ValueError: Invalid attention arguments: AttnType.self_attn, None

[chen-lee-li]

Traceback (most recent call last):
File "/data/lee/Megatron-LM/pretrain_gpt.py", line 158, in
pretrain(train_valid_test_datasets_provider, model_provider,
File "/data/lee/Megatron-LM/megatron/training.py", line 129, in pretrain
model, optimizer, opt_param_scheduler = setup_model_and_optimizer(model_provider,
File "/data/lee/Megatron-LM/megatron/training.py", line 376, in setup_model_and_optimizer
model = get_model(model_provider_func, model_type)
File "/data/lee/Megatron-LM/megatron/training.py", line 262, in get_model
model = model_provider_func(
File "/data/lee/Megatron-LM/pretrain_gpt.py", line 35, in model_provider
model = GPTModel(
File "/data/lee/Megatron-LM/megatron/model/gpt_model.py", line 74, in init
self.language_model, self._language_model_key = get_language_model(
File "/data/lee/Megatron-LM/megatron/model/language_model.py", line 75, in get_language_model
language_model = TransformerLanguageModel(
File "/data/lee/Megatron-LM/megatron/model/language_model.py", line 373, in init
self.encoder = ParallelTransformer(
File "/data/lee/Megatron-LM/megatron/model/transformer.py", line 1182, in init
[build_layer(i + 1 + offset) for i in range(self.num_layers)])
File "/data/lee/Megatron-LM/megatron/model/transformer.py", line 1182, in
[build_layer(i + 1 + offset) for i in range(self.num_layers)])
File "/data/lee/Megatron-LM/megatron/model/transformer.py", line 1130, in build_layer
return ParallelTransformerLayer(
File "/data/lee/Megatron-LM/megatron/model/transformer.py", line 877, in init
self.self_attention = ParallelAttention(
File "/data/lee/Megatron-LM/megatron/model/transformer.py", line 590, in init
raise ValueError(f"Invalid attention arguments: {attention_type}, {self.attention_head_type}")
ValueError: Invalid attention arguments: AttnType.self_attn, None

The parameters and commands are as follows. May I ask what the problem is？

GPUS_PER_NODE=1
MASTER_ADDR=localhost
MASTER_PORT=6001
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
CHECKPOINT_PATH=/data/lee/Megatron-LM/experiments/0606
VOCAB_FILE=vocab.json
MERGE_FILE=merges.txt
DATA_PATH=starcoder-abap_content_document
GPT_ARGS="--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size 12 \
--global-batch-size 192 \
--lr 0.0005 \
--train-iters 150000 \
--lr-decay-iters 150000 \
--lr-decay-style cosine \
--lr-warmup-iters 2000 \
--weight-decay .1 \
--adam-beta2 .999 \
--fp16 \
--log-interval 10 \
--save-interval 2000 \
--eval-interval 200 \
--eval-iters 10"

# --finetune
TENSORBOARD_ARGS="--tensorboard-dir experiments/tensorboard"


python -m torch.distributed.launch $DISTRIBUTED_ARGS \
        pretrain_gpt.py \
        --tensor-model-parallel-size 1 \
        --pipeline-model-parallel-size 1 \
        $GPT_ARGS \
        --vocab-file $VOCAB_FILE \
        --merge-file $MERGE_FILE \
        --save $CHECKPOINT_PATH \
        --data-path $DATA_PATH \
        $TENSORBOARD_ARGS

[KOVVURISATYANARAYANAREDDY]

Adding

             --attention-head-type multihead \

in GPT_ARGS helped me