megatron 微调 qwen3vl-235b-a22b OOM

以下是我用megatron lora sft qwen3vl-235b-a22b 模型的配置（8x8H800-80G)，但是如果seq_len调整到 32k以上会OOM，可以怎么优化么：

#!/bin/bash
set -euo pipefail
set -x

########################

自动分布式环境设置

NNODES=$WORLD_SIZE RANK=$RANK MASTER_ADDR=$MASTER_ADDR MASTER_PORT=$MASTER_PORT

########################

单机模式检测，如果没有 MASTER_ADDR，则默认为单机

if [ -z "${MASTER_ADDR:-}" ]; then
export MASTER_ADDR=127.0.0.1
export MASTER_PORT=$(shuf -n 1 -i 20000-65000)
export NNODES=1
export NODE_RANK=0
export NPROC_PER_NODE=8
export WORLD_SIZE=8
else
# 多机模式，平台需要提供 MASTER_ADDR, RANK, WORLD_SIZE
export MASTER_ADDR=$MASTER_ADDR
export MASTER_PORT=$MASTER_PORT
export NNODES=${WORLD_SIZE:-1} # 平台的WORLD_SIZE是节点数
export NODE_RANK=${RANK:-0} # 节点排名
export RANK=${RANK:-0}
export NPROC_PER_NODE=8 # 默认每个节点8张卡

fi

动态调整批处理大小（可选，根据节点数量调整）

MICRO_BATCH_SIZE=${MICRO_BATCH_SIZE:-1}

全局批处理大小应该根据节点数量进行调整

GLOBAL_BATCH_SIZE=${GLOBAL_BATCH_SIZE:-$((4 * NNODES))}

其他配置

export TF_CPP_MIN_LOG_LEVEL=3

echo "=== 分布式配置信息 ==="
echo "MASTER_ADDR: ${MASTER_ADDR}"
echo "MASTER_PORT: ${MASTER_PORT}"
echo "NNODES: ${NNODES}"
echo "NODE_RANK: ${NODE_RANK}"
echo "NPROC_PER_NODE: ${NPROC_PER_NODE}"
echo "TOTAL_PROCESSES: $((NNODES * NPROC_PER_NODE))"
echo "MICRO_BATCH_SIZE: ${MICRO_BATCH_SIZE}"
echo "GLOBAL_BATCH_SIZE: ${GLOBAL_BATCH_SIZE}"
echo "========================"

export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export NCCL_DEBUG=INFO

export WANDB_NAME=1217_h800_8node_image_85w
export WANDB_API_KEY=""
export WANDB_PROJECT=default
export WANDB_MODE=online

image

export MAX_PIXELS=1003520

video

export VIDEO_MAX_TOKEN_NUM=64 # 256x256
export FPS_MAX_FRAMES=140

PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True'

export MEGATRON_LM_PATH='/Megatron-LM'
export MODELSCOPE_CACHE='/shared_8node'

$(which megatron) sft
--model /mnt/tidalfs-bdsz01/dataset/llm_ckpt/video_agent/models/Qwen3-VL-235B-A22B-Instruct
--load_safetensors true
--save_safetensors true
--dataset
/gaea_train/gaea_train_10w_msformat.jsonl
--load_from_cache_file true
--train_type lora
--lora_rank 8
--lora_alpha 32
--target_modules all-linear
--moe_permute_fusion true
--tensor_model_parallel_size 2
--expert_tensor_parallel_size 1
--expert_model_parallel_size $((8 * NNODES))
--moe_grouped_gemm true
--moe_shared_expert_overlap false
--moe_aux_loss_coeff 1e-3
--micro_batch_size ${MICRO_BATCH_SIZE}
--global_batch_size ${GLOBAL_BATCH_SIZE}
--recompute_granularity full
--recompute_method uniform
--recompute_num_layers 1
--max_epochs 1
--finetune true
--cross_entropy_loss_fusion true
--lr 1e-4
--lr_warmup_fraction 0.05
--min_lr 1e-5
--save 1217_h800_8node_image_85w
--eval_interval 8000
--save_interval 8000
--max_length 9000
--num_workers 4
--dataset_num_proc 4
--no_save_optim true
--no_save_rng true
--sequence_parallel true
--attention_backend flash
--model_author xiangyi
--model_name 1217_h800_8node_image_85w
--wandb-project $WANDB_PROJECT
--wandb-exp-name $WANDB_NAME

Start by reproducing the supplied Megatron SFT command with the listed 8x8 H800 configuration, comparing the reported sequence length above 32k with the command's --max_length 9000 setting. Inspect memory usage around recomputation, parallelism, MoE, and multimodal token limits. Done means identifying a reproducible cause and documenting a validated configuration or optimization that avoids OOM.