3.distributed-training-mistral-mathstral.sbatch

#!/bin/bash

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0

#SBATCH --nodes=4 # number of nodes to use
#SBATCH --job-name=FSDP # name of your job
#SBATCH --exclusive # job has exclusive use of the resource, no sharing

set -ex;

###########################
###### User Variables #####
###########################

GPUS_PER_NODE=8 # 4 for G5.12x, 8 for P4/P5

###########################
## Environment Variables ##
###########################

## Plenty of EFA level variables
## Comment out for non-efa instances (G4d, P3)
## For G5.12x, Comment out RDMA and Fork safe
## For G4dn and other G5, comment out all

export FI_EFA_USE_DEVICE_RDMA=1 # use for p4d
export FI_EFA_FORK_SAFE=1
export FI_LOG_LEVEL=1
export FI_PROVIDER=efa
export NCCL_DEBUG=INFO
## Switching SYNC_MEMOPS to zero can boost throughput with FSDP
## Disables CU_POINTER_ATTRIBUTE_SYNC_MEMOPS
## Reduces memory synchronizations
## https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__UNIFIED.html
export FI_EFA_SET_CUDA_SYNC_MEMOPS=0

###########################
####### Torch Dist  #######
###########################

declare -a TORCHRUN_ARGS=(
    --nproc_per_node=$GPUS_PER_NODE \
    --nnodes=$SLURM_JOB_NUM_NODES \
    --rdzv_id=$SLURM_JOB_ID \
    --rdzv_backend=c10d \
    --rdzv_endpoint=$(hostname) \
)

export TORCHRUN=./pt_fsdp/bin/torchrun
export TRAIN_SCRIPT=./train.py

############################
# Mistral Training Params ##
############################

declare -a TRAINING_ARGS=(
    --train_batch_size=1 \
    --val_batch_size=1 \
    --max_steps=5000 \
    --seed=42 \
    --grad_clip=1.0 \
    --weight_decay=0.2 \
    --beta1=0.9 \
    --beta2=0.95 \
    --activation_checkpointing=1 \
    --intermediate_size=14336 \
    --num_key_value_heads=8 \
    --logging_freq=1 \
    --max_context_width=32768 \
    --vocab_size=32768 \
    --hidden_width=4096 \
    --num_layers=32 \
    --num_heads=32 \
    --resid_pdrop=0.1 \
    --embd_pdrop=0.1 \
    --attn_pdrop=0.1 \
    --summary_first_pdrop=0.1 \
    --initializer_range=0.02 \
    --model_type="mistral" \
    --rotary_pct=0.25 \
    --rotary_emb_base=10000 \
    --lr=0.0001 \
    --lr_decay_style="cosine" \
    --min_lr=1e-5 \
    --warmup=0.0032 \
    --plateau=0.0 \
    --dataset="c4" \
    --tokenizer="mistralai/mathstral-7B-v0.1" \
    --epochs=3 \
    --checkpoint_dir="./checkpoints/mathstral-7B" \
    --resume_from_checkpoint="./checkpoints/mathstral-7B" \
    --checkpoint_freq=50 \
    --validation_freq=500 \
    --dataset_config_name="en" \
    --limit_all_gathers=1 \
    --sharding_strategy="full" \ # https://pytorch.org/docs/stable/fsdp.html
    --offload_activations=1
)
## added auto-resume flag for resiliency checks
AUTO_RESUME=""
if [ -d "/opt/sagemaker_cluster" ]; then
	echo "Detected Hyperpod cluster.. enabling --auto-resume=1"
	AUTO_RESUME="--auto-resume=1"
fi

srun ${AUTO_RESUME} -l ${TORCHRUN} "${TORCHRUN_ARGS[@]}" $TRAIN_SCRIPT "${TRAINING_ARGS[@]}"