docker/bloom13b/Megatron-DeepSpeed/tools/retro/examples/get_preprocess_cmd.sh

#!/bin/bash

# Build preprocessing command for Retro.

set -u
DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )

################ Required environment variables. ################
# Required environment variables:
# - REPO_DIR : Root directory of Megatron codebase.
# - RETRO_WORKDIR : Root directory of this Retro project's processed data. (For
#     example, this project directory might be for a blended dataset, while
#     another project directory might be for just a Wikipedia dataset, and
#     another for just Book Corpus data, etc.) This project directory will
#     contain a complete set of processed data, including the retrieval
#     database, search index, and pretraining neighbors.
# - RETRO_TASKS : One of 'build', 'db-build', 'index-build', or
#     'pretraining-query-neighbors'. See 'Retro tasks' below for task
#     descriptions.
# - DATA_BLEND_SCRIPT : Path to blended dataset definition file.
# - GPT_VOCAB_FILE : GPT vocab file.
# - GPT_MERGE_FILE : GPT merge file.
# - GPT_TOKENIZER : GPT tokenizer type (e.g., GPT2BPETokenizer)
# - BERT_LOAD_PATH : Bert checkpoint directory.
# - BERT_VOCAB_FILE : Bert vocab file.
# - BERT_TOKENIZER : Bert tokenizer type (e.g., BertWordPieceLowerCase,
#     BertWordPieceCase).
# - BERT_EMBEDDER_TYPE : One of 'megatron' or 'huggingface'.
# - EXTRA_ARGS : Extra arguments (else, leave empty).

################ Data blend. ################
. ${DATA_BLEND_SCRIPT}
DATA_PATH=${DATA_BLEND}

################ Retro setup. ################
RETRO_GPT_SEQ_LENGTH=2048
RETRO_GPT_CHUNK_LENGTH=64
RETRO_GPT_MICRO_BATCH_SIZE=1 # *8
RETRO_GPT_GLOBAL_BATCH_SIZE=256

################ Retro tasks. ################
# The '--retro-tasks' argument is a comma-separated list of tasks to run, in
# sequential order. For a quick start, simply set this to 'build' to run the
# entire preprocessing pipeline. For finer control, you may specify the list of
# tasks to run. This is desirable for tuning computational resources. For
# example, training the search index is relatively fast and utilizes GPUs,
# while querying the search index is relatively slow, CPU-only, and memory
# intensive (i.e., multiple populated search indexes are loaded simultaneously).

# *Note* : Once the task(s) below have been completed -- by running either
#    1) 'build', or 2) the sequential combination of 'db-build', 'index-build',
#    and 'pretraining-query-neighbors' -- we are ready to pretrain Retro by
#    calling pretrain_retro.py.

# ---- Option #1 : Run entire pipeline. ----

# RETRO_TASKS="build" # (*note*: default tasks)

# ---- Option #2 : Run specific stages. ----
# *Note*: Run the following stages in the given order. Optionally, tune your
#   cluster setup for each stage, as described above.

# RETRO_TASKS="db-build" # ....................... run 1st
# RETRO_TASKS="index-build" # .................... run 2nd
# RETRO_TASKS="pretraining-query-neighbors" # .... run 3rd

################ Megatron args. ################
MEGATRON_ARGS=" \
    --seed 1234 \
    --distributed-timeout-minutes 600 \
    --tokenizer-type ${BERT_TOKENIZER} \
    --tensor-model-parallel-size 1 \
    --pipeline-model-parallel-size 1 \
    --num-layers 24 \
    --hidden-size 1024 \
    --num-attention-heads 16 \
    --micro-batch-size ${RETRO_GPT_MICRO_BATCH_SIZE} \
    --global-batch-size ${RETRO_GPT_GLOBAL_BATCH_SIZE} \
    --seq-length 512 \
    --max-position-embeddings 512 \
    --train-samples ${RETRO_GPT_TRAIN_SAMPLES} \
    --load ${BERT_LOAD_PATH} \
    --exit-on-missing-checkpoint \
    --no-load-optim \
    --data-path ${DATA_PATH} \
    --vocab-file ${BERT_VOCAB_FILE} \
    --data-impl mmap \
    --split 98,2,0 \
    --distributed-backend nccl \
    --lr 0.0001 \
    --lr-decay-style linear \
    --min-lr 1.0e-5 \
    --lr-decay-samples ${LR_DECAY_SAMPLES} \
    --lr-warmup-samples ${LR_WARMUP_SAMPLES} \
    --weight-decay 1e-2 \
    --clip-grad 1.0 \
    --eval-interval ${RETRO_GPT_EVAL_INTERVAL} \
    --eval-iters ${RETRO_GPT_EVAL_ITERS} \
    --fp16 \
    --DDP-impl local \
    --dataloader-type ${DATALOADER_TYPE} \
    --no-data-sharding \
    --no-gradient-accumulation-fusion \
    --no-async-tensor-model-parallel-allreduce \
"

################ Retro args. ################
RETRO_ARGS=" \
    --bert-embedder-type ${BERT_EMBEDDER_TYPE} \
    --output-bert-embeddings \
    \
    --retro-gpt-vocab-file ${GPT_VOCAB_FILE} \
    --retro-gpt-merge-file ${GPT_MERGE_FILE} \
    --retro-gpt-tokenizer-type ${GPT_TOKENIZER} \
    --retro-gpt-seq-length ${RETRO_GPT_SEQ_LENGTH} \
    --retro-gpt-chunk-length ${RETRO_GPT_CHUNK_LENGTH} \
    --retro-bert-vocab-file ${BERT_VOCAB_FILE} \
    --retro-bert-tokenizer-type ${BERT_TOKENIZER} \
    \
    --retro-tasks ${RETRO_TASKS} \
    --retro-index-str ${RETRO_INDEX_STR} \
    --retro-ef-search ${RETRO_EF_SEARCH} \
    --retro-nprobe ${RETRO_NPROBE} \
    \
    --retro-workdir ${RETRO_WORKDIR} \
    --retro-nchunks-sampled ${RETRO_NCHUNKS_SAMPLED} \
    \
    --retro-return-doc-ids \
"

################ Command. ################
RETRO_PREPROCESS_CMD=" \
    ./tools/retro/main.py \
    ${MEGATRON_ARGS} \
    ${RETRO_ARGS} \
    ${EXTRA_ARGS} \
"