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scripts / tr13f-6b3-mtf-tasky.sh

Muennighoff

Rename tr13f-6b3-mtf-tasky to tr13f-6b3-mtf-tasky.sh

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	#!/bin/bash
	#SBATCH --job-name=tr13f-6b3-mtf-tasky
	#SBATCH --partition=gpu_p5
	#SBATCH --constraint=a100
	#SBATCH --qos=qos_gpu-gc # up to 100h
	#SBATCH --nodes=8
	#SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
	#SBATCH --cpus-per-task=64 # number of cores per tasks
	#SBATCH --hint=nomultithread # we get physical cores not logical
	#SBATCH --gres=gpu:8 # number of gpus
	#SBATCH --time 12:00:00 # maximum execution time (HH:MM:SS)
	#SBATCH --output=%x-%j.out # output file name
	#SBATCH --account=ajs@a100

	set -x -e

	source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
	echo "START TIME: $(date)"

	variant=tasky

	DATA_OUTPUT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13f-6B3-ml-t0
	CHECKPOINT_PATH=$DATA_OUTPUT_PATH/checkpoints/$variant
	REPO_PATH=$DATA_OUTPUT_PATH/tr13f-6B3-ml-t0-logs
	TENSORBOARD_PATH=$REPO_PATH/tensorboard/$variant
	LOGS_PATH=$REPO_PATH/logs/$variant
	mkdir -p $LOGS_PATH
	mkdir -p $TENSORBOARD_PATH

	MEGATRON_DEEPSPEED_REPO=/gpfswork/rech/six/commun/code/tr13f-6B3-ml-t0/megdslossseq/Megatron-DeepSpeed
	cd $MEGATRON_DEEPSPEED_REPO

	KILL_SWITCH_PATH=$MEGATRON_DEEPSPEED_REPO/kill-switch-tr13f-6B3-mtf

	TRAIN_DATA_PATH=$six_ALL_CCFRWORK/code/tr13f-6B3-ml-t0/Megatron-DeepSpeed/data/tasky_train.txt
	VALID_DATA_PATH=$six_ALL_CCFRWORK/code/tr13f-6B3-ml-t0/Megatron-DeepSpeed/data/p31_validation.txt
	TOKENIZER_NAME_OR_PATH=bigscience/tokenizer

	# defining the right environment variables
	export TRANSFORMERS_CACHE=$six_ALL_CCFRWORK/models
	export HF_DATASETS_CACHE=$six_ALL_CCFRWORK/datasets
	export HF_MODULES_CACHE=$six_ALL_CCFRWORK/modules
	export HF_METRICS_CACHE=$six_ALL_CCFRWORK/metrics
	export HF_DATASETS_OFFLINE=1
	export TRANSFORMERS_OFFLINE=1

	# testing for potential faulty nodes
	# srun --jobid $SLURM_JOBID bash -c 'python -c "import torch, socket; print(socket.gethostname(), torch.cuda.is_available())"'

	# so processes know who to talk to
	MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST \| head -n 1)
	MASTER_PORT=6001

	GPUS_PER_NODE=8
	NNODES=$SLURM_NNODES

	PP_SIZE=1
	TP_SIZE=1

	# T0 paper:
	# ...truncate input and target sequences to 1024 and 256 tokens...
	# ...use a batch size of 1024 sequences ... 2^20 total input tokens per batch...
	# We use 2048 total tokens and 512 batch size = 2**20
	MICRO_BATCH_SIZE=4
	GLOBAL_BATCH_SIZE=2048

	NLAYERS=30
	NHIDDEN=4096
	NHEADS=32
	SEQ_LEN=2048

	SAVE_INTERVAL=250

	TRAIN_SAMPLES=6_348_800

	# T0 paper:
	# "...we use a learning rate of 1e-3..."
	# However, they use Adafactor, which adapts the LR
	# For Adam we likely want a lower one
	# FLAN:
	# "...decay of 1e-4..""

	# Uncomment for the first step
	# --no-load-optim \
	OPTIMIZER_ARGS=" \
	--optimizer adam \
	--adam-beta1 0.9 \
	--adam-beta2 0.95 \
	--adam-eps 1e-8 \
	--lr 2e-5 \
	--lr-decay-style constant \
	--lr-warmup-samples 0 \
	--clip-grad 1.0 \
	--weight-decay 1e-4 \
	--no-load-optim \
	"
	# for 20h 1190, for 100h 5990
	# --exit-duration-in-mins 1190 \
	EXIT_OPTS=" \
	--exit-duration-in-mins 5990 \
	"

	GPT_ARGS=" \
	--pp-partition-method 'type:transformer\|embedding' \
	--num-layers $NLAYERS \
	--hidden-size $NHIDDEN \
	--num-attention-heads $NHEADS \
	--seq-length $SEQ_LEN \
	--max-position-embeddings $SEQ_LEN \
	--micro-batch-size $MICRO_BATCH_SIZE \
	--global-batch-size $GLOBAL_BATCH_SIZE \
	--train-samples $TRAIN_SAMPLES \
	--tokenizer-type PretrainedFromHF \
	--tokenizer-name-or-path $TOKENIZER_NAME_OR_PATH \
	--init-method-std 0.0048 \
	--embed-layernorm \
	--fp16 \
	--seed 42 \
	--position-embedding-type alibi \
	--checkpoint-activations \
	--abort-on-unmet-fused-kernel-constraints \
	--kill-switch-path $KILL_SWITCH_PATH \
	--pad-vocab-size-to 250880 \
	$OPTIMIZER_ARGS \
	$EXIT_OPTS \
	"

	OUTPUT_ARGS=" \
	--log-interval 1 \
	--save-interval $SAVE_INTERVAL \
	--eval-interval 250 \
	--eval-iters 50 \
	--tensorboard-dir $TENSORBOARD_PATH \
	--tensorboard-queue-size 5 \
	--log-timers-to-tensorboard \
	--log-batch-size-to-tensorboard \
	--log-validation-ppl-to-tensorboard \
	"

	ZERO_STAGE=1

	config_json="./ds_config.$SLURM_JOBID.json"

	# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
	cat <<EOT > $config_json
	{
	"train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE,
	"train_batch_size": $GLOBAL_BATCH_SIZE,
	"gradient_clipping": 1.0,
	"zero_optimization": {
	"stage": $ZERO_STAGE
	},
	"fp16": {
	"enabled": true,
	"loss_scale": 0,
	"loss_scale_window": 500,
	"hysteresis": 2,
	"min_loss_scale": 1,
	"initial_scale_power": 12
	},
	"steps_per_print": 2000,
	"wall_clock_breakdown": false
	}
	EOT


	DEEPSPEED_ARGS=" \
	--deepspeed \
	--deepspeed_config ${config_json} \
	--zero-stage ${ZERO_STAGE} \
	--deepspeed-activation-checkpointing \
	"

	export LAUNCHER="python -u -m torch.distributed.run \
	--nproc_per_node $GPUS_PER_NODE \
	--nnodes $NNODES \
	--rdzv_endpoint $MASTER_ADDR:$MASTER_PORT \
	--rdzv_backend c10d \
	--max_restarts 0 \
	--tee 3 \
	"

	export CMD=" \
	`pwd`/finetune_t0.py \
	--tensor-model-parallel-size $TP_SIZE \
	--pipeline-model-parallel-size $PP_SIZE \
	$GPT_ARGS \
	$OUTPUT_ARGS \
	--save $CHECKPOINT_PATH \
	--load $CHECKPOINT_PATH \
	--train-weighted-split-paths-path $TRAIN_DATA_PATH \
	--valid-weighted-split-paths-path $VALID_DATA_PATH \
	--dataloader-type single \
	--data-impl mmap \
	--distributed-backend nccl \
	$DEEPSPEED_ARGS \
	"

	echo $CMD

	# do not remove or the training will hang and nodes will be lost w/o this workaround
	export CUDA_LAUNCH_BLOCKING=1

	# hide duplicated errors using this hack - will be properly fixed in pt-1.12
	export TORCHELASTIC_ERROR_FILE=/tmp/torch-elastic-error.json

	clear; srun --jobid $SLURM_JOBID bash -c "$LAUNCHER --node_rank \$SLURM_PROCID $CMD" 2>&1 \| tee -a $LOGS_PATH/main_log.txt

	echo "END TIME: $(date)"