr_en_pre_annealing="oi"
+                en_es_pre_annealing="oi"
+                es_en_pre_annealing="oi"
+                en_it_pre_annealing="oi"
+                it_en_pre_annealing="oi"
+                en_nl_pre_annealing="oi"
+                nl_en_pre_annealing="oi"
+                en_pt_pre_annealing="oi"
+                pt_en_pre_annealing="oi"
+                en_ru_pre_annealing="oi"
+                ru_en_pre_annealing="oi"
+                en_zh_pre_annealing="oi"
+                zh_en_pre_annealing="oi"
+                en_ko_pre_annealing="oi"
+                ko_en_pre_annealing="oi"
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=603
+                    es=603
+                    de=603
+                    fr=603
+                    nl=603
+                    pt=603
+                    it=603
+                    ru=603
+                    zh=603
+                    ko=603
+                    en_de=0
+                    de_en=0 
+                    en_fr=0 
+                    fr_en=0 
+                    en_es=0 
+                    es_en=0 
+                    en_it=0 
+                    it_en=0 
+                    en_nl=0 
+                    nl_en=0 
+                    en_pt=0 
+                    pt_en=0 
+                    en_ru=0 
+                    ru_en=0 
+                    en_zh=0 
+                    zh_en=0 
+                    en_ko=0 
+                    ko_en=0 
+                    en_synth=67
+                    es_synth=67
+                    de_synth=67
+                    fr_synth=67
+                    nl_synth=67
+                    pt_synth=67
+                    it_synth=67
+                    ru_synth=67
+                    zh_synth=67
+                    ko_synth=67
+                    instructions=0
+                    en_de_pre_annealing=183
+                    de_en_pre_annealing=183
+                    en_fr_pre_annealing=183
+                    fr_en_pre_annealing=183
+                    en_es_pre_annealing=183
+                    es_en_pre_annealing=183
+                    en_it_pre_annealing=183
+                    it_en_pre_annealing=183
+                    en_nl_pre_annealing=183
+                    nl_en_pre_annealing=183
+                    en_pt_pre_annealing=183
+                    pt_en_pre_annealing=183
+                    en_ru_pre_annealing=183
+                    ru_en_pre_annealing=183
+                    en_zh_pre_annealing=183
+                    zh_en_pre_annealing=183
+                    en_ko_pre_annealing=183
+                    ko_en_pre_annealing=183
+            )
+        )
+
+    datamix_weights_annealing=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=0
+                    es=0
+                    de=0
+                    fr=0
+                    nl=0
+                    pt=0
+                    it=0
+                    ru=0
+                    zh=0
+                    ko=0
+                    en_de=833
+                    de_en=833
+                    en_fr=833 
+                    fr_en=833 
+                    en_es=833 
+                    es_en=833 
+                    en_it=833 
+                    it_en=833 
+                    en_nl=833 
+                    nl_en=833 
+                    en_pt=833 
+                    pt_en=833 
+                    en_ru=833 
+                    ru_en=833 
+                    en_zh=833 
+                    zh_en=833 
+                    en_ko=833 
+                    ko_en=833
+                    en_synth=0
+                    es_synth=0
+                    de_synth=0
+                    fr_synth=0
+                    nl_synth=0
+                    pt_synth=0
+                    it_synth=0
+                    ru_synth=0
+                    zh_synth=0
+                    ko_synth=0
+                    instructions=85000
+                    en_de_pre_annealing=0
+                    de_en_pre_annealing=0
+                    en_fr_pre_annealing=0
+                    fr_en_pre_annealing=0
+                    en_es_pre_annealing=0
+                    es_en_pre_annealing=0
+                    en_it_pre_annealing=0
+                    it_en_pre_annealing=0
+                    en_nl_pre_annealing=0
+                    nl_en_pre_annealing=0
+                    en_pt_pre_annealing=0
+                    pt_en_pre_annealing=0
+                    en_ru_pre_annealing=0
+                    ru_en_pre_annealing=0
+                    en_zh_pre_annealing=0
+                    zh_en_pre_annealing=0
+                    en_ko_pre_annealing=0
+                    ko_en_pre_annealing=0
+            )
+        )
+
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+                en_synth=20000000
+                es_synth=20000000
+                de_synth=20000000
+                fr_synth=20000000
+                nl_synth=20000000
+                pt_synth=20000000
+                it_synth=20000000
+                ru_synth=20000000
+                zh_synth=20000000
+                ko_synth=20000000
+                instructions="oi"
+                en_de_pre_annealing="oi"
+                de_en_pre_annealing="oi"
+                en_fr_pre_annealing="oi"
+                fr_en_pre_annealing="oi"
+                en_es_pre_annealing="oi"
+                es_en_pre_annealing="oi"
+                en_it_pre_annealing="oi"
+                it_en_pre_annealing="oi"
+                en_nl_pre_annealing="oi"
+                nl_en_pre_annealing="oi"
+                en_pt_pre_annealing="oi"
+                pt_en_pre_annealing="oi"
+                en_ru_pre_annealing="oi"
+                ru_en_pre_annealing="oi"
+                en_zh_pre_annealing="oi"
+                zh_en_pre_annealing="oi"
+                en_ko_pre_annealing="oi"
+                ko_en_pre_annealing="oi"
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+        en_synth=False
+        es_synth=False
+        de_synth=False
+        fr_synth=False
+        nl_synth=False
+        pt_synth=False
+        it_synth=False
+        ru_synth=False
+        zh_synth=False
+        ko_synth=False
+        instructions="oi"
+        en_de_pre_annealing="oi"
+        de_en_pre_annealing="oi"
+        en_fr_pre_annealing="oi"
+        fr_en_pre_annealing="oi"
+        en_es_pre_annealing="oi"
+        es_en_pre_annealing="oi"
+        en_it_pre_annealing="oi"
+        it_en_pre_annealing="oi"
+        en_nl_pre_annealing="oi"
+        nl_en_pre_annealing="oi"
+        en_pt_pre_annealing="oi"
+        pt_en_pre_annealing="oi"
+        en_ru_pre_annealing="oi"
+        ru_en_pre_annealing="oi"
+        en_zh_pre_annealing="oi"
+        zh_en_pre_annealing="oi"
+        en_ko_pre_annealing="oi"
+        ko_en_pre_annealing="oi"
+    )   
+
+    lp=(Dataset:
+        en=""
+        es=""
+        de=""
+        fr=""
+        nl=""
+        pt=""
+        it=""
+        ru=""
+        zh=""
+        ko=""
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+        en_synth=""
+        es_synth=""
+        de_synth=""
+        fr_synth=""
+        nl_synth=""
+        pt_synth=""
+        it_synth=""
+        ru_synth=""
+        zh_synth=""
+        ko_synth=""
+        instructions="oi"
+        en_de_pre_annealing="oi"
+        de_en_pre_annealing="oi"
+        en_fr_pre_annealing="oi"
+        fr_en_pre_annealing="oi"
+        en_es_pre_annealing="oi"
+        es_en_pre_annealing="oi"
+        en_it_pre_annealing="oi"
+        it_en_pre_annealing="oi"
+        en_nl_pre_annealing="oi"
+        nl_en_pre_annealing="oi"
+        en_pt_pre_annealing="oi"
+        pt_en_pre_annealing="oi"
+        en_ru_pre_annealing="oi"
+        ru_en_pre_annealing="oi"
+        en_zh_pre_annealing="oi"
+        zh_en_pre_annealing="oi"
+        en_ko_pre_annealing="oi"
+        ko_en_pre_annealing="oi"
+    )   
+
+    min_perplexity=0
+
+    size=(Size: 2)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=11430
+    train_steps_annealing=1270
+    
+    lr_scheduler=constant
+    warmup_steps=32
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    lr_scheduler_annealing=linear
+    warmup_steps_annealing=0
+    lr_annealing=3e-5
+    lr_min_annealing=3e-6
+
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4 5 6 7 8)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=24
+    grad_accum_steps=4
+    vocab_size=256000
+
+    cpu_workers=16
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+
+    glu_activation=geglu
+    kv_channels=256
+    layernorm_epsilon=1e-6
+
+    seq_length=2048
+}

multilinguality_megatron/ducttape/gemma_7B_20B_all_cleaned_mc4_parallel.tconf

@@ -0,0 +1,280 @@
+global {
+    model_type="gemma"
+    ducttape_output=/mnt/data_2/shared/experiments_megatron/continue_pretraining_gemma_7B
+    repo=/mnt/data/jpombal/multilinguality_megatron
+
+    external_model_dir=/mnt/data_2/shared/experiments_megatron/continue_pretraining_gemma_7B/old_recipe_checkpoints
+    external_model_dir_annealing=/mnt/data_2/shared/experiments_megatron/continue_pretraining_gemma_7B/old_recipe_checkpoints
+    model_path=/mnt/data_2/cache/models--google--gemma-7b/snapshots/bc0790ce8e02c6b2240e2b94bf01fb0453dc90f6
+    tokenizer_path=/mnt/data_2/cache/models--google--gemma-7b/snapshots/bc0790ce8e02c6b2240e2b94bf01fb0453dc90f6
+
+    tokenizer_type=PretrainedFromHF
+
+    dataset=(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)
+    datamix_weights_annealing=""
+
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/pre-training/tower_llm_data/en/data 
+            es=/mnt/data_2/shared/pre-training/tower_llm_data/es/3/0000.json.gz 
+            de=/mnt/data_2/shared/pre-training/tower_llm_data/de/2/0000.json.gz 
+            fr=/mnt/data_2/shared/pre-training/tower_llm_data/fr/1/0000.json.gz 
+            nl=/mnt/data_2/shared/pre-training/tower_llm_data/nl/0000.json.gz 
+            pt=/mnt/data_2/shared/pre-training/tower_llm_data/pt/0000.json.gz              
+            it=/mnt/data_2/shared/pre-training/tower_llm_data/it/0000.json.gz 
+            ru=/mnt/data_2/shared/pre-training/tower_llm_data/ru/6/0000.json.gz
+            zh=/mnt/data_2/shared/pre-training/tower_llm_data/zh/0000.json.gz 
+            ko=/mnt/data_2/shared/pre-training/tower_llm_data/ko/0000.json.gz
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=670
+                    es=670
+                    de=670
+                    fr=670
+                    nl=670
+                    pt=670
+                    it=670
+                    ru=670
+                    zh=670
+                    ko=670
+                    en_de=183
+                    de_en=183 
+                    en_fr=183 
+                    fr_en=183 
+                    en_es=183 
+                    es_en=183 
+                    en_it=183 
+                    it_en=183 
+                    en_nl=183 
+                    nl_en=183 
+                    en_pt=183 
+                    pt_en=183 
+                    en_ru=183 
+                    ru_en=183 
+                    en_zh=183 
+                    zh_en=183 
+                    en_ko=183 
+                    ko_en=183 
+            )
+        )
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+    )   
+
+    lp=(Dataset:
+        en="none"
+        es="none"
+        de="none"
+        fr="none"
+        nl="none"
+        pt="none"
+        it="none"
+        ru="none"
+        zh="none"
+        ko="none"
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+    )   
+
+    min_perplexity=0
+
+    size=(Size: 2 7)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=12700
+    train_steps_annealing=0
+    
+    lr_scheduler=cosine
+    warmup_steps=127
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    lr_scheduler_annealing=linear
+    warmup_steps_annealing=0
+    lr_annealing=3e-5
+    lr_min_annealing=3e-6
+
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4 5 6 7 8)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=2
+    grad_accum_steps=24
+    vocab_size=256000
+
+    cpu_workers=16
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+
+    glu_activation=geglu
+    kv_channels=256
+    layernorm_epsilon=1e-6
+
+    seq_length=4096
+}

multilinguality_megatron/ducttape/llama_3_flavio.tconf

@@ -0,0 +1,546 @@
+global {
+    model_type="llama3"
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama3_flavio
+    repo=/mnt/data/pmartins/code/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama3_flavio/pre_annealing_checkpoints
+    external_model_dir_annealing=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama3_flavio/checkpoints_annealed
+    model_path=/mnt/data_2/cache/models--meta-llama--Meta-Llama-3-8B/snapshots/cd892e8f4da1043d4b01d5ea182a2e8412bf658f/
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Meta-Llama-3-8B/snapshots/cd892e8f4da1043d4b01d5ea182a2e8412bf658f/
+
+    tokenizer_type=PretrainedFromHF
+
+    dataset=(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 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_synth es_synth de_synth fr_synth nl_synth pt_synth it_synth ru_synth zh_synth ko_synth instructions)
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data
+            en_synth=""
+            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
+            es_synth=""
+            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
+            de_synth=""
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            fr_synth=""
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            nl_synth=""
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz   
+            pt_synth=""           
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            it_synth=""
+            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+            ru_synth=""
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            zh_synth=""
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            ko_synth=""
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            instructions="oi"
+            en_de_pre_annealing="oi"
+            de_en_pre_annealing="oi"
+            en_fr_pre_annealing="oi"
+            fr_en_pre_annealing="oi"
+            en_es_pre_annealing="oi"
+            es_en_pre_annealing="oi"
+            en_it_pre_annealing="oi"
+            it_en_pre_annealing="oi"
+            en_nl_pre_annealing="oi"
+            nl_en_pre_annealing="oi"
+            en_pt_pre_annealing="oi"
+            pt_en_pre_annealing="oi"
+            en_ru_pre_annealing="oi"
+            ru_en_pre_annealing="oi"
+            en_zh_pre_annealing="oi"
+            zh_en_pre_annealing="oi"
+            en_ko_pre_annealing="oi"
+            ko_en_pre_annealing="oi"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+            en_synth=False
+            es_synth=False
+            de_synth=False
+            fr_synth=False
+            nl_synth=False
+            pt_synth=False
+            it_synth=False
+            ru_synth=False
+            zh_synth=False
+            ko_synth=False
+            instructions="oi"
+            en_de_pre_annealing="oi"
+            de_en_pre_annealing="oi"
+            en_fr_pre_annealing="oi"
+            fr_en_pre_annealing="oi"
+            en_es_pre_annealing="oi"
+            es_en_pre_annealing="oi"
+            en_it_pre_annealing="oi"
+            it_en_pre_annealing="oi"
+            en_nl_pre_annealing="oi"
+            nl_en_pre_annealing="oi"
+            en_pt_pre_annealing="oi"
+            pt_en_pre_annealing="oi"
+            en_ru_pre_annealing="oi"
+            ru_en_pre_annealing="oi"
+            en_zh_pre_annealing="oi"
+            zh_en_pre_annealing="oi"
+            en_ko_pre_annealing="oi"
+            ko_en_pre_annealing="oi"
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+                en_synth=100000
+                es_synth=100000
+                de_synth=100000
+                fr_synth=100000
+                nl_synth=100000
+                pt_synth=100000
+                it_synth=100000
+                ru_synth=100000
+                zh_synth=100000
+                ko_synth=100000
+                instructions="oi"
+                en_de_pre_annealing="oi"
+                de_en_pre_annealing="oi"
+                en_fr_pre_annealing="oi"
+                fr_en_pre_annealing="oi"
+                en_es_pre_annealing="oi"
+                es_en_pre_annealing="oi"
+                en_it_pre_annealing="oi"
+                it_en_pre_annealing="oi"
+                en_nl_pre_annealing="oi"
+                nl_en_pre_annealing="oi"
+                en_pt_pre_annealing="oi"
+                pt_en_pre_annealing="oi"
+                en_ru_pre_annealing="oi"
+                ru_en_pre_annealing="oi"
+                en_zh_pre_annealing="oi"
+                zh_en_pre_annealing="oi"
+                en_ko_pre_annealing="oi"
+                ko_en_pre_annealing="oi"
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=603
+                    es=603
+                    de=603
+                    fr=603
+                    nl=603
+                    pt=603
+                    it=603
+                    ru=603
+                    zh=603
+                    ko=603
+                    en_de=0
+                    de_en=0 
+                    en_fr=0 
+                    fr_en=0 
+                    en_es=0 
+                    es_en=0 
+                    en_it=0 
+                    it_en=0 
+                    en_nl=0 
+                    nl_en=0 
+                    en_pt=0 
+                    pt_en=0 
+                    en_ru=0 
+                    ru_en=0 
+                    en_zh=0 
+                    zh_en=0 
+                    en_ko=0 
+                    ko_en=0 
+                    en_synth=67
+                    es_synth=67
+                    de_synth=67
+                    fr_synth=67
+                    nl_synth=67
+                    pt_synth=67
+                    it_synth=67
+                    ru_synth=67
+                    zh_synth=67
+                    ko_synth=67
+                    instructions=0
+                    en_de_pre_annealing=183
+                    de_en_pre_annealing=183
+                    en_fr_pre_annealing=183
+                    fr_en_pre_annealing=183
+                    en_es_pre_annealing=183
+                    es_en_pre_annealing=183
+                    en_it_pre_annealing=183
+                    it_en_pre_annealing=183
+                    en_nl_pre_annealing=183
+                    nl_en_pre_annealing=183
+                    en_pt_pre_annealing=183
+                    pt_en_pre_annealing=183
+                    en_ru_pre_annealing=183
+                    ru_en_pre_annealing=183
+                    en_zh_pre_annealing=183
+                    zh_en_pre_annealing=183
+                    en_ko_pre_annealing=183
+                    ko_en_pre_annealing=183
+            )
+        )
+
+    datamix_weights_annealing=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=0
+                    es=0
+                    de=0
+                    fr=0
+                    nl=0
+                    pt=0
+                    it=0
+                    ru=0
+                    zh=0
+                    ko=0
+                    en_de=833
+                    de_en=833
+                    en_fr=833 
+                    fr_en=833 
+                    en_es=833 
+                    es_en=833 
+                    en_it=833 
+                    it_en=833 
+                    en_nl=833 
+                    nl_en=833 
+                    en_pt=833 
+                    pt_en=833 
+                    en_ru=833 
+                    ru_en=833 
+                    en_zh=833 
+                    zh_en=833 
+                    en_ko=833 
+                    ko_en=833
+                    en_synth=0
+                    es_synth=0
+                    de_synth=0
+                    fr_synth=0
+                    nl_synth=0
+                    pt_synth=0
+                    it_synth=0
+                    ru_synth=0
+                    zh_synth=0
+                    ko_synth=0
+                    instructions=85000
+                    en_de_pre_annealing=0
+                    de_en_pre_annealing=0
+                    en_fr_pre_annealing=0
+                    fr_en_pre_annealing=0
+                    en_es_pre_annealing=0
+                    es_en_pre_annealing=0
+                    en_it_pre_annealing=0
+                    it_en_pre_annealing=0
+                    en_nl_pre_annealing=0
+                    nl_en_pre_annealing=0
+                    en_pt_pre_annealing=0
+                    pt_en_pre_annealing=0
+                    en_ru_pre_annealing=0
+                    ru_en_pre_annealing=0
+                    en_zh_pre_annealing=0
+                    zh_en_pre_annealing=0
+                    en_ko_pre_annealing=0
+                    ko_en_pre_annealing=0
+            )
+        )
+
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+                en_synth=20000000
+                es_synth=20000000
+                de_synth=20000000
+                fr_synth=20000000
+                nl_synth=20000000
+                pt_synth=20000000
+                it_synth=20000000
+                ru_synth=20000000
+                zh_synth=20000000
+                ko_synth=20000000
+                instructions="oi"
+                en_de_pre_annealing="oi"
+                de_en_pre_annealing="oi"
+                en_fr_pre_annealing="oi"
+                fr_en_pre_annealing="oi"
+                en_es_pre_annealing="oi"
+                es_en_pre_annealing="oi"
+                en_it_pre_annealing="oi"
+                it_en_pre_annealing="oi"
+                en_nl_pre_annealing="oi"
+                nl_en_pre_annealing="oi"
+                en_pt_pre_annealing="oi"
+                pt_en_pre_annealing="oi"
+                en_ru_pre_annealing="oi"
+                ru_en_pre_annealing="oi"
+                en_zh_pre_annealing="oi"
+                zh_en_pre_annealing="oi"
+                en_ko_pre_annealing="oi"
+                ko_en_pre_annealing="oi"
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+        en_synth=False
+        es_synth=False
+        de_synth=False
+        fr_synth=False
+        nl_synth=False
+        pt_synth=False
+        it_synth=False
+        ru_synth=False
+        zh_synth=False
+        ko_synth=False
+        instructions="oi"
+        en_de_pre_annealing="oi"
+        de_en_pre_annealing="oi"
+        en_fr_pre_annealing="oi"
+        fr_en_pre_annealing="oi"
+        en_es_pre_annealing="oi"
+        es_en_pre_annealing="oi"
+        en_it_pre_annealing="oi"
+        it_en_pre_annealing="oi"
+        en_nl_pre_annealing="oi"
+        nl_en_pre_annealing="oi"
+        en_pt_pre_annealing="oi"
+        pt_en_pre_annealing="oi"
+        en_ru_pre_annealing="oi"
+        ru_en_pre_annealing="oi"
+        en_zh_pre_annealing="oi"
+        zh_en_pre_annealing="oi"
+        en_ko_pre_annealing="oi"
+        ko_en_pre_annealing="oi"
+    )   
+
+    lp=(Dataset:
+        en=""
+        es=""
+        de=""
+        fr=""
+        nl=""
+        pt=""
+        it=""
+        ru=""
+        zh=""
+        ko=""
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+        en_synth=""
+        es_synth=""
+        de_synth=""
+        fr_synth=""
+        nl_synth=""
+        pt_synth=""
+        it_synth=""
+        ru_synth=""
+        zh_synth=""
+        ko_synth=""
+        instructions="oi"
+        en_de_pre_annealing="oi"
+        de_en_pre_annealing="oi"
+        en_fr_pre_annealing="oi"
+        fr_en_pre_annealing="oi"
+        en_es_pre_annealing="oi"
+        es_en_pre_annealing="oi"
+        en_it_pre_annealing="oi"
+        it_en_pre_annealing="oi"
+        en_nl_pre_annealing="oi"
+        nl_en_pre_annealing="oi"
+        en_pt_pre_annealing="oi"
+        pt_en_pre_annealing="oi"
+        en_ru_pre_annealing="oi"
+        ru_en_pre_annealing="oi"
+        en_zh_pre_annealing="oi"
+        zh_en_pre_annealing="oi"
+        en_ko_pre_annealing="oi"
+        ko_en_pre_annealing="oi"
+    )   
+
+    min_perplexity=50
+
+    size=(Size: 8)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=11430
+    train_steps_annealing=1270
+    
+    lr_scheduler=constant
+    warmup_steps=32
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    lr_scheduler_annealing=linear
+    warmup_steps_annealing=0
+    lr_annealing=3e-5
+    lr_min_annealing=3e-6
+
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4 5 6 7 8)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=128256
+
+    cpu_workers=16
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+
+    seq_length=4096
+
+    glu_activation=swiglu
+    kv_channels=""
+    layernorm_epsilon=1e-5
+}

multilinguality_megatron/ducttape/llama_3_flavio_wmt_annealing.tconf

@@ -0,0 +1,570 @@
+global {
+    model_type="llama3"
+    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama3_flavio
+    repo=/mnt/data/pmartins/code/multilinguality_megatron
+
+    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama3_flavio/pre_annealing_checkpoints
+    external_model_dir_annealing=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama3_flavio/checkpoints_annealed_wmt
+    model_path=/mnt/data_2/cache/models--meta-llama--Meta-Llama-3-8B/snapshots/cd892e8f4da1043d4b01d5ea182a2e8412bf658f/
+    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Meta-Llama-3-8B/snapshots/cd892e8f4da1043d4b01d5ea182a2e8412bf658f/
+
+    tokenizer_type=PretrainedFromHF
+
+    dataset=(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 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_synth es_synth de_synth fr_synth nl_synth pt_synth it_synth ru_synth zh_synth ko_synth instructions en_de_wmt en_ru_wmt en_zh_wmt)
+    dataset_path=(Dataset: 
+            en=/mnt/data_2/shared/tower_llm_data/en/data
+            en_synth=""
+            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
+            es_synth=""
+            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
+            de_synth=""
+            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
+            fr_synth=""
+            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
+            nl_synth=""
+            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz   
+            pt_synth=""           
+            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
+            it_synth=""
+            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
+            ru_synth=""
+            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
+            zh_synth=""
+            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
+            ko_synth=""
+            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
+            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
+            instructions="oi"
+            en_de_pre_annealing="oi"
+            de_en_pre_annealing="oi"
+            en_fr_pre_annealing="oi"
+            fr_en_pre_annealing="oi"
+            en_es_pre_annealing="oi"
+            es_en_pre_annealing="oi"
+            en_it_pre_annealing="oi"
+            it_en_pre_annealing="oi"
+            en_nl_pre_annealing="oi"
+            nl_en_pre_annealing="oi"
+            en_pt_pre_annealing="oi"
+            pt_en_pre_annealing="oi"
+            en_ru_pre_annealing="oi"
+            ru_en_pre_annealing="oi"
+            en_zh_pre_annealing="oi"
+            zh_en_pre_annealing="oi"
+            en_ko_pre_annealing="oi"
+            ko_en_pre_annealing="oi"
+            en_de_wmt="oi"
+            en_ru_wmt="oi" 
+            en_zh_wmt="oi"
+        )
+    
+    is_hf_dataset=(Dataset: 
+            en=True 
+            es=False
+            de=False
+            fr=False
+            nl=False
+            pt=False      
+            it=False
+            ru=False
+            zh=False
+            ko=False
+            en_de=False
+            de_en=False 
+            en_fr=False 
+            fr_en=False 
+            en_es=False 
+            es_en=False 
+            en_it=False 
+            it_en=False 
+            en_nl=False 
+            nl_en=False
+            en_pt=False 
+            pt_en=False 
+            en_ru=False 
+            ru_en=False 
+            en_zh=False 
+            zh_en=False 
+            en_ko=False 
+            ko_en=False
+            en_synth=False
+            es_synth=False
+            de_synth=False
+            fr_synth=False
+            nl_synth=False
+            pt_synth=False
+            it_synth=False
+            ru_synth=False
+            zh_synth=False
+            ko_synth=False
+            instructions="oi"
+            en_de_pre_annealing="oi"
+            de_en_pre_annealing="oi"
+            en_fr_pre_annealing="oi"
+            fr_en_pre_annealing="oi"
+            en_es_pre_annealing="oi"
+            es_en_pre_annealing="oi"
+            en_it_pre_annealing="oi"
+            it_en_pre_annealing="oi"
+            en_nl_pre_annealing="oi"
+            nl_en_pre_annealing="oi"
+            en_pt_pre_annealing="oi"
+            pt_en_pre_annealing="oi"
+            en_ru_pre_annealing="oi"
+            ru_en_pre_annealing="oi"
+            en_zh_pre_annealing="oi"
+            zh_en_pre_annealing="oi"
+            en_ko_pre_annealing="oi"
+            ko_en_pre_annealing="oi"
+            en_de_wmt="oi"
+            en_ru_wmt="oi"
+            en_zh_wmt="oi"
+        )
+
+    threshold=(Dataset:
+                en=516       
+                es=275
+                de=611
+                fr=322
+                nl=649
+                pt=257
+                it=332
+                ru=334
+                zh=2041
+                ko=198
+                en_de=100000
+                de_en=100000 
+                en_fr=100000 
+                fr_en=100000 
+                en_es=100000 
+                es_en=100000 
+                en_it=100000 
+                it_en=100000 
+                en_nl=100000 
+                nl_en=100000 
+                en_pt=100000 
+                pt_en=100000 
+                en_ru=100000 
+                ru_en=100000 
+                en_zh=100000 
+                zh_en=100000 
+                en_ko=100000 
+                ko_en=100000
+                en_synth=100000
+                es_synth=100000
+                de_synth=100000
+                fr_synth=100000
+                nl_synth=100000
+                pt_synth=100000
+                it_synth=100000
+                ru_synth=100000
+                zh_synth=100000
+                ko_synth=100000
+                instructions="oi"
+                en_de_pre_annealing="oi"
+                de_en_pre_annealing="oi"
+                en_fr_pre_annealing="oi"
+                fr_en_pre_annealing="oi"
+                en_es_pre_annealing="oi"
+                es_en_pre_annealing="oi"
+                en_it_pre_annealing="oi"
+                it_en_pre_annealing="oi"
+                en_nl_pre_annealing="oi"
+                nl_en_pre_annealing="oi"
+                en_pt_pre_annealing="oi"
+                pt_en_pre_annealing="oi"
+                en_ru_pre_annealing="oi"
+                ru_en_pre_annealing="oi"
+                en_zh_pre_annealing="oi"
+                zh_en_pre_annealing="oi"
+                en_ko_pre_annealing="oi"
+                ko_en_pre_annealing="oi"
+                en_de_wmt="oi"
+                en_ru_wmt="oi"
+                en_zh_wmt="oi"
+            )
+
+    # rougly 67% for mc4, 33% for total parallel data
+    datamix_weights=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=603
+                    es=603
+                    de=603
+                    fr=603
+                    nl=603
+                    pt=603
+                    it=603
+                    ru=603
+                    zh=603
+                    ko=603
+                    en_de=0
+                    de_en=0 
+                    en_fr=0 
+                    fr_en=0 
+                    en_es=0 
+                    es_en=0 
+                    en_it=0 
+                    it_en=0 
+                    en_nl=0 
+                    nl_en=0 
+                    en_pt=0 
+                    pt_en=0 
+                    en_ru=0 
+                    ru_en=0 
+                    en_zh=0 
+                    zh_en=0 
+                    en_ko=0 
+                    ko_en=0 
+                    en_synth=67
+                    es_synth=67
+                    de_synth=67
+                    fr_synth=67
+                    nl_synth=67
+                    pt_synth=67
+                    it_synth=67
+                    ru_synth=67
+                    zh_synth=67
+                    ko_synth=67
+                    instructions=0
+                    en_de_pre_annealing=183
+                    de_en_pre_annealing=183
+                    en_fr_pre_annealing=183
+                    fr_en_pre_annealing=183
+                    en_es_pre_annealing=183
+                    es_en_pre_annealing=183
+                    en_it_pre_annealing=183
+                    it_en_pre_annealing=183
+                    en_nl_pre_annealing=183
+                    nl_en_pre_annealing=183
+                    en_pt_pre_annealing=183
+                    pt_en_pre_annealing=183
+                    en_ru_pre_annealing=183
+                    ru_en_pre_annealing=183
+                    en_zh_pre_annealing=183
+                    zh_en_pre_annealing=183
+                    en_ko_pre_annealing=183
+                    ko_en_pre_annealing=183
+                    en_de_wmt=0
+                    en_ru_wmt=0
+                    en_zh_wmt=0
+            )
+        )
+
+    datamix_weights_annealing=(
+        DataMix: 
+            mc4_parallel_uniform=(
+                Dataset:
+                    en=0
+                    es=0
+                    de=0
+                    fr=0
+                    nl=0
+                    pt=0
+                    it=0
+                    ru=0
+                    zh=0
+                    ko=0
+                    en_de_wmt=833
+                    en_ru_wmt=833
+                    en_zh_wmt=833
+                    en_de=0
+                    de_en=833
+                    en_fr=833 
+                    fr_en=833 
+                    en_es=833 
+                    es_en=833 
+                    en_it=833 
+                    it_en=833 
+                    en_nl=833 
+                    nl_en=833 
+                    en_pt=833 
+                    pt_en=833 
+                    en_ru=0 
+                    ru_en=833 
+                    en_zh=0 
+                    zh_en=833 
+                    en_ko=833 
+                    ko_en=833
+                    en_synth=0
+                    es_synth=0
+                    de_synth=0
+                    fr_synth=0
+                    nl_synth=0
+                    pt_synth=0
+                    it_synth=0
+                    ru_synth=0
+                    zh_synth=0
+                    ko_synth=0
+                    instructions=85000
+                    en_de_pre_annealing=0
+                    de_en_pre_annealing=0
+                    en_fr_pre_annealing=0
+                    fr_en_pre_annealing=0
+                    en_es_pre_annealing=0
+                    es_en_pre_annealing=0
+                    en_it_pre_annealing=0
+                    it_en_pre_annealing=0
+                    en_nl_pre_annealing=0
+                    nl_en_pre_annealing=0
+                    en_pt_pre_annealing=0
+                    pt_en_pre_annealing=0
+                    en_ru_pre_annealing=0
+                    ru_en_pre_annealing=0
+                    en_zh_pre_annealing=0
+                    zh_en_pre_annealing=0
+                    en_ko_pre_annealing=0
+                    ko_en_pre_annealing=0
+            )
+        )
+
+
+    # number such that final tokens for each language are around 1B
+    n_tokens=(Dataset:
+                en=1000000000
+                es=833333330
+                de=833333330
+                fr=833333330
+                nl=833333330
+                pt=833333330
+                it=833333330
+                ru=500000000
+                zh=13888888
+                ko=250000000
+                en_de_wmt="oi"
+                en_ru_wmt="oi"
+                en_zh_wmt="oi"
+                en_de=20000000
+                de_en=20000000 
+                en_fr=20000000 
+                fr_en=20000000 
+                en_es=20000000 
+                es_en=20000000
+                en_it=20000000 
+                it_en=20000000 
+                en_nl=20000000 
+                nl_en=20000000 
+                en_pt=20000000 
+                pt_en=20000000 
+                en_ru=20000000 
+                ru_en=20000000 
+                en_zh=20000000
+                zh_en=20000000 
+                en_ko=20000000
+                ko_en=20000000
+                en_synth=20000000
+                es_synth=20000000
+                de_synth=20000000
+                fr_synth=20000000
+                nl_synth=20000000
+                pt_synth=20000000
+                it_synth=20000000
+                ru_synth=20000000
+                zh_synth=20000000
+                ko_synth=20000000
+                instructions="oi"
+                en_de_pre_annealing="oi"
+                de_en_pre_annealing="oi"
+                en_fr_pre_annealing="oi"
+                fr_en_pre_annealing="oi"
+                en_es_pre_annealing="oi"
+                es_en_pre_annealing="oi"
+                en_it_pre_annealing="oi"
+                it_en_pre_annealing="oi"
+                en_nl_pre_annealing="oi"
+                nl_en_pre_annealing="oi"
+                en_pt_pre_annealing="oi"
+                pt_en_pre_annealing="oi"
+                en_ru_pre_annealing="oi"
+                ru_en_pre_annealing="oi"
+                en_zh_pre_annealing="oi"
+                zh_en_pre_annealing="oi"
+                en_ko_pre_annealing="oi"
+                ko_en_pre_annealing="oi"
+            )
+
+    is_parallel=(Dataset:
+        en=False
+        es=False
+        de=False
+        fr=False
+        nl=False
+        pt=False
+        it=False
+        ru=False
+        zh=False
+        ko=False
+        en_de_wmt="oi"
+        en_ru_wmt="oi"
+        en_zh_wmt="oi"
+        en_de=True
+        de_en=True
+        en_fr=True
+        fr_en=True
+        en_es=True
+        es_en=True
+        en_it=True
+        it_en=True
+        en_nl=True
+        nl_en=True
+        en_pt=True
+        pt_en=True
+        en_ru=True
+        ru_en=True
+        en_zh=True
+        zh_en=True
+        en_ko=True
+        ko_en=True
+        en_synth=False
+        es_synth=False
+        de_synth=False
+        fr_synth=False
+        nl_synth=False
+        pt_synth=False
+        it_synth=False
+        ru_synth=False
+        zh_synth=False
+        ko_synth=False
+        instructions="oi"
+        en_de_pre_annealing="oi"
+        de_en_pre_annealing="oi"
+        en_fr_pre_annealing="oi"
+        fr_en_pre_annealing="oi"
+        en_es_pre_annealing="oi"
+        es_en_pre_annealing="oi"
+        en_it_pre_annealing="oi"
+        it_en_pre_annealing="oi"
+        en_nl_pre_annealing="oi"
+        nl_en_pre_annealing="oi"
+        en_pt_pre_annealing="oi"
+        pt_en_pre_annealing="oi"
+        en_ru_pre_annealing="oi"
+        ru_en_pre_annealing="oi"
+        en_zh_pre_annealing="oi"
+        zh_en_pre_annealing="oi"
+        en_ko_pre_annealing="oi"
+        ko_en_pre_annealing="oi"
+    )   
+
+    lp=(Dataset:
+        en=""
+        es=""
+        de=""
+        fr=""
+        nl=""
+        pt=""
+        it=""
+        ru=""
+        zh=""
+        ko=""
+        en_de_wmt="oi"
+        en_ru_wmt="oi"
+        en_zh_wmt="oi"
+        en_de="en-de"
+        de_en="de-en"
+        en_fr="en-fr"
+        fr_en="fr-en"
+        en_es="en-es"
+        es_en="es-en"
+        en_it="en-it"
+        it_en="it-en"
+        en_nl="en-nl"
+        nl_en="nl-en"
+        en_pt="en-pt"
+        pt_en="pt-en"
+        en_ru="en-ru"
+        ru_en="ru-en"
+        en_zh="en-zh"
+        zh_en="zh-en"
+        en_ko="en-ko"
+        ko_en="ko-en"
+        en_synth=""
+        es_synth=""
+        de_synth=""
+        fr_synth=""
+        nl_synth=""
+        pt_synth=""
+        it_synth=""
+        ru_synth=""
+        zh_synth=""
+        ko_synth=""
+        instructions="oi"
+        en_de_pre_annealing="oi"
+        de_en_pre_annealing="oi"
+        en_fr_pre_annealing="oi"
+        fr_en_pre_annealing="oi"
+        en_es_pre_annealing="oi"
+        es_en_pre_annealing="oi"
+        en_it_pre_annealing="oi"
+        it_en_pre_annealing="oi"
+        en_nl_pre_annealing="oi"
+        nl_en_pre_annealing="oi"
+        en_pt_pre_annealing="oi"
+        pt_en_pre_annealing="oi"
+        en_ru_pre_annealing="oi"
+        ru_en_pre_annealing="oi"
+        en_zh_pre_annealing="oi"
+        zh_en_pre_annealing="oi"
+        en_ko_pre_annealing="oi"
+        ko_en_pre_annealing="oi"
+    )   
+
+    min_perplexity=50
+
+    size=(Size: 8)
+
+    log_interval=1
+    save_interval=635
+    eval_interval=635
+    train_steps=11430
+    train_steps_annealing=1270
+    
+    lr_scheduler=constant
+    warmup_steps=32
+    lr=3e-5
+    lr_min=3e-6
+    weight_decay=0.1
+    
+    lr_scheduler_annealing=linear
+    warmup_steps_annealing=0
+    lr_annealing=3e-5
+    lr_min_annealing=3e-6
+
+    n_gpus=8
+    gpu_ids=0,1,2,3,4,5,6,7
+    tp=(TP: 1 2 3 4 5 6 7 8)
+    pp=(PP: 1 2 3 4)
+    micro_batch_size=4
+    grad_accum_steps=12
+    vocab_size=128256
+
+    cpu_workers=16
+    wikipedia=False
+    freeze_layers=""
+    posterior_tokens=False
+    n_posterior_tokens=0
+    eval_iters=1
+
+    seq_length=4096
+
+    glu_activation=swiglu
+    kv_channels=""
+    layernorm_epsilon=1e-5
+}