#! /bin/bash
echo "------ARGUMENTS LIST --------"
for ARGUMENT in "$@"
do
   KEY=$(echo $ARGUMENT | cut -f1 -d=)

   KEY_LENGTH=${#KEY}
   VALUE="${ARGUMENT:$KEY_LENGTH+1}"

   export "$KEY"="$VALUE"
   echo "$KEY=$VALUE"
done
echo "---------------------------------"

set -exo pipefail
if [[ -z $MBS ]]; then MBS=4; fi
if [[ -z $GBS ]]; then GBS=32; fi
if [[ -z $MOE_GROUPED_GEMM ]]; then MOE_GROUPED_GEMM=0; fi
if [[ -z $ALLOW_NONDETERMINISTIC ]]; then ALLOW_NONDETERMINISTIC=0; fi

GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))

command="export CUDA_DEVICE_MAX_CONNECTIONS=1;"

TRAINING_DTYPE=fp16
TRANSFORMER_IMPL=local

if [[ $ALLOW_NONDETERMINISTIC -eq 1 ]]; then
   command="$command export NVTE_ALLOW_NONDETERMINISTIC_ALGO=1;"
else
   command="$command export NVTE_ALLOW_NONDETERMINISTIC_ALGO=0; export NCCL_ALGO=Tree;"
   ADDITIONAL_PARAMS+=" --deterministic-mode"
fi

USE_LEGACY=1
if [[ $USE_CORE -eq 1 ]]; then
       echo "Running using megatron core"
       TRANSFORMER_IMPL=local
       TRAINING_DTYPE=bf16
       unset USE_LEGACY
fi

if [[ $MOE_GROUPED_GEMM -eq 1 ]]; then
       echo "Running MoE with Grouped GEMM"
       TRAINING_DTYPE=bf16  # Currently GroupedGEMM for MoE only supports bf16 dtype
fi

if [[ $USE_TE -eq 1 ]]; then
       echo "Running with TransformerEngine ..."
       TRANSFORMER_IMPL=transformer_engine
       TRAINING_DTYPE=bf16
       ADDITIONAL_PARAMS+=" --attention-softmax-in-fp32"
else
       echo "Running with local transformer implementation ..."
fi
if [[ $CHECKPOINT_RESUME_TEST -eq 1 ]]; then
       echo "Running checkpoint resume test..."
       __SAVE_INTERVAL=50
       ADDITIONAL_PARAMS+=" --use-checkpoint-opt_param-scheduler"
       if [[ $MAX_STEPS -ne 100 ]]; then
         echo "Overriding MAX_STEPS=100"
         MAX_STEPS=100
       fi
else
       __SAVE_INTERVAL=10000  # inf
fi
if [[ -n "$CKPT_FORMAT" ]] && [[ "$CKPT_FORMAT" != 'torch' ]]; then
       echo "Using distributed checkpoint format $CKPT_FORMAT..."
       [[ "$CKPT_FORMAT" == 'zarr' ]] && command="$command pip install zarr tensorstore==0.1.45;"
       ADDITIONAL_PARAMS+=" --use-dist-ckpt --dist-ckpt-format $CKPT_FORMAT --use-mcore-models"
fi
set +x

DISTRIBUTED_ARGS="--max-restarts 3 --nproc_per_node $GPUS_PER_NODE --nnodes $NUM_NODES"

build_torch_run_cmd() {
  torch_run_cmd="torchrun $DISTRIBUTED_ARGS \
    pretrain_vlm.py \
      --num-layers 12 \
      --hidden-size 512 \
      --attention-dropout 0.0 \
      --hidden-dropout 0.0 \
      --num-attention-heads 8 \
      --log-params-norm \
      --log-num-zeros-in-grad \
      --log-validation-ppl-to-tensorboard \
      --log-timers-to-tensorboard \
      --tensorboard-dir ${TENSORBOARD_DIR} \
      --micro-batch-size ${MBS:-4} \
      --global-batch-size ${GBS:-32} \
      --seq-length 1024 \
      --max-position-embeddings 1024 \
      --train-iters $MAX_STEPS \
      --timing-log-level 2 \
      --lr-decay-iters 320000 \
      --save $CHECKPOINT_PATH \
      --load $CHECKPOINT_PATH \
      --split 949,50,1 \
      --tokenizer-type NullTokenizer \
      --vocab-size=8192 \
      --distributed-backend nccl \
      --lr 0.00015 \
      --lr-decay-style cosine \
      --min-lr 1.0e-5 \
      --weight-decay 1e-2 \
      --clip-grad 1.0 \
      --lr-warmup-fraction .01 \
      --log-interval 1 \
      --save-interval $__SAVE_INTERVAL \
      --eval-interval 1000 \
      --eval-iters 10 \
      --transformer-impl $TRANSFORMER_IMPL \
      --tensor-model-parallel-size $TP_SIZE \
      --pipeline-model-parallel-size $PP_SIZE \
      ${VP_SIZE:+--num-layers-per-virtual-pipeline-stage "$VP_SIZE"} \
      ${ADDITIONAL_PARAMS:+$ADDITIONAL_PARAMS} \
      ${USE_LEGACY:+--use-legacy-models} \
      --no-gradient-accumulation-fusion \
      --${TRAINING_DTYPE} \
      --img-h 336 \
      --img-w 336 \
      --patch-dim 14 \
      --mock-data"

  if [[ "${TRAINING_DTYPE}" == "fp16" ]]; then
      torch_run_cmd+=" --apply-query-key-layer-scaling"
      # NVTE_APPLY_QK_LAYER_SCALING=1 is required if using:
      #  1. --apply-query-key-layer-scaling
      #  2. transformer_impl="transformer_engine"
      #  3. TE >= 0.11
      #  4. fp16
      export NVTE_APPLY_QK_LAYER_SCALING=1
  fi
}

build_torch_run_cmd
command="$command $torch_run_cmd"
if [[ $CHECKPOINT_RESUME_TEST -eq 1 ]]; then
  echo "------RESUME OVERRIDES ARGS LIST --------"
  # apply all env vars starting from 'RESUME_OVERRIDE_' (after removing prefix)
  _OVERRIDE_PREFIX="RESUME_OVERRIDE_"
  _OVERRIDE_PREFIX_LENGTH=${#_OVERRIDE_PREFIX}
  _NONEMPTY_OVERRIDES=0
  for ARGUMENT in "$@"
  do
    KEY=$(echo $ARGUMENT | cut -f1 -d=)
    if [[ $KEY == ${_OVERRIDE_PREFIX}* ]]; then
      KEY_LENGTH=${#KEY}
      VALUE="${ARGUMENT:$KEY_LENGTH+1}"
      KEY="${KEY:$_OVERRIDE_PREFIX_LENGTH}"
      if [[ -n "${VALUE}" ]]; then
        export "$KEY"="$VALUE"
        echo "$KEY=$VALUE"
        _NONEMPTY_OVERRIDES=1
      fi
    fi
  done
  echo "---------------------------------"
  if [[ $_NONEMPTY_OVERRIDES == 1 ]]; then
    ADDITIONAL_PARAMS+=" --no-load-rng"  # assuming TPxPP mismatch
  fi

  build_torch_run_cmd
  command="$command; rm -rf $CHECKPOINT_PATH/iter_0000100; echo 50 > $CHECKPOINT_PATH/latest_checkpointed_iteration.txt; $torch_run_cmd"
fi
echo "-------------------- THE FINAL PRETRAIN SCRIPT COMMAND THAT WILL BE RUN ------------"
echo "$command"
echo "-----------------------------------------------------------------------------"

echo "$command" > $SCRIPTS_DIR/pretrain_llava_distributed_command.sh
eval $command

echo "Saving test results to $TENSORBOARD_DIR"
PYTHONPATH=$PWD python3 ./tests/functional_tests/python_test_utils/get_test_results_from_tensorboard_logs.py $TENSORBOARD_DIR "$JOB_NAME" | \
    tee ${TENSORBOARD_DIR}/results.json

if [[ $SKIP_PYTEST != 1 ]]; then
    echo "-----------------------------------------------------------------------------"
    if [[ $CHECKPOINT_RESUME_TEST -eq 1 ]]; then
        echo "Running pytest 1st vs 2nd run comparison"
        export LOGS_DIR=$TENSORBOARD_DIR
        pytest ./tests/functional_tests/python_test_utils/test_resume_checkpoint_pipeline.py
    else
        echo "Running pytest checks against golden values"
        export EXPECTED_METRICS_FILE="./tests/functional_tests/test_results/jet/${JOB_NAME}.json"
        export LOGS_DIR=$TENSORBOARD_DIR
        pytest ./tests/functional_tests/python_test_utils/test_ci_pipeline.py
    fi
fi
