RuntimeError: Invalid function argument. Expected parameter tensor to be of type

[FannierPeng]

I was trying to run Megatron with ZeRO 2 config when I encountered this error
The code version is Megatron-LM-v1.1.5-3D_parallelism.

Traceback (most recent call last):
File "pretrain_gpt2.py", line 158, in <module>
args_defaults={'tokenizer_type': 'GPT2BPETokenizer'})
File "DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/training.py", line 100, in pretrain
train_data_iterator, valid_data_iterator)
File "DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/training.py", line 485, in train
lr_scheduler)
File "DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/training.py", line 325, in train_step
return train_step_pipe(model, data_iterator)
File "DeepSpeedExamples/Megatron-LM-v1.1.5-3D_parallelism/megatron/training.py", line 359, in train_step_pipe
loss = model.train_batch(data_iter=data_iterator)
File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/pipe/engine.py", line 283, in train_batch
self._exec_schedule(sched)
File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/pipe/engine.py", line 1161, in _exec_schedule
self._exec_instr(**cmd.kwargs)
File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/pipe/engine.py", line 219, in _exec_reduce_tied_grads
self.module.allreduce_tied_weight_gradients()
File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/pipe/module.py", line 409, in allreduce_tied_weight_gradients
File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/pipe/module.py", line 409, in allreduce_tied_weight_gradients
dist.all_reduce(weight.grad, group=comm['group'])
File "/opt/conda/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 890, in all_reduce
_check_single_tensor(tensor, "tensor")
File "/opt/conda/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 210, in _check_single_tensor
"to be of type torch.Tensor.".format(param_name))
RuntimeError: Invalid function argument. Expected parameter `tensor` to be of type torch.Tensor.

It seems that File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/pipe/module.py", line 409, in allreduce_tied_weight_gradients
dist.all_reduce(weight.grad, group=comm['group'])
the weight.grad is not Tensor. But this error doesn't occur with ZERO 0 and 1 config

My script is like this:

#! /bin/bash

GPUS_PER_NODE=16
# Change for multinode config
NNODES=1
NODE_RANK=0
MASTER_ADDR=localhost
MASTER_PORT=6000
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))

export DLWS_NUM_WORKER=${NNODES}
export DLWS_NUM_GPU_PER_WORKER=${GPUS_PER_NODE}

DATA_PATH=/userhome/ChineseCorpus/Megatron-training/all-sample100G-samplebyfile-combine10M/text_document
VOCAB_PATH=bpe_3w_new/vocab.json
MERGE_PATH=bpe_3w_new/merges.txt
CHECKPOINT_PATH=checkpoints/gpt2_345m_ds

script_path=$(realpath $0)
script_dir=$(dirname $script_path)
config_json="$script_dir/ds_zero_stage_2_config.json"
# config_json="$script_dir/ds_config.json"

# Megatron Model Parallelism
mp_size=2
# DeepSpeed Pipeline parallelism
pp_size=2

NLAYERS=24
NHIDDEN=1024
BATCHSIZE=4
LOGDIR="tensorboard_data/${NLAYERS}l_${NHIDDEN}h_${NNODES}n_${GPUS_PER_NODE}g_${pp_size}pp_${mp_size}mp_${BATCHSIZE}b_ds4"

GAS=16

#ZeRO Configs
stage=2
reduce_scatter=true
contigious_gradients=true
rbs=50000000
agbs=5000000000

#Actication Checkpointing and Contigious Memory
chkp_layers=1
PA=true
PA_CPU=false
CC=true
SYNCHRONIZE=true
PROFILE=false


gpt_options=" \
        --model-parallel-size ${mp_size} \
        --pipe-parallel-size ${pp_size} \
        --num-layers $NLAYERS \
        --hidden-size $NHIDDEN \
        --num-attention-heads 16 \
        --seq-length 1024 \
        --max-position-embeddings 1024 \
        --batch-size $BATCHSIZE \
        --gas $GAS \
        --train-iters 320000 \
        --lr-decay-iters 320000 \
        --save $CHECKPOINT_PATH \
        --load $CHECKPOINT_PATH \
        --data-path $DATA_PATH \
        --vocab-file $VOCAB_PATH \
        --merge-file $MERGE_PATH \
        --data-impl mmap \
        --split 949,50,1 \
        --distributed-backend nccl \
        --lr 1.5e-4 \
        --lr-decay-style cosine \
        --min-lr 1.0e-5 \
        --weight-decay 1e-2 \
        --clip-grad 1.0 \
        --warmup 0.01 \
        --checkpoint-activations \
        --log-interval 1 \
        --save-interval 500 \
        --eval-interval 100 \
        --eval-iters 10 \
        --fp16 \
        --tensorboard-dir ${LOGDIR}
"
  
 deepspeed_options=" \
                --deepspeed \
                --deepspeed_config ${config_json} \
                --zero-stage ${stage} \
                --zero-reduce-bucket-size ${rbs} \
                --zero-allgather-bucket-size ${agbs} 
            "

if [ "${contigious_gradients}" = "true" ]; then
deepspeed_options="${deepspeed_options} \
                --zero-contigious-gradients"
fi

if [ "${reduce_scatter}" = "true" ]; then
deepspeed_options="${deepspeed_options} \
                --zero-reduce-scatter"
fi

chkp_opt=" \
--checkpoint-activations \
--checkpoint-num-layers ${chkp_layers}"

if [ "${PA}" = "true" ]; then
chkp_opt="${chkp_opt} \
        --partition-activations"
fi

if [ "${PA_CPU}" = "true" ]; then
chkp_opt="${chkp_opt} \
        --checkpoint-in-cpu"
fi

if [ "${SYNCHRONIZE}" = "true" ]; then
chkp_opt="${chkp_opt} \
        --synchronize-each-layer"
fi

if [ "${CC}" = "true" ]; then
chkp_opt="${chkp_opt} \
        --contigious-checkpointing"
fi

if [ "${PROFILE}" = "true" ]; then
chkp_opt="${chkp_opt} \
        --profile-backward"
fi

full_options="${gpt_options} ${deepspeed_options} ${chkp_opt}"

run_cmd="deepspeed --num_nodes ${DLWS_NUM_WORKER} --num_gpus ${DLWS_NUM_GPU_PER_WORKER} --master_addr ${MASTER_ADDR} --master_port ${MASTER_PORT} pretrain_gpt2.py $@ ${full_options}"
echo ${run_cmd}
eval ${run_cmd}

set +x

The zero 2 config is like this:

{
  "train_batch_size":256,
  "gradient_accumulation_steps": 1,
  "steps_per_print": 1,
  "zero_optimization": {
    "stage": 2,
    "allgather_partitions": true,
    "reduce_scatter": true,
    "allgather_bucket_size": 50000000,
    "reduce_bucket_size": 50000000,
    "overlap_comm": true
  },
  "optimizer": {
    "type": "Adam",
    "params": {
      "lr": 0.00015,
      "max_grad_norm": 1.0,
      "betas": [0.9, 0.95]
    }
  },
  "gradient_clipping": 1.0,
  "fp16": {
    "enabled": true,

    "loss_scale": 0,
    "loss_scale_window": 1000,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "wall_clock_breakdown": true,
  "zero_allow_untested_optimizer": false
}

@ShadenSmith @jeffra

[melissamao]

hello~I also face the same problem and I wonder how to solve it. Thanks!

[FannierPeng]

您好，已收到您的邮件。感谢！

[tjruwase]

@melissamao and @FannierPeng, that codebase is deprecated.

Please use https://github.com/microsoft/Megatron-DeepSpeed as recommended.