Add extrapolation experiment slurm scripts

train/tr4-1B3-rotary/tr4c-1B3-extrapolation-law-checkpoint-177k.slurm

@@ -0,0 +1,185 @@
+#!/bin/bash
+#SBATCH --job-name=1B3-rotary-extrapolation-checkpoint-117k
+#SBATCH --qos=qos_gpu-t3
+#SBATCH --nodes=16
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=40         # number of cores per tasks
+#SBATCH --hint=nomultithread         # we get physical cores not logical
+#SBATCH --gres=gpu:4                 # number of gpus
+#SBATCH --time 20:00:00              # maximum execution time (HH:MM:SS)
+#SBATCH --output=/gpfsdswork/projects/rech/six/uue59kq/logs/rotary/%x-%j.out
+#SBATCH --account=six@gpu
+
+set -x -e
+
+# TODO: modify these for your training setup, just Ctrl-F replace <YOUR_TRAINING_NAME>
+DATA_OUTPUT_PATH=$SCRATCH/synched_exps/tr4c-1B3-rotary-oscar
+CHECKPOINT_PATH=$DATA_OUTPUT_PATH/checkpoints
+REPO_PATH=$DATA_OUTPUT_PATH/tr4c-1B3-rotary-oscar-logs
+TENSORBOARD_PATH=$REPO_PATH/tensorboard
+CODECARBON_PATH=$REPO_PATH/codecarbon
+LOGS_PATH=$REPO_PATH/logs
+VAL_LOGS_PATH=$REPO_PATH/val-logs
+MEGATRON_DEEPSPEED_REPO=$SCRATCH/checkpoints/tr7d-1B3-alibi/code/Megatron-DeepSpeed # use code fixed alibi
+
+# TODO: you may change the dataset, some examples are at tr3-1B3-baseline (tr3 = c4 + t5-tokenizer, tr3m = the Pile)
+VOCAB_FILE=$SCRATCH/checkpoints/tr7d-1B3-alibi/data/gpt2-vocab.json
+MERGE_FILE=$SCRATCH/checkpoints/tr7d-1B3-alibi/data/gpt2-merges.txt
+DATA_PATH=$six_ALL_CCFRWORK/datasets-custom/oscar-en/meg-gpt2_text_document
+
+# defining the right environment variables
+source $six_ALL_CCFRWORK/start-prod
+export TRANSFORMERS_CACHE=$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
+cd $MEGATRON_DEEPSPEED_REPO
+
+mkdir -p $VAL_LOGS_PATH
+
+# so processes know who to talk to
+MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
+MASTER_PORT=6000
+
+# TODO: this is our base config for 1B3, edit PP/TP/batch size/model config if smaller or bigger
+GPUS_PER_NODE=4
+NNODES=16
+PP_SIZE=4                                                 # NLAYERS must be a multiple of PP_SIZE here
+TP_SIZE=4                                                 # always fixed to the size of a single node
+DP_SIZE=$((NNODES * GPUS_PER_NODE / (PP_SIZE * TP_SIZE))) # will get derived automatically by trainer
+
+MICRO_BATCH_SIZE=8
+GLOBAL_BATCH_SIZE=512
+TRAIN_ITER=73_242_187
+
+NLAYERS=24
+NHIDDEN=2048
+NHEADS=16
+FFN_HIDDEN_SIZE=8192
+SEQ_LEN=2048
+
+SAVE_INTERVAL=1500
+
+OPTIMIZER_ARGS=" \
+    --optimizer adam \
+    --adam-beta1 0.9 \
+    --adam-beta2 0.999 \
+    --adam-eps 1e-8 \
+    --lr 2e-4 \
+    --min-lr 1e-5 \
+    --lr-decay-style cosine \
+    --lr-decay-samples 73_242_187 \
+    --lr-warmup-samples 183_105 \
+    --clip-grad 1.0 \
+    --weight-decay 1e-1 \
+    --eval-only 1\
+    "
+
+EXIT_OPTS=" \
+    --exit-duration-in-mins 1190 \
+    "
+
+for increment in {0..4000..100}; do
+    SEQ_LEN_2=$(($increment + $SEQ_LEN))
+    echo "***** Extrapolation for a seq length of $SEQ_LEN_2 *****"
+
+    GPT_ARGS=" \
+    --num-layers $NLAYERS \
+    --hidden-size $NHIDDEN \
+    --num-attention-heads $NHEADS \
+    --ffn-hidden-size $FFN_HIDDEN_SIZE \
+    --seq-length $SEQ_LEN_2 \
+    --position-embedding-type rotary \
+    --micro-batch-size $MICRO_BATCH_SIZE \
+    --global-batch-size $GLOBAL_BATCH_SIZE \
+    --rampup-batch-size 32 32 2_000_000 \
+    --train-samples $TRAIN_ITER \
+    --vocab-file $VOCAB_FILE \
+    --merge-file $MERGE_FILE \
+    --loss-scale 12 \
+    --clip-grad 1.0 \
+    --fp16 \
+    --checkpoint-activations \
+    $OPTIMIZER_ARGS \
+    $EXIT_OPTS \
+    "
+
+    OUTPUT_ARGS=" \
+    --log-interval 200 \
+    --save-interval $SAVE_INTERVAL \
+    --eval-interval 1000 \
+    --eval-iters 100 \
+    --tensorboard-dir $TENSORBOARD_PATH \
+    --tensorboard-queue-size 5 \
+    --log-timers-to-tensorboard \
+    --log-batch-size-to-tensorboard \
+    --log-validation-ppl-to-tensorboard \
+    "
+    # TODO: Add --codecarbon-dir $CODECARBON_PATH \ if you want to use codecarbon, not adding it for now to make the current
+    # series of experiments consistent, especially speed-wise. Adding it once Tr6 and Tr7 are done
+
+    ZERO_STAGE=1
+
+    config_json="./ds_config.$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.launch \
+    --nproc_per_node $GPUS_PER_NODE \
+    --nnodes $NNODES \
+    --master_addr $MASTER_ADDR \
+    --master_port $MASTER_PORT \
+    "
+
+    export CMD=" \
+    `pwd`/pretrain_gpt.py \
+    --tensor-model-parallel-size $TP_SIZE \
+    --pipeline-model-parallel-size $PP_SIZE \
+    $GPT_ARGS \
+    $OUTPUT_ARGS \
+    --save $CHECKPOINT_PATH \
+    --load $CHECKPOINT_PATH \
+    --data-path $DATA_PATH \
+    --data-impl mmap \
+    --split 949,50,1 \
+    --distributed-backend nccl \
+    $DEEPSPEED_ARGS \
+    "
+
+    # # clear old checkpoint as it'd mismatch while we sort things out
+    #     rm -rf $SAVE_CHECKPOINT_PATH
+
+    echo $CMD
+
+    # to debug - add echo (it exits and prints what it would have launched)
+    srun --jobid $SLURM_JOBID bash -c '$LAUNCHER --node_rank $SLURM_PROCID $CMD' 2>&1 | tee -a $VAL_LOGS_PATH/tr4c-1B3-rotary-extrapolation.$SLURM_JOBID.out
+done