Create tr13-176B-ml-p31lossseq

tr13-176B-ml-p31lossseq

@@ -0,0 +1,209 @@
+#!/bin/bash
+#SBATCH --job-name=taskytr13f-6B3-ml-t0
+#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)"