#!/bin/bash
set -e
export CUDA_VISIBLE_DEVICES=7
START_TIME=$SECONDS
MASTER_ADDR=localhost
MASTER_PORT=$(shuf -n 1 -i 10000-65535)

MODEL_SIZE=$1
SOURCE_CKPT_PATH=$2
TARGET_CKPT_PATH=$3
TP=$4
PP=$5
EP=$6
PR=$7
USE_TE=$8
MG2HF=$9
HF_CKPT_PATH=${10}

CURRENT_DIR="$( cd "$( dirname "$0" )" && pwd )"
MEGATRON_PATH=$( dirname $(dirname $( dirname ${CURRENT_DIR})))
export PYTHONPATH=$PYTHONPATH:${MEGATRON_PATH}:${MEGATRON_PATH}/PAI-Megatron-LM-240718

if [ $MODEL_SIZE = A2.4B ]; then

HIDDEN_SIZE=2048
NUM_ATTENTION_HEADS=16
NUM_HIDDEN_LAYERS=27
INTERMEDIATE_SIZE=10944
MOE_INTERMEDIATE_SIZE=1408
MAX_POSITION_EMBEDDINGS=163840
EXTRA_VOCAB_SIZE=2400
KV_LORA_RANK=512
QK_NOPE_HEAD_DIM=128
QK_ROPE_HEAD_DIM=64
V_HEAD_DIM=128
ROPE_THETA=10000
SCALE_FACTOR=40
NUM_EXPERTS=64
ROUTER_TOPK=6
NUM_SHARED_EXPERTS=2
MOE_LAYER_FREQ=1
RMS_NORM_EPS=1e-6

moe_options=" \
    --moe-router-topk ${ROUTER_TOPK} \
    --num-experts ${NUM_EXPERTS} \
    --target-expert-model-parallel-size ${EP} \
    --moe-ffn-hidden-size ${MOE_INTERMEDIATE_SIZE} \
    --moe-router-load-balancing-type aux_loss \
    --moe-aux-loss-coeff 1e-2 \
    --enable-shared-expert \
    --moe-layer-freq ${MOE_LAYER_FREQ} \
    --num-shared-experts ${NUM_SHARED_EXPERTS} \
    --kv-lora-rank ${KV_LORA_RANK} \
    --qk-nope-head-dim ${QK_NOPE_HEAD_DIM} \
    --qk-rope-head-dim ${QK_ROPE_HEAD_DIM} \
    --v-head-dim ${V_HEAD_DIM} \
    "


cpu_options=" \
            --use-cpu-initialization"

elif [ $MODEL_SIZE = A21B ]; then

HIDDEN_SIZE=5120
NUM_ATTENTION_HEADS=128
NUM_HIDDEN_LAYERS=60
INTERMEDIATE_SIZE=12288
MOE_INTERMEDIATE_SIZE=1536
MAX_POSITION_EMBEDDINGS=163840
EXTRA_VOCAB_SIZE=2400
Q_LORA_RANK=1536
KV_LORA_RANK=512
QK_NOPE_HEAD_DIM=128
QK_ROPE_HEAD_DIM=64
V_HEAD_DIM=128
ROPE_THETA=10000
SCALE_FACTOR=40
NUM_EXPERTS=160
ROUTER_TOPK=6
NUM_SHARED_EXPERTS=2
MOE_LAYER_FREQ=1
RMS_NORM_EPS=1e-6

moe_options=" \
    --moe-router-topk ${ROUTER_TOPK} \
    --num-experts ${NUM_EXPERTS} \
    --target-expert-model-parallel-size ${EP} \
    --moe-ffn-hidden-size ${MOE_INTERMEDIATE_SIZE} \
    --moe-router-load-balancing-type aux_loss \
    --moe-aux-loss-coeff 1e-2 \
    --enable-shared-expert \
    --moe-layer-freq ${MOE_LAYER_FREQ} \
    --num-shared-experts ${NUM_SHARED_EXPERTS} \
    --q-lora-rank ${Q_LORA_RANK} \
    --kv-lora-rank ${KV_LORA_RANK} \
    --qk-nope-head-dim ${QK_NOPE_HEAD_DIM} \
    --qk-rope-head-dim ${QK_ROPE_HEAD_DIM} \
    --v-head-dim ${V_HEAD_DIM} \
    "

cpu_options=" \
            --use-cpu-initialization"

fi


if [ $MG2HF = true ]; then
    convert_options=" \
                --convert-checkpoint-from-megatron-to-transformers \
                --hf-ckpt-path ${HF_CKPT_PATH}"

elif [ $MG2HF = false ]; then
    convert_options=""
fi

if [ $USE_TE = true ]; then
    te_options=" \
                --transformer-impl transformer_engine \
                "

elif [ $USE_TE = false ]; then
    te_options=" \
                --transformer-impl local \
                "
fi

if [ $PR = fp16 ]; then
    pr_options=" \
		    --fp16"

elif [ $PR = bf16 ]; then
    pr_options=" \
        --bf16"

fi


DISTRIBUTED_ARGS="--nproc_per_node 1 --nnodes 1 --node_rank 0 --master_addr $MASTER_ADDR --master_port $MASTER_PORT"

torchrun ${DISTRIBUTED_ARGS} hf2mcore_deepseek_v2_moe.py \
    --load ${SOURCE_CKPT_PATH} \
    --save ${TARGET_CKPT_PATH} \
    --target-tensor-model-parallel-size ${TP} \
    --target-pipeline-model-parallel-size ${PP} \
    --micro-batch-size 1 \
    --save-interval 1 \
    --swiglu \
    --num-layers ${NUM_HIDDEN_LAYERS} \
    --hidden-size ${HIDDEN_SIZE} \
    --ffn-hidden-size ${INTERMEDIATE_SIZE} \
    --num-attention-heads ${NUM_ATTENTION_HEADS} \
    --max-position-embeddings ${MAX_POSITION_EMBEDDINGS} \
    --seq-length 1 \
    --no-async-tensor-model-parallel-allreduce \
    --patch-tokenizer-type DeepSeekV2Tokenizer \
    --extra-vocab-size ${EXTRA_VOCAB_SIZE} \
    --untie-embeddings-and-output-weights \
    --no-bias-swiglu-fusion \
    --no-rope-fusion \
    --use-rotary-position-embeddings \
    --disable-bias-linear \
    --normalization RMSNorm \
    --norm-epsilon ${RMS_NORM_EPS} \
    --use-mcore-models \
    --attention-dropout 0.0 \
    --hidden-dropout 0.0 \
    --rotary-base ${ROPE_THETA} \
    --rotary-scaling-factor ${SCALE_FACTOR} \
    --qk-layernorm \
    --kv-channels ${V_HEAD_DIM} \
    ${moe_options} \
    ${te_options} \
    ${convert_options} \
    ${pr_options} \
    ${cpu_options}


ELAPSED_TIME=$(($SECONDS - $START_TIME))
echo "$(($ELAPSED_TIME/60)) min $(($ELAPSED_TIME%60)) sec"